Power training

BACKGROUND: Precapillary pulmonary hypertension (PH) is characterized by a sustained increase in right ventricular (RV) afterload, impairing systolic function. Two-dimensional (2D) echocardiography is the most performed cardiac imaging tool to assess RV systolic function; however, an accurate evaluation requires expertise. We aimed to develop a fully automated deep learning (DL)-based tool to estimate the RV ejection fraction (RVEF) from 2D echocardiographic videos of apical four-chamber views in patients with precapillary PH...

Shifts among functional pollinator groups are commonly regarded as sources of floral morphological diversity (disparity) through the formation of distinct pollination syndromes. While pollination syndromes may be used for predicting pollinators, their predictive accuracy remains debated, and they are rarely used to test whether floral disparity is indeed associated with pollinator shifts. We apply classification models trained and validated on 44 functional floral traits across 252 species with empirical pollinator observations and then use the validated models to predict pollinators for 159 species lacking observations...

This paper proposes an advanced control approach to controlling a DC-DC buck converter for a proton exchange membrane (PEM) electrolyzer within the framework of a direct current (DC) microgrid. The proposed adaptive backstepping terminal sliding mode control (ABTSMC) leverages a physics-informed neural network (PINN) to accurately estimate and compensate for system uncertainty. The composite controller achieves finite-time convergence of the tracking error by combining backstepping control and terminal sliding mode control (TSMC)...

Brain state classification by applying deep learning techniques on neuroimaging data has become a recent topic of research. However, unlike domains where the data is low dimensional or there are large number of available training samples, neuroimaging data is high dimensional and has few training samples. To tackle these issues, we present a sparse feedforward deep neural architecture for encoding and decoding the structural connectome of the human brain. We use a sparsely connected element-wise multiplication as the first hidden layer and a fixed transform layer as the output layer...

In a rapidly evolving digital landscape autonomous tools and robots are becoming commonplace. Recognizing the significance of this development, this paper explores the integration of Large Language Models (LLMs) like Generative pre-trained transformer (GPT) into human-robot teaming environments to facilitate variable autonomy through the means of verbal human-robot communication. In this paper, we introduce a novel simulation framework for such a GPT-powered multi-robot testbed environment, based on a Unity Virtual Reality (VR) setting...

OBJECTIVE: FDG PET imaging plays a crucial role in the evaluation of demented patients by assessing regional cerebral glucose metabolism. In recent years, both radiomics and deep learning techniques have emerged as powerful tools for extracting valuable information from medical images. This article aims to provide a comparative analysis of radiomics features, 3D-deep learning convolutional neural network (CNN) and the fusion of them, in the evaluation of 18F-FDG PET whole brain images in patients with dementia and normal controls...

BACKGROUND: Given the urgency of transitioning towards sustainable nutrition, dietary shifts that provide co-benefits to human health and the environment are imperative. There is currently no database of the environmental impacts of foods that reflects Canada's unique geographical and agri-climatic context and regional inputs and emissions. To determine sustainable diets, harmonising nutritional considerations with environmental impacts is also essential for an equitable comparison of foods...

BACKGROUND: Major depressive disorder (MDD) is prevalent, yet sub-optimally treated among persons with multiple sclerosis (MS). We propose that exercise training may be a promising approach for treating depression in persons with MS who have MDD. Our primary hypothesis predicts a reduction in depression severity immediately after an exercise training intervention compared with minimal change in an attention control condition, and the reduction will be maintained during a follow-up period...

Spiking neural networks (SNNs) are receiving increased attention because they mimic synaptic connections in biological systems and produce spike trains, which can be approximated by binary values for computational efficiency. Recently, the addition of convolutional layers to combine the feature extraction power of convolutional networks with the computational efficiency of SNNs has been introduced. This paper studies the feasibility of using a convolutional spiking neural network (CSNN) to detect anticipatory slow cortical potentials (SCPs) related to braking intention in human participants using an electroencephalogram (EEG)...

Purpose: Existing research suggests that Acceptance and Commitment Therapy (ACT) training is beneficial for healthcare workers' professional practice and personal well-being. This review aimed to further understanding of healthcare workers' experiences of ACT training by synthesizing existing qualitative studies. Methods: A systematic literature review identified papers published up until April 2022 using the Embase, Ovid MEDLINE, and PsycINFO databases as well as relevant studies within the gray literature...

Classical training theory postulates that performance fatigability following a training session should be proportional to the total work done (TWD); however, this notion has been questioned. This study investigated indices of performance and perceived fatigability after primary sessions of high-intensity interval (HIIT) and constant-work rate (CWR) cycling, each followed by a cycling time-to-task-failure (TTF) bout. On separate days, 16 participants completed an incremental cycling test, and, in a randomized order, i) a TTF trial at 80% of peak power output (PPO), ii) a HIIT session and iii) a CWR session, both of which were immediately followed by a TTF trial at 80% PPO...

Humanoid robots that can autonomously operate in diverse environments have the potential to help address labor shortages in factories, assist elderly at home, and colonize new planets. Although classical controllers for humanoid robots have shown impressive results in a number of settings, they are challenging to generalize and adapt to new environments. Here, we present a fully learning-based approach for real-world humanoid locomotion. Our controller is a causal transformer that takes the history of proprioceptive observations and actions as input and predicts the next action...

BACKGROUND: Chronic pain affects approximately 8 million Canadians (~20%), impacting their physical and mental health while burdening the health care system with costs of upwards of US $60 billion a year. Indeed, patients are often trialed on numerous medications over several years without reductions to their symptoms. Therefore, there is an urgent need to identify new therapies for chronic pain to improve patients' quality of life, increase the availability of treatment options, and reduce the burden on the health care system...

The long-term monitoring stability of electronic current transformers is crucial for accurately obtaining the current signal of the power grid. However, it is difficult to accurately distinguish between the fluctuation of non-stationary random signals on the primary side of the power grid and the gradual error of the transformers themselves. A current transformer error prediction model, CNN-MHA-BiLSTM, based on the golden jackal optimization (GJO) algorithm, which is used to obtain the optimal parameter values, bidirectional long short-term memory (BiLSTM) network, convolutional neural networks (CNNs), and multi-head attention (MHA), is proposed to address the difficulty of measuring error evaluation...

OBJECTIVE: To recognize elements that facilitated or hindered the PlanificaSUS implementation stages. METHODS: A multiple case study was carried out in four pre-selected health regions in Brazil-Belo Jardim (PE), Fronteira Oeste (RS), Sul-Mato-Grossense (MT) and Valença (BA) using systemic arterial hypertension and maternal and child care as tracer conditions. Participant observation (in regional interagency commissions) and in-depth interviews with key informants from state and municipal management and primary health care and specialized outpatient care service professionals within the project were carried out in these four regions...

With the rapid development of the national economy, power security is very important for the security of the country and people's happiness. Electricity is an important energy source for a country. Even if the power system malfunctions for a short period of time, it would cause incalculable losses to social production and people's lives. Among them, one of the most important reasons for power system faults is the occurrence of power line faults, so diagnosing faulty lines has great research significance. On the basis of analyzing the structure and working principle of the deep learning model convolutional neural network (CNN), this article used the CNN model to diagnose faults in power lines and analyzed the simulation results...

This paper presents a study investigating the performance of functionally graded material (FGM) annular fins in heat transfer applications. An annular fin is a circular or annular structure used to improve heat transfer in various systems such as heat exchangers, electronic cooling systems, and power generation equipment. The main objective of this study is to analyze the efficiency of the ring fin in terms of heat transfer and temperature distribution. The fin surfaces are exposed to convection and radiation to dissipate heat...

INTRODUCTION: Chagas disease is a severe parasitic illness that is prevalent in Latin America and often goes unaddressed. Early detection and treatment are critical in preventing the progression of the illness and its associated life-threatening complications. In recent years, machine learning algorithms have emerged as powerful tools for disease prediction and diagnosis. METHODS: In this study, we developed machine learning algorithms to predict the risk of Chagas disease based on five general factors: age, gender, history of living in a mud or wooden house, history of being bitten by a triatomine bug, and family history of Chagas disease...

Although deep-learning methods can achieve human-level performance in boundary detection, their improvements mostly rely on larger models and specific datasets, leading to significant computational power consumption. As a fundamental low-level vision task, a single model with fewer parameters to achieve cross-dataset boundary detection merits further investigation. In this study, a lightweight universal boundary detection method was developed based on convolution and a transformer. The network is called a "transformer with difference convolutional network" (TDCN), which implies the introduction of a difference convolutional network rather than a pure transformer...

Recent advancements in pre-trained language-image models have ushered in a new era of visual comprehension. Leveraging the power of these models, this paper tackles two issues within the realm of visual analytics: (1) the efficient exploration of large-scale image datasets and identification of data biases within them; (2) the evaluation of image captions and steering of their generation process. On the one hand, by visually examining the captions generated from language-image models for an image dataset, we gain deeper insights into the visual contents, unearthing data biases that may be entrenched within the dataset...