International Journal of Knowledge Processing Studies
Volume:5 Issue: 1, Winter 2025

The aim of this research was to present an optimization model for a human resource management audit based on a genetic algorithm. This study is exploratory in nature due to the presentation of the model, and because its results are utilized by users, it is also considered practical. The Non-dominated Sorting Genetic Algorithm II (NSGA-II) was employed as a meta-heuristic method to solve nine simulation problems. The results obtained from this method were then compared with those from the epsilon constraint method. The relationship between the results indicates that the NSGA-II algorithm is capable of reaching optimal solutions in a shorter time compared to the epsilon method, although it has specific limitations when applied to large-scale problems. The results of solving the proposed mathematical model were demonstrated through nine simulations using the desired algorithms, which were implemented in GAMS and MATLAB software. The model considered in this research is a bi-objective model aimed at minimizing inter-cell movements and human resource management audit actions (cell formation), while maximizing the relationships among management audit operators, taking into account network considerations and the efficiency of operators in human resource allocation. This model not only enhances the efficiency of human resource management but also offers the flexibility to adapt to various organizational challenges by providing a new and effective approach. Therefore, the application of this optimization model can significantly improve performance and efficiency in human resource management, contributing to development and progress within the organizational environment.

Climate change is a significant issue that directly affects the environment and well-being of every living organism on Earth. In addition to environmental concerns, it exacerbates socio-economic burdens, especially in developing countries, and further worsens ongoing social inequalities. The effects of climate change and the difficulty of reversing these effects necessitate international collaboration and policies that prioritize sustainability. At this point, it is important to predict how climate change and trends will evolve in the future in order to minimize the adverse effects and take necessary measures. The aim of this study is to forecast the trajectory of global climate change, based on the goals of the United Nations 2030 Agenda for Sustainable Development, with a focus on the G20 countries, which are considered to be most affected by environmental issues. For analysis, the Artificial Neural Networks method, which has recently emerged in forecasting studies, is used. The dependent variable in the analysis is the change in temperature, which is an indicator of climate change. The independent variables include per capita CO2 emissions, population, GDP change, consumption of oil, coal, natural gas, nuclear energy, urban population, and carbon tax implementation. Referring to the United Nations Environment Conference, the data range is limited to 1972-2022, and the average temperature change until 2030 is predicted. According to the analysis results, it is found that the average global temperature change by 2030 will exceed the United Nations target of 1.5 degrees Celsius.

For many years, legislators have been concerned that the change in the auditor, along with the incentive to buy the audit opinion, can have a negative impact on the quality of the audit. Therefore, in all the researches conducted in this field, the audit quality after the change of auditor has been investigated. This study investigates the effect of auditor change probability on audit quality using a Random Forest Classifier model. In this research, using the machine learning technique and Random Forest Classifier model, the probability of auditor change in companies listed on the Tehran Stock Exchange for the years 2003 to 2021 and the effect of this probability on audit quality in companies without a change in auditors have been reviewed. The results show that the companies in which the probability of auditor change is high; They have a lower audit quality. In the following, according to the hypotheses related to the reduction of audit costs in large companies based on the familiarity discount framework, the above result has been analysed separately in large and small companies. The results show that larger companies, where there is a possibility of changing auditors, experience a greater decrease in audit quality. Such results indicate that, by using the model presented in this research, legislators and investors can identify the behaviours that occur by auditors and under the pressure of audited companies to obtain desirable results.

The purpose of this research is to present an interpretive structural model of employee behavioral competence through a workplace information and knowledge framework. The research was conducted using both exploratory and survey methods. This research is applied in purpose and exploratory in nature. The participants consisted of 14 experts from the Ministry of Islamic Guidance in Iraq, specifically from Baghdad, Diyala, and Karbala. They were selected using purposive judgment sampling. The structural interaction analysis method and MICMAC software were utilized for data processing. The findings from the structural analysis encompassed several key areas: environmental analysis, management challenges, employee behavioral competency analysis, organizational culture assessment, legal and policy applications, competency determination, dimensions of leadership competency, the development of an employee behavioral competency model, interpersonal skills, communication skills, analytical skills, leadership skills, establishment of behavioral criteria, model evaluation and development, implementation and monitoring, intra-organizational success, key competencies, extra-organizational success, and employee behavioral competency through an information and knowledge approach to the workplace. In this approach, workplace information—including operational data, information processes, and decision-making systems—plays a crucial role in enhancing employees' behavioral skills for managing complex situations and adapting to dynamic changes in the workplace. Applying this structural model facilitates the development of skills and behaviors that align with the strategic needs of the organization, thereby enhancing employee flexibility and dynamism.

This paper presents a novel key pre-distribution scheme designed to improve secure communication within distributed systems by using noncommutative ring theory and Linear Feedback Shift Registers (LFSRs). The use of noncommutative rings enables the creation of more complex and secure mathematical frameworks for cryptographic key generation, enhancing resilience against common security threats. LFSRs are selected for their efficiency and minimal overhead in key generation, making this approach particularly effective for resource-constrained environments, such as Internet of Things (IoT) and sensor networks. By combining noncommutative rings with LFSR-based generation techniques, the proposed scheme delivers a lightweight, scalable, and highly secure solution for key pre-distribution. Simulation results demonstrate notable improvements in both security and efficiency. In order to enhance scalability, it is advisable to explore optimizations within the Key Agreement and Link Setup phases, as well as the implementation of parallel processing strategies. Furthermore, conducting empirical testing in various IoT environments would yield valuable insights regarding the practicality of the approach and its capacity for further enhancements. This analysis highlights the algorithm's promise for secure and efficient key management in small to medium-scale IoT systems, while also identifying areas for refinement necessary to support larger networks.

Economic algorithmology is used to understand data structure, relationships, and dynamics and the models used to analyze them. In macroeconomics, data includes complex information such as inflation rates, interest rates, GDP, and related financial variables that significantly impact financial markets. This requires specialized knowledge of economic, statistical, and information technology concepts. Therefore, given the importance of this issue, the present study aims to analyze dynamic algorithmic data on the stock market with an emphasis on the economic turbulences of the TARCH BEKK and VAR models in the Tehran Stock Exchange. For this purpose, using information related to macro variables and capital market indices over 10 years, from 2013 to 2022, the research hypotheses were examined and in this regard, the TARCH-BEKK, VAR, and Granger causality models were used to test the research hypotheses. The results show that the release of news resulting from changes in inflation rates, interest rates, exchange rates, and oil prices can affect stock returns.

The increasing development of the competitive environment and the globalization of the product market have caused organizations to make significant efforts to supply, procure, produce, and distribute their company's goods in order to be able to respond to the diverse needs of customers in the shortest time and at the lowest cost. In order to achieve the goals of the model, the research approach is exploratory and the qualitative method of the grounded theory technique has been used to extract the components. The statistical population includes interviews with 17 experts. The results of the research showed that the proposed model includes six components with the categories of causal conditions, pivotal conditions, background category, intervening conditions, strategies, and consequences and is related to them, and the causal conditions consist of main and sub-themes. The cost management approach includes sub-components such as cost analysis and planning, supply chain optimization, cost driver analysis, and cross-border cost allocation. The results indicated that the use of management accounting as a strategic tool can help improve the performance and value of listed companies.