The enhancement of heat transfer continues to be a critical objective across various high-performance applications, including electronics cooling, automotive thermal systems, and renewable energy systems. Among emerging passive and active strategies, oscillating fin technology has attracted growing interest due to its potential to disrupt thermal boundary layers and augment convective heat transfer. In this review, a systematic analysis of 120 peer-reviewed studies indexed in Scopus, Web of Science, and Google Scholar was conducted, employing the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) methodology to ensure transparency and reproducibility. Search terms such as “oscillating fins,” “heat transfer enhancement,” “numerical simulations,” and “experimental techniques” were used to capture the breadth of relevant literature. Emphasis was placed on the interplay between oscillation parameters—namely frequency, amplitude, and mode of oscillation—and fin geometry, with particular focus on their influence on local and average heat transfer coefficients. Numerical methodologies, including Computational Fluid Dynamics (CFD) and Finite Element Thermal Analysis (FETA), were utilized extensively to characterize fluid motion and thermal gradients around oscillating structures. The reliability of these simulations was critically assessed in light of experimental validations, with instrumentation precision and laboratory conditions considered as key metrics of model fidelity. Challenges related to continuous fin movement, mechanical fatigue, and manufacturing constraints were also identified. To address these issues, recent developments in fatigue-resistant composite materials and advanced fabrication techniques—such as additive manufacturing—were reviewed. Furthermore, the incorporation of novel materials, including porous metals, nanofluids, and piezoelectric components, was explored for their synergistic effects on thermal performance and system durability. This review not only consolidates the current understanding of oscillating fin mechanisms but also highlights gaps in knowledge and opportunities for future research in the development of high-efficiency thermal management systems.
Diabetes is a chronic metabolic disorder that affects millions of people worldwide, making early detection crucial for effective management. This study assesses the effectiveness of three machine learning (ML) models, Logistic Regression (LR), Naïve Bayes (NB), and Decision Tree (DT), in predicting diabetes based on data from 392 individuals, including their demographic and clinical characteristics. The dataset underwent preprocessing to maintain data integrity, was standardized for model compatibility, and analyzed through feature correlation heatmaps, feature importance assessments, and statistical significance tests. The findings revealed that LR surpassed the other models, with the highest accuracy (78%), precision (73%), and F1-score (65%) for diabetic cases. NB showed moderate performance with 75% accuracy, while DT demonstrated the lowest accuracy (71%) due to overfitting. Receiver Operating Characteristic (ROC) analysis revealed strong discriminative power across all models, although perfect Area Under the Curve (AUC) scores indicate potential overfitting needing further validation. The study emphasizes the significance of key features like Glucose, Body Mass Index (BMI), and Age, which showed notable differences between diabetic and non-diabetic individuals. By enabling early detection and proactive management, these models can contribute to reducing diabetes-related complications, enhancing patient outcomes, and lessening the burden on healthcare systems. Future research should investigate ensemble learning, deep learning, and real-time data integration from Internet of Things (IoT) devices to improve predictive accuracy and scalability.
This study investigates the relationship between financial risk management, corporate social responsibility (CSR), and sustainable development within the petrochemical industry. The research aims to explore the impact of financial risk management practices on CSR initiatives and to assess how these factors collectively contribute to the long-term sustainability of petrochemical companies. A key focus of the study is the role that CSR plays in advancing sustainable development, particularly in sectors facing significant financial and operational risks. The research is applied in nature, offering practical insights for improving risk management strategies in petrochemical corporations. The study sample consisted of 130 experienced managers from the petrochemical industry, selected based on the number of items in the survey questionnaire. The measurement tool used was a researcher-developed questionnaire, which was designed following an extensive review of relevant literature and consultations with subject matter experts. To ensure the validity of the instrument, content validity was assessed, and reliability was confirmed through the calculation of Cronbach's alpha coefficient. Data were analyzed using Partial Least Squares (PLS) software, which revealed significant findings regarding the influence of financial risk management on CSR and sustainable development. The results underscore the crucial role of effective financial risk management in facilitating CSR initiatives and enhancing the sustainability of petrochemical companies. Additionally, CSR was found to positively affect sustainable development, with a particular emphasis on the integration of social activities, product and service innovation, and human resource management practices. It is concluded that prioritizing CSR, along with strategic financial risk management, is essential for achieving long-term sustainability in the petrochemical sector. These findings offer valuable insights for both academic research and industry practice, contributing to the development of more effective risk management frameworks in the context of sustainable development.
A novel composite mechanical bulging process suitable for the manufacture of medium-duty commercial vehicle drive axle housings is proposed. The analytical expression for the limit bulging forming coefficient of tube blanks under conditions below the metal recrystallization temperature is derived, and the influence of the matching of various force parameters on the limit bulging forming coefficient is analyzed. The appropriate range for the axial auxiliary load during radial bulging is also presented. Based on the derived theory, a 5-ton commercial vehicle drive axle housing is selected as the research object. The key processes in the forming process are numerically simulated to obtain the metal flow state, stress-strain distribution, and wall thickness variation. The types and locations of defects that may occur during the bulging process are also predicted. To address the phenomenon of local wall thinning in the composite mechanical bulging process of the drive axle housing, a set of orthogonal simulation experiments is designed, focusing on the wall thickness thinning rate in the bridge arch bulging area and the crack-prone region, with respect to the process parameters. Based on the numerical simulation results, response surface equations are established for the expansion core's movement speed and axial auxiliary thrust in relation to the wall thickness thinning rate. Through parameter estimation of the response surface equation and regression analysis of significant influencing factors, the effects of process parameters on wall thickness thinning are obtained: the thinning rate in the bridge arch bulging area decreases with increasing expansion core movement speed and axial auxiliary thrust, while the thinning rate in the crack-prone region increases. The optimization of the response surface model and the determination of the optimal process parameter combination, based on field production conditions, show that the numerical simulation results and the wall thickness measurements from process experiments are in close agreement. No cracks occur in the axle housing, and the thinning is effectively alleviated. In contrast, mechanical bulging without axial auxiliary thrust leads to cracks, thus validating the feasibility of the proposed process scheme and the effectiveness of the parameter optimization. This research provides valuable technical reference for upgrading the manufacturing technology of large-span axle-tube products.
The forecasting of wheat commodity prices plays a crucial role in mitigating financial risks for stakeholders across the agricultural supply chain. In this study, the predictive performance of three models—Simple Moving Average (SMA), Extreme Gradient Boosting (XGBoost), and a hybrid SMA-XGBoost model—was evaluated to determine their efficacy in capturing both linear trends and complex nonlinear patterns inherent in wheat price data. A 10-lag structure was employed to integrate historical dependencies and seasonal fluctuations, thereby enhancing the accuracy of trend identification. The dataset was partitioned into training (75%) and testing (25%) subsets to facilitate an objective performance assessment. The XGBoost model, known for its capability in modelling nonlinear dependencies, demonstrated the highest forecasting precision, achieving a Mean Absolute Percentage Error (MAPE) of 1.64%. The hybrid SMA-XGBoost model, which leveraged the complementary strengths of both SMA and XGBoost, yielded a MAPE of 1.75%, outperforming the standalone SMA model, which exhibited a MAPE of 2.60%. While the hybrid model displayed slightly lower accuracy than XGBoost, it offered greater stability and robustness by effectively balancing trend extraction and nonlinear adaptability. These findings highlight the hybrid approach as a viable alternative to purely machine learning-based forecasting methods, particularly in scenarios requiring resilience to diverse market fluctuations. The proposed methodology provides a valuable tool for policymakers, agricultural producers, and market analysts seeking to enhance decision-making strategies and optimize risk management within the agricultural sector.
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.
Open Access
