Survey on the Status Quo of Emergency Response Capabilities of Public Health Emergencies in Grassroots Disease Control Institutions in a Certain Area

Objective:To survey and study the emergency response capabilities of grassroots disease control institutions in a certain area for public health emergencies,and to put forward suggestions for rectification.Methods:The study was carried out from March 2022 to March 2023.Field surveys,questionnaire surveys,and interviews were used to investigate and analyze the emergency response capabilities of public health emergencies in 5 county Centers for Disease Control and Prevention(CDCs)in the region.Results:Through the survey,it was found that the professional level of the existing emergency team personnel of the grassroots disease control institutions in this region needs to be improved.There was an overall lack of emergency plans,and the compliance rate of the equipment,inspection,and testing items was low.The health emergency system of the CDCs in the region and the ability of the talent team need to be further improved.Conclusion:The emergency response capacity of grassroots disease control institutions in this region needs to be improved.For this reason,government departments need to increase investment and strengthen the construction of talent teams and hardware settings,and grassroots disease control institutions need to strengthen the construction of the public health emergency system to improve the ability to respond to public health emergencies.

The application of feedforward control in the nursing emergency management of COVID-19

Objective:To explore the application of feedforward control in the nursing emergency management of COVID-19.Methods:The feedforward control theory was applied to the emergency management of COVID-19 nursing,including grasping the latest epidemic information,preparing for the early stage,formulating nursing workflow,implementing flexible management,standardizing isolation and protection measures,unifying nursing document record format,and implementing humanistic care.Results:During the period of support to Wuhan,the nursing work in the isolated area was orderly,the nursing staff's job satisfaction was high,no nursing errors and hospital infections occurred.Conclusion:Efficient feedforward control in the nursing emergency management that could avoid work blindness to a certain extent and play a guiding role in maintaining the normal operation of treatment and nursing work and protecting the safety of patients and medical staff in the ward during the epidemic period of COVID-19.

DC Emergency Power Control Strategy for AC/DC Multi-Channel Interconnected System

Inter-regional transmission line fault often results in power flow transferring, tie-line overloading and system islanding. Traditional control methods such asgenerator tripping and load shedding are costly, and also have undesirable impacts on theloadside. In this paper, a new emergency power control strategy is proposed for multi-channel interconnected system by using the overload capacity of non-fault DC lines. First of all, the capacity of emergency power control can be acquired by critical transmission power of a certain tie-line for stability. Secondly, the shortest electric distance can be calculated by Dijkstra algorithm, and then the priority of emergency control of the DC lines can be obtained by the entropy weight method. When the inter-regional transmission power decreases and the effect of single DC line emergency control is poor, the multi-channel cooperative emergency control strategy is proposed to ensure the system stability. Simulation results verify the effectiveness of the method proposed.

Emergency Control Strategy Based on Multi-agent Theory under Blackout

The multi-agent theory is introduced and applied in the way to strike the control amount of emergency control according to stability margin, based on which an emergency control strategy of the power system is presented. The multi-agent control structure which is put forward in this article has three layers: system agent, areal agent and local agents. System agent sends controlling execution signal to the load-local agent according to the position and the amount of load shedding upload from areal agent;The areal agent judges whether the power system is stable by monitoring and analyzing the maximum relative power angle. In the condition of instability, determines the position of load-shedding, and the optimal amount of load-shedding according to the stability margin based on the corrected transient energy function, upload control amount to system agent;local-generator agent is mainly used for real-time monitoring the power angle of generator sets and uploading it to the areal agency, local-loads agent control load by receiving the control signal from system agent. Simulations on IEEE39 system show that the proposed control strategy improves the system stability.

Expert consensus on emergency medicine procedure optimization guided by routine prevention and control strategy for COVID-19

The outbreak of coronavirus disease 2019(COVID-19)was declared a global public health emergency on 31 January 2020.Emergency medicine procedures in Emergency Department should be optimized to cope with the current COVID-19 pandemic by providing subspecialty services,reducing the spread of nosocomial infections,and promoting its capabilities to handle emerging diseases.Thus,the Chinese Society of Emergency Medicine and Wuhan Society of Emergency Medicine drafted this consensus together to address concerns of medical staffs who work in Emergency Department.Based on in-depth review of COVID-19 diagnosis and treatment plans,literatures,as well as management approval,this consensus proposes recommendations for improving the rationalization and efficiency of emergency processes,reducing the risk of nosocomial infections,preventing hospital viral transmission,and ensuring patient safety.

Dynamics and adaptive control of a dual-arm space robot with closed-loop constraints and uncertain inertial parameters

A dynamics-based adaptive control approach is proposed for a planar dual-arm space robot in the presence of closed-loop constraints and uncertain inertial parameters of the payload. The controller is capable of controlling the po- sition and attitude of both the satellite base and the payload grasped by the manipulator end effectors. The equations of motion in reduced-order form for the constrained system are derived by incorporating the constraint equations in terms of accelerations into Kane＇s equations of the unconstrained system. Model analysis shows that the resulting equations perfectly meet the requirement of adaptive controller design. Consequently, by using an indirect approach, an adaptive control scheme is proposed to accomplish position/attitude trajectory tracking control with the uncertain parameters be- ing estimated on-line. The actuator redundancy due to the closed-loop constraints is utilized to minimize a weighted norm of the joint torques. Global asymptotic stability is proven by using Lyapunov＇s method, and simulation results are also presented to demonstrate the effectiveness of the proposed approach.

Enlightenment from the COVID-19 Pandemic:The Roles of Environmental Factors in Future Public Health Emergency Response

The coronavirus disease 2019(COVID-19)pandemic is challenging the current public health emergency response systems(PHERSs)of many countries.Although environmental factors,such as those influencing the survival of viruses and their transmission between species including humans,play important roles in PHERSs,little attention has been given to these factors.This study describes and elucidates the roles of environmental factors in future PHERSs.To improve countries’capability to respond to public health emergencies associated with viral infections such as the COVID-19 pandemic,a number of environmental factors should be considered before,during,and after the responses to such emergencies.More specifically,to prevent pandemic outbreaks,we should strengthen environmental and wildlife protection,conduct detailed viral surveillance in animals and hotspots,and improve early-warning systems.During the pandemic,we must study the impacts of environmental factors on viral behaviors,develop control measures to minimize secondary environmental risks,and conduct timely assessments of viral risks and secondary environmental effects with a view to reducing the impacts of the pandemic on human health and on ecosystems.After the pandemic,we should further strengthen surveillance for viruses and the prevention of viral spread,maintain control measures for minimizing secondary environmental risks,develop our capability to scientifically predict pandemics and resurgences,and prepare for the next unexpected resurgence.Meanwhile,we should restore the normal life and production of the public based on the“One Health”concept,that views global human and environmental health as inextricably linked.Our recommendations are essential for improving nations’capability to respond to global public health emergencies.

Integrated risk management: a Petrobras application in offshore well construction safety to minimize critical emergency disconnections

In order to avoid mistakes and to save a great deal of time in analysis, an innovative methodology was developed that can analyze the well operations and rig characteristics involved to define the best emergency disconnect sequence （EDS） available. A solution was developed based on the characteristics of the rigs and blowout preventers （BOPs）, and six variables were considered that directly affect the choice of EDS. All possible combinations of 64 scenarios were analyzed, and the priority of choice of the EDS was defined empirically. This paper presents an approach to EDS risk management and examples of exposure time （time without riser safety margin and shear capability） for the same well, which can be lowered from 13% to 0.1%. The impact of this reduction is related to the ability of the BOP to cut some of the heavy casings, in addition to improved availability of EDS modes. This implementation opened up many possibilities for the performance of risk exposure analysis, enabling comparison of several BOP configurations of contracted rigs and selection of the best options. This innovative approach allowed a better management of the rig schedules, prioritizing safety aspects and making it possible to allocate the fleet in a systematic way.

Application of Single Neuron Adaptive PID Regulators with Auto-tuning Gain in Industrial Parameter(Pressure)Closed-loop Process Control Systems

A type of single neuron adaptive PID regulator with auto-tuning gain is proposed and applied to the work control of fans, waterpumps and air-pressers etc. in Handan Iron & Steel Compel China. The robusthess of induStrial parameter closed-loop process controlsystems is improved, and the work quality of the systems bettered.

Retrospective Diagnosis of COVID-19 in an Asymptomatic Patient Undergoing Emergency Surgery

Background: With reports of higher mortality and complications occurring in patients with perioperative 2019 novel coronarvirus disease (COVID-19), most elective surgeries have been postponed. However, evidence regarding emergency surgeries in patients with COVID-19 remains scarce. We report the case of a patient with asymptomatic perioperative COVID-19, presenting with an acute abdomen requiring surgery. Case: A 25-year-old male, with a prior nasopharyngeal swab that was negative for SARS-CoV-2, presented with classical signs and symptoms of acute appendicitis. Clinical examination and investigations were not suggestive of COVID-19 infection. He underwent laparoscopic appendicectomy with infection control precautions. Post-operatively, he was found to be positive for SARS-CoV-2 but remained asymptomatic and had an uneventful recovery. Conclusion: In asymptomatic individuals with higher risks, negative test results should be viewed cautiously. The benefits of urgent surgical interventions must be weighed against the risks of complications due to perioperative COVID-19 in these patients.

Interference Management for DS-CDMA Systems through Closed-Loop Power Control, Base Station Assignment, and Beamforming

In this paper, we propose a smart step closed-loop power control (SSPC) algorithm and a base station assignment method based on minimizing the transmitter power (BSA-MTP) technique in a direct sequence-code division multiple access (DS-CDMA) receiver with frequency-selective Rayleigh fading. This receiver consists of three stages. In the first stage, with constrained least mean squared (CLMS) algorithm, the desired users’ signal in an arbitrary path is passed and the inter-path interference (IPI) is reduced in other paths in each RAKE finger. Also in this stage, the multiple access interference (MAI) from other users is reduced. Thus, the matched filter (MF) can use for more reduction of the IPI and MAI in each RAKE finger in the second stage. Also in the third stage, the output signals from the matched filters are combined according to the conventional maximal ratio combining (MRC) principle and then are fed into the decision circuit of the desired user. The simulation results indicate that the SSPC algorithm and the BSA-MTP technique can significantly reduce the network bit error rate (BER) compared to the other methods. Also, we observe that significant savings in total transmit power (TTP) are possible with our methods.

Study on Optimization of Earthquake Emergency Traffic Scheduling Based on The Lifting of Cyberspace

How to improve the rescue efficiency of the network after the earthquake is the key content of emergency management decision-making, improve the efficiency of emergency rescue, and reduce the impact of emergency rescue to the non victims. Using cyberspace of lifeline network traffic and emergency transportation problem, considering the network restoration problem of disaster area, earthquake emergency supplies distribution model is established. In the model, to consider the need to repair the damaged sections and the existing emergency rescue generated traffic volume of emergency rescue network effects. And design heuristic algorithm for solving the model. Finally the example shows that in emergency rescue, emergency rescue of critical damage repair the road and traffic control of the whole lifeline network rescue efficiency highest, with the average nuisance greatly reduce, the lifeline network connectivity reliability.

Model Reference Adaptive Control for Switched Systems with Closed-loop Reference Model Under Arbitrary Switching

This paper explores the model reference adaptive control problem for a class of switched linear systems under arbitrary switching with no need for the measurability of the system state.Based on the state of reference model and the measurable output error, adaptive laws and controllers are designed for switched systems.Each subsystem may have its individual reference model and controller, which increases the design flexibility.The introduction of the closed-loop reference model is to get a better transient performance of the whole switched systems.A numerical example is provided to verify the effectiveness of the main results.

Emergency Prioritized and Congestion Handling Protocol for Medical Internet of Things

Medical Internet of Things(MIoTs)is a collection of small and energyefficient wireless sensor devices that monitor the patient’s body.The healthcare networks transmit continuous data monitoring for the patients to survive them independently.There are many improvements in MIoTs,but still,there are critical issues that might affect the Quality of Service(QoS)of a network.Congestion handling is one of the critical factors that directly affect the QoS of the network.The congestion in MIoT can cause more energy consumption,delay,and important data loss.If a patient has an emergency,then the life-critical signals must transmit with minimum latency.During emergencies,the MIoTs have to monitor the patients continuously and transmit data(e.g.,ECG,BP,heart rate,etc.)with minimum delay.Therefore,there is an efficient technique required that can transmit emergency data of high-risk patients to the medical staff on time with maximum reliability.The main objective of this research is to monitor and transmit the patient’s real-time data efficiently and to prioritize the emergency data.In this paper,Emergency Prioritized and Congestion Handling Protocol for Medical IoTs(EPCP_MIoT)is proposed that efficiently monitors the patients and overcome the congestion by enabling different monitoring modes.Whereas the emergency data transmissions are prioritized and transmit at SIFS time.The proposed technique is implemented and compared with the previous technique,the comparison results show that the proposed technique outperforms the previous techniques in terms of network throughput,end to end delay,energy consumption,and packet loss ratio.

Low Dose Ketamine Used for Acute Pain Management in the Emergency Department

The use of opioid analgesics has created an opioid addiction epidemic in the United States and around the world. One of the main responsibilities of the emergency department is to address acute pain. In this paper we examine the use of low dose ketamine as a safe substitute for opioid analgesics. Types of pain are identified using prior established taxonomy, followed by discussing types of management and treatments. The opioid epidemic and its societal implications are considered. We also examine the side effects of ketamine and the use of ketamine as an adjunct to analgesics used for pain control. Recommendations and considerations for the use of ketamine are suggested, and a low dose ketamine administration sample hospital policy is reviewed.

Joint Closed-Loop Power Control and Adaptive Beamforming for Wireless Networks with Antenna Arrays in a 2D Urban Environment

The interference reduction capability of antenna arrays and the power control algorithms have been considered separately as means to decrease the interference in wireless communication networks. In this paper, we propose smart step closed-loop power control (SSPC) algorithm in wireless networks in a 2D urban environment with constrained least mean squared (CLMS) algorithm. This algorithm is capable of efficiently adapting according to the environment and able to permanently maintain the chosen frequency response in the look direction while minimizing the output power of the array. Also, we present switched-beam (SB) technique for enhancing signal to interference plus noise ratio (SINR) in wireless networks. Also, we study an analytical approach for the evaluation of the impact of power control error (PCE) on wireless networks in a 2D urban environment. The simulation results indicate that the convergence speed of the SSPC algorithm is faster than other algorithms. Also, we observe that significant saving in total transmit power (TTP) are possible with our proposed algorithm. Finally, we discuss three parameters of the PCE, number of antenna elements, and path-loss exponent and their effects on capacity of the system via some computer simulations.

Clinical Effect of Intelligent Emergency Nursing Mode in Patients with Severe Traumatic Brain Injury

Objective: Severe traumatic brain injury (sTBI) is one of the common acute and critical diseases in neurosurgery. So we aim to explore the clinical effectiveness of an intelligent emergency care model in patients with severe traumatic brain injury. Methods: Eighty patients with severe traumatic brain injury (sTBI) who were treated in Zhuji People’s Hospital of Zhejiang Province from January 2019 to December 2021 were selected as the study subjects. The patients were divided into an observation group and a control group with 40 patients in each group according to the random number table method. Patients in the control group received conventional first-aid nursing mode intervention, and the intelligent emergency nursing mode was used for the observation group based on the control group. Comparisons were conducted between the two groups on the time of arrival to the emergency room, the time from the emergency room to the operating room, Glasgow Coma Scale (GCS) score before surgery, GCS score when leaving the Intensive Care Unit (ICU), the average length of ICU stay, the average length of hospital stay, the total hospital costs. Results: The time of arrival to the emergency room, the time from the emergency room to the operating room, the average length of ICU stay, the average length of hospital stay, and the total hospital costs in the observation group were significantly lower than those in the control group, and the differences were statistically significant (All P Conclusion: Intelligent emergency nursing mode can shorten the time of sTBI rescue, the length of ICU stay, and the average length of hospital stay, reduce the total hospitalization cost, improve the prognosis, with good efficacy, reduce the total cost of hospitalization, and improve the prognosis with better efficacy.

A Deep Reinforcement Learning-Based Power Control Scheme for the 5G Wireless Systems

In the fifth generation(5G)wireless system,a closed-loop power control(CLPC)scheme based on deep Q learning network(DQN)is introduced to intelligently adjust the transmit power of the base station(BS),which can improve the user equipment(UE)received signal to interference plus noise ratio(SINR)to a target threshold range.However,the selected power control(PC)action in DQN is not accurately matched the fluctuations of the wireless environment.Since the experience replay characteristic of the conventional DQN scheme leads to a possibility of insufficient training in the target deep neural network(DNN).As a result,the Q-value of the sub-optimal PC action exceed the optimal one.To solve this problem,we propose the improved DQN scheme.In the proposed scheme,we add an additional DNN to the conventional DQN,and set a shorter training interval to speed up the training of the DNN in order to fully train it.Finally,the proposed scheme can ensure that the Q value of the optimal action remains maximum.After multiple episodes of training,the proposed scheme can generate more accurate PC actions to match the fluctuations of the wireless environment.As a result,the UE received SINR can achieve the target threshold range faster and keep more stable.The simulation results prove that the proposed scheme outperforms the conventional schemes.

Design and Simulation of an Audio Signal Alerting and Automatic Control System

A large part of our daily lives is spent with audio information. Massive obstacles are frequently presented by the colossal amounts of acoustic information and the incredibly quick processing times. This results in the need for applications and methodologies that are capable of automatically analyzing these contents. These technologies can be applied in automatic contentanalysis and emergency response systems. Breaks in manual communication usually occur in emergencies leading to accidents and equipment damage. The audio signal does a good job by sending a signal underground, which warrants action from an emergency management team at the surface. This paper, therefore, seeks to design and simulate an audio signal alerting and automatic control system using Unity Pro XL to substitute manual communication of emergencies and manual control of equipment. Sound data were trained using the neural network technique of machine learning. The metrics used are Fast Fourier transform magnitude, zero crossing rate, root mean square, and percentage error. Sounds were detected with an error of approximately 17%;thus, the system can detect sounds with an accuracy of 83%. With more data training, the system can detect sounds with minimal or no error. The paper, therefore, has critical policy implications about communication, safety, and health for underground mine.

Data-Driven Model Identification and Control of the Inertial Systems

In the synthesis of the control algorithm for complex systems, we are often faced with imprecise or unknown mathematical models of the dynamical systems, or even with problems in finding a mathematical model of the system in the open loop. To tackle these difficulties, an approach of data-driven model identification and control algorithm design based on the maximum stability degree criterion is proposed in this paper. The data-driven model identification procedure supposes the finding of the mathematical model of the system based on the undamped transient response of the closed-loop system. The system is approximated with the inertial model, where the coefficients are calculated based on the values of the critical transfer coefficient, oscillation amplitude and period of the underdamped response of the closed-loop system. The data driven control design supposes that the tuning parameters of the controller are calculated based on the parameters obtained from the previous step of system identification and there are presented the expressions for the calculation of the tuning parameters. The obtained results of data-driven model identification and algorithm for synthesis the controller were verified by computer simulation.