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.

Investigation on the Performance of Coated Carbide Tool during Dry Turning of AISI 4340 Alloy Steel.

The machinability of materials is highly affected by their hardness, and it affects power consumption, cutting tool life as well as surface quality while machining the component. This work deals with machining of annealed AISI 4340 alloy steel using a coated carbide tool under a dry environment. The microhardness of annealed and non-annealed workpieces was compared and a significant reduction was found in the microhardness of annealed samples. Microstructure examination of the annealed sample revealed the formation of coarse pearlite which indicated a reduction of hardness and improved ductility. A commercially CVD multilayer (TiN/TiCN/Al2O3/ZrCN) coated cemented carbide cutting tool was employed for turning quenched and tempered structural AISI 4340 alloy steel by varying machining speed, rate of feed, and depth of cut to evaluate the surface quality, machining forces, flank wear, and chip morphology. According to the findings of experiments, the feed rate possesses a high impact on surface finish, followed by cutting speed. The prominent shape of the serrated saw tooth chip was noticed at a higher cutting speed. Machined surface finish and cutting forces during turning is a function of the wear profile of the coated carbide insert. This study proves that annealing is a low-cost and economical process to enhance the machinability of alloy steel.

Predicting Characteristics of Dissimilar Laser Welded Polymeric Joints Using a Multi-Layer Perceptrons Model Coupled with Archimedes Optimizer.

This study investigates the application of a coupled multi-layer perceptrons (MLP) model with Archimedes optimizer (AO) to predict characteristics of dissimilar lap joints made of polymethyl methacrylate (PMMA) and polycarbonate (PC). The joints were welded using the laser transmission welding (LTW) technique equipped with a beam wobbling feature. The inputs of the models were laser power, welding speed, pulse frequency, wobble frequency, and wobble width; whereas, the outputs were seam width and shear strength of the joint. The Archimedes optimizer was employed to obtain the optimal internal parameters of the multi-layer perceptrons. In addition to the Archimedes optimizer, the conventional gradient descent technique, as well as the particle swarm optimizer (PSO), was employed as internal optimizers of the multi-layer perceptrons model. The prediction accuracy of the three models was compared using different error measures. The AO-MLP outperformed the other two models. The computed root mean square errors of the MLP, PSO-MLP, and AO-MLP models are (39.798, 19.909, and 2.283) and (0.153, 0.084, and 0.0321) for shear strength and seam width, respectively.

The performance enhancement of solar cooker integrated with photovoltaic module and evacuated tubes using ZnO/Acalypha Indica leaf extract: response surface study analysis.

In this study, the effect of employing ZnO/Acalypha Indica leaf extract (ZAE) on the energy absorption of a coated portable solar cooker has been examined using an experimental setup. A prototypical model has been developed to corroborate in associating an investigative outcome per constituents of the experiments. The studied heat transfer process in ZAE is stable for harsh conditions. The design analysis and an estimation of the system performance were done given various parameters including the pressure of the vacuum envelope, bar plate coating digestion, emissivity, and solar rays. The fabricated solar was tested with and without ZAE to investigate the impact of this coating material on the solar cooker's thermal performance. To observe the performance of the new design, two figures of merit (F1 and F2) have been introduced. The factual food cooking assessments were for a family of four people, which operated in ZAE coating (0.8, 1.0, 1.2 µm) of the solar cooker. The values of F1 and F2 for the proposed cooker were obtained as 0.1520 and 0.4235, respectively, which is intact with the BIS values. The results revealed that employing ZAE instead of a thermal NHC-PV solar cooker reduced the time required to boil 2 L of water for about 47 min. The overall thermal energy productivity of the solar cooker with electrical backup was obtained as 42.65%, indicating that the ZAE coating can improve the thermal efficiency by 10.35%.

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.

Bistable Morphing Composites for Energy-Harvesting Applications.

Bistable morphing composites have shown promising applications in energy harvesting due to their capabilities to change their shape and maintain two different states without any external loading. In this review article, the application of these composites in energy harvesting is discussed. Actuating techniques used to change the shape of a composite structure from one state to another is discussed. Mathematical modeling of the dynamic behavior of these composite structures is explained. Finally, the applications of artificial-intelligence techniques to optimize the design of bistable structures and to predict their response under different actuating schemes are discussed.

Experimental investigation on dish solar distiller with modified absorber and phase change material under various operating conditions.

The shortage of potable water is a global problem. One of the techniques used to participate solving this problem is the solar distiller. The main demerit of solar distiller is its low output yield. So, this work aims at improving the freshwater productivity of a dish solar distiller by enlarging its absorber surface area and increasing its evaporation rate. As a result, the effect of using three different shapes of absorber liner was investigated: convex dish absorber, stepped absorber, and corrugated surface over the stepped absorber. In addition, the absorber of dish distiller was covered by a cotton wick to enlarge the wetted surface area. Also, different water depths (1.0, 2.0, 3.0, 4.0, and 5.0 cm) in dish distiller with stepped absorber were investigated. Moreover, the distance below basin liner was occupied by energy storing medium (paraffin wax + titanium oxide nanoparticles). The performances of dish distiller, dish distiller with stepped absorber, and dish distiller with corrugated absorber were evaluated and compared to that of a conventional solar distiller. Experimental results revealed that the performance of dish distiller with corrugated absorber, wick, and energy storing material was higher than that of dish distiller with stepped absorber, which was better than that of dish distiller, which was higher than that of conventional distiller. As well, the highest improvement in productivity of dish distiller with stepped absorber was 125% compared to conventional distiller and took place at 2-cm water depth. In addition, the productivity of dish distiller with corrugated absorber and wick was improved by 160% compared to that of conventional distiller. Additionally, the highest performance was obtained for dish distiller with corrugated absorber, wick, and energy storing material, where the productivity was augmented by about 183%, and the thermal efficiency reached 69.5%.

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.

Machinability Investigation of Nitronic 60 Steel Turning Using SiAlON Ceramic Tools under Different Cooling/Lubrication Conditions.

The machining of nickel-based super alloys is challenging, owing to the generation of high cutting temperatures, as well as difficulty in maintaining dimensional accuracy and minimizing surface roughness, which compels the use of cutting fluids for reducing these issues due to efficient cooling/lubrication strategies. The present work investigates the comparative performance of four cooling/lubrication techniques: dry cutting, wet, minimum quantity lubricant (MQL) and compressed-air modes in turning Nitronic 60 steel using a new-generation SiAlON ceramic inserts. Several machinability parameters were analyzed for performance evaluation. For this purpose, 16 cycles of turning trials were performed based on Taguchi's L16 orthogonal array experimental design by varying cutting conditions and lubrication modes. MQL exhibits beneficial effects as compared to the other lubrication conditions concerning low cutting force, improved surface finish, decreased cutting temperature, longer tool life, and lower white layer thickness on machined surface. Burr formation on the saw-tooth chip surface, as well as friction, greatly influenced the tool flank wear due to improper cooling and poor lubrication approach in dry, wet, and compressed-air-cooled machining environments in comparison to MQL-machining. From an economical perspective, the tool life in MQL machining improved by 11%, 72%, and 138% in the comparison with flooded, compressed-air, and dry conditions, respectively. The results of the study demonstrate that using the MQL system can help with heat extraction capability, and provide some promising outcomes.

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.

Microcontroller PIC 16F877A standard based on solar cooker using PV-evacuated tubes with an extension of heat integrated energy system.

The unavailability of sunlight during nighttime and cloudy weather condition has limited the usage of solar cookers throughout the day. This study will attempt to engineer a solar cooker with PV (photovoltaic panel), evacuated tubes with CPC reflectors, battery, and charge controller using the microcontroller PIC 16F877A. A mathematical model is developed to predict the electrical power (Ep) required during cloudy weather condition and nighttime as well as the temperatures occurring at different parts of the cooker. The proposed model is validated against experimental observations gathered for one of the typical working days of the system. The cooker is tested for various cooking loads to find the cooking time, and it is proven that the proposed cooker can be utilized over 24/7 without interruption.

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.

Augmenting the productivity of stepped distiller by corrugated and curved liners, CuO/paraffin wax, wick, and vapor suctioning.

This paper aimed to improve the thermal performance of the stepped solar still. So, the effects of using different types of basin liners (corrugated and curved basin liners), jute cloth wick, CuO/paraffin wax, and vapor suction on the stepped solar still performance were investigated. The CuO/paraffin wax was placed into a groove parallel to the basin steps' liner. Also, the stepped distiller was integrated with an external condensation unit. The experimental results revealed that using the corrugated and curved liners increased the productivity of the modified stepped solar still (MSSS) by 42% and 33%, respectively. In addition, using the corrugated liner with wick, corrugated liner with wick and CuO/paraffin wax, corrugated liner with wick, CuO/paraffin wax, and vapor suction improved the freshwater productivity of the distiller by 95%, 127%, and 170%, respectively. At the last studied case (MSSS with corrugated liner, wick, CuO/paraffin wax, and vapor suction), the daily freshwater productivities of the MSSS and conventional still (CSS) were 7000 and 2600 mL/m2·day, respectively. Also, the thermal efficiency of MSSS was calculated as 59% which was obtained at the last studied case. And the efficiency of the CSS was 35%. Besides, the distilled water cost of CSS and MSSS with corrugated liner with wick, CuO/paraffin wax, and vapor suction was 0.023 and 0.014 $/L, respectively.

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.

Improving the performance of solar still using different heat localization materials.

This work aimed to explore a new technique for improving the performance of solar stills (SSs) through utilizing three different types of a new hybrid structure of heat localization materials (HSHLM) floating on the water surface to increase the evaporation rate as well as water production and minimize heat losses. The three types were exfoliated graphite flakes with wick (type A), carbon foam with wick (type B), and exfoliated graphite flakes with wick and carbon foam (type C). These hybrid structures had good features such as high absorption and hydrophilic capillary forces to interconnected pores for fluid flow through the structure. Two identical SSs were designed, fabricated, and investigated to assess SSs' performance with and without HSHLM (modified and conventional SSs). The obtained results showed that the daily productivity was enhanced by 34.5, 28.6, and 51.8% for type A, type B, and type C, respectively, relative to the conventional one. Moreover, the efficiency of the SS reached about 37.6% for type C; while, it reached about 27% for the conventional SS. Contrary to conventional SSs, the use of HSHLM resulted in increasing the productivity proportional to water depth.