Intrusion Detection Model Using Chaotic MAP for Network Coding Enabled Mobile Small Cells

Wireless Network security management is difficult because of the ever-increasing number of wireless network malfunctions,vulnerabilities,and assaults.Complex security systems,such as Intrusion Detection Systems(IDS),are essential due to the limitations of simpler security measures,such as cryptography and firewalls.Due to their compact nature and low energy reserves,wireless networks present a significant challenge for security procedures.The features of small cells can cause threats to the network.Network Coding(NC)enabled small cells are vulnerable to various types of attacks.Avoiding attacks and performing secure“peer”to“peer”data transmission is a challenging task in small cells.Due to the low power and memory requirements of the proposed model,it is well suited to use with constrained small cells.An attacker cannot change the contents of data and generate a new Hashed Homomorphic Message Authentication Code(HHMAC)hash between transmissions since the HMAC function is generated using the shared secret.In this research,a chaotic sequence mapping based low overhead 1D Improved Logistic Map is used to secure“peer”to“peer”data transmission model using lightweight H-MAC(1D-LM-P2P-LHHMAC)is proposed with accurate intrusion detection.The proposed model is evaluated with the traditional models by considering various evaluation metrics like Vector Set Generation Accuracy Levels,Key Pair Generation Time Levels,Chaotic Map Accuracy Levels,Intrusion Detection Accuracy Levels,and the results represent that the proposed model performance in chaotic map accuracy level is 98%and intrusion detection is 98.2%.The proposed model is compared with the traditional models and the results represent that the proposed model secure data transmission levels are high.

Effects of invigorating-spleen and anticancer prescription on extracellular signal-regulated kinase/mitogen-activated protein kinase signaling pathway in colon cancer mice model

BACKGROUND Colon cancer(CC)is one of the most common malignant tumors in the gastrointestinal system.Overall,CC had the third highest incidence but the second highest mortality rate globally in 2020.Nowadays,CC is mainly treated with capecitabine chemotherapy regimen,supplemented by radiotherapy,immunotherapy and targeted therapy,but there are still limitations,so Chinese medicine plays an important role.AIM To investigate the effects of invigorating-spleen and anticancer prescription(ISAP)on body weight,tumor inhibition rate and expression levels of proteins in extracellular-signal-regulated kinase(ERK)/mitogen-activated protein kinase(MAPK)signaling pathway in CC mice model.METHODS The CC mice model were established and the mice were randomly divided into 5 groups,including the control group,capecitabine group,the low-dose,mediumdose and high-dose groups of ISAP,with 8 mice in each group,respectively.After 2 weeks of intervention,the body weight and tumor inhibition rate of mice were observed,and the expression of RAS,ERK,phosphorylated ERK(p-ERK),C-MYC and matrix metalloproteinase 2(MMP2)proteins in the tissues of tumors were detected.RESULTS Compared with the control group,the differences of body weight before and after treatment was much smaller in the groups of ISAP,with the smallest difference in the high-dose group of ISAP,while the capecitabine group had the greatest difference,indicating ISAP had a significant inhibiting effect on the growth of transplanted tumor in mice.The expression of RAS protein was decreased in the low-and medium-dose groups of ISAP,and the change of p-ERK was significant in the medium-and high-dose groups of ISAP.MMP2 protein expression was significantly decreased in both the low-dose and medium-dose groups of ISAP.There were no significant changes in ERK in the ISAP group compared to the capecitabine group,while RAS,MMP2,and C-MYC protein expression were reduced in the ISAP group.The expression level of C-MYC protein decreased after treated with ISAP,and the decrease was the most significant in the medium-dose group of ISAP.CONCLUSION ISAP has a potential inhibiting effect on transplanted tumor in mice,and could maintain the general conditions,physical strength and body weight of mice.The expression levels of RAS,p-ERK,MMP2 and c-myc were also decreased to a certain extent.By inhibiting the expression of upstream proteins,the expression levels of downstream proteins in ERK/MAPK signaling pathway were significantly decreased.Therefore,it can be concluded that ISAP may exert an anti-tumor effect by blocking the ERK/MAPK signaling pathway and inhibiting the expression of MMP2 and c-myc proteins.

A Model of Cloud-Based Enterprise Resource Planning (ERP) for Small and Medium Enterprise

Cloud Computing is an uprising technology in the rapid growing IT world. The adaptation of cloud computing is increasing in very large scale business organizations to small institutions rapidly due to many advanced features of cloud computing, such as SaaS, PaaS and IaaS service models. So, nowadays, many organizations are trying to implement Cloud Computing based ERP system to enjoy the benefits of cloud computing. To implement any ERP system, an organization usually faces many challenges. As a result, this research has introduced how easily this cloud system can be implemented in an organization. By using this ERP system, an organization can be benefited in many ways;especially Small and Medium Enterprises (SMEs) can enjoy the highest possible benefits from this system.

Small-signal modeling and parameter extraction method for a multigate GaAs pHEMT switch

This paper presents an accurate small-signal model for multi-gate GaAs pHEMTs in switching-mode.The extraction method for the proposed model is developed.A 2-gate switch structure is fabricated on a commercial 0.5μm AlGaAs/GaAs pHEMT technology to verify the proposed model.Excellent agreement has been obtained between the measured and simulated results over a wide frequency range.

Numerical modeling of DPSK pressure signals and their transmission characteristics in mud channels

A numerical model and transmission characteristic analysis of DPSK （differential phase shift keying） pressure signals in mud channels is introduced. With the control logic analysis of the rotary valve mud telemetry, a logical control signal is built from a Gate function sequence according to the binary symbols of transmitted data and a phase-shift function is obtained by integrating the logical control signal. A mathematical model of the DPSK pressure signal is built based on principles of communications by modulating carrier phase with the phase-shift function and a numerical simulation of the pressure wave is implemented with the mathematical model by MATLAB programming. Considering drillpipe pressure and drilling fluid temperature profile along drillpipes, the drillpipe of a vertical well is divided into a number of sections. With water-based drilling fluids, the impacts of travel distance, carrier frequency, drillpipe size, and drilling fluids on the signal transmission were studied by signal transmission characteristic analysis for all the sections. Numerical calculation results indicate that the influences of the viscosity of drilling fluids and volume fraction of gas in drilling fluids on the DPSK signal transmission are more notable than the others and the signal will distort in waveform with differential attenuations of the signal frequent component.

Cognitive impairment in cerebral small vessel disease induced by hypertension

Hypertension is a primary risk factor for the progression of cognitive impairment caused by cerebral small vessel disease,the most common cerebrovascular disease.Howeve r,the causal relationship between hypertension and cerebral small vessel disease remains unclear.Hypertension has substantial negative impacts on brain health and is recognized as a risk factor for cerebrovascular disease.Chronic hypertension and lifestyle factors are associated with risks for stro ke and dementia,and cerebral small vessel disease can cause dementia and stroke.Hypertension is the main driver of cerebral small vessel disease,which changes the structure and function of cerebral vessels via various mechanisms and leads to lacunar infarction,leukoaraiosis,white matter lesions,and intracerebral hemorrhage,ultimately res ulting in cognitive decline and demonstrating that the brain is the to rget organ of hypertension.This review updates our understanding of the pathogenesis of hypertensioninduced cerebral small vessel disease and the res ulting changes in brain structure and function and declines in cognitive ability.We also discuss drugs to treat cerebral small vessel disease and cognitive impairment.

Investigating the elliptic anisotropy of identified particles in p-Pb collisions with a multi-phase transport model

The elliptic azimuthal anisotropy coefficient(v_(2))of the identified particles at midrapidity(|η|<0.8)was investigated in p-Pb collisions at√s_(NN)=5.02 TeV using a multi-phase transport model(AMPT).The calculations of differential v_(2)based on the advanced flow extraction method of light flavor hadrons(pions,kaons,protons,andΛ)in small collision systems were extended to a wider transverse momentum(p_(T))range of up to 8 GeV/c for the first time.The string-melting version of the AMPT model provides a good description of the measured p_(T)-differential v_(2)of the mesons but exhibits a slight deviation from the baryon v_(2).In addition,we observed the features of mass ordering at low p_(T)and the approximate number-of-constituentquark(NCQ)scaling at intermediate p_(T).Moreover,we demonstrate that hadronic rescattering does not have a significant impact on v_(2)in p-Pb collisions for different centrality selections,whereas partonic scattering dominates in generating the elliptic anisotropy of the final particles.This study provides further insight into the origin of collective-like behavior in small collision systems and has referential value for future measurements of azimuthal anisotropy.

Analysis of Additional Damping Control Strategy and Parameter Optimization for Improving Small Signal Stability of VSC-HVDC System

The voltage source converter based high voltage direct current(VSC-HVDC)system is based on voltage source converter,and its control system is more complex.Also affected by the fast control of power electronics,oscillation phenomenon in wide frequency domain may occur.To address the problem of small signal stability of the VSCHVDC system,a converter control strategy is designed to improve its small signal stability,and the risk of system oscillation is reduced by attaching a damping controller and optimizing the control parameters.Based on the modeling of the VSC-HVDC system,the general architecture of the inner and outer loop control of the VSCHVDC converter is established;and the damping controllers for DC control and AC control are designed in the phase-locked loop and the inner and outer loop control parts respectively;the state-space statemodel of the control system is established to analyze its performance.And the electromagnetic transient simulation model is built on the PSCAD/EMTDC simulation platform to verify the accuracy of the small signal model.The influence of the parameters of each control part on the stability of the system is summarized.The main control parts affecting stability are optimized for the phenomenon of oscillation due to changes in operation mode occurring on the AC side due to faults and other reasons,which effectively eliminates system oscillation and improves system small signal stability,providing a certain reference for engineering design.

C-Vine Pair Copula Based Wind Power Correlation Modelling in Probabilistic Small Signal Stability Analysis

The increasing integration of wind power generation brings more uncertainty into the power system. Since the correlation may have a notable influence on the power system,the output powers of wind farms are generally considered as correlated random variables in uncertainty analysis. In this paper, the C-vine pair copula theory is introduced to describe the complicated dependence of multidimensional wind power injection, and samples obeying this dependence structure are generated. Monte Carlo simulation is performed to analyze the small signal stability of a test system. The probabilistic stability under different correlation models and different operating conditions scenarios is investigated. The results indicate that the probabilistic small signal stability analysis adopting pair copula model is more accurate and stable than other dependence models under different conditions.

Prediction models for recurrence in patients with small bowel bleeding

Obscure gastrointestinal bleeding(OGIB)has traditionally been defined as gastrointestinal bleeding whose source remains unidentified after bidirectional endoscopy.OGIB can present as overt bleeding or occult bleeding,and small bowel lesions are the most common causes.The small bowel can be evaluated using capsule endoscopy,device-assisted enteroscopy,computed tomography enterography,or magnetic resonance enterography.Once the cause of smallbowel bleeding is identified and targeted therapeutic intervention is completed,the patient can be managed with routine visits.However,diagnostic tests may produce negative results,and some patients with small bowel bleeding,regardless of diagnostic findings,may experience rebleeding.Predicting those at risk of rebleeding can help clinicians form individualized surveillance plans.Several studies have identified different factors associated with rebleeding,and a limited number of studies have attempted to create prediction models for recurrence.This article describes prediction models developed so far for identifying patients with OGIB who are at greater risk of rebleeding.These models may aid clinicians in forming tailored patient management and surveillance.

Modeling random telegraph signal noise in CMOS image sensor under low light based on binomial distribution

The random telegraph signal noise in the pixel source follower MOSFET is the principle component of the noise in the CMOS image sensor under low light. In this paper, the physical and statistical model of the random telegraph signal noise in the pixel source follower based on the binomial distribution is set up. The number of electrons captured or released by the oxide traps in the unit time is described as the random variables which obey the binomial distribution. As a result,the output states and the corresponding probabilities of the first and the second samples of the correlated double sampling circuit are acquired. The standard deviation of the output states after the correlated double sampling circuit can be obtained accordingly. In the simulation section, one hundred thousand samples of the source follower MOSFET have been simulated,and the simulation results show that the proposed model has the similar statistical characteristics with the existing models under the effect of the channel length and the density of the oxide trap. Moreover, the noise histogram of the proposed model has been evaluated at different environmental temperatures.

A Support Data-Based Core-Set Selection Method for Signal Recognition

In recent years,deep learning-based signal recognition technology has gained attention and emerged as an important approach for safeguarding the electromagnetic environment.However,training deep learning-based classifiers on large signal datasets with redundant samples requires significant memory and high costs.This paper proposes a support databased core-set selection method(SD)for signal recognition,aiming to screen a representative subset that approximates the large signal dataset.Specifically,this subset can be identified by employing the labeled information during the early stages of model training,as some training samples are labeled as supporting data frequently.This support data is crucial for model training and can be found using a border sample selector.Simulation results demonstrate that the SD method minimizes the impact on model recognition performance while reducing the dataset size,and outperforms five other state-of-the-art core-set selection methods when the fraction of training sample kept is less than or equal to 0.3 on the RML2016.04C dataset or 0.5 on the RML22 dataset.The SD method is particularly helpful for signal recognition tasks with limited memory and computing resources.

Adversarial attacks and defenses for digital communication signals identification

As modern communication technology advances apace,the digital communication signals identification plays an important role in cognitive radio networks,the communication monitoring and management systems.AI has become a promising solution to this problem due to its powerful modeling capability,which has become a consensus in academia and industry.However,because of the data-dependence and inexplicability of AI models and the openness of electromagnetic space,the physical layer digital communication signals identification model is threatened by adversarial attacks.Adversarial examples pose a common threat to AI models,where well-designed and slight perturbations added to input data can cause wrong results.Therefore,the security of AI models for the digital communication signals identification is the premise of its efficient and credible applications.In this paper,we first launch adversarial attacks on the end-to-end AI model for automatic modulation classifi-cation,and then we explain and present three defense mechanisms based on the adversarial principle.Next we present more detailed adversarial indicators to evaluate attack and defense behavior.Finally,a demonstration verification system is developed to show that the adversarial attack is a real threat to the digital communication signals identification model,which should be paid more attention in future research.

A Vertex Network Model of Arabidopsis Leaf Growth

Biology provides many examples of complex systems whose properties allow organisms to develop in a highly reproducible,or robust,manner.One such system is the growth and development of flat leaves in Arabidopsis thaliana.This mechanistically challenging process results from multiple inputs including gene interactions,cellular geometry,growth rates,and coordinated cell divisions.To better understand how this complex genetic and cellular information controls leaf growth,we developed a mathematical model of flat leaf production.This two-dimensional model describes the gene interactions in a vertex network of cells which grow and divide according to physical forces and genetic information.Interestingly,the model predicts the presence of an unknown additional factor required for the formation of biologically realistic gene expression domains and iterative cell division.This two-dimensional model will form the basis for future studies into robustness of adaxial-abaxial patterning.

Comparative transcriptomic analysis reveals the molecular changes of acute pancreatitis in experimental models

BACKGROUND Acute pancreatitis(AP)encompasses a spectrum of pancreatic inflammatory conditions,ranging from mild inflammation to severe pancreatic necrosis and multisystem organ failure.Given the challenges associated with obtaining human pancreatic samples,research on AP predominantly relies on animal models.In this study,we aimed to elucidate the fundamental molecular mechanisms underlying AP using various AP models.AIM To investigate the shared molecular changes underlying the development of AP across varying severity levels.METHODS AP was induced in animal models through treatment with caerulein alone or in combination with lipopolysaccharide(LPS).Additionally,using Ptf1αto drive the specific expression of the hM3 promoter in pancreatic acinar cells transgenic C57BL/6J-hM3/Ptf1α(cre)mice were administered Clozapine N-oxide to induce AP.Subsequently,we conducted RNA sequencing of pancreatic tissues and validated the expression of significantly different genes using the Gene Expression Omnibus(GEO)database.RESULTS Caerulein-induced AP showed severe inflammation and edema,which were exacerbated when combined with LPS and accompanied by partial pancreatic tissue necrosis.Compared with the control group,RNA sequencing analysis revealed 880 significantly differentially expressed genes in the caerulein model and 885 in the caerulein combined with the LPS model.Kyoto Encyclopedia of Genes and Genomes enrichment analysis and Gene Set Enrichment Analysis indicated substantial enrichment of the TLR and NOD-like receptor signaling pathway,TLR signaling pathway,and NF-κB signaling pathway,alongside elevated levels of apoptosis-related pathways,such as apoptosis,P53 pathway,and phagosome pathway.The significantly elevated genes in the TLR and NOD-like receptor signaling pathways,as well as in the apoptosis pathway,were validated through quantitative real-time PCR experiments in animal models.Validation from the GEO database revealed that only MYD88 concurred in both mouse pancreatic tissue and human AP peripheral blood,while TLR1,TLR7,RIPK3,and OAS2 genes exhibited marked elevation in human AP.The genes TUBA1A and GADD45A played significant roles in apoptosis within human AP.The transgenic mouse model hM3/Ptf1α(cre)successfully validated significant differential genes in the TLR and NOD-like receptor signaling pathways as well as the apoptosis pathway,indicating that these pathways represent shared pathological processes in AP across different models.CONCLUSION The TLR and NOD receptor signaling pathways play crucial roles in the inflammatory progression of AP,notably the MYD88 gene.Apoptosis holds a central position in the necrotic processes of AP,with TUBA1A and GADD45A genes exhibiting prominence in human AP.

Multilevel complexity of calcium signaling:Modeling angiogenesis

Intracellular calcium signaling is a universal,evolutionary conserved and versatile regulator of cell biochemistry.The complexity of calcium signaling and related cell machinery can be investigated by the use of experimental strategies,as well as by computational approaches.Vascular endothelium is a fascinating model to study the specific properties and roles of calcium signals at multiple biological levels.During the past 20 years,live cell imaging,patch clamp and other techniques have allowed us to detect and interfere with calcium signaling in endothelial cells(ECs),providing a huge amount of information on the regulation of vascularization(angiogenesis) in normal and tumoral tissues.These data range from the spatiotemporal dynamics of calcium within different cell microcompartments to those in entire multicellular and organized EC networks.Beside experimental strategies,in silico endothelial models,specifically designed for simulating calcium signaling,are contributing to our knowledge of vascular physiol-ogy and pathology.They help to investigate and predict the quantitative features of proangiogenic events moving through subcellular,cellular and supracellular levels.This review focuses on some recent developments of computational approaches for proangiogenic endothelial calcium signaling.In particular,we discuss the creation of hybrid simulation environments,which combine and integrate discrete Cellular Potts Models.They are able to capture the phenomenological mechanisms of cell morphological reorganization,migration,and intercellular adhesion,with single-cell spatiotemporal models,based on reaction-diffusion equations that describe the agonist-induced intracellular calcium events.

A New Efficient Finite Volume Modeling of Small Amplitude Free Surface Flows with Unstructured Grid

A staggered finite-volume technique for non-hydrostatic, small amplitude free surface flow governed by the incompressible Navier-Stokes equations is presented there is a proper balance between accuracy and computing time. The advection and horizontal diffusion terms in the momentum equation are discretized by an integral interpolation method on the orthogonal unstructured staggered mesh and, while it has the attractive property of being conservative. The pressure-correction algorithm is employed for the non-hydrostatic pressure in order to achieve second-order temporal accuracy. A conservative scalar transport algorithm is also applied to discretize k - c equations in this model. The eddy viscosity is calculated from the k-c turbulent model. The resulting model is mass and momentum conservative. The model is verified by two examples to simulate unsteady small amplitude free surface flows where non-hydrostatic pressures have a considerable effect on the velocity field, and then applied to simulate the tidal flow in the Bohai Sea.

A theoretical study of the signal enhancement mechanism of coaxial DP-LIBS

In the field of dual-pulse laser-induced breakdown spectroscopy(DP-LIBS)research,the pursuit of methods for determining pulse intervals and other parameters quickly and conveniently in order to achieve optimal spectral signal enhancement is paramount.To aid researchers in identification of optimal signal enhancement conditions and more accurate interpretation of the underlying signal enhancement mechanisms,theoretical simulations of the spatiotemporal processes of coaxial DP-LIBS-induced plasma have been established in this work.Using a model based on laser ablation and two-dimensional axisymmetric fluid dynamics,plasma evolutions during aluminum–magnesium alloy laser ablation under single-pulse and coaxial dualpulse excitations have been simulated.The influences of factors,such as delay time,laser fluence,plasma temperature,and particle number density,on the DP-LIBS spectral signals are investigated.Under pulse intervals ranging from 50 to 1500 ns,the time evolutions of spectral line intensity,dual-pulse emission enhancement relative to the single-pulse results,laser irradiance,spatial distribution of plasma temperature and species number density,as well as laser irradiance shielded by plasma have been obtained.The study indicates that the main reason behind the radiation signal enhancement in coaxial DP-LIBS-induced plasma is attributed to the increased species number density and plasma temperature caused by the second laser,and it is inferred that the shielding effect of the plasma mainly occurs in the boundary layer of the stagnation point flow over the target surface.This research provides a theoretical basis for experimental research,parameter optimization,and signal enhancement tracing in DP-LIBS.

Modeling Distortion Signals of Power Grid Based on Wiener-G Functionals

The uniform mathematical model of distortion signals in power grid has been setup with the theory of Wiener-G Functional. Firstly,the Matlab simulation models were established. Secondly,the Wiener kernel of power load was found based on the Gaussian white noise as input. And then the uniform mathematical model of the power grid signal was established according to the homogeneous of the same order of Wiener functional series. Finally,taking three typical distortion sources which are semiconductor rectifier,electric locomotive and electric arc furnace in power grid as examples,we have validated the model through the Matlab simulation and analyzed the simulation errors. The results show that the uniform mathematical model of distortion signals in power grid can approximation the actual model by growing the items of the series under the condition of the enough storage space and computing speed.