درخت حوزه‌های تخصصی

In recent years, off-road (4×4) tourism has become one of the most popular forms of nature-based recreation in the forests of Gilan. The combination of forest trails, pristine landscapes, and the thrill of driving through rugged terrain has attracted many visitors to the region. However, the unbalanced development of this tourism activity has led to significant environmental and social challenges. This study aims to examine the environmental and social impacts of off-road tourism in Gilan’s Forest ecosystems and to propose practical strategies for the sustainable management of such activities. A descriptive-analytical approach was adopted, and data were collected through field observations, interviews with local communities, and a review of environmental reports. The findings reveal that off-road driving causes soil erosion, vegetation destruction, noise pollution, and a decline in wildlife population within the forests. From a social perspective, the growing influx of off-road tourists has altered local lifestyles, created cultural conflicts, and reduced community satisfaction with tourism activities. To mitigate these challenges, it is recommended that designated routes be established for off-road vehicles, environmental monitoring systems be strengthened, and educational programs be provided to raise awareness among visitors and local residents. Furthermore, the development of alternative forms of tourism—such as ecotourism and nature-based cycling—can help preserve the environment while fostering positive engagement between tourists and local communities.

This study investigates the bibliometric analysis on asset allocation for portfolio optimization using machine learning algorithms. The primary objective is to identify and analyze the scientific literature through bibliometric analysis to uncover key themes, authors, sources, highly-cited articles, and countries involved in portfolio management research. To achieve this, 304 articles indexed in Scopus and Web of Science from 1990 to 2023 were analyzed. Using RStudio software, the study highlights various models employed in this field, along with tables, graphs, maps, and key performance metrics related to article production and citation impact. The findings reveal an upward trend in the use of machine learning for optimal portfolio management, asset allocation, and risk management since 2016. Additionally, the United States and China emerged as leading contributors to this literature. The results provide practical insights for market participants, especially those in fintech and finance sectors, to identify optimal machine learning solutions for decision-making processes. These findings also guide students in focusing their research efforts on underexplored areas within this domain.

Objective : In the Fourth Industrial Revolution, advanced technologies are revolutionizing supply chains by enhancing data collection, processing, and analysis across material, financial, and information flows. This shift enables businesses to adopt intelligent supply chain processes with unprecedented efficiency. The integration of intelligent strategies and process-oriented approaches, supported by tools like Intelligent Business Process Management Systems (iBPMS), holds transformative potential for supply chain management, paving the way for Intelligent Supply Chain Management (iSCM) models. This study aims to identify the key dimensions and sub-dimensions of intelligent supply chain processes within the context of Industry 4.0 technologies. Methods : The research employs a meta-synthesis methodology, systematically reviewing peer-reviewed literature and international publications from 2016 to 2025. Following strict meta-synthesis protocols, the study involved keyword screening, thematic evaluation, and iterative refinement, resulting in a curated selection of 62 high-impact journal articles and 4 seminal books. These sources underwent rigorous validation to ensure scholarly relevance before analysis.  Results : The findings identified 117 open codes related to intelligent supply chain processes, which were consolidated into 18 core codes and further classified into five key dimensions: (1) Intelligent Supply Chain Management (covering SCM and intelligent procurement); (2) Process Intelligent Automation (including automation approaches, intelligent processes, and equipment); (3) Process Management (focused on process-oriented approaches, systems, and modeling); (4) Technological Infrastructure (encompassing emerging technologies, ICT infrastructure, software maturity, and robotics); and (5) Macro & Structural Dimensions (addressing managerial, industrial, e-business, market, and organizational factors). Conclusion : The study concludes that Industry 4.0 technologies—such as IoT, AI, blockchain, robotics, and big data analytics—facilitate advanced data-driven supply chain management. When integrated with iBPMS, these innovations enhance efficiency, agility, and end-to-end visibility, establishing a foundation for next-generation intelligent supply chains