Organizational commitment (OC) has gained popularity in recent times. It’s a very crucial determinant of employee retention, productivity, and a contributor to organizational growth and prosperity. The role of a faculty is very important in shaping the careers of students and in the overall growth of higher education institutions. The current study emphasizes how job-related factors like designation and demographic profiles like age, gender, education qualification, marital status, and area of belongingness affect OC of the faculty working in the HEIs of Uttarakhand, India. A simple random technique was used to determine the sample of the study; the sample of the study was 235 faculty members engaged in the higher education institutions (HEIs). 15 items were adapted from the questionnaire on OC developed by Mowday. To collect the data, an online questionnaire was sent to the faculty engaged in the HEIs through email and the WhatsApp application. To check the internal consistency, Cronbach's Alpha test was applied. The data were distributed normally, hence a parametric test was used. The study reveals that age, gender, experience, and marital status influence OC, and designation and area of belongingness have no impact on the OC. The policymakers need to develop strategies and policies keeping in mind both demographic and job-related factors to embrace and foster commitment amongst the employees.
Urban air pollution remains a persistent challenge in the Global South, where rapid urbanization, limited monitoring infrastructure, and weak regulatory frameworks hinder effective environmental governance. In Lima, Peru—one of the most polluted capitals in Latin America—elevated PM2.5 and PM10 concentrations continue to pose serious threats to public health and sustainable urban development. Traditional Air Quality Index (AQIs), such as the U.S. EPA standard, often struggle to account for data uncertainty, pollutant interactions, and spatial heterogeneity. To address these gaps, this study introduces a novel AQI based on grey systems theory, applying a grey clustering framework enhanced with center-point triangular whitenization weight functions (CTWF). The model was specifically designed to handle ambiguous data and overlapping pollution categories. It was applied to daily PM2.5 and PM10 data from nine monitoring stations across metropolitan Lima, with validation conducted against both Peru’s national air quality standards and the U.S. EPA AQI. Results showed that the proposed index outperformed conventional methods under uncertain conditions, revealing critical spatial disparities often missed by traditional models. Beyond diagnostic accuracy, the index offers a scalable and transferable tool for urban planners and decision-makers to support targeted interventions, inform policy development, and advance Sustainable Development Goals—specifically SDG 3 (Good Health and Well-Being) and SDG 11 (Sustainable Cities and Communities).
Road surface cracks are a major contributor to vehicular accidents, particularly in high-speed and high-traffic environments. Conventional crack detection techniques that rely on grayscale imaging often fail to maintain accuracy under varying lighting conditions and in the presence of noise. To address these challenges, a robust detection methodology is proposed, based on a Gradient-based Crack Enhancement, Color Consistency, and Smoothness Regularization Model (GCSM). This model leverages Gaussian smoothing to reduce noise, gradient-based enhancement to accentuate crack features, and color consistency to effectively differentiate cracks from surrounding textures. Smoothness regularization ensures the continuity of crack patterns and minimizes false positives, enhancing the accuracy of detection. The resulting crack maps form the foundation for advanced risk analysis, directly linking crack detection to safety evaluation. The integration of crack detection with accident prediction is achieved by a hybrid model that estimates the likelihood of accidents induced by road surface deterioration. This hybrid model combines logistic regression to assess variables such as crack density, width, traffic volume, vehicle speed, and pavement condition, with a fuzzy inference system (FIS) to handle the imprecision inherent in road condition assessments. The final accident risk score is computed as a weighted combination of these components, offering enhanced prediction accuracy. Experimental results on datasets from Peshawar, Khyber Pakhtunkhwa, demonstrate that GCSM outperforms existing methods in terms of Intersection over Union (IoU), Precision, Recall, and Structural Similarity Index Measure (SSIM), with statistical significance (p < 0.01) confirmed via ANOVA. The hybrid prediction model achieves an accuracy of 88.23% and a mean squared error (MSE) of 0.042, highlighting its efficiency and robustness. This framework facilitates automated crack visualization and accident risk classification, providing valuable insights for engineers and urban planners. Future work will focus on real-time deployment and system adaptability to various road conditions, supporting intelligent transportation systems and proactive road safety management.
This study proposes a novel approach to driver drowsiness detection using the Video Vision Transformer (ViViT) model, which captures both spatial and temporal dynamics simultaneously to analyze eye conditions and head movements. The National Tsing Hua University Driver Drowsiness Detection (NTHU-DDD) dataset, which consists of 36,000 annotated video clips, was utilized for both training and evaluation. The ViViT model is compared to traditional Convolutional Neural Network (CNN) and Convolutional Neural Network-Long Short-Term Memory (CNN-LSTM) models, demonstrating superior performance with 96.2% accuracy and 95.9% F1-Score, while maintaining a 28.9 ms/frame inference time suitable for real-time deployment. The ablation study indicates that integrating spatial and temporal attention yields a notable improvement in model accuracy. Furthermore, positional encoding proves essential in preserving spatial coherence within video-based inputs. The model’s resilience was tested across a range of challenging conditions including low-light settings, partial occlusions, and drastic head movements and it consistently maintained reliable performance. With a compact footprint of just 89 MB, the ViViT model has been fine-tuned for deployment on embedded platforms such as the Jetson Nano, making it well-suited for edge AI applications. These findings highlight ViViT’s promise as a practical and high-performing solution for real-time driver drowsiness detection in real-world scenarios.
This study investigated sustainable tourism practices in the aviation sector by assessing how passenger awareness and carbon offset pricing could be integrated into travel behaviors. With the International Civil Aviation Organization (ICAO) Carbon Emissions Calculator, the analysis covered five Thai Airways routes from Thailand to Shanghai, Guangzhou, Beijing, Kunming, and Chengdu. The calculated offset costs per passenger ranged between 6.55 and 36.99 CNY, which were derived by applying a benchmark of 95 CNY/tCO2e (≈ 445 THB) to per-passenger emissions. These proposed offset contributions were not obtained from evidence of direct survey on the offset cost per passenger. On the other hand, the benchmark selected was based on the estimate in the international literature, anticipated price trends, and the goal of encouraging broader participation. The findings prioritized the importance of consistent terminology, explicit standards, and collaborative policies between public and private stakeholders to strengthen travelers’ engagement in carbon offset programs.
Urban building energy modeling (UBEM) is essential for understanding energy consumption and developing sustainable policies at the city scale. However, current UBEM approaches overlook spatial and temporal interactions and lack generalizability across diverse urban contexts. This study introduces a hybrid framework that integrates physics-based simulations with machine learning based residual learning to enhance prediction accuracy using real energy consumption data. The methodology incorporates GIS-supported data collection and processing. Multiple ML models were applied to predict monthly consumption and validate their performance. Meanwhile, a physics-based model is used to simulate hourly energy consumption. The best performing ML model was later used for daily residual learning to calibrate physics-based simulation outputs. The framework was tested on residential buildings connected to the District Heating Network in Turin, Italy. Results showed LGBM achieved the highest performance with a R2 of 0.883 and a MAPE below 15% in most months. Residual learning reduced daily prediction error in 80% of cases, with up to 75% improvement in extreme cases. After model calibration, 65% of buildings achieved a daily MAPE below 30%, and 55% fell below 20%, demonstrating consistent error reduction across varied building types and consumption levels. This confirms the effectiveness of the hybrid approach in enhancing accuracy and reliability at the urban scale.
Generative Artificial Intelligence (Gen-AI) has emerged as a transformative technology with considerable potential to enhance information management and decision-making processes in the public sector. The present study examined how Gen-AI, with specific attention to Microsoft Copilot, can be integrated into local government organizations to support routine operations and strategic tasks. An Integrative Literature Review (ILR) methodology was applied, through which scholarly sources were systematically evaluated and findings were synthesized across predefined research questions and thematic categories. The review emphasized three focal areas: the conceptual foundations of Gen-AI, the challenges associated with its integration, and the opportunities for improving public sector information analysis and administrative practices. Evidence indicated that Gen-AI adoption in local government contexts can substantially improve efficiency in data retrieval, accelerate decision-making processes, enhance service responsiveness, and streamline administrative workflows. At the same time, significant risks were identified, including fragmented data infrastructures, limited digital and Artificial Intelligence (AI) literacy among personnel, and ongoing ethical, transparency, and regulatory challenges. Recommendations were formulated for future research, including empirical assessments of Gen-AI deployment across diverse local government contexts and longitudinal studies to evaluate the sustainability of AI-driven transformations. The insights generated from this study provide actionable guidance for local government organizations seeking to evaluate both the benefits and the risks of integrating Gen-AI technologies into information management and decision-support systems, thereby contributing to ongoing debates on public sector innovation and digital governance.
Local wisdom-based ecopedagogy learning approach plays a strategic role in growing critical consciousness and environmental care behavior among students. This research aims to explore the effect of local wisdom-based ecopedagogy learning approach on students’ critical consciousness and environmental care behavior in the context of Sociology learning in Senior High School Sequential mixed method approach is used by collecting quantitative data through online questionnaire distributed to 644 students coming from many provinces in Indonesia and qualitative data through in-depth interview with Sociology teachers. The result of research shows a positive significant correlation between students’ critical consciousness and environmental care behavior (b = 0.869, p $<$ 0.05), where 61.3% of behavior variability is explained by the students’ critical consciousness level (R$^2$ = 0.613). Qualitative data supports the quantitative finding indicating that teachers applied some learning strategies: environmental project, case study, activity out of classroom, and interactive discussion to give meaningful experience encouraging the students to think critically and to take real action to care for the environmental problem. This research also identifies the challenges faced by teachers in the implementation of local wisdom-based ecopedagogy learning approach including limited module as learning reference, limited practical training for the teachers, and limited time because the curriculum is not flexible. This study contributes theoretically to expanding the literature about ecopedagogy and likewise offers practical recommendation to improve the facilitation of training for teachers and the development of local value-based teaching module for Sociology subject.
National parks are designated natural areas set aside for the preservation of their resources. However, they suffer from several environmental problems resulting from human actions, exacerbated by a lack of effective management planning, including unsustainable biodiversity loss, deforestation, and wildfires. This qualitative research proposes practical sustainability conservation management based on the experience of Thab Lan National Park in Thailand, utilizing Community-Based Natural Resource Management (CBNRM) and Sustainable Development Goal (SDG) targets. Through in-depth interviews, data were collected from three residents and two operations-level staff members of the Thab Lan National Park. The findings highlighted local resource protection, park residency legality, and agricultural expertise as supportive factors. In contrast, ecosystem protection from slosh equity enabled them, which was detrimental due to the skewed distribution of benefits. Furthermore, the management level was found to have an impact on the long-term ecological benefits. Most importantly, unequal resource allocation has hampered conservation efforts, highlighting the need for community participation in sustainable resource management. This management strategy is a working approach that local authorities and regional policymakers can adopt as guidelines for the sustainable conservation of natural resources in the Thab Lan National Park and other similar settings.
The study carried out in the Puñun Peasant Community had as its main objective the inventory of springs and the planning of agroecological zones, assessing water availability in a semi-arid environment. The methodology included the georeferencing of 139 springs and flow measurement using the volumetric method in Sector II. Measurements were taken quarterly on three key dates during the 2024 dry season: April, June, and December. Agroecological zones were delimited considering soil and climate factors and morphological factors, using Arc GIS 10.8 GIS software. A mixed approach was also applied to collect quantitative and qualitative data, including interviews with experts. The results showed that springs contribute significantly to the available flow in the agroecological zones, with a total water volume of 631.56 m³ in Sector II, distributed among four identified zones. According to experts, the spring inventory had a strong influence on agroecological planning, reaching an index of 0.89. Likewise, the Pearson correlation test between the area of the agroecological zones and the volume of water available in the springs showed a nearly perfect positive relationship (r = 0.99). The conclusions highlighted the importance of springs for agricultural sustainability and the urgent need to implement efficient water management strategies, promoting responsible water use and environmental conservation. It is estimated that the total available volume can support agricultural irrigation of approximately 29.19 hectares.
This research explores the important role of soil physics and irrigation technology in water conservation in sustainable agriculture. With increasing global water shortage and wasteful irrigation practices posing a threat to agricultural productivity, water use optimization is critical. The study seeks to evaluate soil physical properties influencing irrigation efficiency, contrast various irrigation techniques, examine the effect of fertilization on water quality, and categorize farms according to irrigation performance. Mixed methods utilize statistical modeling, exploratory data analysis (EDA), and K-means clustering to assess soil properties, irrigation efficiency, and water-saving methods. The research identifies that precision irrigation methods like subsurface and drip irrigation substantially increase water-use efficiency through reduced evaporation and runoff. Organic matter and soil texture are important in retaining moisture, affecting irrigation requirements. Overfertilization is associated with nitrogen runoff, highlighting the significance of the regulated application of nutrients to avoid groundwater pollution. Another unique contribution of the research is using clustering methods to categorize farms according to their irrigation efficiency and providing specific suggestions for improving water use. The study offers actionable recommendations for farmers, policymakers, and environmental agencies to promote precision irrigation, sustainable soil management, and data-driven decision-making to maximize agricultural water conservation. Such findings add value to global efforts towards sustainable food security and environmental conservation.
West Nusa Tenggara (NTB) Province possesses considerable natural resource potential, exhibiting a wide array of distinctive ecosystems. However, the province is confronted with environmental challenges arising from escalating economic activities and population growth, including deforestation, land degradation, water pollution, and marine ecosystem degradation. In an effort to address this issue, the NTB Provincial Government has implemented a series of priority policies, one of which is Governor Regulation Number 60 of 2022 concerning Monitoring and Evaluation of Financial Assistance Expenditure. This regulation serves as a mechanism for providing fiscal incentives based on ecological performance to district, city, and village governments. The objective of this study is to evaluate the implementation of the policy by employing a retrospective policy valuation approach. The data will be collected through observation, interviews, literature reviews, and Focus Group Discussions (FGDs). The data will then be analyzed using a Likert scale on five main criteria: effectiveness, efficiency, responsiveness, adequacy, and determination. The findings of the study indicate that the implementation of Governor Regulation 60 of 2022 is classified as high, with an average value of 2.56, particularly in terms of effectiveness, as evidenced by the allocation of awards and specialized financial assistance to villages and regencies/cities in environmental management. Nevertheless, the monitoring and evaluation of the implementation of financial assistance must be improved to ensure transparency, accountability, and program continuity. This finding underscores the necessity of calibrating ecological indicators within the fiscal transfer scheme in accordance with the local characteristics of NTB, as well as the imperative for cross-government collaboration to promote sustainable development. The implementation of this policy can serve as a model for other regions seeking to enhance fiscal incentives for environmental conservation in an effective and equitable manner.
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