Search Results (153)

This study examines the effect of values-based leadership on workplace engagement and explores the moderating role of sustainable social responsibility. The proposed study is based on the social learning theory, the leader–member exchange theory, and the social exchange theory, proposing a multidimensional model of values-based leadership, leadership qualities (LQ), ethical values (EV) and balance in achieving interests (BAI). The quantitative survey design was employed in the collection of data amongst 390 employees of the Ministry of Interior, Qatar. The measurement and the structural models were tested using the partial least squares structural equation modeling (PLS-SEM) using WarpPLS V. 7 Software. The findings show that the three dimensions of values-based leadership make important and positive contributions to engagement in the workplace. The results indicated that sustainable social responsibility had no significant moderating effect on the relationship between leadership qualities and workplace engagement, or on the relationship between achieving a balance of interests and workplace engagement. However, sustainable social responsibility significantly moderated the relationship between ethical values and workplace engagement. The study adds value to the literature on leadership and workplace engagement by separating the dimensions of values-based leadership and the contextualized enhancing role of sustainable social responsibility. Full article

The present paper suggests a federated learning-based distributed solar forecasting model based on gated recurrent unit (GRU) networks (FL-GRU) to smart buildings in Muscat, Oman. The growing adoption of rooftop photovoltaic (PV) systems in urban settings needs precise, privatizing, and scalable forecasting models able to manage geographically dispersed and statistically heterogeneous data. The suggested solution will include federated learning and GRU networks to train a global forecasting model across several smart buildings and avoid the exchange of raw energy data to overcome these challenges. The local GRU models are trained on local PV generation data and only parameters of the model are relayed to a central aggregation server. This provides privacy of data without compromising the effectiveness of collaborative learning. The proposed framework is tested in a variety of realistic scenarios such as scalability analysis, non-identically distributed (non-IID) data, client dropout, communication constraints, seasonal variability, and privacy saving noise injection. Simulation outcomes show that the proposed FL-GRU model presents a final RMSE of 0.129, MAE of 0.100 and forecasting accuracy of 97%. When increasing the number of clients involved in the process, 2 to 10, RMSE decreases to 0.129, which supports the high scalability advantages. In non-IID scenarios, RMSE ranges between 0.129 and 0.167, and even with half of the clients dropping, the system is robust with an RMSE of 0.172. The proposed FL-GRU is better than the benchmark models, Local GRU, centralized GRU, FL-LSTM, and FL-ANN with a maximum improvement of 22.29% in RMSE reduction. Also, the best predictive consistency is found with correlation analysis with R² = 0.957. On the whole, the suggested approach can offer an efficient, privacy-aware, and scalable solution to distributed solar energy prediction in smart cities. Full article

Sleep disorders, such as insomnia, obstructive sleep apnea (OSA), narcolepsy, REM sleep behavior disorder, and circadian rhythm disturbances, represent a rapidly expanding global health burden that is strongly associated with cardiovascular, metabolic, neurological, and psychiatric diseases. Advancements in wearable sensing technologies and Internet of Medical Things (IoMT) infrastructures have expanded the possibilities for continuous, home-based sleep assessment beyond conventional polysomnography laboratories. These Sleep Health Internet of Things (S-HIoT) systems combine multimodal physiological sensing (EEG, ECG, SpO₂, respiratory effort and actigraphy) with wireless communication and cloud-based analytics for automated sleep-stage classification and disorder detection. Nonetheless, the digitization of sleep medicine brings about significant cybersecurity concerns. The constant transmission of sensitive biomedical information makes S-HIoT networks open to anomalous traffic flows, signal manipulation, replay attacks, spoofing, and data integrity violation. Existing studies mostly focus on analyzing physiological signals and network intrusion detection independently, resulting in a systemic vulnerability of cyber–physical sleep monitoring ecosystems. With the aim of addressing this empirical deficiency, this review integrates emerging advances (2022–2026) in the AI-assisted categorization of sleep phases and IoMT anomaly detector designs on the finer analysis of CNN, LSTM/BiLSTM, Transformer-based systems, and a component part of federated schemes and the lightweight, edge-deployable intruder assessor models available. The aim of this study is to uncover a gap in the literature: integrated architectures to trade off audiences of faithfulness of physiological modeling with communication-layer security. To counter it, we present a single framework to include CNN-based spatial feature extraction, Bidirectional Long Short-Term Memory (BiLSTM)-based temporal models and Random Forest-based ensemble classification using a dual task-learning approach. We propose a multi-objective optimization framework to jointly optimize the performance of sleep-stage prediction and that of network anomaly detection. Performance on publicly available datasets (Sleep-EDF and CICIoMT2024) confirms that hybrid integration can be tailored to achieve high accuracy [99.8% sleep staging; 98.6% anomaly detection] whilst being characterized by low inference latency (<45 ms), which is promising for feasibility in real-time deployment in view of targeting edge devices. This work presents a comprehensive framework for developing secure, intelligent, and clinically robust digital sleep health ecosystems by bridging chronobiological signal modeling with cybersecurity mechanisms. Furthermore, it highlights future research directions, including explainable AI, federated secure learning, adversarial robustness, and energy-aware edge optimization. Full article

This study introduces a physics-guided deep learning architecture designed for the simulation, reconstruction, and pattern recognition of biomedical images. By explicitly integrating physical priors into the learning model, the framework addresses the black-box nature of traditional artificial intelligence (AI). It provides an explainable AI pathway that enhances diagnostic accuracy, robustness, and clinical interpretation. The proposed framework was evaluated through systematic simulation studies. It involved complex geometric configurations, multimodal physical fields, and noise-corrupted synthetic three-dimensional brain volumes. Quantitative analysis demonstrates consistent improvements in reconstruction fidelity, with the peak signal-to-noise ratio (PSNR) reaching 47 dB and the structural similarity index exceeding 0.90 across all scenarios. Notably, at moderate noise levels (0.05), the framework maintains a PSNR greater than 32 dB, ensuring structural integrity essential for computer-aided diagnosis. Volumetric brain experiments further reveal a 38–44% reduction in activation localization errors, highlighting the framework’s utility in functional imaging and disease prognosis. By grounding deep learning in physical constraints, this study provides a transparent and robust solution for automated disease classification and advanced biomedical imaging tasks within clinical decision support systems. Full article

Genetically modified foods represent an important application of modern biotechnology and remain a subject of public debate. Attitudes toward consumption are more likely to be influenced by varying levels of scientific knowledge. University students from the College of Science and the College of Agricultural and Food Sciences at King Faisal University, Saudi Arabia, are expected to possess the basic knowledge that may affect their attitudes toward consumption of genetically modified foods. This study aimed to assess undergraduate students’ knowledge as a predictor of future attitudes toward consumption of genetically modified foods. Using a descriptive method, an electronic questionnaire was administered to a random sample of 300 participants during the first semester of the academic year 2025/2026. Data were analyzed using confirmatory factor analysis and t-tests. The results indicate that students possess a moderate level of scientific knowledge. Their future attitudes toward consuming genetically modified foods were also moderate. Prior studying of genetics and biotechnology courses significantly affects students’ scientific knowledge and future attitudes toward consumption of genetically modified food. The students strongly supported strict regulations, but they expressed hesitation regarding consumption regardless of scientific assurances of safety. No statistically significant differences in knowledge or attitudes based on specialization or gender were found. The authors recommend integrating ethical and social considerations of this kind of food into educational curricula to support informed decision-making among future professionals. Full article

Multitarget-directed ligands offer a promising strategy for overcoming tumor complexity through simultaneous modulation of complementary oncogenic pathways. In this work, a novel (E)-6-(3-(4-methyl-2-thioxo-2,3-dihydrothiazol-5-yl)-3-oxoprop-1-en-1-yl)-2H-chromen-2-one (compound 6) was synthesized and evaluated as a dual inhibitor of tubulin polymerization and tumor-associated carbonic anhydrases (CAs) IX and XII. Compound 6 displayed potent antiproliferative activity, particularly against MDA-MB-231 triple-negative breast cancer cells (IC₅₀ = 0.37 µM), with excellent selectivity toward non-tumorigenic cells. Mechanistic studies demonstrated strong tubulin polymerization inhibition (IC₅₀ = 3.40 ± 0.09 µM) and submicromolar inhibition of CA IX (IC₅₀ = 0.102 ± 0.005 µM) and CA XII (IC₅₀ = 0.213 ± 0.004 µM), accompanied by downregulation of CA-IX and CA-XII protein expression. Cellular investigations revealed pronounced G₂/M phase arrest and apoptosis induction via mitochondrial signaling and caspase activation. Anti-angiogenic activity was supported by inhibition of endothelial migration and concentration-dependent suppression of VEGFR-2 (Tyr1175) phosphorylation in HUVEC cells. Human liver microsomal assays indicated measurable metabolic stability, while molecular docking and in silico ADMET predictions supported target engagement and drug-like properties. Collectively, these findings identify compound 6 as a promising multitarget anticancer lead integrating antimitotic, metabolic, and anti-angiogenic mechanisms. Full article

In recent years, the emergence of drug-resistant pathogens and the limitations of current therapies have highlighted the need for innovative strategies to combat emerging viral infections. Natural compounds, derived from plants, are playing an increasingly significant role in the research of novel and effective therapies. Boswellia sacra, a frankincense-producing tree widely distributed in Yemen and Oman, and Nerium oleander, a common ornamental and medicinal plant, are examples of plants with well-documented antimicrobial properties. Their extracts have demonstrated good activity against a wide range of infections, which is attributed to the anti-inflammatory and immunomodulatory compounds they contain. Based on these findings, we assessed, in vitro, the broad-spectrum antiviral activity of combined extracts obtained from Boswellia sacra and Nerium oleander. The extract mixture NOBS7(1) was found to be active against the respiratory virus RSV, Herpesvirus simplex type 1, and Coxsackievirus 5. Furthermore, a combination of cell-based assays was performed to provide additional insights into their potential mechanism of action. Full article

Entrepreneurship education (EE) is increasingly considered an important tool in promoting sustainable economic development, yet the empirical base for its effect on entrepreneurial intention (EI) is dispersed and not consistent. However, the literature fails to address EE as a direct antecedent of EI and pays little attention to conditional mechanisms that explain how education contributes to shaping entrepreneurial cognition. To address this gap, this article performs a systematic–integrative review of the literature where entrepreneurship education is a moderating variable in entrepreneurial intentionality. Based on PRISMA 2020, peer-reviewed journal papers from 2000 to 2025 were collected through Scopus and Web of Science and systematized with the theory-building integrative method. Using the Theory of Planned Behavior, Shapero’s Entrepreneurial Event Model, Social Cognitive Theory and Human Capital Theory, we show in the review that entrepreneurship education primarily moderates how entrepreneurial attitudes, subjective norms and perceived behavioral control predict entrepreneurial intention rather than exert uniform direct effects. The results also reveal that the moderating effect of EE is dependent on pedagogical quality, level of experiential depth, extent of cultural fit and institutional support, with strong implications for emerging and collectivist economies. Holistic in approach, the study demonstrates how education for entrepreneurship can focus entrepreneurial intention on sustainable value creation, economic diversification and inclusive development contributing directly to SDGs 4 (quality education), 8 (decent work and economic growth) and 9 (industry innovation and infrastructure). The paper introduces a context-dependent conceptual framework, and discusses some implications for sustainability-related educational design and policy. Full article

Sustainable pavement solutions that lessen the dependency on virgin materials are required due to mounting environmental and economic pressures. Although recycled asphalt concrete (RAC) has structural and environmental advantages, binder heterogeneity and non-linear material interactions make it difficult to predict the ideal bitumen content in RAC mixtures. This study predicts the bitumen content of asphalt mixtures infused with RAC by combining sophisticated machine learning (ML) with traditional laboratory testing. While this study combines AI-driven predictions with experimental insights to create a state-of-the-art framework for sustainable pavement engineering, 780 data points were obtained from the preparation and testing of three mixtures (0%, 30%, and 50% RAC) for volumetric and mechanical characteristics. Controlled Autoregressive Integrated Moving Average (CARIMA), Swapped Autoregressive Integrated Moving Average (SARIMA), radial basis function artificial neural network (RBF), bagging (BAG), multilayer perceptron (MLP) artificial neural network, and boosting (BOT) ensembles were among the models created. BAG-CARIMA-LGM is a new hybrid model that combines logistic probabilistic generalization, ensemble variance reduction, and time-series forecasting. Higher predictive accuracy and resilience across different RAC levels were attained by the hybrid BAG-CARIMA-LGM model, which performed noticeably better than standalone algorithms. The findings demonstrated improved Marshall stability and controlled flow along with a progressive decrease in mean bitumen content as RAC increased. While 50% RAC with rejuvenators maintained durability and structural integrity, the 30% RAC mixture produced the most balanced performance. The model’s capacity to manage non-linear interactions, volumetric variability, and aging effects was validated by statistical analyses. The BAG-CARIMA-LGM hybrid model optimizes RAC incorporation in asphalt mixtures, supports circular economy goals, and improves technical accuracy. The results point to a revolutionary route towards intelligent, environmentally friendly road systems that support international sustainability objectives. Full article

This study reveals the effects of artificial space-like proton irradiation on three meteorite samples that are Northwest Africa (NWA) 4560 LL3.2 and NWA 5838 H6 chondrite meteorites, as well as the Dhofar (Dho) 007 eucrite. We used low-vacuum scanning electron microscopy (LV-SEM) and Raman Spectroscopy to examine the structure and composition of olivine and pyroxene grains in the meteorites before and after the irradiation events. This article focuses on the strongest and most intense irradiation, which was performed by protons up to 12 keV with a fluence value of 10¹⁹ ions/cm² that lasted ~30 h. According to the Raman spectra, significant lattice disruption in all analyzed silicates occurred, and a more extensive amorphous, glassy layer developed under the strongest irradiation conditions. Relative to the second irradiation, peak 1 (820.0 cm⁻¹) shifts slightly negatively (–0.46 cm⁻¹) with a small FWHM increase (+0.88 cm⁻¹), while peak 2 (850.3 cm⁻¹) shifts positively in both parameters (+0.40 and +4.04 cm⁻¹) in NWA 4560 olivines. In NWA 5838 olivines, both olivine peaks (820.5 and 850.8 cm⁻¹) shift positively (+7.40 and +7.90 cm⁻¹) and broaden (+2.75 and +4.29 cm⁻¹). In Dho 007 pyroxenes, peak 1 (997.1 cm⁻¹) shifts positively (+3.01 cm⁻¹) with an FWHM decrease (−0.46 cm⁻¹), peak 2 (669.7 cm⁻¹) shifts slightly negatively (−0.75 cm⁻¹) while broadening strongly (+29.23 cm⁻¹), and peak 3 (327.7 cm⁻¹) shifts positively (+0.86 cm⁻¹) with reduced FWHM (−4.55 cm⁻¹). Three characteristic amorphous bands appear in all examined meteorite silicates, located at ~550–1000 cm⁻¹, ~1100–1700 cm⁻¹, and ~1700–1850 cm⁻¹. Olivines in NWA 4560 and NWA 5838 exhibited similar responses across all irradiation events. In contrast, Dho 007 pyroxenes showed variable compositional changes without a consistent or well-defined pattern in our SEM dataset. The Fo decrease in our experiments likely results from preferential Mg sputtering in the olivine lattice, leading to relative Fe enrichment, similar to but more pronounced than after the first irradiation. Pyroxenes exhibit a comparable response, with Fs and En increasing and Wo sharply decreasing, reflecting preferential Ca loss relative to Mg alongside Fe enrichment. Investigating these processes improves the interpretation of planetary remote sensing data and advances our understanding of planetary surface evolution, while also clarifying how surface materials respond to space environmental conditions. Full article

This research paper focuses on the role of average working hours (AVHs) of the labor force in explaining the variation in QOL across countries, which is an important but unexplored area in the empirical literature. Using data from 62 countries and employing several econometric techniques, we show that long AVHs are detrimental for improved QOL. The sub-sample results demonstrate that AVHs have a significant detrimental impact on the QOL of the population only in the case of developing countries. However, in the case of developed countries, the influence of AVHs is insignificant as these countries are enjoying relatively reduced AVHs as compared to developing countries. Moreover, our results indicate that the labor force participation rate, human capital, government expenditures, internet use, and electricity consumption are the main driving forces behind a better QOL both in developed and developing countries. Finally, we found evidence that trade openness is an irrelevant factor in explaining the variation in QOL as it is insignificant in most of the specifications despite possessing a positive coefficient. Full article

Multicomponent mixed lanthanide oxide (MMLO) nanoparticles possess considerable potential as multimodal imaging agents because they integrate diverse excellent optical and magnetic properties within a single nanoparticle. Herein, we present triply and quadruply mixed lanthanide oxide nanoparticles, namely, gadolinium (Gd)/dysprosium (Dy)/europium (Eu) oxide (GDEO), Gd/Dy/terbium (Tb) oxide (GDTO), and Gd/Dy/Eu/Tb oxide (GDETO) nanoparticles. Gd³⁺ can strongly induce positive (T₁) contrast in magnetic resonance imaging (MRI), Dy³⁺ and Tb³⁺ can generate negative (T₂) contrast in MRI, and Eu³⁺ and Tb³⁺ emit visible photons that are applicable to fluorescence imaging (FI). All the nanoparticles were grafted with hydrophilic, biocompatible polyacrylic acid (PAA) to enhance colloidal stability and biocompatibility and further grafted with small amounts of an organic photosensitizer, 2,6-pyridinedicarboxylic acid (PDA), to obtain a high absolute fluorescent quantum yield (QY) with an extended fluorescent lifetime (τ). All PAA-MMLO and PAA/PDA-MMLO nanoparticles exhibited nearly monodispersed particle-size distributions with average particle diameters of ~2 nm and displayed considerably higher longitudinal (r₁) and transverse (r₂) water proton spin relaxivities than commercial molecular MRI contrast agents. The PAA/PDA-GDEO, PAA/PDA-GDTO, and PAA/PDA-GDETO nanoparticles exhibited high absolute QYs of 45, 29, and 61%, respectively, and long τ values of 1–2 ms, making them suitable for time-delayed noise-free fluorescence signal detection. These findings confirm the high potential of PAA-MMLO nanoparticles as T₁ and/or T₂ MRI contrast agents and PAA/PDA-MMLO nanoparticles as both T₁ and/or T₂ MRI and FI agents. Full article

This study investigates the interplay between asset quality, financial ratios, and market valuation in Indian commercial banks over a twelve-year period (2014–2025). Using a hybrid approach combining Structural Equation Modeling, correlation analysis, and trend evaluation, the research examines whether Non-Performing Assets (NPAs) influence market capitalization directly or through Return on Equity (ROE) as an intermediary. The findings reveal that NPAs exert a significant negative impact on both ROE and market value, while Net Interest Margin (NIM) emerges as a strong positive determinant of valuation. Conversely, Capital Adequacy Ratio (CAR), though vital for regulatory compliance, shows no direct effect on market prices. Mediation analysis challenges conventional assumptions, indicating that profitability alone does not fully explain valuation dynamics. These insights underscore the need for integrated strategies addressing asset quality and operational efficiency, offering practical implications for policymakers, investors, and bank management in strengthening resilience and optimizing shareholder value. Full article

This study investigates the configurational pathways enabling women in Oman to translate entrepreneurial intentions into technology venture creation. By integrating institutional theory and resource-based view, we develop a novel framework examining how formal institutional support (FIS), informal institutional support (IIS), and digital self-efficacy (DSE) interact in Oman’s conservative context. We emphasize the significant enabling role of work–life balance resources (WLBR) and the cultural legitimacy of spousal endorsement. Our mixed-methods design utilizes survey data from 418 female IT graduates and 20 semi-structured interviews, analyzed through fuzzy-set Qualitative Comparative Analysis (fsQCA). The findings indicate that FIS predicts entrepreneurial intention (β = 0.34, p < 0.001) but not venture creation (OR = 0.85, p = 0.298), revealing a visibility gap in policy implementation. IIS predicts venture creation (OR = 1.43, p = 0.033), with spousal endorsement acting as a cultural legitimacy signal. DSE alone fails to predict venture creation but is vital when combined with WLBR. FsQCA identifies a sufficient configuration pathway characterized by the combination of spousal endorsement, domestic support, DSE, and WLBR with solution consistency of 0.93 and coverage of 0.78. WLBR is a necessary condition with necessity consistency of 0.96, demonstrating that venture creation is improbable without it. Qualitative evidence shows founders reposition conservative norms as legitimacy signals, while non-founders emphasize funding barriers despite policy awareness. We recommend that policymakers subsidize care infrastructure, leverage women-led community networks for targeted outreach, and formalize state-backed legitimacy programs that reduce kinship dependency while building autonomy-focused alternatives. Full article

The growth of green hydrogen technologies is changing modern power systems with the addition of large, dynamic and flexible electricity consumers. Electrolyzers, as the fundamental technology for green hydrogen production, have a specific set of features upon their operation that will largely impact grid stability, voltage regulation, harmonic distortion and frequency control. Their massive penetration brings several protection and operational issues that are distinct from the classic industrial loads. Sophisticated protection techniques are needed to deal with overcurrent, overvoltage and short-circuit problems as well as power quality variations whilst maintaining the coordination with existing protections. This review is aimed at summarizing the status of green hydrogen production and its aspired relationship to electrical grids. It is an investigation that systematically looks at technical, operational and protection challenges connected with electrolyzers’ integration. Moreover, this paper investigates the analysis of protection strategies and various grid integration scenarios in renewable-rich/hybrid power systems. Finally, this paper presents some research challenges and cutting-edge technologies to aid in innovations of advanced techniques for the protection of electrolyzers and power systems. The results are expected to provide guidance for the future study and application of reliable, secure and economically sound integration of green hydrogen production in emerging power networks. Full article