Examining the effects of non-immersive virtual reality game-based training on knee hyperextension control and balance in chronic stroke patients: a single-blind randomized controlled study.

Background: Post-stroke hemiparesis can lead to decreased mobility, gait disturbances, impaired balance, postural instability, limitations in activities of daily living (ADL), and long-term disability.

Aims: The aim of this study was to examine the effect of non-immersive virtual reality game-based training (nIVRGT) in addition to conventional rehabilitation in stroke patients on dynamic balance, knee hyperextension control, and ADL.

Methods: Twenty-five chronic stroke patients aged between 51 and 70 were included in the study.

The relationship between burnout and work engagement levels of nurses and physiotherapists working during the COVID-19 pandemic period.

Background: The COVID-19 pandemic has affected all health professionals worldwide. This has also influenced their working lives, affecting burnout and work engagement.

Objective: This study aims to investigate the relationship between burnout and work engagement among nurses and physiotherapists during the COVID-19 pandemic.

Optimization of voltammetric parameters for sensitive and simultaneous determination of ferulic acid and vanillin using a glassy carbon electrode based on 2-aminonicotinic acid in the presence of surfactant media.

The electrochemical sensor for simultaneous determination of ferulic acid (FA) and vanillin (VA) was prepared by electrochemical deposition of 2-aminonicotinic acid (2-ANA) on the glassy carbon (GC) electrode. The voltammetric determination of FA and VA was performed in the BR buffer solution in the presence of sodium dodecyl sulfate as a surfactant with SWV. The parameters of the SWV technique were optimized by response surface methodology experimental design.

An Out-of-Sample Extension to Manifold Learning via Meta-Modelling.

Unsupervised manifold learning has become accepted as an important tool for reducing dimensionality of a dataset by finding its meaningful low-dimensional representation lying on an unknown nonlinear subspace. Most manifold learning methods only embed an existing dataset, but do not provide an explicit mapping function for novel out-of-sample data, thereby potentially resulting in an ineffective tool for classification purposes, particularly for iterative methods such as active learning. To address this issue, out-of-sample extension methods have been introduced to generalize an existing embedding of new samples.

Feature Selection Based on High Dimensional Model Representation for Hyperspectral Images.

In hyperspectral image analysis, the classification task has generally been addressed jointly with dimensionality reduction due to both the high correlation between the spectral features and the noise present in spectral bands, which might significantly degrade classification performance. In supervised classification, limited training instances in proportion with the number of spectral features have negative impacts on the classification accuracy, which is known as Hughes effects or curse of dimensionality in the literature. In this paper, we focus on dimensionality reduction problem, and propose a novel feature-selection algorithm, which is based on the method called high dimensional model representation.