ESTIMATION OF AVERAGE DIFFERENTIAL ENTROPY FOR A STATIONARY ERGODIC SPACE-TIME RANDOM FIELD ON A BOUNDED AREA

In this paper,we mainly discuss a discrete estimation of the average differential entropy for a continuous time-stationary ergodic space-time random field.By estimating the probability value of a time-stationary random field in a small range,we give an entropy estimation and obtain the average entropy estimation formula in a certain bounded space region.It can be proven that the estimation of the average differential entropy converges to the theoretical value with a probability of 1.In addition,we also conducted numerical experiments for different parameters to verify the convergence result obtained in the theoretical proofs.

Detection method of forward-scatter signal based on Rényi entropy

The application scope of the forward scatter radar(FSR)based on the Global Navigation Satellite System(GNSS)can be expanded by improving the detection capability.Firstly,the forward-scatter signal model when the target crosses the baseline is constructed.Then,the detection method of the for-ward-scatter signal based on the Rényi entropy of time-fre-quency distribution is proposed and the detection performance with different time-frequency distributions is compared.Simula-tion results show that the method based on the smooth pseudo Wigner-Ville distribution(SPWVD)can achieve the best perfor-mance.Next,combined with the geometry of FSR,the influence on detection performance of the relative distance between the target and the baseline is analyzed.Finally,the proposed method is validated by the anechoic chamber measurements and the results show that the detection ability has a 10 dB improvement compared with the common constant false alarm rate(CFAR)detection.

Novel Investigation of Stochastic Fractional Differential Equations Measles Model via the White Noise and Global Derivative Operator Depending on Mittag-Leffler Kernel

Because of the features involved with their varied kernels,differential operators relying on convolution formulations have been acknowledged as effective mathematical resources for modeling real-world issues.In this paper,we constructed a stochastic fractional framework of measles spreading mechanisms with dual medication immunization considering the exponential decay and Mittag-Leffler kernels.In this approach,the overall population was separated into five cohorts.Furthermore,the descriptive behavior of the system was investigated,including prerequisites for the positivity of solutions,invariant domain of the solution,presence and stability of equilibrium points,and sensitivity analysis.We included a stochastic element in every cohort and employed linear growth and Lipschitz criteria to show the existence and uniqueness of solutions.Several numerical simulations for various fractional orders and randomization intensities are illustrated.

基于Dematel-Entropy耦合赋权的煤层顶板富水性评价

矿井的安全问题一直是矿业领域关注的重点,而准确评估含水层的富水性是进行涌水或突水灾害风险分析的首要前提。以大南湖七号煤矿一、二分区3煤顶板为研究对象,首先,在分析采区的地质及水文地质条件的基础上,选取了含水层厚度、隔水指数、煤层倾角、岩心采取率、砂泥互层指数、岩性结构指数6个因素作为评价富水性的主要指标;然后,采用Dematel(决策实验室法)-Entropy(熵值法)确定主客观权重,利用信息熵原理协调2种评价方法之间既竞争又一致的关系,得到各影响因素组合权重;最后,结合GIS分析功能对含水层富水性进行评价分区。研究结果表明:一分区整体为弱富水性,二分区以中等-强富水性为主,评价结果准确率达80%以上,可为煤层顶板含水层的疏水方案提供参考。

IGED:Towards Intelligent DDoS Detection Model Using Improved Generalized Entropy and DNN

As the scale of the networks continually expands,the detection of distributed denial of service(DDoS)attacks has become increasingly vital.We propose an intelligent detection model named IGED by using improved generalized entropy and deep neural network(DNN).The initial detection is based on improved generalized entropy to filter out as much normal traffic as possible,thereby reducing data volume.Then the fine detection is based on DNN to perform precise DDoS detection on the filtered suspicious traffic,enhancing the neural network’s generalization capabilities.Experimental results show that the proposed method can efficiently distinguish normal traffic from DDoS traffic.Compared with the benchmark methods,our method reaches 99.9%on low-rate DDoS(LDDoS),flooded DDoS and CICDDoS2019 datasets in terms of both accuracy and efficiency in identifying attack flows while reducing the time by 17%,31%and 8%.

Entropy of deterministic trajectory via trajectories ensemble

We present a formulation of the single-trajectory entropy using the trajectories ensemble. The single-trajectory entropy is affected by its surrounding trajectories via the distribution function. The single-trajectory entropies are studied in two typical potentials, i.e., harmonic potential and double-well potential, and in viscous environment by interacting trajectory method. The results of the trajectory methods are in agreement well with the numerical methods(Monte Carlo simulation and difference equation). The single-trajectory entropies increasing(decreasing) could be caused by absorption(emission) heat from(to) the thermal environment. Also, some interesting trajectories, which correspond to the rare evens in the processes, are demonstrated.

A Novel Derivation of Black Hole Entropy in all Dimensions from Truly Point Mass Sources

It is explicitly shown how the Schwarzschild Black Hole Entropy (in all dimensions) emerges from truly point mass sources at r=0due to a non-vanishing scalar curvature involving the Dirac delta distribution. In order to achieve this, one is required to extend the domain of r to negative values −∞≤r≤+∞. It is the density and anisotropic pressure components associated with the point mass delta function source at the origin r=0which furnish the Schwarzschild black hole entropy in all dimensions D≥4after evaluating the Euclidean Einstein-Hilbert action. Two of the most salient results are i) that the observed spacetime dimension D=4is precisely singled out from all the other dimensions when the strong and weak energy conditions are met, and ii) the point mass source described in this work is not the result of a spherically symmetric gravitational collapse of a star as described by the Oppenheimer-Snyder model because we are not neglecting the pressure. As usual, it is required to take the inverse Hawking temperature βHas the length of the circle Sβ1obtained from a compactification of the Euclidean time in thermal field theory which results after a Wick rotation, it=τ, to imaginary time. This approach can be generalized to the Reissner-Nordstrom and Kerr-Newman metrics. The physical implications of this finding warrant further investigation since it suggests a profound connection between the notion of gravitational entropy and spacetime singularities.

Research on a deconvolution algorithm for laser-induced fluorescence diagnosis based on the maximum entropy principle

Laser-induced fluorescence(LIF)spectroscopy is employed for plasma diagnosis,necessitating the utilization of deconvolution algorithms to isolate the Doppler effect from the raw spectral signal.However,direct deconvolution becomes invalid in the presence of noise as it leads to infinite amplification of high-frequency noise components.To address this issue,we propose a deconvolution algorithm based on the maximum entropy principle.We validate the effectiveness of the proposed algorithm by utilizing simulated LIF spectra at various noise levels(signal-to-noise ratio,SNR=20–80 d B)and measured LIF spectra with Xe as the working fluid.In the typical measured spectrum(SNR=26.23 d B)experiment,compared with the Gaussian filter and the Richardson–Lucy(R-L)algorithm,the proposed algorithm demonstrates an increase in SNR of 1.39 d B and 4.66 d B,respectively,along with a reduction in the root-meansquare error(RMSE)of 35%and 64%,respectively.Additionally,there is a decrease in the spectral angle(SA)of 0.05 and 0.11,respectively.In the high-quality spectrum(SNR=43.96 d B)experiment,the results show that the running time of the proposed algorithm is reduced by about98%compared with the R-L iterative algorithm.Moreover,the maximum entropy algorithm avoids parameter optimization settings and is more suitable for automatic implementation.In conclusion,the proposed algorithm can accurately resolve Doppler spectrum details while effectively suppressing noise,thus highlighting its advantage in LIF spectral deconvolution applications.

Characterization of local chemical ordering and deformation behavior in high entropy alloys by transmission electron microscopy

Short-range ordering(SRO)is one of the most important structural features of high entropy alloys(HEAs).However,the chemical and structural analyses of SROs are very difficult due to their small size,complexed compositions,and varied locations.Transmission electron microscopy(TEM)as well as its aberration correction techniques are powerful for characterizing SROs in these compositionally complex alloys.In this short communication,we summarized recent progresses regarding characterization of SROs using TEM in the field of HEAs.By using advanced TEM techniques,not only the existence of SROs was confirmed,but also the effect of SROs on the deformation mechanism was clarified.Moreover,the perspective related to application of TEM techniques in HEAs are also discussed.

Efficient slope reliability analysis under soil spatial variability using maximum entropy distribution with fractional moments

Spatial variability of soil properties imposes a challenge for practical analysis and design in geotechnical engineering.The latter is particularly true for slope stability assessment,where the effects of uncertainty are synthesized in the so-called probability of failure.This probability quantifies the reliability of a slope and its numerical calculation is usually quite involved from a numerical viewpoint.In view of this issue,this paper proposes an approach for failure probability assessment based on Latinized partially stratified sampling and maximum entropy distribution with fractional moments.The spatial variability of geotechnical properties is represented by means of random fields and the Karhunen-Loève expansion.Then,failure probabilities are estimated employing maximum entropy distribution with fractional moments.The application of the proposed approach is examined with two examples:a case study of an undrained slope and a case study of a slope with cross-correlated random fields of strength parameters under a drained slope.The results show that the proposed approach has excellent accuracy and high efficiency,and it can be applied straightforwardly to similar geotechnical engineering problems.

Decoding Retinoblastoma: Differential Gene Expression

Background: Retinoblastoma, the most common intraocular pediatric cancer, presents complexities in its genetic landscape that necessitate a deeper understanding for improved therapeutic interventions. This study leverages computational tools to dissect the differential gene expression profiles in retinoblastoma. Methods: Employing an in silico approach, we analyzed gene expression data from public repositories by applying rigorous statistical models, including limma and de seq 2, for identifying differentially expressed genes DEGs. Our findings were validated through cross-referencing with independent datasets and existing literature. We further employed functional annotation and pathway analysis to elucidate the biological significance of these DEGs. Results: Our computational analysis confirmed the dysregulation of key retinoblastoma-associated genes. In comparison to normal retinal tissue, RB1 exhibited a 2.5-fold increase in expression (adjusted p Conclusions: Our analysis reinforces the critical genetic alterations known in retinoblastoma and unveils new avenues for research into the disease’s molecular basis. The discovery of chemoresistance markers and immune-related genes opens potential pathways for personalized treatment strategies. The study’s outcomes emphasize the power of in silico analyses in unraveling complex cancer genomics.

Boron-doped high-entropy oxide toward high-rate and long-cycle layered cathodes for wide-temperature sodium-ion batteries

03-type layered metal oxides hold great promise for sodium-ion batteries cathodes owing to their energy density advantage.However,the severe irreversible phase transition and sluggish Na^(+)diffusion kinetics pose significant challenges to achieve high-performance layered cathodes.Herein,a boron-doped03-type high entropy oxide Na(Fe_(0.2)Co_(0.15)Cu_(0.05)Ni_(0.2)Mn_(0.2)Ti_(0.2))B_(0.02)O_(2)(NFCCNMT-B_(0.02))is designed and the covalent B-O bonds with high entropy configuration ensure a robust layered structure.The obtained cathode NFCCNMT-B_(0.02)exhibits impressive cycling performance(capacity retention of 95%and 82%after100 cycles and 300 cycles at 1 and 10 C,respectively)and outstanding rate capability(capacity of 83 mAh g^(-1)at 10 C).Furthermore,the NFCCNMT-B_(0.02)demonstrates a superior wide-temperature performance,maintaining the same capacity level(113,4 mAh g^(-1)@-20℃,121 mAh g^(-1)@25℃,and 119 mAh g^(-1)@60℃)and superior cycle stability(90%capacity retention after 100 cycles at 1 C at-20℃).The high-entropy configuration design with boron doping strategy contributes to the excellent sodium-ion storage performance.The high-entropy configuration design effectively suppresses irreversible phase transitions accompanied by small volume changes(ΔV=0.65 A3).B ions doping expands the Na layer distance and enlarges the P3 phase region,thereby enhancing Na^(+)diffusion kinetics.This work offers valuable insights into design of high-performance layered cathodes for sodium-ion batteries operating across a wide temperature.

Blade Wrap Angle Impact on Centrifugal Pump Performance:Entropy Generation and Fluid-Structure Interaction Analysis

The centrifugal pump is a prevalent power equipment widely used in different engineering patterns,and the impeller blade wrap angle significantly impacts its performance.A numerical investigation was conducted to analyze the influence of the blade wrap angle on flow characteristics and energy distribution of a centrifugal pump evaluated as a low specific speed with a value of 69.This study investigates six impellermodels that possess varying blade wrap angles(95°,105°,115°,125°,135°,and 145°)that were created while maintaining the same volute and other geometrical characteristics.The investigation of energy loss was conducted to evaluate the values of total and entropy generation rates(TEG,EGR).The fluid-structure interaction was considered numerically using the software tools ANSYS Fluent and ANSYSWorkbench.The elastic structural dynamic equation was used to estimate the structural response,while the shear stress transport k–ωturbulence model was utilized for the fluid domain modeling.The findings suggest that the blade wrap angle has a significant influence on the efficiency of the pump.The impeller featuring a blade wrap angle of 145°exhibits higher efficiency,with a notable increase of 3.76%relative to the original model.Variations in the blade wrap angle impact the energy loss,shaft power,and pump head.The model with a 145°angle exhibited a maximum equivalent stress of 14.8MPa and a total deformation of 0.084 mm.The results provide valuable insights into the intricate flow mechanism of the centrifugal pump,particularly when considering various blade wrap angles.

Deep Structure Optimization for Incremental Hierarchical Fuzzy Systems Using Improved Differential Evolution Algorithm

The optimization of the rule base of a fuzzy logic system (FLS) based on evolutionary algorithm has achievednotable results. However, due to the diversity of the deep structure in the hierarchical fuzzy system (HFS) and thecorrelation of each sub fuzzy system, the uncertainty of the HFS’s deep structure increases. For the HFS, a largenumber of studies mainly use fixed structures, which cannot be selected automatically. To solve this problem, thispaper proposes a novel approach for constructing the incremental HFS. During system design, the deep structureand the rule base of the HFS are encoded separately. Subsequently, the deep structure is adaptively mutated basedon the fitness value, so as to realize the diversity of deep structures while ensuring reasonable competition amongthe structures. Finally, the differential evolution (DE) is used to optimize the deep structure of HFS and theparameters of antecedent and consequent simultaneously. The simulation results confirm the effectiveness of themodel. Specifically, the root mean square errors in the Laser dataset and Friedman dataset are 0.0395 and 0.0725,respectively with rule counts of rules is 8 and 12, respectively.When compared to alternative methods, the resultsindicate that the proposed method offers improvements in accuracy and rule counts.

A Differential Privacy Federated Learning Scheme Based on Adaptive Gaussian Noise

As a distributed machine learning method,federated learning(FL)has the advantage of naturally protecting data privacy.It keeps data locally and trains local models through local data to protect the privacy of local data.The federated learning method effectively solves the problem of artificial Smart data islands and privacy protection issues.However,existing research shows that attackersmay still steal user information by analyzing the parameters in the federated learning training process and the aggregation parameters on the server side.To solve this problem,differential privacy(DP)techniques are widely used for privacy protection in federated learning.However,adding Gaussian noise perturbations to the data degrades the model learning performance.To address these issues,this paper proposes a differential privacy federated learning scheme based on adaptive Gaussian noise(DPFL-AGN).To protect the data privacy and security of the federated learning training process,adaptive Gaussian noise is specifically added in the training process to hide the real parameters uploaded by the client.In addition,this paper proposes an adaptive noise reduction method.With the convergence of the model,the Gaussian noise in the later stage of the federated learning training process is reduced adaptively.This paper conducts a series of simulation experiments on realMNIST and CIFAR-10 datasets,and the results show that the DPFL-AGN algorithmperforms better compared to the other algorithms.

Effective approach for conformal subarray design based on maximum entropy of planar mappings

In this paper,an effective algorithm for optimizing the subarray of conformal arrays is proposed.The method first divides theconformal array into several first-level subarrays.It uses the X algorithm to solve the feasible solution of first-level subarray tiling and employs the particle swarm algorithm to optimize the conformal array subarray tiling scheme with the maximum entropy of the planar mapping as the fitness function.Subsequently,convex optimization is applied to optimize the subarray amplitude phase.Data results verify that the method can effectively find the optimal conformal array tiling scheme.

Blockchain-Enabled Federated Learning with Differential Privacy for Internet of Vehicles

The rapid evolution of artificial intelligence(AI)technologies has significantly propelled the advancement of the Internet of Vehicles(IoV).With AI support,represented by machine learning technology,vehicles gain the capability to make intelligent decisions.As a distributed learning paradigm,federated learning(FL)has emerged as a preferred solution in IoV.Compared to traditional centralized machine learning,FL reduces communication overhead and improves privacy protection.Despite these benefits,FL still faces some security and privacy concerns,such as poisoning attacks and inference attacks,prompting exploration into blockchain integration to enhance its security posture.This paper introduces a novel blockchain-enabled federated learning(BCFL)scheme with differential privacy(DP)tailored for IoV.In order to meet the performance demanding IoV environment,the proposed methodology integrates a consortium blockchain with Practical Byzantine Fault Tolerance(PBFT)consensus,which offers superior efficiency over the conventional public blockchains.In addition,the proposed approach utilizes the Differentially Private Stochastic Gradient Descent(DP-SGD)algorithm in the local training process of FL for enhanced privacy protection.Experiment results indicate that the integration of blockchain elevates the security level of FL in that the proposed approach effectively safeguards FL against poisoning attacks.On the other hand,the additional overhead associated with blockchain integration is also limited to a moderate level to meet the efficiency criteria of IoV.Furthermore,by incorporating DP,the proposed approach is shown to have the(ε-δ)privacy guarantee while maintaining an acceptable level of model accuracy.This enhancement effectively mitigates the threat of inference attacks on private information.

Spin logic devices based on negative differential resistance -enhanced anomalous Hall effect

Owing to rapid developments in spintronics,spin-based logic devices have emerged as promising tools for next-generation computing technologies.This paper provides a comprehensive review of recent advancements in spin logic devices,particularly focusing on fundamental device concepts rooted in nanomagnets,magnetoresistive random access memory,spin–orbit torques,electric-field modu-lation,and magnetic domain walls.The operation principles of these devices are comprehensively analyzed,and recent progress in spin logic devices based on negative differential resistance-enhanced anomalous Hall effect is summarized.These devices exhibit reconfigur-able logic capabilities and integrate nonvolatile data storage and computing functionalities.For current-driven spin logic devices,negative differential resistance elements are employed to nonlinearly enhance anomalous Hall effect signals from magnetic bits,enabling reconfig-urable Boolean logic operations.Besides,voltage-driven spin logic devices employ another type of negative differential resistance ele-ment to achieve logic functionalities with excellent cascading ability.By cascading several elementary logic gates,the logic circuit of a full adder can be obtained,and the potential of voltage-driven spin logic devices for implementing complex logic functions can be veri-fied.This review contributes to the understanding of the evolving landscape of spin logic devices and underscores the promising pro-spects they offer for the future of emerging computing schemes.

Identification of differentially expressed mRNAs as novel predictive biomarkers for gastric cancer diagnosis and prognosis

BACKGROUND Gastric cancer(GC)has a high mortality rate worldwide.Despite significant progress in GC diagnosis and treatment,the prognosis for affected patients still remains unfavorable.AIM To identify important candidate genes related to the development of GC and iden-tify potential pathogenic mechanisms through comprehensive bioinformatics analysis.METHODS The Gene Expression Omnibus database was used to obtain the GSE183136 dataset,which includes a total of 135 GC samples.The limma package in R software was employed to identify differentially expressed genes(DEGs).Thereafter,enrichment analyses of Gene Ontology(GO)terms and Kyoto Encyclopedia of Genes and Genomes(KEGG)pathways were performed for the gene modules using the clusterProfile package in R software.The protein-protein interaction(PPI)networks of target genes were constructed using STRING and visualized by Cytoscape software.The common hub genes that emerged in the cohort of DEGs that was retrieved from the GEPIA database were then screened using a Venn Diagram.The expression levels of these overlapping genes in stomach adenocarcinoma samples and non-tumor samples and their association with prognosis in GC patients were also obtained from the GEPIA database and Kaplan-Meier curves.Moreover,real-time quantitative polymerase chain reaction(RT-qPCR)and western blotting were performed to determine the mRNA and protein levels of glutamic-pyruvic transaminase(GPT)in GC and normal immortalized cell lines.In addition,cell viability,cell cycle distribution,migration and invasion were evaluated by cell counting kit-8,flow cytometry and transwell assays.Furthermore,we also conducted a retrospective analysis on 70 GC patients diagnosed and surgically treated in Wenzhou Central Hospital,Dingli Clinical College of Wenzhou Medical University,The Second Affiliated Hospital of Shanghai University between January 2017 to December 2020.The tumor and adjacent normal samples were collected from the patients to determine the potential association between the expression level of GPT and the clinical as well as pathological features of GC patients.RESULTS We selected 19214 genes from the GSE183136 dataset,among which there were 250 downregulated genes and 401 upregulated genes in the tumor samples of stage III-IV in comparison to those in tumor samples of stage I-II with a P-value<0.05.In addition,GO and KEGG results revealed that the various upregulated DEGs were mainly enriched in plasma membrane and neuroactive ligand-receptor interaction,whereas the downregulated DEGs were primarily enriched in cytosol and pancreatic secretion,vascular smooth muscle contraction and biosynthesis of the different cofactors.Furthermore,PPI networks were constructed based on the various upregulated and downregulated genes,and there were a total 15 upregulated and 10 downregulated hub genes.After a comprehensive analysis,several hub genes,including runt-related transcription factor 2(RUNX2),salmonella pathogenicity island 1(SPI1),lysyl oxidase(LOX),fibrillin 1(FBN1)and GPT,displayed prognostic values.Interestingly,it was observed that GPT was downregulated in GC cells and its upregulation could suppress the malignant phenotypes of GC cells.Furthermore,the expression level of GPT was found to be associated with age,lymph node metastasis,pathological staging and distant metastasis(P<0.05).CONCLUSION RUNX2,SPI1,LOX,FBN1 and GPT were identified key hub genes in GC by bioinformatics analysis.GPT was significantly associated with the prognosis of GC,and its upregulation can effectively inhibit the proliferative,migrative and invasive capabilities of GC cells.

Enhanced Redox Electrocatalysis in High‑Entropy Perovskite Fluorides by Tailoring d–p Hybridization

High-entropy catalysts featuring exceptional properties are,in no doubt,playing an increasingly significant role in aprotic lithium-oxygen batteries.Despite extensive effort devoted to tracing the origin of their unparalleled performance,the relationships between multiple active sites and reaction intermediates are still obscure.Here,enlightened by theoretical screening,we tailor a high-entropy perovskite fluoride(KCoMnNiMgZnF_(3)-HEC)with various active sites to overcome the limitations of conventional catalysts in redox process.The entropy effect modulates the d-band center and d orbital occupancy of active centers,which optimizes the d–p hybridization between catalytic sites and key intermediates,enabling a moderate adsorption of LiO_(2)and thus reinforcing the reaction kinetics.As a result,the Li–O2 battery with KCoMnNiMgZnF_(3)-HEC catalyst delivers a minimal discharge/charge polarization and long-term cycle stability,preceding majority of traditional catalysts reported.These encouraging results provide inspiring insights into the electron manipulation and d orbital structure optimization for advanced electrocatalyst.