In light of the recurring tsunami threats faced by coastal cities, the significance of transportation infrastructure resilience is underscored, particularly in regions such as Padang City, Indonesia, which has previously experienced the devastating impacts of tsunamis, notably the Mentawai event. This study is aimed at developing a robust evacuation planning strategy to mitigate potential loss of life during tsunami occurrences. Through a quantitative analysis utilizing the PTV Visum software, optimal evacuation routes were identified, emphasizing the importance of infrastructure performance in emergency scenarios. The analysis revealed that certain road segments, including Jl. Raya Balai Baru 2, Jl. Mustika Raya, Jl. Rimbo Tarok - Belimbing, Jl. Koto Baru Banuaran, Jl. Thui Raya 2, Jl. Raya Gadut, and Jl. Durian Taruang, achieved a level of service A, indicating very good performance. These routes are essential for an effective evacuation plan, demonstrating superior efficiency and playing a pivotal role in disaster response strategies. The findings advocate for the integration of these optimal routes into urban planning and disaster preparedness initiatives, aiming to enhance the city’s resilience to tsunami threats. Recommendations are extended to the relevant authorities, highlighting the criticality of incorporating advanced transportation planning tools like PTV Visum in the development of evacuation strategies. Such measures are deemed instrumental in minimizing casualties during tsunami events, thereby contributing significantly to the safety and well-being of the populace.

The purpose of the article is to propose a new methodological approach to optimizing ecological taxation by determining its role in environmental protection. The object of study is the environment and ecological taxation. The scientific question is to establish how to optimize ecological taxation in such a way as to ensure a high level of environmental safety. For this purpose, simulation modeling and correlation and regression analysis were carried out. Based on the results, it was determined that it was the scenario of increasing revenues from emissions into the atmosphere, from discharges into water bodies, and from the ecological taxation that is levied for the generation of radioactive waste that is the best in terms of optimization. It was proposed to increase revenues from emissions into the atmosphere, from discharges into water bodies, and the ecological taxation levied for the generation of radioactive waste: a review and increase in tax rates on pollutant emissions. The study is limited by taking into account the specifics of only one country during optimization and therefore the variables are selected only after this.

Over a thirty-year period (1990-2020), the spatiotemporal changes in riverbank erosion and accretion along the Jamuna River in Shariakandi Upazila, Bogura District, Bangladesh, were investigated using Landsat satellite imagery processed through ArcGIS 10.8 and Erdas Imagine 2015. The analysis delineated significant alterations in the riverbank, quantifying a decrease in the river area from 108 km² to 79.99 km², with a net erosion of 50.02 km² and an accretion of 78.03 km². Among the nine unions affected, Karnibari, Kazla, and Chaulabari were most impacted, with erosion accounting for 14.79%, 25.98%, and 28.42% of the total, respectively. This study established a direct correlation between riverbank erosion and increased vulnerability for local populations, characterized by loss of homesteads and agricultural lands, displacement, income reduction, and a cycle of poverty. Environmental repercussions included deteriorated water quality and an increased prevalence of diseases. The effectiveness of various local adaptation strategies, such as financial reliance on external sources, migration, and occupational shifts, was also assessed, revealing a spectrum of success and underscoring the necessity for more sustainable, holistic approaches. This research emphasizes the imperative for integrated riverbank management strategies that concurrently address the geological and socio-economic ramifications of riverbank erosion.

Airports serve as critical nodes for tourist ingress within nations and cities; yet, the efficacy of public transportation systems connecting these gateways to final destinations remains suboptimal. This systematic literature review interrogates public transport integration systems (PTIS) to elucidate determinants of their efficacy and to explore their capacity as a service that enhances passenger mobility. An analysis of the extant literature indicates that the success of PTIS is contingent upon an array of factors that collectively influence the physical, operational, and institutional quality of transport integration. It has been identified that governmental entities play a pivotal role in provisioning reliable transport amenities, with an emphasis on infrastructure and operations predicated on integration to augment passenger mobility, diminish expenses, and curtail transfer durations. Nonetheless, the enactment of collaborative measures between regulatory bodies and service providers in the PTIS domain emerges as a formidable challenge, given its intrinsic linkage to business operations, revenue allocation, promotional strategies, and fiscal policies regarding subsidies.

This research aims to analyze the characteristics of consumer behavior regarding the use of shopping bags and the factors that influence it based on an environmental approach. The method in this research is based on ten (10) factors from the Theory of Planned Behavior (TPB) model to understand the behavior of carrying shopping bags instead of using plastic bags based on ten (10) variables namely; 1) Attitude (AT); 2) Subjective Norms (SN); 3) Perceived Behavioral Control (PBC); 4) Environmental Concern (EC); 5) Personal Norms (PN); 6) Response Efficacy (RE); 7) Self-efficacy (SE); 8) Behavioral Intention (BI); 9) Anti-Plastic Bag Behavior (APB); and 10) Behavioral Willingness (BW). The results show a significant relationship between trustworthy AT, SN, PBC, EC, PN, and SE. This is evident from the significance value (sig) which is less than 0.05, indicating a fairly high level of confidence. Overall, the results of the research provide a better understanding of the factors that influence consumer decisions in carrying shopping bags, especially among BI consumers. The implications of these findings can be used as a basis for developing more effective strategies for promoting more eco-friendly and sustainable behavior in the future.

Decision-making while commuting in big cities is still challenging for many citizens in developing countries. The implementation of diverse transportation modes operating in silos combined with the inaccessibility of real-time travel information prevents commuters from these countries from making informed travel decisions. Commuters often have to choose the specific means of transport that will yield the highest value in terms of cost, safety, convenience, and timeliness among alternatives. This paper uses a case study of Cape Town in South Africa to explore stakeholders' perspectives on implementing an integrated real-time information system (IRIS) and the requirements that must be satisfied. We employed a qualitative methodology, utilising semi-structured interviews and co-design sessions as the means of data collection. Four categories of stakeholders associated with transportation, including taxis, trains, Bus Rapid Transit (BRT), and municipal buses, within the context of South Africa, participated in the study. The findings reveal that the commuters and the public transport operators agreed that challenges around socio-traffic incidents, infrastructure development, lack of technology resources and lack of real-time travel information are major concerns that must be addressed for successful IRIS implementation. Functional features, change management, data privacy, system integration and information sharing were the main priorities on the list of requirements. The study represents a first attempt at understanding the requirements of an IRIS from the stakeholders' perspective in the context of South Africa. It extends the discussion on using IRIS to support transportation in developing countries, which has received limited attention thus far in the literature. The study is relevant for developing futuristic policies, advanced infrastructure, and optimised service delivery in developing countries because it provides a good foundation for understanding the critical requirements for the design and development of IRIS.

To facilitate early intervention and control efforts, this study proposes a soybean leaf disease detection method based on an improved Yolov5 model. Initially, image preprocessing is applied to two datasets of diseased soybean leaf images. Subsequently, the original Yolov5s network model is modified by replacing the Spatial Pyramid Pooling (SPP) module with a simplified SimSPPF for more efficient and precise feature extraction. The backbone Convolutional Neural Network (CNN) is enhanced with the Bottleneck transformer (BotNet) self-attention mechanism to accelerate detection speed. The Complete Intersection over Union (CIoU) loss function is replaced by EIoU-Loss to increase the model's inference speed, and Enhanced Intersection over Union (EIoU)-Non-Maximum Suppression (NMS) is used instead of traditional NMS to optimize the handling of prediction boxes. Experimental results demonstrate that the modified Yolov5s model increases the mean Average Precision (mAP) value by 4.5% compared to the original Yolov5 network model for the detection and identification of soybean leaf diseases. Therefore, the proposed method effectively detects and identifies soybean leaf diseases and can be validated for practicality in actual production environments.

In recent years, environmental protection has become an indispensable component of China's economic development, with its significance increasingly emphasized. National efforts towards environmental governance have expanded from traditional high-pollution industries to encompass all sectors with potential environmental impacts, demonstrating a comprehensive and multi-layered commitment to environmental management. However, within the domain of environmental cost accounting, research and practice have predominantly concentrated on traditional heavy industries such as coal and chemical sectors, leaving a gap in other industries, particularly in light industries such as the sugar industry. Given that the sugar industry is one of the top ten water polluting industries in China, it is particularly necessary to explore its environmental cost accounting. One side, this study addresses this gap by shifting the research focus to the sugar industry, thereby broadening the scope of environmental cost accounting. On the other side, utilizing Material Flow Cost Accounting (MFCA), this research quantifies the environmental costs incurred during the sugar production process, applying its accounting principles to divide materials in enterprise production activities into positive and negative products, elucidating the extent of environmental pollution and resource wastage. This approach not only enhances corporate environmental responsibility but also provides practical insights for the sustainable development of the industry and the formulation of governmental policies.

Rice, a global staple crop, plays a crucial role in feeding approximately half of the global population. Nevertheless, the persistent spread of diseases poses a significant threat to rice production. Therefore, accurately identifying rice diseases is of paramount practical importance. The proposed approach introduces an innovative hybrid architecture for image classification, harnessing the strengths of both Vision Transformers (ViT) and Convolutional Neural Networks (CNNs). This research investigates five primary diseases affecting rice crops: Blast, Brown Spot, Tungro, False smut, and Bacterial Sheath Blight. Approximately 8000 images of these specific rice leaf diseases were employed for training purposes in the study. What distinguishes this method is its unique integration of a CNN block within the transformer layers, deviating from the traditional ViT architecture. Vision Transformers (ViTs), recognized for their exceptional performance in image classification, excel in providing global insights through attention-based mechanisms. Nevertheless, their model complexity can obscure the decision-making process, and ambiguous attention maps can lead to erroneous correlations among image patches. The incorporation of CNNs in this approach serves to address these challenges by effectively capturing local patterns. This synergistic combination enhances the model's robustness to variations in input data, such as changes in scale, perspective, or context. With the utilization of the proposed hybrid ViT-CNN model architecture, the model achieves remarkable results, boasting 100 percent accuracy and top-5 accuracy, along with a precision of 93.84 percent. Through this hybrid model, we have obtained satisfactory outcomes, surpassing the performance of the latest transformer models in the realm of rice leaf disease identification.

Waste oil represents a major challenge to soil quality and environmental sustainability. This study was conducted to determine the environmental impact of refinery effluent on the soil within the refinery site and surrounding area. Six different test sites were selected to measure soil pollutant concentrations according to their distance from the untreated effluent discharge lagoon. The study period lasted six months, as the testing work began in April and ended in September 2023. Standard methods were used to determine the physical, chemical, and biological pollutants of the soil, and soil pollution indices (PLI, CF, and I_geo) were adopted in determining soil quality. The results showed a clear effect of untreated wastewater on soil properties. The soil of the testing sites near the refinery appeared to be highly contaminated with organic pollutants (O&G, TOC, and phenol) and gradually decreased towards the city. The concentrations of heavy metals in the soil were less than the permissible limit of WHO at the various testing sites, with the exception of the concentrations of Cd and Pb. The Cd concentration was approximately 200% higher than the permissible limit, while the Pb concentration was close to the permissible limit or slightly more. Generally, the measured concentrations of these metals followed the order: Ni_geo) indicated that the soil was highly polluted with cadmium, while it did not indicate contamination with other heavy metals. The study recommends that in order to reduce pollution from refinery effluent, the responsible administration must implement waste drainage networks, establish a treatment plant for the untreated effluent, and also use modern technologies to reduce gaseous emissions and their deposits on the soil.

The objectives of this research are: (1) identify the community’s willingness to pay (WTP) for domestic water environmental services and their WTP value; (2) analyze the factors that influence the domestic water WTP value; (3) analyze the potential for self-financing for water conservation; and (4) formulate mechanisms and strategies for the management of conservation self-help funds. This research was conducted in the upstream area of the Renggung Watershed with 30 sample households. Data were analyzed using a mathematical approach to calculate the WTP value and multiple regression analysis was used to determine the factors influencing WTP. The research results are as follows: (1) the majority (83.33%) of the community is willing to pay for domestic water environmental services with an average WTP value for each household of IDR 6,633 per month; (2) there are three factors that have a significant influence (p<0.10) on the WTP value, namely: age of the head of the family, household expenditure, and education of the head of the family; (3) the potential for self-help conservation funds sourced from domestic water WTP is IDR 744,001,115 per year; and (4) mechanisms and strategies for managing conservation self-help funds are carried out by empowering BUMDES as managers and local non-governmental organizations as supervisors.

Selective laser sintering (SLS) is a typical procedure in powder-based 3D printing technology that produces items with great accuracy and precision. The powders used in SLS are granular and discontinuous, making them difficult to simulate using traditional computational techniques that rely on continuous methods, such as the finite element method (FEM) or finite difference (FD). This paper presents a system for accurately depicting the physical interactions of particles affected by a moving laser source using the discrete element method (DEM), performed numerically in Python. This DEM framework was used on polyamide 12 powder with various laser powers (2W, 4W, 5W) and scanning speeds (0.5m/s, 1m/s). The results and comparison with previous literature confirm that the DEM framework accurately depicts the temperature distribution in the laser-scanned powder bed. The effect of laser power and scan speed on fused surface size is explored and corroborated using previous studies, confirming the DEM's dependability and applicability for modelling powder-based additive manufacturing processes.

The necessity to optimize feed crop cultivation in Kazakhstan's steppe zone is underscored by evolving climatic conditions and sustainable agriculture demands. This study, conducted from 2021 to 2023 in the Akmola region, evaluated the nutritional value and production efficiency of annual and perennial grass mixtures. A randomized complete block design was utilized for annual grasses, while a sequential scheme was applied for perennial grasses, each with three replications per plot. Statistical data processing was employed to analyze the outcomes. Results indicated that mixed-feed crops exhibited superior nutritional composition and energy value. Specific combinations of annual grasses, such as oats with peas or oats, peas, and vetch, alongside multicomponent mixtures incorporating legumes for perennial grasses, demonstrated optimal results. The ideal harvest timings for these grass mixtures were also established. It is recommended to cultivate combinations like Sudan grass with peas and vetch, or oats with peas and vetch for green feed, and for hay and pasture, combinations of red fescue, bluegrass, wheatgrass, and alfalfa, as well as red fescue, bluegrass, brome, sainfoin, and alfalfa. This research emphasizes the importance of diverse crop mixtures to enhance feed nutritional value, thereby contributing to sustainable agricultural practices, food security, and environmental resilience amid climate change.

In the realm of high-definition surveillance for dense traffic environments, the accurate detection and classification of vehicles remain paramount challenges, often hindered by missed detections and inaccuracies in vehicle type identification. Addressing these issues, an enhanced version of the You Only Look Once version v5s (YOLOv5s) algorithm is presented, wherein the conventional network structure is optimally modified through the partial integration of the Swin Transformer V2. This innovative approach leverages the convolutional neural networks' (CNNs) proficiency in local feature extraction alongside the Swin Transformer V2's capability in global representation capture, thereby creating a symbiotic system for improved vehicle detection. Furthermore, the introduction of the Similarity-based Attention Module (SimAM) within the CNN framework plays a pivotal role, dynamically refocusing the feature map to accentuate local features critical for accurate detection. An empirical evaluation of this augmented YOLOv5s algorithm demonstrates a significant uplift in performance metrics, evidencing an average detection precision (mAP@0.5:0.95) of 65.7%. Specifically, in the domain of vehicle category identification, a notable increase in the true positive rate by 4.48% is observed, alongside a reduction in the false negative rate by 4.11%. The culmination of these enhancements through the integration of Swin Transformer and SimAM within the YOLOv5s framework marks a substantial advancement in the precision of vehicle type recognition and reduction of target miss detection in densely populated traffic flows. The methodology's success underscores the efficacy of this integrated approach in overcoming the prevalent limitations of existing vehicle detection algorithms under complex surveillance scenarios.