Current understanding on artificial intelligence and machine learning in orthopaedics - A scoping review.

Background: Artificial Intelligence (AI) has improved the way of looking at technological challenges. Today, we can afford to see many of the problems as just an input-output system rather than solving from the first principles. The field of Orthopaedics is not spared from this rapidly expanding technology.

Evaluation of potential toxic heavy metal contamination in soil, fly ash, vegetables and grain crops along with associated ecological and health risk assessment of nearby inhabitants of a thermal power station in Jharkhand (India).

The present study aimed to evaluate the potential toxic heavy metal (PTHM) concentrations and associated ecological and health risk of nearby inhabitants (child and adult populations) of the Bokaro thermal power station (BTPS), Jharkhand, India. The fly ash containing PTHMs is released from the thermal power plant and contaminates the soil of the nearby agricultural area, roadside area and residential area. Furthermore, PTHMs cause ecological risk as well as carcinogenic health effects in adults.

Groundwater Suitability Evaluation Using Entropy Weightage Quality Index (EWQI) Model and Human Health Cancer Risk Assessment of Heavy Metal in Eastern India.

This study evaluated the groundwater using the Entropy Weightage Quality Index model (EWQI). Eighteen samples were taken from the different wellbores during premonsoon seasons in 2021. The present study is aimed at developing a comprehensive approach for groundwater quality assessment and associated health risk along with the cancer risk due to the presence of heavy metals.

Recent advancements in the challenges and strategies of globally used traffic noise prediction models.

It is the need of an era to develop efficient traffic noise prediction models with optimum accuracy. In this context, the present work tries to comprehend the performance-related potential parameters based on earlier published articles worldwide that are responsible for deviation in noise values for different traffic noise prediction models and find out critical gaps. This study reviewed the process involved in source modeling and sound propagation algorithms, applicability, limitations, and recent modification in 9 principal traffic noise prediction models adapted by different countries all around the globe.

Vehicular traffic noise modelling of urban area-a contouring and artificial neural network based approach.

Road traffic vehicular noise is one of the main sources of environmental pollution in urban areas of India. Also, steadily increasing urbanization, industrialization, infrastructures around city condition causes health risks among the urban populations. In this study, we have explored noise descriptors (L, L, L, LNI, TNI, NC), contour plotting and find the suitability of artificial neural networks (ANN) for the prediction of traffic noise all around the Dhanbad township in 15 monitoring stations.

In situ phytoremediation of heavy metal-contaminated soil and groundwater: a green inventive approach.

The heavy metal contamination of soil and groundwater is a serious threat to environment worldwide. The survival of human being primarily relies upon soil and groundwater sources. Therefore, the remediation of heavy metal-contaminated soil and groundwater is a matter of utmost concern.

Assessment of Mine Water Quality Using Heavy Metal Pollution Index in a Coal Mining Area of Damodar River Basin, India.

A total no. of 16 mine water (underground and opencast coal mine pump discharges) samples were collected from East Bokaro coalfield during pre-monsoon, monsoon and post-monsoon seasons. The concentrations of Fe, Mn, Cu, Pb, Zn, Ni, As, Se, Al, Cd and Cr were determined using inductively coupled plasma mass spectrometry for the assessment of spatio-temporal variations, source apportionment and heavy metal pollution indexing.

Estimation of Heavy Metal Contamination in Groundwater and Development of a Heavy Metal Pollution Index by Using GIS Technique.

Heavy metal (Al, As, Ba, Cr, Cu, Fe, Mn, Ni, Se and Zn) concentration in sixty-six groundwater samples of the West Bokaro coalfield were analyzed using inductively coupled plasma-mass spectroscopy for determination of seasonal fluctuation, source apportionment and heavy metal pollution index (HPI). Metal concentrations were found higher in the pre-monsoon season as compared to the post-monsoon season. Geographic information system (GIS) tool was attributed to study the metals risk in groundwater of the West Bokaro coalfield.

Evaluation of Surface Water Quality by Using GIS and a Heavy Metal Pollution Index (HPI) Model in a Coal Mining Area, India.

Twenty eight surface water samples were collected from fourteen sites of the West Bokaro coalfield, India. The concentration of Mn, Cu, Zn, Ni, As, Se, Al, Cr, Ba, and Fe were analyzed using inductively coupled plasma mass spectrometry (ICP-MS) for determination of seasonal fluctuations and a heavy metal pollution index (HPI). The HPI values were below the critical pollution index value of 100.