Removing microplastics from wastewater using leading-edge treatment technologies: a solution to microplastic pollution-a review.

Microplastics (MPs) in environmental studies have revealed that public sewage treatment plants are a common pathway for microplastics to reach local surroundings. Microplastics are becoming more of a worry, posing a danger to both marine wildlife and humans. These plastic items not only contribute to the macrocosmic proliferation of plastics but also the scattering of microplastics and the concentration of other micropollutant-containing objects, increasing the number of pollutants identified. Microplastics' behavior, movement, transformation, and persistence mechanisms, as well as their mode of action in various wastewater effluent treatment procedures, are still unknown. They are making microplastics made from wastewater a big deal. We know that microplastics enter wastewater treatment facilities (WWTPs), that wastewater is released into the atmosphere, and that this wastewater has been considered to represent a threat to habitats and ground character based on our literature assessment. The basic methods of wastewater and sewage sludge, as well as the treatment procedure and early characterization, are covered throughout the dissection of the problematic scientific conceptualization.

Economic and life cycle cost analysis of building-integrated photovoltaic system for composite climatic conditions.

An insulated building-integrated photovoltaic (PV) roof prototype is designed, developed, and experimentally monitored for the composite climatic conditions in the current work. The prototype is monitored based on hourly indoor room temperature, relative humidity, discomfort index, decrement factor time lag, and power generation. To validate the results, a heat conduction equation was developed and simulated considering actual lower income group (LIG) building size and materials. Second-order polynomial equations were derived from simulation results to optimize insulation thickness. Additionally, the economic analysis of the insulated building-integrated Photovoltaic (BIPV) roof was analyzed and compared to the reinforced concrete cement (RCC) roof. The results reveal that insulated BIPV roofs outperform the RCC roof, reducing indoor temperatures by 3.34 ℃ to 1.37 ℃ within an optimum thickness range of 0.0838-0.1056 m. A time lag of 1 h and a significant reduction in decrement factor up to 0.29 are achieved. The average discomfort index of the proposed roof during sunshine hours was found to be between 23 and 26.5. The insulated BIPV roofs with levelized cost of electricity (LCOE) of the 3.38 Rs/kWh gave a payback period of 6.32 years and a higher internal rate of return of 29.4 compared to RCC roof. The current study increases the feasibility of PV modules to be used as building material.

Photovoltaic-wind-battery and diesel generator-based hybrid energy system for residential buildings in smart city Coimbatore.

The building consumes almost 40% of the energy generated in the building. Investigating the photovoltaic system, wind, battery, and diesel generators for residential buildings can reduce energy utilization. In this work, various energy sources are combined to form hybrid energy sources, which are designed based on the load of the residential building. The Hybrid Optimization of Multiple Energy Resources tool optimizes various energy sources such as photovoltaic (PV), wind, diesel generator (DG), and battery. An investigation on the residential load in the smart city of Coimbatore, Tamil Nadu, India, is being carried out. This article examined the technological and economic feasibility of solar photovoltaic, wind, diesel generators, and batteries combined to form a hybrid energy source (HES). With 2 kW of photovoltaic, 1 kW wind, 1 kW of DG, 1 kW of the power converter, and five batteries, system case 1 (photovoltaic/wind/diesel generator/battery model) had the best results in the simulation and was recommended for use in the proposed residential building. As a result, it has a minimum net present value of $14,568 and an energy cost of $0.312/kWh, which is about 39% cheaper than system base cases. The sensitivity and environmental analysis are carried out to analyze the system's feasibility.

A mathematical model for investigation of dry band location on 22 kV bushing with and without RTV coating: Experimental study.

As different pollutants are deposited on the high voltage bushings, a dry band forms, which causes a flashover. The bushing's contaminated layer will weaken its insulation and have an impact on its electrical characteristics. The performance of bushings in dry band conditions of various lengths was investigated in this proposed piece of work, and a dynamic arc model is presented for the arc process in polluted bushings. It shows satisfactory performance in modelling the arc variables for various dry band positions. The developed dynamic open model for contaminated bushings with and without RTV coating predicted the flashover voltage and dry band positions. Any type of contamination, such as sea salt, road salt, and industrial pollutants prevalent in several sites, can be studied using the established model. Ultimately, it was discovered that there was good agreement between the model's results and the outcomes of the experiments. •Mathematical modeling of 22 kV bushing is conceded out for diverse polluted dry band location at lead-in, lead-out and middle region of bushing surface.•Dynamic arc modeling involved in bushing flashover process for different dry band location is done and flashover voltage is predicted•Experimental work is carried out to find FOV for the bushing with different dry location and compared with predicted FOV.

Productivity enhancement of solar still through heat transfer enhancement techniques in latent heat storage system: a review.

Solar still is one of the sustainable and renewable technology which converts brackish or salty water into fresh water. The technology helps in CO2 mitigation, global warming effect, and the use of solar desalination contributes towards decarbonization, mitigation of CO2 and other adverse global warming effect, and it contributes to the sustainable development goals (SDG). However, due to the low production rate of the distillate, the performance of solar still gets affected. The phase change materials (PCMs) as latent heat storage systems can enhance the thermal performance of solar still (SS). Further, techniques like increasing the area of contact and thermal conductivity can be practiced to enhance the heat transfer in PCM-SS. The article reviewed the performance of various designs of solar still integrated with PCM. Furthermore, the effect of nanoparticles enhanced PCM-integrated solar still with different absorber designs and configurations was seen. Compared to conventional solar still (CSS), the heat transfer techniques in PCM's SS can significantly improve the overall distillate productivity of Tubular SS by 218%, followed by single basin single slope SS 149%, pyramidal 125%, hemispherical 94%, and stepped 68%, respectively. In addition, the night time productivity was increased by 235%. Also, it was observed that in comparison to tubular PCM-SS, the nanodisbanded tubular PCM-SS increases the productivity by 68%, whereas in stepped solar still by using external condenser arrangement the productivity was increased by 48%. In single basin single slope, the nanoparticle disbanded PCMSS increases the productivity from 11 to 33%.

Energy forecasting of the building-integrated photovoltaic façade using hybrid LSTM.

Effective building energy management systems need a reliable approach to estimating future energy needs using renewable energy sources. However, nonlinear and nonstationary trends in building energy use data make prediction more challenging for integrating the photovoltaic system. To estimate future energy forecast, this work presents a hybrid approach based on random forest (RF) and long short-term memory (LSTM) using complete ensemble empirical mode decomposition with adaptive noise (CEEMDAN). Initial steps in our suggested procedure include utilizing CEEMDAN to translate the raw energy usage data into multiple components. Then, the component with the most significant frequency is predicted using RF, and the other components are forecasted using hybrid LSTM. Finally, all of the individual parts' predictions are combined to form a whole. Real-world output energy usage data has been predicted to test the suggested strategy. Results from the experiments show that the suggested strategy outperforms the reference methods.

Artificial neural network-based output power prediction of grid-connected semitransparent photovoltaic system.

The solar photovoltaic system is an emerging renewable energy resource. The performance of the solar photovoltaic system is predicted based on the historical experimental dataset. In this work, the real-time prediction models are developed for the output power prediction of the STPV system. The performance of the semitransparent photovoltaic system is predicted for the Kovilpatti region where the climatic condition is hot and humid. The short-term power is predicted for the hourly, daily, and weekly average are considered. The feature selected for the prediction of the output power of the STPV system comprises of the solar radiation, ambient temperature, and wind velocity of the Kovilpatti region. The result reveals that the output power prediction of the hourly, daily, and weekly power have the very high value of the correlation coefficient of R. The final model produced accurate forecasts, with a Root mean square (RMSE) of 0.25 in ELMAN and 0.30 in FFN and 0.426 in GRN. These features of the training algorithm indicate that the model is not dependent on the model's position or configuration in the simulation.

Diagnosing Breast Cancer Based on the Adaptive Neuro-Fuzzy Inference System.

In this work, a novel hybrid neuro-fuzzy classifier (HNFC) technique is proposed for producing more accuracy in input data classification. The inputs are fuzzified using a generalized membership function. The fuzzification matrix helps to create connectivity between input pattern and degree of membership to various classes in the dataset. According to that, the classification process is performed for the input data. This novel method is applied for ten number of benchmark datasets. During preprocessing, the missing data is replaced with the mean value. Then, the statistical correlation is applied for selecting the important features from the dataset. After applying a data transformation technique, the values normalized. Initially, fuzzy logic has been applied for the input dataset; then, the neural network is applied to measure the performance. The result of the proposed method is evaluated with supervised classification techniques such as radial basis function neural network (RBFNN) and adaptive neuro-fuzzy inference system (ANFIS). Classifier performance is evaluated by measures like accuracy and error rate. From the investigation, the proposed approach provided 86.2% of classification accuracy for the breast cancer dataset compared to other two approaches.

Human Centered Decision-Making for COVID-19 Testing Center Location Selection: Tamil Nadu-A Case Study.

This paper proposes a blend of three techniques to select COVID-19 testing centers. The objective of the paper is to identify a suitable location to establish new COVID-19 testing centers. Establishment of the testing center in the needy locations will be beneficial to both public and government officials. Selection of the wrong location may lead to lose both health and wealth. In this paper, location selection is modelled as a decision-making problem. The paper uses fuzzy analytic hierarchy process (AHP) technique to generate the criteria weights, monkey search algorithm to optimize the weights, and Technique for Order of Preference by Similarity to Ideal Solution (TOPSIS) method to rank the different locations. To illustrate the applicability of the proposed technique, a state named Tamil Nadu, located in India, is taken for a case study. The proposed structured algorithmic steps were applied for the input data obtained from the government of India website, and the results were analyzed and validated using the government of India website. The ranks assigned by the proposed technique to different locations are in aligning with the number of patients and death rate.

Prediction of the Age and Gender Based on Human Face Images Based on Deep Learning Algorithm.

In recent times, nutrition recommendation system has gained increasing attention due to their need for healthy living. Current studies on the food domain deal with a recommendation system that focuses on independent users and their health problems but lack nutritional advice to individual users. The proposed system is developed to suggest nutritional food to people based on age and gender predicted from their face image. The designed methodology preprocesses the input image before performing feature extraction using the deep convolution neural network (DCNN) strategy. This network extracts D-dimensional characteristics from the source face image, followed by the feature selection strategy. The face's distinctive and identifiable traits are chosen utilizing a hybrid particle swarm optimization (HPSO) technique. Support vector machine (SVM) is used to classify a person's age and gender. The nutrition recommendation system relies on the age and gender classes. The proposed system is evaluated using classification rate, precision, and recall using Adience dataset and UTKface dataset, and real-world images exhibit excellent performance by achieving good prediction results and computation time.

Diagnosis of Retinal Diseases Based on Bayesian Optimization Deep Learning Network Using Optical Coherence Tomography Images.

Retinal abnormalities have emerged as a serious public health concern in recent years and can manifest gradually and without warning. These diseases can affect any part of the retina, causing vision impairment and indeed blindness in extreme cases. This necessitates the development of automated approaches to detect retinal diseases more precisely and, preferably, earlier. In this paper, we examine transfer learning of pretrained convolutional neural network (CNN) and then transfer it to detect retinal problems from Optical Coherence Tomography (OCT) images. In this study, pretrained CNN models, namely, VGG16, DenseNet201, InceptionV3, and Xception, are used to classify seven different retinal diseases from a dataset of images with and without retinal diseases. In addition, to choose optimum values for hyperparameters, Bayesian optimization is applied, and image augmentation is used to increase the generalization capabilities of the developed models. This research also provides a comparison of the proposed models as well as an analysis of them. The accuracy achieved using DenseNet201 on the Retinal OCT Image dataset is more than 99% and offers a good level of accuracy in classifying retinal diseases compared to other approaches, which only detect a small number of retinal diseases.

Investigating the Role of Image Fusion in Brain Tumor Classification Models Based on Machine Learning Algorithm for Personalized Medicine.

Image fusion can be performed on images either in spatial domain or frequency domain methods. Frequency domain methods will be most preferred because these methods can improve the quality of edges in an image. In image fusion, the resultant fused images will be more informative than individual input images, thus more suitable for classification problems. Artificial intelligence (AI) algorithms play a significant role in improving patient's treatment in the health care industry and thus improving personalized medicine. This research work analyses the role of image fusion in an improved brain tumour classification model, and this novel fusion-based cancer classification model can be used for personalized medicine more effectively. Image fusion can improve the quality of resultant images and thus improve the result of classifiers. Instead of using individual input images, the high-quality fused images will provide better classification results. Initially, the contrast limited adaptive histogram equalization technique preprocess input images such as MRI and SPECT images. Benign and malignant class brain tumor images are applied with discrete cosine transform-based fusion method to obtain fused images. AI algorithms such as support vector machine classifier, KNN classifier, and decision tree classifiers are tested with features obtained from fused images and compared with the result obtained from individual input images. Performances of classifiers are measured using the parameters accuracy, precision, recall, specificity, and F1 score. SVM classifier provided the maximum accuracy of 96.8%, precision of 95%, recall of 94%, specificity of 93%, F1 score of 91%, and performed better than KNN and decision tree classifiers when extracted features from fused images are used. The proposed method results are compared with existing methods and provide satisfactory results.

Review on phase change materials for solar energy storage applications.

The energy storage application plays a vital role in the utilization of the solar energy technologies. There are various types of the energy storage applications are available in the todays world. Phase change materials (PCMs) are suitable for various solar energy systems for prolonged heat energy retaining, as solar radiation is sporadic. This literature review presents the application of the PCM in solar thermal power plants, solar desalination, solar cooker, solar air heater, and solar water heater. Even though the availability and cost of PCMs are complex and high, the PCMs are used in most solar energy methods due to their significant technical parameters improvisation. This review's detailed findings paved the way for future recommendations and methods for the investigators to carry work for further system developments.

COVID-19 Detection Based on Lung Ct Scan Using Deep Learning Techniques.

SARS-CoV-2 is a novel virus, responsible for causing the COVID-19 pandemic that has emerged as a pandemic in recent years. Humans are becoming infected with the virus. In 2019, the city of Wuhan reported the first-ever incidence of COVID-19. COVID-19 infected people have symptoms that are related to pneumonia, and the virus affects the body's respiratory organs, making breathing difficult. A real-time reverse transcriptase-polymerase chain reaction (RT-PCR) kit is used to diagnose the disease. Due to a shortage of kits, suspected patients cannot be treated promptly, resulting in disease spread. To develop an alternative, radiologists looked at the changes in radiological imaging, like CT scans, that produce comprehensive pictures of the body of excellent quality. The suspected patient's computed tomography (CT) scan is used to distinguish between a healthy individual and a COVID-19 patient using deep learning algorithms. A lot of deep learning methods have been proposed for COVID-19. The proposed work utilizes CNN architectures like VGG16, DeseNet121, MobileNet, NASNet, Xception, and EfficientNet. The dataset contains 3873 total CT scan images with "COVID" and "Non-COVID." The dataset is divided into train, test, and validation. Accuracies obtained for VGG16 are 97.68%, DenseNet121 is 97.53%, MobileNet is 96.38%, NASNet is 89.51%, Xception is 92.47%, and EfficientNet is 80.19%, respectively. From the obtained analysis, the results show that the VGG16 architecture gives better accuracy compared to other architectures.

Synthesis and Workability Behavior of Cu-X wt.% TiC (x = 0, 4, 8, and 12) Powder Metallurgy Composites.

In this work, copper (Cu) matrix composite reinforced with titanium carbide (TiC) was fabricated by powder metallurgy (PM) method with the varying TiC content from 0% to 12% by weight in the step of 4%. The required weight percentage of powders was milled in an indigenously developed ball milling setup. Green compacts were made using a computer-controlled hydraulic press (400 kN) and sintered in a muffle furnace at a temperature of 950°C. Scanning electron microscope (SEM) was used to analyze the distribution of TiC particles in Cu matrix in as-sintered conditions. X-ray diffraction (XRD) analysis resulted in the existence of respective phases in the produced composites. The structural characteristics such as stress, strain, dislocation density, and grain size of the milled composites were evaluated. Cold upsetting was conducted for the sintered composites at room temperature to evaluate the axial (σ z ), hoop (σ Ó© ), hydrostatic (σ m ), and effective (σ eff ) true stresses. These stresses were analyzed against true axial strain (ε z ). Results showed that the increase in the inclusion of weight percentage of TiC into the Cu matrix increases density, hardness, (σ z ), (σ Ó© ), (σ m ), (σ eff ), and stress ratio parameters such as (σ z /σ eff ), (σ θ /σ eff ), (σ m /σ eff ), and (σ z /σ θ ) of the composites.

Investigation on Inorganic Salts K2TiF6 and KBF4 to Develop Nanoparticles Based TiB2 Reinforcement Aluminium Composites.

In the current research, AA6082 aluminium alloy matrix composites (AAMCs) incorporated with various weight fractions of titanium diboride (0, 3, 6, and 9 wt%) were prepared via an in situ casting technique. The exothermic reaction between inorganic powders like dipotassium hexafluorotitanate (K2TiF6) and potassium tetrafluoroborate (KBF4) in molten Al metal contributes to the development of titanium diboride content. The manufactured AA6082-TiB2 AAMCs were evaluated using a scanning electron microscope (SEM) and X-ray diffraction (XRD). The mechanical properties and wear rate (WR) of the AAMCs were investigated. XRD guarantees the creation of TiB2 phases and proves the nonappearance of reaction products in the AMCs. SEM studies depict the even dispersion of TiB2 in the matrix alloy. The mechanical and tribological properties (MTP) of the AAMCs showed improvement by the dispersion of TiB2 particles. The WR decreases steadily with TiB2 and the least WR is seen at nine weight concentrations of TiB2/AA6082 AAMCs. Fabricated composites revealed 47.9% higher flexural strength and 14.2% superior compression strength than the base AA6082 alloy.

Review on progress in concrete solar water collectors.

The solar-powered water heating method is the best way to use the available free solar radiation for thermal energy. Electrical water heating systems all over the world consume more electrical energy for their operation. The Solar Water Heating System (SWHS) has a higher efficiency than the electrical water heating system. As a result, SWHS plays an important role in the home, industry, hostel, and hotel. Various types of SWHS are published by different researchers. Concrete Collector Solar Water Heating (CCSWH) System used for dual purposes is one of the published works (space cooling and hot water production). The CCSWH system is discussed in this review, which includes both traditional and recent developments. Also, future research opportunities in this field are provided.

Review on the progress of building-applied/integrated photovoltaic system.

Integration of photovoltaic (PV) technologies with building envelopes started in the early 1990 to meet the building energy demand and shave the peak electrical load. The PV technologies can be either attached or integrated with the envelopes termed as building-attached (BA)/building-integrated (BI) PV system. The BAPV/BIPV system applications are categorized under the building envelope roof and facades as PV-roof, PV-skin facade, PV-Trombe wall, PV claddings, and louvers. This review covers various factors that affect the design and performance of the BAPV/BIPV system applications. The factors identified are air gap, ventilation rate, a tilt angle of PV shading devices, adjacent shading, semitransparent PV (STPV) glazing design, cell coverage ratio (CCR), transmittance, window to wall ratio (WWR), and glazing orientation. Furthermore, the results of the possible factors are compared to building locations. This review article will be beneficial for researchers in designing the BAPV/BIPV system and provides future research possibilities.

A Novel Context Aware Joint Segmentation and Classification Framework for Glaucoma Detection.

Glaucoma is a chronic ocular disease characterized by damage to the optic nerve resulting in progressive and irreversible visual loss. Early detection and timely clinical interventions are critical in improving glaucoma-related outcomes. As a typical and complicated ocular disease, glaucoma detection presents a unique challenge due to its insidious onset and high intra- and interpatient variabilities. Recent studies have demonstrated that robust glaucoma detection systems can be realized with deep learning approaches. The optic disc (OD) is the most commonly studied retinal structure for screening and diagnosing glaucoma. This paper proposes a novel context aware deep learning framework called GD-YNet, for OD segmentation and glaucoma detection. It leverages the potential of aggregated transformations and the simplicity of the YNet architecture in context aware OD segmentation and binary classification for glaucoma detection. Trained with the RIGA and RIMOne-V2 datasets, this model achieves glaucoma detection accuracies of 99.72%, 98.02%, 99.50%, and 99.41% with the ACRIMA, Drishti-gs, REFUGE, and RIMOne-V1 datasets. Further, the proposed model can be extended to a multiclass segmentation and classification model for glaucoma staging and severity assessment.

An Expert System for COVID-19 Infection Tracking in Lungs Using Image Processing and Deep Learning Techniques.

The proposed method introduces algorithms for the preprocessing of normal, COVID-19, and pneumonia X-ray lung images which promote the accuracy of classification when compared with raw (unprocessed) X-ray lung images. Preprocessing of an image improves the quality of an image increasing the intersection over union scores in segmentation of lungs from the X-ray images. The authors have implemented an efficient preprocessing and classification technique for respiratory disease detection. In this proposed method, the histogram of oriented gradients (HOG) algorithm, Haar transform (Haar), and local binary pattern (LBP) algorithm were applied on lung X-ray images to extract the best features and segment the left lung and right lung. The segmentation of lungs from the X-ray can improve the accuracy of results in COVID-19 detection algorithms or any machine/deep learning techniques. The segmented lungs are validated over intersection over union scores to compare the algorithms. The preprocessed X-ray image results in better accuracy in classification for all three classes (normal/COVID-19/pneumonia) than unprocessed raw images. VGGNet, AlexNet, Resnet, and the proposed deep neural network were implemented for the classification of respiratory diseases. Among these architectures, the proposed deep neural network outperformed the other models with better classification accuracy.