This study investigates the complex interrelationships between environmental quality, economic growth, and human capital across 34 provinces in Indonesia from 2017 to 2023, employing a vector autoregression (VAR) approach. The analysis seeks to elucidate how these three critical dimensions influence one another and to provide insights for formulating sustainable development policies that balance economic progress with environmental preservation and human capital enhancement. The findings reveal a bidirectional causality between environmental quality and economic growth, indicating that improvements in one are likely to promote advances in the other. A similar bidirectional causality is observed between environmental quality and human capital, suggesting that better environmental conditions may enhance human capital development, which in turn can contribute to environmental sustainability. However, the relationship between economic growth and human capital is found to be unidirectional, with evidence showing that human capital positively influences economic growth, but not vice versa. This unidirectional causality highlights the importance of investing in human capital to sustain economic growth without compromising environmental integrity. The study underscores the necessity of integrated policy approaches that simultaneously address environmental quality, economic growth, and human capital development. Focusing narrowly on economic growth without considering its environmental and social dimensions may lead to adverse outcomes, undermining long-term sustainability objectives. Therefore, it is recommended that policymakers in Indonesia adopt a holistic perspective, integrating environmental, economic, and social policies to achieve sustainable development goals. The findings of this study provide a nuanced understanding of the interplay among these factors and offer valuable guidance for designing policies that ensure balanced and sustainable development in Indonesia.
The growing global population has placed increasing pressure on the agriculture sector to meet rising food demand, posing significant environmental and ecological challenges. This review systematically examines 70 studies selected from the Scopus database, with a focus on the environmental impacts of agriculture and potential mitigation strategies. Of the 70 articles, 38 studies explore the macroeconomic environmental effects of agriculture. While 10 studies report positive environmental contributions from the sector, 23 highlight adverse ecological consequences. Additionally, various studies indicate U-shaped, inverted U-shaped, or N-shaped relationships between agricultural activities and pollution levels. Livestock production and the extensive use of synthetic fertilisers are identified as major contributors to greenhouse gas (GHG) emissions, while the widespread use of pesticides and herbicides has been shown to cause soil and water contamination. Further environmental degradation is linked to deforestation driven by agricultural expansion, which reduces carbon sinks and biodiversity. The agriculture sector's dependence on fossil fuels also exacerbates its GHG emissions, while its significant freshwater consumption heightens concerns about water scarcity. Moreover, soil degradation, often resulting from monocropping and conventional farming practices, presents an ongoing challenge. However, sustainable agricultural practices, such as agroforestry, crop rotation, conservation tillage, and organic farming, offer promising solutions to mitigate these environmental impacts. These practices not only enhance soil health by reducing chemical inputs but also promote biodiversity within farming systems. Precision agriculture, optimisation of water, fertiliser, and pesticide usage, the adoption of native plant species, and the integration of renewable energy sources have been identified as key strategies for improving the sustainability of agricultural operations. Additionally, genetic advancements in crop development may play a critical role in addressing the sector’s environmental footprint. By adopting these sustainable methods, the agriculture sector has the potential to increase productivity while significantly reducing its environmental impact, contributing to the overall goal of ecological sustainability.
The Sustainable Development Goals (SDGs), particularly Goal 11 (Sustainable Cities and Communities) and Goal 13 (Climate Action), underscore the interconnectedness between air quality and climate change. Escalating levels of air pollution in both urban and rural regions of Indonesia necessitate a deeper understanding of the factors contributing to air quality degradation. This study employs a generalized linear modeling approach, specifically focusing on ordinal logistic regression, to explore the determinants influencing the Air Quality Index (AQI) across 34 provinces in Indonesia. Key predictors, including motor vehicle density, population density, Greenhouse Gas (GHG) emissions, and forest cover, are analyzed to assess their impact on air quality levels. The findings indicate that the number of motor vehicles and the extent of forest cover are significant predictors of air quality. Elevated motor vehicle density is shown to deteriorate the AQI, while larger forest cover areas are associated with improvements in air quality. These results emphasize the importance of targeted environmental interventions, particularly those aimed at reducing vehicle emissions and preserving forest ecosystems. The study highlights the need for the development and enforcement of policies that promote sustainable urban mobility and forest conservation to mitigate air pollution. By providing a comprehensive statistical framework through ordinal logistic regression, this research offers actionable insights for policymakers. The findings can guide the formulation of effective environmental management strategies, supporting efforts to achieve sustainable development objectives. Moreover, this study demonstrates the relevance of adopting rigorous statistical models to address complex environmental challenges, contributing to the broader discourse on sustainability and climate action.
Kendal Regency faces significant challenges concerning the management of solid waste due to the constraints of its only landfill, Darupono Baru, which is situated adjacent to the environmentally sensitive Pagerwunung Nature Reserve. Recent assessments have indicated that the landfill has suffered from landslides on its northern and western flanks. The regency generates approximately 410 tons of waste daily, while the landfill's operational capacity is limited to 150 tons per day, leading to predictions of overload by 2027. In light of these issues, this study employed overlay scoring techniques and network analysis, specifically the fastest route methodology, in accordance with the standards set forth in SNI No. 03-3241-1994, to identify potential new landfill sites across a total area of 2,566 hectares within the regency. Six sites were identified as viable candidates: Gebangan Village in Pageruyung District, Kalibareng Village in Patean District, Kedungasri Village in Ringinarum District, Kalices Village in Patean District, Sojomerto Village in Gemuh District, and Singorojo Village in Singorojo District. The evaluation process employed elimination assessments, which rated Kedungasri Village the highest with a score of 548 out of a maximum of 690, while Singorojo Village received the lowest score of 393. The existing Darupono Baru landfill was found to score 424 out of 690, meeting only 5 out of the 10 assessment criteria established for new sites. Additionally, it was noted that Kendal Regency operates 155 temporary waste disposal sites and maintains 44 waste collection routes, which include 8 routes for tricycles, 20 for armrolls, and 16 for dump trucks. This study contributes valuable insights into waste management strategies and landfill site selection in Kendal Regency, emphasizing the urgent need for sustainable solutions in the context of increasing waste generation.
Radar warning receivers (RWRs) are critical for swiftly and accurately identifying potential threats in complex electromagnetic environments. Numerous methods have been developed over the years, with recent advances in artificial intelligence (AI) significantly enhancing RWR capabilities. This study presents a machine learning-based approach for emitter identification within RWR systems, leveraging a comprehensive radar signal library. Key parameters such as signal frequency, pulse width, pulse repetition frequency (PRF), and beam width were extracted from pulsed radar signals and utilized in various machine learning algorithms. The preprogramming phase of RWRs was optimized through the application of multiple classification algorithms, including k-Nearest Neighbors (KNN), Decision Tree (DT), the ensemble learning method, support vector machine (SVM), and Artificial Neural Network (ANN). These algorithms were compared against conventional methods to evaluate their performance. The machine learning models demonstrated a high degree of accuracy, achieving over 95% in training phases and exceeding 99% in test simulations. The findings highlight the superiority of machine learning algorithms in terms of speed and precision when compared to traditional approaches. Furthermore, the flexibility of machine learning techniques to adapt to diverse problem sets underscores their potential as a preferred solution for future RWR applications. This study suggests that the integration of machine learning into RWR emitter identification not only enhances the operational efficiency of electronic warfare (EW) systems but also represents a significant advancement in the field. The increasing relevance of machine learning in recent years positions it as a promising tool for addressing complex signal processing challenges in EW.
Blast-induced ground vibration, a by-product of rock fragmentation, presents significant challenges, particularly in areas adjacent to residential structures, where excessive vibration can cause structural damage and propagate cracks. This study proposes a novel framework integrating Principal Component Analysis (PCA) and Artificial Neural Networks (ANN) to predict Peak Particle Velocity (PPV), a critical metric for assessing ground vibration intensity. Field data were gathered from Singareni coal mines, capturing a range of blasting parameters, including burden, spacing, explosive quantity, and maximum charge per delay. PCA was employed to identify and retain the most influential variables, reducing dimensionality while preserving essential information. The optimised subset of features was subsequently used to train the ANN model. The model’s performance was evaluated using regression analysis, yielding a high coefficient of determination (R² = 0.92), indicating its robustness and accuracy in predicting PPV. A comparative analysis with conventional empirical equations demonstrated the superiority of the ANN model, which consistently provided more precise estimates of vibration intensity. The integration of PCA not only improved model performance but also enhanced computational efficiency by eliminating redundant parameters. This research underscores the potential of combining advanced statistical techniques with machine learning models to improve the predictability of blast-induced ground vibrations. The proposed framework offers a practical tool for mine operators to mitigate the environmental impact of blasting activities, particularly in sensitive areas.
The identification of optimal stock or portfolio options is a critical concern for investors aiming to maximize profitability within financial markets. With the increasing complexity of available alternatives and the growing volume of financial data, selecting the most suitable investment has become more challenging. Decision-makers often face difficulties in navigating these vast data sets and require robust support tools to simplify and enhance the decision-making process. This study proposes a three-phase approach designed to reduce data complexity and facilitate more detailed analysis. In the initial phase, firms demonstrating low operational efficiency, as indicated by their inventory turnover ratio, were excluded from further consideration. In the subsequent phase, data envelopment analysis (DEA) was employed to assess the efficiency of remaining firms, with those exhibiting efficiency scores lower than one being removed from further investigation. Finally, the third phase involved determining the relative importance of each financial ratio through the calculation of their respective weights, allowing for the ranking of firms based on these adjusted values. The results of this approach provide decision-makers with a refined list of viable investment options, contributing to more informed stock portfolio optimization decisions.
An innovative context-aware fuzzy logic transmission map adjustment method is proposed for road image defogging, aimed at improving visibility and clarity under varying fog conditions. Unlike conventional defogging techniques that rely on a uniform transmission map, the presented approach introduces a fuzzy logic framework that dynamically adjusts the transmission map based on local fog density and contextual factors. Fuzzy membership functions are employed to classify fog density into low, medium, and high categories, enabling an adaptive and context-sensitive adjustment process. Road images are segmented into distinct regions using edge detection and texture analysis, with each region treated independently to preserve critical details such as road markings, lane boundaries, and traffic signs. A key contribution is the integration of proximity-based adjustments for areas near high-intensity light sources, such as streetlights, to maintain brightness and enhance visibility in illuminated zones. The final transmission map is generated through the combination of fuzzy density-based adjustments and an iterative Gaussian filter, which smooths transitions and minimizes potential artifacts. This approach prevents over-darkening while enhancing contrast, even in dense fog conditions. Experimental results demonstrate that the proposed method significantly outperforms traditional defogging techniques in terms of brightness, contrast, and detail retention. The results underscore the utility of fuzzy logic in road image defogging, offering a robust solution for applications in autonomous driving, surveillance, and remote sensing. This method sets a new benchmark for visibility enhancement in challenging environments, providing a high-quality, adaptive solution for real-world applications.
Maintaining wheat moisture content within a safe range is of critical importance for ensuring the quality and safety of wheat. High-precision, rapid detection of wheat moisture content is a key factor in enabling effective control processes. A microwave detection system based on metasurface lens antennas was proposed in this study, which facilitates accurate, non-invasive, and contactless measurement of wheat moisture content. The system measures the attenuation characteristics of wheat with varying moisture content from 23.5 GHz to 24.5 GHz in the frequency range. A linear regression equation (coefficient of determination R2=0.9946) was established by using the measured actual moisture content obtained through the standard drying method, and was used as the prediction model for wheat moisture. Totally, 72 wheat samples were selected for moisture content prediction, yielding a root mean square error (RMSE) of 0.193%, mean absolute error (MAE) of 0.16%, and maximum relative error (MRE) of 5.25%. The results indicate that the proposed microwave detection system, based on metasurface lens antennas, provides an effective method for detecting wheat moisture content.
Automated Guided Vehicles (AGVs) represent a transformative advancement in the automation of transport operations, facilitating unmanned mobility within a wide array of environments, including production lines, warehouses, freight hubs, and terminal operations. In container terminals, where AGVs are increasingly deployed, the routing of these vehicles is a critical task aimed at minimising operational inefficiencies such as travel time, fuel consumption, and overall transportation costs. Routing in this context refers to the determination of optimal paths for a fleet of AGVs, which must satisfy a variety of operational constraints while also adhering to predefined user requirements. Given the high complexity of these problems, characterised by a large solution space, finding exact solutions is computationally intractable for most scenarios. As a result, heuristic methods are commonly employed to approximate optimal solutions. Among the various heuristic techniques, the nearest neighbor algorithm and Tabu search have been identified as promising approaches for determining efficient AGV routes in container terminal environments. These methods are applied to identify paths that minimise travel distance and time, enhancing resource utilisation and improving the overall reliability of goods delivery. The application of these algorithms is expected to lead to a significant reduction in the number of kilometres travelled by AGVs, thereby lowering operational costs and improving service efficiency. This paper examines the efficacy of the "nearest neighbor" and Tabu search algorithms in the context of AGV routing at container terminals, highlighting their potential to optimise fleet operations in the face of complex logistical challenges. Emphasis is placed on the comparative analysis of both algorithms, with a focus on their ability to approximate optimal solutions in dynamic and highly constrained environments.
The Smoothed Particle Hydrodynamics (SPH) method has been applied to solve the Boussinesq equations in order to simulate hypothetical one-dimensional dam break flows (DBFs) across varying depth ratios. Initial simulations reveal that the influence of Boussinesq terms remains minimal during the early stages of DBF when the depth ratio is less than 0.4. However, these terms become increasingly significant at later stages of the flow. In comparison to simulations based on the Saint-Venant equations, the Boussinesq-SPH model underestimates flow depths in regions of constant elevation while overestimating the propagation speed of the positive surge wave, with this overestimation becoming more pronounced as the depth ratio increases. Notably, the first and third Boussinesq terms exert the greatest influence on the simulation results. The findings also indicate the presence of non-hydrostatic pressure distributions within the DBF, which contribute to the accelerated movement of the positive surge. This study offers valuable insights into the modelling of flows that exhibit non-hydrostatic behaviour, and the results may be instrumental in improving the analysis of similar flow phenomena, especially those involving complex pressure distributions and wave propagation dynamics.
Water-cooled heat sinks are efficient cooling solutions for high-heat dissipation applications in industrial and electronic systems. This study investigates water-cooled heat sinks' thermal and hydrodynamic performance through Computational Fluid Dynamics (CFD) simulations. The fluid flow distribution, heat transfer characteristics, and thermal efficiency of various cooling channel geometries were examined under controlled conditions, including a mass flow rate of 0.05 kg/s, an inlet fluid temperature of 22℃, and a convection film coefficient of 80 W/m²℃ between the fluid and heat sink. Additionally, the convection coefficient between the heat sink body and its fins to the environment was set at 10 W/m²℃, with an ambient temperature of 22℃ and a heat flux of 10,000 W/m² applied to the heat sink's base. The analysis reveals that the coolant channel geometry, flow velocity, and the materials' thermophysical properties strongly influence the system's thermal performance and pressure drop. The optimized channel configuration significantly enhanced the heat dissipation efficiency, achieving an increase of 49.1% and a temperature reduction of 59℃. Furthermore, a thermal efficiency of 40.97% and an overall system efficiency of 45.04% were attained. These findings highlight the substantial role of optimized channel geometries in enhancing the performance of water-cooled heat sinks, leading to more efficient and effective cooling systems. The study demonstrates that CFD simulations can be a powerful tool in identifying key design parameters that maximize heat transfer efficiency in water-cooled heat sinks.
The occurrence of market anomalies has been steadily increasing in contemporary stock markets, particularly within the context of the current economic climate. The volatility of stock markets, exacerbated by the recent inflation crisis, has heightened the need for anomaly detection and informed investment decisions. This study focuses on the BIST 100 index in Turkey, specifically examining the XU030 spot market and the XU030D1 futures market, where significant economic fluctuations are prevalent. The Three Sigma Rule was applied to establish threshold values for anomaly detection, and a directional impact analysis was conducted based on these thresholds. The findings indicate that a positive anomaly in the spot market leads to an average increase of 7.65% in the futures market, while a negative anomaly in the spot market results in an average decrease of 8.69% in the futures market. Conversely, a positive anomaly in the futures market has an average positive impact of 7.82% on the spot market, while a negative anomaly in the futures market results in an average negative impact of 3.99% on the spot market. These results underscore the interconnected nature of the spot and futures markets, particularly in times of economic volatility, and provide insights into how anomalies in one market can influence the other. The study’s findings have significant implications for investors, highlighting the need for careful monitoring of market anomalies and their potential directional effects on investment strategies.
Open Access
