Source-Storage-Load Coordinated Master-Slave Control Strategy for Islanded Microgrid Considering Load Disturbance and Communication Interruption.

When there is a sudden load disturbance in an islanded microgrid, the peer-to-peer control model requires the energy resource to maintain a margin of generation, resulting in a relatively limited regulation range, that is, voltage/frequency sometimes requires additional control to maintain stability. A "source-storage-load" coordinated master-slave control strategy is proposed in this study to address the aforementioned issues. The system voltage and frequency will be stable as long as the output frequency and voltage of the master resource are stable. Furthermore, it can fully utilize the power supply capacity of resources to support the supply-demand balance. The following tasks are included in the proposed strategy: 1) to improve the operational security in the face of load disruption, a source-storage-load coordinated control method based on the "ramping speed" ratio is proposed, which can quickly restore the balance of supply and demand; 2) to improve the communication reliability in the face of interruption, a channel planning method is proposed, which can address the communication interruption problem by constructing an internal network among source-storage-load; and 3) to improve the mode switching stability of resources subjected to external disturbance, the external disturbance suppression and stability analysis involved in the regulation process are completed using sliding-mode control and small signal model methods. Related case studies are carried out to verify the effectiveness of the proposed strategies.

Output Formation Containment for Multiagent Systems Under Multipoint Multipattern FDI Attacks: A Resilient Impulsive Compensation Control Approach.

The increasing number of devices and frequent interactions of agents from networked multiagent systems (MASs) exacerbate the risks of potential cyber attacks, especially the different point attacks and multiple pattern attacks. This article considers the output formation-containment problem for MASs under multipoint multipattern false data injection (FDI) attacks. The multipoint describes the attacks simultaneously occurring on the sensors, actuators, and communication channels; the multipattern captures that sensor and actuator attack signals are both continuous deterministic variables, and the communication channel attack signals are intermittent random variables, obeying the Bernoulli distribution. For such compromised MASs, a novel hybrid protocol is proposed, which integrates a state observer, an attack estimator, an impulsive interactor and a compensation controller. Thereinto, the state observer and the attack estimator are constructed to recover the unmeasured system states and the unknown FDI attack signals, respectively; the impulsive interactor is designed to guarantee that the neighbor's signals are transmitted only at impulsive instants, and meanwhile the channel attacks are randomly launched; using the recovered signals, the compensation controller is devised to alleviate the effect of attacks. A sufficient condition is identified, under which the output formation containment is achieved with cooperative uniform ultimate boundedness (UUB). Finally, simulation results are carried out to validate the effectiveness and advantages of the proposed approach.

Predefined Accuracy Adaptive Tracking Control for Nonlinear Multiagent Systems With Unmodeled Dynamics.

This article focuses on an adaptive dynamic surface tracking control issue of nonlinear multiagent systems (MASs) with unmodeled dynamics and input quantization under predefined accuracy. Radial basis function neural networks (RBFNNs) are employed to estimate unknown nonlinear items. A dynamic signal is established to handle the trouble introduced by the unmodeled dynamics. Moreover, the predefined precision control is realized with the aid of two key functions. Unlike the existing works on nonlinear MASs with unmodeled dynamics, to avoid the issue of "explosion of complexity", the dynamic surface control (DSC) method is applied with the nonlinear filter. By using the designed controller, the consensus errors can gather to a precision assigned a priori. Finally, the simulation results are given to demonstrate the effectiveness of the proposed strategy.

Security Event-Trigger-Based Distributed Energy Management Of Cyber-Physical Isolated Power System With Considering Nonsmooth Effects.

Due to cyber-physical fusion and nonsmooth characteristics of energy management, this article proposes a security event-trigger-based distributed approach to address these issues with developed smoothing technique. To tackle with nonconvex and nondifferentiable issue, a randomized gradient-free-based successive convex approximation is developed to smooth economic objective function. Due to resilience ability against security issue, a security event-triggered mechanism-based distributed energy management is proposed to optimize social welfare, which coordinately controls both power generators and load demand. The security event-triggered mechanism is designed to reduce power system security risks, and relieve communication burden caused by smoothing calculation, the convergence of proposed distributed algorithm is also properly proved. According to those obtained results on both IEEE 9-bus and IEEE 39-bus systems, it reveals that the proposed approach can achieve good convergence performance and have less security risks than other alternatives, which also proves that the proposed approach can be a viable and promising way for tackling with energy management issue of cyber-physical isolated power system.

Design and Application of Virtual Reality Simulation for Rehabilitation Nursing of Patients With Cervical Spondylosis Under the Background of New Medical Science.

Objectives: Our objective was to develop and implement a system to solve the problems that students have as a result of few opportunities for consultation and hands-on exercise in nursing practice teaching, including an inability to participate in the whole process of patients' treatment and nursing, and lack of humanistic care for patients. Methods: The application of the system was conducted among undergraduate nursing students. In 2020, we cooperated with companies and jointly developed a virtual reality (VR) simulation of rehabilitation nursing for patients with cervical spondylosis (CS) and applied it to undergraduate nursing students. Results:The cumulative online training time of 79 students was (30.52±16.28) minutes/person and the average number of learning times was (3.12±1.78) times/person. Overall, 97.5% of the students rated the system as excellent. Conclusions: In thi s paper, we introduce the design, system construction, teaching design, and preliminary application effects of the system. In addition, we discuss the advantages, characteristics, limitations and countermeasures of the system, to provide reference for the construction of VR simulation experimental teaching courses for undergraduate nursing students under the background of new medical science.

Effects of a virtual simulation-based interprofessional education activity for rehabilitation nursing using shared resources: A quasi-experimental study.

BACKGROUND: Interprofessional education (IPE) is crucial for effective clinical practice but remains challenging to be implemented. The IPE activity using virtual simulation (VS) may potentially solve the time and space challenges of in-person interprofessional simulations. Using shared VS resources may increase the popularity of virtual teaching in conditions of limited resources. OBJECTIVES: Using shared resources, this study aimed to design and implement a VS-based IPE activity for undergraduate healthcare students, exploring the effects. DESIGN: A quasi-experimental design was used, with assessments conducted before and after the activity. SETTINGS: One university and its affiliated hospitals in south China. PARTICIPANTS: Forty-two undergraduate students majoring in nursing, clinical medicine, and rehabilitation therapy participated in this study. METHODS: A test composed of ten questions was used to evaluate knowledge of rehabilitation. The Chinese version of Critical Thinking Disposition Inventory (CTDI-CV) and the Chinese version of Assessment of Interprofessional Team Collaboration in Student Learning Scale (AITCS-II (Student)-CV) were used to evaluate critical thinking and interprofessional collaboration. Participants' opinions about the activity were assessed, considering satisfaction, perceived effectiveness, the ease of shared VS platform use, and suggestions about the activity. RESULTS: Significant improvements were shown in pre- and post-test total scores on knowledge of rehabilitation, mean scores for overall critical thinking disposition, and mean item scores on overall interprofessional team collaboration. CONCLUSIONS: The study provides a reference for designing and implementing VS-based IPE but the effects of this innovative pedagogy on students' rehabilitation knowledge, critical thinking, and interprofessional collaboration ability still need to be further confirmed. Most of the students gave positive feedback on the activity. Technical issues should be addressed to decrease their impacts on the VS practice experience.

Resilient Optimal Defensive Strategy of TSK Fuzzy-Model-Based Microgrids' System via a Novel Reinforcement Learning Approach.

With consideration of false data injection (FDI) on the demand side, it brings a great challenge for the optimal defensive strategy with the security issue, voltage stability, power flow, and economic cost indexes. This article proposes a Takagi-Sugeuo-Kang (TSK) fuzzy system-based reinforcement learning approach for the resilient optimal defensive strategy of interconnected microgrids. Due to FDI uncertainty of the system load, TSK-based deep deterministic policy gradient (DDPG) is proposed to learn the actor network and the critic network, where multiple indexes' assessment occurs in the critic network, and the security switching control strategy is made in the actor network. Alternating direction method of multipliers (ADMM) method is improved for policy gradient with online coordination between the actor network and the critic network learning, and its convergence and optimality are proved properly. On the basis of security switching control strategy, the penalty-based boundary intersection (PBI)-based multiobjective optimization method is utilized to solve economic cost and emission issues simultaneously with considering voltage stability and rate-of-change of frequency (RoCoF) limits. According to simulation results, it reveals that the proposed resilient optimal defensive strategy can be a viable and promising alternative for tackling uncertain attack problems on interconnected microgrids.

Two-Layered Hierarchical Optimization Strategy With Distributed Potential Game for Interconnected Hybrid Energy Systems.

Due to the existence of different stakeholders, it makes competitive game characteristic in hybrid energy systems (HESs). Combined with the high-dimensional complexity and output uncertainty of distributed energy resources, the optimal operation of HESs can be a more challenging problem. Here, this article proposes a potential game-based two-layered hierarchical optimization strategy to deal with this problem. With consideration of its high-dimensional complexity, a two-layered hierarchical HES model is created, consisting of an upper-level and a lower-level model. For properly solving competitive relationships among different stakeholders in the upper-level model, a multiagent system for stakeholders is created and a potential game is employed with a distributed primal-dual perturbed algorithm, and its convergence and optimality have been both proved. Moreover, an uncertainty and robustness analysis is done with coordination between lower and upper models, which deduces a feasible robust uncertainty interval in the lower-level model. For better dealing with the lower-level model, a gradient descent-based multiobjective differential evolution (GD-MODE) algorithm is utilized to optimize the economic cost and emission issue simultaneously, producing a set of Pareto-optimal schemes. Combined with simulation results, it is proven that the proposed method can reduce computational complexity as well as properly deal with uncertainty problems for the optimal operation of HESs.

Plasma cell subtypes analyzed using artificial intelligence algorithm for predicting biochemical recurrence, immune escape potential, and immunotherapy response of prostate cancer.

Background: Plasma cells as an important component of immune microenvironment plays a crucial role in immune escape and are closely related to immune therapy response. However, its role for prostate cancer is rarely understood. In this study, we intend to investigate the value of a new plasma cell molecular subtype for predicting the biochemical recurrence, immune escape and immunotherapy response in prostate cancer. Methods: Gene expression and clinicopathological data were collected from 481 prostate cancer patients in the Cancer Genome Atlas. Then, the immune characteristics of the patients were analyzed based on plasma cell infiltration fractions. The unsupervised clustering based machine learning algorithm was used to identify the molecular subtypes of the plasma cell. And the characteristic genes of plasma cell subtypes were screened out by three types of machine learning models to establish an artificial neural network for predicting plasma cell subtypes. Finally, the prediction artificial neural network of plasma cell infiltration subtypes was validated in an independent cohort of 449 prostate cancer patients from the Gene Expression Omnibus. Results: The plasma cell fraction in prostate cancer was significantly decreased in tumors with high T stage, high Gleason score and lymph node metastasis. In addition, low plasma cell fraction patients had a higher risk of biochemical recurrence. Based on the differential genes of plasma cells, plasma cell infiltration status of PCa patients were divided into two independent molecular subtypes(subtype 1 and subtype 2). Subtype 1 tends to be immunosuppressive plasma cells infiltrating to the PCa region, with a higher likelihood of biochemical recurrence, more active immune microenvironment, and stronger immune escape potential, leading to a poor response to immunotherapy. Subsequently, 10 characteristic genes of plasma cell subtype were screened out by three machine learning algorithms. Finally, an artificial neural network was constructed by those 10 genes to predict the plasma cell subtype of new patients. This artificial neural network was validated in an independent validation set, and the similar results were gained. Conclusions: Plasma cell infiltration subtypes could provide a potent prognostic predictor for prostate cancer and be an option for potential responders to prostate cancer immunotherapy.

Evaluating the effects of simulated interprofessional teaching on the development of clinical core competence in nursing students: a mixed methods study.

BACKGROUND: While single-method studies have reported on the effectiveness of simulated interprofessional teaching, our understanding of its full effects remains incomplete. Teaching design also provides no relevant theoretical guidance, which reduces the scientific quality and rigor of research. The purpose of this work was to study the effects of the simulated interprofessional education (SIPE) teaching model based on the 3P theory on the course of "Clinical Critical Thinking Training" through a convergent mixed method, and to provide the basis for future teaching design. METHODS: A convergent mixed-method design was used, which consisted of a survey and a semi-structured interview. Data collection took place from September 2021 to July 2022. A cluster sampling method was used to select 60 full-time nursing students from a school in China, and randomly divide them into a control group of 36 and an experimental group of 24. According to the principle of voluntary participation, 6 students majoring in clinical medicine and 6 students majoring in pharmacy were recruited to join the experimental group to form an interprofessional team. The students studied "Clinical Critical Thinking Training" together, in which the control group used traditional simulation teaching and the experimental group used SIPE. The CCTDI (California Critical Thinking Disposition Inventory) and AITCS-II Student (Assessment of Interprofessional Team Collaboration in Student Learning Scale) were used for quantitative evaluation before and after the course, and descriptive statistics and Mann-Whitney U test were used to compare the critical thinking and interprofessional collaboration skills of the two groups of students. Semi-structured interviews were used for qualitative evaluation. Thematic analysis was used to understand student development on the basis of inter-professional core competencies and learning experience. RESULTS: The students' interprofessional cooperation abilities and critical thinking scores improved compared with the beginning of the course, but the scores of the experimental group were significantly higher than the control group (p < 0.05). Three themes emerged regarding simulated interprofessional teaching: clarifying team positioning, improving team efficiency, and optimizing the learning experience. CONCLUSION: SIPE can build students' critical thinking, teamwork, and interprofessional core competencies, which makes it a useful teaching design.

A Three-Stage Optimal Operation Strategy of Interconnected Microgrids With Rule-Based Deep Deterministic Policy Gradient Algorithm.

The ever-increasing requirements of demand response dynamics, competition among different stakeholders, and information privacy protection intensify the challenge of the optimal operation of microgrids. To tackle the above problems, this article proposes a three-stage optimization strategy with a deep reinforcement learning (DRL)-based distributed privacy optimization. In the upper layer of the model, the rule-based deep deterministic policy gradient (DDPG) algorithm is proposed to optimize the load migration problem with demand response, which enhances dynamic characteristics with the interaction between electricity prices and consumer behavior. Due to the competition among different stakeholders and the information privacy requirement in the middle layer of the model, a potential game-based distributed privacy optimization algorithm is improved to seek Nash equilibriums (NEs) with encoded exchange information by a distributed privacy-preserving optimization algorithm, which can ensure the convergence as well as protect privacy information of each stakeholder. In the lower layer of the model of each stakeholder, economic cost and emission rate are both taken as operation objectives, and a gradient descent-based multiobjective optimization method is employed to approach this objective. The simulation results confirm that the proposed three-stage optimization strategy can be a viable and efficient way for the optimal operation of microgrids.

Resilient Optimal Defensive Strategy of Micro-Grids System via Distributed Deep Reinforcement Learning Approach Against FDI Attack.

The ever-increasing false data injection (FDI) attack on the demand side brings great challenges to the energy management of interconnected microgrids. To address those aspects, this article proposes a resilient optimal defensive strategy with the distributed deep reinforcement learning (DRL) approach. To evaluate the FDI attack on demand response (DR), an online evaluation approach with the recursive least-square (RLS) method is proposed to evaluate the extent of supply security or voltage stability of the microgrids system is affected by the FDI attack. On the basis of evaluated security confidence, a distributed actor network learning approach is proposed to deduce optimal network weight, which can generate an optimal defensive scheme to ensure the economic and security issue of the microgrids system. From the methodology's view, it can also enhance the autonomy of each microgrid as well as accelerate DRL efficiency. According to those simulation results, it can reveal that the proposed method can evaluate FDI attack impact well and an improved distributed DRL approach can be a viable and promising way for the optimal defense of microgrids against the FDI attack on the demand side.

Static and Dynamic Event-Triggered Mechanisms for Distributed Secondary Control of Inverters in Low-Voltage Islanded Microgrids.

Due to the high resistance/reactance (R/X) ratio of a low-voltage microgrid (LVMG), virtual complex impedance-based P-· V/Q-ω droop control is adopted in this article as the primary control (PC) technique for stabilizing the system. A distributed event-triggered restoration mechanism (ETSM) is proposed as the secondary control (SC) technique to restore the output-voltage frequency and improve power sharing accuracy. The proposed ETSM ensures that neighboring communication happens only at some discrete instants when a predefined event-triggering condition (ETC) is fulfilled. In general, the design of the ETC is the crucial challenge of an event-triggered mechanism (ETM). Thus, in this article, a static ETM (SETM) is proposed as the ETC at first, where two static parameters are utilized to reduce the triggering frequency. Bounded stability is ensured under the SETM, which means that the output-voltage frequency is restored to the vicinity of its nominal value, and close to fair utilization of the distributed generators (DGs) is achieved. To further improve the power sharing accuracy and accelerate the regulation process, a dynamic ETM (DETM) is then introduced. In the DETM, two dynamic parameters that converge to zero in the steady state are designed, which promises asymptotic stability of the system. Besides, Zeno behavior is excluded in both mechanisms. An LVMG consisting of four DGs is constructed in MATLAB/Simulink to illustrate the effectiveness of the proposed methods, and the simulations correspond with our theoretical analysis.

Event-Triggered Multiagent Optimization for Two-Layered Model of Hybrid Energy System With Price Bidding-Based Demand Response.

Due to uncertainty and dynamic characteristics from intermittent energy and load demand response (DR), the optimal operation of the hybrid energy system is a great challenge. This article proposes an event-triggered multiagent coordinated optimization strategy with two-layered architecture. First, the price-bidding-based DR model is proposed with different stakeholders, and it also deduces the optimal bidding price with the Nash equilibrium theory. Then, four agents are designed to control different kinds of energy resources: agent 1 mainly analyzes the uncertainty or randomness caused by intermittent power, agent 2 takes charge of the dynamic economic dispatch (DED) within thermal units, agent 3 manages the optimal scheduling of energy storage, and agent 4 mainly undertakes the load-shifting strategy from consumers. In the upper-layer level, all agents coordinate together to ensure the stability of the hybrid energy system with an event-triggered mechanism, and the intelligent control approach mainly depends on switching ON/OFF power generators or curtailing system load, and the consensus algorithm is utilized to optimize the subsystem problem in the lower-layer level. Furthermore, the simulation results can further verify the efficiency of the proposed method, and it also reveals that the event-triggered multiagent optimization strategy can be a promising way to solve the hybrid energy system problem.

A Packet Loss-Dependent Event-Triggered Cyber-Physical Cooperative Control Strategy for Islanded Microgrid.

In this article, a cyber-physical cooperative control strategy is proposed for islanded microgrid (MG), which divides the MG into cyber and physical layers. And the main designs in these two layers are two event-triggered mechanisms, where one mechanism is used to improve the voltage and frequency stability of MG considering the packet loss problem, the other is used to reduce the communication burden in the control process. More specifically, the control process of the first mechanism can be understood as we use these event-triggered mechanisms to complete the secondary control in the physical layer based on the information in the cyber layer. In this mechanism, the packet loss situation in one communication channel is divided into three categories: 1) to handle the case where the loss rate is small, an adaptive virtual leader-following consensus controller (AVLFCC) is proposed in the cyber layer; 2) to handle the case where the loss rate is large and the forecasted data can be used, a hybrid forecast supplement method (HFSM) is proposed in the physical layer; and 3) to handle the case where the loss rate is large and the forecasted data cannot be used, a path reconstruction method combined with a novel sliding-mode control (SMC) is proposed in the cyber layer. In the second mechanism, an event-triggered protocol is designed for the consensus controller to reduce the communication burden based on the designs in 1)-3). Finally, based on these designs in the two mechanisms, a novel secondary controller is designed. And the experimental results have confirmed the validity of the contributed strategy.

Adaptive PI Control for Consensus of Multiagent Systems With Relative State Saturation Constraints.

The relative state between neighbors represents the difference of two connected agents' states, and it possesses specific physical meanings in practice. Under this background, the saturation constraints in the relative state inevitably occur. This article studies the consensus problems under the relative state saturation constraints. Novel adaptive proportional-integral (PI) protocols are designed to solve the constrained consensus problem. Specifically, the adaptive coupling weights and the saturation functions are embedded into the proposed protocols, and the former can render the protocols independent of any global topology graph information, while the latter can confine the relative state to stay in its constrained set. Sufficient conditions are identified under which the constrained consensus can be achieved. Considering that the solution matrix is required to be diagonally dominant, an iterative learning-based heuristic algorithm is proposed to seek the diagonally dominant positive-definite solution matrix. For the special case that the input matrix is row full rank, more stringent saturation functions are constructed, and it not only achieves the constrained consensus but also realizes the nonovershoot and shorter settling time associated with edge states. Besides, this result can be applied to preserve connectivity of the communication network. The theoretical analyses are validated by a simulation example.

The Effect of Peer Support on Individuals with Overweight and Obesity: A Meta-Analysis.

Background: Scarce data exists about the effect of peer support on individuals with overweight or obesity. This study aimed to conduct a meta-analysis regarding the effect of peer support on weight, BMI, waist circumference, blood pressure, quality of life, social support and depressive symptoms in individuals with overweight and obesity. Methods: PubMed, Embase, and CENTRAL were searched for relevant studies from their inceptions to 1 Mar 2020, and 14 randomized controlled trials (RCTS) were included. Data were pooled with Review Manager 5.3. Results: Significantly small improvement in weight (-0.78 kg) was found in individuals who received peer support than those who received usual care (MD= -0.78 kg, 95% CI-1.33 to -0.22, P=0.02). And peer support appeared to be associated with significant decrease in BMI levels (MD= -0.16 kg/m2, 95% CI -0.32 to -0.01, P=0.04). However, there was no statistically significant improvement in the levels of waist circumference, systolic blood pressure, diastolic blood pressure, quality of life, social support and depressive symptoms after peer support. Conclusion: Peer support appears to be associated with decreased weight and BMI levels in individuals with overweight and obesity. However, additional research is warranted due to insufficient evidence for the effects of peer support on the other health indicators.

Observer-Based Event-Triggered Control for Networked Linear Systems Subject to Denial-of-Service Attacks.

This paper is concerned with the observer-based event-triggered control for a continuous networked linear system subject to denial-of-service (DoS) attacks, where the attacks are launched periodically to block the data transmission in control channels. First, a new observer state-based resilient event-triggering scheme is developed in the presence of DoS attacks. Second, a novel event-based switched system model is established by considering the effect of the event-triggering scheme and DoS attacks simultaneously. By virtue of this new model combined with a piecewise Lyapunov-Krasovskii functional method, the sufficient conditions are derived to guarantee exponential stability of the resulting switched system. It is shown that the proposed results can establish a quantitative relationship among the launching/sleeping periods of the attacks, the event-triggering parameters, the sampling period, and the exponential decay rate. Third, criteria for designing a desired observer-based event-triggered controller are provided and expressed in terms of a set of linear matrix inequalities. Finally, an offshore structure model is presented to illustrate the efficiency of the developed control method.

Security control of cyber-physical system based on switching approach for intermittent denial-of-service jamming attack.

In this study, the effects caused by intermittent denial-of-service jamming attack (I-DoS-JA) on different communication channels and communication topology transformations have been deeply analyzed. According to the analyzation, each different communication topology is taken as a subsystem of switching system. Based on switching system, finite-time nonlinear system robust stability conditions are derived. Both the theoretical deduction and example simulation have corroborated that this approach has special effective in resisting the intermittent DoS attack.

Data-Driven Distributed Optimal Consensus Control for Unknown Multiagent Systems With Input-Delay.

This paper is concerned with data-driven distributed optimal consensus control for unknown multiagent systems (MASs) with input delays. The input-delayed MAS model is first converted into a delay-free form using a model reduction method. By establishing an equivalent relationship on the predesigned performance indices of the two MASs, optimal consensus control of input-delayed MAS can be fully transformed to that of delay-free MAS. Based on the coupled Hamilton-Jacobi equations and Bellman's optimality principle, optimal consensus control policies are derived for the transformed delay-free MAS. Then a policy iteration algorithm based on distributed asynchronous update mechanism is proposed to learn the coupled Hamilton-Jacobi-Bellman equations online. To perform the proposed data-driven adaptive dynamic programming algorithm, we adopt the measured data-based critic-actor neural networks to approximate the value functions and the control policies, respectively. Finally, a simulation example is given to illustrate the effectiveness of the proposed method.