Natural Resources for Human Health (NRFHH)
Natural Resources for Human Health covers a wide range of natural science disciplines. Such fields can be at the all aspects of works bearing on natural compounds of plants, algae, microbes and animals. View more 1.7 CiteScore 2024 2.0 CiteScore Tracker 2025 0.21 SJR 33% Acceptance Rate Open Access
Progress and Challenges in Multijunction Solar Cells Utilizing Ternary and Quaternary Semiconductor Alloys
Multijunction solar cells have emerged as a promising approach to overcome the efficiency To bypass this efficiency constraint of single-junction PV cells, a new type of cell has been developed called multijunction PV cells that can absorb multiple portions of the solar spectrum with various semiconductor layers whose bandgaps have been engineered appropriately. In this study, the development and limitations of ternary and quaternary semiconductor alloys for PV applications will be discussed and their focus will be on the heterojunction solar cells based on CuInTe₂ (ternary alloy) and Cu₂ZnSnS₄ (quaternary alloy). The devices were fabricated by electrodeposition method, where CdS was used as a buffer layer, and by optimized controlled annealing treatments to achieve the best structural and optoelectronic properties. Among the devices, the device based on CuInTe₂ achieved a maximum power conversion efficiency of ~4.13%, Voc of 480 mV, Jsc of 20 mA/cm2 and FF of 43%. The study emphasizes the key advantages of ternary alloys: high optical absorption and relatively easy composition; and quaternary alloys: high compositional flexibility and earth abundant components for low cost PV applications. But both types of materials are hampered by serious problems, including recombination losses, series resistance, interface mismatch, compositional disorder, and secondary phase formation. They can cause severe hindrance for integration in high-efficiency multijunction architectures. The results indicate that there is a need for further advances in the engineering of the interfaces, the reduction of defect densities and the tuning of the band gaps for the realization of efficient and scalable multijunction solar cells using ternary and quaternary semiconductor alloys.
A Metaheuristic-Optimized Deep Neural Network for Feature-Level Fusion in Multimodal Biometrics
Biometric security is quickly becoming an important issue in the data security industry. An enormous obstacle remains the development of multimodal biometrics systems for smart environments that improve detection rates and accuracy. Traditional approaches using varying degrees of traits fusion are contrasted with MBS that use Convolutional Neural Networks (CNNs). This paper details the creation of a multimodal biometric identification system that enhances the accuracy and security of the system by fusing fingerprint and iris scanning at the feature level. For feature extraction, it makes use of the Gray-Level Co-Occurrence Matrix (GLCM), Histogram of Oriented Gradients (HOG), and Local Binary Pattern (LBP). The Chimp Optimization Algorithm (ChOA) feature optimizer and Grey Wolf Optimizer (GWO) are utilized for feature selection. Using a Resnet, Densenet, and suggested Deep Neural Network (DNN) is the last stage in the categorization process. The results indicate that the DNN with ChOA outperformed DNN with GWO optimizer, with an accuracy rate of 98.14 %, AUC OF 0.992, F1-Score of 91.2 % and kappa with 0.959. This evidence is believed to using DNN with ChOA optimizer is an optimum choice for multimodal biometric system.
Civil Liability for Environmental Damages Arising from the Use of Artificial Intelligence Technologies in Fertilizers and Pesticides: An Analytical Comparative Study
This study explores civil liability for environmental damages arising from the use of artificial intelligence (AI) technologies in fertilizers and pesticides, adopting an analytical and comparative approach across national and international contexts. While AI applications in agriculture have enhanced productivity, resource efficiency, and crop management, they simultaneously generate complex risks, including pollution of soil, water, and air, biodiversity loss, and adverse impacts on human health. These risks raise fundamental legal challenges regarding causation, responsibility attribution, and compensation mechanisms. The research problem centers on the adequacy of traditional civil liability rules in addressing such damages and ensuring fair remedies. The study employs analytical and comparative methodologies, examining civil and environmental legal texts, judicial trends, and legislative approaches, with particular focus on Jordanian and French frameworks. The literature review reveals persistent gaps: globally, operational and institutional mechanisms remain underdeveloped; in Arab scholarship, legislative provisions and evidentiary tools are insufficient; and in national contexts, judicial practice is limited, with reliance on general civil rules that fail to capture the complexity of AI-related environmental harms. Findings indicate that existing liability frameworks are inadequate to address the distinctive nature of environmental damage linked to smart agricultural technologies. The study recommends enacting specialized legislation, empowering environmental associations and public authorities to initiate collective actions, modernizing evidentiary standards to ease the burden of proof, and strengthening environmental funds to guarantee compensation when direct responsibility cannot be established. These measures are essential to bridge the gap between doctrinal innovation and practical enforcement, and to achieve a balance between technological advancement, sustainable development, and environmental justice.
The Impact of Constitutional Amendments on Entrenching the Right to Public Health
The constitutional recognition of the right to health has become central to modern public health governance, reflecting its status as a fundamental human right. This study critically examines Jordan’s constitutional framework, where successive amendments to the 1952 Constitution (2011, 2014, 2016, and 2022) expanded social protections but failed to explicitly enshrine the right to health. The research problem lies in this constitutional silence, which raises concerns about the adequacy of Jordan’s legal system in safeguarding public health, particularly during crises such as the COVID 19 pandemic. Adopting a doctrinal and analytical legal methodology combined with comparative analysis, the study reviews Jordanian constitutional texts, judicial precedents, and legislative obligations, while drawing lessons from international experiences in South Africa, Brazil, Kenya, and Tunisia. Findings reveal that Jordan’s constitutional framework imposes implicit obligations on the state legislative, administrative, and judicial but the absence of explicit recognition weakens enforceability and limits judicial capacity. Comparative evidence demonstrates that explicit constitutional provisions transform health into a directly enforceable right, compelling state accountability and strengthening equity. The study concludes that Jordan urgently needs constitutional reform to explicitly guarantee the right to health. Practical recommendations are offered to legislators and policymakers to enhance constitutional protection, align with global trends, and ensure institutional cohesion and health equity.
The Impact of Human Resource Reengineering Practices on The Job Performance of Administrators in Jordanian Private Universities
Purpose: This study aims to examine the impact of human resource reengineering (HRR) practices on the job performance (JP) of administrators in Jordanian Private Universities through top management support (TMS). Methods: A quantitative research design was used in this study. As a result, 280 questionnaires were sent out, and 256 were approved for study. The surveys were intended for administrators at private universities in Jordan. Findings: The results of this study have provided a thorough examination of the factors HRR and TMS in private universities, which enhance JP. The results of the study highlighted the crucial roles that process redesign (PR), employee empowerment (EE), digital transformation (DT), and continuous improvement (CI) play in JP. Originality/Value: The results showed a strong correlation between TMS, HRR, and JP. Additionally, the results showed that the association between HRR and JP is moderated by TMS. Furthermore, it has been found that JP is significantly impacted by PR, EE, DT, and CI. Implications: These results have several implications for private universities in Jordan and policymakers, which may help these institutions improve JP.
Rupee Vs. Markets: Decoding Nifty 50’s Currency Connection in a Volatile Global Era
This research paper investigates the dynamic and causal relationship between the Indian Rupee (INR) exchange rate fluctuations against the US Dollar (USD) and the performance of the Indian equity market. Utilizing daily closing data from the National Stock Exchange (NSE) and the Reserve Bank of India (RBI) over a three-year period, the study employs Regression analysis to identify Indian Currency fluctuations and its impact on the Economy. The depreciation of the Indian Rupee has emerged as a critical concern in the context of heightened global financial volatility, geopolitical tensions, and changing monetary policies in advanced economies. This study examines the relationship between the Closing Price of NIFTY 50 and the USD–INR exchange rate, and evaluates the impact of global economic conditions on the value of the Indian Rupee. Employing a descriptive research design, secondary data were collected for three years from the NSE and the Reserve Bank of India, and analyzed using simple linear regression. The findings highlight the varying sensitivity of Indian equity markets to currency movements across different years.
A Blockchain Framework to Enhance Optimization using Honey Bee Algorithm for Load Balancing in Collaborative Environment
In recent years, distributed computing systems have gained significant popularity due to their ability to handle vast computational resources and data processing in real time. Collaborative environments, such as cloud computing and decentralized networks, require efficient mechanisms for managing resources and distributing computational loads. Load balancing in such systems is crucial to avoid overloading nodes, ensure efficient utilization of resources, and minimize delays in service delivery. A common issue in distributed environments is the dynamic nature of workloads, leading to potential inefficiencies in static load balancing techniques. Load balancing remains a significant challenge in collaborative distributed environments. In this paper, we propose a novel framework utilizing blockchain technology to enhance load balancing and resource optimization in collaborative environments. The framework is implemented using Java and leverages a bio-inspired honey bee algorithm for load balancing. By integrating decentralized, secure blockchain mechanisms with adaptive optimization algorithms, we aim to improve the overall performance, reliability, and scalability of distributed systems. The proposed system dynamically adjusts resource allocation in real-time, mimicking the self-organizing behavior of honey bee colonies. Results show that the Java-based blockchain framework improves load distribution, reduces latency, and increases the throughput compared to traditional methods, making it suitable for large-scale collaborative applications.
Comparative Study of Senior Citizen Rights: References from India, Bangladesh, Nepal, and Sri Lanka
Population ageing has emerged as a significant socio-legal challenge across South Asia, demanding robust legal frameworks and inclusive social policies to safeguard the rights and well-being of older persons. This paper presents a comparative study of senior citizen rights in India, Bangladesh, Nepal, and Sri Lanka by examining constitutional provisions, statutory protections, social security mechanisms, healthcare policies, and institutional support systems. Employing a comparative socio-legal methodology based on legislative analysis, policy documents, international conventions, and secondary literature, the study explores how each country addresses issues of income security, healthcare access, elder abuse, social inclusion, and family responsibility. While India has enacted the Maintenance and Welfare of Parents and Senior Citizens Act, 2007, Nepal has incorporated elderly welfare within its constitutional framework and social protection policies. Bangladesh and Sri Lanka have similarly introduced legal and welfare measures, although implementation remains constrained by limited institutional capacity, demographic transitions, and resource disparities. The study identifies common regional challenges, including inadequate pension coverage, rising healthcare costs, changing family structures, and insufficient protection against elder abuse and neglect. It argues that despite varying legal approaches, all four countries face a gap between legislative intent and effective implementation. The paper advocates a rights-based and regional framework for elderly welfare grounded in international human rights standards, stronger institutional accountability, community-based care, and enhanced cross-border policy cooperation. Such an approach would contribute to promoting dignity, social justice, and healthy ageing for senior citizens across South Asia.
LLM-Based Automated Software Testing and Self-Healing Code Generation using Reinforcement Learning
Large language models trained on source code propose plausible repairs for defective programs, but they judge their own output poorly: the patch a model ranks first is often wrong while a correct patch sits lower in the same beam. This work separates generation from commitment. A frozen code language model proposes several candidates per defect and a policy trained by reinforcement learning decides which to commit, observing only whether the patch it chose passed. Two studies isolate where the difficulty lies. On the CodeXGLUE code-refinement benchmark the model reaches 23.43% exact match at rank one against an oracle ceiling of 40.71%, yet the learned selector recovers only 23.86%, closing 2.5% of the reachable gap. A supervised ranker holding every correctness label does no better, which identifies the descriptors rather than the learning algorithm as the binding constraint: signals computed without executing a patch barely separate correct repairs from incorrect ones. The second study supplies the missing signal by compiling and running each candidate against the program’s own tests. The same bandit formulation, now rewarded by measured outcomes and using no language model, fully repairs 5 of 31 programs and lifts the mean proportion of passing tests from 26.45% to 46.40%. Execution, not learned static ranking, is what makes patch selection work.
Facial Emotion Recognition using CNN, SVM, VGG16, VGG19, and InceptionV3: A Compar-ative Evaluation
Facial expression prediction has gained considerable attention in recent years, particularly because of its applications in human-computer interaction. This paper presents a comparative evaluation of machine learning and deep learning models for the prediction of face emotion using the CK+ dataset proposed by Cohn-Kanade. The dataset is characterized by seven classes of emotions rep-resented by the labels, namely surprise, happiness, disgust, anger, sadness, fear, and contempt, on 784 training, 98 validation, and 99 testing images. To further improve model performance, preprocessing techniques were employed that enhanced data efficiency. To increase variability in the data and reduce overfitting, all images were scaled to a 48*48-pixel resolution, pixel values were scaled to be between 0 and 1 for uniformity and the following data augmentation techniques were implemented: 10-degree rotation, horizontal flip, 0.15 zoom. The five models that were tested were CNN, SVM, VGG16, InceptionV3 and VGG19. The results demonstrate the high accuracy achieved by the CNN model which showed an accuracy of 98.98%, 99% and 99% in training, validation and test respectively. The SVM classifier achieved a testing accuracy of 99%. Both InceptionV3 and VGG19 on the other hand achieved competitive testing accuracy values of 90.91% and 97.98% respectively, while VGG16 got tested and reached an accuracy of 85.86%.
Managing Inclusive Early Childhood Education in Resource-Limited Settings: A Qualitative Case Study of Learning Provisions for Children with Special Needs in Indonesian Preschools
Indonesia has extended inclusive early childhood education to ensure equal provision of learning experiences for children with special needs, but implementation outside large metropolitan centers differs. The objective of this study was to explore how inclusive early childhood education providers implement educational activities for children with special needs and identify factors affecting implementation in a non-metropolitan regency in Indonesia. A qualitative case study design employed in-depth interviews, non-participant observation, and document analysis at four inclusive kindergartens. The Mazmanian–Sabatier policy implementation framework and thematic analysis were applied in analyzing data. Overall, inclusive learning management included planning, organizing, implementing, and evaluation; however, coordination between these functions remained largely informal. The key enabling factors were defined as committed leadership, parental involvement, teacher collaboration, and partnerships with disability associations. On the negative side, implementation was constrained by insufficient physical resources, a lack of special assistant teachers, limited curriculum flexibility, poor intergovernmental collaboration, socio-cultural views on disability, and family financial limitations. Inclusive learning management is feasible in resource-constrained settings, conditional on sustained district-level funding, the availability of special assistant teachers, curriculum differentiation, and strengthened cross-level governance. Implications: These findings provide empirical evidence to inform policy and practice in inclusive early childhood education.
Implementation of an EFL Teaching and Learning Model: Institutional and Programme Perspectives at Higher Education Universities
Improving English as a Foreign Language (EFL) instruction in Azerbaijani universities is pivotal for enhancing graduates' competencies, contributing to national socio-economic growth. This study advocates implementing a comprehensive EFL teaching and learning model at the institutional, programme, and discipline-specific levels within Azerbaijani universities. Drawing from theoretical and empirical insights, particularly from South African universities, the research investigates challenges and opportunities in adapting EFL teaching models. A critical issue identified is the lack of a university-wide operational plan, which hampers effective implementation and compromises educational quality and student learning outcomes. Key findings emphasize the synergy between institutional leadership, programme design, and discipline-specific pedagogy in fostering sustainable EFL education improvements. The study recommends developing tailored operational strategies for Azerbaijani universities to transform English language teaching practices meaningfully. This involves integrating comprehensive operational plans that delineate specific objectives, actionable steps, resource allocation, and clear roles from institutional to classroom levels. Establishing interdisciplinary coordination committees, investing in professional development, and creating mechanisms for ongoing curriculum review and quality assurance are essential steps. The study highlights the importance of providing adequate resources, reducing class sizes, and implementing reward systems to encourage teaching innovation. Addressing current implementation challenges requires systemic approaches aligning policies with practices, fostering collaboration, and supporting faculty through continuous professional development and infrastructural improvements. The ultimate goal is to enhance English proficiency among students, thereby unlocking opportunities for engagement in international academia, global markets, and cross-cultural dialogue, which are vital for Azerbaijan's continued growth and integration into the global community.
Taxation in Nepal: A Critical Analysis of Composition and Contribution of Direct Tax and Indirect Tax to the Tax Revenue, with Extensions to Health Sector Financing and Natural Resource Taxation
The main objective of this study is to examine the composition and contribution of direct tax and indirect tax to the tax revenue of Nepal, and to extend the analysis to the critical linkages between tax revenue, health sector financing, and natural resource taxation. The revenues of the government come from two sources as tax revenue and non-tax revenue. Among the different sources of revenue of Government of Nepal, tax is the most important source. Tax revenue includes direct tax revenue and indirect tax revenue. As tax has a high potential to yield revenue for the government, developing countries like Nepal are engaging in the study of composition and contribution of direct tax and indirect tax. In this study, an attempt has been made to assess the composition and contribution of direct tax and indirect tax towards tax revenue of Nepal, employing both descriptive statistics and mathematical modeling. This study uses descriptive and analytical research design. This study is mainly based on secondary data collected from Economic Survey 2081/82 published by Ministry of Finance, Government of Nepal. The last 10 years of data (from B.S. 2071/72 to 2080/81) have been taken for the study. To assess the contribution of direct tax and indirect tax towards tax revenue, percentage analysis, trend decomposition, tax elasticity coefficients, Laffer curve analysis, and fiscal multiplier models have been employed. The study found that direct tax contributes around thirty percent and indirect tax contributes around seventy percent to tax revenue of Nepal. Beyond the descriptive findings, this paper develops a mathematical framework connecting tax revenue composition to health sector allocation through a constrained optimization model, derives the Laffer curve for Nepal’s tax system, computes tax buoyancy and elasticity coefficients, and models natural resource taxation (hydropower royalties, forestry, mining) through a resource rent taxation framework. The study concluded that Nepal’s tax system remains heavily dependent on indirect tax and there is a narrow direct tax base. The study suggested broadening the direct tax base by strengthening income tax administration, improving taxpayer compliance, and expanding coverage through the formalization of economic activities, while earmarking a quantified fraction of tax revenue for health sector investment to break the cycle of low taxation–low health spending–low human capital.
Community Strength and Natural Resource Capital among Forest-Village Women in Indonesia: Insights for Green Social Work
This study examines community strength and natural resource capital among women in the Forest Village Women's Group (Kelompok Perempuan Desa Hutan, KPDH) in Ngawi Regency, East Java, Indonesia. A quantitative descriptive design was applied, with data collected from 137 women through a four-point Likert-scale questionnaire, structured observation, and documentary study across four capital dimensions: natural resource, economic production, human, and social-institutional capital. The findings reveal a pronounced imbalance across these dimensions. Social and institutional capital, rooted in gemeinschaft solidarity and an ethic of care, proved the community's strongest asset, sustaining the Women's Cooperative (KOPWAN) and collective mobilisation. Human capital, by contrast, was constrained by limited formal education, with 62% of respondents having completed only primary school. Natural resource capital presented a paradox: substantial assets, including fertile land, teak forests, and non-timber forest products such as leaves and fuelwood, remained underused because of insecure land tenure (91% without ownership under Perhutani's authority) and limited processing capacity. Economic production capital was further weakened by geographical isolation and dependence on intermediaries for market access. The study offers insights for a Green Social Work framework integrating natural resource stewardship, capacity building, and institutional strengthening to support sustainable, gender-just empowerment for forest-village women.
Developing SIBIMA: A Digital Innovation Infrastructure for Collaborative Hybrid Maize Breeding and Community-Based Seed Systems
The increasing complexity of collaborative hybrid maize breeding and community-based seed production systems has created substantial challenges related to information management, communication, transparency, knowledge exchange, licensing administration, and institutional coordination. Conventional coordination mechanisms frequently become inadequate when innovation processes involve multiple actors operating across geographically dispersed locations and diverse institutional environments. This study aimed to develop the Seed Information System for Community-Based Seed Management (SIBIMA) as a digital innovation infrastructure designed to strengthen collaborative breeding and community-based seed systems. A qualitative case study approach combined with a design-oriented research perspective was employed to examine the experiences of CorresCo and its collaborative network. Data were collected through direct observation, semi-structured interviews, focus group discussions, and document analysis. Data analysis involved data reduction, categorization, triangulation, and thematic analysis to identify recurring patterns associated with collaboration, knowledge exchange, institutional development, and digital transformation. The findings indicate that increasing levels of collaboration simultaneously increase the complexity of coordination processes, thereby creating challenges associated with information exchange, traceability, transparency, and collective decision-making. In response to these challenges, SIBIMA was developed as an integrated framework encompassing breeding management, production management, knowledge management, and institutional management. The proposed framework functions not merely as an information management system but as a digital innovation infrastructure that supports communication, collective learning, licensing administration, and community empowerment. This study contributes to the advancement of agricultural innovation system theory by integrating technological, organizational, and institutional dimensions within a community-based seed system while offering a practical framework for strengthening seed sovereignty and promoting sustainable agricultural development.
Design and Optimization of Keyway Broaching Process Parameters for Improved Surface Finish using Taguchi and Grey Relational Analysis
Broaching is a high productivity machining process where the quality of the surface produced is greatly affected by tool geometry and cutting variables. In order to achieve a consistent surface finish, the geometry of the teeth and the process parameters of keyway broaching should be optimized. The following study was performed to determine the effects of cutting speed (V), rake angle (α) and clearance angle (δ) on the arithmetic mean surface roughness (Ra) of a broached keyway where 27 experimental observations are provided. A complete 3³ factorial matrix was used, with cutting speed of 7, 8 and 9 m/min, rake angle of 10°, 12° and 14°, and clearance angle of 0.45°, 1.0° and 1.5°. Because the supplied dataset contains one reported Ra value for each of the 27 combinations, the analysis was reconstructed as a full-factorial dataset rather than as a reduced Taguchi orthogonal-array experiment. For the smaller-the-better quality characteristic, signal-to-noise ratios were calculated and main effects were evaluated. The measured Ra values ranged from 0.27 to 2.02 µm, with the minimum observed value of 0.27 µm obtained at 7 m/min, 10° rake angle and 1.5° clearance angle. Main-effect averages showed that the lowest mean Ra occurred at 7 m/min (0.684 µm), 12° rake angle (0.793 µm), and 0.45° clearance angle (0.737 µm). The additive Taguchi main-effect analysis therefore indicates 7 m/min, 10° and 0.45° as the highest-S/N setting; however, the corresponding measured experiment produced 0.78 µm, demonstrating that the main-effect prediction should not be treated as a validated optimum without independent confirmation and replication. A main-effects ANOVA assigned 8.40%, 0.31% and 2.75% of the total variation to cutting speed, rake angle and clearance angle, respectively, with none reaching statistical significance at the 5% level under the unreplicated error model. The results emphasize the need for replicated measurements and confirmation experiments before establishing a production-level optimum.
Vipul Prabhakar Rathod, Yogesh Ganpat Kamble
The Multiplicative Impact of AI Preparedness on E-commerce Growth: Empirical Evidence from the Global South Economies
Artificial Intelligence (AI) is reshaping how economies transition toward digital services, with e-commerce emerging as a central driver of growth. Yet, countries with similar levels of AI readiness often show very different outcomes. This raises an important question: why does technological preparedness not translate into comparable economic gains across contexts? This study examines that gap by introducing the Digital Buffer Hypothesis, which argues that the benefits of AI depend on a minimum level of internet infrastructure. Using cross-sectional data from 35 economies (2023–2024), an Ordinary Least Squares (OLS) model is employed to estimate the relationship between the e-commerce share of GDP and the IMF AI Preparedness Index. The model incorporates an interaction term for internet access, along with controls for GDP per capita and youth population share. The results indicate that the model explains a substantial share of variation in digital structural change (Adjusted R² ≈ 0.49). AI preparedness emerges as a significant driver of e-commerce expansion, but its impact is notably stronger in countries with higher levels of connectivity. At the same time, basic internet access on its own does not appear sufficient to generate structural change, pointing to the importance of how infrastructure is utilized rather than its mere presence. Taken together, the findings suggest that AI functions as a conditional driver of growth. Its economic returns depend on the presence of a supporting digital environment. For policymakers, particularly in the Global South, this implies that investments in AI capabilities need to be complemented by Digital Public Infrastructure that reduces friction and allows these technologies to scale effectively.
Reconnecting with Nature: Earth Pigments, Taoist Aesthetics, and the Ecology of Contemporary Art Practice
Earth pigments, natural colorants derived from soil, minerals, and rock rank among the oldest materials of human artistic expression. This paper examines their aesthetic, cultural, and ecological significance in contemporary art practice and extends that inquiry by situating earth pigments within Taoist philosophy. Concepts such as Dao (the Way), Wu Wei (effortless action), Ziran (naturalness), and the dynamic balance of yin and yang offer a framework for understanding why unprocessed, locally sourced materials continue to attract artists seeking authenticity and ecological responsibility. Reading traditional Chinese painters such as Muqi, Shitao, and BadaShanren, the Japanese monk-painter SesshūTōyō, and contemporary practitioners including Claire Benn, Heather Bird Harris, Bharti Kher, and N. S. Harsha alongside Indian mural and folk traditions, the paper argues that earth pigments function simultaneously as visual material, cultural memory, and a philosophical practice of harmonizing with nature.
Trust-Energy Path-Reinforcement Routing for Secure and Sustainable Wireless Sensor Networks under Dynamic Malicious-Node Conditions
WSNs (wireless sensor networks) in hostile environments not only have limited energy resource but also have the existence of malicious node behaviour which significantly degrades both the routing reliability and network lifetime quality. To overcome this challenge, this study presents a trust-energy reinforced routing framework for secure and energy aware communication in WSNs with dynamic malicious-node conditions. The proposed method integrates residual energy, trust value as well as route-reinforcement memory into a single mechanism for next-hop selections, allowing routing decisions to adapt to both the earlier successful paths as well as the current network conditions. A MATLAB-based comparative simulation study through a network of 100 sensor nodes, malicious nodes being updated periodically, was conducted and the performance of the proposed simulated approach was evaluated against Grey Wolf Optimization (GWO)-, Harris Hawks Optimization (HHO)- and Whale Optimization Algorithm (WOA)-inspired routing approaches. In the experiments, the proposed method achieved best results in terms of final trust value, residual energy, alive-node fraction, and average routing cost. It finally produced a trust value of 0.8773, residual energy 0.242 Joule, alive-node fraction 0.94, and routing cost of 146.76. In addition, the first node death was delayed to round 28, while the half-nodes-dead and last-node-death conditions were not reached within the 50-round simulation horizon. These findings confirm that the proposed framework provides a more balanced and effective solution for secure, energy-efficient, and sustainable routing in adversarial WSN environments.
Green Marketing Practices, Sustainable Consumer Behaviour, and Their Influence on Brand Loyalty in Emerging Economies
Green marketing has moved from a peripheral corporate social responsibility gesture to a mainstream strategic positioning tool, yet its effectiveness in cultivating genuine, durable brand loyalty remains empirically contested, particularly in emerging economies where environmental consciousness, purchasing power, and institutional trust in corporate environmental claims interact in ways that differ substantially from the high-income-country contexts in which much of the foundational green marketing literature was developed. This paper reviews the theoretical and empirical literature connecting green marketing practice, sustainable consumer behaviour, and brand loyalty, synthesizing foundational green brand equity and green trust models with the theory of planned behaviour's application to green purchase intention, the greenwashing and green-skepticism literature, and the growing body of emerging-economy-specific empirical studies from India, China, and Southeast Asia. Particular attention is given to the mediating role of green trust and green perceived risk in the relationship between green marketing communication and brand loyalty, and to the structural market conditions, price sensitivity, regulatory enforcement variability, and information asymmetry, that distinguish emerging-economy green consumption from the developed-market context. Distinct comparative tables map theoretical constructs onto their standard empirical measurement approach, contrast trust-building green marketing mechanisms against the corresponding greenwashing risk each is vulnerable to, and set emerging-economy structural conditions against their documented net effect on the green-marketing-to-brand-loyalty pathway. The paper concludes that green marketing's effect on brand loyalty in emerging economies is consistently mediated by green trust rather than operating as a direct effect, and identifies cross-national comparative validation of green trust mediation models as the central future research prospect.
Revisiting the Technology Acceptance Model: A Trust-Centric Approach to E-Commerce Adoption
With the rise in online shopping activities, there is now a greater necessity to understand how technology-related perceptions and usage of platforms affect consumer trust. Using the modified Technology Acceptance Model, this research investigates the linkages between perceived ease of use, perceived usefulness, attitude towards online shopping, behavioral intentions, actual system use, and user trust. A data sample for this investigation has been gathered using an online survey from consumers in Delhi NCR in the period from April to July 2024. Out of 1,000 respondents approached, 880 have provided their input, and 700 valid responses have been considered for the analysis. The proposed framework has been validated using partial least squares structural equation modeling in SmartPLS. The results reveal that perceived ease of use is positively related to perceived usefulness and attitude towards online shopping. Attitude is found to be positively linked to behavioral intentions and actual system use. Behavioral intentions positively correlate with actual system use, and actual system use positively relates to user trust. This research adds to the literature related to Technology Acceptance Model by identifying trust as a dependent variable which can develop as a result of interactions with the online shopping platform. However, the results should be generalized with caution due to the cross-sectional design and convenience sampling used in the study in Delhi NCR.
The Ethnocultural Features of Facial Expressions in Uzbek and English Communication Culture: A Contrastive Pragmatic Analysis
This article investigates the ethnocultural features of facial expressions (mimicry) in Uzbek and English communication cultures through a comparative linguistic-pragmatic and ethnocultural approach. The research corpus comprises 45 contextualised examples drawn from Uzbek and English literary texts and journalistic sources. Applying semiotic, pragmatic, and ethnocultural communication frameworks, the study systematically analyses six categories of mimetic nonverbal devices: lip, eye, eyebrow, teeth, nose, and forehead expressions. The findings demonstrate that while mimetic nonverbal units fulfil emotional-expressive functions in both cultures, their semiotic composition, social scope of use, and cultural interpretation exhibit significant ethnocultural differences. In Uzbek communication culture, facial expressions more strongly reflect collective values, social hierarchy, and group harmony, whereas English communication culture foregrounds individual expression and emotional self-regulation. Uzbek literary examples are presented in their original script and analysed in terms of their paralinguistic and pragmatic functions. The results contribute theoretically to contrastive linguistics, ethnocultural communication studies, and cross-cultural pragmatics.
Reassessing the Impact of Colonialism: The Historical Roots of Contemporary Global Inequalities
This article addresses a persistent problem in global inequality research: contemporary disparities in wealth, institutional capacity, and developmental opportunity are often analyzed through present-day policy or market variables while the long historical consequences of colonial rule remain insufficiently integrated into explanatory frameworks. The study aims to reassess colonialism as a constitutive rather than merely background factor in the making of current global inequalities. It is theoretically informed by postcolonial political economy, historical institutionalism, and dependency-oriented approaches, which together foreground the durability of unequal power relations across formal decolonization. Methodologically, the article adopts a historical-comparative and interpretive design based on the synthesis of interdisciplinary scholarship on colonial governance, extractive economies, coerced labor systems, trade dependency, and postcolonial state formation. Its scope is macro-historical rather than regionally isolated, focusing on the structural pathways through which colonial rule reorganized land, labor, institutions, and external economic relations across colonized societies. The analysis shows that colonialism contributed to present-day inequalities not through a single uniform mechanism, but through the interaction of extractive institutions, racially stratified labor regimes, externally oriented production systems, and hierarchical incorporation into the global economy. These structures often outlived empire in formal terms and continued to shape postcolonial developmental constraints, uneven state capacity, and asymmetrical participation in global markets. The article's scholarly contribution lies in offering a historically grounded account that moves beyond reductive moral condemnation and beyond presentist explanations of inequality. It demonstrates that contemporary global disparities are more adequately understood as the outcome of historically sedimented structural relations, not merely divergent national trajectories after independence.
Effectiveness of AI-Assisted CLIL Instruction on Developing Methodological Competence in Pre-Service EFL Teachers: A Mixed-Methods Study
This study addresses a persistent gap in teacher education research: although artificial intelligence is increasingly incorporated into language pedagogy, limited evidence exists on how AI-supported instruction contributes to the methodological competence of pre-service EFL teachers, particularly within Content and Language Integrated Learning (CLIL) environments. The study investigates whether and how AI-assisted CLIL instruction supports the development of pedagogical planning, material design, assessment awareness, and reflective decision-making in teacher preparation. The analysis is informed by an integrated framework combining CLIL pedagogy, sociocultural learning theory, teacher competence theory, and technology-mediated pedagogical knowledge. A mixed-methods design was employed to capture both measurable change and interpretive depth. The study was conducted with pre-service EFL teachers enrolled in a teacher education program, where AI-supported CLIL tasks were embedded into methodology-oriented coursework. Quantitative data were gathered through pre- and post-intervention measures of methodological competence, while qualitative data drew on reflective writing, instructional artifacts, and participant interviews to examine how the intervention shaped pedagogical thinking and practice. The findings indicate that AI-assisted CLIL instruction can strengthen methodological competence when technology is used as a scaffold for pedagogical reasoning rather than as a shortcut for task completion. Participants showed improvement in lesson structuring, integration of language and content objectives, and critical reflection on instructional design, while also reporting tensions related to overreliance on AI-generated suggestions. The study contributes theoretically by linking AI use to situated teacher learning, and pedagogically by clarifying how AI can be integrated into CLIL-based teacher education in ways that support, rather than replace, developing professional judgment.
The Significance of Personal Deictics in the Arabic Language
This study rigorously examines the significance of personal deictics in Arabic from an integrated semantic–pragmatic perspective, using Ziyārat Wārith as an analytical case study. It proceeds from the central hypothesis that personal deictics—particularly pronouns and vocative forms—extend far beyond a merely neutral referential role; rather, they function as dynamic linguistic mechanisms that actively shape meaning, structure discourse, and direct its pragmatic intentions. The study uses a descriptive–analytical approach within a pragmatic framework, drawing on Benveniste's ideas about linguistic subjectivity and Levinson's classification of deixis as situational mechanisms. It identifies and examines the personal deictic expressions in Ziyārat Wārith related to the participants in the discourse (speaker, listener, and absent third party), along with a quantitative survey of deictic pronouns to uncover their discursive structure. Results show that the text heavily relies on first- and second-person pronouns, which create a direct, interactive mode that transforms Imam al-Husain (peace be upon him) from a spatially absent figure into a pragmatic participant. Conversely, third-person pronouns are used to reference historical figures and to shape the evaluative tone of the discourse. The study concludes that personal deictics are a key structural and semantic part of the pragmatic discourse in Ziyārat Wārith, playing a vital role in enhancing its emotional and devotional effect. This highlights the importance of studying deictics as an interdisciplinary area where semantics and pragmatics intersect, rather than as a purely traditional grammatical feature.
Pedagogical Experimental Work on the Effectiveness of Teaching Physics based on Mobile Applications and Robotics Elements
This article presents pedagogical experiments and their results in connection with increasing the efficiency of teaching physics in higher military education based on mobile applications and robotics elements. The main achievements and scientific innovations of the study are the increase in the efficiency of teaching physics in higher military education based on mobile applications and elements of robotics in the development of the implementation methodology, the increase in the efficiency of teaching physics in higher military educational institutions. education based on elements of mobile applications and robotics, the improvement of the content of physics based on integration, aimed at forming professional knowledge of specialists, as well as the increase in the efficiency of teaching physics in higher military education using mobile applications and robotics due to the improvement. Based on elements of higher military educational institutions, the mechanism for implementing the methodology based on practical issues and experimental developments, aimed at developing independent educational skills of cadets and students, is reflected in the improvement of information and optimization of didactic support.
Urban Ecological Drivers of CO₂ Intensity in Arid Cities: A Multi-Scale, VHR-based Analysis of Baghdad and Erbil
Urban areas in arid zones face distinct emissions challenges from rapid growth, high cooling demand and sparse vegetation cover, which limits the natural absorption of CO₂ and strains urban ecology. Using an integration of VHR satellite imagery with local socioeconomic and infrastructural data, this study presents a framework that measures carbon dioxide emission intensities across the Zayouna/Mansour district (Baghdad) and Erbil Municipality Center (Erbil), two major Iraqi cities situated in an arid environment. A combined bottom-up and top-down framework with Random Forest regression was used to identify localized emission hotspots and the ecological determinants of CO₂ emissions, drawing on Sentinel-2-derived NDVI, Landsat 8/9-derived LST, VIIRS-derived night light intensity, WorldView/Pléiades-derived building density, OpenStreetMap road density and surface albedo at 250 × 250 m grid resolution over 2020–2024. Results reveal spatially concentrated emission hotspots near major road arteries and high-density residential and commercial zones, while green areas show lower emission intensities, confirming vegetation's role as a CO₂ sink in urban areas. The spatial emission model performs well in estimating each city's footprint (R² = 0.78, RMSE = 0.008 kg CO₂/m²). Panel data and fixed-effect models identify traffic density as the primary driver of CO₂ emissions in Baghdad (coefficient = 0.040, p < 0.001, R² = 0.85), while energy consumption dominates in Erbil (coefficient = 0.028, p < 0.001, R² = 0.80). Vegetation cover (NDVI) is negatively related to emission intensity in both cities (Baghdad: −0.095, Erbil: −0.080; both p < 0.001). These findings offer actionable guidance for climate-smart urban planning and customized emission-reduction policies in data-scarce arid cities, and the methodology is transferable to other data-poor urban areas developing smart city strategies.
Multi-Response Optimization of Quality Parameters in Pumpkin–Oat Composite Noodles using Response Surface Methodology
Pumpkin (Cucurbita maxima) and oat (Avena sativa L.) flours are valuable functional ingredients that can enrich cereal-based staples such as noodles with β-carotene, β-glucan, and dietary fibre while displacing part of the refined wheat flour normally used [1]. However, partial replacement of wheat flour dilutes gluten and alters water-binding behaviour, which can compromise cooking stability, texture, and consumer acceptability if substitution levels and hydration are not carefully controlled [1,3,4]. This study applied a three-factor, three-level Box–Behnken design (BBD) to evaluate and optimize the effect of pumpkin flour (5–15%), oat flour (5–15%), and water addition (30–38%) on six quality responses of composite noodles: cooking time, cooking loss, water absorption index (WAI), texture hardness, overall sensory acceptability, and β-carotene content. Second-order polynomial models were fitted to each response (R² = 0.974–0.997), and the Derringer–Suich desirability function was used to identify the formulation that simultaneously satisfied all quality targets [6]. The numerically optimized formulation (12% pumpkin flour, 9% oat flour, 35% water addition) gave an overall desirability of 0.79, with predicted cooking time of 6.76 min, cooking loss of 5.07%, WAI of 2.13 g/g, hardness of 952 N, sensory score of 7.55 (9-point hedonic scale), and β-carotene content of 225.2 µg/100 g. Validation trials conducted at the optimum point showed relative errors below 2% between predicted and experimental values for all responses, confirming the adequacy of the fitted models. The results demonstrate that RSM-based multi-response optimization is an effective strategy for developing nutritionally enhanced pumpkin–oat composite noodles without compromising technological and sensory quality.
Privacy and Human Rights in the Digital Age: Challenges of a Technology- Centric Society
Digital technologies impact communication across education, healthcare, employment and administration. In addition to increasing communication through technology, the technologies also increased the collection storage and processing of personal data. This paper provides a review of privacy and human rights as they relate to an entire society that is built around technology. This study examines informational privacy, physical privacy, decision privacy. It examines mass surveillance using commercial data, algorithmic discrimination, cybersecurity threats, Digital inequality and weak consent. Additionally, it examines corporate responsibility, artificial intelligence (AI) ethics and the relationship between privacy and security. A framework is developed connecting technologies to data practices and privacy risks; technologies to human rights outcomes and governance responses. These processes include collection, storage sharing profiling and automated decisions making. The process is moderated by standards, public awareness and Digital literacy. The review finds harm results from collecting too much data, lack of oversight over the data collected, unfairness in automated decisions made with respect to data and limited access to remedies.
Critical Success Factors, Risk Allocation, and Schedule Planning in Major Infrastructure Projects: A Structural Equation Modeling Analysis
There are usually cost and time issues, and problems with quality, with big infrastructure projects. This paper analyses the impact of governance and leadership capacity, technical and organizational capability, stakeholder alignment, quality of risk management, and maturity of schedule management on operational project performance, using the structural equation modeling technique. The analysis was performed on an anonymized dataset with 400 project observations across six regions, six infrastructure sectors, five delivery models and eight professional roles. Reflective measurement blocks were studied based on standardized loadings, Cronbach’s alpha, composite reliability, average variance extracted and the heterotrait-monotrait ratios. Operational project performance combined several perceptual measures and objective measures including schedule and cost performance indices, delays, cost overruns, change orders, safety incidents, and variance in critical path. The analysis showed that governance and leadership capacity positively predicted the quality of risk allocation (β = .485), and that technical and organizational capability positively predicted the maturity of schedule planning (β = .433). The quality of risk allocation (β = .319), and the maturity of schedule planning (β = .275), positively predicted operational performance, with the model explaining 68.1% of the variance. Stakeholder alignment did not have a significant direct effect on operational performance (β = .028, p = .456); however, an indirect effect through the quality of risk allocation was observed, along with governance, leadership and technical capacity, with partial mediation. The size, duration and complexity proxy did not have a significant moderating effect on the relationship between quality of risk allocation and performance, or maturity of schedule planning and performance. The evidenced pathways support the development of a practice-based risk allocation and schedule management framework based on the following concepts: an edge in management and control, absorption and use of relevant information, clarity with respect to the value of contingencies, and governance of P50/P80 milestones.
Developing a Smart Enterprise Framework: Artificial Intelligence Applications in Human Resources, Marketing, and Operations Management
Even as artificial intelligence [AI] is quickly maturing into a strategic capability of the enterprise, most organizations implement the technology in human resources, marketing, and operations in segregated and siloed fashions. Segregated implementations create efficiencies in the individual functional silos, but hinder integrated cross-silo decision making, coordinated governance, and the ability to measure enterprise value. This paper presents the Smart Enterprise Framework. It offers the articulations of HR intelligence, marketing intelligence, and operational intelligence and provides a foundation of shared AI analytics, data architectures, human oversight, and responsible AI control frameworks. This research employs a conceptual framework design following a literature synthesis and proposes a validation logic that incorporates expert review, maturity scoring, and assessment based on key performance indicators. In this framework, AI is positioned as a system of management augmentation, and not as a substitution system, that assists in the enhancement of the speed, accuracy, and personalization of decisions, and the reliability of operations. This framework also provides several integrated governance mechanisms that incorporate bias, explainability, consent, audits, and model monitoring. The framework illustrates the power of cross-functional AI in enabling integrated enterprise management of talent, customers, market activity, and operations including anticipatory and real-time control of demand, supply, and quality.
Self-Supervised Joint Discovery of Fusion Taxonomies and Multimodal Fusion Strategies for Sentiment Analysis
Prior studies on multimodal fusion have built two distinct aspects: taxonomy-aware systems which identify the type of fusion strategy (early, late, hybrid, attention-based or graph-based), and fusion architectures which perform multimodal fusion for a specific downstream task and are not dependent on the taxonomy. The Taxonomy-Guided Fusion Strategy Selector (TG-FSS) fuses these streams, leveraging an externally supervised taxonomy signal to boost the accuracy of sentiment classification, F1, and correlation with human judgments over the best prior systems on CMU-MOSEI, CMU-MOSI, and the Multimodal Fusion Strategy Corpus (MFSC), and it uses segment-level and taxonomy annotations to train its taxonomy-alignment component. This paper proposes a Taxonomy–Fusion Co-Discovery Network (TFCD-Net) that breaks this supervision dependency by proposing to reformulate the two tasks of taxonomy discovery and the execution of the taxonomy–fusion as two nested optimization problems that are jointly solved by a single self-supervised objective. A shared soft-assignment vector is used to assign each instance to its taxonomy category and provides a gating signal for a bank of candidate fusion operators, thereby enabling gradient to flow directly back to taxonomy induction from fusion performance, while also incorporating the consistency of cross-modal assignments, the separability of taxonomy assignments, a fusion utility, and a regularizing signal based on assignment-balance. Classified with the same protocol as TG-FSS and its sub-systems, TFCD-Net achieves better sentiment classification accuracy, F1 score and correlation with human judgement (87.1% vs. 85.4%, 0.85 vs. 0.83, 0.84 vs. 0.81 respectively) with zero modality supervision in its training, and the gap in accuracy grows with modality occlusion and cross-dataset transfer. Ablation results confirm that the improvement is directly due to the shared taxonomy-fusion gradient path, rather than to additional model capacity, and post-hoc analysis confirms that the model self-learns a taxonomy cardinality (K = 5) and routing behavior that closely match the hand designed taxonomy used in the field for fusion. The results show that taxonomy discovery and execution of the taxonomy-guided pipelines are two distinct problems, but when they are optimized together, they are reinforcing each other, and that reinforcement can be achieved without annotation cost which is needed for supervised taxonomy-guided pipelines.
A Trust-Aware and Energy Conversion Optimization Framework for Secure and Energy-Efficient Routing in Mobile Ad Hoc Networks
Mobile Ad Hoc Networks (MANETs) are very dynamic, infrastructure free networks prone to constant topology changes, energy limitation and vulnerability to security and as such, efficient routing is an important challenge. The research will contribute to improving the performance of routing in MANETs by resolving challenges pertaining to trust assessment, energy imbalance, and routing unsteadiness. The main aim of this work is to come up with a safe, energy efficient, and adaptable routing structure that enhances network lifetime and reliability of communication. Towards this end, a Modular Aging Optimization Algorithm is proposed to dynamically estimate and refresh node trust basing on direct, indirect and recent trust factors, and an Energy Conversion Optimization Algorithm is proposed to optimize residual energy consumption and routing choices. The proposed framework combines the elements of trust-conscious routing and energy optimization to guarantee the maintenance of the stability and security of data transit. The experimental findings indicate that the proposed solution is more superior to the current machine learning and deep learning-based routing solutions since it has a greater accuracy of 93.50, better energy efficiency of 0.88, shorter end-to-end delay of 14.0 ms, and higher network lifespan. All in all, the proposed methodology is efficient to improve the routing reliability, security, and energy efficiency in MANETs, which is why it can be regarded as a good solution to the future mobile wireless networks.
Exploring the Factors Influencing Employee Adoption of Digital Information Systems
Digital Technology has revolutionized organizational processes, communication, information management, decision-making and much more. Digital Information Systems (DIS) are now so much more than tools to improve operational efficiency, productivity and timely access to information. But how successful are they, as well as how well they work? These systems are dependent on the employees’ willingness and ability to take advantage and use them. In this study “Exploring the factors influencing employee adoption of Digital Information Systems” we will investigate the factors that affect employees’ adoption of digital information systems in the workplace. We will focus on the perceived usefulness, perceived ease of use, system quality, information quality, management support, digital competency and facilitating conditions. The primary data will be collected from employees using a structured survey and questionnaire based on a five-point Likert scale. The collected data will be analyzed using descriptive statistics, reliability analysis, correlation analysis and multiple linear regression to determine the relationship between those factors and employee adoption. To find the factors that significantly affect employees’ adoption of digital information systems and what is their relation to adoption behaviour, we will provide managers and organisations with information to help them plan for technology implementation, training, organizational support, etc. This may also contribute to the literature on information system adoption by providing a comprehensive picture of technological, individual and organizational factors affecting employee adoption in contemporary workplaces.
Impact of AI-Enabled Information Systems and Management Training on Students’ Interview Performance: A Pre- and Post-Training Study
Communication skills play an important role in students’ interview performance and employability. This study examines the impact of a communication skills training programme on students’ communication competencies using a pre- and post-training research design. The study was conducted among 520 students, and data were collected using a five-point Likert-scale questionnaire covering five dimensions: verbal communication skills, non-verbal communication skills, active listening skills, confidence, and interaction skills. A paired-samples t-test was employed to determine whether significant differences existed between students’ scores before and after the training programme. The findings revealed a statistically significant improvement in all five dimensions after the training. The results indicate that the training programme enhanced students’ verbal and non-verbal communication, active listening, confidence, and interaction skills. Among the dimensions, confidence demonstrated the highest improvement. The study concludes that structured communication skills training can significantly enhance students’ communication competencies and contribute to their preparedness for interviews and professional interactions. The findings highlight the importance of integrating communication and soft-skills training programmes into student development and employability initiatives.
The Impact of Emergency Federal Intervention on the Autonomy of Federal Units (A State of Emergency as Case Study)
Declaring a state of emergency is one of the most prominent and influential means of federal emergency intervention in the structure of the federal state, due to the temporary redistribution of powers between the federal authority and the units, and the impact this has on the autonomy of the latter. In this context, the study reveals that most studies have focused on examining the declaration of a state of emergency from the angle of protecting security and the impact on rights and freedoms, without extending to examining its impact on the autonomy of federal units. Based on this research gap, the study adopts a comparative analytical approach to examine the legal organization of declaring a state of emergency and its practical applications, and its impact on the autonomy of units, in three federal systems in which the philosophy of the legislator and the degree of this autonomy differ: (the United States of America, India, and Iraq) within two related criteria: the first is the direct impact of declaring a state of emergency on the autonomy of units, and the second is the effectiveness of constitutional and oversight guarantees in limiting the federal authority’s deviation in using this exceptional measure or normalizing it. The study concluded that the American constitutional system shows a tendency to normalize and politicize the declaration of a state of emergency due to the expansion of legislative authorization and the multiplicity of sources of emergency powers, which is indirectly reflected in the autonomy of federal units. In India, the ambiguity of some constitutional texts and the expansion of others, coupled with the experience of the 1975-1977 state of emergency declaration, contributed to highlighting the tendency towards centralization and the expansion of the impact of the state of emergency on the autonomy of the units, which necessitated the introduction of subsequent constitutional amendments. In Iraq, the absence of a complementary legislative regulation that regulates the provisions of the declaration of a state of emergency contributed to weakening the effectiveness of the constitutional text and the ambiguity of the potential impact of using the state of emergency declaration to undermine the autonomy of the region. Accordingly, the study calls for the development of a legal framework that regulates the declaration of a state of emergency in a federal context based on a balance between the requirements of necessity and the preservation of the autonomy of the units.
Khitam Hamadi Mahmoud, Maha Bahjat Younis
Comparative Analysis of Saffron- and Mint-Enriched Soy Yoghurt: Effects on Physicochemical Characteristics, Quality Attributes, and Storage Stability
The goal is to develop plant-based fermented products that improve gel stability, antioxidant activity, and sensory attributes. Also, the viewpoint should not impair the activity of the food fermentation starter cultures. The current study invented a targeted formulated dataset to analyze day 0, 7, 14 and 21 samples of 3 independent production batches of control Soy Yoghurt (SY0), Saffron-enriched Soy Yoghurt (SYS) and Mint-enriched Soy Yoghurt (SYM) at 4 ± 1°C for 21 days. Physical, rheological, phenolic, antioxidant, and microbial analyses were performed on the samples. Two-way Analysis of Variance (ANOVA), Tukey’s tests, partial eta squared, and Pearson’s correlations were used to analyze the datasets. A second dataset of (24 panelists) was used to assess the sensory attributes. SYM and SYS were shown to have better microbial stability and better antioxidant activity and gel strength, respectively. SYM was shown to have a better syneresis value. ```PHYSCHEMICAL, RHEOLOGICAL ANALYSES, ANTIOXIDANT, MICROBIOLOGICAL ANALYSES , Hardness and b* color of SYS and SYM were better than SYO. The results also indicated that SYM has the best gel stability, SYS has the best antioxidant activity, and SYM has the best syneresis. Saffron has the best sensory color value. The results may indicate that the use of mint and saffron may improve the overall gel strength and storage stability of the product. More validation is needed to support product claims.
Administrative Principles Derived from the Holy Qur'an: A Comparative Study between Iraq and Egypt
This study aims to identify administrative principles derived from the Holy Quran, using a comparative study of Iraq and Egypt. The descriptive, analytical, and comparative methods were employed. The study is divided into two sections: the first, entitled "Quranic Principles Governing the Occupation and Practice of Public Office," and the second, entitled "Quranic Principles Guaranteeing the Proper Functioning of Public Institutions, In conclusion, it was found that there is a noticeable convergence between the Iraqi and Egyptian administrative systems in drawing inspiration from Quranic principles governing public service, despite differences in legislative mechanisms. The researcher recommends benefiting from comparative legislative experiences between Iraq and Egypt to develop the administrative system in a way that achieves harmony between Quranic values and the requirements of modern public administration.
Emotional Intelligence in Leadership: A Cross-Cultural Analysis of Employee Engagement and Retention
Emotional intelligence has increasingly emerged as a critical leadership competency for managing employee relationships, engagement, commitment, and retention in culturally diverse organizational environments. This study examines how leaders’ emotional intelligence influences employee engagement and retention across different cultural contexts, with particular emphasis on self-awareness, self-regulation, empathy, social awareness, and relationship management. The study proposes that emotionally intelligent leadership strengthens trust, communication, psychological safety, organizational commitment, and employees’ willingness to remain with their organizations. It further argues that the effectiveness of emotional intelligence is not culturally uniform because national culture, power distance, collectivism, individualism, communication patterns, and expectations regarding leadership can influence how emotionally intelligent behaviours are perceived and experienced. Drawing on emotional intelligence theory, social exchange theory, leader–member exchange theory, and cultural intelligence perspectives, the study develops an integrated conceptual framework connecting leadership emotional intelligence with employee engagement and retention. The proposed framework provides a basis for examining cultural differences in these relationships and offers implications for leadership development, human resource management, employee well-being, and talent-retention strategies in multinational and multicultural organizations.
The Importance of National Values in Raising Environmental Culture in The Educational System of Uzbekistan
The article examines the importance of national values in shaping environmental culture within the education system. The author emphasizes the significant role of environmental education and upbringing in fostering a healthy relationship between society and nature. The article also discusses the principles of international environmental cooperation, including ensuring healthy environmental conditions for all individuals and recognizing each country's right to use natural resources for its own benefit. Today, educating young people who care about the environment, possess ecological awareness and values, and actively protect nature is considered an urgent task. This study is based on a qualitative approach, including the analysis of pedagogical literature, national educational standards, and policy documents related to environmental education in Uzbekistan. In addition, comparative and descriptive methods were used to examine the role of national values in developing environmental culture. Observations and analysis of teaching practices were also conducted to evaluate current approaches in the educational process. The findings indicate that integrating national values into the educational process significantly contributes to the development of students’ environmental culture. Traditional beliefs, customs, and ethical norms foster respect for nature, conservation of resources, and responsible behavior. The study also shows that interdisciplinary approaches and interactive methods enhance students’ ecological awareness and engagement. In conclusion, the integration of national values into environmental education is an effective strategy for developing environmental culture among students in Uzbekistan. It is recommended to strengthen the role of national traditions and cultural heritage in curricula, apply innovative pedagogical technologies, and promote value-based education to ensure sustainable development and environmental responsibility among future generations.
Regulatory Reform and Digital Transformation of Pharmacovigilance in Uzbekistan: A National Mixed-Methods Analysis of Reporting Patterns, 2023–Mid-2026
Background: Pharmacovigilance systems in emerging regulatory settings are increasingly shaped by concurrent legal, institutional, and digital reforms. Evidence on how these reforms affect national reporting patterns remains limited in Central Asia. Objective: To assess the development of Uzbekistan’s national pharmacovigilance system and changes in safety-reporting patterns between January 2023 and June 2026. Methods: A retrospective mixed-methods study combined structured analysis of national legislation, regulatory standards, implementation records, and training reports with analysis of aggregated pharmacovigilance data. All 1,500 safety reports recorded during the study period were included. Reporting volume, source, and seriousness were summarised using counts and percentages. Between-period differences were assessed using Pearson’s chi-square test and Cramér’s V. Relative risk was calculated for serious-report proportions in 2025 and the first half of 2026. Results: The national system received 380 reports in 2023, 295 in 2024, 285 in 2025, and 540 during January–June 2026. Healthcare professionals’ contribution increased from 21.1% in 2023 to 53.7% in the first half of 2026, while that of marketing authorisation holders decreased from 53.7% to 32.6%. Reporter-source composition differed significantly across periods (χ²=120.02; df=6; p<0.001; Cramér’s V=0.20). Serious-report proportions also differed (χ²=87.58; df=3; p<0.001; Cramér’s V=0.24), decreasing from 47.0% in 2025 to 16.7% in the first half of 2026 (RR=0.35; 95% CI: 0.28–0.44). By June 2026, approximately 700 public healthcare institutions had joined the electronic platform, and 1,880 healthcare professionals had received pharmacovigilance training. Conclusions: Electronic reporting was introduced alongside regulatory reform, institutional registration, and professional training and coincided with expanded direct reporting by healthcare professionals. Because the interventions were concurrent and the 2026 observation covered only six months, the findings indicate temporal association rather than causality. Further evaluation should use equivalent observation periods and case-level indicators of report quality, signal management, and regulatory outcomes.
The Impact of Artificial Intelligence on Financial Decision-Making: An Empirical Study
Artificial Intelligence (AI) is increasingly being used in financial decision-making to analyse large volumes of financial data, identify patterns, assess risks, and provide timely insights and recommendations. Its ability to improve the speed, accuracy, and efficiency of financial analysis has made AI an important decision-support tool for individuals and organisations. The present study examines the impact of Artificial Intelligence utilization on financial decision-making, with particular emphasis on whether greater use of AI contributes to more effective, accurate, and confident financial decisions. The study adopts a quantitative research approach and collects primary data through a structured questionnaire from 128 valid respondents. AI utilization is considered the independent variable, while financial decision-making is the dependent variable. The study uses descriptive statistics, correlation analysis, and regression analysis to examine the relationship and impact between the two variables. The findings are expected to demonstrate a significant positive relationship between AI utilization and financial decision-making, thereby indicating that effective use of AI can enhance the quality and efficiency of financial decisions.
The Impact of AI Usage Frequency on Critical Thinking Skills among University Students: A One-Way ANOVA Analysis
The rapid adoption of generative artificial intelligence (AI) tools in higher education has sparked debate regarding their impact on students’ critical thinking abilities. While concerns about cognitive offloading are prevalent, empirical evidence regarding the rela-tionship between AI usage frequency and critical thinking remains limited. This study examines whether students who use AI tools at different frequencies demonstrate varying levels of critical thinking competence. A cross-sectional survey was administered to 400 university students, measuring critical thinking skills using a 12-item scale based on the California Critical Thinking Skills Test framework. Participants were categorized into five groups based on AI usage frequency: Never (n = 25), Rarely (n = 43), Weekly (n = 59), Few times a week (n = 154), and Daily (n = 119). A one-way analysis of variance (ANOVA) was conducted to examine group differences. Results indicated no statisti-cally significant difference in critical thinking scores across AI usage frequency groups, F (4, 395) = 1.47, p = .212, η2 = .015. All groups demonstrated mean scores above the scale midpoint, with “Never” users (M = 3.86, SD = 0.68) and “Daily” users (M = 3.78, SD = 0.74) showing slightly higher scores than moderate users. The findings suggest that the frequency of AI use is not associated with critical thinking abilities, challenging assumptions that AI engagement necessarily impairs cognitive development. Implications for educational policy and pedagogy are discussed.
COVID-19 Pandemic’s Impact on Education Systems. Gendered Language Use and Equity in Technology-Mediated English Learning: An Empirical Linguistic Study
Background: The integration of Information and Communication Technology (ICT) into English Language Teaching (ELT) signifies a major transformation in pedagogical approaches, a shift accelerated by the COVID-19 pandemic’s impact on education systems. ICT adoption offers novel opportunities to enhance learner engagement, foster inclusivity, and address gender disparities in language education. This study, “Technology-mediated language pedagogy and gender equity: An exploration in Azerbaijan,” aims to investigate how different ICT tools and platforms contribute to promoting gender equity and inclusivity within ELT classrooms across Azerbaijan’s key regions of Baku, Ganja, and Sumgait. Methods: Employing a mixed-methods research design, the study combined quantitative data collected via structured surveys with qualitative insights derived from in-depth interviews and classroom observations. The research evaluated the accessibility, utilization, and effectiveness of various technology-mediated resources including course management systems (CMS), virtual meeting software, interactive language applications, and computer-assisted language learning (CALL) tools. Additionally, it assessed educator and learner readiness, institutional support, and policy frameworks influencing gender equitable practices in digital ELT environments. Results: Analysis revealed notable gender-based differences in access to and use of ICT tools. While male learners generally reported higher levels of independent access and greater confidence with technology, female learners exhibited comparable or higher motivation to engage with digital resources when access barriers were minimized. Systemic and sociocultural challenges persisted that disproportionately affected female students’ ability to fully benefit from technology-enhanced learning. Insights: The findings underscore the pivotal role of targeted interventions to bridge gender gaps in technology access and usage within ELT. Technology, when implemented thoughtfully, can foster balanced gender participation, increase learner autonomy, and enable personalized, inclusive language instruction aligned with diverse learner needs. The study highlights the importance of culturally responsive pedagogy and gender-sensitive professional development to equip educators in maximizing the equitable integration of ICT.
Biodiesel Production from Waste Cooking Oil: Opportunities for Sustainable and Circular Energy Development
The primary aim of this review paper is to present a step-by-step detail, problem and solutions to boost the output of the biodiesel production by incorporating different microorganisms that have the ability of producing the enzyme to transform used cooking oil to biodiesel. The article discusses the current developments in biodiesel production, with an emphasis on the use of waste cooking oils (WCOs) as a green raw material. The existing methods of transesterification, including heterogeneous acid catalysts, alkali catalysts and enzyme-catalyzed reactions are also outlined. The enzymes required in such kinds of reactions are also addressed as well as the benefits of the enzymatic method compared to the other methods. The most promising enzymes to convert biodiesel are the lipases at the moment. The tenets of the circular economy are observed by employing waste cooking oils (WCOs) in the biodiesel production process, as well as by tackling the issues of poor disposal of these products. Finally, the possibility of biodiesel production through the utilization of leftover cooking oils provides an opportunity to decrease fossil fuel reliance, which can help advance a sustainable agenda and the use of the circular economy.
When Meaning Is Not Enough: Cultural, Phonological, and Semiotic Constraints in Arabic-English Trademark Translation
Trademark translation between Arabic and English in Saudi Arabia is not a routine act of lexical transfer but a high-stakes form of cross-cultural brand mediation. Even where denotational meaning is preserved, brand value can be weakened by phonological mismatch (unlicensed consonants, stress patterns, syllable structures), script-direction effects on visual salience, and semiotic interference (unintended indexical meanings, prestige/register shifts, taboo adjacency, or religious sensitivity). This article develops a theory-dense account of Arabic-English trademark translation by modelling it as multi-constraint optimisation rather than equivalence. We propose a constraint framework-the Cultural-Phonological-Semiotic (CPS) model-in which successful brand-name transfer maximises (i) cultural acceptability, (ii) phonological fit and processability, and (iii) semiotic coherence with brand positioning and category cues, under the additional requirements of legal distinctiveness and consumer trust. Using a curated dataset of thirty Saudi-market trademark pairs (Arabic/English forms used on packaging, storefronts, or apps) and an expanded bank of constructed examples to isolate specific mechanisms, we show why literal translation often produces semantic dilution and register drift, while pure transliteration can introduce unintended meanings or weak memorability. We then derive innovative strategies that move from “translation” to brand-oriented transcreation: hybrid semantic-phonetic blends, culturally filtered naming, semiotic mapping of associative fields, consumer-tested validation, and bounded AI-assisted screening for semantic and reputational risk. The contribution to translation theory is threefold: (1) it specifies the translation unit as a brand sign rather than a word, (2) it operationalises semiotic constraints (iconic, indexical, symbolic) as design-relevant criteria, and (3) it formalises trademark translation as constraint satisfaction in which trade-offs can be justified and empirically tested.
Abdul-Wasea Abdul-Ghani Saif Al-Mekhlafi
The Impact of Translation Quality on Brand Equity and Consumer Trust in the Saudi Market
Translation is often treated as a downstream production task: a campaign is written, approved, and only then sent for conversion into Arabic. That sequence overlooks the commercial work performed by translated language. In the Saudi market, a product page, mobile interface, warranty, advertisement, chatbot reply, or complaint response does more than transfer information. It tells consumers whether a brand is competent, attentive, culturally present, and likely to honour its promises. This article develops an integrative conceptual account of how translation quality shapes consumer trust and customer-based brand equity in Saudi Arabia. Drawing on translation studies, signalling theory, processing-fluency research, e-commerce trust literature, and brand-equity scholarship, it proposes a Translation Quality–Trust–Equity framework. Translation quality is defined through seven connected dimensions: semantic and factual accuracy; functional clarity and completeness; terminological and cross-touchpoint consistency; cultural-pragmatic congruence; brand-voice coherence; digital usability; and transparency, legal, and ethical adequacy. The framework argues that these dimensions influence trust indirectly through cognitive fluency, perceived product and service quality, brand credibility, cultural respect and authenticity, and lower perceived risk. Trust then strengthens awareness, favourable associations, loyalty, willingness to recommend, and resistance to competitors. The effects are expected to be particularly strong for high-risk or technically complex purchases and in digital settings where language substitutes for physical inspection and face-to-face reassurance. The analysis also rejects a simplistic Arabic-versus-English prescription: language choice creates value only when it fits the audience, category, touchpoint, and brand promise. The article concludes with eight research propositions, a practical governance model for Saudi-facing brands, and an empirical agenda for testing causal and longitudinal effects.
Abdul-Wasea Abdul-Ghani Saif Al-Mekhlafi, Sayed M. Ismail
Testing the L2 WTC Pyramid in Business English: Anxiety, Boredom, Mental Toughness, and Resilience
In Saudi Arabia, Business English courses often assess speaking, yet many undergraduates still avoid initiating classroom talk. Willingness to communicate (WTC) explains why students with adequate language knowledge may remain silent. This study tests an integrated model of Business English WTC by combining MacIntyre’s L2 WTC pyramid with two emotion mechanisms (anxiety as threat; boredom as low value) and two coping resources (mental toughness; resilience). A cross-sectional survey is implemented in a College of Business Administration at a public university in Saudi Arabia. Students in credit-bearing Business English courses complete a scenario-based WTC measure and short scales for course-referenced situational self-confidence, desire to communicate, speaking anxiety, boredom, mental toughness, and resilience. Confirmatory factor analysis and structural equation modelling estimate direct effects, indirect effects via situated antecedents, and buffering (moderation) effects of coping resources. Anxiety and boredom are expected to reduce WTC directly and indirectly by lowering situational self-confidence and desire to communicate. Mental toughness and resilience are expected to predict higher WTC and to weaken the negative impact of anxiety and boredom. The study models two routes to reduced participation in an ESP setting—threat-based inhibition and value-based disengagement—within one pyramid-consistent framework, providing a replicable analytic blueprint for Saudi Business English programmes. WTC in Business English is a situated intention shaped jointly by emotions and coping resources, not language competence alone.
Motivational Regulation in Business Undergraduates’ EFL Learning: A Self-Determination Theory Study
Self-determination theory explains motivational quality through the satisfaction of autonomy, competence and relatedness. In business-college EFL, English functions both as a programme requirement and as a career resource, so students may pursue learning for mixed self-determined and controlled reasons. This study aims to extend SDT-based measurement of motivational regulations and basic psychological needs among business-college EFL learners in Saudi Arabia, and to examine how need satisfaction relates to intrinsic motivation, internalised regulation and external regulation. A cross-sectional survey will be administered to EFL students at the College of Business Administration, Prince Sattam bin Abdulaziz University (Al-Kharj, Saudi Arabia). Instruments will measure motivational regulations and need satisfaction using SDT-informed EFL scales. Data will be analysed using factor analysis to validate the measurement structure and regression/structural modelling to estimate relationships between needs and motivation types. Reliability and construct validity will be examined using internal consistency and model-fit evidence. The analysis is expected to support three motivation factors (intrinsic; identified/introjected; external) and three need factors (autonomy; competence; relatedness). Higher need satisfaction is expected to relate more strongly to intrinsic and internalised regulations than to external regulation, with autonomy showing a negative association with external regulation. The study provides an SDT-based diagnostic workflow for business-oriented EFL programmes and identifies actionable need-support levers that can strengthen higher-quality motivation in Saudi higher education. Validating SDT measures in this setting and modelling need–motivation links can inform targeted pedagogical support aligned with students’ academic and career goals. Future research should complement cross-sectional evidence with longitudinal and classroom-intervention studies that strengthen autonomy support, competence-building feedback and relatedness in EFL instruction.
Strategic Agility, Entrepreneurial Orientation, and Operational Performance in Small Enterprises: An Integrative Review
This article examines how entrepreneurial orientation, strategic agility, and operational performance are related in small enterprises. It argues that entrepreneurial orientation is best understood as a strategic posture, while strategic agility explains whether that posture is translated into coordinated operational action. The article uses a focused integrative review of 20 published academic sources drawn from strategic management, entrepreneurship, organizational behavior, and operations management. The review is interpretive rather than systematic and aims to clarify conceptual relationships, identify recurring tensions, and develop a more precise account of how performance emerges in small-firm settings. The literature converges on four main points. First, entrepreneurial orientation explains how firms approach opportunity, innovation, and risk more convincingly than it explains operational performance directly. Second, strategic agility is better understood as a disciplined capacity to sense, prioritize, and reconfigure than as generic flexibility or speed. Third, in small enterprises, leadership judgment, informal coordination, and resource scarcity strongly condition whether entrepreneurial intent is enacted effectively. Fourth, operational performance depends on implementation quality, routine alignment, and responsiveness rather than on innovativeness or risk-taking alone.The article offers an integrative argument that strategic agility is the translating mechanism through which entrepreneurial orientation becomes operational performance in small enterprises. In doing so, it shifts attention from broad entrepreneurship–performance claims to the organizational work of prioritization, adaptation, and enactment. For owner-managers, the central implication is that entrepreneurial energy must be matched by lightweight but explicit decision and operating routines. Small firms improve performance not simply by pursuing more opportunities, but by evaluating them selectively, reallocating scarce effort quickly, and converting strategic intent into reliable execution.
Willingness to Communicate in Business English: Testing MacIntyre’s L2 WTC Pyramid Model Through Anxiety, Boredom, Mental Toughness, and Resilience
Willingness to communicate (WTC) is widely recognized as a proximal driver of second language use, yet Business English programs often struggle to translate classroom competence into spontaneous participation in professional genres such as meetings, presentations, negotiations, and problem-solving talk. Building on MacIntyre, Clément, Dörnyei, and Noels’ L2 willingness-to-communicate (L2 WTC) pyramid, this article develops a context-sensitive model for Saudi business administration colleges that connects two inhibiting achievement emotions (anxiety and boredom) with two adaptive psychological resources (mental toughness and resilience) (MacIntyre et al., 1998; MacIntyre, 2007; Dudley-Evans & St John, 1998; Nickerson, 2005). Anxiety and boredom are posited to constrain Business English willingness to communicate (WTC) both directly and indirectly through two situated antecedents in the pyramid: state communicative self-confidence and desire to communicate with specific interlocutors. Conversely, mental toughness and resilience are conceptualized as adaptive resources that consolidate self-confidence, sustain engagement under evaluative pressure or monotony, and attenuate the detrimental influence of anxiety and boredom on communicative intention (Horwitz et al., 1986; Pekrun, 2006; Li et al., 2021; Clough et al., 2002; Smith et al., 2008). The article is structured as a theory-driven research paper and delineates an empirical design for testing these relationships in a multi-site survey of undergraduate business administration students enrolled in Business English courses in Saudi Arabia (target N = 500–700). Measures are adapted to authentic Business English scenarios and analyzed via confirmatory factor analysis, structural equation modeling, and latent interaction techniques. The proposed contribution is an integrated account of affective barriers and endurance-oriented resources within the WTC pyramid, alongside pedagogical implications for designing Business English classrooms in which communication becomes both instrumentally worthwhile and psychologically manageable.
Constructing the Transforming University: Governance, Leadership, Academic Culture, and Quality Assurance in the Institutional Discourse of Prince Sattam bin Abdulaziz University
Saudi higher education is being reshaped by a policy environment that simultaneously expands institutional responsibility and intensifies evaluation, accreditation, performance monitoring, and alignment with national development priorities. This article examines how Prince Sattam bin Abdulaziz University (PSAU) discursively constructs that changing institutional order. It integrates a substantial body of Saudi scholarship on governance, leadership, quality assurance, academic accreditation, human capital, and institutional performance with a qualitative institutional discourse analysis of publicly available PSAU documents issued or maintained between 2019 and 2026. The corpus comprises the University’s strategic, identity, governance, quality, accreditation, academic-programme, and Vision 2030 texts. The analysis identifies five interconnected constructions. First, autonomy is represented as governed discretion exercised within national frameworks, authorised procedures, and measurable objectives. Second, leadership is presented as participatory and capacity-building, yet remains embedded in formally hierarchical approval structures. Third, academic culture is constituted through the recurring values of quality, mastery, transparency, teamwork, innovation, responsibility, and continuous improvement. Fourth, accreditation is transformed from an external inspection event into an internal architecture of evidence, review, benchmarking, and programme redesign. Fifth, institutional transformation is legitimised through alignment with Saudi Vision 2030, labour-market relevance, digitalisation, research impact, sustainability, and community partnership. These constructions are productive but not tension-free: participation coexists with central authorisation, innovation with standardisation, and improvement with compliance. The article contributes a discourse-centred account of Saudi university transformation and proposes practical safeguards for ensuring that governance and quality systems develop institutional learning rather than merely expanding documentary workload.
A Data-Informed Governance Model for Enhancing Institutional Decision-Making and Effectiveness at Prince Sattam bin Abdulaziz University
Universities now possess more data than ever, yet greater data volume does not automatically produce better decisions. The decisive issue is governance: who defines institutional questions, which data are trusted, how evidence is interpreted, how professional judgement enters deliberation and whether decisions are later evaluated. This study develops a data-informed governance model tailored to Prince Sattam bin Abdulaziz University (PSAU). It combines an integrative review of research on higher education governance, data governance, analytics, quality assurance and organisational learning with a qualitative analysis of public PSAU strategic and organisational documents current to 21 August 2026. The analysis identifies important enabling assets, including a strategic commitment to quality and continuous development, a Data and AI Office, a Business Intelligence Unit, a Statistical Studies and Decision Support Administration, an Institutional Excellence Unit, open-data commitments, enterprise-architecture work and a 2026-2028 digital transformation strategy. Public documents, however, do not demonstrate an end-to-end routine linking priority questions, shared definitions, validated evidence, deliberation, implementation and learning. The proposed PSAU Data-Informed Governance Model addresses this coordination challenge through six stages: framing the decision, assembling trusted evidence, analysing and contextualising findings, deliberating and deciding, implementing and monitoring action, and evaluating outcomes. Decision rights, stewardship, privacy, ethical review, data literacy and change leadership operate across the cycle. The term data-informed is deliberate: university decisions require contextual knowledge, academic judgement and attention to values alongside quantitative evidence. A phased roadmap, governance-role matrix and evaluation framework are provided.
Human Capital Development and Its Contribution to Institutional Sustainability and Community Impact at Prince Sattam bin Abdulaziz University
Universities are increasingly judged not only by the education they provide and the research they publish, but also by their capacity to remain resilient and create visible value for the communities they serve. This article examines human capital development as the connecting mechanism between those expectations at Prince Sattam bin Abdulaziz University (PSAU). It adopts a theory-informed integrative review and institutional document analysis, bringing together research on human capital, sustainable human resource management, organizational learning, resilience, university sustainability, and the third mission of higher education. Official PSAU and Saudi Vision 2030 documents are read alongside this literature to identify how investment in faculty, professional staff, leaders, and students can generate durable institutional and social outcomes. Five mutually reinforcing pathways are identified: teaching and learning quality; research and innovation capacity; digital and administrative capability; leadership, succession, and organizational resilience; and community-engaged knowledge application. The analysis argues that counts of courses, participants, publications, or volunteer hours are insufficient on their own. PSAU needs an integrated measurement system that traces development inputs through capability gains, workplace transfer, institutional outcomes, and community-level effects. A Human Capital–Institutional Sustainability–Community Impact framework is proposed to connect workforce planning, professional learning, knowledge retention, partnerships, and impact evaluation. The framework offers a practical basis for aligning human capital policy with PSAU’s strategic goals, Saudi Arabia’s Human Capability Development Program, and the wider responsibilities of a regional public university.
Digital Dining and Grocery Shopping: Consumer Behaviour Change in Covid-19 Pandemic-Era in South Korea
The COVID-19 pandemic changed conventional consumption habits profoundly and pushed the development of digital food and grocery delivery services around the world. The present study investigates changes in South Korean consumer behaviours in the usage of food delivery and online grocery apps before and during the pandemic, and whether these changes will be sustained. Survey results were gathered from 200 respondents using a quantitative, deductive approach, and evaluated by descriptive statistics, cross-tabulations, and chi-square tests of homogeneity. The results show a statistically significant increase in usage for both types of applications. The number of respondents who used food delivery apps at least three times a week jumped from 8.5% pre-pandemic to 44.5% during the pandemic (χ²(2, N = 393) = 93.97, p < .001, Cramér's V = .49). Similarly, weekly online grocery usage considerably increased (χ²(2, N = 211) = 47.01, p < .001, Cramér's V = .47). Convenience was the primary self-reported rationale for increasing use, and not infection worries or social distancing rules, and 80.5% of respondents said they intended to continue using at least one sort of delivery app after the pandemic. The results indicate that the pandemic served as a large-scale forced experiment that shifted from situational adoption to habitual use motivated by convenience. This study provides theoretical implications for the consumer behaviour change during the crisis, and practical suggestions for delivery platforms that want to convert the pandemic-acquisition to long-term retention.
Traditional Paper-Based Versus Electronic Formative Assessments in Achievement of Course Learning Outcomes
Formative assessment serves as both a measurement and instructional tool in academia by providing continuous feedback to refine teaching strategies in real time and guides students towards achievement of course learning outcomes. While traditional paper-based method of formative assessments remains widely used in practice, electronic assessments are rapidly gaining momentum due to scalability, immediate feedback and enhanced student engagement. This study evaluated the impact of formative assessments on students’ achievement of CLOs across different domains of learning and comparison of traditional versus electronic formats. Data was collected from selected courses within a six-year bachelor’s degree program during the academic year 2024–2025 with predetermined inclusion and exclusion criteria to ensure consistency and reliability. Convenience non-probability sampling technique was adopted, and sample size determination was conducted using G*power software, and data analysis was performed using SPSS version 28. Pearson correlation analysis demonstrated positive relationship between performance of students in formative assessments and CLO achievement in the two learning domains out of three at the end of the course {knowledge (r=0.80) and values (r=0.88)}, with overall r=0.86. The number of assessments carried out in electronic mode showed a stronger correlation with CLO achievement (r=0.61) than the traditional paper-based methods (r=0.29). Furthermore, study findings indicate that the formative assessments frequency especially through electronic mode strongly influences CLO achievement and overall student learning. Hence, institutions can integrate formative assessments in its academic structure, assessment schedules, and quality of feedback, and implement digital platforms to increase student engagements in the courses.
Burden of Irritable Bowel Syndrome on Quality of Life Among Saudi Adults
Background: Irritable Bowel Syndrome (IBS) is a prevalent functional gastrointestinal disorder. The consequences of IBS extend beyond physical discomfort, as the condition severely impacts patients' quality of life. This study assessed the impact of IBS on behavioural, emotional, and social dimensions of quality of life among Saudi adults. Methods: A quantitative cross-sectional study was conducted among the Saudi population. Data were collected from the participants via online questionnaires. All participants were asked to complete self-administered questionnaires. Participants completed the validated IBS-36 questionnaire and demographic measures. Descriptive and inferential analyses (Mann–Whitney U and Kruskal–Wallis tests) were used to evaluate associations between quality-of-life domains and sociodemographic variables. Results: A total of 317 respondents were included (73.2% female, 26.8% male; mean age 24 years). Emotional distress was pronounced, with 53.3% fearing symptom worsening and 50.8% reporting frequent tearfulness. Behavioural adjustments were common: 43.8% avoided favourite foods and 42.3% left social settings suddenly due to symptoms. Social and occupational functioning were also affected, with 40.4% reporting work interference and 30% difficulty enjoying leisure time. Female patients reported significantly greater emotional symptoms and bowel severity than males (p≤0.003). Married individuals experienced greater disruption in daily activities compared to singles (p≤0.001). Conclusion: These findings underscore the pervasive impact of IBS on quality of life and emphasize the need for comprehensive management strategies for IBS patients in Saudi Arabia. Future research exploring the role of dietary and lifestyle modifications could provide further insights into effective management strategies.
Deep Learning-Based Real-Time Classification of Regional Garlic Varieties of Karnataka for Food Quality Assurance
Being a medicinal food, garlic (Allium sativum) is used for its antimicrobial, antioxidant, cardio protective and immune-modulatory properties, as well as for culinary use as a spice; its nutraceutical contents vary systematically among cultivars, and correct identification of regional garlic varieties is also important to ensure consistency in nutraceutical quality, food-industry standardisation, and preventing adulteration or mislabelling in the functional-food supply chain. At present, in Karnataka, regional cultivars like Agrifound White (G-41), Yamuna Safed (G-1), Bhima Omkar and Bhima Purple are differentiated manually by visual inspection, which is subjective, time-consuming and requires skilled personnel, and is likely to create quality-control risks in the downstream process under varying light conditions. To overcome this deficiency, in this study a garlic-variety classification system was developed and evaluated based on image processing and deep learning to classify garlic varieties in real-time. First, bulb images were pre-processed with OpenCV, localised in video or uploaded images using a YOLOv8-based object detection network, then classified into their regional variety or not using a VGG19-based CNN trained on clove-pattern, skin-texture and colour features and presented in a simple, user-friendly PyQt5 desktop application without any technical skills required. The overall accuracy of the system was 91% on the 5 class evaluation set, with the F1 scores of three out of four varieties being above 0.90. The proposed system could help maintain the nutraceutical quality and authenticity of garlic brought into food and medicinal supply chain in Karnataka by providing consistent and cost-effective variety verification.
Administrative Overload as an Implementation Cost: How Frequent Policy Reforms Affect Frontline Maternal Healthcare Governance in India
This comprehensive empirical study meticulously examines the administrative friction and operational bottlenecks introduced by high-frequency policy reforms in maternal healthcare governance in India. In recent years, the Indian healthcare architecture has undergone a radical transformation towards digital governance, biometric tracking, and conditional cash transfers, all intended to enhance service delivery and eliminate systemic leakages. However, this study hypothesizes that these rapid rollouts generate severe, unquantified "implementation costs" at the grassroots level, disproportionately borne by India’s frontline health workforce. Utilizing an extensive mixed-methods approach, including the novel development of the Frontline Administrative Burden Index (FABI), we surveyed 450 Accredited Social Health Activists (ASHAs) and Auxiliary Nurse Midwives (ANMs) across three highly vulnerable Indian states (Uttar Pradesh, Bihar, Madhya Pradesh). Our simulated, granular findings indicate an alarming 42% increase in documentation time over the last three years. Crucially, this administrative explosion inversely correlates with a 28% reduction in direct patient-care time. Using case studies related to the Ayushman Bharat Card saturation campaign and the Pradhan Mantri Matru Vandana Yojana (PMMVY), this article shows the exact manner through which documentation fatigue and portal mandates hinder the effectiveness of maternal health services. In conclusion, this paper recommends a restructuring of frontline bureaucratic tasks in order to put priority on patients’ care rather than compliance to algorithms.
When Toxicity Breeds Compliance: Sycophancy, Alienation, and the Transformation of Workplace Relations
This study examines how workplace toxicity reshapes social relations in organizations by fostering sycophantic behaviors and reinforcing organizational alienation, ultimately influencing employee withdrawal. Moving beyond linear models of turnover, we draw on social exchange theory to conceptualize toxicity not merely as a stressor but as a relational condition that reconfigures patterns of interaction, power, and compliance. Using survey data from 239 employees in India and structural equation modeling, we test a mediated framework linking workplace toxicity, sycophancy, organizational alienation, and turnover intention. The findings reveal that workplace toxicity does not directly predict turnover intention; rather, it operates indirectly by encouraging sycophantic behaviors, which in turn intensify feelings of alienation. Organizational alienation emerges as a key mechanism translating these altered social dynamics into withdrawal intentions. These results challenge dominant assumptions that toxic environments lead to immediate exit, instead highlighting how employees may initially adapt through political compliance before disengaging. By foregrounding the relational and processual consequences of toxicity, the study contributes to a deeper understanding of how organizational contexts produce compliance and disconnection. The findings offer critical insights into the reproduction of power and the erosion of authentic work relations in contemporary organizations.
AI-Driven Blockchain Framework for Optimizing Latency, Reliability, and Energy-Efficient QoS in 5G/6G Mobile Networks
The 5G and beyond-6G next-generation wireless networks require ultra-low latency, ultra-high reliability, and ultra-low power consumption in order to accommodate mission-critical and massive-scale IoT cases. Classical centralized network control cannot fulfill such high-quality QoS needs, particularly under explosive data expansion and device proliferation. This paper outlines a new AI-enabled blockchain architecture that collaborates edge computing with federated learning for intelligent control of network resources, yet exploits blockchain's distributed trust for added security and confidentiality. In this paper an architecture in which AI agents at the edge of a network dynamically adjust resource control (spectrum, compute, power) for minimum latency and power consumption, and maximum reliability, such that decisions and information exchange are immutably stored on a blockchain ledger for security and visibility is described. A reinforcement learning-based algorithm with adaptive power management is developed to orchestrate base station activity and edge service placement, guided by smart contracts that enforce fair and reliable resource sharing across the network. We evaluate the framework against state-of-the-art approaches through simulations. The results demonstrate that our approach significantly reduces end-to-end latency and network energy consumption while improving reliability (packet success rates and outage probability) compared to conventional methods. The federated learning mechanism integrated helps maintain data privacy for users while not compromising model quality. In general, this AI-blockchain configuration offers a scalable, secure QoS administration paradigm for 5G/6G networks, which helps pave the way for intelligent, automated future wireless networks.
A Mathematical Optimization Framework for Cost-Efficient Inventory Management in MSMEs
One of the major difficulties in inventory management that Micro, Small, and Medium Enterprises (MSMEs) encounter is fluctuating market demand, resource limitations, and rising operational costs. In this paper, we present a mathematically optimized formulation aimed at reducing the total inventory and supply chain operational costs while maximizing efficiency within a door hinge manufacturing MSME. The optimization formulation integrates procurement cost, transportation cost (inbound and outbound), facility operational cost, and inventory holding cost together with operational constraints such as supplier capacities, facility production capacities, inventory balance equations, and customer demand requirements. The optimization problem is developed using Mixed Integer Linear Programming (MILP) and solved using Microsoft Excel Solver to achieve an optimal allocation of resources. Real industrial data from a door hinge manufacturer is utilized to validate the model. Comparative benchmark evaluation reveals that the proposed MILP model reduces total supply chain operating costs by 30.2% compared to traditional MSME practices, slashes ending inventory capital by 51.4%, and achieves a 100% customer demand fulfilment service level.
Natural-Resource-Based Approaches to Employee Well-Being under Flexible Work Arrangements: Evidence from IT Companies
Flexible work arrangements have transformed the nature of work in IT organizations, creating opportunities for greater autonomy while simultaneously increasing digital overload, blurred work–life boundaries, and difficulties in psychological recovery. This study examines how natural-resource-based approaches can support employee well-being under flexible work arrangements, with particular emphasis on natural-resource exposure and employee recovery. A hybrid mixed-method approach is proposed, integrating a three-wave longitudinal quantitative design, qualitative interviews, and machine-learning techniques. Illustratively, data from 450 IT professionals are analysed using descriptive statistics, correlation analysis, time-lagged regression, mediation and moderation analysis, while qualitative interviews provide contextual insights into employees’ experiences of flexible work and nature-based recovery. Clustering and classification algorithms are additionally employed to identify heterogeneous employee well-being profiles and generate data-driven recommendations. The illustrative findings indicate that flexible work arrangements are positively associated with employee well-being, while natural-resource exposure demonstrates significant positive relationships with employee recovery and well-being. Recovery partially mediates the relationship between natural-resource exposure and well-being, while natural-resource exposure strengthens the positive association between flexible work and well-being. Machine-learning analysis further identifies distinct employee profiles, highlighting differences in environmental resources, recovery, stress, and well-being. The study extends the Job Demands–Resources perspective by conceptualising natural-resource exposure as an environmental resource that can complement flexible work and facilitate employee recovery. The findings offer implications for developing healthier, sustainable, and differentiated flexible-work policies in IT organizations.
ResTAC-Net: A Residual Texture-Attention Convolutional Network for Robust Thyroid Nodule Classification from Ultrasound Image
Thyroid cancer is a type of endocrine malignancy that is increasing rapidly worldwide. Prompt and accurate diagnosis is necessary to optimize patient outcomes. Ultrasound is frequently employed for evaluating thyroid nodules because it is non-invasive, cost-effective, and provides real-time imaging. However, interpretation accuracy often relies on the radiologist's experience, which can result in inter-observer variability. This paper presents a fully automated deep learning system for more precise classification of thyroid nodules. The approach incorporates advanced image preprocessing techniques and a custom residual convolutional neural network (CNN), trained from the initial stages to address variability challenges. The utilized dataset is extensive, comprising 5,195 thyroid nodule images from two public databases, the Digital Database of Thyroid Image (DDTI) and the Thyroid Nodule Ultrasound Image (TNUI) dataset. Both datasets include benign and malignant cases verified by pathology. The proposed network integrates residual learning modules to enhance feature extraction, as well as adaptive feature aggregation, spatial attention mechanisms, and multi-scale convolutional operations to improve performance. Preprocessing steps include standardizing the region of interest, enhancing contrast, normalizing intensity, and reducing speckle noise to facilitate efficient feature learning by the model. Experimental results indicate high effectiveness, with an overall accuracy of 97.3 percent, sensitivity of 96.8 percent, specificity of 97.7 percent, F1 score of 97.1 percent, Matthew’s correlation coefficient of 0.94, and an AUC of 0.99. Comparative analysis demonstrates that the proposed custom residual CNN outperforms established deep learning architectures under similar conditions. The suggested framework provides a robust and reliable computer-aided diagnostic tool that can support clinicians in thyroid cancer screening, improve diagnostic precision, and reduce inter-observer variability in ultrasound radiation.
CNN-Based System for Voice Disorder Severity Assessment Using Speech Signals
Voice disorders in children can affect communication, speech development, and academic performance, making early identification and severity assessment important. This study proposes an Intelligent CNN-Based System for Voice Disorder Severity Assessment Using Speech Signals for children below 12 years of age. The study was conducted to develop an accessible, non-invasive, and cost-effective automated approach for assessing voice-disorder severity using short speech recordings obtained through commonly available mobile devices. Speech samples of 0.2–0.8 seconds were recorded using a mobile phone in a controlled recording room. From each speech signal, a combined set of acoustic features, including Mel-Frequency Cepstral Coefficients (MFCCs), Jitter, Shimmer, and Harmonics-to-Noise Ratio (HNR), was extracted. The extracted features were used to develop four classification models: Support Vector Machine (SVM), K-Nearest Neighbors (KNN), Random Forest (RF), and Convolutional Neural Network (CNN). Experimental evaluation demonstrates that the proposed framework achieves an accuracy of approximately 95%, indicating its ability to distinguish different severity levels from voice recordings. Compared with conventional machine-learning approaches. The models make use of three categories for severity classification of voice disorders namely Low, Moderate, and High. Data augmentation was employed to improve the generalization of the model and tackle the class imbalance. The performance of both the proposed and the comparative model was evaluated using several performance metrics including accuracy, sensitivity, specificity, precision, and F1-score. Results from the experiment illustrate the effectiveness of machine learning and deep learning techniques in identifying severity. The proposed CNN-based system provides a promising approach for rapid and automated preliminary voice-disorder severity assessment in children and may support early screening in resource-limited and non-clinical environments.
Automated Variety Identification and Polishing Status Classification of Tur Dal Grains Using Computer Vision and Machine Learning
Manual grading of tur dal (Cajanus cajan) remains subjective, inconsistent, and poorly scalable, underscoring the need for objective, image-based inspection. This study develops an integrated computer vision–machine learning framework for simultaneous variety identification and polishing-status classification of tur dal grains. Twenty-five morphometric, colorimetric, surface-optical, and defect-related features were extracted from 1,500 grain images (five varieties, two polishing states) and validated statistically (one-way ANOVA, chi-square, p < .001) before training a multi-layer perceptron classifier. On a held-out test set (n = 300), the model achieved 99.50% accuracy, 0.0006 loss, and precision/recall/F1 nearly equalling to 1.00 across grades, with ROC–AUC approaching 1.00; permutation importance identified weevil damage, colour uniformity, and broken-grain percentage as dominant predictors. Findings confirm feasibility under controlled imaging conditions, though generalisability to field-acquired, variably lit samples remains untested. The study's originality lies in unifying three inspection tasks within one interpretable, statistically grounded pipeline for an understudied pulse crop.
Artificial Intelligence–Driven Medical Image Processing for Early Cancer Diagnosis and Clinical Decision Support
Cancer remains one of the leading causes of mortality worldwide, and early diagnosis plays a crucial role in improving treatment outcomes and patient survival. Recent advances in artificial intelligence (AI) and medical image processing have transformed computer-aided diagnosis by enabling automated detection, segmentation, and classification of cancerous lesions from medical imaging modalities such as computed tomography (CT), magnetic resonance imaging (MRI), mammography, histopathology, ultrasound, and positron emission tomography (PET). Deep learning algorithms, particularly convolutional neural networks (CNNs), have demonstrated remarkable capability in extracting discriminative imaging features while reducing diagnostic variability associated with manual interpretation. This study presents an AI-driven medical image processing framework for early cancer diagnosis and clinical decision support. The framework integrates image preprocessing, lesion segmentation, deep feature extraction, classification, and decision support into a unified pipeline designed to improve diagnostic efficiency and accuracy. In addition, the study discusses the challenges associated with data heterogeneity, model interpretability, limited annotated datasets, and clinical deployment. The proposed framework highlights the potential of AI-assisted imaging systems to support radiologists and clinicians by facilitating early cancer detection, reducing diagnostic workload, and enabling precision medicine.
From Explanation to Intervention: An Audit-Debug-Refine Pipeline for Sequential Recommendation
Explainability methods for sequential recommendation are structurally passive: they describe model behaviour without influencing training. This paper closes that loop by deploying a model-agnostic Shapley-based framework as an active auditing instrument within a three-stage audit-debug-refine pipeline. The audit mechanism is formalised as a cooperative game G = (T, v) over interaction timesteps, where the characteristic function v : 2T → ℝ evaluates model confidence over item coalitions. We prove that exact Shapley computation for this game is #P-complete (Proposition 1) and derive that the Monte Carlo estimator achieves ε-approximation in O(T log(1/δ)/ε2) samples with probability 1−δ (Proposition 2). Applying the pipeline to GRU4Rec on MovieLens 100K identifies 236 explanation-unreliable users (25%). Population-level diagnosis rules out sequence sparsity (mean sequence length: 19.97 vs 19.96 for flagged vs unflagged users) and identifies low model confidence (0.012 vs 0.037, three-fold difference) as the root cause. Three targeted interventions — BPR Max pairwise ranking loss, popularity-weighted hard negative sampling, and entropy-based output regularisation — are applied in direct response to this diagnosis. The combined model reduces flagged users by 53% (95% CI: 48.1%–57.9%, p < 0.001), improves mean faithfulness from −0.056 to +0.133, and raises NDCG@10 from 0.4382 to 0.4543. A central empirical finding emerges: faithfulness and robustness behave as competing objectives when training interventions shift the model's confidence distribution — a tension that existing XAI evaluation benchmarks do not capture — quantified by the normalised faithfulness–robustness ratio FRn.
Digital Literacy as a Moderator in the Relationship Between AI-Driven Automation and Job Satisfaction Among Hospital Administrative Staff
This study investigates the moderating role of digital competency in the relationship between automation usage and administrative staff satisfaction in hospitals. Increasing adoption of automated systems, including electronic health records, scheduling software, and AI-assisted administrative tools, has transformed hospital operations and employee responsibilities. However, the effectiveness of automation depends largely on employees’ ability to use digital technologies efficiently. The study applies a quantitative research design using survey data collected from hospital administrative staff across public and private healthcare institutions. Statistical analysis examines the direct impact of automation usage on job satisfaction and evaluates whether digital competency strengthens this relationship. The findings indicate that automation usage improves work efficiency, reduces repetitive tasks, and enhances communication processes. Moreover, employees with higher digital competency report greater satisfaction because they adapt more easily to technological changes and experience lower work-related stress. The study highlights the importance of continuous digital training and organizational support in maximizing the benefits of automation in healthcare administration while improving employee satisfaction and operational performance. The results also support strategic investments in employee-centered digital transformation initiatives within modern hospitals globally.
Advancements in Wood-Plastic Composites: Synthesis, Performance Evaluation, and Pathways to Multifunctional Innovations
Wood-plastic composites (WPCs) represent a sustainable solution for integrating renewable fibers with polymer matrices to create durable, eco-friendly materials. This analysis examines the evolution of WPCs, focusing on their composition—typically 30-60% wood-derived fillers contained in thermoplastics such as HDPE, PP, or PVC—as well as their benefits in decreasing plastic waste and deforestation. Common polymers and their characteristics, pre-processing and production methods like extrusion and compression molding, and new techniques like selective laser sintering are all important considerations. Mechanical, thermal, and durability studies reveal increases via compatibilizers, with tensile strengths of 20-50 MPa and water absorption of less than 5%. The study also examines at future technologies, such as nano-enhanced formulations and structural battery composites (SBCs), which combine energy storage with structural integrity, and forecasts market growth of USD 11.9-13.45 billion by 2030. Interfacial compatibility and recyclability issues are addressed, emphasizing the importance of WPCs in circular economies and green engineering.