Leaf diseases pose a significant threat to global agricultural productivity, impacting both crop yields and quality. Traditional detection methods often rely on expert knowledge, are labor-intensive, and can be time-consuming. To address these limitations, a novel framework was developed for the segmentation and detection of leaf diseases, incorporating complex fuzzy set (CFS) theory and advanced spatial averaging and difference techniques. This approach leverages the Hue, Saturation, and Value (HSV) color model, which offers superior contrast and visual clarity, to effectively distinguish between healthy and diseased regions in leaf images. The HSV space was utilized due to its ability to enhance the visibility of subtle disease patterns. CFSs were introduced to manage the inherent uncertainty and imprecision associated with disease characteristics, enabling a more accurate delineation of affected areas. Spatial techniques further refine the segmentation, improving detection precision and robustness. Experimental validation on diverse datasets demonstrates the proposed method’s high accuracy across a variety of plant diseases, highlighting its reliability and adaptability to real-world agricultural conditions. Moreover, the framework enhances interpretability by offering insights into the progression of disease, thus supporting informed decision-making for crop protection and management. The proposed model shows considerable potential in practical agricultural applications, where it can assist farmers and agronomists in timely and accurate disease identification, ultimately improving crop management practices.

This study uses the Theory of Planned Behavior to examine how saltwater intrusion information affects dry season migratory intentions in impacted areas. The study collected data from coastal communities in Ca Mau, Bac Lieu, Soc Trang, and Ben Tre using online and in-person questionnaires. The data that was gathered was then evaluated using Partial Least Square – structural equation modeling. The findings indicate that individual attitudes toward saltwater intrusion play a significant role in shaping their perception of its impact. Notably, there is a willingness to adapt. Although subjective norms are not yet clearly expressed, this factor influences salinity intrusion awareness, meaning that people actively monitor and apply adaptive solutions to respond to environmental changes. Moreover, perceived behavioral control directly influences migration intentions, suggesting that enhanced resilience and coping strategies could mitigate migration pressures. Government policies and infrastructure play a crucial moderating role by providing essential support and adaptations, which influence residents’ responses to environmental challenges. This study underscores the need for targeted governmental and community-focused interventions to enhance resilience and reduce migration driven by environmental stressors in the Mekong Delta, contributing to the broader discourse on climate change adaptation and community resilience.

When the word "disaster" is used, it usually refers to both human-caused situations that have a negative impact on the community and its environment as well as natural disasters like hurricanes, earthquakes, floods, and similar phenomena. Good logistics management is crucial to reducing the bad effects of these kinds of circumstances. This typically entails tasks like planning, organizing, acquiring, moving, and other associated duties. The distribution of supplies to impacted individuals in an effort to save lives is the main objective of humanitarian logistics. The location of humanitarian goods and equipment, which are kept in makeshift humanitarian logistics centers, is crucial for ensuring prompt response in such circumstances. As a result, when deciding where to locate these centers, it is crucial to take into account particular local factors. Numerous factors might impact this kind of selection, which is why finding a location for a humanitarian logistics center is considered a multi-criteria challenge. This research suggests using the ADAM (Axial-Distance-Based Aggregated Measurement Method) and SWARA (Stepwise Weight Assessment Ratio Analysis) techniques to solve this kind of issue. An example of their application is provided by a case study that centers on where Serbia's humanitarian logistics hub is located. The creation of a framework and a special set of standards for choosing the locations of humanitarian logistics centers are the main results of this study. This can help decision-makers, authorities, individuals, non-governmental groups, and logistical service providers make well-informed decisions that have the potential to save countless lives.

In the production of high-precision electronic connectors, the proper alignment and insertion quality of pins are critical to ensuring product reliability. Any pin misalignment or deformation can lead to electrical failures in connectors, such as poor contact or pin breakage. To address this issue, this paper conducts a systematic dynamic analysis of the pin insertion mechanism in continuous pin insertion machines, aiming to minimize defects during production and inspection processes. The study first outlines the working principles of continuous pin insertion machines and provides a comprehensive analysis of the pin insertion mechanism, control system, and visual inspection system. By establishing a dynamic model of the pin insertion mechanism, the research uses Matlab for simulation to explore the effects of clearance values, motor speeds, and different materials on the dynamic characteristics of the pin bar. Additionally, a comprehensive test platform was constructed, comprising a feeding module, pinhead, servo worktable, pressure sensor, infrared displacement sensor, and an industrial control computer. The experimental results confirm the accuracy of the simulations and reveal specific trends regarding how clearance values, motor driving speeds, and material selection impact the dynamics of the pin bar. The findings of this study not only enhance the operational stability of continuous pin insertion machines but also provide scientific guidance for quality control and defect prevention in precision connector manufacturing.

The present study will attempt to investigate the energy dissipation in a stair-shaped stilling basin, developed as an improved model of the USBR Type III basin. For this purpose, an initial step of planning and modeling of the flume was undertaken, followed by experimental setup and data collection on the water level, critical depth, velocity, and discharge. In each of these two models, experiments were conducted for 10 variations in discharge. The energy dissipation ratio for the stair-shaped model reached 81.59%, as opposed to 78.99% for the USBR Type III. That means that the efficiency in the stair-shaped model is 2.6% higher. The velocity varied between 19.17 and 29.80 m/s for the USBR Type III model and between 17.42 and 28.14 m/s for the stair-shaped model. The maximum water level in USBR Type III was 'this', while in the stair-shaped model, it is +22.95, thus showing better energy dissipation. The stair-shaped model, also closely lies with the hydraulic jump state according to Elevatorski's formula and shows a value of 7% skewness. Further recommendations on topographic and geological conditions are warranted for the application of a stair-shaped basin.

Forests play a pivotal role in environmental conservation, necessitating effective management strategies to ensure sustainability. This research aims to evaluate the potential implementation of forest endowments (waqf) through a strengths, weaknesses, opportunities, and threats (SWOT) analysis. The study employs two methodologies: a comprehensive SWOT analysis to examine internal and external factors and the Internal Factor Evaluation (IFE) and External Factor Evaluation (EFE) methods within the SWOT matrix framework. Data were gathered via questionnaires distributed to representatives of environmental care communities. The findings indicate that internal factors, particularly potential strengths, support the implementation of waqf forests. Internal and external factors contribute to these strengths, enhancing the potential for successful implementation. Weaknesses can be mitigated by leveraging existing strengths. External factors are categorized into opportunities, which can promote the development of waqf forest strategies, and threats, which necessitate strategic interventions. The analysis reveals that the strength factor scores higher overall than the weakness factor, suggesting a promising outlook for successful implementation. These research findings contribute to a deeper understanding of waqf forest implementation by thoroughly analyzing the relevant internal and external factors. The identified strengths, weaknesses, opportunities, and threats provide valuable guidance for stakeholders aiming to optimize the use of waqf forests for environmental conservation and sustainable management.

The traditional K-means clustering algorithm has unstable clustering results and low efficiency due to the random selection of initial cluster centres. To address the limitations, an improved K-means clustering algorithm based on adaptive guided differential evolution (AGDE-KM) was proposed. First, adaptive operators were designed to enhance global search capability in the early stages and accelerate convergence in later stages. Second, a multi-mutation strategy with a weighted coefficient was introduced to leverage the advantages of different mutation strategies during various evolutionary phases, balancing global and local search capabilities and expediting convergence. Third, a Gaussian perturbation crossover operation was proposed based on the best individual in the current population, providing individuals with superior evolution directions while preserving population diversity across dimensions, thereby avoiding the local optima of the algorithm. The optimal solution output at the end of the algorithm implementation was used as the initial cluster centres, replacing the cluster centres randomly selected by the traditional K-means clustering algorithm. The proposed algorithm was evaluated on public datasets from the UCI repository, including Vowel, Iris, and Glass, as well as a synthetic dataset (Jcdx). The sum of squared errors (SSE) was reduced by 5.65%, 19.59%, 13.31%, and 6.1%, respectively, compared to traditional K-means. Additionally, clustering time was decreased by 83.03%, 81.33%, 77.47%, and 92.63%, respectively. Experimental results demonstrate that the proposed improved algorithm significantly enhances convergence speed and optimisation capability, significantly improving the clustering effectiveness, efficiency, and stability.

Energy dependency plays a pivotal role in shaping the performance of stock markets, particularly in energy-sensitive indices such as the BIST Industrial Index in Turkey. This study presents a comparative evaluation of traditional statistical models and machine learning (ML) techniques in capturing the complex relationship between energy variables and the BIST Industrial Index. A dataset encompassing energy imports, production levels, and energy prices is utilised to assess the effectiveness of Ordinary Least Squares (OLS) regression, Random Forest (RF), and Gradient Boosting (GB) models. The results reveal that ML models substantially outperform traditional statistical methods in their ability to capture nonlinear, intricate relationships between energy metrics and market behaviour. Among the ML models, RF demonstrates the highest predictive accuracy. Feature importance analysis identifies crude oil production as the most significant variable, underscoring the dominant influence of domestic energy dynamics in shaping the BIST Industrial Index. While ML models offer superior forecasting capabilities, they introduce challenges in terms of model interpretability. In contexts where transparency is crucial, statistical models such as OLS remain more favoured for their simplicity and explainability. The findings highlight the need for a balanced approach in model selection, with hybrid models potentially offering the best of both worlds by combining the strengths of traditional and modern methodologies. The insights derived from this study can inform policymakers and investors, particularly within emerging markets, providing a nuanced understanding of the trade-offs between predictive power and model transparency in forecasting energy-sensitive financial indices.

In the realm of expressway development, the importance of vehicle identification technology is steadily rising. However, conventional systems often struggle to maintain optimal recognition accuracy in adverse weather conditions such as heavy rain, snow, or haze. This study proposes an enhanced approach to license plate recognition utilizing radar fusion technology to synthesize multiple information bands and improve image visibility. By addressing the challenges posed by inclement weather, our algorithm aims to overcome limitations observed in conventional de-fogging methods. Through cooperative image processing, the proposed algorithm automatically identifies and enhances license plate regions, subsequently employing a character recognition model to identify clipped characters. This enhanced technique effectively mitigates the impact of complex backgrounds and noise, thus boosting recognition accuracy. Simulation analyses conducted in MATLAB validate the efficacy of our approach. Through simulation analysis, it is found that the recognition accuracy rate can reach 97%, demonstrating its superior recognition accuracy even in adverse weather conditions.

Parking management systems often need to improve due to outdated infrastructure, inefficient manual processes, and the lack of automation, resulting in increased congestion and poor user experience. Traditional parking management systems, reliant on manual license plate entry and outdated payment methods, need help to meet modern demands. In response, this research introduces a novel Intelligent Parking System (IPS) that leverages automatic License Plate Recognition (LPR) with a YOLO model for real-time detection and recognition of vehicle license plates. By eliminating the need for sensors and manual data entry, our system enhances accuracy, reduces maintenance costs, and optimizes parking operations. Furthermore, integrating QR code-based payment simplifies and accelerates the payment process, reducing wait times and improving user experience. This approach addresses the growing need for scalable and adaptable parking solutions in smart city initiatives. Through this research, the IPS provides a scalable, intelligent alternative to conventional parking systems, contributing to smarter urban environments.

Driver identification is vital in connected transport and has various benefits like usage-based insurance, personalized assisted driving, fleet management, etc. The data collected from behind the wheel makes it possible to identify unique driving styles as an alternative to adding extra costs or compromising drivers' biometric fingerprint privacy, such as facial recognition. The variable nature of drivers causes problems for traditional techniques because they become less accurate when faced with new drivers. This paper presents an innovative method of driver identification using few-shot learning techniques based 1D CNN-LSTM Attention model that can effectively solve the N-driver identification problem, given very few training examples on driving. Our findings reveal that this model can be generalized correctly from just a few examples, making it essential in real-life situations. We compare our proposed method with several baseline models such as LSTM Attention, LSTM, CNN, and ANN. Furthermore, applying our model to 3-way and 5-way classification problems using 1-shot and 5-shot methods further evidences its effectiveness in changing environments. Consequently, from this research, it is clear that knowledge based on the training dataset could be applied successfully to new drivers. Impressive results obtaining when trained on all raw databases but still getting correct identifications even with a small number of instances per driver label.

The suspension system design has been one of the most challenging tasks for engineers due to the importance of its role in reducing the road vibrations transmitted to the vehicle, which have a harmful effect on the human body. This research aims to use and compare different optimization techniques used to design the passive suspension parameters, including the spring stiffness (Ks), damping coefficient (Cs), and tire stiffness (Kt), to minimize body accelerations and subsequently enhance ride comfort for vehicles. The quarter car is modelled as a two-degrees-of-freedom system by using MATLAB/Simulink. Different optimization techniques were introduced and used, such as Taguchi, Genetic Algorithms (GA), and Simulated Annealing (SA), to design the passive suspension parameters. The results showed that the optimal design parameters for suspension systems were obtained using GA and SA methods, which reduced the value of the root mean square of vertical vibration by approximately 44% and the peak of acceleration by approximately 60% compared to the original values. The Taguchi approach reduces the value of the root mean square by approximately 32% and the peak of the acceleration by approximately 28% compared to the original values.

Indonesia is generous in renewable energy resources since the country benefits from abundant solar energy. There has been an increase in the energy demand, and thus, it is essential to consider shifting to renewable energy sources to sustain future energy demand. This research looks at how renewable energy could be formed in an airport, specifically alleviating the use of fossil fuel-powered vehicles. Among them is the engineering of a standalone Public Electric Vehicle Charging Station (SPKLU) powered by other energies. The researcher applies a HOMER-Grid simulation approach to design an approximate daily electrical load of 424.25kW. According to the projections made in the simulation, approximately 254,078kWh of electricity will be produced annually from this renewable energy system. The percentage contribution of the energy from this system to the total energy load is 26.11%. Harnessing renewable energy at the airport is about developing a green technology approach, which can reduce the operational carbon footprint efficiency of the airport and help make the operation more sustainable.

Technology has significantly improved transportation services through information and communication technologies (ICT), and it has enhanced efficiency, accessibility, security, and sustainability in transportation systems. This study aims to comprehensively understand how ICT impacts the transportation system through a systematic literature review. Moreover, this study systematically reviews and synthesizes literature on the impact of Information and Communication Technologies (ICT) on various facets of transportation. It specifically investigates the transformative potential of ICT, including blockchain and IoT technologies, in replacing traditional transportation systems. This review followed the PRISMA guidelines for reporting systemic reviews and meta-analyses. The review process involves several stages, including initial search queries, screening studies, eligibility assessments, and the final selection of articles. The study used only one database, Scopus and it found 425 articles, only 10 papers matched the selection criteria. The Study findings suggest that information and communication technologies have played an important role in transportation services, like smart traffic management, real-time data analysis, and enhanced user interfaces. Despite this, data privacy, infrastructure integration, and equitable access remain challenges. This review contributes to a deeper understanding of the relationship between ICT and transportation. The findings offer valuable insights for policymakers, researchers, and practitioners striving to harness the potential of ICT for creating more efficient, sustainable, and user-centric transportation systems. Based on the study, future research should explore the integrated impact of blockchain, IoT, and AI on transportation efficiency, user acceptance, regulatory adaptations, and environmental implications.

The Indonesian government, through the Directorate General of Railways, Ministry of Transportation, plans to operate the trans-Sumatra Rail Way or train transportation mode (TSRW), so that cities on the island of Sumatra will be fully connected. Meanwhile, on the one hand, the construction costs are very high, starting from the rails and bridges, stations and trains themselves, and on the other hand, cities in Sumatra are already operating with satisfactory service to travelers using road transportation modes, both ICIP buses and Small passenger cars (LPC) have very easy access, while on trains each passenger has to go to the station first or access is low, so it is feared that once this Trans Sumatra Rail Way (TSRW) or train operates, it will not be of interest to people traveling between cities on the island of Sumatra. So, to ensure that the Trans Sumatra Rail Way (TSRW) or train is in demand by people traveling between cities and provinces on the island of Sumatra, it is necessary to carry out a study by presenting a new service attribute that is not yet available in other modes of transportation besides the Trans Sumatra Rail Way (TSRW) or train and also so far. Currently, there are no studies that discuss this mode choice which includes this new service attribute, namely continuous integration between trains and online transportation such as Go-Car, Grab and Maxim by combining the payment of one ticket on the train ticket so that train passengers can be picked up at home and delivered to the train station for free which is called seamless service. The results of the study show that with the existence of this new service attribute as a new variable, it turns out that this trans Sumatra rail-way mode has a great opportunity to be used by people traveling between cities on the island of Sumatra with great opportunities with a market share of 81 percent.