Culture or local wisdom is very important for environmental sustainability in areas such as Jember, where the ecological potential is that there are many “Gumuk” fields that used to stand strong but are now starting to be eroded or exploited by local and foreign communities. This research aims to analyze the role of local wisdom in preserving “Gumuk” in Kaliwates District, Jember Regency, and develop a concept for local wisdom-based community education for “Gumuk” conservation in Jember. This research was carried out using phenomenological methods and descriptive analysis, and then the Likert scale became the data analysis technique used in this research. This research shows that local wisdom is valuable and benefits people’s lives. Local wisdom is part of life’s way of solving all life’s problems. There needs to be a role for local wisdom of the community to maintain and preserve existing “Gumuk” so that they are not continuously exploited following the increasing needs of the community. Applying local wisdom in keeping “Gumuk” is the government’s basis for creating conservation policies. Community-based education is a mechanism that provides opportunities for everyone in society to enrich knowledge and technology through lifelong learning.

This study examines how top management’s environmental awareness and green innovation mediate the effects of digital transformation on sustainable development performance. The current study also looks at the institutional environment’s moderating role in the relationship between mediators (i.e., green innovation and top management’s environmental awareness) and digital transformation. The research uses regression analysis to examine hypotheses on how digital transformation affects firms’ sustainable development performance. It does this by using an imbalanced panel dataset including 1,805 Chinese publicly listed manufacturing companies from 2010 to 2021. The findings reveal that digital transformation is positively related to sustainable development performance. Besides, the relationship between digital transformation and sustainable development performance is mediated by increased green innovation and environmental awareness among top management. Furthermore, a supportive institutional environment enhances the impact of digital transformation on top management’s environmental awareness and green innovation. The study provides new insights into the mechanisms by which digital transformation promotes economic and environmental sustainability in China’s industrial sector. The findings have important implications for businesses looking to use digital technology to increase competitiveness while also achieving China’s "dual carbon" aims of green innovation and environmentally responsible leadership. Theoretical contributions include integrating an institution-based perspective to better comprehend contextual implications on long-term digital transformation.

The use of pesticides in the agricultural sector has become a major concern today, especially with the increasing worries about environmental, health, and sustainability impacts. A similar situation is also a focus in Indonesia, known as an agrarian country. Therefore, the objective of this research is to comprehend farmers’ intentions in using pesticides through the planned behavior theory perspective. The method employed in this study is quantitative, utilizing a questionnaire as the research instrument. The questionnaire was developed from 25 research indicators using a seven-point Likert scale. This research adopts the "rule of thumb" formula to determine the sample size, recommending that the sample size should be more significant than 10 times the number of manifest variables. Consequently, the resulting sample size is 250 respondents. The data analysis technique in this research employs Structural Equation Modeling (SEM) with the SmartPLS software. The findings of this study highlight a strong relationship between knowledge, attitude, and perceived behavioral control with the intention of pesticide use. In this context, knowledge plays a central role in shaping a positive attitude, while perceived behavioral control is also significant. Although subjective norms do not significantly influence individual intentions to use pesticides, subjective norms remain an essential element in understanding individual behavior because they essentially reflect social pressure and norms accepted by individuals from their environment.

This investigation employs statistical learning techniques to analyze service quality within Nigeria's aviation industry, a sector integral to the nation's economic vitality and connectivity. The industry has faced challenges exacerbated by economic downturns, notably the rise in fuel prices and the devaluation of the Nigerian Naira since early 2022. Previously reported customer dissatisfaction prompted a thorough examination of passenger and stakeholder experiences. A cross-sectional survey methodology was adopted, yielding data subsequently analyzed through advanced machine learning algorithms. A principal component analysis (PCA) model was refined via leave-one-out cross-validation (LOOCV), an unsupervised learning approach. Findings reveal that crew member performance is the most influential factor on service quality, exhibiting an inverse relationship with other variables in the first principal component. In the second principal component, flight rescheduling emerges as a significant negative determinant. Recommendations from this analysis are directed at aviation industry practitioners, policymakers, and stakeholders, emphasizing the enhancement of crew member recruitment and training processes. Additionally, strategies to adhere to scheduled travel times are advocated. These insights are pivotal for advancing service standards in Nigeria's airline industry.

The study investigates the efficacy of eco-friendly cooling fluids, specifically vegetable oil and water mixtures, in the machining of EN19 steel, with a focus on enhancing performance metrics while promoting environmental sustainability. Machining parameters, including cutting speed, feed rate, and depth of cut (DOC), were analyzed for their effects on surface roughness, tool temperature, cutting forces, and material removal rate (MRR). The study employed a hybrid optimization approach, integrating Taguchi's orthogonal array (OA) method with grey relational analysis (GRA), to evaluate the effectiveness of these eco-friendly cutting fluids. The analysis revealed that spindle speed significantly influenced the MRR, while the DOC notably affected cutting force and tool temperature. The choice of coolant was found to have a considerable impact on surface roughness. Although the Taguchi method effectively optimized individual machining parameters, GRA provided a more comprehensive evaluation by synthesizing multiple performance metrics into a single index, achieving an accuracy of 80.17%, which surpassed the 72.44% accuracy of the Taguchi method. These findings underscore the potential of GRA to optimize the machining process of EN19 steel, offering substantial improvements in manufacturing efficiency and sustainability. The study highlights the importance of adopting eco-friendly practices in industrial machining, demonstrating that the integration of GRA and Taguchi methods can lead to more sustainable and efficient manufacturing processes.

Developing insight into the determinants that impact communities' willingness to accept autonomous buses has become a crucial aspect of smart city advancement. This study investigated the inclination of residents to utilize autonomous buses by employing the expanded Unified Theory of Acceptance and Use of Technology model, encompassing satisfaction, trust, and perceived risk. The UTAUT model is an influential theoretical framework used to forecast and elucidate the acceptance of new technology by people or organizations. The results show that (1) Effort expectancy, performance expectancy, social influence, and facilitating conditions have a considerable beneficial effect on both behavioral intention and satisfaction. (2) A significant positive correlation exists between behavioral intention and satisfaction and trust. (3) Perceived risk also has a detrimental moderating impact. The results offer governments and public transportation operators a valuable blueprint for the development and promotion of autonomous buses in metropolitan regions. Current findings can play as a helpful point of reference for enhancing development of autonomous public transportation in China.

In light of the International Energy Agency’s (IEA) 2020 special report, which estimates the global capacity for carbon dioxide (CO₂) storage to range between 8,000 and 55,000 gigatons, the imperative to enhance carbon storage efficiency and develop superior distribution systems has never been more critical. This investigation focuses on the optimization of adsorption-based carbon storage units through a comprehensive systems analysis, employing the finite element method within the COMSOL Multi-physics™ framework to devise a two-dimensional axisymmetric model that integrates energy, mass, and momentum conservation principles in accordance with thermodynamic constraints. The analysis entails examining the charging and discharging processes of the storage unit under a designated pressure of 9 MPa and an initial temperature of 302 K, with refrigeration provided by ice water. Findings from the simulation underscore the significance of observing pressure and temperature fluctuations during operational phases, revealing higher temperatures in the central region of the tank at the end of the charging cycle, contrasted with lower temperatures upon discharge completion. Moreover, a gradient in velocity is observed, diminishing from the entry point along the tank’s axis. The study underscores the feasibility of storing significantly more CO₂ than the 100 Gt projected by the IEA’s “sustainable development” scenario by 2055, with land-based storage potential notably surpassing offshore capacities. The research advances by developing a predictive model for a novel CO₂ adsorbent throughout the adsorption-desorption cycle, encompassing all relevant transport phenomena. This model is validated against extant data for H₂ storage, facilitating predictions of pressure and temperature variations across different tank locations. This work not only contributes to the field by enhancing the understanding of thermal effects within carbon storage units but also emphasizes the role of advanced modeling techniques in bolstering environmental protection efforts through improved liquid carbon storage solutions.

Underwater vehicles are now mainly researched using the 6-DOF equations of motion. The research on 4-DOF Autonomous Underwater Vehicles (AUV) for small Underwater Vehicles regularly focuses on fully actuated control algorithms. Research on underactuated systems has been conducted frequently for surface ships, while 4-DOF underactuated AUV using a nonlinear control system has received little attention. Little research focuses on devices with quadrotor UAV configuration, also known as QUV, but evaluations have yet to be conducted to advise on which controller to use for different cases. Therefore, in this article, the authors focus on building a control algorithm for an AUV object that lacks a typical recursive executive structure, which is the Backstepping controller when dividing the 4-order strict backpropagation nonlinear system into subsystems to design feedback controllers and Lyapunov control functions for each subsystem. Using this same approach, the authors built a controller that combines Backstepping controller and Hierarchical Sliding Mode Controller (HSMC). This is the guiding premise for research on improving the quality of 4-DOF AUV control before comparing and evaluating the two controllers for specific cases. Newly proposed algorithms and stability analyses are based on Lyapunov's theory, and an evaluation survey is carried out through simulation by Matlab software.

Public transport plays an important role in facilitating productivity and allows transporting skills, labor, and knowledge within and between countries. Many studies were conducted to enhance the public transit system performance, especially the travel time. Travel time in this study represents the total journey time including time on bus, delay time, and waiting time at stops. In this study, two predicting models were developed to estimate the bus travel time by employing two different techniques statistical analysis which involve the use of mathematical models, methods, and tools to analyze and interpret data using SPSS program and Gene Expression Programming (GEP) techniques which is a type of evolutionary algorithm inspired by biological evolution to find computer programs that perform a user-defined task, using GeneXproTools. Four routes have been selected that are served by minibus with a capacity between 22-28 sets, the length of these routes was (11.9, 7.2, 9.0 and 15.2 km), respectively. In this study sixteen trips have been observed for each route (eight trips for each direction) through five weekdays and two weekend days at peak and off-peak period for each day using En-route survey the form of datasheet has been using to obtain the required data. Forty-three data points have been observed from all routes. The first model has developed a relationship between operating bus speed (Vo) and the other independent variables affecting bus speed while the second model has predicted the relation between bus operating speed, private vehicle speed, and the number of stops. The results of model 1 showed that the number of bus stops, signalized intersections, route length, and the average traffic volume is the most effective factors that affect Bus operating speed. Also, the predicted model has a high coefficient of determination (R-square) with 0.888 and 0.93 for SPSS and GeneXpro5.0, respectively. On the other hand, the second model showed that the number of bus stops and the speed of the private vehicle also have a strong relationship with the bus operating speed with the coefficient of determination (R-square) with 0.96 and 0.97 for SPSS and GeneXpro5.0, respectively. The main recommendations that there are several strategies that can contribute to enhancing the travel time of a public transit system: Increase service frequency during peak hours, Enhance the reliability of transit services, improve quality control over the bus operators, and use the bus with multi-door to reduce the dwelling time.

In military defence and wildlife conservation operations, detecting camouflage in images poses a significant challenge. This research investigates the efficacy of deep learning techniques, including Convolutional Neural Networks (CNN), Artificial Neural Networks (ANN), and Long Short-Term Memory (LSTM), in addressing this challenge. The study meticulously evaluates each model's performance using metrics such as average accuracy, validation accuracy, and loss measures across well-known benchmark datasets comprising camouflaged and non-camouflaged images. Notably, the CNN + ANN Pipeline model emerges as the most effective, achieving a remarkable average accuracy of 91.37%. This model, together with the standalone CNN, outperforms the ANN and LSTM models in terms of camouflage detection. The discoveries advance the state-of-the-art in image analysis while also having practical implications for real-world applications. In military settings, good camouflage detection can improve situational awareness and danger detection capabilities. Similarly, automated camouflage detection helps monitor and protect endangered species by detecting hidden creatures or potential threats. Overall, this study highlights the ability of deep learning techniques to greatly improve visual analytic tasks across a variety of domains.

Detecting and managing coastal water pollution is crucial for preserving ecological functions and ecosystem services. However, it is challenging due to the complex nature of the coastal environment, large spatio-temporal scales, and high operational costs. To improve situational awareness, this study used a top-down approach, integrating multi-spectral data from satellites and drones with different resolutions. By combining these data sources, the researchers obtained complementary results and were able to focus on the same phenomenon from multiple perspectives. The study successfully applied this approach to monitor a polluted water plume in the Domitia coast, Italy, originating from wastewater plants and illicit discharges. The results confirmed the effectiveness of the proposed method in assessing water quality and increasing situational awareness in coastal areas. Implementing this approach can aid in the proper management of water resources.

This investigation addresses the issue of premature failure or damage to bearing components in aeroengines, which often results from the release of dissolved gases in the lubricant due to environmental pressure changes during operation. Employing the three-dimensional Reynolds equation and focusing on an ideal lubricating oil, a lubrication model for the engine camshaft's oil film was developed. The formation and extent of gaseous voids within plain bearings were analyzed. The study systematically explored how fit clearance and lubricating oil viscosity influence oil film pressure and thickness. It was found that a reduced fit gap increases the oil film pressure gradient while decreasing the film's thickness. Additionally, although variations in lubricating oil viscosity do not affect the distribution of oil film thickness, they significantly impact the pressure exerted on the oil film, with higher viscosities leading to increased pressures. These findings provide essential theoretical guidance for the safety assessment of aeroengine plain bearings.

Road accidents are the leading cause of death; this increase is usually due to speeding. For road safety, intelligent systems have been designed to keep a constant speed and a safe distance between vehicles in a convoy. This article focuses on the synthesis of an inter-distance control system using intelligent methods and algorithms. The main idea presented in this article is to implement a model-free control for physical model "spring-damper" known as intelligent control based on an algebraic filter. Our comparative analysis extends beyond comparing the use of a simple derivative and an algebraic filter for intelligent control. We also take into account the effect of noise directly affecting the model's behaviour to demonstrate the robustness of our approach. Through MATLAB simulations, we highlight that our approach exhibits better robustness and stable tracking in the inter-distance control system.

Public transportation integration is essential for sustainable tourism development, so it is necessary to improve transportation services. This study aims to determine the indicators needed in designing transportation integration procedures. This study uses a decision-based Fuzzy Delphi method assessed by experts to determine the ranking of each indicator in transportation integration. The location of this study is in West Sumatra Province because it has favorite tourist attractions in 19 districts and cities that foreign and domestic tourists can visit. The findings of this study include nine indicators of physical integration, four indicators of operational integration, three indicators of ticket integration, four indicators of information integration, and one indicator of institutional integration. Thus, it can be used as a guideline in planning transportation integration to improve accessibility to tourist areas.

The dog is considered the man’s best friend, and noise can significantly affect its behavior. In this context, the aim of the research was to determine the effects of fireworks noise on dogs. The methodology applied was the PRISMA 2020 statement; the literature review was conducted on digital databases such as Scopus, ScienceDirect, Taylor & Francis, Wiley, and Ebsco, the annual growth of scientific production was calculated using the digital tool Calcuvio, and data analysis was carried out using Microsoft Office Excel and VOSviewer. The annual growth of production (between 1965 and 2023) was 6.11%, the highest scientific production per year was concentrated in 2018 and 2020, and the pioneering country in scientific production was the United States, the keywords with the highest number of appearances are ‘dog’ and ‘magnetic resonance imaging’. The study concludes that the effects of fireworks noise on dogs were observed in changes in behavior and physiological responses. Furthermore, specific regulations should be in place to help reduce the hearing damage to which dogs are exposed and thus improve people’s emotional relationships with their pets. It is recommended that future research determine the effects on different breeds of dogs.