Fortifying Smart Grids: A Holistic Assessment Strategy against Cyber Attacks and Physical Threats for Intelligent Electronic Devices

Intelligent electronic devices(IEDs)are interconnected via communication networks and play pivotal roles in transmitting grid-related operational data and executing control instructions.In the context of the heightened security challenges within smart grids,IEDs pose significant risks due to inherent hardware and software vulner-abilities,as well as the openness and vulnerability of communication protocols.Smart grid security,distinct from traditional internet security,mainly relies on monitoring network security events at the platform layer,lacking an effective assessment mechanism for IEDs.Hence,we incorporate considerations for both cyber-attacks and physical faults,presenting security assessment indicators and methods specifically tailored for IEDs.Initially,we outline the security monitoring technology for IEDs,considering the necessary data sources for their security assessment.Subsequently,we classify IEDs and establish a comprehensive security monitoring index system,incorporating factors such as running states,network traffic,and abnormal behaviors.This index system contains 18 indicators in 3 categories.Additionally,we elucidate quantitative methods for various indicators and propose a hybrid security assessment method known as GRCW-hybrid,combining grey relational analysis(GRA),analytic hierarchy process(AHP),and entropy weight method(EWM).According to the proposed assessment method,the security risk level of IEDs can be graded into 6 levels,namely 0,1,2,3,4,and 5.The higher the level,the greater the security risk.Finally,we assess and simulate 15 scenarios in 3 categories,which are based on monitoring indicators and real-world situations encountered by IEDs.The results show that calculated security risk level based on the proposed assessment method are consistent with actual simulation.Thus,the reasonableness and effectiveness of the proposed index system and assessment method are validated.

High-sensitivity closed-loop three-axis atomic magnetometer using two elliptically polarized laser beams

The high-sensitivity three-axis detection of magnetic fields is widely used in various applications.Our study demonstrates an atomic magnetometer detecting vector magnetic field,of which the core components are one glass cell and two elliptically polarized laser beams.The light-intensity noise is suppressed by differential detection technology,so a higher sensitivity is obtained compared with our previous work.The three-axis average sensitivities of the atomic magnetometer simultaneously reached 14 fT/Hz^(1/2)along the x axis,11 f T/Hz^(1/2)along the y axis,and 25 fT/Hz^(1/2)along the z axis.Furthermore,the atomic magnetometer's dynamic range was further improved to 150 n T,and its bandwidth was increased to over 200 Hz using a closed-loop control system.The proposed three-axis atomic magnetometer with a large dynamic range and a wide bandwidth holds great promise for biomagnetic measurement in a more challenging environment.

Research Progress in the Application of Chinese Herbal Medicines in Aquaculture： A Review

Due to increasing safety concerns regarding human consumption of fish products, an increasing number of medicinal chemicals are prohibited from use in aquaculture. As a result, Chinese herbal medicines are being increasingly used, coining the use of the term ＂green medicine.＂ Research shows that Chinese herbal medicines have many beneficial effects on fish, including growth promotion, enhancement of disease resistance, and improvement in meat quality. Many effective ingredients have been discovered in Chinese herbal medicines, which function to promote feed intake, improve meat flavor, and increase digestive enzyme activity. They also regulate and participate in processes that improve the specific and non-specific immunity of fish; however, the composition of Chinese herbal medicines is very complex and it is often difficult to identify the effective ingredients. This article reviews the latest research and application progress in Chinese herbal medicines regarding growth and feed utilization, immunity and disease resistance, and the meat quality of cultured fish. It also discusses research on the chemical constituents of classical Chinese medicinal herbs and problems with the application of Chinese herbal medicines in fish culture. This article concludes by proposing that future studies on Chinese herbal medicines should focus on how to cheaply refine and extract the effective ingredients in classical Chinese medicinal herbs, as well as how to use them efficiently in aquaculture.

Spatial variability effect of internal friction angle on the post-failure behavior of landslides using a random and non-Newtonian fluid based SPH method

This study proposed a random Smoothed Particle Hydrodynamics method for analyzing the post-failure behavior of landslides,which is based on the Karhunen-Loeve(K-L) expansion,the non-Newtonian fluid model,and the OpenMP parallel framework.Then,the applicability of this method was validated by comparing the generated random field with theoretical result and by simulating the post-failure process of an actual landslide.Thereafter,an illustrative landslide example was created and simulated to obtain the spatial variability effect of internal friction angle on the post-failure behavior of landslides under different coefficients of variation(COVs) and correlation lengths(CLs).As a conclusion,the reinforcement with materials of a larger friction angle can reduce the runout distance and impact the force of a landslide.As the increase of COV,the distribution range of influence zones also increases,which indicates that the deviation of influence zones becomes large.In addition,the correlation length in Monte Carlo simulations should not be too small,otherwise the variation range of influence zones will be underestimated.

A spatial-explicit dynamic vegetation model that couples carbon,water,and nitrogen processes for arid and semiarid ecosystems

Arid and semiarid ecosystems, or dryland, are important to global biogeochemical cycles. Dryland＇s community structure and vegetation dynamics as well as biogeochemical cycles are sensitive to changes in climate and atmospheric composition. Vegetation dynamic models has been applied in global change studies, but the com- plex interactions among the carbon （C）, water, and nitrogen （N） cycles have not been adequately addressed in the current models. In this study, a process-based vegetation dynamic model was developed to study the responses of dryland ecosystems to environmental changes, emphasizing on the interactions among the C, water, and N proc- esses. To address the interactions between the C and water processes, it not only considers the effects of annual precipitation on vegetation distribution and soil moisture on organic matter （SOM） decomposition, but also explicitly models root competition for water and the water compensation processes. To address the interactions between C and N processes, it models the soil inorganic mater processes, such as N mineralization/immobilization, denitrifica- tion/nitrification, and N leaching, as well as the root competition for soil N. The model was parameterized for major plant functional types and evaluated against field observations.

Enhanced stability of Pt/Al_(2)O_(3) modified by Zn promoter for catalytic dehydrogenation of ethane

Catalytic ethane dehydrogenation(EDH) to ethylene over Pt-based catalysts has received increasing interests in recent years as it is a potential alternative route to conventional steam cracking. However, the catalysts used in this reaction often suffer from rapid deactivation due to serious coke deposition and metal sintering. Herein, we reported the effects of Zn modification on the stability of Pt/Al2 O3 for EDH.The Zn-modified sample(PtZn2/Al2 O3) exhibits stable ethane conversion(20%) with over 95% ethylene selectivity. More importantly, it exhibits a significantly low deactivation rate of only 0.003 h-1 at 600 °C for70 h, which surpasses most of previously reported catalysts. Detailed characterizations including in situ FT-IR, ethylene adsorption microcalorimetry, and HAADF-STEM etc. reveal that Zn modifier reduces the number of Lewis acid sites on the catalyst surface. Moreover, it could modify Pt sites and preferentially cover the step sites, which decrease surface energy and retard the sintering of Pt particle, then prohibiting the further dehydrogenation of ethylene to ethylidyne. Consequently, the good stability is realized due to anti-sintering and the decrease of coke formation on the Pt Zn2/Al2 O3 catalyst.

Reliability and validity of the Chinese version of the Head and Neck Information Needs Questionnaire for patients with head and neck cancer and their caregivers

Objectives:This study aimed to assess the reliability and validity of the Chinese version of the Head and Neck Information Needs Questionnaire(HaNiQ).Methods:The HaNiQ was translated into a Chinese version using internationally recognized forwardand back-translation procedures.The reliability and validity of the HaNiQ were measured using Cronbach’s a coefficient,split-half reliability,exploratory factor analysis,and Pearson correlation analysis.Results:A total of 207 patients in different head and neck cancer(HNC)stages and 174 caregivers completed the Chinese version of the HaNiQ.Internal consistencies varied between good and very well(Cronbach’s α coefficient 0.74-0.90);the split-half coefficient and the content validity index(CVI)of the questionnaire were 83.5%and 83.33%,respectively.The cumulative contribution rates of the 5 subscales in patients with HNCand their caregivers were 62.41%and 61.19%,respectively.However,there are some differences between the Chinese questionnaire for caregiver and the original questionnaire regarding the attribution of items.Items 22,23,and 27 in the Psychosocial subscale of the English version were assigned to the Survivorship subscale in the Chinese version for caregivers.Conclusions:The results demonstrated that the Chinese version of the HaNiQ is a reliable and valid instrument for measuring the information needs of patients with HNC and that of their caregivers.Though the structure of the Chinese versionwas different from the English version for caregivers of HNC patients,the Chinese version of the HaNiQ appears to be reliable and would benefit from further testing.

Electrospinning of a sandwich-structured membrane with sustained release capability and long-term anti-inflammatory effects for dental pulp regeneration

Current electro spun membranes used for pulp capping still lack the sustained-release capability and long-term anti-inflammatory effects that are favorable for dental pulp regeneration.In this work,a single-layered poly(lac tic acid)(PLA)electro spun membrane loaded with amorphous calcium phosphate(ACP)and aspirin(PLA/ACP/Aspirin membrane,i.e.,PA A membrane)is sandwiched between two poly(lactic-co-glycolic acid)(PLGA)electro spun membranes as a novel sandwich-structured PLGA and PA A composite electro spun membrane(PLGA-PAA membrane)to resolve the need for sustained-release design and anti-inflammatory effects.Contact angle measurements indicate that the PLGA-PAA membrane is more hydrophilic than the PAA membrane.An in vitro release study reveals that PLGA membranes coated on PAA membrane could slightly slow down ion release,while signiificantly prolonging aspirin release.We also co-cultured membranes with dental pulp stem cells(DPSCs)and human monocytic THP-1 cells to evaluate their osteogenic ability and anti-inflammatory effects,respectively.Compared with the PAA membrane,the PLGA-PAA membrane promotes cell adhesion,proliferation,and osteogenic differentiation.A prolonged anti-inflammatory effect of up to 18 days is also observed in the PLGA-PAA group.The results suggest a promising strategy for fabricating an electro spun membrane system with controlled release capabilities and long-term anti-inflammatory effects for use as pulp-capping material for regeneration of the dentin-pulp complex.

Noise level estimation method with application to EMD-based signal denoising

This paper proposes a new signal noise level estimation approach by local regions. The estimated noise variance is applied as the threshold for an improved empirical mode decomposition（EMD） based signal denoising method. The proposed estimation method can effectively extract the candidate regions for the noise level estimation by measuring the correlation coefficient between noisy signal and a Gaussian filtered signal. For the improved EMD based method, the situation of decomposed intrinsic mode function（IMFs） which contains noise and signal simultaneously are taken into account. Experimental results from two simulated signals and an X-ray pulsar signal demonstrate that the proposed method can achieve better performance than the conventional EMD and wavelet transform（WT） based denoising methods.

An Intelligent Tumors Coding Method Based on Drools

In order to solve the problems of low efficiency and heavy workload of tumor coding in hospitals,we proposed a Drools-based intelligent tumors coding method.At present,most tumor hospitals use manual coding,the trained coders follow the main diagnosis selection rules to select the main diagnosis from the discharge diagnosis of the tumor patients,and then code all the discharge diagnoses according to the coding rules.Owing to different coders have different familiarity with the main diagnosis selection rules and ICD-10 disease coding,it will reduce the efficiency of the artificial coding results and affect the quality of the whole medical record.We first analyze the ICD library information,doctor's diagnostic information,radiotherapy information or chemotherapy information,surgery information,hospitalization information and other related information,and then generated Drools rule files based on the main diagnostic selection principles and coding principles,we also combined the text similarity analysis algorithm to construct an intelligent diagnostic information coding method.Practice shows that the coding method can be used to make the work efficiently and at the same time obtain the coding results which meet the standard and have high accuracy,so that the coders can be free from the repeated work and pay more attention to coding quality control and the coding logic adjustment.

Improved Prediction and Understanding of Glass-Forming Ability Based on Random Forest Algorithm

As an ideal material,bulk metallic glass(MG)has a wide range of applications because of its unique properties such as structural,functional and biomedical materials.However,it is difficult to predict the glass-forming ability(GFA)even given the criteria in theory and this problem greatly limits the application of bulk MG in industrial field.In this work,the proposed model uses the random forest classification method which is one of machine learning methods to solve the GFA prediction for binary metallic alloys.Compared with the previous SVM algorithm models of all features combinations,this new model is successfully constructed based on the random forest classification method with a new combination of features and it obtains better prediction results.Simultaneously,it further shows the degree of feature parameters influence on GFA.Finally,a normalized evaluation indicator of binary alloy for machine learning model performance is put forward for the first time.The result shows that the application of machine learning in MGs is valuable.

Effect of Cross-Modal Perceptual Training on Audiovisual Integration in Older Adults

Background: Previous studies have demonstrated the plasticity of perceptual sensitivity and compensatory mechanisms of audiovisual integration (AVI) in older adults. However, the impact of perceptual training on audiovisual integrative abilities remains unclear. Methods: This study randomly assigned 40 older adults to either a training or control group. The training group underwent a five-day audiovisual perceptual program, while the control group received no training. Participants completed simultaneous judgment (SJ) and audiovisual detection tasks before and after training. Results: Findings indicated improved perceptual sensitivity to audiovisual synchrony in the training group, with AVI significantly higher post-test compared to pre-test (9.95% vs. 13.87%). No significant change was observed in the control group (9.61% vs. 10.77%). Conclusion: These results suggested that cross-modal perceptual training might be an effective candidate cognitive intervention to ease the dysfunction of unimodal sensory.

The Controllability of Quantum Correlation under Geometry and Entropy Discords

Quantum correlation plays a critical role in the maintenance of quantum information processing and nanometer device design.In the past two decades,several quantitative methods had been proposed to study the quantum correlation of certain open quantum systems,including the geometry and entropy style discord methods.However,there are differences among these quantification methods,which promote a deep understanding of the quantum correlation.In this paper,a novel time-dependent three environmental open system model is established to study the quantum correlation.This system model interacts with two independent spin-environments(two spin-environments are connected to the other spin-environment)respectively.We have calculated and compared the changing properties of the quantum correlation under three kinds of geometry and two entropy discords,especially for the freezing phenomenon.At the same time,some original and novel changing behaviors of the quantum correlation under different timedependent parameters are studied,which is helpful to achieve the optimal revival of the quantum discord and the similar serrated form of the freezing phenomenon.Finally,it shows the controllability of the freezing correlation and the robustness of these methods by adjusting time-dependent parameters.This work provides a new way to control the quantum correlation and design nanospintronic devices.

Molecular mechanisms of cellular metabolic homeostasis in stem cells

Many tissues and organ systems have intrinsic regeneration capabilities that are largely driven and maintained by tissue-resident stem cell populations. In recent years, growing evidence has demonstrated that cellular metabolic homeostasis plays a central role in mediating stem cell fate, tissue regeneration, and homeostasis. Thus, a thorough understanding of the mechanisms that regulate metabolic homeostasis in stem cells may contribute to our knowledge on how tissue homeostasis is maintained and provide novel insights for disease management. In this review, we summarize the known relationship between the regulation of metabolic homeostasis and molecular pathways in stem cells. We also discuss potential targets of metabolic homeostasis in disease therapy and describe the current limitations and future directions in the development of these novel therapeutic targets.

An Image Localization System Based on Single Photon

As an essential part of artificial intelligence,many works focus on image processing which is the branch of computer vision.Nevertheless,image localization faces complex challenges in image processing with image data increases.At the same time,quantum computing has the unique advantages of improving computing power and reducing energy consumption.So,combining the advantage of quantum computing is necessary for studying the quantum image localization algorithms.At present,many quantum image localization algorithms have been proposed,and their efficiency is theoretically higher than the corresponding classical algorithms.But,in quantum computing experiments,quantum gates in quantum computing hardware need to work at very low temperatures,which brings great challenges to experiments.This paper proposes a single-photon-based quantum image localization algorithm based on the fundamental theory of single-photon image classification.This scheme realizes the operation of the mixed national institute of standards and technology database(MNIST)quantum image localization by a learned transformation for non-noise condition,noisy condition,and environmental attack condition,respectively.Compared with the regular use of entanglement between multi-qubits and low-temperature noise reduction conditions for image localization,the advantage of this method is that it does not deliberately require low temperature and entanglement resources,and it improves the lower bound of the localization success rate.This method paves a way to study quantum computer vision.

Formal Verification Platform as a Service:WebAssembly Vulnerability Detection Application

In order to realize a general-purpose automatic formal verification platform based on WebAssembly technology as a web service(FVPS),which aims to provide an automated report of vulnerability detections,this work builds a Hyperledger Fabric blockchain runtime model.It proposes an optimized methodology of the functional equivalent translation from source program languages to formal languages.This methodology utilizes an external application programming interface(API)table to replace the source codes in compilation,thereby pruning the part of housekeeping codes to ease code inflation.Code inflation is a significant metric in formal language translation.Namely,minor code inflation enhances verification scale and performance efficiency.It determines the efficiency of formal verification,involving launching,running,and memory usage.For instance,path explosion increases exponentially,resulting in out-of-memory.The experimental results conclude that program languages like golang severely impact code inflation.FVPS reduces the wasm code size by over 90%,achieving two orders of optimization magnitude,from 2000 kilobyte(KB)to 90 KB.That means we can cope with golang applications up to 20 times larger than the original in scale.This work eliminates the gap between Hyperledger Fabric smart contracts and WebAssembly.Our approach is pragmatic,adaptable,extendable,and flexible.Nowadays,FVPS is successfully applied in a Railway-Port-Aviation blockchain transportation system.

Effects of Torreya grandis Root Rot on Seedling Growth and Screening of Its Chemical Control Agents

[Objectives]This study was conducted to prevent the occurrence of root rot disease in Torreya grandis and improve the yield and quality of T.grandis.[Methods]One-year-old and two-year-old seedlings of Torreya grandis‘Xifei’and‘Cufei’were inoculated with the root rot pathogen Fusarium fujikuroi,and the changes in photosynthesis,chlorophyll content,malondialdehyde,and defense enzyme system(superoxide dismutase,peroxidase,and catalase)activity in leaves of T.grandis seedlings were investigated using water as a control.Meanwhile,the control effects of 80%carbendazim wettable powder,64%metalaxyl·mancozeb wettable powder,430 g/L tebuconazole suspension and 30%difenoconazole·cyproconazole EC on root rot in T.grandis were investigated.[Results]After inoculation with the pathogen F.fujikuroi,the net photosynthetic rates and transpiration rates in leaves of T.grandis‘Xifei’and‘Cufei’decreased,and the contents of chlorophyll decreased,while the contents of malondialdehyde increased,and the contents of superoxide dismutase and catalase increased with time.However,peroxidase showed a high activity in T.grandis‘Cufei’only,but a trend of"increasing-decreasing-increasing"in T.grandis‘Xifei’.Among the four chemical control agents,64%metalaxyl·mancozeb wettable powder had the best control effect on root rot of T.grandis caused by the pathogenic fungus F.fujikuroi.[Conclusions]The net photosynthetic rate,transpiration rate,chlorophyll content,malondialdehyde content,superoxide dismutase activity and catalase activity could all be used as screening indicators for T.grandis varieties resistant to root rot.Meanwhile,64%metalaxyl·mancozeb wettable powder could be used as a control agent for root rot on T.grandis.

The Interaction of Heat and Lipopolysaccharide on the Expression Levels of Receptor Activator of NF-κB Ligand and Osteoprotegerin in Human Periodontal Ligament Cells

Aim: To investigate the expression levels of receptor activator of NF-κB ligand (RANKL) and oste-oprotegerin (OPG) in human periodontal ligament fibroblasts when stimulated with heat in infective conditions. Methods: Periodontal ligament fibroblasts were subjected to various temperature increases for 5 min with or without 10 ng/mL lipopolysaccharide (LPS) and then maintained at 37℃ for 6 h. After that, the expression levels of RANKL and OPG were investigated using real-time RT-PCR and ELISA. As a positive or negative control, the cells were cultured at 37℃ with or without 10 ng/mL LPS. Data were analyzed using one-way ANOVA at a significant level of p = 0.05. Results: The mRNA expression levels of RANKL and OPG were both down-regulated when the cells were heated in infective conditions. The release of sRANKL was increased at low temperatures in such infection;while at high temperatures heat treatment down-regulated the release of sRANKL induced by LPS. The relative RANKL/OPG expression ratios were increased at low temperatures in infective conditions. Conclusions: The interactions between heat and LPS would affect the balance between RANKL and OPG in periodontal ligament fibroblasts when they were heated in infective conditions. Such infection may be the reason why bone resorption occurs in the local area after warm vertical compaction.

Online News Sentiment Classification Using DistilBERT

The ability of pre-trained BERT model to achieve outstanding performances on many Natural Language Processing(NLP)tasks has attracted the attention of researchers in recent times.However,the huge computational and memory requirements have hampered its widespread deployment on devices with limited resources.The concept of knowledge distillation has shown to produce smaller and faster distilled models with less trainable parameters and intended for resource-constrained environments.The distilled models can be fine-tuned with great performance on a wider range of tasks,such as sentiment classification.This paper evaluates the performance of DistilBERT model and other pre-canned text classifiers on a Covid-19 online news binary classification dataset.The analysis shows that despite having fewer trainable parameters than the BERT-based model,the DistilBERT model achieved an accuracy of 0.94 on the validation set after only two training epochs.The paper also highlights the usefulness of the ktrain library in facilitating the building,training,and application of state-of-the-art Machine Learning and Deep Learning models.

Dynamic Target Detection and Tracking Based on Quantum Illumination LIDAR

In the detection process of classic radars such as radar/lidar,the detection performance will be weakened due to the presence of background noise and loss.The quantum illumination protocol can use the spatial correlation between photon pairs to improve image quality and enhance radar detection performance,even in the presence of loss and noise.Based on this quantum illumination LIDAR,a theoretic scheme is developed for the detection and tracking of moving targets,and the trajectory of the object is analyzed.Illuminated by the quantum light source as Spontaneous Parametric Down-Conversion(SPDC),an opaque target can be identified from the background in the presence of strong noise.The static objects obtained by classical and quantum illumination are compared,respectively,and the advantages of quantum illumination are verified.The moving objects are taken at appropriate intervals to obtain the images of the moving objects,then the images are visualized as dynamic images,and the three-frame difference method is used to obtain the target contour.Finally,the image is performed by a series of processing on to obtain the trajectory of the target object.Several different motion situations are analyzed separately,and compared with the set object motion trajectory,which proves the effectiveness of the scheme.This scheme has potential practical application value.