Convection heat transfer enhancement techniques play a vital role in many industrial thermal processing applications, including food thermal processing, and the pharmaceutical, and chemical manufacturing industries. These techniques contribute to reducing the size and cost of heat exchangers, conserving energy, improving product quality, and enhancing both energy efficiency and thermal performance. Among passive solutions, corrugated wall tubes are widely adopted in heat exchangers for such applications. This study applies the inverse heat conduction problem (IHCP) method combined with infrared thermography data to estimate the local temperature and convective heat transfer coefficient distributions for forced convection in a transversally corrugated wall tube with high viscosity fluid flow under laminar conditions. The IHCP is solved within the corrugated wall domain using measured external wall temperatures as input. Thermal performance was evaluated over a Reynolds number range of 290–1200. The findings showed that at Re $<$ 350, irregular local temperature and convective heat transfer distributions led to reduced thermal efficiency, unreliable sterilization, and increased microbial risk, whereas for 650 $<$ Re $<$ 1200, thermal efficiency improved significantly. These findings support the development of more efficient heat exchanger designs, offering significant benefits to industries requiring precise thermal management.
Traffic congestion in urban commercial districts presents a critical challenge to sustainable mobility, particularly in developing cities. This study addresses this issue by developing and calibrating a mesoscopic simulation model to optimize traffic performance parameters in the commercial district of Ayacucho, Peru. The methodology was based on extensive fieldwork to gather traffic volume, travel time, and parking data. Using this data, a PTV Vissim model was developed and rigorously calibrated, with its accuracy validated through the Geoffrey E. Havers (GEH) statistic. Various traffic management strategies, including signal timing adjustments and parking supply regulation, were simulated and evaluated. The results indicate a substantial improvement in network performance: Average intersection delay was reduced from 10.72 seconds to 7.40 seconds, and a significant decrease in queue lengths was observed. The findings confirm that calibrated mesoscopic simulation serves as a robust and effective tool for quantitatively assessing traffic interventions, thereby providing municipal authorities with reliable data for evidence-based urban planning.
Overurbanization poses environmental challenges that threaten human health and biodiversity. Nature-Based Solutions (NBS) enhance urban livability, restore biodiversity, and provide vital Ecosystem Services (ES), such as mitigating the Urban Heat Island (UHI) effect. This study evaluates environmental monitoring at Marco Biagi Park (Reggio Emilia, Italy) as part of the Life City AdapT3 project. Following the introduction of micro-forests, rural edges, tree rows, and a wetland, data were collected to assess local climate mitigation and carbon storage. Microclimatic effects were analyzed using satellite images (Landsat 8) and on-site measurements. Between 2021-2024, summer Land Surface Temperature (LST) decreased in post-intervention period by 2.1℃. Air temperature in urban forest areas averaged 1.2℃ lower, while humidity increased by 10% compared to built-up areas. Using the i-Tree model, it was estimated that Marco Biagi Park stored 332.20 kg of carbon in 2024 and 825.20 kg in 2025—representing a 148.4% increase in just one year. Species of the Quercus genus, Prunus avium and Tilia platyphyllos contributed 58.26% to this carbon storage in 2025. Findings highlight NBS effectiveness in improving urban microclimates and carbon sequestration, reinforcing their role in sustainable city planning.
The Load Haul Dumper (LHD) is essential machinery utilized for moving ore in the underground mining industry, in order to fulfil production targets. In this connection, the efficiency of the equipment should be maintained at an ideal standard, to be accomplished by reducing unexpected failure of components or subsystems in this intricate system. Downtime analysis helped identify faulty components and subsystems, which require the development of complementary maintenance plans to facilitate the replacement or fixing of parts. Proper practices of maintenance management improve the performance of the equipment. In this research, the efficiency of the LHD machine was assessed through reliability methods. Initially, the assumption of independent and identical distribution (IID) for the data sets was validated using trend and serial correlation analyses. The statistical tests indicated that the data sets adhered to the IID assumption. Therefore, a renewal process method was utilized for additional examination. The Kolmogorov-Smirnov (K-S) test was utilized to identify the most suitable distribution for the data sets. The theoretical probability distributions were estimated parametrically using the Maximum Likelihood Estimate (MLE) approach. The dependability of each separate subsystem was determined using the optimal fit distribution. Based on the reliability outcomes, preventive maintenance (PM) time plans were created to reach the targeted 90% reliability. Different maintenance strategies, in addition, were suggested to the maintenance team to extend the lifespan of the machine.
This study proposed a novel pin-level dynamic compensation strategy to combat the critical challenge of springback in the three-dimensional numerically controlled bending of ship hull plates. A collaborative prediction model combining convolutional and bidirectional recurrent networks (CNN-BiLSTM) was optimized using an improved metaheuristic algorithm, the Modified Sparrow Search Algorithm (MCSSA), to achieve millimeter-level precision in springback compensation. Based on the 225-pin independent control architecture, the system enabled real-time compensation with millisecond-level response ($\leq$ 50 ms) on standard industrial computing hardware, to overcome the limitations of conventionally fixed compensation methods. The optimized algorithm enhanced global search capability, population diversity, and convergence efficiency, hence yielding a prediction accuracy of RMSE = 4.41 $\times$ $10^{-5}$ mm. The integrated spatiotemporal learning framework effectively combined feature extraction, sequential modeling, and critical region emphasis, to achieve a test-set $R^2$ of 0.969. Industrial validation of the SKWB-1600 system demonstrated significant improvements in traditional stepwise approximation methods: (i) Post-compensation forming errors were reduced to 0.13–0.26 mm with a 47–62% improvement; and (ii) Curvature errors in high-stress zones were maintained within $\pm$ 0.02 mm, thus forming iterations decreased by 42% and energy consumption reduced by 35%. This certified pin-level dynamic compensation solution provides a new methodology for forming precision of complex curved ship hull plates under industrial conditions and establishes a technical paradigm for manufacturing related components requiring high precision and efficiency.
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.
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.
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