Search

Background: The application of motor learning (ML) principles and research in physical therapy can optimize patient outcomes. However, the translation of the accumulated knowledge in ML to clinical practice is limited. Knowledge translation interventions, which are designed to promote changes in clinical behaviors, have the potential to address this implementation gap. We developed, implemented, and evaluated a knowledge translation intervention for ML implementation that focuses on building clinical capacity among physical therapists for the systematic application of ML knowledge in clinical practice., Methods: A total of 111 physical therapists underwent the intervention, which consisted of the following: (1) an interactive didactic 20-hour course; (2) an illustrated conceptual model of ML elements; and (3) a structured clinical-thinking form. Participants completed the Physical Therapists' Perceptions of Motor Learning (PTP-ML) questionnaire pre and post intervention. The PTP-ML was used to assess ML-related self-efficacy and implementation. Participants also provided post-intervention feedback. A sub-sample (n = 25) provided follow-up feedback more than a year after the completion of the intervention. Pre-post and post-follow-up changes in the PTP-ML scores were calculated. The information gathered from the open-ended items of the post-intervention feedback was analyzed to identify emerging themes., Results: Comparing pre- and post-intervention scores, significant changes were found in the total questionnaire scores, self-efficacy subscale scores, reported implementation subscale scores (P < .0001), and general perceptions and work environment subscale score (P < .005). The mean changes in the total questionnaire and self-efficacy scores also significantly exceeded the Reliable Change Index. In the follow-up sample, these changes were maintained. Participants felt that the intervention helped them organize their knowledge in a structured manner and consciously link their practice elements to concepts in ML. Discussion of clinical cases was reported to be the most valuable educational method, and the illustrated conceptual model of ML elements was the least valued. Respondents also suggested support activities to maintain and enhance the learning experience, including on-site mentorship and hands-on experience., Conclusions: Findings support the positive effect of an educational tool, most prominently on physical therapists' ML self-efficacy. The addition of practical modeling or ongoing educational support may enhance intervention effects., (© 2023. The Author(s).)

Decreasing costs of distributed generation and storage, alongside increasing network charges, provide consumers with a growing incentive to defect from the main grid. On a large scale, this may lead to price inflation, hindrance of the energy transition, and even a "death spiral" - a domino effect of disconnections. Here, we develop a game-theoretic framework that demonstrates how conflicting interests among consumers - an aspect that previous studies overlooked - may lead to complex dynamics of grid defection. Our results reveal that although individual consumers benefit from staying connected at the distribution level, the defection of small energy communities from the grid may lead to the defection of larger communities. We also demonstrate that centralized design approaches may lead to inefficient outcomes, e.g., redundant grid expansions, because of the inherent inability to predict potential defections. However, we indicate how, by properly incorporating defection considerations into the grid's design, social welfare can be improved., Competing Interests: The authors declare no competing interests., (© 2023 The Author(s).)

In recent years, explainable artificial intelligence (XAI) techniques have been developed to improve the explainability, trust and transparency of machine learning models. This work presents a method that explains the outputs of an air-handling unit (AHU) faults classifier using a modified XAI technique, such that non-AI expert end-users who require justification for the diagnosis output can easily understand the reasoning behind the decision. The method operates as follows: First, an XGBoost algorithm is used to detect and classify potential faults in the heating and cooling coil valves, sensors, and the heat recovery of an air-handling unit. Second, an XAI-based SHAP technique is used to provide explanations, with a focus on the end-users, who are HVAC engineers. Then, relevant features are chosen based on user-selected feature sets and features with high attribution scores. Finally, a sliding window system is used to visualize the short history of these relevant features and provide explanations for the diagnosed faults in the observed time period. This study aimed to provide information not only about what occurs at the time of fault appearance, but also about how the fault occurred. Finally, the resulting explanations are evaluated by seven HVAC expert engineers. The proposed approach is validated using real data collected from a shopping mall.

In this paper we explore how the COVID-19 pandemic, also known as Coronavirus pandemic, affected the operation of small electric grids, and what can this event teach us on the readiness of such grids in the face of future global health crises. We focus on three major effects: changing patterns of generation and consumption, frequency stability, and the joint impact of low consumption and high share of renewable energy sources. Specifically, we analyze changes in consumption in the Israeli, Estonian, and Finnish grids, and attempt to identify patterns of consumption changes that may be explained by the pandemic. We also analyze changes in voltage and frequency, and show that the low consumption caused significant deviations from the nominal values of both parameters. One main conclusion is that the reduced energy consumption during the pandemic is critical, and has a major effect on the operation of small electric grids. Another conclusion is that since the pandemic pushed the relative share of renewable energy to record highs, this event may help us to better understand the influence of a high share of renewables on small grids, thus offering a glance into a renewable-rich future., (This work is licensed under a Creative Commons Attribution 4.0 License. For more information, see https://creativecommons.org/licenses/by/4.0/.)

This work focuses on the design of transmitting coils in weakly coupled magnetic induction communication systems. We propose several optimization methods that reduce the active, reactive and apparent power consumption of the coil. These problems are formulated as minimization problems, in which the power consumed by the transmitting coil is minimized, under the constraint of providing a required magnetic field at the receiver location. We develop efficient numeric and analytic methods to solve the resulting problems, which are of high dimension, and in certain cases non-convex. For the objective of minimal reactive power an analytic solution for the optimal current distribution in flat disc transmitting coils is provided. This problem is extended to general three-dimensional coils, for which we develop an expression for the optimal current distribution. Considering the objective of minimal apparent power, a method is developed to reduce the computational complexity of the problem by transforming it to an equivalent problem of lower dimension, allowing a quick and accurate numeric solution. These results are verified experimentally by testing a number of coil geometries. The results obtained allow reduced power consumption and increased performances in magnetic induction communication systems. Specifically, for wideband systems, an optimal design of the transmitter coil reduces the peak instantaneous power provided by the transmitter circuitry, and thus reduces its size, complexity and cost., Competing Interests: The authors have declared that no competing interests exist.

Objective: To determine the prevalence of placental complications in patients conceived through donor versus autologous oocytes., Study Design: A retrospective cohort study including 2 groups of patients who conceived through in vitro fertilization using: (1) donor oocyte (n = 139) and (2) autologous oocyte (n = 126). Only singleton gestations were included. The rate of placental complications including preeclampsia, gestational hypertension, and intrauterine growth restriction was compared between these 2 groups., Results: The women who conceived using donor oocytes were older compared with women who conceived using autologous oocytes (median maternal age 45 vs 41, P < .01). The rate of hypertensive diseases of pregnancy including gestational hypertension and preeclampsia was significantly higher in ovum donor recipients compared with women conceived with autologous oocytes (25% vs 10%, P < .01). Similarly, the rate of intrauterine growth restriction was also higher among patients conceived through oocyte donation although it did not reach statistical significance (9.3% vs 4%, P = .08). When maternal age was restricted to ≤45 years, the rate of hypertensive diseases of pregnancy remained significantly higher among ovum donor compared with autologous oocyte recipients (22% vs 10%, P = .02). Adjustment for maternal age, gravidity, parity, and chronic hypertension revealed that oocyte donation was independently associated with higher rate of hypertensive diseases of pregnancy (P = .01)., Conclusion: Patients conceived through oocyte donation have an increased risk for placental complications of pregnancy. These findings support the 'immunologic theory' suggesting that immunologic intolerance between the mother and the fetus may play an important role in the pathogenesis of preeclampsia., (Copyright © 2014 Elsevier Inc. All rights reserved.)