While the healthcare landscape continues to evolve, rural-based hospitals face unique challenges in providing quality patient care amidst resource constraints and geographical isolation. This study evaluates the impact of big data analytics in rural-based hospitals in relation to service delivery and shaping future policies. Evaluating the impact of big data analytics in rural-based hospitals will assist in discovering the benefits and challenges pertinent to this hospital. The study employs a positivist paradigm to quantitatively analyze collected data from rural-based hospital professionals from the Information Technology (IT) departments. Through a comprehensive evaluation of big data analytics, this study seeks to provide valuable insights into the feasibility, infrastructure, policies, development, benefits and challenges associated with incorporating big data analytics into rural-based hospitals for day-to-day operations. The findings are expected to contribute to the ongoing discourse on healthcare innovation, particularly in rural-based hospitals and inform strategies for optimizing the implementation and use of big data analytics to improve patient care, decision-making, operations and healthcare sustainability in rural-based hospitals.

Humanitarian workers face numerous challenges when providing assistance to people affected by natural disasters, armed conflicts, and other crises, which often leads to burnout and psychological distress. This qualitative study investigates the interplay of factors that contribute to burnout among Malaysian employees of a refugee-focused humanitarian organization. Ten staff members participated in focus group discussions, which revealed five themes: positive and meaningful emotions; difficult and negative emotions; vicarious trauma, stress, and burnout; work environment, culture, and managerial policies; and structural and governmental stressors. The study emphasizes the need for improved support and resources for humanitarian workers, as well as enhanced organizational policies and practices to prevent and mitigate burnout. The findings suggest that culturally adapted interventions, such as Acceptance and Commitment Therapy (ACT), can help humanitarian workers address their unique psychological challenges. More research is needed to examine the issues present within humanitarian organizations using qualitative methods and adapt appropriate interventions to prevent the development of psychopathology in these settings.

This research uses both quantitative and qualitative research methodologies to examine the complex factors affecting community resilience in various settings. In this case, the research explores how social cohesion, governance effectiveness, adaptability, community involvement, and the specified difficulties influence resilience results by using the five pillars of resilience as variables. Descriptive and inferential statistics are used to test hypotheses on the relationships between social cohesion, governance effectiveness, adaptive capacity, and community resilience variables. Qualitative data provides further insights into the quantitative results by providing broader views and experiences of the community. The study shows how social capital is important in increasing community capacity, stressing the importance of social relations and trust in developing community solutions to disasters. Another major factor that stands out is the governance factor that ensures that decisions are made, and actions taken in line with the community’s best interest in improving its ability to prepare for and respond to disasters. Adaptive capacity is seen as a key component of resilience and this paper emphasizes the importance of communities to come up with measures that can be adjusted to the changing circumstances. In summary, this study enriches theoretical understanding and offers practical applications of the processes that can enhance community resilience based on the principles of social inclusion, sound governance, and context-specific solutions.

Electric cars are manufactured to address environmental problems, reduce dependence on fossil fuels, and nullify climate change. Their production aligns with sustainability objectives by encouraging cleaner transportation options, promoting energy efficiency, and contributing to a transition towards eco-friendly mobility in an answer to global environmental challenges. In Jordan, similar to any international market, car dealers and traders import electric cars. However, the prevailing perceptions and attitudes of Jordanian consumers need strong consideration. Nevertheless, there is still uncertainty and a need for more trust in electric vehicles among Jordanian consumers. Therefore, this research aims to ascertain whether electric cars have a lasting positive perception among Jordanians through an inductive research approach. Employing thematic qualitative analysis, this research is supported by the diffusion of innovation theory. Notably, the research findings provided robust insights, further leading to reinforcing the idea about the pervasive attitudes of Jordanian consumers. Thus, this research concludes that there still needs to be more confidence regarding electric vehicles among most consumers in Jordan. Furthermore, this research offers practical and theoretical contributions to Jordan’s policymakers and electric vehicle companies.

The goal of this work was to create and assess machine-learning models for estimating the risk of budget overruns in developed projects. Finding the best model for risk forecasting required evaluating the performance of several models. Using a dataset of 177 projects took into account variables like environmental risks employee skill level safety incidents and project complexity. In our experiments, we analyzed the application of different machine learning models to analyze the risk for the management decision policies of developed organizations. The performance of the chosen model Neural Network (MLP) was improved after applying the tuning process which increased the Test R² from −0.37686 before tuning to 0.195637 after tuning. The Support Vector Machine (SVM), Ridge Regression, Lasso Regression, and Random Forest (Tuned) models did not improve, as seen when Test R² is compared to the experiments. No changes in Test R²’s were observed on GBM and XGBoost, which retained same Test R² across different tuning attempts. Stacking Regressor was used only during the hyperparameter tuning phase and brought a Test R² of 0. 022219.Decision Tree was again the worst model among all throughout the experiments, with no signs of improvement in its Test R²; it was −1.4669 for Decision Tree in all experiments arranged on the basis of Gender. These results indicate that although, models such as the Neural Network (MLP) sees improvements due to hyperparameter tuning, there are minimal improvements for most models. This works does highlight some of the weaknesses in specific types of models, as well as identifies areas where additional work can be expected to deliver incremental benefits to the structured applied process of risk assessment in organizational policies.

In older adults with disabilities, muscle weakness reduces mobility and causes postural issues. Electrical muscle stimulation (EMS) training is effective for increasing strength by inducing involuntary skeletal muscle contractions. Thus, this study aimed to examine changes in lumbar movement, muscle activity during walking, and maximum muscle strength of lower-limb joints based on an 8-week EMS exercise program for older adults with physical disabilities. Sixteen older adults (aged 56–78 years) with physical disabilities were selected and randomly assigned to either the exercise group (EG, n = 8) or the control group (CG, n = 8). EG participants were instructed to wear EMS suits and participate in an elastic band and bare-body exercise program, whereas CG participants performed the exercise program without the EMS suits. Lumbar range of motion (ROM), body circumference, partial volume, maximum muscle strength, and muscle activity were measured. Statistical analysis was performed using SPSS 28.0 for Windows, with the critical value of α = 0.05. Compared to CG participants, EG participants had a superior ROM, body volume, partial volume, maximum muscle strength (knee joints), and muscle activity while walking. These positive outcomes highlight the efficacy of combining EMS with an exercise program for strength and rehabilitation training in older adults with physical disabilities.