The wide spread of Covid-19 has led to infecting a huge number of patients, simultaneously. This resulted in a massive number of requests for medical care, at the same time. During the first wave of Covid-19, many people were not able to get admitted to appropriate hospitals because of the immense number of patients. Admitting patients to suitable hospitals can decrease the in-bed time of patients, which can lead to saving many lives. Also, optimizing the admission process can minimize the waiting time for medical care, which can save the lives of severe cases. The admission process needs to consider two main criteria: the admission time and the readiness of the hospital that will accept the patients. These two objectives convert the admission problem into a Multi-Objective Problem (MOP). Pareto Optimization (PO) is a common multi-objective optimization method that has been applied to different MOPs and showed its ability to solve them. In this paper, a PO-based algorithm is proposed to deal with admitting Covid-19 patients to hospitals. The method uses PO to vary among hospitals to choose the most suitable hospital for the patient with the least admission time. The method also considers patients with severe cases by admitting them to hospitals with the least admission time regardless of their readiness. The method has been tested over a real-life dataset that consisted of 254 patients obtained from King Faisal specialist hospital in Saudi Arabia. The method was compared with the lexicographic multi-objective optimization method regarding admission time and accuracy. The proposed method showed its superiority over the lexicographic method regarding the two criteria, which makes it a good candidate for real-life admission systems.

Multi-Objective Problems (MOPs) are common real-life problems that can be found in different fields, such as bioinformatics and scheduling. Pareto Optimization (PO) is a popular method for solving MOPs, which optimizes all objectives simultaneously. It provides an effective way to evaluate the quality of multi-objective solutions. Swarm Intelligence (SI) methods are population-based methods that generate multiple solutions to the problem, providing SI methods suitable for MOP solutions. SI methods have certain drawbacks when applied to MOPs, such as swarm leader selection and obtaining evenly distributed solutions over solution space. Whale Optimization Algorithm (WOA) is a recent SI method. In this paper, we propose combining WOA with Tabu Search (TS) for MOPs (MOWOATS). MOWOATS uses TS to store non-dominated solutions in elite lists to guide swarm members, which overcomes the swarm leader selection problem. MOWOATS employs crossover in both intensification and diversification phases to improve diversity of the population. MOWOATS proposes a new diversification step to eliminate the need for local search methods. MOWOATS has been tested over different benchmark multi-objective test functions, such as CEC2009, ZDT, and DTLZ. Results present the efficiency of MOWOATS in finding solutions near Pareto front and evenly distributed over solution space.

This paper presents a comprehensive machine learning (ML)-based framework for microbial classification, addressing the growing need for scalable, accurate, and low-resource alternatives to traditional manual methods. Given the critical role of microbes across domains such as environmental monitoring, biotechnology, and healthcare, efficient classification methods are essential for advancing both scientific understanding and practical applications. Traditional manual microbial classification methods are labor-intensive, time-consuming, and hinder scalability. In response, this research proposes a comprehensive and systematic comparative framework evaluating large number of ML algorithms to identify their relative strengths and weaknesses in microbial classification tasks. The framework leverages a large-scale, feature-based dataset comprising over 21k samples, avoiding the high computational costs associated with image-based data acquisition and processing. Extensive hyperparameter tuning, model validation, and rigorous statistical assessments were conducted to ensure robustness and prevent over-fitting. Notably, more than five different classifiers achieved accuracy exceeding 90%, with the best-performing reaching 97.4% accuracy. Furthermore, by prioritizing biologically meaningful features, this work provides a scalable and effective computational approach to microbial trait analysis. To the best of our knowledge, this is the first study to conduct such a broad and rigorous evaluation of ML models for microbial classification, offering valuable insights for both computer science and microbiology communities, and paving the way for more efficient, data-driven approaches in microbial research and medicinal development.

This paper introduces G-SQL, a schema-aware and rule-guided framework for translating Natural Language Queries (NLQ) into SQL, designed to support users with limited technical expertise. G-SQL bridges the limitations of traditional rule-based and deep learning approaches by combining JSON-based schema serialization with Natural Language Processing (NLP) tools, such as GloVe and Stanford CoreNLP. The system encodes database schemas—including tables, columns, keys, and relationships—into a structured JSON format, enabling efficient and interpretable mapping between query tokens and schema elements. SQL statements are generated using a guided template-driven strategy. Evaluated across three benchmark datasets (IMDB, Yelp, MAS), For easy queries, it achieved 100% execution accuracy on all three datasets. On medium queries, G-SQL reached 95% on IMDB, 85% on Yelp, and 90% on MAS. For hard queries, the accuracy was 78% on IMDB, 65% on Yelp, and 72% on MAS. Finally, on extra-hard queries, G-SQL achieved 55% on IMDB, 45% on Yelp, and 50% on MAS., demonstrating robust performance across varying query complexities.

Improvement of indoor air quality is a necessity for sustainable green building design. Mechanically ventilated systems are a viable alternative to pollutant control, but careful optimization of the configuration system is required to assess these effects: airflow dynamics, pollutant dispersion, and temperature comfort performance. This study is an extensive numerical study of the effectiveness of mechanical ventilation in ensuring IAQ in green buildings, via Computational Fluid Dynamics (CFD). A mathematical model containing mass flow, momentum, energy, and species transport equations is used to model airflow and pollutant disposition. Supporting parameters include air exchange rate, ventilation effectiveness, pollutant purge efficiency, and Predictive Mean Vote (PMV), all of which determine both IAQ and comfort levels of occupants. A series of ventilation case studies is solved serially by an optimization algorithm, changing the flow rates and boundaries to determine the optimal structure. Simulation results are given in plots that demonstrate the trends of the pollutant concentration, airflow distribution, and comfort index. In the best case, the pollutant removal efficiency obtained approaches 95.2% with an airflow of 100 m ³ /h and marked improvements in thermal comfort. The results show that within properly designed mechanical ventilation, it is possible to achieve substantially improved IAQ and satisfy thermal comfort goals. This methodology presents a scalable, simulation-driven methodology for designing efficient ventilation systems in green buildings. Possible future works may investigate dynamic indoor environments concerning real-time adaptive control strategies.