Analysis of the Correspondence of the Degree of Fragility with the Way to Exercise the Force of the Hand.

BACKGROUND: Frailty is a geriatric syndrome characterized by increased individual vulnerability with an increase in both dependence and mortality when exposed to external stressors. The use of Frailty Indices in routine clinical practice is limited by several factors, such as the cognitive status of the patient, times of consultation, or lack of prior information from the patient. OBJECTIVES: In this study, we propose the generation of an objective measure of frailty, based on the signal from hand grip strength (HGS). DESIGN AND MEASUREMENTS: This signal was recorded with a modified Deyard dynamometer and processed using machine learning strategies based on supervised learning methods to train classifiers. A database was generated from a cohort of 138 older adults in a transverse pilot study that combined classical geriatric questionnaires with physiological data. PARTICIPANTS: Participants were patients selected by geriatricians of medical services provided by collaborating entities. SETTING AND RESULTS: To process the generated information 20 selected significant features of the HGS dataset were filtered, cleaned, and extracted. A technique based on a combination of the Synthetic Minority Oversampling Technique (SMOTE) to generate new samples from the smallest group and ENN (technique based on K-nearest neighbors) to remove noisy samples provided the best results as a well-balanced distribution of data. CONCLUSION: A Random Forest Classifier was trained to predict the frailty label with 92.9% of accuracy, achieving sensitivities higher than 90%.

Assessment of groundwater salinity using principal component analysis (PCA): a case study from Mewat (Nuh), Haryana, India.

In the present study, principal component analysis (PCA) is used to investigate the processes controlling groundwater salinity in the Mewat (Nuh) district, Haryana, India. Twenty groundwater samples were collected from salinity-affected areas in the March-April months of years 2018 and 2019 and were analyzed for chemical variables pH, EC, Ca2+, Mg2+, Na+, K+, [Formula: see text], Cl-, SO42-, [Formula: see text], TDS, and total hardness. Three principal components were selected based on the eigen value, which explains 79.58% and 85.08% of the total variation in the years 2018 and 2019, respectively. The first principal component (PC-1) is identified with salinity, the second principal component (PC-2) with alkalinity, and the third principal component (PC-3) described the pollution. When the yearly comparison was made, the samples collected in 2019 were found to have an increased salinity compared to 2018, which shows an increased vulnerability to the aquifer of Mewat on account of the decline in rainfall recharge. It was also evident that declining recharge also triggered the recharge from other sources; thus, the impact of pollution is more pronounced in 2019 compared to 2018.