Abstract-There are more large-scale PV plants being established in rural areas due to availability of low priced land. However, distribution grids in such areas traditionally have feeders with low X/R ratios, which makes the independent reactive power compensation method less effective on voltage regulation. Consequently, upstream Step Voltage Regulator (SVR) may suffer from excessive tap operations with PV induced fast voltage fluctuations. Although a battery energy storage system (BESS) can successfully smooth PV generation, frequent charge/discharge will substantially affect its cost effectiveness. In this paper, a real-time method is designed to coordinate PV inverters and BESS for voltage regulation. To keep up with fast fluctuations of PV power, this method will be executed in each 5s control cycle. In addition, charging/discharging power of BESS is adaptively retuned by an active adjustment method in order to avoid BESS premature energy exhaustion in a long run. Finally, through a voltage margin control scheme, the upstream SVR and downstream PV inverters and BESS are coordinated for voltage regulation without any communication. This research is validated via an RTDS-MatLab co-simulation platform, and it will provide valuable insights and applicable strategies to both utilities and PV owners for large-scale PV farm integration into rural networks.Index Terms--Coordinated voltage control, photovoltaic (PV), battery energy storage system (BESS), real-time control, state of charge (SOC) regulation.

Objective: Hospital violence remains a global public health problem. This study aims to analyze serious hospital violence causes in China and the characteristics of perpetrators. It likewise seeks to understand frontline personnel's needs and put forward targeted suggestions.Methods: Serious hospital violence cases from 2011 to 2020 in the China Judgment Online System (CJOS) were selected for descriptive statistical analysis. A total of 72 doctors, nurses, hospital managers, and security personnel from 20 secondary and tertiary hospitals in China were selected for semi-structured interviews.Results: Of the incidents, 62.17% were caused by patients' deaths and dissatisfaction with their treatment results. Moreover, it was found that out-of-hospital disputes (11.14%) were also one of the main reasons for serious hospital violence. The perpetrators were mainly males (80.3%), and had attained junior high school education or lower (86.5%). Furthermore, most of them were family members of the patients (76.1%). Healthcare workers urgently hope that relevant parties will take new measures in terms of legislation, security, and dispute handling capacity.Conclusion: In the past 10 years, serious hospital violence's frequency in China has remained high. Furthermore, their harmful consequences are more serious. The causes of hospital violence are diverse, and the characteristics of perpetrators are obvious. Frontline healthcare workers urgently need relevant parties to take effective measures in terms of legislation, security, and dispute handling capacity, to prevent the occurrence of violence and protect medical personnel's safety.

With the increasing carbon emissions worldwide, lithium-ion batteries have become the main component of energy storage systems for clean energy due to their unique advantages. Accurate and reliable state-of-charge (SOC) estimation is a central factor in the widespread use of lithium-ion batteries. This review, therefore, examines the recent literature on estimating the SOC of lithium-ion batteries using the hybrid methods of neural networks combined with Kalman filtering (NN-KF), classifying the methods into Kalman filter-first and neural network-first methods. Then the hybrid methods are studied and discussed in terms of battery model, parameter identification, algorithm structure, implementation process, appropriate environment, advantages, disadvantages, and estimation errors. In addition, this review also gives corresponding recommendations for researchers in the battery field considering the existing problems.

Lithium battery state of health (SOH) is a key parameter to characterize the actual battery life. SOH cannot be directly measured. In order to further improve the accuracy of SOH estimation of lithium batteries, a model combining incremental capacity analysis (ICA) and bidirectional long- and short-term memory (Bi-LSTM) neural networks based on health characteristic parameters is proposed to predict the SOH of lithium-ion batteries. First, the health characteristic parameters are initially selected from the lithium battery charging curve, and the health characteristics are extracted by the Pearson correlation coefficient, including the charging time of constant current, charging time of constant voltage, voltage change rate from 300 s to 1000 s, 200s of voltage per cycle at a time. Second, ICA was used to deeply mine the deep associations related to SOH and the peaks of IC curves and their corresponding voltages were extracted as additional inputs to the model. Then, Bi-LSTM is used to form a combined SOH estimation model through adaptive weighting factors. Finally, the verification is based on the 5th battery parameters of the NASA lithium battery data set. The experimental results show that the proposed combined model reduces the mean square error by 55.17%, 49.28%, and 41.47%, respectively, compared with single models such as BP neural network (BPNN), LSTM, and gated recurrent neural network (GRU).