Solar desalination/purification (solar stills, humidification-dehumidification, solar disinfection) in high altitude during COVID19: Insights of gastrointestinal manifestations and systems' mechanism.

From the starting of the pandemic different transmission routes of the pathogen was brought into the spotlight by researchers from different disciplines. This matter in high-altitudes was more boosted as the main parameters were not exactly realized. In this review we are about to highlight the possibility of consuming contaminated water generated form solar water desalination/disinfection systems in highlands. Three systems including solar still, solar disinfection (which experimented by the authors in 2019 in high altitude) and humidification-dehumidification were consider in this context. Ascribe to the risks of pathogens transmission in solar desalination/disinfection systems where the water resources are heavily polluted in every corner of the world, highlighting the risk of consuming water in high-altitude where there are many other parameters associated with spread of pathogen is of great importance. As it was reported, reliability of solar desalination and solar water disinfections systems against contaminated water by the novel coronavirus remained on the question because the virus can be transmitted by vapor in solar stills due to tiny particle size (60-140 nm) and would not be killed by solar disinfections due to low-temperature of operation <40 °C while for HDH contamination of both water and air by sars-cov-2 could be a concern. Although the SARS-CoV-2 is not a waterborne pathogen, its capability to replicate in stomach and infection of gastrointestinal glandular suggested the potential of transmission via fecal-oral. Eventually, it was concluded that using solar-based water treatment as drinking water in high altitude regions should be cautiously consider and recommendations and considerations are presented. Importantly, this critical review not only about the ongoing pandemic, but it aims is to highlight the importance of produced drinking water by systems for future epidemic/pandemic to prevent spread and entering a pathogen particularly in high-altitude regions via a new routes.

Microstructure, Mechanical Characteristics, and Wear Performance of Spark Plasma Sintered TiB2-Si3N4 as Affected by B4N Doping.

This study aims to analyze the effect of boron nitride (B4N) additive (3-6%) on the densification, microstructure, mechanical properties, and wear performance of TiB2-15%Si3N4 and TiB2-30%Si3N4 sintered composites. When the B4N (3%) was added to the TiB2-30Si3N4 composite, the density increased to 99.5%, hardness increased to 25.2 MPa, and the fracture toughness increased to 4.62 MPam1/2. Microstructural analysis shows that in situ phases such as TiB2 help to improve the relative mechanical characteristics. However, raising the B4N additive to 6% in the above-sintered composite reduces the composites' relative density and hardness. The tested sintered composites demonstrated that their superior wear resistance can be attributed to their increased density and hardness.

In Vitro Degradability, Microstructural Evaluation, and Biocompatibility of Zn-Ti-Cu-Ca-P Alloy.

According to the modern era, zinc is one of the best replacements for human bio-implants due to its acceptable degradation, nominal degradable rate, and biocompatibility. However, alloying zinc with other nutrient metals is mandatory to improve the mechanical properties. In this research, Zn-4Ti-4Cu was alloyed with calcium and phosphorous through a powder metallurgical process to make guided bone regeneration (GBR). First, the sintering temperature of the alloy was found with the usage of thermogravimetric analysis (TGA). Tensile and compression tests showed the suitability of the alloy in strength. The microstructural characteristics were provided with EDS and SEM. The different phases of the alloy were detected with X-ray diffraction (XRD). We can clearly depict the precipitates formed and the strengthening mechanism due to titanium addition. An electrochemical corrosion (ECM) test was carried out with simulated body fluid (Hank's solution) as the electrolyte. Cytotoxicity, biocompatibility, mechanical properties, and corrosion resistance properties were studied and discussed.

A biological sub-sequences detection using integrated BA-PSO based on infection propagation mechanism: Case study COVID-19.

The longest common consecutive subsequences (LCCS) play a vital role in revealing the biological relationships between DNA/RNA sequences especially the newly discovered ones such as COVID-19. FLAT is a Fragmented local aligner technique which is an accelerated version of the local pairwise sequence alignment algorithm based on meta-heuristic algorithms. The performance of FLAT needs to be enhanced since the huge length of biological sequences leads to trapping in local optima. This paper introduces a modified version of FLAT based on improving the performance of the BA algorithm by integration with particle swarm optimization (PSO) algorithm based on a novel infection mechanism. The proposed algorithm, named BPINF, depends on finding the best-explored solution using BA operators which can infect the agents during the exploitation phase using PSO operators to move toward it instead of moving toward the best-exploited solution. Hence, moving the solutions toward the two best solutions increase the diversity of generated solutions and avoids trapping in local optima. The infection can be propagated through the agents where each infected agent can transfer the infection to other non-infected agents which enhances the diversification of generated solutions. FLAT using the proposed technique (BPINF) was validated to detect LCCS between a set of real biological sequences with huge lengths besides COVID-19 and other well-known viruses. The performance of BPINF was compared to the enhanced versions of BA in the literature and the relevant studies of FLAT. It has a preponderance to find the LCCS with the highest percentage (88%) which is better than other state-of-the-art methods.

Artificial Intelligence for Forecasting the Prevalence of COVID-19 Pandemic: An Overview.

Since the discovery of COVID-19 at the end of 2019, a significant surge in forecasting publications has been recorded. Both statistical and artificial intelligence (AI) approaches have been reported; however, the AI approaches showed a better accuracy compared with the statistical approaches. This study presents a review on the applications of different AI approaches used in forecasting the spread of this pandemic. The fundamentals of the commonly used AI approaches in this context are briefly explained. Evaluation of the forecasting accuracy using different statistical measures is introduced. This review may assist researchers, experts and policy makers involved in managing the COVID-19 pandemic to develop more accurate forecasting models and enhanced strategies to control the spread of this pandemic. Additionally, this review study is highly significant as it provides more important information of AI applications in forecasting the prevalence of this pandemic.

Boosting COVID-19 Image Classification Using MobileNetV3 and Aquila Optimizer Algorithm.

Currently, the world is still facing a COVID-19 (coronavirus disease 2019) classified as a highly infectious disease due to its rapid spreading. The shortage of X-ray machines may lead to critical situations and delay the diagnosis results, increasing the number of deaths. Therefore, the exploitation of deep learning (DL) and optimization algorithms can be advantageous in early diagnosis and COVID-19 detection. In this paper, we propose a framework for COVID-19 images classification using hybridization of DL and swarm-based algorithms. The MobileNetV3 is used as a backbone feature extraction to learn and extract relevant image representations as a DL model. As a swarm-based algorithm, the Aquila Optimizer (Aqu) is used as a feature selector to reduce the dimensionality of the image representations and improve the classification accuracy using only the most essential selected features. To validate the proposed framework, two datasets with X-ray and CT COVID-19 images are used. The obtained results from the experiments show a good performance of the proposed framework in terms of classification accuracy and dimensionality reduction during the feature extraction and selection phases. The Aqu feature selection algorithm achieves accuracy better than other methods in terms of performance metrics.

Optimization of Abrasive Water Jet Machining of SiC Reinforced Aluminum Alloy Based Metal Matrix Composites Using Taguchi-DEAR Technique.

Since the importance of introducing new engineering materials is increasing, the need for machining such higher strength materials has also considerably increased. In the present research, an endeavor was made to introduce a Taguchi-DEAR methodology for the abrasive water-jet machining process, while machining a SiC-reinforced aluminum composite. Material removal rate, taper angle, and surface roughness were considered as the quality measures. The optimal arrangement of input process factors in the AWJM process was found to be 2800 bar (WP), 400 mg/min (AF), 1000 mm/min (FR), and 4 mm (SOD), among the chosen factors, with an error accuracy of 0.8%. The gas pressure had the most significance for formulating the performance measures, owing to its ability to modify the impact energy and crater size of the machined specimen.

Utilization of random vector functional link integrated with manta ray foraging optimization for effluent prediction of wastewater treatment plant.

An innovative predictive model was employed to predict the key performance indicators of a full-scale wastewater treatment plant (WWTP) operated with an activated sludge treatment process. The data-driven model was obtained using data gathered from Cairo, Egypt. The proposed model consists of Random Vector Functional Link (RVFL) Networks incorporated with Manta Ray Foraging Optimizer (MRFO). RVFL is used as an advanced Artificial Neural Network (ANN) that avoids the common conventional ANN problems such as overfitting. MRFO is employed to determine the best RVFL parameters to maximize the prediction accuracy of the model. The developed MRFO-RVFL is compared with conventional RVFL to figure out the role of MRFO as an optimization tool to enhance model performance. Both models were trained and tested using experimental data measured during a long period of 222 days. This study aims to provide an accurate prediction of the most widely treated effluent indicators of BOD5 and TSS in the wastewater treatment plants. In this study, ten well-known influent wastewater parameters, BOD5, TSS, and VSS, influent flow rate, pH, ambient temperature, F/M ratio, SRT, WAS, and RAS, the output BOD5 and TSS were modeled and predicted using the integrated MRFO-RVFL algorithms and compared with the standalone RVFL model. The performance of the models was evaluated using different assessment measures such as R2, RMSE, and others. The obtained results of R2 and RMSE for the MRFO-RVFL model were 0.924 and 3.528 for BOD5 and 0.917 and 6.153 for TSS, which were much better than the results of conventional RVFL with 0.840 and 6.207 for BOD5 and 0.717 and 10.05 for TSS. Based on the obtained results, the selective model (MRFO-RVFL) exhibited a higher performance and validity to predict the TSS and optimal BOD5.

Efficient artificial intelligence forecasting models for COVID-19 outbreak in Russia and Brazil.

COVID-19 is a new member of the Coronaviridae family that has serious effects on respiratory, gastrointestinal, and neurological systems. COVID-19 spreads quickly worldwide and affects more than 41.5 million persons (till 23 October 2020). It has a high hazard to the safety and health of people all over the world. COVID-19 has been declared as a global pandemic by the World Health Organization (WHO). Therefore, strict special policies and plans should be made to face this pandemic. Forecasting COVID-19 cases in hotspot regions is a critical issue, as it helps the policymakers to develop their future plans. In this paper, we propose a new short term forecasting model using an enhanced version of the adaptive neuro-fuzzy inference system (ANFIS). An improved marine predators algorithm (MPA), called chaotic MPA (CMPA), is applied to enhance the ANFIS and to avoid its shortcomings. More so, we compared the proposed CMPA with three artificial intelligence-based models include the original ANFIS, and two modified versions of ANFIS model using both of the original marine predators algorithm (MPA) and particle swarm optimization (PSO). The forecasting accuracy of the models was compared using different statistical assessment criteria. CMPA significantly outperformed all other investigated models.

Deep learning-based forecasting model for COVID-19 outbreak in Saudi Arabia.

COVID-19 outbreak has become a global pandemic that affected more than 200 countries. Predicting the epidemiological behavior of this outbreak has a vital role to prevent its spreading. In this study, long short-term memory (LSTM) network as a robust deep learning model is proposed to forecast the number of total confirmed cases, total recovered cases, and total deaths in Saudi Arabia. The model was trained using the official reported data. The optimal values of the model's parameters that maximize the forecasting accuracy were determined. The forecasting accuracy of the model was assessed using seven statistical assessment criteria, namely, root mean square error (RMSE), coefficient of determination (R2), mean absolute error (MAE), efficiency coefficient (EC), overall index (OI), coefficient of variation (COV), and coefficient of residual mass (CRM). A reasonable forecasting accuracy was obtained. The forecasting accuracy of the suggested model is compared with two other models. The first is a statistical based model called autoregressive integrated moving average (ARIMA). The second is an artificial intelligence based model called nonlinear autoregressive artificial neural networks (NARANN). Finally, the proposed LSTM model was applied to forecast the total number of confirmed cases as well as deaths in six different countries; Brazil, India, Saudi Arabia, South Africa, Spain, and USA. These countries have different epidemic trends as they apply different polices and have different age structure, weather, and culture. The social distancing and protection measures applied in different countries are assumed to be maintained during the forecasting period. The obtained results may help policymakers to control the disease and to put strategic plans to organize Hajj and the closure periods of the schools and universities.

Forecasting the prevalence of COVID-19 outbreak in Egypt using nonlinear autoregressive artificial neural networks.

SARS-CoV-2 (COVID-19) is a new Coronavirus, with first reported human infections in late 2019. COVID-19 has been officially declared as a universal pandemic by the World Health Organization (WHO). The epidemiological characteristics of COVID-2019 have not been completely understood yet. More than 200,000 persons were killed during this epidemic (till 1 May 2020). Therefore, developing forecasting models to predict the spread of that epidemic is a critical issue. In this study, statistical and artificial intelligence based approaches have been proposed to model and forecast the prevalence of this epidemic in Egypt. These approaches are autoregressive integrated moving average (ARIMA) and nonlinear autoregressive artificial neural networks (NARANN). The official data reported by The Egyptian Ministry of Health and Population of COVID-19 cases in the period between 1 March and 10 May 2020 was used to train the models. The forecasted cases showed a good agreement with officially reported cases. The obtained results of this study may help the Egyptian decision-makers to put short-term future plans to face this epidemic.