https://www.transoscience.ir/ Transactions on Data Analysis in Social Science en daily 1 Sat, 01 Mar 2025 00:00:00 +0330 Sat, 01 Mar 2025 00:00:00 +0330 https://www.transoscience.ir/article_228265.html Gold is widely recognized as one of the most volatile and potentially profitable financial instruments, yet it can also result in significant losses. Consequently, even small price movements can generate substantial gains or losses for traders. In the present study, we developed a machine learning model for short-term forecasting of gold prices with a minimum expected accuracy of 60%. The forecasting problem was formulated as a binary classification task. Considering sound capital management principles, a model with 60% predictive accuracy can enable a trader to achieve profitability in the financial market. Assuming an initial capital of $100 per trade, a profit of $1 per successful trade, a loss of $1 per unsuccessful trade (i.e., a risk-reward ratio of 1:1), and a model success rate of at least 60%, one could achieve a net gain of $10 or a 10% return over 100 trades—an acceptable result. After building and optimizing the model, we achieved an accuracy of 66%, approximately 6% higher than the baseline assumption. To further improve model reliability and validate predictions, we tested the model using weekly and monthly data. The model performed poorly on weekly data, likely due to the limited sample size at this time scale. In contrast, the model demonstrated acceptable accuracy on monthly data, suggesting its utility for validating daily predictions. Monthly data typically contain lower noise and volatility than other time frames, which may explain the higher accuracy observed at this scale. For comparison with previous studies, we selected two articles that predicted gold prices as a regression task and one article that predicted price direction. Results indicate that the proposed method has two key advantages over prior approaches: 1) the predictive power of deep neural networks and 2) the effectiveness of incorporating the price action methodology—particularly the inside bar technique—in forecasting gold price direction. https://www.transoscience.ir/article_228268.html In today’s competitive business environment, supply chain management (SCM) has become a critical factor for organizations seeking to increase market share and enhance customer satisfaction. The nature of competition has shifted from being limited to individual companies to encompassing entire supply chains. Consequently, supply chain evaluation has emerged as one of the most vital elements in SCM, requiring continuous refinement of assessment methods and criteria. Previous studies have introduced a variety of models and approaches to evaluate supply chains; however, many of these frameworks lack comprehensiveness and fail to adequately address the evolving needs of organizations, particularly regarding environmental considerations. This study aims to fill this gap by employing Shannon’s entropy technique to evaluate and rank companies operating within the automotive industry, with a specific focus on environmental performance indicators. By integrating environmental factors into the assessment process, this research contributes to a more sustainable and holistic evaluation of supply chain effectiveness. The findings indicate that Company A3 outperforms other firms in the sample, positioning itself as the most effective organization according to the selected criteria. The results provide both theoretical insights and practical implications, highlighting the importance of adopting integrated evaluation models to strengthen competitive advantage and promote sustainable practices across supply chains. https://www.transoscience.ir/article_228271.html The foundation of sustainable industrial development lies in maintaining a continuous flow of resources from nature to the industrial ecosystem and their return to life cycles. Traditional management approaches, characterized by linear flows, lead to unstable resource streams, which are the primary causes of severe environmental degradation and resource scarcity. Shifting the resource flow paradigm from linear or intermittent patterns toward continuous and circular streams constitutes a major challenge for sustainable resource management in industrial ecosystems. Existing research has primarily focused on improving resource efficiency or reducing waste; however, systematic management studies on resource flows from the perspective of the industrial ecosystem remain scarce. To address this gap, this paper proposes a life cycle management approach grounded in the concepts and mechanisms of industrial symbiosis. By analyzing the life cycle system of resource flows and the symbiosis model, a framework for circular resource flows is developed. Based on this framework, a life cycle model is introduced that leverages the symbiosis of existing resource streams, coupled with an integrated assessment method that supports goal-oriented life cycle management while also addressing environmental impacts and sustainable utilization prospects. The case study demonstrates the capability of this approach to assist decision-makers in identifying key issues and formulating integrated, targeted strategies to facilitate sustainable resource flows within industrial ecosystems. To illustrate the evaluation and outcomes, the case study employs Nash theory. https://www.transoscience.ir/article_228299.html Participation in tenders and investment in construction projects involves numerous factors and significant complexities. Decision-making in this context is highly sensitive and critical due to constant fluctuations in the economic conditions of the construction industry and the target investment environment. Consequently, companies require accurate information and rigorous data analysis, often expending substantial time and financial resources on human expertise to address these challenges. It is evident that a comprehensive and precise evaluation of project conditions prior to winning a tender plays a fundamental role in achieving ultimate success for all project stakeholders. To address this need, and within the context of Iran’s construction industry, key decision-making variables were identified to develop a deterministic mathematical model based on an operational research approach. The model incorporates four primary variables: 1) maximum investment cost and bid price, 2) project duration and associated time-based costs, 3) projected net profit, and 4) likelihood of claims occurrence and related claim costs. Using this model, tender decisions can be assessed according to overall price indices, construction duration, expected profit, and potential claims with their corresponding costs. Ultimately, it is concluded that such a model cannot rely solely on analytical operational research solutions and must be calculated using numerical approaches and approximations. https://www.transoscience.ir/article_228300.html This study provides a comprehensive bibliometric analysis of research trends in malware detection and analysis within communication networks, with particular emphasis on the application of machine learning and federated learning techniques. Using Bibexcel and VOSviewer, a total of 2,915 research documents indexed in the Scopus database between 2008 and 2024 were systematically examined. The analysis explores publication trends, key contributing countries, frequently cited works, and core thematic areas in the field. Statistical findings reveal that concepts such as malware, machine learning, and malware detection dominate scholarly discussions, highlighting their central role in advancing detection frameworks. Moreover, the study identifies India, the United States, and China as the top three leading contributors in terms of research output, reflecting their growing academic and industrial engagement in cybersecurity innovation. Emerging trends such as federated learning indicate a strong research orientation toward privacy-preserving and decentralized approaches, which are becoming increasingly critical in large-scale and distributed communication systems. Overall, the study provides valuable insights into the intellectual structure and global research landscape of malware detection, offering guidance for future studies and the development of more robust, intelligent, and collaborative defense mechanisms against evolving cyber threats. https://www.transoscience.ir/article_228301.html It is well recognized that just as humans influence their environment, the environment also shapes human behavior. Educational spaces, in particular, occupy a significant part of individuals’ lives during the formative stages of personality development. Therefore, careful design and construction of these spaces play a vital role in addressing students’ physical and psychological needs. Among these needs, the sense of belonging to a place is especially important and is strongly linked to spatial design. Rigid and conventional school environments cannot fully address students’ diverse requirements; instead, flexible design strategies are essential. Flexibility in spatial design fosters a sense of belonging by enabling both personal and group-based spatial arrangements. This study investigates the effect of flexible and transformable partitions in educational spaces on students’ sense of belonging and proposes practical design strategies. A mixed descriptive–analytical approach was adopted, beginning with a literature review and theoretical analysis, followed by case study evaluations. Subsequently, a structured questionnaire was administered to the target population, and the data were analyzed using structural equation modeling with a partial least squares (PLS) approach, employing SPSS and SmartPLS3 software. The results confirm that flexible partitions significantly enhance students’ sense of belonging through various design interventions, including the integration of large windows, grouping of spaces with deliberate adjacency, flexible forms, movable furniture and panels, purposeful circulation, potential for future expansion, collective and individual rest/study areas, enhanced transparency, the use of terraces or courtyards, and the creation of multi-functional spaces.