https://eutils.ncbi.nlm.nih.gov/entrez/eutils/efetch.fcgi?db=pubmed&id=31374773&retmode=xml&tool=Litmetric&email=readroberts32@gmail.com&api_key=61f08fa0b96a73de8c900d749fcb997acc09https://eutils.ncbi.nlm.nih.gov/entrez/eutils/esearch.fcgi?db=pubmed&term=free+moments&datetype=edat&usehistory=y&retmax=5&tool=Litmetric&email=readroberts32@gmail.com&api_key=61f08fa0b96a73de8c900d749fcb997acc09https://eutils.ncbi.nlm.nih.gov/entrez/eutils/efetch.fcgi?db=pubmed&WebEnv=MCID_67957aa2b70cb60e7e0b1c4b&query_key=1&retmode=xml&retmax=5&tool=Litmetric&email=readroberts32@gmail.com&api_key=61f08fa0b96a73de8c900d749fcb997acc09
Wearable actuators in lower-extremity active orthoses or prostheses have the potential to address a variety of gait disorders. However, whenever conventional joint actuators exert moments on specific limbs, they must simultaneously impose opposing reaction moments on other limbs, which may reduce the desired effects and perturb posture. Momentum exchange actuators exert free moments on individual limbs, potentially overcoming or mitigating these issues.We simulate unperturbed gait to compare conventional joint actuators placed on the knee or hip of the swing leg, and equivalent angular momentum exchange actuators placed on the shank or thigh. Our results indicate that, while conventional joint actuators excel at increasing toe clearance when assisting knee flexion, free moments can yield greater increases in stride length when assisting knee extension or hip flexion.
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http://dx.doi.org/10.1109/ICORR.2019.8779389 | DOI Listing |
Sensors (Basel)
January 2025
Department of Electrical Engineering and Information Technology, University of Naples Federico II, 80125 Naples, Italy.
Parkinson's disease (PD) is characterized by a slow, short-stepping, shuffling gait pattern caused by a combination of motor control limitations due to a reduction in dopaminergic neurons. Gait disorders are indicators of global health, cognitive status, and risk of falls and increase with disease progression. Therefore, the use of quantitative information on the gait mechanisms of PD patients is a promising approach, particularly for monitoring gait disorders and potentially informing therapeutic interventions, though it is not yet a well-established tool for early diagnosis or direct assessment of disease progression.
View Article and Find Full Text PDFBehav Sci (Basel)
January 2025
IRyS Group, Escuela de Educación Física, Pontificia Universidad Católica de Valparaíso, Valparaíso 2340025, Chile.
In this study, we investigated the impact of a 10-week free weight resistance training (RT) program on cognitive function in healthy young adults. In this randomized controlled trial, 18 participants were assigned to either an experimental or control group. We assessed cognitive function by using eye-tracking (ET) technology during text processing tasks.
View Article and Find Full Text PDFSci Rep
January 2025
Applied Optics Laboratory, Institute of Optics and Precision Mechanics, University Setif 1, Setif, 19000, Algeria.
This prediction evaluates the different physical characteristics of magnetic materials XFeO (X = Mg, Ca and Sr) by using density functional theory (DFT). The generalized gradient approximation (GGA) approach is chosen to define the exchange and correlation potential. The structural study of the compounds XFeO (X = Mg, Ca and Sr) shows that the ferromagnetic phase is the more stable ground state, where all the parameters of the network are given at equilibrium.
View Article and Find Full Text PDFBMC Nurs
January 2025
Department of Nursing, Kwame Nkrumah University of Science and Technology, Kumasi, Ghana.
Background: The nursing profession plays a crucial role in the quality of healthcare services. While nurses face occupational injury challenges globally, mental workload, which is often overlooked, plays a significant role in these injuries. Understanding nurses' coping strategies can help develop effective interventions.
View Article and Find Full Text PDFJ Chem Phys
January 2025
Microsoft Research AI for Science, 21 Station Road, Cambridge CB1 2FB, United Kingdom.
Variational ab initio methods in quantum chemistry stand out among other methods in providing direct access to the wave function. This allows, in principle, straightforward extraction of any other observable of interest, besides the energy, but, in practice, this extraction is often technically difficult and computationally impractical. Here, we consider the electron density as a central observable in quantum chemistry and introduce a novel method to obtain accurate densities from real-space many-electron wave functions by representing the density with a neural network that captures known asymptotic properties and is trained from the wave function by score matching and noise-contrastive estimation.
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