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.

Our research used a SVAR to analyze the underlying copper price shocks, that is, a commodity shock for the Junín department in Peru. The results of a short- and long-run SVAR were based on traditional matrix constraints that capture the fact that domestic shocks do not affect international prices. The main conclusion is that before the pandemic a shock in the international price of copper decreases economic growth and inflation in the department of Junín in Peru, after the pandemic the opposite happens. As a result of the model, the short and long-run effects of the international copper price on the main macroeconomic variables of Junín in Peru are statistically significant. Before the pandemic, the dynamics of the international copper price reflected the existence of the curse of mining resources in copper institutions. Before the pandemic, a percentage increase in the international price of copper decreased economic growth, reflecting the existence of the mining resource curse in copper institutions. After the pandemic, a percentage increase in the price of copper increases economic growth by up to 0.0488%, then decreases over time, noting the transitory effect of economic recovery and poor management of mineral resources.

The issue of integrated scheduling and sequencing operation of unloading and loading equipment in container ports has been one of the most important issues concerning time efficiency. In addition, with the emergence of green harbor concepts, the inclusion of criteria for minimizing energy consumption, fuel and emission reduction are among the other issues that have been noticed by planners in the field of energy efficiency. Furthermore, due to the complexity and scope of activities of a container terminal, uncertainty in operational parameters such as transportation time, time of readiness and entry of work into the system and the velocity of the transportation fleet are inevitable in this operational environment. Therefore, this research with the aim of sharing trucks among loading and unloading equipment, proposes a robust multi-objective integer programming model for the synchronized scheduling of truck operations with other handling equipment to decrease the fuel consumption of trucks and the flow time of containers, considering the uncertainty in operational parameters as fuzzy numbers. To find the Pareto solutions for this model, the ε-Constraint technique is employed. Finally, the performance of the model in deterministic and uncertain modes is evaluated, compared and analyzed employing the inputs gathered from Shahid Rajaei port. The findings demonstrate that using this model will result in a substantial decrease in both fuel consumption and flow time of containers in comparison to the current procedure. Additionally, results will demonstrate the extent to which the terminal's fuel and time consumption will increase under conditions of uncertainty in operational parameters when the optimal plans derived from the robust model are implemented.

The expansion of tourism locations that are both creative and of high quality is a significant contributor to the expansion of the economy. The stages of tourism development that are influenced by the progression of information technology are represented by the term "smart tourism" in the context of the ecosystem of smart villages. Integrating micro-enterprises with tourist practices is one of the ways that may be utilised to speed up the development of villages. By implementing the concept of smart tourism, tourism integrated with information and communication technology (ICT) can potentially improve both the economics and the services provided by the tourism industry. This research aims to analyse the clustering of smart tourism potential possibilities within the Kemang sub-district. These areas' clustering depends on some variables, including infrastructure (access for tourists), innovation, technology, local wisdom, distinctiveness, and economic conditions. The Fuzzy C-Means (FCM) clustering approach is utilised. A Geographic Information System (GIS) is utilised to facilitate the process of determining which villages are included in each cluster. This is done to describe potential areas better. The value of the cluster evaluation using the Davies Boulding Index (DBI) obtained is 0.3819, and the number of clusters with the best performance is 3. There is a very potential cluster in Cluster 3, comprising two villages (Kemang and Atangsanjaya). A potential cluster was also detected in Cluster 2, comprising three villages (Tegal, Pondokudik, and Parakanjaya). Furthermore, a fairly potential cluster was detected in Cluster 1, consisting of three villages (Jampang, Pabuaran, and Bojong). Specifically, in the Kemang sub-district, it is anticipated that the findings of this study will provide an overview of possible sites for implementing environmentally conscious tourism.

Protein IDO1 (indoleamine 2,3-dioxygenase) occupies a critical position in the regulation of the immune system and is involved in cancer progression and the development of immune diseases. Being a therapeutic target for such critical diseases, we aimed to investigate the IDO1 inhibition activity of thirty-nine triazole derivatives using a quantitative structure-activity relationship. The dataset was under principal component analysis, multiple linear regression, and multiple non-linear regression from which two models were generated. The best 2D-QSAR model was generated using linear regression, demonstrating a determination coefficient of R2=0.680, a good acceptable internal cross-validated coefficient of R2cv=0.700, an error of MSE=0.074, and a good predictive potential of R2test=0.809. The QSAR model was further investigated using the applicability domain, which showed that all molecules were within the applicability domain, hence the absence of an outlier. Overall, the obtained results provide a reliable and highly predictive model for the design and prediction of new IDO1 inhibitors thereby influencing cancer progression and autoimmune disease development.