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ObjectiveThis study aims to investigate the relationship between muscle strength, working memory, and activities of daily living (ADL) in older adults. Additionally, it seeks to clarify the pathways and effects of working memory in mediating the relationship between muscle strength and ADL.MethodsUsing a cross-sectional study design, we recruited 245 older adults individuals from nursing homes. We collected data on grip strength, the 30-s sit-to-stand test, the N-back task, and ADL. The data were analyzed using independent sample t-tests, χ2 tests, correlation analysis, and structural equation modeling.ResultsGrip strength significantly influenced ADL (effect size = −0.175, 95% CI: −0.226 to −0.124). Grip strength also had a significant direct effect on ADL (effect size = −0.114, 95% CI: −0.161 to −0.067). The 1-back task correct rate significantly mediated the relationship between grip strength and ADL (effect size = 0.054, 95% CI: −0.084 to −0.029). The 30-s sit-to-stand test significantly impacted ADL (effect size = −0.280, 95% CI: −0.358 to −0.203). It also had a significant direct effect on ADL (effect size = −0.095, 95% CI: −0.183 to −0.007). The 1-back task correct rate significantly mediated the relationship between the 30-s sit-to-stand test and ADL (effect size = −0.166, 95% CI: −0.236 to −0.106).ConclusionThere exists a strong correlation between muscle strength, working memory, and ADL. Increased muscle strength leads to better ADL performance and improved working memory tasks. Low cognitive load working memory tasks can mediate the relationship between muscle strength and ADL. Regular physical exercise can enhance muscle strength, slow down the decline of working memory, thereby maintaining or improving ADL in older adults.

An actor-critic based distributed deep reinforcement learning approach is proposed to optimize the reactive power of the distribution network under the access of distributed photovoltaics, wind turbines and other power sources. This approach can optimize and dispatch the resources of the distribution network in real time under the change of power output such as distributed photovoltaics and wind turbines, so as to optimize the reactive power of the distribution network. First, this paper builds an optimization model with the objective function of minimizing the reactive power of the distribution network, and considers the operating constraints. Then, the agents of the proposed approach are trained, and the well-trained agents can schedule and optimize the resources of the distribution network in real time. Finally, based on the actual source-load output data in a certain place, reactive power optimization simulation experiments are carried out on the IEEE 33-bus, IEEE 123-bus simulation systems and the actual power distribution system in a region of China. Simulation results show that the proposed distributed deep reinforcement learning approach (DDRLA) can optimize distribution network reactive power online in real time.

Abstract Objectives To investigate the method of resting EEG assessment of depressive symptoms in college students and to clarify the relationship between physical activity level and depressive symptoms in college students. Methods Using a cross-sectional study design, 140 current full-time college students were recruited to complete the Self-Rating Depression Scale and the International Physical Activity Questionnaire, and 10-min resting EEGs were obtained. Results 1) The power values of δ and α2 in the central (C3, C4) and parietal (P3, P4) regions of depressed college students were significantly higher than those of normal college students. And the degree of lateralization of δ, θ, α1, and α2 in the prefrontal regions (F3, F4) of depressed college students was significantly higher than that of normal college students (all P

In the process of decarbonization, the configuration of renewable energy and energy storage plays a crucial role. In current research, there is often a singular focus on the isolated optimization of either renewable energy configurations or energy storage configurations, resulting in limitations within the optimized outcomes. Therefore, we propose a collaborative configuration approach for renewable energy and energy storage under fixed investment, considering the impact of uncertainty on optimization results. By employing the W/S (wind-to-solar ratio) and E/P (energy-to-power ratio) and constructing a model with an hourly granularity, we can obtain the configurations of renewable energy and energy storage at crucial time points. Using the UK as a case study, we calculate the configurations for renewable energy and energy storage from 2020 to 2050, offering effective recommendations for the decarbonization efforts in the UK.

In recent years, large-scale and high-density operations have caused a dramatic increase in the energy consumption of metro systems. For overcrowded metro systems, the original energy-optimized timetable is no longer optimal after unexpected dwell disturbances occur. In this paper, we propose an actor–critic deep reinforcement learning (ACDRL) approach for solving the energy-aimed train schedule rescheduling (ETTR) problem in a real-time and energy-efficient manner. The proposed ACDRL approach can reduce metro systems’ net traction energy consumption by rearranging trains in millisecond time after the occurrence of unpredictable dwell disturbances. The simulation experimental results show that the average response time of ACDRL under unpredictable disturbances is only 0.0009 s and 0.0016 s in two-train and five-train metro systems, which can save energy by 4.73% and 6.95% on average, respectively. This means that a lot of energy can be saved.

Abstract Matrix metalloproteinase-10 (MMP-10) is a zinc-dependent endopeptidase involved in regulating a wide range of biologic processes, such as apoptosis, cell proliferation, and tissue remodeling. However, the role of MMP-10 in the pathogenesis of acute kidney injury (AKI) is unknown. In this study, we show that MMP-10 was upregulated in the kidneys and predominantly localized in the tubular epithelium in various models of AKI induced by ischemia/reperfusion (IR) or cisplatin. Overexpression of exogenous MMP-10 ameliorated AKI, manifested by decreased serum creatinine, blood urea nitrogen, tubular injury and apoptosis, and increased tubular regeneration. Conversely, knockdown of endogenous MMP-10 expression aggravated kidney injury. Interestingly, alleviation of AKI by MMP-10 in vivo was associated with the activation of epidermal growth factor receptor (EGFR) and its downstream AKT and extracellular signal-regulated kinase-1 and 2 (ERK1/2) signaling. Blockade of EGFR signaling by erlotinib abolished the MMP-10-mediated renal protection after AKI. In vitro, MMP-10 potentiated EGFR activation and protected kidney tubular cells against apoptosis induced by hypoxia/reoxygenation or cisplatin. MMP-10 was colocalized with heparin-binding EGF-like growth factor (HB-EGF) in vivo and activated it by a process of proteolytical cleavage in vitro. These studies identify HB-EGF as a previously unrecognized substrate of MMP-10. Our findings also underscore that MMP-10 can protect against AKI by augmenting EGFR signaling, leading to promotion of tubular cell survival and proliferation after injury.

Considering that uncertain dwell disturbances often occur at metro stations, researchers have proposed many methods for solving the train timetable rescheduling (TTR) problem. This paper proposes a Modified Genetic Algorithm-Gate Recurrent Unit (MGA-GRU) method, which is a real-time TTR method based on deep learning. The proposed method takes the Gate Recurrent Unit (GRU) network as the decision network and uses the results produced by the Modified Genetic Algorithm (MGA) as the training set of the decision network. A well-trained decision network can provide effective solutions in real time after random disturbances occur, in order to optimize the net traction energy consumption of trains in metro systems. Based on the Shanghai Metro Line One (SML1) pilot network, this paper establishes a comprehensive model of the metro system as a training and testing environment to verify the energy-saving effect and real-time performance of the proposed method in solving the TTR problem. The experimental results show that in the two-train metro system, the three-train metro system, and the five-train metro system, the MGA-GRU method can save an average of energy by 4.45%, 6.16%, and 7.19%, while the average decision time is only 0.15 s, 0.27 s, and 0.33 s, respectively.

The article discusses chronic kidney disease (CKD) has become a major public health problem worldwide. Topics include endothelial cell injury leading to microvascular rarefaction has a characteristic feature of chronic kidney disease; and the prevalence of CKD has high, ranging from 10 to 13% in the adult population.