Deep Reinforcement Learning Based Optimal Energy Management of Multi-energy Microgrids with Uncertainties

Multi-energy microgrid (MEMG) offers an effective approach to deal with energy demand diversification and new energy consumption on the consumer side. In MEMG, it is critical to deploy an energy management system (EMS) for efficient utilization of energy and reliable operation of the system. To help EMS formulate optimal dispatching schemes, a deep reinforcement learning (DRL)-based MEMG energy management scheme with renewable energy source (RES) uncertainty is proposed in this paper. To accurately describe the operating state of the MEMG, the off-design performance model of energy conversion devices is considered in scheduling. The nonlinear optimal dispatching model is expressed as a Markov decision process (MDP) and is then addressed by the twin delayed deep deterministic policy gradient (TD3) algorithm. In addition, to accurately describe the uncertainty of RES, the conditional-least squares generative adversarial networks (C-LSGANs) method based on RES forecast power is proposed to construct the scenarios set of RES power generation. The generated data of RES is used for scheduling to obtain caps and floors for the purchase of electricity and natural gas. Based on this, the superior energy supply sector can formulate solutions in advance to tackle the uncertainty of RES. Finally, the simulation analysis demonstrates the validity and superiority of the method.Comment: Accepted by CSEE Journal of Power and Energy System

Development and Validation of a Questionnaire for Analyzing Real-Life Falls in Long-Term Care Captured On Video

Background Falls are the number one cause of injuries in older adults, and are particularly common in long-term care (LTC). Lack of objective evidence on the mechanisms of falls in this setting is a major barrier to prevention. Video capture of real-life falls can help to address this barrier, if valid tools are available for data analysis. To address this need, we developed a 24-item fall video analysis questionnaire (FVAQ) to probe key biomechanical, behavioural, situational, and environmental aspects of the initiation, descent, and impact stages of falls. We then tested the reliability of this tool using video footage of falls collected in LTC. Methods Over three years, we video-captured 221 falls experienced by 130 individuals in common areas (e.g., dining rooms, hallways, and lounges) of two LTC facilities. The FVAQ was developed through literature review and an iterative process to ensure our responses captured the most common behaviours observed in preliminary review of fall videos. Inter-rater reliability was assessed by comparing responses from two teams, each having three members, who reviewed 15 randomly-selected videos. Intra-rater reliability was measured by comparing responses from one team at baseline and 12 months later. Results In 17 of the 24 questions, the percentage of inter- and intra-rater agreement was over 80% and the Cohen\u27s Kappa was greater than 0.60, reflecting good reliability. These included questions on the cause of imbalance, activity at the time of the fall, fall direction, stepping responses, and impact to specific body sites. Poorer agreement was observed for footwear, contribution of clutter, reach-to-grasp responses, and perceived site of injury risk. Conclusions Our results provide strong evidence of the reliability of the FVAQ for classifying biomechanical, behavioural, situational, and environmental aspects of falls captured on video in common areas in LTC. Application of this tool should reveal new and important strategies for the prevention and treatment of falls and fall-related injuries in this setting

Short-term power load forecasting method based on CNN-SAEDN-Res

In deep learning, the load data with non-temporal factors are difficult to process by sequence models. This problem results in insufficient precision of the prediction. Therefore, a short-term load forecasting method based on convolutional neural network (CNN), self-attention encoder-decoder network (SAEDN) and residual-refinement (Res) is proposed. In this method, feature extraction module is composed of a two-dimensional convolutional neural network, which is used to mine the local correlation between data and obtain high-dimensional data features. The initial load fore-casting module consists of a self-attention encoder-decoder network and a feedforward neural network (FFN). The module utilizes self-attention mechanisms to encode high-dimensional features. This operation can obtain the global correlation between data. Therefore, the model is able to retain important information based on the coupling relationship between the data in data mixed with non-time series factors. Then, self-attention decoding is per-formed and the feedforward neural network is used to regression initial load. This paper introduces the residual mechanism to build the load optimization module. The module generates residual load values to optimize the initial load. The simulation results show that the proposed load forecasting method has advantages in terms of prediction accuracy and prediction stability.Comment: in Chinese language, Accepted by Electric Power Automation Equipmen

How Practical Phase-shift Errors Affect Beamforming of Reconfigurable Intelligent Surface?

Reconfigurable intelligent surface (RIS) is a new technique that is able to manipulate the wireless environment smartly and has been exploited for assisting the wireless communications, especially at high frequency band. However, it suffers from hardware impairments (HWIs) in practical designs, which inevitably degrades its performance and thus limits its full potential. To address this practical issue, we first propose a new RIS reflection model involving phase-shift errors, which is then verified by the measurement results from field trials. With this beamforming model, various phase-shift errors caused by different HWIs can be analyzed. The phase-shift errors are classified into three categories: (1) globally independent and identically distributed errors, (2) grouped independent and identically distributed errors and (3) grouped fixed errors. The impact of typical HWIs, including frequency mismatch, PIN diode failures and panel deformation, on RIS beamforming ability are studied with the theoretical model and are compared with numerical results. The impact of frequency mismatch are discussed separately for narrow-band and wide-band beamforming. Finally, useful insights and guidelines on the RIS design and its deployment are highlighted for practical wireless systems

Association Between Family Atmosphere and Internet Addiction Among Adolescents: The Mediating Role of Self-Esteem and Negative Emotions

Objectives: Family atmosphere is a significant predictor of internet addiction in adolescents. Based on the vulnerability model of emotion and the compensatory internet use theory, this study examined whether self-esteem and negative emotions (anxiety, depression) mediated the relationship between family atmosphere and internet addiction in parallel and sequence.Methods: A total of 3,065 Chinese middle school and high school students (1,524 females, mean age = 13.63 years, SD = 4.24) participated. They provided self-reported data on demographic variables, family atmosphere, self-esteem, anxiety, depression, and internet addiction through the Scale of Systemic Family Dynamic, Self-Esteem Scale, Self-Rating Anxiety Scale, Self-Rating Depression Scale, and Internet Addiction Test, respectively. We employed Hayes PROCESS macro for the SPSS program to scrutinize the suggested mediation model.Results: It revealed that self-esteem, anxiety, and depression mediated the relationship between family atmosphere and internet addiction in parallel and sequence. The pathway of family atmosphere-self-esteem-internet addiction played a more important role than others.Conclusion: The present study confirmed the mediating role of self-esteem and negative emotions between family atmosphere and internet addiction, providing intervention studies with important targeting factors

Melatonin Prevents Osteoarthritis-Induced Cartilage Degradation via Targeting MicroRNA-140

Abstract Osteoarthritis (OA) is characterized by the progressive destruction of articular cartilage, which is involved in the imbalance between extracellular matrix (ECM) synthesis and degradation. MicroRNA-140-5p (miR-140) is specifically expressed in cartilage and plays an important role in OA-induced matrix degradation. The aim of this study was to investigate (1) whether intra-articular injection of melatonin could ameliorate surgically induced OA in mice and (2) whether melatonin could regulate matrix-degrading enzymes at the posttranscriptional level by targeting miR-140. In an in vitro OA environment induced by interleukin-1 beta (IL-1β), melatonin treatment improved cell proliferation of human chondrocytes, promoted the expression of cartilage ECM proteins (e.g., type II collagen and aggrecan), and inhibited the levels of IL-1β-induced proteinases, such as matrix metalloproteinase 9 (MMP9), MMP13, ADAMTS4 (a disintegrin and metalloproteinase with thrombospondin motifs 4), and ADAMTS5. Both the microarray and polymerase chain reaction (PCR) experiments revealed that miR-140 was a melatonin-responsive microRNA and melatonin upregulated miR-140 expression, which was suppressed by IL-1β stimulation. In vivo experiments demonstrated that intra-articular injection of melatonin prevented disruptions of cartilage matrix homeostasis and successfully alleviated the progression of surgery-induced OA in mice. Transfection of miR-140 antagomir completely counteracted the antiarthritic effects of melatonin by promoting matrix destruction. Our findings demonstrate that melatonin protects the articular cartilage from OA-induced degradation by targeting miR-140, and intra-articular administration of melatonin may benefit patients suffering from OA

Scapular kinematics and muscle activity during Yi Jin Bang exercises

Introduction: Scapular dyskinesis is commonly associated with subacromial pain syndrome (SAPS). Addressing scapular dyskinesis is widely accepted as an important component of shoulder rehabilitation. Our previous randomized controlled trial showed that Yi Jin Bang (YJB) exercises could effectively manage SAPS, but scapular motions and muscle activity during YJB exercises remain unknown. This study examined scapular kinematics synchronously with scapular muscle activation during YJB exercises.Methods: Thirty healthy participants with no shoulder complaints were enrolled in this study. Three-dimensional (3D) scapular kinematics and electromyography (EMG) activation of the upper trapezius, middle trapezius, lower trapezius, serratus anterior, anterior deltoid, middle deltoid, and posterior deltoid were synchronously measured during nine YJB movements.Results: During all YJB movements, the scapula was upwardly rotated and anteriorly tilted, with more upward rotation and a similar or less anterior tilt than the mean resting scapular angle. Column rotation, arm crossover, shoulder support circle, and armpit support high lift generated more internal rotation than the mean resting scapular angle, with the angles of internal rotation significantly greater than the other five movements (p < 0.001). Regarding EMG activity, all YJB movements elicited low activity (1.42%–19.19% maximal voluntary isometric contraction [MVIC]) from the upper trapezius and posterior deltoid and low to moderate activity (0.52%–29.50% MVIC) from the middle trapezius, lower trapezius, serratus anterior, anterior deltoid, and middle deltoid.Conclusion: YJB exercises could be useful in the middle to later phases of shoulder rehabilitation. For patients with insufficient external rotation, some YJB movements should be prescribed with caution

Pseudomonas syringae Type III Effector HopBB1 Promotes Host Transcriptional Repressor Degradation to Regulate Phytohormone Responses and Virulence

Independently evolved pathogen effectors from three branches of life (ascomycete, eubacteria, and oomycete) converge onto the Arabidopsis TCP14 transcription factor to manipulate host defense. However, the mechanistic basis for defense control via TCP14 regulation is unknown. We demonstrate that TCP14 regulates the plant immune system by transcriptionally repressing a subset of the jasmonic acid (JA) hormone signaling outputs. A previously unstudied Pseudomonas syringae (Psy) type III effector, HopBB1, interacts with TCP14 and targets it to the SCFCOI1 degradation complex by connecting it to the JA signaling repressor JAZ3. Consequently, HopBB1 de-represses the TCP14-regulated subset of JA response genes and promotes pathogen virulence. Thus, HopBB1 fine-tunes host phytohormone crosstalk by precisely manipulating part of the JA regulon to avoid pleiotropic host responses while promoting pathogen proliferation