The aim of the paper is to identify a set of the key performance indicators (KPIs) in order to provide managers and employees from the healthcare system with recommendations for evaluating, monitoring, and controlling the critical factors that influence the performance of the healthcare sector in Algeria during a pandemic crisis. During February–August 2020, a cross-sectional survey design was administrated to medical employees from hospitals situated in the northeastern part of Algeria. Our findings proved that the four groups of KPIs correlate to each other, and during this period, the triple relationship among human factor-technology-medication plays a decisive role in reducing the pressure on the medical system and overcoming the crisis. In order to increase the efficiency of the decision-making process, a hierarchy of KPIs is recommended in terms of their impact on the performance of medical staff. The practical importance of our research consists in ranking KPIs on four clusters that support managers to focus on both the human factor (clinical errors, infection rate, and medication errors) and the technical elements of maximum importance (laboratory test time, location of the facility, and sufficient air).

This research aims to explore the capabilities of Artificial Intelligence model applications in sustainable urban planning design, aiming to create an AI-powered model that detects urban sprawl, urban decay, infrastructure deficiencies, and environmental degradation, and generates sustainable solutions. The methodology includes model development, data preprocessing, architecture design, training, and comprehensive questionnaire design and statistical analysis. Insights into the model's performance and perception were gathered from 200 participants, including researchers, developers, architects, and urbanists Results reveal significant variations in perceived model performance across demographic groups. Statistical tests and correlations study showed that participants from urban areas rated the model's effectiveness in detecting urban sprawl significantly higher than those from rural areas, and urban planners found the model's infrastructure deficiency detection less accurate compared to architects. These findings provide valuable guidance for enhancing urban planning strategies and underscore AI's potential in transforming urban planning with more effective and inclusive solutions.

This paper aims to assess the impact of the COVID-19 pandemic on the link between urban planning practices and public health. A triangulated study was conducted to gain a comprehensive understanding of the topic. The first phase consisted of semi-structured interviews with health and urban planning experts, which were analyzed with the aid of Artificial Intelligence tools. The second phase involved an on-site investigation in the city of Algiers, including a survey, site visits, and a thorough analysis of the master plan for land use and urban planning. The findings emphasize the critical importance of a comprehensive health-centric approach to city design, improved governance and management practices, community involvement, and political commitment to prioritize health in urban planning. Furthermore, the results proved a strong correlation between prioritizing public health in urban planning practices and residents’ satisfaction with the city’s response to the COVID-19 pandemic. In conclusion, it is necessary to consider public health as a priority in urban planning practices and as a need for all stakeholders to work towards a healthier and more equitable urban environment.

This study presents the design and evaluation of a multi-model artificial intelligence (AI) framework for proactive quality risk management in projects. A dataset comprising 2000 risk records was developed, containing four columns: Risk Description (input), Risk Category, Trigger, and Impact (outputs). Each output variable was modeled using three independent classifiers, forming a multi-step decision-making pipeline where one input is processed by multiple specialized models. Two feature extraction techniques, Term Frequency–Inverse Document Frequency (TF-IDF) and GloVe100 Word Embeddings, were compared in combination with several machine learning algorithms, including Logistic Regression, Support Vector Machines (SVMs), Random Forest, Multinomial Naive Bayes, and XGBoost. Results showed that model performance varied with task complexity and the number of output classes. Trigger prediction (28 classes), Logistic Regression, and SVM achieved the best performance, with a macro-average F1-score of 0.75, while XGBoost with TF-IDF features produced the highest accuracy for Risk Category classification (five classes). In Impact prediction (15 classes), SVM with Word Embeddings demonstrated superior results. The implementation, conducted in Python (v3.9.12, Anaconda), utilized Scikit-learn, XGBoost, SHAP, and Gensim libraries. SHAP visualizations and confusion matrices enhanced model interpretability. The proposed framework contributes to scalable, text-based, predictive, quality risk management, supporting real-time project decision-making.

The study aims to investigate the economic benefits of salutogenic architecture in public healthcare facilities. The research design involves a quantitative approach that will utilize a survey to gather pertinent data. The survey comprises a meticulously designed set of questions aimed at investigating the economic benefits of salutogenic architecture in public healthcare facilities across Algeria. For comprehensive coverage, the study will encompass a sample size of 60 healthcare facilities, representing approximately 10% of the total establishments distributed across various provinces (wilayas). It aims to assess the economic advantages resulting from incorporating salutogenic design principles, such as natural light, access to nature, and a sense of environmental control, into the construction and renovation of public healthcare facilities. This research underscores the significance of salutogenic architectural principles in healthcare facility design, highlighting the importance of fostering positive atmospheres, providing access to natural elements, ensuring flexibility, and nurturing community connections to enhance patient and staff well-being by providing valuable insights into public healthcare sorting based on salutogenic architectural criteria and the demographic characteristics of the sample population. The findings highlighted the need for improvement in certain criteria such as flexibility and adaptability, a positive and healing atmosphere, and access to nature. The study also emphasized the significance of salutogenic architectural principles in healthcare facility design and suggested incorporating strategies that enhance patient and staff well-being.

Digital twin technology is revolutionizing healthcare by providing a real-time digital replica of hospital systems, enabling better decision-making and performance optimization. This study examines the implementation of digital twin models in healthcare through an analysis of four real-world case studies: The Moorfields Eye Hospital (UK), Singapore General Hospital, Duke Health (USA), and The Karolinska University Hospital (Sweden). By reviewing these cases, the study highlights their impact on operational efficiency, resource utilization, and patient care outcomes. Challenges faced during implementation and key lessons learned are discussed to guide future adoption of digital twin technology in healthcare.