In the 1960s, coinciding with the massive demand for credit cards, financial companies needed a method to know their exposure to risk insolvency. It began applying credit-scoring techniques. In the 1980s credit-scoring techniques were extended to loans due to the increased demand for credit and computational progress. In 2004, new recommendations of the Basel Committee (as called Basel II) on banking supervision appeared. With the ensuing global financial crisis, a new document, Basel III, appeared. It introduced more demanding changes on the control of borrowed capital.
Nowadays, one of the main problems not addressed is the presence of large datasets. This research is focused on calculating probabilities of default in home equity loans, and measuring the computational efficiency of some statistical and data mining methods. In order to do these, some Monte Carlo experiments with known techniques and algorithms have been developed.
These computational experiments reveal that large datasets need BigData techniques and algorithms that yield faster and unbiased estimators.
Using oil-spill booms as floating barriers must respect environmental conditions, mechanical limitations and operational constraints. Numerical modelling of boom behaviour can be used in order to prepare or validate booming plans, which respect these constraints. We present simulations of boom behaviour during an exercise in Galicia to support existing contingency plans. The main inputs of the modelled simulations are: environmental data on meteorology and oceanography, pollution field data and technical specifications of commercially available booms. The barrier structural analysis uses four-step modelling with an adaptive geometry. Modelled results are used in two ways. Firstly, a pre-paredness approach is conducted with a three-section boom plan to protect a mussel farm near the Puebla del Caramiñal. Secondly, a post-experiment analysis is made with a four-section plan and time- dependant boundary conditions given by the five GPS buoys position records carried out during the experiment. This numerical validation of the boom plan is complementary to the operational training of the boom deployment. The model results reproduce the barriers’ behaviour during the exercise and improve contingency planning for future response. The proposed approach has been generalized to other environments such as estuaries, ports and lakes.
The depth of closure of the beach profile, from now on termed as DoC, is a key parameter to perform effective evaluations of beach nourishments or coastal defence works. It is defined for a given time interval, as the closest depth to the shore at which there is no significant change in seabed elevation and no significant net sediment transport between the nearshore and offshore. To obtain this point it is necessary to compare profile surveys at a given period of time, and evaluate them to find the point in the profile where the depth variation is equal to, or less than, a pre-selected criteria. In order to manage all this information, a software application has been developed. On providing the input of the beach profiles, this tool offers the possibility of selecting the dates of the desired period of study, graph the profiles and then obtain, for each XY coordinate, all the required parameters, such as offshore distance, maximum, average and minimum depth, standard deviation and area difference between profiles. By evaluating each point along the profile, the DoC can be obtained at that point that meets the criteria. Moreover, this tool allows to graph not only the initial and final profile of the period, but all the beach profiles recorded, creating its maximum and minimum envelope. In addition, if the user introduces the parameters related to the equilibrium beach profile, this tool also corrects the area difference, taking into account the morphological changes (erosion– accretion) that may have occurred during the period studied. In conclusion, this tool has a friendly interface for obtaining the DoC with accuracy by interactive selection of the period of study. It also stores all the information and exports it to different formats.
One of the main problems of coastlines around the world is their erosion. There are many studies that have tried to link coastal erosion with different parameters such as: maritime climate, sediment transport, sea level rise etc. However, it is unclear to what extent these factors influence coastal erosion. For example, the Intergovernmental Panel on Climate Change (IPCC) has predicted an increase in sea level at a much faster rate than that experienced in the first part of this century, reaching 1 m of elevation in some areas. Another factor to consider is the lack of sediment supply, since currently the contribution of new sediments from rivers or ravines is interrupted by anthropic activities carried out in their basins (dams, channelling, etc.). The big storms, increasingly frequent due to climate change, also should be considered, since they produce an off-shore sediments transport, so that these cross the depth of clo- sure, causing nonreturn of the sediment to the beach. Also, the sediment undergoes a process of wear due to various reasons such as the dissolution of the carbonate fraction and/or breakage and separation of the components of the particles. All these elements, to a greater or lesser extent, lead to the retreat of the coastline. Therefore, the aim of this study is to analyse the different factors causing the retreat of the coastline, in order to determine the degree of involvement of each of them and, therefore, be able to pose different proposals to reduce the consequences of coastal erosion.
An axle bearing is one of the most important components to guarantee the service life of a rail car. In order to ensure the stable and reliable bearing life, it is essential to estimate the fatigue life of an axle bearing under the loading conditions. The fatigue life of a bearing is affected by many parameters such as material properties, heat treatment, lubrication conditions, operating temperature, loading conditions, bearing geometry, the internal clearance of bearing, and so on. Because these factors are so complicatedly related to each other, it is very important to investigate the effects of these factors on the axle bearing life. This paper presents the process of estimating the fatigue life of a railroad roller bearing, which takes into account geometric parameters of the bearing in the life calculation. The load distributions of the bearing were determined by solving numerically force and moment equilibrium equations with Lundberg’s approximate model. This paper focuses on analyzing the effects of bearing geometric parameters on the fatigue life using Taguchi method.
The design and optimisation of a latent heat thermal storage system require knowledge of flow, heat and mass transfer during the melting (charging) and solidification (discharging) processes of high-temperature phase change materials (PCMs). Using fluent, numerical modeling was performed to study the impact of natural convection and turbulence in the melting process of a high- temperature PCM in a latent heat storage system with Ra = 1012. Numerical calculation was conducted, considering a two dimensional symmetric grid of a dual-tube element in a parallel flow shell and tube configuration where the heat transfer fluid passes through the tube and PCM fills the shell. Three melting processes of PCM were considered; pure conduction, conduction and natural convection, and finally the latter with turbulence. The first study showed a one dimensional melt front, evolving parallel to the tube, which results in lower peak temperatures and temperature gradients, higher heat transfer area for a longer period of time, however lower heat transfer rate due to natural convection being ignored. The second study presented a two dimensional melt front which evolves mainly perpendicular to the tube, shrinking downward, resulting in the loss of heat transfer area and higher peak temperatures and temperature gradient, however, the higher rate of heat transfer rate due to the creation of convection cells which facilitate mass and heat transfer. Including turbulence led to a higher mixing effect due to the higher velocity of convection cells, resulting in a more uniform process with lower peak temperature and temperature gradients and higher heat transfer rate. In a melting process with Ra>1011, including convection and turbulence impact provides more realistic data of flow, mass and heat transfer.
Polyhydroxyalkanoates (PHAs) are a family of biodegradable and biocompatible polyesters that have recently attracted much industrial attention. The most representative PHA is poly(3-hydroxybutyrate) (PHB), though it presents several shortcomings such as brittleness and poor impact resistance. 3-hydroxy- hexanoate units can be incorporated in PHB to obtain poly(3-hydroxybutyrate-co-3-hydroxyhexanoate) (PHBHHx), a copolymer with improved mechanical properties, processability and biodegradability, more suitable for biomedical applications. In this study, chitosan-grafted polycaprolactone (CS-g- PCL)/PHBHHx fiber blends in different compositions were developed by wet electrospinning, and their morphology, biodegradability, mechanical and tribological properties were investigated. A direct correlation was found between the wear rate and the mechanical properties, pointing that fiber breakage is the mechanism responsible for both the abrasive wear and yield. The interactions between the components led to a synergistic effect on tensile and tribological properties at a blend composition of 70/30, resulting in an optimum combination of maximum stiffness, strength, ductility and toughness and minimum coefficient of friction and wear rate, ascribed to the lower porosity and higher crystallinity of this sample. Further, it exhibits the slowest degradation rate. These fiber blends are ideal candidates as scaffolds for tissue engineering applications.
This paper discussed about the consequences of using different filler metal by metal inert gas (MIG) welding process on aluminium alloys Al 7075 sheet metal joint. Nowadays, Al 7075 is widely used in automobile and aviation industry due to its light weight, strong, and high hardness. Fusion welding, such as MIG and TIG were commonly used in joining the aluminium alloys due to its low cost. However, defects usually occurred using fusion welding because of the inaccurate welding parameters and types of filler metal used. The purpose of this study is to determine whether the filler metal with different elements and welding parameters affect the mechanical properties of welded Al 7075. Welding parameters used were current, voltage, welding speed, and Argon (Ar) as shielding gas. Two different types of filler metal were used which is Electrode Rod (ER) 4043 and ER5356 which is from Al-Si and Al-Mg based element, respectively. From microstructure analysis, fusion zone (FZ) of sample welded with ER4043 has a smaller grain size than that of with ER5356. Both filler produced equiaxed dendritic grain at FZ. Both samples welded with ER4043 and ER5356 has lower hardness value than heat affected zone (HAZ) and base metal (BM) due to the differences in their elements where ER4043 from Al-Si and ER5356 from Al-Mg group. The weld efficiency of sample welded using ER5356 was 61% which was higher compared to sample welded using ER4043 which at 43% and both sample was brittle fractured. Sample welded with ER5356 was fractured at HAZ due to porosity while sample welded with ER4043 fractured at FZ due to the oxide inclusion.
In this study, we develop an efficient topology optimisation method with the H -matrix method and the boundary element method (BEM). In sensitivity analyses of topology optimisation, we need to solve a set of two algebraic equations whose coefficient matrices are common, particularly in many cases. For such cases, by using a direct solver such as LU decomposition to factorise the coefficient matrix, we can reduce the computational time for the sensitivity analysis. A coefficient matrix derived by the BEM is, however, fully populated, which causes high numerical costs for the LU decomposition. In this research, the LU decomposition is accelerated by using the H -matrix method for the sensitivity analyses of topology optimisation problems. We demonstrate the efficiency of the proposed method by a numerical example of a multi-objective optimisation problem for 2D electromagnetic field.
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.
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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