Optimization of a real municipal sewage treatment plant using CRFSMA algorithm and a mathematical model.

This study presents the development, calibration, and validation of a mathematical model tailored for biological wastewater treatment at an actual urban sanitation facility. Utilizing multi-criteria optimization techniques, the research identified the most effective MCO algorithm by assessing Pareto optimal solutions. The model incorporated three primary performance measures energy consumption, overall volume, mean quality of effluent, and optimized 12 process parameters. Three algorithms, CRFSMA, particle swarm algorithm, and adaptive non-dominated sorting genetic algorithm III, were rigorously tested using MATLAB. The CRFSMA method emerged as superior, achieving enhanced Pareto optimal solutions for three-dimensional optimization. Quantitative improvements were observed with a 14.8 % increase in wastewater quality and reductions in total nitrogen (TN), chemical oxygen demand (COD), total phosphorus (TP), and ammonium nitrogen ( N H 4 + - N ) concentrations by 0.95, 2.38, 0.04, and 0.14 mg/L, respectively. Additionally, the total cost index and overall volume were decreased, contributing to an 18.27 % reduction in overall volume and an 18.83 % decrease in energy utilization. The adapted anaerobic-anoxic-Oxic (A2O) framework, based on real-world wastewater treatment plants, demonstrated compatibility with observed effluent variables, signifying the potential for energy savings, emission reductions, and urban sanitation enhancements.

Modified osprey algorithm for optimizing capsule neural network in leukemia image recognition.

The diagnosis of leukemia is a serious matter that requires immediate and accurate attention. This research presents a revolutionary method for diagnosing leukemia using a Capsule Neural Network (CapsNet) with an optimized design. CapsNet is a cutting-edge neural network that effectively captures complex features and spatial relationships within images. To improve the CapsNet's performance, a Modified Version of Osprey Optimization Algorithm (MOA) has been utilized. Thesuggested approach has been tested on the ALL-IDB database, a widely recognized dataset for leukemia image classification. Comparative analysis with various machine learning techniques, including Combined combine MobilenetV2 and ResNet18 (MBV2/Res) network, Depth-wise convolution model, a hybrid model that combines a genetic algorithm with ResNet-50V2 (ResNet/GA), and SVM/JAYA demonstrated the superiority of our method in different terms. As a result, the proposed method is a robust and powerful tool for diagnosing leukemia from medical images.

Feature engineering of environmental covariates improves plant genomic-enabled prediction.

Introduction: Because Genomic selection (GS) is a predictive methodology, it needs to guarantee high-prediction accuracies for practical implementations. However, since many factors affect the prediction performance of this methodology, its practical implementation still needs to be improved in many breeding programs. For this reason, many strategies have been explored to improve the prediction performance of this methodology. Methods: When environmental covariates are incorporated as inputs in the genomic prediction models, this information only sometimes helps increase prediction performance. For this reason, this investigation explores the use of feature engineering on the environmental covariates to enhance the prediction performance of genomic prediction models. Results and discussion: We found that across data sets, feature engineering helps reduce prediction error regarding only the inclusion of the environmental covariates without feature engineering by 761.625% across predictors. These results are very promising regarding the potential of feature engineering to enhance prediction accuracy. However, since a significant gain in prediction accuracy was observed in only some data sets, further research is required to guarantee a robust feature engineering strategy to incorporate the environmental covariates.

WebGIS-Based Real-Time Surveillance and Response System for Vector-Borne Infectious Diseases.

The diseases transmitted through vectors such as mosquitoes are named vector-borne diseases (VBDs), such as malaria, dengue, and leishmaniasis. Malaria spreads by a vector named Anopheles mosquitos. Dengue is transmitted through the bite of the female vector Aedes aegypti or Aedes albopictus mosquito. The female Phlebotomine sandfly is the vector that transmits leishmaniasis. The best way to control VBDs is to identify breeding sites for their vectors. This can be efficiently accomplished by the Geographical Information System (GIS). The objective was to find the relation between climatic factors (temperature, humidity, and precipitation) to identify breeding sites for these vectors. Our data contained imbalance classes, so data oversampling of different sizes was created. The machine learning models used were Light Gradient Boosting Machine, Random Forest, Decision Tree, Support Vector Machine, and Multi-Layer Perceptron for model training. Their results were compared and analyzed to select the best model for disease prediction in Punjab, Pakistan. Random Forest was the selected model with 93.97% accuracy. Accuracy was measured using an F score, precision, or recall. Temperature, precipitation, and specific humidity significantly affect the spread of dengue, malaria, and leishmaniasis. A user-friendly web-based GIS platform was also developed for concerned citizens and policymakers.

Erratum: Alnowibet et al. Healthcare Human Resources: Trends and Demand in Saudi Arabia. Healthcare 2021, 9, 955.

Some details of the authors' names, affiliations, and email addresses were incorrect in the original version of the article [...].

Healthcare Human Resources: Trends and Demand in Saudi Arabia.

This paper estimates the impact of policies on the current status of Healthcare Human Resources (HHR) in Saudi Arabia and explores the initiatives that will be adopted to achieve Saudi Vision 2030. Retrospective time-series data from the Ministry of Health (MOH) and statistical yearbooks between 2003 and 2015 are analyzed to identify the impact of these policies on the health sector and the number of Saudi and non-Saudi physicians, nurses and allied health specialists employed by MOH, Other Government Hospitals (OGH) and Private Sector Hospitals (PSH). Moreover, multiple regressions are performed with respect to project data until 2030 and meaningful inferences are drawn. As a local supply of professional medical falls short of demand, either policy to foster an increase in supply are adopted or the Saudization policies must be relaxed. The discrepancies are identified in terms of a high rate of non-compliance of Saudization in the private sector and this is being countered with alternative measures which are discussed in this paper. The study also analyzed the drivers of HHR demand, supply and discussed the research implications on policy and society. The findings suggest that the 2011 national Saudization policy yielded the desired results mostly regarding allied health specialists and nurses. This study will enable decision-makers in the healthcare sector to measure the effectiveness of the new policies and, hence, whether to continue in implementing them or to revise them.