Javascript is required
Search

Abstract

Full Text|PDF|XML
This study investigates the relationship between rapid urbanization and poverty levels in Somalia, employing annual data spanning from 1990 to 2022. The analysis focused on critical variables, including urbanization rates, CO2 emissions as a measure of climate change, and unemployment rates, with poverty quantified by real GDP per capita. A Johansen cointegration approach is utilized to ascertain long-term equilibrium relationships, while a Vector Error Correction Model (VECM) captures short-term dynamics. Results indicate that urbanization exerts a significant positive influence on poverty in the long term; specifically, a 1% increase in urbanization correlates with a 1.73% rise in poverty levels. Additionally, unemployment demonstrates a substantial and statistically significant positive effect, whereby a 1% increase in unemployment results in a 9.64% increase in poverty. In contrast, CO2 emissions were found to be statistically insignificant. The long-run equilibrium adjustment rate is approximately 12.66% per period, suggesting a moderate pace of return to equilibrium. In the short run, the unemployment rate negatively influences poverty, with a coefficient of -2.369508. Furthermore, CO2 emissions exhibit a delayed yet significant positive effect on poverty, indicated by a coefficient of 0.681835. Granger causality tests reveal strong causal relationships between past unemployment rates and future poverty levels, as well as between past urbanization trends and subsequent poverty levels. The findings underscore the necessity for integrated policies that address urbanization, enhance climate resilience, and promote employment, aiming to alleviate poverty in Somalia.

Abstract

Full Text|PDF|XML

Incremental sheet metal forming (ISMF) is a promising manufacturing technique that has gained significant attention due to its ability to produce complex geometries and high-quality products, particularly for small-scale production and rapid prototyping. The integration of industrial robots into the ISMF process, referred to as roboforming, has enabled advancements in this field. However, the inherent limitations of industrial robots—particularly the reduced rigidity of robotic arms with rotary joints—can lead to dimensional inaccuracies and deviations in the final product. These limitations are primarily due to the lack of precise force control during the forming process. To address these challenges, this study introduces a novel approach to roboforming that incorporates force control alongside the position control of the industrial robot. The contact force between the tool and the workpiece is considered as an additional variable in the control loop, with the objective of improving dimensional accuracy and the overall quality of the formed product. A regression analysis was conducted to determine the mean process force required for conical geometries, with the starting radius, infeed depth, wall angle, and supporting angle serving as input variables. Experimental validation revealed that force-controlled incremental forming with a constant contact force is unfeasible, as the pressure force is highly dependent on the current radius of the workpiece and varies during the forming process. Therefore, a new control strategy is proposed, which involves the dynamic adjustment of the contact force, using the variable pressure force as an input parameter. This approach is expected to significantly enhance the precision and reliability of robot-assisted ISMF, offering a pathway for overcoming current limitations in industrial applications.

Open Access
Research article
Mathematical Modelling of the Vacuum Degassing Process for Hydrogen Removal in Precision Steel Production
nenad milijić ,
natalya safronova ,
ivan mihajlović ,
aca jovanović
|
Available online: 12-24-2024

Abstract

Full Text|PDF|XML

Precision steel is a critical material in modern engineering, particularly in precision mechanics and high-performance construction. In this study, a mathematical model is presented to simulate the vacuum degassing (VD) process employed to reduce the hydrogen content in steel produced via the basic oxygen furnace (BOF) process. The steel, which is subsequently used for ingot casting, requires a significant reduction in hydrogen levels— from 7 ppm to below 1.5 ppm—to meet the stringent quality requirements for high-precision applications. This reduction is achieved through the VD process in combination with argon bottom stirring. The model, developed in collaboration with an industrial project in Bosnia and Herzegovina, is designed to predict the necessary degassing time and the temperature variation during the process. The model accounts for the operational parameters specified by the project sponsor and the constraints of the process. Results indicate that the hydrogen content can be reduced within 8.39 minutes under optimal conditions. Furthermore, for a molten steel starting temperature of 1670℃, the final temperature after degassing is predicted to be 1637℃. The applicability of the model has been validated through practical implementation in a new industrial facility, constructed based on the model’s predictions. This study demonstrates the broader utility of the model in designing and optimizing VD processes for precision steel production, offering significant potential for enhancing steel quality and process efficiency in similar industrial settings.

Abstract

Full Text|PDF|XML

Crowd logistics (CL) represents an innovative model within the logistics sector, leveraging the participation of individuals to enhance service provision, optimize resource utilization, and reduce operational costs. Among the various applications of CL, crowd distribution has emerged as one of the most prevalent methods. This study introduces a Multi-Criteria Decision-Making (MCDM) framework for the selection of CL platforms, examining key factors that contribute to their success. A comprehensive review of relevant literature and an in-depth analysis of both domestic and global platforms were conducted, revealing critical performance indicators for successful platform implementation. The Step-wise Weight Assessment Ratio Analysis (SWARA) and Measurement of Alternatives and Ranking according to Compromise Solution (MARCOS) methods were employed to evaluate essential criteria, including cost efficiency, delivery speed, reliability, environmental sustainability, flexibility, and customer support quality. The results of this analysis demonstrate that platforms such as Company 1, Company 2, and Company 3 have achieved market dominance in Serbia, attributed to their optimal balance across these performance criteria. This study’s proposed model serves as a practical tool for businesses and consumers seeking to select the most suitable CL platforms, while also providing actionable insights for further enhancement of logistics systems. The findings contribute to the growing body of knowledge on CL, highlighting the importance of comprehensive evaluation in the selection process.

Open Access
Research article
Risk Assessment in the Transportation of Dangerous Goods: Application of ALOHA and GIS Tools in Montenegro
milanko damjanović ,
aleksandra petrović ,
vladimir ilić ,
marko radetić ,
predrag stanojević
|
Available online: 12-23-2024

Abstract

Full Text|PDF|XML
The transportation of dangerous goods (DG) presents significant risks due to their hazardous chemical properties, which, in the event of an accident, can have detrimental effects on the environment, public health, and infrastructure. Although the transport of such materials is generally prohibited, the growing demand for DG transportation over long distances necessitates compliance with stringent international regulations (e.g., ADR, RID). In urban areas, where transport routes may intersect with residential zones, incidents involving DG can lead to severe consequences, including fatalities, environmental damage, evacuation of local populations, and disruptions to traffic. To mitigate these risks, effective risk management is essential, encompassing analysis, assessment, and reduction strategies. Risk assessment for DG transport can be conducted using various quantitative and qualitative methods, with this study employing the Areal Locations of Hazardous Atmospheres (ALOHA) software and Geographic Information System (GIS) tools for both risk evaluation and visualization. The study area is located in the capital of Montenegro, specifically within the Stari Aerodrom District. This research focuses on evaluating the potential impact of DG transport incidents in this area and the consequences of hazardous material releases in confined spaces. Three specific DGs—benzene, chlorine, and methane—are considered, each presenting distinct environmental, health, and property-related risks. Chlorine is selected as the worst-case scenario, with its impact radius extending approximately 10 km from the release point. The primary objective of this study is to provide a comprehensive assessment of the risks associated with DG transportation, highlighting the importance of safety improvements and effective emergency response strategies. The findings underscore the need for enhanced safety measures during transport and the development of more robust emergency management frameworks for DG-related incidents.
Open Access
Research article
Innovative Hybrid Deep Learning Models for Financial Sentiment Analysis
ridwan b. marqas ,
abdulazeez mousa ,
fatih özyurt
|
Available online: 12-23-2024

Abstract

Full Text|PDF|XML

This study explores hybrid deep learning architectures for the classification of financial sentiment, focusing on the integration of the Convolutional Neural Network (CNN) with the Support Vector Machine (SVM) and the Random Forest (RF). CNN, with its powerful feature extraction capabilities, was combined with SVM’s ability to handle non-linear decision boundaries, while RF enhanced model generalization through ensemble learning. The proposed hybrid frameworks addressed two fundamental challenges in sentiment analysis: overfitting and class imbalance. These challenges were mitigated, resulting in improved model accuracy and reliability compared to standalone methods. Empirical evaluations demonstrated that the CNN-SVM model achieved competitive or superior validation accuracy and loss, indicating its suitability for precise financial sentiment classification. By enabling more accurate sentiment categorization, the model provides actionable insights for financial analysts and investors, thereby supporting better market assessment and investment decision-making. Future work is suggested to incorporate advanced techniques such as adversarial training and domain-specific pre-trained models to further enhance model performance.

Abstract

Full Text|PDF|XML
The evolution of educational systems, marked by an increasing number of institutions, has prompted the integration of advanced data mining techniques to address the limitations of traditional pedagogical models. Predicting students’ academic performance, derived from large-scale educational data, has emerged as a critical application within educational data mining (EDM), a multidisciplinary field combining education and computational science. As educational institutions seek to enhance student outcomes and reduce the risk of failure, the ability to anticipate academic performance has gained considerable attention. A novel methodology, employing cluster analysis in combination with Bayesian networks, was introduced to predict student performance and classify academic quality. Students were first categorized into two distinct clusters, followed by the use of Bayesian networks to model and predict academic performance within each cluster. The proposed framework was evaluated against existing approaches using several standard performance metrics, demonstrating its superior accuracy and robustness. This method not only enhances predictive capabilities but also provides a valuable tool for early intervention in educational settings. The results underscore the potential of integrating machine learning techniques with educational data to foster more effective and personalized learning environments.

Abstract

Full Text|PDF|XML
This paper addresses the issues of incomplete safety management systems and the challenge of optimizing multiple safety objectives concurrently in wind power project construction. An approach for solving Multi-objective Optimization Problem (MOP) based on the Non-Dominated Sorting Genetic Algorithm (NSGA) is proposed. First, key safety risk factors in the construction process of wind power projects are systematically analyzed and identified. A multi-dimensional evaluation index system, including personnel safety, equipment safety, environmental safety, and management safety, is established. Next, a mathematical model is developed with safety, cost, and construction period as the optimization objectives. The NSGA-II and NSGA-III algorithms are applied to solve the model. Case study results show that: (1) the proposed MOP model effectively balances the multiple objectives in wind power project construction; (2) compared with traditional methods, the NSGA demonstrates significant advantages in solution efficiency and diversity; (3) the obtained Pareto optimal solution set provides multiple feasible options for engineering decision-making. The research results provide theoretical foundations and practical guidance for safety management in wind power project construction.

Abstract

Full Text|PDF|XML
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.

Abstract

Full Text|PDF|XML
The evaluation of supply chain (SC) efficiency in the presence of uncertainty presents significant challenges due to the multi-criteria nature of SC performance and the inherent ambiguities in both input and output data. This study proposes an innovative framework that combines Rough Set Theory (RST) with Data Envelopment Analysis (DEA) to address these challenges. By employing rough variables, the framework captures uncertainty in the measurement of inputs and outputs, defining efficiency intervals that reflect the imprecision of real-world data. In this approach, rough sets are used to model the vagueness and granularity of the data, while DEA is applied to assess the relative efficiency of decision-making units (DMUs) within the SC. The effectiveness of the proposed model is demonstrated through case studies that highlight its capacity to handle ambiguous and incomplete data. The results reveal the model’s superiority in providing actionable insights for identifying inefficiencies and areas for improvement within the SC, thus offering a more robust and flexible evaluation framework compared to traditional methods. Moreover, this integrated approach allows decision-makers to assess the efficiency of SC more effectively, taking into account the uncertainty and complexity inherent in the data. These findings contribute significantly to the field of supply chain management (SCM) by offering an enhanced tool for performance assessment that is both comprehensive and adaptable to varying operational contexts.
- no more data -

Journals