Health hazards from perchlorate enriched groundwater of a semi-arid river basin of south India and suggesting in-situ remediation through Managed Aquifer Recharge.

The present study was conducted to assess the groundwater quality and perchlorate contents in the pre- and post-monsoon groundwater samples along with its associated health concerns with suggesting in-situ remediation in semi-arid Arjunanadi River basin of south India. Most of the samples (86 in pre-monsoon and 84 in post-monsoon out of total 94) showed secondary salinity and secondary alkalinity, and the perchlorate showed positive relations with Na, K, SO, Cl, and NO contents. The main source of perchlorate could be fireworks manufacturing industries with 23 % of pre-monsoon (246.

Assessment of groundwater fluoride and human health effects in a hard rock province of south India: Implications from Pollution Index Model (PIM) and Geographical Information System (GIS) techniques.

This research examines whether the groundwater in the Sivakasi Region of South India is suitable for consumption, and assesses the possible health hazards for various age demographics including infants, children, teenagers, and adults. A total of 77 groundwater samples were gathered, covering a total area of 580 km and analyzed for major and minor ions. The hydrogen ion concentration (pH) of the samples indicates neutral to marginally alkaline.

DDA-SSNets: Dual decoder attention-based semantic segmentation networks for COVID-19 infection segmentation and classification using chest X-Ray images.

Background: COVID-19 needs to be diagnosed and staged to be treated accurately. However, prior studies' diagnostic and staging abilities for COVID-19 infection needed to be improved. Therefore, new deep learning-based approaches are required to aid radiologists in detecting and quantifying COVID-19-related lung infections.

A novel machine learning model for breast cancer detection using mammogram images.

The most fatal disease affecting women worldwide now is breast cancer. Early detection of breast cancer enhances the likelihood of a full recovery and lowers mortality. Based on medical imaging, researchers from all around the world are developing breast cancer screening technologies.

Encapsulation of quercetin fraction from Musa balbisiana banana blossom in chitosan alginate solution, its optimization and characterizations.

This study comprises the isolation of quercetin from the bhimkol banana (Musa balbisiana) blossom, encapsulation, and its characterizations. An isolated quercetin rich fraction was obtained from HPLC followed by column chromatography and subsequently encapsulated with chitosan-alginate polyelectrolyte complex at optimum encapsulation conditions obtained by ant colony optimization. Quercetin fraction and encapsulated quercetin were characterized for their physicochemical properties (by HPLC, FTIR, NMR, XRD, Dynamic Light Scattering, and release study).

An efficient breast cancer classification model using bilateral filtering and fuzzy convolutional neural network.

BC (Breast cancer) is the second most common reason for women to die from cancer. Recent workintroduced a model for BC classifications where input breast images were pre-processed using median filters for reducing noises. Weighed KMC (K-Means clustering) is used to segment the ROI (Region of Interest) after the input image has been cleaned of noise.

Macromolecular, thermal, and nonthermal technologies for reduction of glycemic index in food-A review.

Consumers rely on product labels to make healthy choices, especially with regard to the glycemic index (GI) and glycemic load (GL), which identify foods that stabilize blood sugar. Employing both thermal and nonthermal processing techniques can potentially reduce the GI, contributing to improved blood sugar regulation and overall metabolic health. This study concentrates on the most current advances in GI-reduction food processing technologies.

A vehicular network based intelligent transport system for smart cities using machine learning algorithms.

Smart cities and the Internet of Things have enabled the integration of communicating devices for efficient decision-making. Notably, traffic congestion is one major problem faced by daily commuters in urban cities. In developed countries, specialized sensors are deployed to gather traffic information to predict traffic patterns.

Gynecological Healthcare: Unveiling Pelvic Masses Classification through Evolutionary Gravitational Neocognitron Neural Network Optimized with Nomadic People Optimizer.

Accurate and early detection of malignant pelvic mass is important for a suitable referral, triage, and for further care for the women diagnosed with a pelvic mass. Several deep learning (DL) methods have been proposed to detect pelvic masses but other methods cannot provide sufficient accuracy and increase the computational time while classifying the pelvic mass. To overcome these issues, in this manuscript, the evolutionary gravitational neocognitron neural network optimized with nomadic people optimizer for gynecological abdominal pelvic masses classification is proposed for classifying the pelvic masses (EGNNN-NPOA-PM-UI).

Sustainable remediation of toxic congo red dye pollution using bio based carbon nanocomposite: Modelling and performance evaluation.

Remediation of synthetic dyes found in aqueous environment poses a serious challenge for treatment due to their resistance to chemical and biological degradation. This research study investigated the application of Chitosan-ZnO-Seaweed bio nanocomposite in the remediation of congo red. The novel bionanocomposite was characterised by FTIR, SEM, TEM, EDS and XRD studies.

Modern PID/FOPID controllers for frequency regulation of interconnected power system by considering different cost functions.

This article presents frequency regulation of an interconnected three-area power system (Thermal + Wind + Hydro). Fractional Order PID (FOPID) and Proportional-Integral-Derivative (PID) controllers are applied as subsidiary regulators to control the electrical power interconnected system at the time of sudden load variation. To accomplish this study, Genetic Algorithm (GA), Grey Wolf Optimizer (GWO), Sine Cosine Inspired Algorithm (SCIA) and Atom Search Inspired Algorithm (ASIA) are implemented to optimize the secondary regulators' gains (PID and FOPID) by considering various cost functions such as Integral Absolute Error (IAE), Integral Time Absolute Error (ITAE), Integral Square Error (ISE), and Integral Time Square Error (ITSE).

Conversion of solid wastes and natural biomass for deciphering the valorization of biochar in pollution abatement: A review on the thermo-chemical processes.

This overview addresses the formation of solid trash and the various forms of waste from a variety of industries, which environmentalists have embraced. The paper investigates the negative effects on the environment caused by unsustainable management of municipal solid trash as well as the opportunities presented by the formal system. This examination looks at the origins of solid waste as well as the typical treatment methods.

MTMC-AUR2CNet: Multi-textural multi-class attention recurrent residual convolutional neural network for COVID-19 classification using chest X-ray images.

Coronavirus disease (COVID-19) has infected over 603 million confirmed cases as of September 2022, and its rapid spread has raised concerns worldwide. More than 6.4 million fatalities in confirmed patients have been reported.

MCSC-Net: COVID-19 detection using deep-Q-neural network classification with RFNN-based hybrid whale optimization.

Background: COVID-19 is the most dangerous virus, and its accurate diagnosis saves lives and slows its spread. However, COVID-19 diagnosis takes time and requires trained professionals. Therefore, developing a deep learning (DL) model on low-radiated imaging modalities like chest X-rays (CXRs) is needed.

Microwave assisted fluidized bed drying of bitter gourd: Modelling and optimization of process conditions based on bioactive components.

Bitter gourds were dried under varied drying conditions in a microwave assisted fluidized bed dryer, and the process was optimized using response surface methodology. Microwave power, temperature and air velocity were used as process variables for drying and the process parameters were varied between 360 and 720 W, 40-60 °C and 10-14 m/s, respectively. The responses determined for deciding the optimal criteria were vitamin C, total phenolics, IC, total chlorophyll content, vitamin A content, rehydration ratio, hardness and total color change of the dried bitter gourd.

Biosorption of methylene blue dye using a novel chitosan pectinase blend.

Biosorption is a key phenomenon that has been used to remove the aquatic pollutants like dyes and heavy metals present in industrial effluents. The current study aims at the development of a chitosan-pectinase blend (CPB) to separate the methylene blue (MB) dye from its synthetic solution. Pectinase, an enzyme isolated from a consortium of Bacillus species, is imbibed in the blend.

Promising bioactive properties of quercetin for potential food applications and health benefits: A review.

Naturally occurring phytochemicals with promising biological properties are quercetin and its derivatives. Quercetin has been thoroughly studied for its antidiabetic, antibacterial, anti-inflammatory, anti-Alzheimer's, anti-arthritic, antioxidant, cardiovascular, and wound-healing properties. Anticancer activity of quercetin against cancer cell lines has also recently been revealed.

Predicting phosphorus and nitrate loads by using SWAT model in Vamanapuram River Basin, Kerala, India.

Evaluations of probable environmental impacts of point and diffuse source pollution at regional sizes are essential to achieve sustainable development of natural resources such as land and water. This research focused on how nitrate and phosphorus load varied over time and space in the Vamanapuram River Basin (VRB). Phosphorus and nitrate loads have been evaluated in the VRB using the semi-distributed Soil and Water Assessment Tool (SWAT) hydrological model.

Bio-fabrication of porous magnetic Chitosan/FeO nanocomposite using Azolla pinnata for removal of chromium - Parametric effects, surface characterization and kinetics.

In this research, a novel porous nanocomposite, namely Chitosan-iron-oxide @ Azolla pinnata nanocomposite, has been synthesized by co-precipitation and hydrothermal method. The effect of process parameter on adsorption process was investigated. Batch removal of chromium (Cr) was optimized with respect to solution pH, batch stirring time, sorbent dose, initial chromium concentration and temperature.

Effect of incorporation of wheat bran, rice bran and banana peel powder on the mesostructure and physicochemical characteristics of biscuits.

Various types of natural fiber-rich ingredients are added into bakery-based products to improve their fiber content for health promotional purposes. But the majority of these products usually include exotic dietary fiber components. The aim of this study was to develop biscuits incorporated with wheat bran, rice bran and banana peel powder and to evaluate the effects on physicochemical properties and sensory acceptability of these different biscuit samples.

Comparative assessment of groundwater vulnerability using GIS-based DRASTIC and DRASTIC-AHP for Thoothukudi District, Tamil Nadu India.

The groundwater is very precious in the world. Rapid urbanization and industrialization create tremendous stress on groundwater quality and quantity. Unscientific groundwater extraction and waste disposal methods impact the groundwater aquifer's susceptibility in the coastal area.

Multi-texture features and optimized DeepNet for COVID-19 detection using chest x-ray images.

The corona virus disease 2019 (COVID-19) pandemic has a severe influence on population health all over the world. Various methods are developed for detecting the COVID-19, but the process of diagnosing this problem from radiology and radiography images is one of the effective procedures for diagnosing the affected patients. Therefore, a robust and effective multi-local texture features (MLTF)-based feature extraction approach and Improved Weed Sea-based DeepNet (IWS-based DeepNet) approach is proposed for detecting the COVID-19 at an earlier stage.