无人机辅助物联网中基于Safe Actor-Critic的信息年龄最小化研究

无人机作为一种新的通信设备,有望在物联网数据采集、监控等业务中发挥关键作用。为保证所采集数据的时效性,利用信息年龄来衡量无人机从物联网设备接收到的数据新鲜度。通过联合优化无人机轨迹和无人机与物联网设备的关联策略以最小化信息年龄加权和,并保证无人机累积飞行能量消耗满足预算要求。由于上述问题同时受短期和长期约束条件的限制,将问题建模为受约束的马尔可夫决策过程(CMDP),并利用Safe Actor-Critic来求解。仿真结果表明,所提算法在最小化信息年龄的同时,能有效保证能量预算。

A cross-sectional study to assess medication safety,knowledge,attitude,and practices regarding nutrition and medication among pregnant women

Objective:To assess pregnant women's knowledge,attitude,and practice regarding nutrition and medication usage,analyse the prescribing pattern,and categorize them based on the Food and Drug Administration(FDA)guidelines.Methods:A cross-sectional study was conducted with 264 pregnant women in the obstetrics and gynaecology department of a tertiary care hospital from October 2022 to August 2023.A knowledge,attitude,and practice(KAP)questionnaire was prepared in English language by the researchers and validated by an expert panel consisting of 12 members.The validated questionnaire was then translated into regional languages,Kannada and Malayalam.The reliability of the questionnaire was assessed with test-retest method with a representative sample population of 30 subjects(10 subjects for each language).The subjects'knowledge,attitude,and practice were evaluated using the validated KAP questionnaire.The safety of the medication was assessed using the FDA drug safety classification for pregnancy.Results:The mean scores for nutritional and medication usage knowledge,attitude,and practice were 4.14±1.15,4.50±1.09,and 3.00±1.47,respectively.Among 30 prescribed medications,3 belong to category A(no risk in human studies),8 belong to category B(no risk in animal studies),18 belong to category C(risk cannot be ruled out)and 1 drug is not classified.A significant association was observed between medication knowledge and practice(r=0.159,P=0.010).Conclusions:Most of the study population knows the need to maintain good dietary and medication practices during pregnancy.Counselling pregnant women regarding diet and medication usage is crucial in maternal care.

A Game-Theoretic Approach to Safe Crowd Evacuation in Emergencies

Obstacle removal in crowd evacuation is critical to safety and the evacuation system efficiency. Recently, manyresearchers proposed game theoreticmodels to avoid and remove obstacles for crowd evacuation. Game theoreticalmodels aim to study and analyze the strategic behaviors of individuals within a crowd and their interactionsduring the evacuation. Game theoretical models have some limitations in the context of crowd evacuation. Thesemodels consider a group of individuals as homogeneous objects with the same goals, involve complex mathematicalformulation, and cannot model real-world scenarios such as panic, environmental information, crowds that movedynamically, etc. The proposed work presents a game theoretic model integrating an agent-based model to removethe obstacles from exits. The proposed model considered the parameters named: (1) obstacle size, length, andwidth, (2) removal time, (3) evacuation time, (4) crowd density, (5) obstacle identification, and (6) route selection.The proposed work conducts various experiments considering different conditions, such as obstacle types, obstacleremoval, and several obstacles. Evaluation results show the proposed model’s effectiveness compared with existingliterature in reducing the overall evacuation time, cell selection, and obstacle removal. The study is potentially usefulfor public safety situations such as emergency evacuations during disasters and calamities.

Risk-Informed Model-Free Safe Control of Linear Parameter-Varying Systems

This paper presents a risk-informed data-driven safe control design approach for a class of stochastic uncertain nonlinear discrete-time systems.The nonlinear system is modeled using linear parameter-varying(LPV)systems.A model-based probabilistic safe controller is first designed to guarantee probabilisticλ-contractivity(i.e.,stability and invariance)of the LPV system with respect to a given polyhedral safe set.To obviate the requirement of knowing the LPV system model and to bypass identifying its open-loop model,its closed-loop data-based representation is provided in terms of state and scheduling data as well as a decision variable.It is shown that the variance of the closedloop system,as well as the probability of safety satisfaction,depends on the decision variable and the noise covariance.A minimum-variance direct data-driven gain-scheduling safe control design approach is presented next by designing the decision variable such that all possible closed-loop system realizations satisfy safety with the highest confidence level.This minimum-variance approach is a control-oriented learning method since it minimizes the variance of the state of the closed-loop system with respect to the safe set,and thus minimizes the risk of safety violation.Unlike the certainty-equivalent approach that results in a risk-neutral control design,the minimum-variance method leads to a risk-averse control design.It is shown that the presented direct risk-averse learning approach requires weaker data richness conditions than existing indirect learning methods based on system identification and can lead to a lower risk of safety violation.Two simulation examples along with an experimental validation on an autonomous vehicle are provided to show the effectiveness of the presented approach.

Enhanced Memory-Safe Linux Security Modules (eLSMs) for Improving Security of Docker Containers for Data Centers

The adoption of Docker containers has revolutionized software deployment by providing a lightweight and efficient way to isolate applications in data centers. However, securing these containers, especially when handling sensitive data, poses significant challenges. Traditional Linux Security Modules (LSMs) such as SELinux and AppArmor have limitations in providing fine-grained access control to files within containers. This paper presents a novel approach using eBPF (extended Berkeley Packet Filter) to implement a LSM that focuses on file-oriented access control within Docker containers. The module allows the specification of policies that determine which programs can access sensitive files, providing enhanced security without relying solely on the host operating system’s major LSM.

A Dual-Task Learning Approach for Bearing Anomaly Detection and State Evaluation of Safe Region

Predictive maintenance has emerged as an effective tool for curbing maintenance costs,yet prevailing research predominantly concentrates on the abnormal phases.Within the ostensibly stable healthy phase,the reliance on anomaly detection to preempt equipment malfunctions faces the challenge of sudden anomaly discernment.To address this challenge,this paper proposes a dual-task learning approach for bearing anomaly detection and state evaluation of safe regions.The proposed method transforms the execution of the two tasks into an optimization issue of the hypersphere center.By leveraging the monotonicity and distinguishability pertinent to the tasks as the foundation for optimization,it reconstructs the SVDD model to ensure equilibrium in the model’s performance across the two tasks.Subsequent experiments verify the proposed method’s effectiveness,which is interpreted from the perspectives of parameter adjustment and enveloping trade-offs.In the meantime,experimental results also show two deficiencies in anomaly detection accuracy and state evaluation metrics.Their theoretical analysis inspires us to focus on feature extraction and data collection to achieve improvements.The proposed method lays the foundation for realizing predictive maintenance in a healthy stage by improving condition awareness in safe regions.

Cloud-Edge Collaborative Federated GAN Based Data Processing for IoT-Empowered Multi-Flow Integrated Energy Aggregation Dispatch

The convergence of Internet of Things(IoT),5G,and cloud collaboration offers tailored solutions to the rigorous demands of multi-flow integrated energy aggregation dispatch data processing.While generative adversarial networks(GANs)are instrumental in resource scheduling,their application in this domain is impeded by challenges such as convergence speed,inferior optimality searching capability,and the inability to learn from failed decision making feedbacks.Therefore,a cloud-edge collaborative federated GAN-based communication and computing resource scheduling algorithm with long-term constraint violation sensitiveness is proposed to address these challenges.The proposed algorithm facilitates real-time,energy-efficient data processing by optimizing transmission power control,data migration,and computing resource allocation.It employs federated learning for global parameter aggregation to enhance GAN parameter updating and dynamically adjusts GAN learning rates and global aggregation weights based on energy consumption constraint violations.Simulation results indicate that the proposed algorithm effectively reduces data processing latency,energy consumption,and convergence time.

A PI+R Control Scheme Based on Multi-Agent Systems for Economic Dispatch in Isolated BESSs

Battery energy storage systems(BESSs)are widely used in smart grids.However,power consumed by inner impedance and the capacity degradation of each battery unit become particularly severe,which has resulted in an increase in operating costs.The general economic dispatch(ED)algorithm based on marginal cost(MC)consensus is usually a proportional(P)controller,which encounters the defects of slow convergence speed and low control accuracy.In order to solve the distributed ED problem of the isolated BESS network with excellent dynamic and steady-state performance,we attempt to design a proportional integral(PI)controller with a reset mechanism(PI+R)to asymptotically promote MC consensus and total power mismatch towards 0 in this paper.To be frank,the integral term in the PI controller is reset to 0 at an appropriate time when the proportional term undergoes a zero crossing,which accelerates convergence,improves control accuracy,and avoids overshoot.The eigenvalues of the system under a PI+R controller is well analyzed,ensuring the regularity of the system and enabling the reset mechanism.To ensure supply and demand balance within the isolated BESSs,a centralized reset mechanism is introduced,so that the controller is distributed in a flow set and centralized in a jump set.To cope with Zeno behavior and input delay,a dwell time that the system resides in a flow set is given.Based on this,the system with input delays can be reduced to a time-delay free system.Considering the capacity limitation of the battery,a modified MC scheme with PI+R controller is designed.The correctness of the designed scheme is verified through relevant simulations.

Study on the Safety and Prevention Technology of Coal Mining under the River in Xingyuan Coal Mine

Coal mining-induced surface subsidence poses significant ecological and infrastructural challenges, necessitating a comprehensive study to ensure safe mining practices, particularly in underwater conditions. This project aims to address the extensive impact of coal mining on the environment, infrastructure, and overall safety, focusing on the Shigong River area above the working face. The study employs qualitative and quantitative analyses, along with on-site engineering measurements, to gather data on crucial parameters such as coal seam characteristics, roof rock lithology, thickness, water resistance, and structural damage degree. The research encompasses a multidisciplinary approach, involving mining, geology, hydrogeology, geophysical exploration, rock mechanics, mine surveying, and computational mathematics. The importance of effective safety measures and prevention techniques is emphasized, laying the foundation for research focused on the Xingyun coal mine. The brief concludes by highlighting the potential economic and social benefits of this project and its contribution to valuable experience for future subsea coal mining.

Energy Economic Dispatch for Photovoltaic-Storage via Distributed Event-Triggered Surplus Algorithm

This paper presents a novel approach to economic dispatch in smart grids equipped with diverse energy devices.This method integrates features including photovoltaic(PV)systems,energy storage coupling,varied energy roles,and energy supply and demand dynamics.The systemmodel is developed by considering energy devices as versatile units capable of fulfilling various functionalities and playing multiple roles simultaneously.To strike a balance between optimality and feasibility,renewable energy resources are modeled with considerations for forecasting errors,Gaussian distribution,and penalty factors.Furthermore,this study introduces a distributed event-triggered surplus algorithm designed to address the economic dispatch problem by minimizing production costs.Rooted in surplus theory and finite time projection,the algorithm effectively rectifies network imbalances caused by directed graphs and addresses local inequality constraints.The algorithm greatly reduces the communication burden through event triggering mechanism.Finally,both theoretical proofs and numerical simulations verify the convergence and event-triggered nature of the algorithm.

Collaborative robust dispatch of electricity and carbon under carbon allowance trading market

The launch of the carbon-allowance trading market has changed the cost structure of the power industry.There is an asynchronous coupling mechanism between the carbon-allowance-trading market and the day-ahead power-system dispatch.In this study,a data-driven model of the uncertainty in the annual carbon price was created.Subsequently,a collaborative,robust dispatch model was constructed considering the annual uncertainty of the carbon price and the daily uncertainty of renewable-energy generation.The model is solved using the column-and-constraint generation algorithm.An operation and cost model of a carbon-capture power plant(CCPP)that couples the carbon market and the economic operation of the power system is also established.The critical,profitable conditions for the economic operation of the CCPP were derived.Case studies demonstrated that the proposed low-carbon,robust dispatch model reduced carbon emissions by 2.67%compared with the traditional,economic,dispatch method.The total fuel cost of generation decreases with decreasing,conservative,carbon-price-uncertainty levels,while total carbon emissions continue to increase.When the carbon-quota coefficient decreases,the system dispatch tends to increase low-carbon unit output.This study can provide important guidance for carbon-market design and the low-carbon-dispatch selection strategies.

Economic Power Dispatching from Distributed Generations: Review of Optimization Techniques

In the increasingly decentralized energy environment,economical power dispatching from distributed generations(DGs)is crucial to minimizing operating costs,optimizing resource utilization,and guaranteeing a consistent and sustainable supply of electricity.A comprehensive review of optimization techniques for economic power dispatching from distributed generations is imperative to identify the most effective strategies for minimizing operational costs while maintaining grid stability and sustainability.The choice of optimization technique for economic power dispatching from DGs depends on a number of factors,such as the size and complexity of the power system,the availability of computational resources,and the specific requirements of the application.Optimization techniques for economic power dispatching from distributed generations(DGs)can be classified into two main categories:(i)Classical optimization techniques,(ii)Heuristic optimization techniques.In classical optimization techniques,the linear programming(LP)model is one of the most popular optimization methods.Utilizing the LP model,power demand and network constraints are met while minimizing the overall cost of generating electricity from DGs.This approach is efficient in determining the best DGs dispatch and is capable of handling challenging optimization issues in the large-scale system including renewables.The quadratic programming(QP)model,a classical optimization technique,is a further popular optimization method,to consider non-linearity.The QP model can take into account the quadratic cost of energy production,with consideration constraints like network capacity,voltage,and frequency.The metaheuristic optimization techniques are also used for economic power dispatching from DGs,which include genetic algorithms(GA),particle swarm optimization(PSO),and ant colony optimization(ACO).Also,Some researchers are developing hybrid optimization techniques that combine elements of classical and heuristic optimization techniques with the incorporation of droop control,predictive control,and fuzzy-based methods.These methods can deal with large-scale systems with many objectives and non-linear,non-convex optimization issues.The most popular approaches are the LP and QP models,while more difficult problems are handled using metaheuristic optimization techniques.In summary,in order to increase efficiency,reduce costs,and ensure a consistent supply of electricity,optimization techniques are essential tools used in economic power dispatching from DGs.

Smart materials for safe lithium-ion batteries against thermal runaway

In recent years,the new energy storage system,such as lithium ion batteries(LIBs),has attracted much attention.In order to meet the demand of industrial progress for longer cycle life,higher energy density and cost efficiency,a quantity of research has been conducted on the commercial application of LIBs.However,it is difficult to achieve satisfying safety and cycling performance simultaneously.There may be thermal runaway(TR),external impact,overcharge and overdischarge in the process of battery abuse,which makes the safety problem of LIBs more prominent.In this review,we summarize recent progress in the smart safety materials design towards the goal of preventing TR of LIBs reversibly from different abuse conditions.Benefiting from smart responsive materials and novel structural design,the safety of LIBs can be improved a lot.We expect to provide a comprehensive reference for the development of smart and safe lithium-based battery materials.

Application and Effect Evaluation of the Integrated“5A and 3+3”Management Model in the Safe Medication Use for Chemotherapy Patients

Objective:To explore the application and effect evaluation of the integrated“5A and 3+3”management model in ensuring safe medication use for chemotherapy patients.Methods:A total of 100 intravenous chemotherapy patients admitted to the oncology department of Shaanxi Provincial People’s Hospital were randomly divided into two groups using a random number list method.Both groups received conventional nursing management during chemotherapy,while the study group additionally received the integrated“5A and 3+3”safety management model.The nursing intervention effects between the two groups were compared.Results:After the intervention,the study group showed higher levels of self-management ability,compliance,and nursing satisfaction compared to the control group.The overall incidence of adverse events during hospitalization was lower in the study group,with statistically significant differences(P<0.05).The knowledge scores of medical staff in the study group,related to the prevention and treatment of chemotherapy drug side effects,daily symptom management,and daily life management,were higher than those in the control group,with statistically significant differences(P<0.05).Conclusion:Implementing the integrated“5A and 3+3”model in the safe medication management of intravenous chemotherapy patients can effectively enhance patients’self-management abilities and compliance,improve medical staff’s ability to safely administer chemotherapy drugs,reduce adverse events caused by chemotherapy,and increase patient satisfaction.

Wind farm active power dispatching algorithm based on Grey Incidence

This study proposes a wind farm active power dispatching(WFAPD) algorithm based on the grey incidence method, which does not rely on an accurate mathematical model of wind turbines. Based on the wind turbine start-stop data at different wind speeds, the weighting coefficients, which are the participation degrees of a variable speed system and a variable pitch system in power regulation, are obtained using the grey incidence method. The incidence coefficient curve is fitted by the B-spline function at a full range of wind speeds, and the power regulation capacity of all wind turbines is obtained. Finally, the WFAPD algorithm, which is based on the regulating capacity of each wind turbine, is compared with the wind speed weighting power dispatching(WSWPD) algorithm in MATLAB. The simulation results show that the active power fluctuation of the wind farm is smaller, the rotating speed of wind turbines is smoother, and the fatigue load of highspeed turbines is effectively reduced.

Bifunctional flame retardant solid-state electrolyte toward safe Li metal batteries

Solid polymer electrolytes(SPEs)are one of the most promising alternatives to flammable liquid electrolytes for building safe Li metal batteries.Nevertheless,the poor ionic conductivity at room temperature(RT)and low resistance to Li dendrites seriously hinder the commercialization of SPEs.Herein,we design a bifunctional flame retardant SPE by combining hydroxyapatite(HAP)nanomaterials with Nmethyl pyrrolidone(NMP)in the PVDF-HFP matrix.The addition of HAP generates a hydrogen bond network with the PVDF-HFP matrix and cooperates with NMP to facilitate the dissociation of Li TFSI in the PVDF-HFP matrix.Consequently,the prepared SPE demonstrates superior ionic conductivity at RT,excellent fireproof properties,and strong resistance to Li dendrites.The assembled Li symmetric cell with prepared SPE exhibits a stable cycling performance of over 1200 h at 0.2 m A cm^(-2),and the solid-state LiFePO_4||Li cell shows excellent capacity retention of 85.3%over 600 cycles at 0.5 C.

Trimming the Degrees of Freedom via a K^(+) Flux Rectifier for Safe and Long‑Life Potassium‑Ion Batteries

High degrees of freedom(DOF)for K^(+)movement in the electrolytes is desirable,because the resulting high ionic conductivity helps improve potassium-ion batteries,yet requiring support from highly free and flammable organic solvent molecules,seriously affecting battery safety.Here,we develop a K^(+)flux rectifier to trim K ion’s DOF to 1 and improve electrochemical properties.Although the ionic conductivity is compromised in the K^(+)flux rectifier,the overall electrochemical performance of PIBs was improved.An oxidation stability improvement from 4.0 to 5.9 V was realized,and the formation of dendrites and the dissolution of organic cathodes were inhibited.Consequently,the K||K cells continuously cycled over 3,700 h;K||Cu cells operated stably over 800 cycles with the Coulombic efficiency exceeding 99%;and K||graphite cells exhibited high-capacity retention over 74.7%after 1,500 cycles.Moreover,the 3,4,9,10-perylenetetracarboxylic diimide organic cathodes operated for more than 2,100 cycles and reached year-scale-cycling time.We fabricated a 2.18 Ah pouch cell with no significant capacity fading observed after 100 cycles.

Mechanical Dispatch Reliability Prediction for Civil Aircraft Considering Operational Parameters

To effectively predict the mechanical dispatch reliability(MDR),the artificial neural networks method combined with aircraft operation health status parameters is proposed,which introduces the real civil aircraft operation data for verification,to improve the modeling precision and computing efficiency.Grey relational analysis can identify the degree of correlation between aircraft system health status(such as the unscheduled maintenance event,unit report event,and services number)and dispatch release and screen out themost closely related systems to determine the set of input parameters required for the prediction model.The artificial neural network using radial basis function(RBF)as a kernel function,has the best applicability in the prediction of multidimensional,small sample problems.Health status parameters of related systems are used as the input to predict the changing trend ofMDR,under the artificial neural network modeling framework.The case study collects real operation data for a certain civil aircraft over the past five years to validate the performance of the model which meets the requirements of the application.The results show that the prediction quadratic error Ep of the model reaches 6.9×10−8.That is to say,in the existing operating environment,the prediction of the number of delay&cancel events per month can be less than once.The accuracy of RBF ANN,BP ANN and GA-BP ANN are compared further,and the results show that RBF ANN has better adaptability to such multidimensional small sample problems.The efforts of this paper provide a highly efficientmethod for theMDR prediction through aircraft system health state parameters,which is a promising model to enhance the prediction and controllability of the dispatch release,providing support for the construction of the civil aircraft operation system.

A low-carbon economic dispatch model for electricity market with wind power based on improved ant-lion optimisation algorithm

Introducing carbon trading into electricity market can convert carbon dioxide into schedulable resources with economic value.However,the randomness of wind power generation puts forward higher requirements for electricity market transactions.Therefore,the carbon trading market is introduced into the wind power market,and a new form of low-carbon economic dispatch model is developed.First,the economic dispatch goal of wind power is be considered.It is projected to save money and reduce the cost of power generation for the system.The model includes risk operating costs to account for the impact of wind power output variability on the system,as well as wind farm negative efficiency operating costs to account for the loss caused by wind abandonment.The model also employs carbon trading market metrics to achieve the goal of lowering system carbon emissions,and analyze the impact of different carbon trading prices on the system.A low-carbon economic dispatch model for the wind power market is implemented based on the following two goals.Finally,the solution is optimised using the Ant-lion optimisation method,which combines Levi's flight mechanism and golden sine.The proposed model and algorithm's rationality is proven through the use of cases.

The collagen type Ⅰ alpha 1 chain gene is an alternative safe harbor locus in the porcine genome

Efficient and stable expression of foreign genes in cells and transgenic animals is important for gain-of-function studies and the establishment of bioreactors.Safe harbor loci in the animal genome enable consistent overexpression of foreign genes,without side effects.However,relatively few safe harbor loci are available in pigs,a fact which has impeded the development of multi-transgenic pig research.We report a strategy for efficient transgene knock-in in the endogenous collagen type I alpha 1 chain(COL1A1)gene using the clustered regularly interspaced short palindromic repeats/CRISPR-associated protein 9(CRISPR/Cas9)system.After the knock-in of a 2A peptide-green fluorescence protein(2A-GFP)transgene in the last codon of COL1A1 in multiple porcine cells,including porcine kidney epithelial(PK15),porcine embryonic fibroblast(PEF)and porcine intestinal epithelial(IPI-2I)cells,quantitative PCR(qPCR),Western blotting,RNA-seq and CCK8 assay were performed to assess the safety of COL1A1 locus.The qPCR results showed that the GFP knock-in had no effect(P=0.29,P=0.66 and P=0.20 for PK15,PEF and IPI-2I cells,respectively)on the mRNA expression of COL1A1 gene.Similarly,no significant differences(P=0.64,P=0.48 and P=0.80 for PK15,PEF and IPI-2I cells,respectively)were found between the GFP knock-in and wild type cells by Western blotting.RNA-seq results revealed that the transcriptome of GFP knock-in PEF cells had a significant positive correlation(P<2.2e–16)with that of the wild type cells,indicating that the GFP knock-in did not alter the global expression of endogenous genes.Furthermore,the CCK8 assay showed that the GFP knock-in events had no adverse effects(P_(24)h=0.31,P_(48)h=0.96,P_(72)h=0.24,P_(96)h=0.17,and P_(120)h=0.38)on cell proliferation of PK15 cells.These results indicate that the COL1A1 locus can be used as a safe harbor for foreign genes knock-in into the pig genome and can be broadly applied to farm animal breeding and biomedical model establishment.