Geographical analysis of fluoride and nitrate and its probabilistic health risk assessment utilizing Monte Carlo simulation and GIS in potable water in rural areas of Mathura region, Uttar Pradesh, northern India.

Human health is being increasingly exposed to fluoride and nitrate ingestion globally due to anthropogenic alternations in groundwater resources. In the present research work, a hazard quotient (HQ), Monte Carlo simulation (MCS), and geographic information systems (GIS) have been used to estimate the non-carcinogenic health risk of nitrate and fluoride in vulnerable adults, teenagers, and children living in far-flung areas of Uttar Pradesh, Northern India. About 110 samples from some nearby populations were collected and analyzed for nitrates by ion chromatography and fluoride by a fluoride-selective electrode.

Groundwater quality assessment using water quality index and principal component analysis in the Achnera block, Agra district, Uttar Pradesh, Northern India.

The qualitative and quantitative assessment of groundwater is one of the important aspects for determining the suitability of potable water. Therefore, the present study has been performed to evaluate the groundwater quality for Achhnera block in the city of Taj, Agra, India, where groundwater is an important water resource. The groundwater samples, 50 in number were collected and analyzed for major ions along with some important trace element.

A historical review and analysis on MOORA and its fuzzy extensions for different applications.

Multi-criteria decision-making (MCDM) methods have been widely used among researchers to provide a trade-off solution between best and worst, considering conflicting criteria and sets of preferences. An efficient and systematic literature review of these methods is needed to maintain their application in distinctive domains. To this end, this paper presents a comprehensive and systematic literature survey on "multi-objective optimization by ratio analysis" (MOORA) method and its fuzzy extensions developed and discussed in recent years.

Performances of Machine Learning Models for Diagnosis of Alzheimer's Disease.

In recent times, various machine learning approaches have been widely employed for effective diagnosis and prediction of diseases like cancer, thyroid, Covid-19, etc. Likewise, Alzheimer's (AD) is also one progressive malady that destroys memory and cognitive function over time. Unfortunately, there are no dedicated AI-based solutions for diagnoses of AD to go hand in hand with medical diagnosis, even though multiple factors contribute to the diagnosis, making AI a very viable supplementary diagnostic solution.

Optimal Design of Wood/Rice Husk-Waste-Filled PLA Biocomposites Using Integrated CRITIC-MABAC-Based Decision-Making Algorithm.

Based on the criteria importance through inter-criteria correlation (CRITIC) and the multi-attributive border approximation area comparison (MABAC), a decision-making algorithm was developed to select the optimal biocomposite material according to several conflicting attributes. Poly(lactic acid) (PLA)-based binary biocomposites containing wood waste and ternary biocomposites containing wood waste/rice husk with an overall additive content of 0, 2.5, 5, 7.

AutoML-ID: automated machine learning model for intrusion detection using wireless sensor network.

Momentous increase in the popularity of explainable machine learning models coupled with the dramatic increase in the use of synthetic data facilitates us to develop a cost-efficient machine learning model for fast intrusion detection and prevention at frontier areas using Wireless Sensor Networks (WSNs). The performance of any explainable machine learning model is driven by its hyperparameters. Several approaches have been developed and implemented successfully for optimising or tuning these hyperparameters for skillful predictions.

A review on properties of magnesium-based alloys for biomedical applications.

With changing lifestyles, the demand for bone implantation has been increasing day by day. The deficiency of nutritious elements within the human body results in certain diseases like osteoporosis, rickets, and other skeletal disorders; lack of physical activities; and the increasing number of accidents are the primary reasons for bone damage/fracture. Metallic implants made up of chrome steel, cobalt-based alloys, and titanium-based alloys are being majorly used worldwide owing to their high strength and high corrosion resistance which makes them permanent orthopedic bioimplant materials, however, they display a stress-shielding effect and it also requires an implant removal surgery.

Automatic detection of arrhythmias from an ECG signal using an auto-encoder and SVM classifier.

Millions of people around the world are affected by arrhythmias, which are abnormal activities of the functioning of the heart. Most arrhythmias are harmful to the heart and can suddenly become life-threatening. The electrocardiogram (ECG) is an important non-invasive tool in cardiology for the diagnosis of arrhythmias.

LT-FS-ID: Log-Transformed Feature Learning and Feature-Scaling-Based Machine Learning Algorithms to Predict the -Barriers for Intrusion Detection Using Wireless Sensor Network.

The dramatic increase in the computational facilities integrated with the explainable machine learning algorithms allows us to do fast intrusion detection and prevention at border areas using Wireless Sensor Networks (WSNs). This study proposed a novel approach to accurately predict the number of barriers required for fast intrusion detection and prevention. To do so, we extracted four features through Monte Carlo simulation: area of the Region of Interest (RoI), sensing range of the sensors, transmission range of the sensor, and the number of sensors.

Design and finite element analysis of femoral stem prosthesis using functional graded materials.

Conventionally biometals were used for design and development of bioimplants. However, the Young's Modulus (YM) of these bioimplants is higher than that of a natural bone. Asymmetric load transfer from a bone to the bioimplant results in aseptic loosening and stress shielding.

Sustainability assessment of hybrid active greenhouse solar dryer integrated with evacuated solar collector.

Environment and Economy are the two important pillars of sustainability. In this paper, the economic viability and environmental impact of the novel greenhouse dryer with an evacuated solar collector are calculated. For this analysis, tomato is dried inside the dryer as it is a high moisture crop that requires a faster drying rate otherwise it starts giving a bad odor and gets contaminated.

ECS-NL: An Enhanced Cuckoo Search Algorithm for Node Localisation in Wireless Sensor Networks.

Node localisation plays a critical role in setting up Wireless Sensor Networks (WSNs). A sensor in WSNs senses, processes and transmits the sensed information simultaneously. Along with the sensed information, it is crucial to have the positional information associated with the information source.

A SIQ mathematical model on COVID-19 investigating the lockdown effect.

This research paper aims at studying the impact of lockdown on the dynamics of novel Corona Virus Disease (COVID-19) emerged in Wuhan city of China in December 2019. Perceiving the pandemic situation throughout the world, Government of India restricted international passenger traffic through land check post (Liang, 2020) and imposed complete lockdown in the country on 24 March 2020. To study the impact of lockdown on disease dynamics we consider a three-dimensional mathematical model using nonlinear ordinary differential equations.

Deep Transfer Learning Based Classification Model for COVID-19 Disease.

The COVID-19 infection is increasing at a rapid rate, with the availability of limited number of testing kits. Therefore, the development of COVID-19 testing kits is still an open area of research. Recently, many studies have shown that chest Computed Tomography (CT) images can be used for COVID-19 testing, as chest CT images show a bilateral change in COVID-19 infected patients.

Mobile phone induced cognitive and neurochemical consequences.

With the rapid advances in technology, extensive use of mobile phones has increased the risk of health problems. This study was performed to find out the effect of mobile phone frequency on male Wistar rats. Animals were divided into two groups (n = 6 in each group).

Evaluation of Copper-Based Alloy (C93200) Composites Reinforced with Marble Dust Developed by Stir Casting under Vacuum Environment.

Copper-based alloy (C93200) composites reinforced with a different weight percentage of marble dust particles (1.5, 3, 4.5, and 6 wt.

Cerebral Small Vessel Disease: A Review Focusing on Pathophysiology, Biomarkers, and Machine Learning Strategies.

Cerebral small vessel disease (cSVD) has a crucial role in lacunar stroke and brain hemorrhages and is a leading cause of cognitive decline and functional loss in elderly patients. Based on underlying pathophysiology, cSVD can be subdivided into amyloidal and non-amyloidal subtypes. Genetic factors of cSVD play a pivotal role in terms of unraveling molecular mechanism.

Biodegradation of wastewater in alternating aerobic-anoxic lab scale pilot plant by Alcaligenes sp. S isolated from agricultural field.

The isolated microbial Alcaligenes sp. S from the agricultural field was used for the biodegradation of synthetic wastewater containing atrazine. This study was conducted in an alternating aerobic-anoxic lab scale pilot plant.

Biocompatibility evaluation and corrosion resistance of tungsten added Co-30Cr-4Mo-1Ni alloy.

Biomaterials are continuously being developed to overcome the drawbacks of existing materials and provide improved function in artificial organs. Currently Co-Cr based alloys are used in many medical applications such as hip and knee implants which still require modification to better perform. In this article, therefore, the influence of tungsten allying element on electrochemical corrosion resistance and biocompatibility behaviour of a recently developed Co-30Cr-4Mo-1Ni alloy composition were investigated.

Telomerase based anticancer immunotherapy and vaccines approaches.

Telomerase is a Reverse Transcriptase that maintains the telomere length. It is absent in most somatic cells but is found in stem cells, germ cells and around 90% of cancers. It plays a crucial role in developing and maintaining cancer cells.

In Silico Screening of Antibacterial Compounds from Herbal Sources Against Vibrio cholerae.

Background: The prolonged use of antibiotic viz., tetracycline, quinolones, ampicillin, etc., to reduce the infection of cholera, may failed due to the emergence of new Vibrio cholerae antibiotics resistant strains.

In silico prediction of B- and T- cell epitope on Lassa virus proteins for peptide based subunit vaccine design.

Background: Lassa fever is a severe, often-fatal and one of the most virulent disease in primates. However, the mechanism of escape of virus from the T-cell mediated immune response of the host cell is not explained in any studies yet. In our studies we had aimed to predict B- and T- cell epitope of Lassa virus protein, for impaling the futuristic approach of developing preventive measures against this disease, further we can also study its presumed viral- host mechanism.

Analyzing the interaction of a herbal compound Andrographolide from as a folklore against swine flu (H1N1).

Objective: To find new bioactive molecules for the treatment of swine flu.

Methods: The present study is an attempt to elucidate inhibition potential of andrographolide and its derivatives along with an associated binding mechanism through virtual screening and molecular docking simulation studies.

Results: Our findings revealed structural conformation changes in 150 loop, secondary sialic acid binding site residues of ACZ97474 {Neuraminidase (A/Blore/NIV236/2009(H1N1)}.

Identification of potential herbal inhibitor of acetylcholinesterase associated Alzheimer's disorders using molecular docking and molecular dynamics simulation.

Cholinesterase inhibitors (ChE-Is) are the standard for the therapy of AD associated disorders and are the only class of approved drugs by the Food and Drug Administration (FDA). Additionally, acetylcholinesterase (AChE) is the target for many Alzheimer's dementia drugs which block the function of AChE but have some side effects. Therefore, in this paper, an attempt was made to elucidate cholinesterase inhibition potential of secondary metabolite from Cannabis plant which has negligible or no side effect.

Antibacterial activity of actinomycetes isolated from different soil samples of Sheopur (A city of central India).

The main objective of the present study was isolation, purification, and characterization of actinomycetes from soil samples, having antimicrobial activity against 12 selected pathogenic strains. Soils samples were taken from different niche habitats of Sheopur district, Madhya Pradesh, India. These samples were serially diluted and plated on actinomycete isolation agar media.