Muck pile characteristics play a pivotal role in optimizing mining operations, particularly in understanding the post-blast behavior of throw, drop, and lateral spread, which directly impacts the selection and performance of loaders. The parameters of blast design are crucial in determining muck pile formation, influencing both loader efficiency and overall operational productivity. This study explores the effects of various blast design parameters on key muck pile attributes through a series of controlled blast experiments. Principal component analysis (PCA) was employed to identify the blast design factors most influential on muck pile characteristics, enabling the formulation of precise blast designs. The experiments were conducted across four phases at the OCI RGIII mines of Singareni Collieries Company Limited (SCCL), using advanced blast planning software to ensure accurate parameter implementation based on PCA results. Muck pile characteristics were assessed with the assistance of sophisticated artificial intelligence (AI) tools, providing valuable insights into blast optimization. The results revealed that blast designs incorporating a 1.35 spacing-to-burden (S/B) ratio, 0.9(B) stemming, 1-meter decking, and a V firing initiation pattern significantly enhanced muck pile performance. Specifically, these configurations reduced drop height by 3 meters, decreased throw distance by 5.9 meters, and increased lateral spread by 19.3 meters. These optimized muck pile attributes facilitated smoother loader operation, ultimately improving loading efficiency and the overall productivity of mining processes.
This study investigates the application of Multi-Criteria Decision-Making (MCDM) techniques in fruit production, specifically focusing on the use of the interval fuzzy rough pivot pairwise relative criteria importance assessment (PIPRECIA) method for criteria evaluation. A total of 11 criteria were evaluated to rank various combinations of plum varieties and rootstocks. The criteria selected represent key aspects of plum production, including phenology, yield, physical fruit characteristics, and the chemical composition and quality of the fruit. Data for the study were collected through surveys of 17 experts and plum producers. The results indicated that the criteria related to overall yield and fruit weight were deemed the most significant, while those concerning the chemical composition and fruit quality were considered of lesser importance. The findings highlight the potential of the interval fuzzy rough PIPRECIA method in addressing both research and managerial challenges in fruit production. It is suggested that future research expand the application of this method to other geographical regions and agricultural sectors. Additionally, the development of accessible software tools featuring user-friendly interfaces could facilitate broader adoption of MCDM techniques in agricultural decision-making.
The petroleum industry is one of the main industries in the world, mainly due to energy demand. However, the activity involves several steps, such as exploration, drilling, production, transportation, and refining of petroleum. All these steps can contribute to environmental accidents, such as accidental oil spills and chronic pollution. Water pollution during the petroleum industry process is quite frequent. Therefore, some procedures and solutions to prevent or clean the water are very important for environmental protection. During the primary extraction of petroleum, seawater or water produced by prior extractions is used. As a result, the water is contaminated mainly by heavy metals and some organics. To minimize the environmental liability of some places near the petroliferous wells, the correct treatment includes neutralization, dissolved air flotation, filtration, and activated carbon treatment.
One of the adopted policies to contribute to this effort is the carbon tax policy, which is being implemented in several countries. However, its effectiveness remains heavily affected by public perceptions and reactions. Therefore, this paper explores the environmental policy implications of carbon tax implementation in Indonesia using a Natural Language Processing (NLP) approach. As seen, the data were directly surveyed from 377 respondents and analyzed using the BERT model. After analysis, most respondents feel positive about the carbon tax, stating that with a policy like that, levels of pollution will be reduced in a green economy. Word clouds of text data bring to the fore important keywords on carbon tax — ‘emission’, ‘climate change’, and ‘green economy’-pointing to the actual gist on which the public discourse is centered. The correlation analysis also shows a strong relationship between perceptions of the carbon tax with views on economic and environmental impacts. The implications are useful for policymakers to come up with a communication strategy optimization and an implementation of the carbon tax in Indonesia, considering public concerns and expectations.
Accurate smoke detection in complex industrial environments, such as chemical plants, remains a significant challenge due to the inherently low contrast, transparency, and weak texture features of smoke, which often exhibits blurred boundaries and diverse spatial scales. To address these limitations, YOLOv8n-AM, an enhanced lightweight detection framework belonging to the YOLO (You Only Look Once) series, was developed by integrating advanced architectural components into the baseline YOLOv8n model. Specifically, the conventional Spatial Pyramid Pooling-Fast (SPPF) module was replaced with an Attention-based Intra-scale Feature Interaction (AIFI) Convolution Synergistic Feature Processing Module (SFPM), i.e., AIFC-SFPM, enabling more effective semantic feature representation and an improvement in detection accuracy. In parallel, the original convolutional module was optimized using a Multi-Scale Downsampling (MSDown) module, which reduces model redundancy and computational overhead, increasing the detection speed. Experimental evaluations demonstrate that the YOLOv8n-AM model achieves a 1.7% improvement in mean Average Precision (mAP), accompanied by a 9.1% reduction in Giga Floating-point Operations Per Second (GFLOPs) and a 15.4% decrease in parameter count when compared to the original YOLOv8n framework. These improvements collectively underscore the model’s suitability for real-time deployment in resource-constrained industrial settings where rapid and reliable smoke detection is critical. The proposed architecture thus provides a computationally efficient and high-precision solution for safety-critical visual monitoring applications.
Efficient management of railway infrastructure is recognized as a cornerstone for the sustainable development of the transport sector, as it plays a critical role in reducing congestion, mitigating environmental pollution, and enhancing mobility. The modernization and optimization of railway systems are essential for the optimal utilization of resources and the advancement of a more competitive and environmentally sustainable sector. Railway infrastructure managers (RIMs) are entrusted with the responsibility of ensuring efficient infrastructure management, maintenance, and modernization, thereby guaranteeing the safety, reliability, and sustainability of railway systems. In this study, a methodological framework was proposed for evaluating the efficiency of RIMs by integrating Pearson’s correlation and the Data Envelopment Analysis (DEA) method. The efficiency evaluation was conducted based on key performance indicators (KPIs) associated with railway infrastructure management. Pearson’s correlation was employed to analyze the relationships among 35 KPIs, while the DEA method was utilized to identify efficient managers. The developed framework offers a novel approach for creating analytical tools tailored to RIMs, providing regulatory bodies and decision-makers with a valuable toolset to implement best practices and enhance competitiveness. The findings of this study have practical implications, enabling performance comparisons, the development of management strategies, and the formulation of policies aimed at fostering a more sustainable and efficient railway industry.
Individuals with disabilities have long faced disproportionate economic disadvantages, including higher poverty rates, poorer health outcomes, limited access to education, and restricted employment opportunities compared to those without disabilities. The green economy, characterized by low carbon emissions, resource efficiency, and social inclusivity, holds the potential to address these persistent inequities by creating jobs that promote income equality and support sustainable livelihoods. However, despite the growing global shift toward carbon neutrality, there is a significant gap in understanding the challenges and opportunities faced by persons with disabilities in this transition. This scoping review aims to assess the current state of knowledge regarding the inclusion of persons with disabilities in the green economy, with a particular focus on the Global North. Literature published between 2012 and 2023 was systematically reviewed, resulting in the identification of 21 relevant studies from an initial pool of 4,311 abstracts. The findings were categorised into three primary themes: conceptual frameworks for inclusion in the green economy, the role of persons with disabilities as workers, and the role of persons with disabilities as consumers. The results underscore a critical lack of literature addressing disability inclusion in green economic development, with existing studies indicating that persons with disabilities have been systemically marginalized in efforts to foster low-carbon economies. This exclusion represents a missed opportunity to harness the talents, perspectives, and contributions of persons with disabilities, whether as workers, consumers, or agents of change in sustainable development. It is therefore imperative that the experiences and epistemologies of persons with disabilities are central to the design, planning, and implementation of green economy initiatives. Future research must address the existing gaps in the literature and explore strategies for fostering greater inclusion in green economic frameworks to ensure equitable opportunities for all individuals in the transition to a carbon-neutral world.
Research on Taiwan's science parks has frequently concentrated on isolated aspects, often neglecting the interplay between diverse indicators and the multifaceted dynamics influencing the development of these parks. Additionally, existing applications of environmental, social, and governance (ESG) frameworks in science parks have been found to inadequately capture the complexity of their performance metrics. This study aims to establish a comprehensive ESG evaluation framework tailored to the unique characteristics of Taiwan's science parks. Through the integration of the Fuzzy Delphi Method (FDM) and cluster analysis, a classification system was developed, demonstrating operational feasibility. The proposed evaluation framework is structured around two primary dimensions-Environmental Resource Management and Socioeconomic Resilience-encompassing ten critical indicators. Findings indicate that indicators under the Environmental Resource Management dimension, including water resource utilization, air quality management, greenhouse gas (GHG) emissions, renewable energy adoption, and waste management, exert the most significant impact on the sustainable development of science parks. In contrast, indicators under the Socioeconomic Resilience dimension, such as transportation planning, labour rights protection, public facility services, and financial sustainability, are deemed moderately influential yet essential to fostering balanced development. Indicators related to high-tech talent cultivation and gender equality in decision-making were determined to have limited relevance to the immediate operational needs of science parks. Consequently, it is suggested that these indicators be excluded from resource allocation priorities in resource-constrained settings. Emphasis is placed on prioritizing investments in the Environmental Resource Management dimension to ensure sustainability and compliance with global environmental standards. Additional resources, if available, should be allocated based on the specific contextual needs of individual science parks. The proposed framework not only provides actionable insights into resource allocation strategies but also establishes a robust, comparable basis for evaluating the ESG performance of science parks in Taiwan and beyond. By addressing the interdependencies among critical indicators, the framework enhances the capacity of science parks to contribute to sustainable industrial development.
Open Access
