This study examines how improvements in digital governance influence urban air quality in global cities. The analysis uses annual PM2.5 and monthly NO2 observations from 280 cities across 120 countries, combined with national e-government indicators, covering the years 2005 to 2023. The study applies various analytical models, including fixed effects models, staggered difference-in-differences, event-study designs, common correlated effects, spatial Durbin models, system-GMM, and panel quantile regression, to capture causal, dynamic, and spatial effects. The results indicate that higher digital government maturity reduces NO2 emissions by approximately 4-5 percent and PM2.5 levels by about 3 percent. The analysis shows that these reductions occur more rapidly following the implementation of e-service initiatives. Event-study estimates confirm the absence of pre-existing trends and demonstrate persistent decreases after the launch of digital services. Spatial models suggest that reforms generate indirect benefits for neighboring cities, and quantile analysis reveals more significant improvements in highly polluted, densely populated areas. The effects are more pronounced in non-OECD countries and cities with higher internet penetration, highlighting the importance of institutional readiness and digital infrastructure for effective environmental outcomes. These findings demonstrate that digital governance reduces administrative mobility, enhances compliance, and improves monitoring, thereby producing measurable environmental benefits. Policymakers can utilize these results to incorporate e-government reforms into national environmental strategies, prioritize urban areas with high pollution levels, and support digital capacity building to maximize environmental gains.
The administration of Ringer's Lactate as initial resuscitation for sepsis and septic shock is recommended in accordance with the Surviving Sepsis Campaign (SSC) guidelines. Early use of first-line vasopressors such as norepinephrine can enhance organ perfusion without the risk of fluid overload. This study analyzed differences in hemodynamic parameters, namely Mean Arterial Pressure (MAP) and Cardiac Index (CI), under the following conditions: before, during septic shock, and after intervention. Porcine (Sus scrofa) subjects were divided into groups: those administered Ringer's lactate, Ringer's lactate and norepinephrine, and norepinephrine alone. The study concluded that all groups experienced a significant decrease in MAP during septic shock and an increase after intervention, with the highest observed in the Ringer's Lactate group (delta 15 mmHg), but no significant differences were found between groups. A decrease in CI was observed during the shock phase, and the highest increase in CI was found in the group receiving Ringer's lactate and norepinephrine (2.43 ± 1.47 L/min/m²), but no significant differences were found between groups (p=0.1). The results highlighted that initial resuscitation with Ringer's lactate and norepinephrine was hemodynamically more stable and more physiological than either intervention alone. This strategy has the potential to optimize perfusion pressure and prevent organ failure in sepsis.
To examine how climate change risk perception (CCRP), perceived environmental governance effectiveness (EGE), and AI‑driven innovation capability (AIIC) jointly influence sustainable geosite tourism behavior (SGTB), including responsible consumption, compliance with site rules, and support for conservation financing. This study uses a mixed‑methods design. Quantitatively, an online/offline survey instrument was designed for geopark visitors and stakeholder groups and analyzed using PLS‑SEM with reliability, convergent validity, discriminant validity, mediation, and moderation testing. Qualitatively, semi‑structured interviews with geopark managers and tourism‑technology actors were used to triangulate mechanisms for climate‑smart governance and AI innovation deployment. The structural model indicates that CCRP increases perceived EGE, which in turn drives SGTB. EGE partially mediates the CCRP→SGTB relationship. AIIC strengthens the direct effect of CCRP on SGTB, suggesting that AI‑enabled information, monitoring, and personalization tools translate climate concern into actionable low‑impact behavior more effectively when innovation capability is high. This research contributes an integrated, climate‑governance‑AI framework tailored to geosite tourism and provides a replicable measurement instrument and analytics workflow. Practically, results inform ASEAN and geopark managers on designing AI‑enabled environmental governance (e.g., transparent rules, adaptive capacity, ethical data practices) to advance sustainable visitation and community benefits, while managing ethical risks such as surveillance creep and algorithmic bias.
The main purpose of this paper is to analyze the financial literacy of undergraduate students at a Thai public university across three academic groups: health and technological sciences, social sciences and humanities, and business and economics. A structured questionnaire and stratified random sampling were used to assess financial literacy. As the main results, the average scores in all academic groups are higher than the benchmark set by the Bank of Thailand. These scores are also statistically significantly different. Moreover, the average financial literacy scores in the business and economics group are the highest, particularly in the area of financial knowledge. On the other hand, the average scores in the social sciences and humanities group are the lowest. Interestingly, the average score for the financial attitude component in the health and technological sciences group is the highest. The estimation results of the ARMA Generalized Least Squares model also reveal that the variables of parental financial socialization, monitoring financial news, and recording student expenses are statistically significant factors affecting financial literacy scores. In conclusion, to develop financial literacy skills among undergraduate students in Thailand, universities should integrate basic financial issues into non-economics courses, complemented by behavioral interventions through appropriate programs.
This study examines the effectiveness of Chinese language curriculum policy among Bangladeshi university students in Chongqing, China. It evaluates how policy intent (PI), implementation process (IP), stakeholder characteristics (SC), and contextual factors (CF) influence perceived policy outcomes, with stakeholder perceptions (SP) acting as an intervening variable. Grounded in Policy Implementation Theory and Resource Dependence Theory, the research adopts a positivist, quantitative, cross-sectional design. Data were collected from 263 Bangladeshi students using a structured questionnaire and analyzed through PLS-SEM (SmartPLS 4) to assess measurement reliability, validity, and structural relationships, including direct and indirect effects. The findings reveal that the implementation process and stakeholder characteristics exert strong, positive, and statistically significant effects on policy effectiveness, as well as on stakeholder perceptions. In contrast, contextual factors demonstrate a small but negative influence. Stakeholder perceptions emerge as the most influential predictor of policy effectiveness and significantly mediate the relationships between antecedent variables and outcomes. Notably, policy intent shows no significant impact, indicating that clearly defined objectives alone are insufficient without effective implementation capacity and active stakeholder engagement. The study highlights the critical importance of strengthening implementation quality, enhancing stakeholder capacity and participation, and ensuring context-sensitive policy design. These factors are essential for improving the effectiveness of Chinese language curriculum policies among Bangladeshi international students in Chongqing universities.
This study aims to examine the direct and indirect effects of strategic entrepreneurship on high-performance organizations, mediated by organizational creativity, in Yemeni pharmaceutical manufacturing firms located in the Capital Municipality of Sana’a. The study adopted a quantitative descriptive-analytical approach and employed a comprehensive census technique to collect data from employees working in eight pharmaceutical manufacturing firms. A total of 307 questionnaires were distributed, of which 199 valid responses were obtained and analyzed. Data were analyzed using Partial Least Squares Structural Equation Modeling (PLS-SEM) through SmartPLS, while descriptive statistics were conducted using SPSS. The findings revealed that strategic entrepreneurship has a positive and significant effect on both high-performance organizations and organizational creativity. In addition, organizational creativity was found to exert a positive and significant influence on high-performance organizations. The results further confirmed that organizational creativity plays a significant partial mediating role in the relationship between strategic entrepreneurship and high-performance organizations. These findings highlight the importance of fostering strategic entrepreneurship and enhancing organizational creativity to improve organizational performance in the pharmaceutical manufacturing sector. The study provides practical implications for managers and policymakers seeking to strengthen competitive capabilities and organizational effectiveness in emerging and conflict-affected economies such as Yemen.
This study examines the role of gender-sensitive governance in shaping the economic integration of displaced women during military conflict, with comparative evidence from Ukraine and Britain. The research analyzes how gender mainstreaming in territorial community management influences financial inclusion, entrepreneurship, and economic outcomes. A mixed-methods design combines qualitative insights with quantitative analysis. A context-specific gender gap index measures disparity across three dimensions: financial inclusion, entrepreneurship rates, and access to support services. Survey and interview data capture business challenges, financial constraints, and institutional barriers, while regression analysis evaluates the impact of gender-sensitive governance, conflict intensity, and financial access on economic outcomes. The findings reveal substantial cross-country differences. Britain demonstrates stronger gender-sensitive governance frameworks that facilitate higher levels of financial inclusion and entrepreneurial activity among displaced women. Ukraine, by contrast, exhibits significant structural and institutional barriers that constrain women’s economic participation, including limited credit access, complex regulatory environments, and social adaptation difficulties. The results confirm that financial access and inclusive governance are fundamental in mitigating the negative economic impact on displaced populations. The study quantifies the strategic relevance of policy interventions, including microfinance programs, institutional capacity-building, and legal support systems, for strengthening economic resilience and inclusion of displaced women in conflict-affected areas.
The study examines how information overload and heuristic judgment mediate between how locus of control and risk perception affect retail investors' digital investment activities. Digital platforms increased financial market access. However, frequent information streams and attention-seeking stimuli for speedy decision-making cause cognitive overload and judgment errors. The paper merges trait-level (locus of control), state-level (risk perception), and cognitive theories (information overload and heuristics) into a single explanatory framework using psychology and behavioral finance fundamentals. The study used structural equation modeling to evaluate direct and mediating correlations from structured surveys of 400 knowledgeable Indian digital investment platform users. Positive investments are associated with an internal control locus, while risk perception decreases involvement. Complex or fuzzy information cognitive prejudices, especially availability and representativeness heuristic biases, aggravate psychological dispositions and lead to extreme behaviors like greater trading, risk-taking, and digital complexity. The paper discusses paradoxical overload and heuristic biases. The former causes decision-making paralysis in some, whereas the latter aids processed decision-making. The research helps digital platform developers and decision aid policymakers customize information to prevent cognitive errors. This highlights the need to simplify information to support confidence and beneficial digital investments.
Classification of brain tumors is a critical issue in neuro-oncology, and the precise and interpretable classification is a challenge. Our paper suggests a hybrid-attention-based multimodal deep learning model, which combines multi-sequence MRI images and radiogenomic features to accomplish explainable and high-quality tumor subtyping. The proposed Explainable Hybrid Attention Multimodal Network (E-HAMNet) employs (i) a spatial stream of attention that, on the fly, highlights salient tumor regions in T1, T1c, T2, and FLAIR images, and (ii) a feature-level attention that weights genomic and radiomic features to capture molecular heterogeneity. A cross-modal attention fusion layer is used to combine these streams and to allow dynamic interaction between imaging and genomic modalities. To achieve robustness, we use a self-supervised pretraining approach to feature extraction and perform supervised fine-tuning on annotated data. To achieve interpretability, we combine Grad-CAM heatmaps, SHAP value attribution, and attention score visualization to give clinicians clear decision support. Experiments on BraTS-2023/2024 and RSNA-MICCAI datasets demonstrate that E-HAMNet is better than recent multimodal CNN, transformer-based, and radiomics pipelines with 99.6% accuracy, 96.4% macro-F1, and 98.2% AUC. It has also been shown that the method has better calibration (ECE 1.9%), as well as strength in missing modalities and domain shift.
This study presents a simplified empirical correlation for predicting barium sulfate (BaSO4) solubility in brine, developed from a comprehensive dataset spanning 1960 to 2022. By fitting a polynomial expression to experimental data, the model accurately accounts for temperature (25–300°C), pressure (1–500 bar), and ionic strength (up to 4 M NaCl). The correlation demonstrates high fidelity to literature values and was further validated through oilfield observations, showing excellent consistency with practical on-site data. Due to its accuracy and computational simplicity, the method is easily integrated into engineering workflows. It serves as a robust tool for predicting and mitigating scale deposition in oilfields, optimizing reverse osmosis membrane performance, and addressing clay-sulfate rock swelling in geotechnical engineering. This reliable predictive approach offers significant utility across diverse scientific and industrial applications where BaSO4 solubility is a critical factor.
The integration of artificial intelligence (AI) into educational frameworks is increasingly prevalent, necessitating rigorous assessment of its impact. This study evaluates digital competencies in university students utilizing ChatGPT as a pedagogical resource. The primary objective was to examine the relationships between AI-mediated learning mechanisms and competency development within higher education. A quantitative empirical study was conducted involving a representative sample of 450 university students. Data were collected via a structured online questionnaire distributed through Google Forms. Data processing involved cleaning and normalization prior to statistical analysis. Psychometric validation yielded a Cronbach's alpha of 0.904, indicating excellent scale reliability. Subsequent principal component analysis (PCA) and Biplot visualization identified the primary dimensions influencing skill acquisition. Results demonstrate that creativity, critical thinking, digital interaction, and collaboration constitute the fundamental variables explaining the majority of observed variability in academic performance. Conversely, satisfaction and motivation emerged as independent factors, suggesting the necessity for personalized learning strategies to optimize engagement. The findings underscore that AI tools like ChatGPT extend beyond technical skill enhancement, fostering holistic student development. Consequently, this research advocates for a humanized approach to digital education, integrating AI to support both cognitive and interpersonal growth within academic contexts. Implementing these insights ensures technological efficiency balances with human-centric learning outcomes, ultimately redefining the role of AI in fostering integral student success.
Occupational burnout among schoolteachers is a significant occupational health problem, yet the tools to measure it require rigorous validation. In Kazakhstan, there has been limited psychometric evaluation of standard diagnostic instruments. This study aimed to assess the applicability, internal structure, temporal stability, and psychometric reliability of the Russian-language version of the Maslach Burnout Inventory (MBI) among schoolteachers in the Republic of Kazakhstan. We conducted a cross-sectional study with 128 female schoolteachers from Karaganda, Kazakhstan. Participants completed the MBI–Educators Survey. Our analysis included internal consistency checks (Cronbach’s alpha), composite reliability (CR), and average variance extracted (AVE). We used exploratory factor analysis (EFA) and confirmatory factor analysis (CFA) to test the instrument's structure. The MBI showed high internal consistency for all three dimensions: emotional exhaustion, depersonalization, and personal accomplishment (Cronbach’s α from 0.942 to 0.965). EFA confirmed the original three-factor structure (over 78% of the total variance). CFA indicated an acceptable model fit. Test–retest reliability was high (r = 0.86). The findings confirm that the Russian-language MBI is a reliable and valid tool for measuring burnout among teachers in Kazakhstan. Educational institutions and occupational health practitioners can use it for diagnosis and monitoring teacher well-being.
This empirical study examines the influence of digital leadership on the performance of banks in the Gulf Cooperation Council (GCC) region from 2010 to 2023. A new Digital Leadership Index is created via contextual content analysis of banks' annual reports to assess the strategic focus on digital transformation. The investigation, employing the panel Generalized Method of Moments (GMM) estimation technique, demonstrates a positive and statistically significant association between digital leadership and bank success, as indicated by return on assets. Further data indicate that bank size, liquidity, and expansion significantly enhance performance, confirming the theoretical foundations of the Resource-Based View and Dynamic Capability Theory. Robustness tests employing alternative proxies validate the dependability of the findings. This study enhances the literature by measuring digital leadership and providing actual data from a region experiencing swift digital and economic development. The urgent need for financial institutions to make digital transformation a strategic priority is brought to light by the considerable and advantageous influence that digital leadership has on the performance of the banking industry. Finally, banks need to establish a culture of innovation, enhance the capabilities of their employees, and ensure that their digital operations are in accordance with the long-term objectives of the firm.
This paper provides an extensive defense mechanism to secure iris recognition systems against adversarial attacks. While deep neural networks can deliver impressive results in biometric authentication applications, they are not immune to intentional data manipulation, which can significantly affect their performance. To overcome this major security issue, the presented system adopts a multi-stage protective strategy by combining sophisticated signal processing with deep learning architectures. The defense strategy uses a discrete wavelet transform to analyze high- and mid-frequency components in wavelet subbands of iris images. This frequency-domain analysis becomes a foundation for the detection and mitigation of adversarial perturbations, which might otherwise deceive the recognition system. The framework combines a sophisticated denoising process specifically designed to handle adversarial iris scans with a powerful classification mechanism based on the U-Net architecture, a deep convolutional neural network known to perform well in image processing tasks. To validate the effectiveness of this protective system, thorough testing was conducted using the IITD iris database, which is well known in the biometric research community as a benchmark for evaluating biometric algorithms. The evaluation involved exposure to three different methods of adversarial attack: FGSM, iGSM, and DeepFool, which represent various types of attack and their respective levels of sophistication. The experimental results show that the proposed defensive model can accurately identify iris patterns under adversarial perturbations, achieving an impressive 95 percent accuracy. Comparative analysis with competing defense approaches in this work shows that this system achieves the highest performance, setting a new standard for security against adversarial attacks in real-world iris recognition systems.
This study examines how international accreditation contributes to the improvement of university degrees and academic programs by strengthening quality assurance, internationalization, and pedagogical innovation. Drawing on a dual bibliometric and qualitative strategy, the research analyzes 1,943 articles published between 1974 and 2025 and indexed in Scopus and Web of Science. Bibliometric techniques using R and VOSviewer were applied to identify research trends, influential journals, international collaboration networks, and thematic clusters, while a qualitative analysis of the most cited contributions enabled the interpretation of conceptual frameworks, methodological approaches, and regional perspectives. Results reveal sustained scientific growth since the mid-2000s, with accelerated expansion after 2012 and a historical peak in 2024. The field is structured around three conceptual cores: quality management and evaluation, internationalization and education policy, and innovation in teaching and learning dominated by journals such as Quality in Higher Education, Higher Education Policy, and Quality Assurance in Education. Collaboration patterns highlight the leadership of the United Kingdom, the United States, and Australia as global hubs, alongside emerging contributions from Asia and selective participation from Latin America and Africa. The findings indicate that international accreditation functions not only as a technical evaluation mechanism but also as an instrument of institutional positioning, academic cooperation, and curricular transformation. Practical implications suggest that universities and accreditation agencies should adopt flexible models integrating global standards with contextual specificities, invest in international networks, and incorporate pedagogical innovation and sustainability into quality assurance agendas.
This study aims to investigate heat and mass transport characteristics in biomagnetic fluid flow, where blood is modeled as a biofluid containing spherical CoFe2O4 magnetic particles. A modified form of Tiwary and Das’s hypothesis is employed to incorporate the combined influence of magnetohydrodynamic (MHD) and ferrohydrodynamic (FHD) effects, an approach that has received little attention in previous works. Using a one-parameter group-theoretic technique, the governing partial differential equations describing momentum and energy transport are reduced to nonlinear ordinary differential equations with appropriate boundary conditions. Numerical solutions are obtained through MATLAB’s bvp4c solver to ensure computational accuracy and stability. The analysis demonstrates that Lorentz and Kelvin forces strongly affect velocity and temperature distributions, while particle radius and volume fraction significantly influence heat transfer rates and skin friction. Results are discussed for both co-moving and counter-moving plate scenarios. The outcomes of this study provide deeper physical insight into the simultaneous action of MHD and FHD mechanisms in biomagnetic fluids. Such understanding may support the development of advanced biomedical technologies, including targeted drug delivery systems, magnetic hyperthermia for cancer therapy, and improved magnetic resonance imaging (MRI)-based diagnostic procedures. The findings also establish a comprehensive framework for future theoretical and experimental investigations in complex biomagnetic transport phenomena under varying magnetic field strengths and physiological conditions.
This paper examines the effects of corporate social responsibility (CSR) spending on dividend policy among Indian non-financial companies, with a specific focus on family-owned businesses operating under the country's compulsory CSR policy. Using both voluntary and mandatory CSR expenditure, the analysis employs logit estimation and system-GMM to assess the impact of these two approaches on dividend strategies, based on 2016-2022 panel data from 429 listed companies on the National Stock Exchange and 267 family firms. Results of the research show that high CSR spending positively affects the likelihood of paying dividends, indicating that companies view CSR as a competitive indicator of financial health and stakeholder intentionality rather than a cost-reducing business component. A similar positive correlation is observed with family-owned companies, meaning that CSR helps them achieve their non-financial goals related to legitimacy and reputation, and reduces agency problems with their minority investors. On the other hand, mandatory CSR requirements alone do not affect dividend payments, suggesting that compliance-based spending is not a strategic option for dividend payouts. The findings extend agency, stakeholder, signaling, and socioemotional wealth theories by demonstrating that CSR is used as a complementary system to dividends in a developing economy with concentrated ownership.
Indonesia’s domestic soybean production meets only about one-third of national demand, resulting in continued reliance on imports. This study develops a System Dynamics (SD) model to assess the eco-efficiency and sustainability of soybean production systems in Wonogiri Regency using primary data (2018–2024) and national statistics. Three scenarios are evaluated: S1 (baseline), S2 (15% reduction in fertilizer use), and S3 (10% increase in yield potential through technological adoption). The simulation results indicate that improved input management reduces greenhouse gas (GHG) emissions by 12–15% while maintaining yields of 1.50–1.65 t/ha. Under scenario S3, yields increase by 9–11% relative to the baseline, accompanied by higher farm profitability without increasing emission intensity. The eco-efficiency index rises from 1.00 in 2020 to 1.17 under S2 and 1.23 under S3 by 2030. The findings suggest that combining fertilizer optimization with technological innovation offers the most effective pathway toward sustainable and low-emission soybean production. This study presents one of the first SD-based eco-efficiency models for Indonesian soybean systems, quantitatively linking productivity, resource-use efficiency, and GHG emissions, providing policy-relevant insights for sustainable intensification in smallholder agriculture.
Trust in government is crucial for shaping public behavior during health crises. The level of confidence can decline if health messages are ambiguous, confusing, lack transparency, or originate from an unreliable source. In changing and shaping public behavior when it comes to health crises, trust is an incumbent agent. This study gathered empirical data on the influence of perceived health messages on public trust in the Malaysian government. Employing the Elaboration Likelihood Model enabled an investigation into the persuasion process of online health messages with a specific focus on the impact of source credibility on public trust. The quantitative method was used to collect 384 samples from an online survey through Google Forms. PLS-SEM analysis revealed a substantial amount of the variance in trust (p <0.05; R² = 0.545). revealed that the health communication messages by the Malaysian government through online platforms were viewed as clear, consistent and transparent. The respondents indicated a favorable perception of the government spokesperson, whom they viewed as a person of integrity, competence, a positive media outlook, and trustworthy. The majority of respondents also expressed a high level of trust in the Malaysian government. The findings provided insights into government and public health authorities, emphasizing the importance of relaying clear, consistent, and transparent messages. The study recommends that the Malaysian government ensure health messages are clear and transparent to combat misinformation and promote accurate public health data, thereby enhancing public trust.
Although necessary for operating in today's digital environment, digital literacy raises unique concerns, including the risk of privacy violations, misinformation, and potentially damaging online behavior. This investigation examines the integration of Islamic ethics into the scope of digital literacy, aiming to help Islamic youth in Malaysia navigate the Internet in a responsible and value-based manner. This study is situated within Islamic social ethics and seeks to determine whether such values can address some of the ethical issues related to the use of digital technologies. A quantitative and descriptive design was chosen for the study, with a population of Malaysian Muslims aged 18 to 30. The relationships among digital engagement, Islamic ethics, and ethics of digital technology use were tested with structural equation modeling. The study demonstrated that Islamic ethical values facilitate the resolution of ethical dilemmas associated with digital engagement. The study also indicates that insufficient digital literacy education, especially its moral aspect, weakens digital engagement, a point that calls for the integration of ethics in digital literacy education. The study demonstrates the value of integrating culture-specific ethical reasoning when enhancing digital literacy. It suggests that incorporating Islamic ethics into education is necessary to promote ethical citizenship and responsible digital engagement among Muslim youth.
