مطالب

This research is aimed to capture EFL teachers’ use of technology in Task-Based Language Teaching (TBLT) by focusing on the variety and intensity of tasks in teaching reading. Driven by the survey research, the data were collected from 71 teachers of English as a Foreign Language (EFL) in Indonesia and analyzed descriptively. The findings show that the EFL teachers perceived that the use of technology in TBLT in teaching reading is important and it can enhance students' language learning experiences. Technology offers engaging materials and activities that aid reading comprehension. Furthermore, EFL teachers' use of various tasks and resources may indicate a need for continuing professional development to improve digital literacy skills and pedagogical strategies of English teachers in EFL context. Although the findings of this survey give an insight into the data of English teachers’ use of technology in TBLT in terms of variety and intensity in teaching reading, this survey has its limitations with regard to the numbers of participants. This research highlights the dynamic relationship among technology, TBLT, and reading instruction in the EFL context, and suggests the need for continued research and exploration of best practices in this area. The current study succeeded in adding new empirical studies related to the variety and intensity in pre-reading, whilst-reading and post-reading activities of EFL teachers enacting teaching reading with the use of TBLT.

This study aims to demonstrate the performance of algorithmic trading strategies compared to traditional trading methods in artificial financial markets. This research uses a hybrid model based on agent-based modeling and machine learning methods to simulate agents' behavior in an artificial financial market. This model includes two categories, traditional agents and intelligent agents. Traditional agents are divided into three groups: liquidity providers, liquidity consumers, and noise traders. Intelligent agents are trained using deep learning techniques and recurrent neural networks. Based on the developed algorithms, the agent-based model simulates both categories of traditional and trained agents in an artificial financial market. Sensitivity analysis tests were used to test the validity and reliability of the model, and the values of the fat-tailed distribution of returns, volatility clustering, autocorrelation of returns, long memory in order flow, concave price impact, and extreme price events are calculated in the model and compared with the standardized values. Historical data was used to predict stock prices, and model simulations were used to generate trading signals and update the limited order book. The results of executing the model show the ability of intelligent agents to trade in artificial financial markets compared to traditional agents.

Although previous research has examined the application of artificial intelligence (AI) across various areas of finance, there remains limited empirical evidence regarding its impact on corporate financial asset allocation. This gap is particularly evident when considering the organisational capabilities that enable firms to utilise AI technologies effectively. In the rapidly evolving technological landscape, artificial intelligence (AI) has emerged as a pivotal force driving innovation and transformation within corporate financial management. By embedding AI into organisational processes, companies have fundamentally reshaped their financial decision-making frameworks. However, the exact mechanisms through which AI adoption shapes the allocation of financial assets are still not fully understood. This study examines how artificial intelligence (AI) technologies influence the allocation of financial assets within corporations, with a specific focus on the moderating influence of three dynamic organisational capabilities: absorptive capacity, innovation capability, and adaptability. Based on panel data collected from companies listed on the Tehran Stock Exchange between 2020 and 2024, AI adoption is measured through textual analysis of management commentary reports obtained from the Codal system. The dependent variable comprises a set of financial ratios, including the proportion of financial assets relative to a firm’s total assets. The analysis employs multiple regression models with interaction terms to test the proposed hypotheses. Findings indicate that the adoption of AI substantially enhances the effectiveness of distributing financial assets. Moreover, absorptive capacity and innovation capability strengthen the association between AI adoption and the allocation of financial assets within firms' performance, while adaptability shows no statistically significant moderating effect. These results highlight the importance of both technological infrastructure and internal capabilities for leveraging advanced technologies to their fullest potential. This research not only enriches the academic discourse with fresh empirical insights but also offers valuable implications for financial managers, capital market regulators, and policymakers engaged in organisational digital transformation strategies.

BACKGROUND AND OBJECTIVES: With rapid population growth and increasing urban complexities, there is a need for innovative and practical solutions. In this context, smart living can play a fundamental role in improving urban security. This study aims to explain and analyze the impact of smart living on the development of urban security in the city of Semnan. METHODS: The research method is descriptive-analytical and statistical, conducted through a mixed approach (quantitative and qualitative). Quantitative data were collected using a standardized questionnaire from a sample of 384 citizens, and qualitative data were gathered through semi-structured interviews with 30 urban and security experts. For data analysis, SPSS and AMOS software were used for the quantitative section, and thematic analysis along with the SOAR technique were employed for the qualitative section. FINDINGS: Statistical analysis using Pearson’s correlation coefficient shows a positive relationship between smart living and urban security in Semnan, with a correlation value of r=0.30 and a significance level of 0.000, indicating that this relationship is statistically very significant and reliable. CONCLUSION: Ultimately, it can be concluded that increasing smart living indicators—including the use of modern technologies, advanced security systems, and smart urban services—leads to improved living conditions, enhanced social interactions, strengthened sense of solidarity and responsibility within the community, and ultimately, the enhancement of urban security.