This paper aims to investigate the commitment of health facilities in the Al-Kharj Province in Saudi Arabia to the dimensions of social responsibility and their disclosure impact on the improvement of the quality of health care. The study gains its importance from the importance of social responsibility which is one of the topics that contributes to the realization of the kingdom's Vision 2030. Partial Least Squares Structural Equation Modeling (PLS-SEM) is utilized in this study, to examine the relationship between dependent and independent variables. The study documents that the human dimension of social responsibility disclosure positively affects the quality of health care, while there is no clear effect of the legal dimension, the economic dimension, and the ethical dimension on the quality of health care institutions in Al-Kharj Province.

Renewable energy development is important for improving energy security and economic growth in Indonesia. This study identifies the best renewable energy potential in Southwest Sumba, East Nusa Tenggara Province, using the Technique for Order of Preference by Similarity to Ideal Solution (TOPSIS) method based on 5C criteria: Consolidated, Controllable, Continue, Clean, and Cheap. The research uses a multi-criteria decision-making approach, using primary data from expert interviews and secondary data from literature reviews. The TOPSIS analysis shows that solar energy has the highest preference value, followed by bioenergy and hydropower. Technical assessments show important implementation requirements for each renewable energy option. The study recommends prioritizing solar energy development, supporting bioenergy projects, improving micro-hydro facilities, and creating clear renewable energy policies. Success depends on cooperation between stakeholders and aligning renewable energy development with regional sustainability and community needs. These efforts can help Southwest Sumba develop its renewable energy sector and contribute to national energy security goals.

Socio-economic development in countries cannot be without the contribution of enterprises, including Vietnam. In particular, factors affecting the decision to access financial resources are a topic of interest in Vietnam and developing countries. The objective of the study is to clarify the factors affecting the decision to access financial resources. Through quantitative analysis, the research results show that enterprise management has a negative impact on the decision to access capital, similar results also show that corporate financial management and policy on financial access have a negative impact on the decision to access capital. The research results show that the business environment and policy on financial access have no impact on the decision to access financial resources at enterprises.

The study's objective is to verify the effects of strategic review on the financial performance and risk management of banks in PortSudan City- Sudan. The descriptive analytical approach was used to accomplish the study's goals. By designing and distributing 180 questionnaires, of which 170 were collected. They were analyzed using path analysis using the partial squares technique. The main results indicated a positive effect of a strategic review on the financial performance of Sudanese banks. It also showed the positive effects of a strategic review on the risk management of Sudanese banks. The value of these results is that improved financial performance will make financial reports more reliable and trustworthy; therefore, it may attract more funds from the public. Investors and other stakeholders are interested in the bank's financial position and expected future operating results. They will use this information to prepare risk reports or make important business decisions. Therefore, if external decision-makers provide a reliable positive return, improving risk management will also contribute positively to shareholder value.

Amid the ongoing pandemic, such as the Covid-19 outbreak, there exists a critical need to comprehend and forecast the dynamic trends of daily confirmed cases to effectively prevent and mitigate the impact of its consequences. This study aims to investigate the essential factors acting as predictors for forecasting daily new confirmed cases specifically within the Indonesian setting. Utilizing advanced Deep Learning (DL) methodologies, including Deep Feedforward Neural Networks (DFNN), Long Short-Term Memory (LSTM), one-dimensional convolutional neural networks (CONV1D), and Gated Recurrent Units (GRU), this research endeavors to predict daily confirmed Covid-19 cases in Indonesia. To achieve this, a comprehensive set of 80 variables (predictors), encompassing the effective reproduction number (Rt), was utilized as input parameters. Before model construction, rigorous variable selection procedures and statistical analyses were conducted to enhance data understanding. The effectiveness of the predictive model was assessed using various metrics, such as Mean Absolute Error (MAE), Mean Absolute Percentage Error (MAPE), Mean Squared Error (MSE), Root Mean Square Error (RMSE), and Mean Absolute Scaled Error (MASE), which evaluates MAE relative to a baseline model. Results indicate that DL models incorporating two key predictors—daily confirmed case count and Rt—exhibited superior predictive performance, capable of forecasting daily confirmed cases up to 13 days in advance. The inclusion of additional variables was found to diminish the predictive accuracy of DL algorithms.

The study aims to identify the effect of environmental sustainability accounting (ESA) on competitive advantage (CA), and the effect of ESA on making decisions (funding, investment, and strategic) in Sudanese banking. A questionnaire was used to collect data from 135 accountants and managers of Sudanese banking. A descriptive method was used to confirm that the study's goals had been met. The questionnaire data is analyzed, and hypotheses are tested, using the Smart pls application. The study found a positive relationship between ESA and CA in the Sudanese banking sector. Additionally, ESA has a positive relationship with funding, investment, and strategic decisions in the Sudanese banking sector. Based on these findings, future research can help accountants comprehend the intricacies of ESA according to national and cultural conditions, especially in developing countries. Also, larger sample sizes may be used in future research on this topic, particularly if it is studied internationally.

In the complex and dynamic stock market landscape, investors seek to optimize returns while minimizing risks associated with price volatility. Various innovative approaches have been proposed to achieve high profits by considering historical trends and social factors. Despite advancements, accurately predicting market dynamics remains a persistent challenge. This study introduces a novel deep reinforcement learning (DRL) architecture to forecast stock market returns effectively. Unlike traditional approaches requiring manual feature engineering, the proposed model leverages convolutional neural networks (CNNs) to directly process daily stock prices and financial indicators. The model addresses overfitting and data scarcity issues during training by replacing conventional Q-tables with convolutional layers. The optimization process minimizes the sum of squared errors, enhancing prediction accuracy. Experimental evaluations demonstrate the model's robustness, achieving a 67% improvement in directional accuracy over the buy-and-hold strategy across short-term and long-term horizons. These findings underscore the model’s adaptability and effectiveness in navigating complex market environments, offering a significant advancement in financial forecasting.

A consideration of the integral variables of customer location, traffic flow, and road conditions to determine the best feasible delivery routes is a big challenge in Logistical operations. A poor routing strategy that delivers products places an ineffective gloss and eventually converts into high operating expenses, over-consumption of fuel, and shipment delays. The paper’s goal is to build a model for the logistics management of the company which aims for effective management of the truck allocation and vehicle routing using K-means clustering and TSP. K-means clustering is often used to classify the sites of delivery based on their closeness in space, hence simplifying the problem by reducing its dimensionality. The proposed algorithm considered customer location prioritization in deliveries, delivery task allocation, and truck allocation to enable timely delivery. Therefore, this paper presented a solution to enhance the logistics operations of beverage brand “KINZA” by optimizing its truck loading and delivery route. The model would ensure that each truck is able to travel optimally, with vehicle-routing algorithms applied in a way to avoid all unnecessary waste of time and distance. Finally, the main scope of this paper is to develop and design a dynamic logistics platform for the KINZA Company distribution network.

This study fills the research gap on the existence of joint control in management control systems—the object-oriented control framework (MCS-OOC)—by focusing on the interaction between results and action control, especially in companies that employ prospector strategies that were not considered in previous studies. This study aims to investigate the functioning of joint control by introducing a novel construct known as effectuation control, which forms effectuation MCS. Effectuation control is the synergistic, complementary, and simultaneous effects of a special relationship between action control and result controls. This study will contribute to the understanding of the dynamics of MCS or the control tightness of MCS-OOC. The Effectuation MCS model modifies the MCS-OOC model to account for uncertainty factors, thereby leveraging its capabilities to ensure the long-term sustainability of the company. In terms of methodology, this research will employ two initial models and two modified models, one for each of the prospector and non-prospector manufacturing companies. By comparing these four models and investigating several hypotheses using SEM-PLS, the results demonstrate that result control is no more significant toward existing capabilities when effectuation control is included in the model. Effectuation control significantly influences existing capabilities, whereas result control significantly influences new capabilities. In times of uncertainty and unpredictability, prospectors who implement a pay-for-performance system (result control) in conjunction with the implementation of sound policies, rules, procedures, and bureaucracy (action control) can leverage the company's existing capabilities and explore new ones, thereby enhancing its performance both now and in the future. Action control, a component of effectuation control, serves as a buffer against complex and confusing situations arising from high uncertainty, as every employee responds and refers to the same guidance, policies, rules, and procedures. On the other hand, result control serves as a buffer as well as a driving force, leveraging its capabilities to discover new capabilities amidst uncertainty with the aim of achieving breakthroughs, leading the market, and maintaining sustainability. This result is relevant only to prospectors, as they possess the ability to quickly adapt to uncertainty and seize opportunities presented by these changes, a trait that non-prospectors lack.

The purpose of this research work is to evaluate the use of statistical tools, specifically Ordinary Least Squares (OLS) and Artificial Neural Networks (ANN) and with the help of these tools to be able to independently and effectively predict the costs. of production in the context of supply logistics in the Peruvian paper industry. Both models that turn out to be different in their analysis, however, turn out to be complementary for a more exact and precise result, highlighting the ANNs for their superior performance in the precision of the evaluated metrics, where they managed to achieve an RMSE of 0.0171 and a MAE of 0.0122 compared to the OLS statistical model that achieved an RMSE of 0.0181 and a MAE of 0.2070. Likewise, the analysis between the dimensions studied, purchasing management stands out with a negative coefficient of -0.4978, which shows that its optimization will generate a positive impact on production costs, contrary to the case with the other two dimensions, which are: storage management and inventory management, which resulted in positive coefficients (0.7457 and 0.4667), which shows that their optimization does not necessarily generate a positive impact on production costs, but quite the opposite, that their inadequate management On the contrary, it can harm production costs. These results highlight the inherent need that Peruvian paper companies must have in being able to implement updated logistics systems, capable of integrating advanced statistical tools such as the use of ANN and MCO, which can scientifically help better decision making, allowing thereby improving your supply processes and thus being able to reduce your production costs.