Environmental water quality prediction based on COOT-CSO-LSTM deep learning.

Water resource management relies heavily on reliable water quality predictions. Predicting water quality metrics in the watershed system, including dissolved oxygen (DO), is the main emphasis of this work. The enhanced long short-term memory (LSTM) model was suggested to improve the model's performance.

Performance evaluation and chemical oxygen demand removal of tannery wastewater through the aerobic-anaerobic route.

With characterized for complex and maximum substance (suspended solids, broke up oil, a mixture of inorganic and chromium sulfides), tannery wastewater was subjected to a treatment process on removal of chemical oxygen demand (COD) via upstream anaerobic sludge blanket reactor where we found reduced departure efficiencies and that process limits were affected by the assortments in regular stacking rates, closeness of chromium, and sulfides. Hence, a combination of the aerobic-anaerobic hybrid reactor was set up for sequential treatment to determine possible COD reduction. This study investigated the biological degradation of tannery wastewater in a laboratory-scale sequential up-flow aerobic-anaerobic reactor.

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.

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.

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.

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).

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 CO mitigation, global warming effect, and the use of solar desalination contributes towards decarbonization, mitigation of CO 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.

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.

Synthesis and Workability Behavior of Cu-X wt.% TiC ( = 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.

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.

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.

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.

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.

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.

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).

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.

Thermal and Flame Retardant Behavior of Neem and Banyan Fibers When Reinforced with a Bran Particulate Epoxy Hybrid Composite.

Awareness of environmental concerns influences researchers to develop an alternative method of developing natural fiber composite materials, to reduce the consumption of synthetic fibers. This research attempted testing the neem () fiber and the banyan () fiber at different weight fractions, under flame retardant and thermal testing, in the interest of manufacturing efficient products and parts in real-time applications. The hybrid composite consists of 25% fiber reinforcement, 70% matrix material, and 5% bran filler.

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.

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.

Optimum conditions of zero-valent iron nanoparticle stabilized foam application for diesel-contaminated soil remediation involving three major soil types.

Stability of foam, enhanced by nano zero-valent iron (nZVI) and its optimized constituents, may have significant potential for effective treatment of soil contaminated with diesel oil-a major environmental problem. The optimum diesel removal efficiency from distinct types of soil accomplished by the unique application of such foams as well as the optimum conditions of the foaming constituents have not been reported in literature so far. Hence, in this work, the removal of diesel contaminant from different soil types (desert, coastal, clay soil) is optimized, and the optimized results are reported for the first time, using response surface methodology (RSM), for alkylpolyglucoside phosphate (APG-Ph) foam, stabilized by nZVI.

Synthesis and Characterization of Mechanical Properties and Wire Cut EDM Process Parameters Analysis in AZ61 Magnesium Alloy + BC + SiC.

Wire Cut Electric Discharge Machining (WCEDM) is a novel method for machining different materials with application of electrical energy by the movement of wire electrode. For this work, an AZ61 magnesium alloy with reinforcement of boron carbide and silicon carbide in different percentage levels was used and a plate was formed through stir casting technique. The process parameters of the stir casting process are namely reinforcement %, stirring speed, time of stirring, and process temperature.

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.

Diagnosis of Cervical Cancer based on Ensemble Deep Learning Network using Colposcopy Images.

Traditional screening of cervical cancer type classification majorly depends on the pathologist's experience, which also has less accuracy. Colposcopy is a critical component of cervical cancer prevention. In conjunction with precancer screening and treatment, colposcopy has played an essential role in lowering the incidence and mortality from cervical cancer over the last 50 years.