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The substitution of cement with coal gangue powder (CGP) offers significant potential for energy conservation, emission reduction, and environmental sustainability. To optimize the mechanical properties of coal gangue cement paste, a modified response surface methodology (RSM) model was developed, incorporating grinding parameters as independent variables and compressive strength as the response variable. The feasibility of the model was validated through coefficient estimation, variance analysis, and fitting statistics. The analysis revealed that milling speed was the most significant factor influencing the compressive strength at 20% substitution, while the ball-to-material ratio predominantly affected the strength at 50% substitution. An increase in milling speed was observed to significantly broaden the particle size distribution, with larger particles (15.14$\mathrm{\mu m}$ to 275.42$\mathrm{\mu m}$) serving primarily as micro-aggregates, and smaller particles (0.32$\mathrm{\mu m}$ to 15.14$\mathrm{\mu m}$) functioning as fillers within ultra-fine pores. Scanning Electron Microscopy (SEM) further corroborated these findings. Numerical optimization based on the RSM model identified optimal grinding parameters: a ball-to-material ratio of 1.40, a milling time of 0.843 hours, and a milling speed of 300 rpm. These parameters are recommended to achieve the target compressive strengths of 25 MPa at 20% CGP substitution and 10 MPa at 50% CGP substitution. This study provides a cost-effective and feasible approach for the utilization of coal gangue in cementitious materials, contributing to the advancement of sustainable construction practices.

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In the contemporary digital era, individuals are afforded the convenience of instantaneous transactions through electronic wallets (e-wallets) when engaging in online shopping. This study aims to investigate the extent to which the adoption of e-wallets influences impulsive purchasing behavior, with a particular focus on the moderating effects of low distribution costs (LDC) and short transit times. A descriptive quantitative methodology was employed, targeting users of Indonesian e-wallets. A non-probability research design was utilized, specifically employing snowball sampling techniques. Data were collected through a Google Forms questionnaire, yielding 297 responses. Partial Least Squares (PLS) analysis was conducted to evaluate the data. The results revealed that perceived risk, perceived usefulness, and perceived ease of use (PEOU) significantly and positively impacted the adoption of e-wallets. However, the adoption of e-wallets did not necessarily result in impulsive purchases driven by utilitarian needs. Moreover, LDC and short transit times did not moderate the relationship between e-wallet usage and impulsive buying (IB) behavior. This suggests that most respondents did not use e-wallets for purchases motivated solely by practical considerations, even when LDC and quick transit times were available. These findings contribute to the existing literature on digital money and e-wallets, offering insights for online merchants and digital wallet providers. It is recommended that digital wallet providers enhance accessibility, improve transparency regarding customer data protection, and disseminate information about the benefits and utility of e-wallets to foster wider adoption. Online retailers are encouraged to offer diverse payment options to attract customers. This study provides valuable implications for the optimization of customer service in the context of Indonesia.

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The current research is to profit from the science of enterprise architecture (Enterprise Architecture) and its application in building the structure of government sector institutions in the Kingdom of Saudi Arabia, in accordance with the Kingdom of Saudi Arabia 2030 vision. while emphasizing the value of enterprise architecture (EA) and the need for knowledge to apply its models and procedures while creating its structures. The research study's scope is determined by how well the descriptive and analytical approaches function together, and this is achieved by choosing a few government sector organizations to focus on. Throughout exploring the possibility of applying the Enterprise Architecture model, as an application case based on the extent of knowledge of the cadres of those entities with the organizations' enterprise architecture, and the presence of supervisory expertise. By relying on the quantitative method of studying and analyzing the situation by conducting a questionnaire on some workers in those bodies under consideration (Research Sample), studying the possibility and feasibility of applying enterprise architecture for organizations and generalizing this in the restructuring of government sector’s institutions in general.

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The evaluation of companies' sustainability performance in generating shareholder wealth is increasingly reliant on environmental, social, and governance (ESG) ratings. This study introduces a novel approach by applying data envelopment analysis (DEA) as an alternative to conventional linear regression methods. A conceptual framework has been developed to integrate E-, S-, and G-scores into DEA models, enabling a more nuanced interpretation of whether a company’s ESG efforts contribute to or undermine wealth creation. This approach also assesses the relative effectiveness of a company’s ESG initiatives compared to its peers, taking into account key wealth creation variables. An empirical analysis was conducted on a sample of 80 listed South African companies, calculating the technical efficiency of each company. The findings indicate that linear regression analysis falls short in benchmarking individual companies' ESG efforts in relation to shareholder wealth creation. In contrast, DEA effectively addresses this challenge by offering a robust benchmarking tool. The practical implications of this study are significant, as the concepts of 'fruitless' and 'fruitful' ESG efforts introduced here provide companies with a transferable framework for comparing their sustainability performance against peers. The empirical application underscores the value of DEA in distinguishing between productive and counterproductive ESG strategies, thereby enhancing the precision of sustainability assessments in the context of shareholder wealth.

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This study evaluates the safety management system at Xuefu Gas Station in Xiangtan City of China through a combination of Preliminary Hazard Analysis (PHA) and Fault Tree Analysis (FTA). Initially, PHA was employed to identify potential hazards and assess the probability of associated accidents. This analysis led to the formulation of preventive measures aimed at mitigating identified risks. Subsequently, FTA was utilized to construct a logical framework for analyzing the various causes of system failures and their interdependencies. The analysis revealed deficiencies in the management system, equipment, ignition sources, and human factors. An approximate calculation method was applied to rank the structural importance of these factors, thereby highlighting key areas of impact. Based on these findings, targeted recommendations were proposed to enhance the safety management practices at the gas station, thereby reducing accident likelihood and safeguarding personnel and property. The results underscore the necessity of improving management practices, upgrading equipment, controlling ignition sources, and bolstering human factors to achieve a comprehensive safety management system.
Open Access
Research article
Optimization of Anti-Drone Defense: Analyzing Non-Kinetic Gun Selection Using DIBR II-Grey MARCOS Methodology
marko radovanović ,
marko crnogorac ,
stefan jovčić ,
elif cirkin ,
mouhamed bayane bouraima
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Available online: 08-18-2024

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The selection of appropriate anti-drone systems is critical for enhancing a military's defensive capabilities. With a range of non-kinetic anti-drone guns available, it is essential to identify the optimal system that meets specific military requirements. This study presents a comprehensive approach, combining Multiple Criteria Decision Making (MCDM) techniques to facilitate this selection process. The Defining Interrelationships Between Ranked Criteria II (DIBR II) method has been employed to determine and calculate the criteria weighting coefficients, while the Grey Measurement of Alternatives and Ranking according to Compromise Solution (MARCOS) method, modified to utilize interval grey numbers, has been applied to rank the alternatives. The criteria weighting coefficients, defined by expert input, are aggregated using the Bonferroni mean. The proposed DIBR II-Grey MARCOS model is then subjected to a sensitivity analysis, which further validates the robustness of the selection process. A comparative analysis of results, based on the applied MCDM methods, underscores the efficacy of the proposed model. The findings demonstrate that this integrated model not only provides a reliable framework for selecting anti-drone guns but also offers a versatile tool for resolving other MCDM challenges across various domains. The study highlights the potential of this model for broader application in diverse operational environments, where complex decision-making is required. The combination of MCDM techniques and sensitivity analysis offers valuable insights into optimizing resource allocation, thereby enhancing strategic decision-making processes. The proposed model's adaptability and effectiveness suggest its significant potential for adoption beyond the military sector.

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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.

Open Access
Research article
Evaluation of Rainwater Harvesting and Bio-pore Infiltration Holes for Flood Mitigation and Soil Conservation
naharuddin naharuddin ,
sudirman daeng massiri ,
hendra pribadi ,
arman maiwa ,
muhammad ihsan
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Available online: 08-12-2024

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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.

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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.

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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.
Open Access
Research article
Integrating Long Short-Term Memory and Multilayer Perception for an Intelligent Public Affairs Distribution Model
hong fang ,
minjing peng ,
xiaotian du ,
baisheng lin ,
mingjun jiang ,
jieyi hu ,
zhenjiang long ,
qiaoxian hu
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Available online: 08-01-2024

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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.

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