To address the challenges in traditional Failure Mode and Effects Analysis (FMEA) related to determining factor weights, identifying risk priority of failure modes, and managing uncertainties in the risk assessment process, this paper proposes an enhanced FMEA risk factor evaluation method. This method integrates incomplete and imprecise expert assessments using a fuzzy multi-criteria compromise ranking technique called the “V1seKriterijumska Optimizacija I Kompromisno Resenje” (VIKOR). By employing Fuzzy Evidence Reasoning (FER), the risk factor ratings are represented using fuzzy belief structures to capture their diversity and uncertainty. Objective weights are adjusted using Shannon entropy to correct subjective weights, and the VIKOR technique is applied to prioritize failure modes based on the principles of minimizing individual regret and maximizing group utility. The improved model is applied to identify key equipment associated with oil and gas leakage risk in the Floating Production Storage and Offloading (FPSO) system. Validity and sensitivity analysis confirm the robustness and reliability of the method, enhancing the accuracy and credibility of the evaluation results.
Rainwater harvesting (RH) techniques, specifically the implementation of Bio-pore Infiltration Holes (BIH), have been investigated as cost-effective and practical methods for managing surface runoff and mitigating flood risks. This study aimed to evaluate the infiltration rates of BIH in secondary forest and agricultural moorland areas, providing a basis for sustainable soil and water conservation practices. A survey methodology was employed to assess infiltration rates using the Horton equation model applied to circular holes with a depth of 50 cm. Soil samples were collected from the vicinity of the BIH for analysis of physical properties at the Soil Science Laboratory, Faculty of Agriculture, Tadulako University. A 4-inch diameter PVC pipe, inserted 30 cm into the soil, was used to measure water infiltration, with water levels recorded up to 60 cm. The findings indicated that infiltration rates in both secondary forest and agricultural lands were moderate. The physical characteristics of the soil, including its texture and organic carbon content, were identified as suboptimal, which constrained the efficiency of waste absorption through the infiltration process. The soil texture in both land types was classified as sandy according to USDA standards, making it susceptible to erosion, which is directly related to the infiltration capacity and the potential for soil transport during erosion events. The carbon organic content was relatively low, at 2.50% in secondary forest land and 1.17% in agricultural land, indicating medium-level criteria for organic content. To enhance soil conservation and flood mitigation, it is recommended that efforts be made to increase organic material content through compost application and post-flood land rehabilitation. Expanding the use of BIH in high-risk flood areas is advocated to effectively reduce and control surface runoff.
The global economic disruption caused by the COVID-19 pandemic has significantly impacted development across various regions, including Penang, where supply chain disruptions, restricted cash flow, and delayed progress have led to reduced economic growth for stakeholders and communities alike. This study aims to explore the interconnected economic, social, and ecological (ESE) systems within the context of sustainable waterfront development in Penang, focusing on how these systems contribute to economic resilience and dynamism. The land and water resources of Penang, strategically situated along vital maritime routes, present substantial potential for economic revitalization. A quantitative research approach was employed, gathering data from questionnaire surveys, revealing a positive correlation between ESE variables. Key attributes such as local authority governance, development scale and impact, business operations, place identity, heritage preservation, and waste management were found to play crucial roles in shaping sustainable waterfront initiatives. The study highlights that Penang’s core economic sectors—agriculture, mining and quarrying, manufacturing, construction, and services—have historically driven the local economy, but the growing focus on waterfront development offers new avenues for economic recovery and growth. The results validate the chosen quantitative methodology and underscore the importance of adopting integrated strategies to address land and water management challenges. By aligning with sustainability goals and adopting a holistic approach, Penang's waterfront development can foster resilience, ensuring long-term socio-economic and ecological balance. The insights provided contribute to a broader understanding of sustainable urban development and offer actionable strategies for policymakers and stakeholders involved in Penang's waterfront projects.
The efficiency of utility vehicle fleets in municipal waste management plays a crucial role in enhancing the sustainability and effectiveness of non-hazardous waste disposal systems. This research investigates the operational performance of a local utility company's vehicle fleet, with a specific focus on waste separation at the source and its implications for meeting environmental standards in Europe and beyond. The study aims to identify the most efficient vehicle within the fleet, contributing to broader goals of environmental preservation and waste reduction, with a long-term vision of achieving "zero waste". Efficiency was evaluated using Data Envelopment Analysis (DEA), where key input parameters included fuel costs, regular maintenance expenses, emergency repair costs, and the number of minor accidents or damages. The output parameter was defined as the vehicle's working hours. Following the DEA results, the Criteria Importance Through Intercriteria Correlation (CRITIC) method was employed to assign weightings to the criteria, ensuring an accurate reflection of their relative importance. The Measurement of Alternatives and Ranking according to Compromise Solution (MARCOS) method was then applied to rank the vehicles based on their overall efficiency. The analysis, conducted over a five-year period (2019-2023), demonstrated that Vehicle 3 (MAN T32-J-339) achieved the highest operational efficiency, particularly in 2020. These findings underscore the potential for optimising fleet performance in waste management systems, contributing to a cleaner urban environment and aligning with global sustainability objectives. The proposed model provides a robust framework for future applications in similar municipal settings, supporting the transition towards more eco-friendly waste management practices.
In light of the intricate interconnection of current global challenges, energy security concerns, and global warming, the strategic pursuit of renewable hydrogen has emerged as a beacon of promise. Consequently, Canada, in alignment with its global environmental commitments and supported by partnerships with entities such as the European Union, is actively working to harness its significant potential in sustainable hydrogen production and distribution. This study undertakes a systematic review and bibliometric analysis of 55 scientific papers focused on hydrogen production and distribution in Canada, published up to September 2023. Firstly, a comprehensive synthesis of these papers is provided across four key dimensions: production, distribution, optimization, and sustainability. Secondly, critical insights into the evolution of hydrogen research and the collaborations shaping the field are unveiled through bibliometric analysis, employing Bibliometrix, an R-package designed for comprehensive science mapping and bibliometric analysis. The findings are intended to offer valuable insights to academic, public, and business communities, enabling them to better utilize available resources, enhance teamwork, and contribute to a more sustainable global energy landscape.
Perovskite solar cells (PSCs) have garnered significant attention in recent years due to their promising potential in photovoltaic applications. Ongoing research aims to enhance the efficiency, stability, and overall performance of PSCs. This study proposes the integration of copper-based metal-organic frameworks (Cu-MOFs) to address critical issues such as inadequate light absorption, instability, and suboptimal power conversion efficiency. Cu-MOFs, synthesized via the hydrothermal method at varying concentrations, have demonstrated an ability to mitigate defects in perovskite films and enhance charge transport. The structural versatility of Cu-MOFs allows for the development of new composites with improved stability and efficiency. By selecting the optimal MOF, hole transport layer (HTL), and counter-electrode materials, the performance of PSCs can be significantly improved. This research focuses on the functionalization of Cu-MOFs within PSCs to boost their efficiency. MOFs, which are porous materials composed of organic and inorganic components, are increasingly utilized in various fields including catalysis, energy storage, pollution treatment, and detection, due to their large surface area, tunable pore size, and adjustable pore volume. Despite their potential, the application of MOFs in aqueous environments has been limited by their poor performance. However, through techniques such as X-ray diffraction (XRD), UV-Vis spectroscopy, Raman spectroscopy, and scanning electron microscopy (SEM), it has been confirmed that Cu-MOFs can be successfully modified. Post-hydrothermal treatment, SEM results indicate enhanced stability and functionality of Cu-MOFs. The integration of Cu-MOFs in PSCs is expected to reduce energy consumption and significantly enhance the efficiency of these solar cells.
This study investigates the relationships between audit reputation, company size, audit fees, and auditor rotation within manufacturing companies listed on the Indonesia Stock Exchange (BEI) from 2018 to 2022. The aim is to analyze the impacts of these factors on auditor rotation decisions, which are hypothesized to enhance trust and transparency in financial reporting. Data from 84 manufacturing companies were analyzed using Partial Least Squares Structural Equation Modeling (PLS-SEM). The findings indicate that larger companies and those with higher audit fees are more likely to change their auditors. However, audit reputation neither influences nor moderates the relationship between these factors and auditor turnover. These insights contribute to understanding the patterns of auditor turnover in Indonesia's manufacturing sector, suggesting that larger firms and those with higher audit fees are inclined to consider changing auditors regardless of the auditor's reputation.
In the realm of urban public affairs management, the necessity for accurate and intelligent distribution of resources has become increasingly imperative for effective social governance. This study, drawing on crime data from Chicago in 2022, introduces a novel approach to public affairs distribution by employing Long Short-Term Memory (LSTM), Multilayer Perceptron (MLP), and their integration. By extensively preprocessing textual, numerical, boolean, temporal, and geographical data, the proposed models were engineered to discern complex interrelations among multidimensional features, thereby enhancing their capability to classify and predict public affairs events. Comparative analysis reveals that the hybrid LSTM-MLP model exhibits superior prediction accuracy over the individual LSTM or MLP models, evidencing enhanced proficiency in capturing intricate event patterns and trends. The effectiveness of the model was further corroborated through a detailed examination of training and validation accuracies, loss trajectories, and confusion matrices. This study contributes a robust methodology to the field of intelligent public affairs prediction and resource allocation, demonstrating significant practical applicability and potential for widespread implementation.
Digital ink Chinese character recognition (DICCR) systems have predominantly been developed using datasets composed of native language writers. However, the handwriting of foreign students, who possess distinct writing habits and often make errors or deviations from standard forms, poses a unique challenge to recognition systems. To address this issue, a robust and adaptable approach is proposed, utilizing a residual network augmented with multi-scale dilated convolutions. The proposed architecture incorporates convolutional kernels of varying scales, which facilitate the extraction of contextual information from different receptive fields. Additionally, the use of dilated convolutions with varying dilation rates allows the model to capture long-range dependencies and short-range features concurrently. This strategy mitigates the gridding effect commonly associated with dilated convolutions, thereby enhancing feature extraction. Experiments conducted on a dataset of digital ink Chinese characters (DICCs) written by foreign students demonstrate the efficacy of the proposed method in improving recognition accuracy. The results indicate that the network is capable of more effectively handling the non-standard writing styles often encountered in such datasets. This approach offers significant potential for the error extraction and automatic evaluation of Chinese character writing, especially in the context of non-native learners.
Magnetic levitation (maglev) transportation represents an advanced rail technology that utilizes magnetic forces to lift and propel trains, eliminating direct contact with tracks. This system offers numerous advantages over conventional railways, including higher operational speeds, reduced maintenance requirements, enhanced energy efficiency, and reduced environmental impact. However, the dynamic interaction between maglev trains and railway bridges, particularly curved bridges, presents challenges in terms of potential instability during operation. To better understand the dynamic behavior of maglev trains on curved bridges, an experimental study was conducted on the Fenghuang Maglev Sightseeing Express Line (FMSEL), the world’s first “Maglev + Culture + Tourism” route. The FMSEL employs a unique ‘U’-shaped girder design, marking its first application in such a setting. Field test data were collected to analyze the dynamic characteristics of the vehicle, suspension bogie, curved rail, and ‘U’-shaped bridge across a range of train speeds. The responses of both the train and bridge were examined in both time and frequency domains, revealing that response amplitudes increased with train speed. Notably, the ride quality of the vehicle remained excellent, as indicated by Sperling index values consistently below 2.5. Furthermore, lateral acceleration of the train was observed to be lower than vertical acceleration, while for the track, vertical acceleration was consistently lower than lateral acceleration. These findings offer insights into the dynamic performance of maglev trains on curved infrastructure, highlighting key factors that must be considered to ensure operational stability and passenger comfort.
Achieving the Sustainable Development Goals (SDG) presents distinct challenges across different income economies, necessitating a comprehensive analysis to identify critical factors influencing progress. This study systematically examines obstacles to SDG attainment across various income groups by analyzing data from 215 nations spanning 2012 to 2021. Principal Component Analysis (PCA) was employed to uncover patterns within the factors, while fuzzy graph modeling elucidated their dynamic influences. The analysis focused on nine key variables: poverty, unemployment, youth literacy, adult literacy, health (undernourishment), food security, access to electricity, carbon dioxide (CO2) emissions, and other greenhouse gas emissions. Findings indicate that CO2 emissions serve as the primary barrier to achieving SDG 13 (climate action) in high-income nations. Conversely, poverty and undernourishment emerge as significant challenges impeding progress in upper-middle-income, lower-middle-income, and low-income groups. The study provides a novel, integrated view of the multifaceted impacts and interactions between socio-economic and environmental factors in addressing SDG challenges. The results offer valuable insights for policymakers, highlighting the need for differentiated strategies tailored to income-specific contexts. It is recommended that governments in high-income countries extend financial support to lower-income groups to alleviate poverty and improve food security, while fostering collaboration in climate mitigation and adaptation to promote balanced and sustainable global development.
Open Access
