Poverty is a complex and multidimensional social problem faced by almost all countries including Indonesia. Poverty alleviation efforts require accurate and up-to-date data to ensure that social welfare programs can effectively run and target appropriate beneficiaries. Bekasi Regency, as one of the regions that faces significant challenges in managing poverty, needs the right strategy in updating and utilizing poverty data to support social welfare programs. This study analyzes a policy framework for updating and utilizing the poverty data. The data were collected from focus group discussions with experts and stakeholders who were competent in updating and utilizing the poverty data. MULTIPOL method was used to analyze the data. The results of the study show that the best strategy for updating and utilizing poverty data in the scenario is to integrate scenarios with priority policy with digital budgeting policy, as well as priority actions providing incentives. This study makes an important contribution to regional development planning, especially poverty alleviation strategies based on accurate and integrative data.

Forest masses participate in the hydrological cycle and precipitation patterns. Therefore, the loss of these forest masses has significant implications for atmosphere-surface dynamics. The objective of this article is to determine the influence of forest cover loss on annual rainfall in the departments of Peru during the period 2013-2022. The methodology was quantitative, longitudinal non-experimental design, with panel data and a random-effects model was estimated. The results reveal a positive and statistically significant relationship between tree cover loss and total annual precipitation, specifically, a 1% increase in deforestation is related to an average increase of 0.186% in annual rainfall. The findings contrast with most previous evidence documenting reductions in precipitation due to deforestation, however, they are consistent with some studies. The research concluded that there is a positive relationship between the loss of forest cover and annual rainfall in the departments of Peru during the period studied.

This study aims to examine the factors affecting Jordanian Islamic banks’ customers’ adoption of electronic banks. The study relied on quantitative methods, where the study designed a questionnaire based on the Technology Acceptance Model (TAM) which derived from the Theory of Reasoned Action (TRA). The study sample consisted of 470 respondents. The study applied the Partial Least Squares Structural Equation Modeling (PLS-SEM), where the results of the Chi-square test and the standardized root mean square residual (SRMR) test showed the validity of the model for analysis. The results showed that usefulness, privacy, and awareness affect the adoption of e-banking, and that ease to use and electronic skills affect the adoption of e-banking through usefulness. The results also show that Infrastructure does not affect the adoption of e-banking, and Convenience does not affect the adoption of e-banking through Intention to use e-banking. The study advises Islamic banks to spread awareness about the importance of electronic banking and design easy-to-use electronic services that enjoy privacy and security.

The current study aims to determine the factors influencing the intention to employ digital libraries among postgraduate students in Jordanian universities. The data were gathered through questionnaires in the format of Google Forms. The questionnaires were distributed to postgraduate students enrolled in Jordanian universities with digital libraries, who were recruited via purposive sampling. A total of 261 responses were received, with 67 deemed unsuitable for the study analysis and subsequently excluded. Resultantly, 194 valid questionnaires were finalized for data analysis. The SMART-partial least squares (PLS) software was utilized to conduct structural equation modelling (SEM) to test the study hypotheses. This study discovered that the variables, namely performance expectancy, effort, and facilitating conditions expectancy, were significantly and positively associated with the outcome variable, which is the intention to employ digital libraries. Meanwhile, social influence was revealed to be insignificant. Performance expectancy also significantly mediated the correlations between technological readiness and the intention to utilize and between online self-efficacy and the intention to utilize. The present study focused only on potential users among postgraduate students at Jordanian universities with digital libraries. The findings contributed valuable insights into the academic sector, especially library management to enhance the rate of digital library or e-library adoption at Jordanian universities. Digital library managers and policymakers could leverage the findings to design pertinent strategies that increase user engagement within digital library environments. The UTAUT model was demonstrated to be capable of predicting users’ intentions of employing digital libraries and corroborated the mediation role of performance expectancy on the associations between technological readiness and the intention to utilize and between online self-efficacy and the intention to use.

This study investigates how key audit matter (KAM) characteristics influence financial statement understandability and subsequent investor decision making. Using Structural Equation Modeling-Variance Based (SEM-VB) through Partial Least Squares (PLS), the analysis was conducted on a diverse global sample of investors from Europe, North America, Asia-Pacific, Africa, and Latin America. The results indicate that KAM accuracy, reliability, audit quality, and financial reporting quality significantly enhance perceived financial statement understandability, which in turn positively impacts investor judgments. The mediating role of understandability is confirmed, emphasizing its crucial influence on investors’ decisions. Additionally, an importance-performance map analysis (IPMA) identified KAM reliability and accuracy as the most critical factors. This study contributes to the theoretical and managerial understanding of audit practices and investor behavior.

Supply chains are vulnerable to various disruption risks that can adversely affect their overall performance and objectives. This study addresses the challenge of designing a resilient and environmentally sustainable mixed open and closed-loop supply chain network that can withstand both operational and disruption risks. The research employs a bi-objective stochastic mathematical model to examine the balance between environmental sustainability and profitability within the SC. To mitigate the impact of disruptions, several resilience strategies are incorporated into the model, significantly reducing their adverse effects. Due to the inherent complexity of the problem, the study introduces a novel hybrid metaheuristic algorithm that combines ant colony optimization with teaching and learning-based optimization, named ACO-TLBO. Additionally, two other enhanced hybrid metaheuristics are proposed. The performance of these solution methods is assessed through various test problems, using specific performance metrics for comparison. Results reveal that the ACO-TLBO algorithm excels in generating high-quality, non-dominated solutions. The model's practical applicability is demonstrated through a case study in the tire industry, validating its effectiveness. The findings indicate that the proposed resilience strategies are crucial for minimizing the negative impacts of disruptions on SC objectives. Furthermore, the results underscore the importance of resilience in maintaining both sustainability and profitability within supply chains.

Asset Liability Management (ALM) can be overseen using financial ratios derived from financial statements. These statements provide a comprehensive picture of a company's status and necessitate analysis to evaluate performance. This research aims to analyze financial ratios to describe the financial condition, measure business development over time, and evaluate the achievement of the company's objectives. An optimization analysis of financial ratios is performed using the Weighted Goal Programming (WGP) model, which addresses multiple objectives by applying weights based on their priorities. The Best-Worst Method (BWM) was used to determine the priority weights of deviation variables from each financial ratio target. Financial ratios were selected based on their impact on profit using factor analysis. The constructed WGP model aims to minimize deviations in Return on Assets, Operating Ratios, Operating Income Ratio, Total Assets Turnover, and Current Ratio. Computational calculations to solve the WGP model are performed using Python, with pseudocode provided. A case study on a company in the garment and textile sector was conducted and found that the Operating Ratio, Return on Assets, Operating Income Ratio, and Current Ratio still need improvement by developing strategies to achieve the targets. Sensitivity analysis was also employed to assess the resilience of the model in response to alterations in data.

This research attempts to investigate the effect of types of AI systems (assisted, augmented, and autonomous) on internal auditing in Saudi Arabia. A questionnaire was used to collect data from 150 internal auditors in Riyadh City. To confirm that the study's goals were met, the descriptive analytical method was used. The questionnaire data is analyzed, and hypotheses are tested, using the Smart pls application. The study’s results show that there is a clear positive effect of AI systems, but different impacts vary according to the kind of AI systems, which is high in augmented systems, moderate in autonomous systems, and weak in assistive intelligence systems. Further studies on this subject can be conducted with larger sample sizes, especially if they are conducted globally. Based on these findings, future research can concentrate on national and cultural conditions. Additionally, companies should adopt more comprehensive involvement methods in internal auditing issues and the development of AI adoption in all company activities.

Communication is essential for humanity today and in the past. However, some individuals lack verbal communication due to their innate disability and physical losses from accidents. There are sign-language communication methods developed for such people to communicate. Artificial intelligence solutions are offered to remove the disadvantaged situations of people with disabilities due to communication in daily life. Nowadays, rapidly developing image processing and artificial intelligence methods are proper solutions for the problem focused on in this study. Convolution neural network techniques, which have become very popular recently, offer solutions to many problems. On the other hand, the YOLO algorithm shows very high performance in real-time object detection. In this study, we proposed a method for identifying the alphabets which each gesture delivers. This work studied hand detection on images and classification according to hand movements. The American Sign Language (ASL) standard was used as the sign language. The most recent version of YOLO, known as YOLOv5x, is used for gesture detection. Concentrating on the Static Sign-language problem, a study was conducted on the definition of hand movements. The letters “J” and “Z” are not included in the data set because movable hand signals are required. Apart from these two letters, a total number of 24 letters are classified. The proposed model achieved a training performance of 99.45% mAP@.5. Moreover, the proposed model has a performance of 97.9% mAP@.5 on the test dataset. The results demonstrate that the model's object detection performance is excellent. A statistical analysis of the training time shows that the training time has been drastically decreased, 4.5 hours with the current model as compared to the existing models in the literature.

The aim of the research is to identify the determinants of investment decisions of individual investors - the empirical evidence of Hanoi, Vietnam. Conducting a survey of 210 individual investors and using quantitative analysis through SPSS software, the research results showed that market stability and project profitability have the greatest impact on investment decisions. Research results also showed that corporate image and investor psychology have no impact on individual investment decisions. However, research results confirmed the positive impact of business growth on investment decisions, so growing businesses have many advantages for investors, so they have many opportunities to attract financial resources from investors to expand production and business.