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.

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.

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.

MIL-100(V) derived porous vanadium oxide/carbon microspheres with oxygen defects and intercalated water molecules as high-performance cathode for aqueous zinc ion battery

The development of aqueous zinc ion battery cathode materials with high capacity and high magnification is still a challenge.Herein,porous vanadium oxide/carbon(p-VO_(x)@C,mainly VO_(2) with a small amount of V_(2)O_(3)) core/shell microspheres with oxygen vacancies are facilely fabricated by using a vanadium-based metal-organic framework(MIL-100(V)) as a sacrificial template.This unique structure can improve the conductivity of the VO_(x),accelerate electrolyte diffusion,and suppress structural collapse during circulation.Subsequently,H_(2)O molecules are introduced into the interlayer of VO_(x) through a highly efficient in-situ electrochemical activation process,facilitating the intercalation and diffusion of zinc ions.After the activation,an optimal sample exhibits a high specific capacity of 464.3 mA h g^(-1) at0.2 A g^(-1) and 395.2 mA h g^(-1) at 10 A g^(-1),indicating excellent rate performance.Moreover,the optimal sample maintains a capacity retention of about 89.3% after 2500 cycles at 10 A g^(-1).Density functional theory calculation demonstrates that the presence of oxygen vacancies and intercalated water molecules can significantly reduce the diffusion barrier for zinc ions.In addition,it is proved that the storage of zinc ions in the cathode is achieved by reversible intercalation/extraction during the charge and discharge process through various ex-situ analysis technologies.This work demonstrates that the p-VO_(x)@C has great potential for applications in aqueous ZIBs after electrochemical activation.

Defective Nickel-Iron Layered Double Hydroxide for Enhanced Photocatalytic NO Oxidation with Significant Alleviation of NO2 Production

Photocatalysis offers a sustainable means for the oxidative removal of low concentrations of NOx(NO,NO2,N2O,N2O5,etc.)from the atmosphere.Layered double hydroxides(LDHs)are promising candidate photocatalysts owing to their unique layered and tunable chemical structures and abundant surface hydroxide(OH)moieties,which are hydroxyl radical(OH)precursors.However,the practical applications of LDHs are limited by their poor charge-separation ability and insufficient active sites.Herein,we developed a facile N_(2)H_(4)-driven etching approach to introduce dual Ni^(2+)and OHvacancies(Niv and OHv,respectively)into NiFe-LDH nanosheets(hereafter referred to as NiFe-LDH-et)to facilitate improved charge-carrier separation and active Lewis acidic site(Fe^(3+)and Ni^(2+)exposed at OHv)formation.In contrast to inert pristine LDH,NiFe-LDH-et actively removed NO under visible-light illumination.Specifically,Ni_(76)Fe_(24)-LDH-et etched with 1.50 mmol·L^(-1)N_(2)H_(4)solution removed 32.8%of the NO in continuously flowing air(NO feed concentration:500 parts per billion(ppb))under visible-light illumination,thereby outperforming most reported catalysts.Experimental and theoretical data revealed that the dual vacancies promoted the production of reactive oxygen species(O_(2)·^(-)andOH)and the adsorption of NO on the LDH.In situ spectroscopy demonstrated that NO was preferentially adsorbed at Lewis acidic sites,particularly exposed Fe^(3+)sites,converted into NO+,and subsequently oxidized to NO3without the notable formation of the more toxic intermediate NO2,thereby alleviating risks associated with its production and emission.

GmAP1d regulates flowering time under long-day photoperiods in soybean

Flowering time is important for adaptation of soybean(Glycine max)to different environments.Here,we conducted a genome-wide association study of flowering time using a panel of 1490 cultivated soybean accessions.We identified three strong signals at the qFT02-2 locus(Chr02:12037319–12238569),which were associated with flowering time in three environments:Gongzhuling,Mengcheng,and Nanchang.By analyzing linkage disequilibrium,gene expression patterns,gene annotation,and the diversity of variants,we identified an AP1 homolog as the candidate gene for the qFT02-2 locus,which we named GmAP1d.Only one nonsynonymous polymorphism existed among 1490 soybean accessions at position Chr02:12087053.Accessions carrying the Chr02:12087053-T allele flowered significantly earlier than those carrying the Chr02:12087053-A allele.Thus,we developed a cleaved amplified polymorphic sequence(CAPS)marker for the SNP at Chr02:12087053,which is suitable for marker-assisted breeding of flowering time.Knockout of GmAP1d in the‘Williams 82’background by gene editing promoted flowering under long-day conditions,confirming that GmAP1d is the causal gene for qFT02-2.An analysis of the region surrounding GmAP1d revealed that GmAP1d was artificially selected during the genetic improvement of soybean.Through stepwise selection,the proportion of modern cultivars carrying the Chr02:12087053-T allele has increased,and this allele has become nearly fixed(95%)in northern China.These findings provide a theoretical basis for better understanding the molecular regulatory mechanism of flowering time in soybean and a target gene that can be used for breeding modern soybean cultivars adapted to different latitudes.

The Effect of Soil Enzymes and Polysaccharides Secreted by the Roots of Salvia miltiorrhiza Bunge under Drought,High Temperature,and Nitrogen and Phosphorus Deficits

Root exudates serve as crucial mediators for information exchange between plants and soil,and are an important evolutionary mechanism for plants’adaptation to environmental changes.In this study,15 different abiotic stress models were established using various stress factors,including drought(D),high temperature(T),nitrogen deficiency(N),phosphorus deficiency(P),and their combinations.We investigated their effects on the seedling growth of Salvia miltiorrhiza Bunge and the activities of Solid-Urease(S-UE),Solid-Nitrite Reductase(S-NiR),Solid-Nitrate Reductase(S-NR),Solid-Phosphotransferase(S-PT),and Solid-Catalase(S-CAT),as well as the contents of polysaccharides in the culture medium.The results showed that the growth of S.miltiorrhiza was inhibited under 15 stress conditions.Among them,13 stress conditions increased the root-shoot ratio.These 15 stress conditions significantly reduced the activity of S-NR,two combinations significantly improved the activity of S-NIR,they were synergistic stresses of high temperature and nitrogen deficiency(TN),and synergistic stresses of drought and nitrogen deficiency(DN)(p<0.05).The activity of S-UE was significantly improved under N,D,T,synergistic stresses of drought and high temperature(DT),DN,synergistic stresses of drought and phosphorus deficiency(DP),and synergistic stresses of high temperature,nitrogen,and phosphorus deficiency(TNP)stress conditions(p<0.05).Most stress combinations reduced the activity of S-PT,but D and T significantly improved it.(p<0.05).The N,DN,and TN stress conditions significantly reduced S-CAT activity.The P,DT,and synergistic stresses of drought,high temperature,and phosphorus deficiency(DTP)significantly decreased the total polysaccharide content of the soil(p<0.05).The research suggested that abiotic stress hindered the growth of S.miltiorrhiza and altered the behavior of root secretion.Roots regulated the secretion of several substances in response to various abiotic stresses,including soil nitrogen cycle enzymes,phosphorus transport-related enzymes,and antioxidant enzymes.In conclusion,plants regulate the utilization of rhizosphere substances in response to abiotic stresses by modulating the exudation of soil enzymes and polysaccharides by the root system.At the same time,soil carbon sequestration was affected by the adverse environment,which restricted the input of organic matter into the soil.

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.

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.

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.

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.

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.

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.