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Some companies manipulate financial statements to users and commit fraud. Therefore, effort to detect fraud is essential. Meanwhile, data mining techniques have increasingly popular. This article aimed to use an advanced model to detect fraudulent financial statements and compare it with the other methods. Crowd optimization algorithms have been considered to solve many optimization problems, but so far, they have not been used in fraud detection research to determine the optimal value of SVM parameters and optimize ANN architecture. In this research, for the first time, the PSO algorithm was used as one of the best innovative optimization algorithms for these optimizations due to its memory and high convergence speed, as well as having solutions for exiting from local optimal points and cooperation and information sharing between particles to detect fraud. For this purpose, the financial statements of companies admitted to the stock exchange from 2017 to 2023 were reviewed. The findings showed that the SVM-PSO method, with 89.86%accuracy, compared to the ANN-PSO method, with 80.43%accuracy, and the LR method, with 69.57%accuracy, performs better in identifying suspected fraudulent financial statements. Combining the PSO algorithm with the SVM method has proven superior to other methods due to SVM's high ability to reduce false negatives and PSO's ability to fine-tune its parameters. This combination can be used for high-accuracy financial statement fraud detection.

This study critically examines the sustainability challenges within the lithium-ion battery (LIB) supply chain in India's electric vehicle (EV) sector, an area of growing importance due to the rapid expansion of EV adoption. While LIBs are essential for EVs due to their high energy density and reliability, their production and disposal pose significant environmental, social, and economic sustainability challenges. These include resource depletion, environmental degradation, ethical concerns in raw material sourcing, and inefficient recycling processes. This study adopts a qualitative case study approach, focusing on three leading Indian automotive companies, to explore these challenges in depth. Data were collected through semi-structured interviews with key stakeholders involved in various stages of the LIB supply chain, including production, waste management, and recycling. Key findings reveal that the primary environmental challenge is the lack of advanced green technologies for recycling and disposal, leading to high water and energy consumption, as well as hazardous waste emissions. Social challenges include unsafe labor practices, particularly in raw material extraction, and a shortage of skilled labor in battery recycling operations. On the economic front, the reliance on imported raw materials, coupled with high production and recycling costs, undermines the sector’s sustainability and profitability. The research contributes to the literature by providing a comprehensive understanding of the environmental, social, and economic dimensions of sustainability in the LIB supply chain. It also offers practical insights for stakeholders and policymakers aiming to foster a greener and more sustainable EV sector in India.  

Objective : The construction industry has been increasingly criticized for its poor sustainability performance in recent decades, creating a chance for the sector to play a key role in global sustainability efforts. Rapid technological advancements and increasing construction project complexities have driven the need for flexible, sustainability-focused project management frameworks. This study introduces a fuzzy inference system designed to evaluate construction project sustainability, built on insights from extensive literature and expert input. Methods : To design the proposed model, the system inputs—criteria for evaluating the sustainability level of construction projects at various layers—were first identified. Next, the necessary if-then rules were developed based on expert opinions. The system output was determined in alignment with the research’s final objective. By offering a comprehensive assessment of construction project sustainability, the model enables organizations to identify their strengths and weaknesses, assess their current position, and make informed decisions to enhance their sustainable performance.  Results : The output of the research includes a detailed analysis of the sustainability performance of construction projects. The designed model, along with its measurement tools, provides an opportunity for leaders and managers in the construction industry who are concerned about sustainability to gradually enhance their sustainability status and advance the sustainability level of projects. This model consists of three subsystems named the Direction, Execution, and Results subsystems. The aforementioned subsystems are the result of a literature review and are considered inputs to the final level of the model. Conclusion : The designed model serves as a tool to identify and implement improvement methods and potential areas for project advancement from a sustainability perspective. By utilizing this model, the quality of project execution in line with sustainability indicators, while addressing all three dimensions—economic, social, and environmental—improves continuously and proportionately.

Temporal databases, quickly rising in size, are distinguished by their capacity to maintain the older version of data objects against actions on them, allowing logical deletions. Queries for historical data are particularly costly due to the linear scanning of temporal versions. Temporal data structures like time-split B-Tree or multiversion B-Tree are working underlying the state-of-the-art temporal databases. So far, most efficient temporal data structures are partially persistent or fully persistent, but none of them support retroactive queries. On the other hand, efficient temporal indexing is required to address bulk loading in a real-life application. To the best of our knowledge, there is no efficient solution for bulk loading and updating retroactive index structures. This article seeks to offer a new data structure, the Retroactive B-Tree (RBT), to facilitate retroactive operations in temporal databases as well as bulk loading. It presents theoretical and empirical research and analysis of the suggested data structure and its relevant operations. The experiments were conducted to demonstrate the performance of the proposed retroactive B-Tree in terms of execution time, I/O complexity, space complexity, and bulk loading. The obtained results show that indexing with a buffer is the most powerful model for existing temporal databases for implementing a retroactive B-Tree. The tree of lists architecture is observed as an I/O efficient data structure for all variants of temporal indexing for large databases.

Historic houses are among the key tourist attractions in arid regions. Kashan, a city located in the arid zones of Iran, is home to numerous historic houses. This study aims to assess the potential of Kashan’s historic houses to attract tourists using the Pralong model. The tourism grade for each house was determined based on several indicators, including physical beauty, scientific value, historical-cultural significance, and socio-economic impact. Data collection involved library research, documentary analysis, and surveys. The results indicate that, in terms of tourism standards, Borujerdi House ranks first. According to statistics from the Ministry of Cultural Heritage, Handicrafts, and Tourism, as well as the Pralong model, Borujerdi House holds the top position in attracting tourists, followed by Sharifian House, Ameri House, Tabatabaei House, and Abbasian House. Borujerdi House received the highest score in aesthetic value, while Ameri House ranked first in scientific value.

The study was conducted with the aim of offering a social media-based digital marketing model for the development of foreign trade. This is an applied-developmental research in nature, following a mixed-method approach in terms of data. Information was gathered through library research, and data collection was conducted via fieldwork. In the qualitative section, the variables, components, and indicators of the model were identified and extracted using content analysis. The qualitative statistical population consisted of 17 marketing managers from exporting companies in the detergent, cosmetic, and hygiene product sectors, selected through non-probability, purposive, and theoretical sampling using the snowball technique. The quantitative statistical population included 151 managers, experts, and marketing experts randomly selected based on stratified sampling. Data collection was done through fieldwork, and the tool for gathering data was a researcher-made questionnaire. The qualitative content analysis results identified the model variables as: “Social Media Trade Strategy in International Markets”, “Marketing Communications in Social Media in International Markets”, “Social Messaging Strategy in Social Media in International Markets”, “Marketing Actions in Social Media in International Markets”, “Customer Value Creation in Social Media”, and “Export Trade Development”. Interpretive-structural modeling was used to generate the model, and the findings from this section indicate that the structural model consists of five levels, with “Social Media Trade Strategy in International Markets” and “Marketing Actions in Social Media in International Markets” being the most influential and independent variables. The variables “Marketing Communications in Social Media in International Markets” and “Social Messaging Strategy in Social Media in International Markets” are interconnected, and finally, “Customer Value Creation in Social Media” is the most dependent and affected variable, with a sequential effect on the others. Subsequently, the relationships within the generated model were tested using SEM. The validation findings demonstrated on export development were positively and significantly confirmed in the model. The results of this study suggest that to enhance social media marketing for export development, customer value creation should be strengthened through marketing communications and the implementation of social media marketing.