The Bau Nyale tradition, practiced by coastal communities in Lombok, West Nusa Tenggara, revolves around the search for sea worms, symbolizing blessings and good fortune. This study aims to 1) identify the behavior of the Kuta village community in the Bau Nyale tradition to address climate change impacts, and 2) analyze the tradition’s role in mitigating these impacts, especially on the marine ecosystem in Lombok. Using a qualitative approach, data collection methods included in-depth interviews, direct observation, and documentation in Sukarara Village, Central Lombok. Data analysis was conducted using NVIVO 12 plus. The findings show that Bau Nyale is not only a cultural ritual but also an environmental adaptation mechanism. It contributes to maintaining marine ecosystem balance through sustainable practices and raises public awareness about environmental conservation and climate change threats. The tradition has significant potential to be integrated with modern scientific approaches, offering a sustainable and resilient strategy for natural resource management in the face of climate change.
The spatiotemporal dynamics of urban expansion and its impact on forest fragmentation within Dhaka City Corporation (DCC), a rapidly urbanizing megacity in South Asia, were critically investigated in this study. While prior research has predominantly focused on broad land-use changes and general vegetation loss, detailed analysis of forest fragmentation and its direct correlation with urban expansion intensity remains limited. This gap was addressed by integrating high-resolution Landsat satellite imagery from 2016, 2020, and 2024 with advanced landscape metrics and urban expansion indices, enabling a comprehensive and replicable assessment of urban-driven ecological disruption. Land use and land cover (LULC) classifications were generated through supervised classification in Google Earth Engine. Urban growth was quantified using the Urban Expansion Intensity Index (UEII) and the Annual Urban Expansion Rate (AUER), while forest fragmentation was evaluated via patch density, edge density, and a comprehensive fragmentation index derived from FRAGSTATS. Results indicated a marked intensification of urban expansion, with the urban area increasing from 133 km² in 2016 to 139 km² in 2024. This growth was accompanied by a rise in UEII from 0.67% to 1.35% and in AUER from 0.37% to 0.73%. Concurrently, forest ecosystems experienced significant fragmentation, as evidenced by an increase in the fragmentation index from 33 to 80 and edge density from 4 to 9 per km², indicating a progressive decline in forest continuity and heightened ecological vulnerability. Pearson correlation analysis revealed strong positive relationships between urban expansion and both edge density (r = 0.953) and the fragmentation index (r = 0.922), confirming the direct influence of urban sprawl on forest disintegration. These findings underscore the urgent need for ecologically informed urban planning. By providing a replicable methodological framework for quantifying urbanization-driven ecological disruption, this study contributes to the broader discourse on sustainable urban development and forest conservation in rapidly transforming urban landscapes.
Accurate assessment of Global Navigation Satellite System (GNSS) observation data quality is essential for ensuring the reliability of positioning and navigation applications. Traditional evaluation methods, which rely on single-index weighting or simplistic combinations of multiple indicators, have proven insufficient in capturing the multifaceted nature of observation quality. To address these limitations, a comprehensive evaluation framework was developed based on a combined weighting strategy that integrates the information entropy weight method and the coefficient of variation method. This hybrid approach enhances the objectivity and sensitivity of index weighting by leveraging the strengths of both methods. Furthermore, fuzzy mathematics theory was incorporated to model the uncertainty and vagueness inherent in GNSS observations, thereby enabling the systematic identification and exclusion of low-quality and low-confidence data. This integration allows for the robust evaluation of multi-constellation GNSS observation data, accommodating complex and variable observational environments. The proposed method was validated through empirical analysis, demonstrating superior performance in distinguishing high-quality data compared to conventional single-indicator and single-weighting approaches. Experimental results confirm that the proposed framework yields more reliable and scientifically grounded quality assessments, contributing to improved accuracy and stability in downstream GNSS applications.
Corporations possess subsidiaries globally, forming a corporate network that engages with both human and natural cultural systems. The process of combining ecological and economic viewpoints presents certain difficulties. To achieve strong sustainability, it is necessary to transition from a business-centric strategy to one that integrates ecological principles into strategic decision-making. The objective of this study was to examine the role of Environmental Management Accounting in promoting company sustainability. An extensive examination of existing research, known as a systematic literature review, was conducted from 2015 to 2024. The Environmental Management Accounting paradigm was utilized in several contexts, encompassing corporate governance, supply chain management, and sustainability management accounting. A total of 868 full-text publications were found. EMA is a systematic approach for combining financial and non-financial measures of performance. This study aims to emphasize the importance of Environmental Management Accounting in addressing the challenges posed by the investigation of future opportunities, and how scholars and practitioners can contribute to the path towards corporate sustainable development. The focus is on the interaction between MA alignment and shifts in the structure and external circumstances. In addition, the study identified prospective areas for future research and highlighted their value for both scholars and practitioners.
The current study aimed to identify the reality of early childhood female teachers’ practice for their roles in spreading environmental awareness among children. The study followed the descriptive analytical method. The study was conducted on a sample of 41 early childhood educators in institutions associated with the Department of Education in the Northern Border Region. The results of the current study indicated that early childhood teachers had an average level of practice for their roles in spreading environmental awareness, as the overall average was (3.77). The distribution of the levels of reality of early childhood teachers’ practice for their roles in spreading environmental awareness among the study sample members was as follows: (22.0%) of early childhood female teachers had a low level, while (17.0%) had a medium level of practice for their roles in spreading environmental awareness, while (61.0%) had a high level. The results of the current study indicated that there were no statistically significant differences at the level of significance (0.05) between the categories of years of experience. The results of the current study indicated that there were statistically significant differences that may be due to the academic qualification e, the academic qualification specialty (kindergarten/other specialty) and the number of training courses variable. The findings suggest several implications for science educators broadly, and specifically for those in Saudi Arabia, are highlighted. It is argued that not only should teachers possess knowledge about environmental issues, but they should also demonstrate environmental concern themselves, as their actions and thoughts greatly influence the students they teach.
This study investigates the application of the horizontal stratified mining method to the extraction of steeply inclined unstable coal seams at the Puxi Mine. The stress environment in the mining area, the relationship between the supports and surrounding rock, the control of the rock layers in the caving zone, and the mechanical analysis of the roof collapse following the extraction of the steeply inclined coal seam were examined. The stress conditions in the mining area under the horizontal stratified mining method were explored, and a numerical analysis model was established using FLAC3D software, based on the rock mechanics parameters of the Puxi Mine’s rock layers and strata. The results indicate that, in the stress environment of the horizontal stratified mining method, the mining area is subject to not only the self-weight stress from the surrounding strata, large horizontal ground stresses, and gas pressures, but also concentrated stresses in both the dip and strike directions. When using this mining method, the stability of the two sides of the tunnel is generally good due to the surrounding rock being of a relatively stable nature. However, the roof collapse in the upper layers during the extraction of the lower layers is one of the factors affecting the safety of the support structures in the lower layers, necessitating enhanced support management. Deformation is expected in the mining face of the lower layers during extraction, and measures must be taken to prevent any instances of roof spalling. Therefore, the horizontal stratified mining method is considered feasible for the extraction of steeply inclined unstable coal seams at the Puxi Mine.
In the context of rapidly advancing digital technology, where touchscreen interactions dominate, the tactile sensory development of children is increasingly compromised. This shift towards digital media can hinder the ability of children to effectively engage with and observe their surroundings. One promising solution to this issue is the integration of art appreciation into educational practices. However, in regions such as Indonesia, there is a noticeable scarcity of comprehensive learning kits aimed at teaching art appreciation. This study addresses this gap by designing and developing an art appreciation learning kit intended for children aged 7 to 11, aiming to teach art appreciation through artful thinking (AT). The kit employs the “see, think, wonder” (STW) thinking routine, a structured three-step process that encourages children to observe art, analyze their observations, and engage with the art through inquiry. The analysis, design, development, implementation, and evaluation (ADDIE) framework was employed as the instructional design model. Additionally, a qualitative research through design (RtD) methodology was adopted to guide the design process and ensure the creation of an innovative educational tool. The developed learning kit integrates physical components, multimedia resources, and hands-on arts and crafts activities that complement the STW routine, thereby fostering deeper engagement and critical thinking skills among young learners. The study emphasizes the value of employing the ADDIE model to assess the learning needs and challenges faced by children, particularly during the STW activity. Collaboration with educators during the design and development phases was identified as crucial for refining the learning kit. Key recommendations include the integration of graphic visualizations, clear demonstrations, and interactive activities to enhance children’s engagement and enthusiasm for art appreciation. The findings offer empirical evidence supporting the effective use of the ADDIE model in educational kit design, providing a valuable reference for future product designers in the educational technology field.
The Golden Triangle consisting of cost, time and quality serves as a fundamental framework for assessing the success of infrastructure projects. Effective risk management is critical for optimising these interconnected dimensions by proactively identifying potential threats implementing risk mitigation strategies and ensuring project control. This study investigates the application of the international standard ISO 31000:2018 in enhancing the Golden Triangle’s dimensions—time management, cost optimization and quality assurance—within the context of large-scale infrastructure projects. A qualitative research methodology was employed incorporating semi-structured interviews, document analysis and site observations to collect comprehensive data. Analytical techniques such as Failure Modes and Effects Analysis (FMEA), Bow-Tie analysis and Fishbone diagrams were utilised to prioritise risks, examine preventive measures and identify underlying causes. A total of forty-three (43) critical risks were identified as having significant impacts on the performance of the Algiers Metro project. The findings revealed that the implementation of a structured risk management approach improved adherence to project timelines, optimised cost control and ensured the delivery of quality outcomes. The integration of ISO 31000:2018 principles in conjunction with tailored analytical tools was found to add considerable value providing practical insights for improving infrastructure project performance. This work underscores the importance of systematic risk management and its role in enhancing the efficiency and success of large infrastructure projects.
A novel approach for analyzing fluid flow dynamics and static pressure distributions within a convergent-divergent nozzle was presented, integrating soft computing techniques with computational fluid dynamics (CFD) simulations performed using Ansys Fluent. The study differs from traditional CFD approaches by leveraging soft computing methods to optimize simulation parameters and enhance the accuracy of predictions. Four distinct fluids—air, hydrogen, nitrogen, and helium—were analyzed across a range of inlet velocities (1 m/s to 5 m/s). The study systematically evaluates the influence of boundary conditions and flow models, including both viscous and inviscid conditions, on the flow patterns and static pressure distributions. The results highlight the substantial impact of fluid density and viscosity on the flow dynamics, particularly for lighter gases such as hydrogen and helium. These gases exhibit higher velocities and less pronounced pressure gradients due to their lower density and viscosity compared to denser fluids like air and nitrogen. Soft computing techniques improve the reliability of these findings by enhancing the predictive capability of the CFD model, allowing for more precise insights into complex fluid behaviors. The implications of these findings are significant across multiple engineering domains, such as aerospace propulsion, chemical processing, and energy systems, where optimizing fluid flow characteristics is critical. The integration of soft computing with CFD provides a robust framework for more accurate modelling of low-density, high-velocity flows and offers valuable insights for the design of more efficient systems. This study underscores the potential of advanced computational techniques in advancing both fluid dynamics research and engineering applications.
According to WHO, in 2024, unsafe food is estimated to potentially cause 600 million cases of foodborne illnesses and 420,000 deaths. In 2017, 106 cases of foodborne illnesses in 24 provinces were confirmed by the National Agency of Drug and Food Control (BPOM). This research aims to evaluate temporary waste disposal sites; presence of flies; sanitation hygiene; room sanitation; chemical parameter testing in food samples; and chemical, biological, and physical parameter testing in drinking and clean water. The type of research used is descriptive research. This study aims to observe sanitation and laboratory examination results of food samples, drinking water samples, clean water samples, and ambient air samples at Pattimura Ambon Airport. The research design used is cross-sectional. Testing of water and food samples was conducted at the Environmental Health and Disease Control Technical Institute (BTKLPP). Based on the evaluation results, compliance with the use of work clothing by workers did not meet standards. In addition, the construction of restaurant walls and floors also did not meet standards, namely, they were not waterproof.
The ability of neural network-based control systems for trajectory tracking in wheeled mobile vehicles was evaluated in this study. A significant challenge often encountered is the deviation from the desired trajectory, particularly in high-speed motion. A robust control scheme, designed using the Nonlinear Auto-Regressive Moving Average-Level 2 (NARMA-L2) approach, was employed to enhance the tracking performance under dynamic conditions. The NARMA-L2 controller, a well-established technique for nonlinear systems, was utilized to improve the accuracy and robustness of trajectory tracking in the presence of external disturbances and noise. In heavy-duty mobile vehicles, such as agricultural machines, maintaining straight-line motion at high speeds is particularly susceptible to external load effects and system noise. The proposed control strategy integrates several stages, including system modeling, controller design, and the training of the neural network. To optimize the parameters of a proportional-integral-derivative (PID) controller, the Particle Swarm Optimization (PSO) algorithm was applied, ensuring precise regulation of the vehicle’s speed. The controller generates a reference velocity, which is fed as a signal to control the motion of the left and right wheels, enabling effective steering and trajectory adherence. Simulation results demonstrate the effectiveness of the proposed controller in mitigating the impact of disturbances and load effects. The optimization of control parameters successfully minimizes the discrepancy between the left and right wheel positions, bringing them closer to zero. The robust parameter optimization approach, which was employed to counteract the influence of external loads, can significantly improve system performance under varying conditions.
The authors approach the scientific evidence for global warming from different points of view. It initially discussed the forcing methods adopted by IPCC and a large part of the scientific community to provide valuable tools for understanding the factors driving climate evolution. It evidences the main limit of the forcing method, which limits radiative heat exchanges according to the first law of thermodynamics. A more exhaustive thermodynamic analysis of the Earth system, considering both the first and second laws of thermodynamics, could offer insights into the energy fluxes and entropy generation associated with climate-related phenomena and better describe the Earth’s heat engine. The exergy analysis is a promising tool for assessing the quality and efficiency of energy utilization and identifying the directions and opportunities for sustainable energy development. It provides a complete evaluation of natural and human-induced climate change phenomena. The results have been analyzed in the light of constructal law, observing that human impacts and the fast-growing GHGs in the atmosphere are moving the planet’s development against this law.
Delay-Tolerant Networks (DTN) are intermittent wireless mobile networks designed to handle communications in environments where network connectivity is often disrupted due to node mobility or the absence of fixed infrastructures. These frequent disconnections lead to repeated communication attempts between nodes, thereby increasing energy consumption. DTN is often deployed in isolated and hard-to-reach environments with limited energy sources, imposing significant constraints on the performance and operational lifetime of individual DTN nodes, as well as the DTN network as a whole. Despite the significant efforts invested by researchers to develop energy-efficient algorithms and models, the problem of energy consumption persists, especially with non-renewable sources. The motivation for this research is based on the major challenges related to powering mobile nodes in DTN networks, notably due to the absence of reliable and constant energy sources. The energy constraints of the nodes, combined with their mobility, raise problems of energy consumption and durability, leading to communication interruptions, delays, data losses, and a decrease in the overall efficiency of the network. To overcome these challenges, the article proposes a long-term energy management strategy by integrating renewable energy sources, notably solar energy, into the architecture of DTN nodes. The contributions include the modeling of an energy-autonomous and sustainable solar-powered DTN node, the evaluation of the energy generated and stored by these nodes, and the validation of the effectiveness of this approach through simulations in the ONE simulator, considering realistic mobility scenarios and communication conditions. The results show that solar DTN nodes have significantly higher residual energy than those with limited power sources. Additionally, social mobility models (MBM, SPMBM) consume more energy than individual models (RW, RWP, RD), while the Spray-and-Wait and PROPHET protocols are more energy-efficient compared to Epidemic and MaxProp. These analyses reveal optimal combinations of DTN protocols and mobility models to reduce energy consumption: the Spray-and-Wait protocol aligns well with social mobility models, while PROPHET is more suited to individual mobility models.
In recent years, representing computer vision data in tensor form has become an important method of data representation. However, due to the limitations of signal acquisition devices, the actual data obtained may be damaged, such as image loss, noise interference, or a combination of both. Using Low-Rank Tensor Completion (LRTC) techniques to recover missing or corrupted tensor data has become a hot research topic. In this paper, we adopt a tensor coupled total variation (t-CTV) norm based on t-SVD as the minimization criterion to capture the combined effects of low-rank and local piecewise smooth priors, thus eliminating the need for balance parameters in the process. At the same time, we utilize the Non-Local Means (NLM) denoiser to smooth the image and reduce noise by leveraging the nonlocal self-similarity of the image. Furthermore, an Alternating Direction Method of Multipliers (ADMM) algorithm is designed for the proposed optimization model, NLM-TCTV. Extensive numerical experiments on real tensor data (including color, medical, and satellite remote sensing images) show that the proposed method has good robustness, performs well in noisy images, and surpasses many existing methods in both quality and visual effects.
Open Access
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