Development of a Post Quantum Encryption Key Generation Algorithm Using Electromagnetic Wave Propagation Theory

In today’s rapid widespread of digital technologies into all live aspects to enhance efficiency and productivity on the one hand and on the other hand ensure customer engagement, personal data counterfeiting has become a major concern for businesses and end-users. One solution to ensure data security is encryption, where keys are central. There is therefore a need to find robusts key generation implementation that is effective, inexpensive and non-invasive for protecting and preventing data counterfeiting. In this paper, we use the theory of electromagnetic wave propagation to generate encryption keys.

Mechanism of internal thermal runaway propagation in blade batteries

Blade batteries are extensively used in electric vehicles,but unavoidable thermal runaway is an inherent threat to their safe use.This study experimentally investigated the mechanism underlying thermal runaway propagation within a blade battery by using a nail to trigger thermal runaway and thermocouples to track its propagation inside a cell.The results showed that the internal thermal runaway could propagate for up to 272 s,which is comparable to that of a traditional battery module.The velocity of the thermal runaway propagation fluctuated between 1 and 8 mm s^(-1),depending on both the electrolyte content and high-temperature gas diffusion.In the early stages of thermal runaway,the electrolyte participated in the reaction,which intensified the thermal runaway and accelerated its propagation.As the battery temperature increased,the electrolyte evaporated,which attenuated the acceleration effect.Gas diffusion affected thermal runaway propagation through both heat transfer and mass transfer.The experimental results indicated that gas diffusion accelerated the velocity of thermal runaway propagation by 36.84%.We used a 1D mathematical model and confirmed that convective heat transfer induced by gas diffusion increased the velocity of thermal runaway propagation by 5.46%-17.06%.Finally,the temperature rate curve was analyzed,and a three-stage mechanism for internal thermal runaway propagation was proposed.In Stage I,convective heat transfer from electrolyte evaporation locally increased the temperature to 100℃.In Stage II,solid heat transfer locally increases the temperature to trigger thermal runaway.In StageⅢ,thermal runaway sharply increases the local temperature.The proposed mechanism sheds light on the internal thermal runaway propagation of blade batteries and offers valuable insights into safety considerations for future design.

Heat transfer enhanced inorganic phase change material compositing carbon nanotubes for battery thermal management and thermal runaway propagation mitigation

Developing technologies that can be applied simultaneously in battery thermal management(BTM)and thermal runaway(TR)mitigation is significant to improving the safety of lithium-ion battery systems.Inorganic phase change material(PCM)with nonflammability has the potential to achieve this dual function.This study proposed an encapsulated inorganic phase change material(EPCM)with a heat transfer enhancement for battery systems,where Na_(2)HPO_(4)·12H_(2)O was used as the core PCM encapsulated by silica and the additive of carbon nanotube(CNT)was applied to enhance the thermal conductivity.The microstructure and thermal properties of the EPCM/CNT were analyzed by a series of characterization tests.Two different incorporating methods of CNT were compared and the proper CNT adding amount was also studied.After preparation,the battery thermal management performance and TR propagation mitigation effects of EPCM/CNT were further investigated on the battery modules.The experimental results of thermal management tests showed that EPCM/CNT not only slowed down the temperature rising of the module but also improved the temperature uniformity during normal operation.The peak battery temperature decreased from 76℃to 61.2℃at 2 C discharge rate and the temperature difference was controlled below 3℃.Moreover,the results of TR propagation tests demonstrated that nonflammable EPCM/CNT with good heat absorption could work as a TR barrier,which exhibited effective mitigation on TR and TR propagation.The trigger time of three cells was successfully delayed by 129,474 and 551 s,respectively and the propagation intervals were greatly extended as well.

Numerical modeling of fracture propagation of supercritical CO_(2)compound fracturing

The exploitation of shale gas is promising due to depletion of the conventional energy and intensification of the greenhouse effect.In this paper,we proposed a heat-fluid-solid coupling damage model of supercritical CO_(2)(SC-CO_(2))compound fracturing which is expected to be an efficient and environmentally friendly way to develop shale gas.The coupling model is solved by the finite element method,and the results are in good agreement with the analytical solutions and fracturing experiments.Based on this model,the fracture propagation characteristics at the two stages of compound fracturing are studied and the influence of pressurization rate,in situ stress,bedding angle,and other factors are considered.The results show that at the SC-CO_(2)fracturing stage,a lower pressurization rate is conducive to formation of the branches around main fractures,while a higher pressurization rate inhibits formation of the branches around main fractures and promotes formation of the main fractures.Both bedding and in situ stress play a dominant role in the fracture propagation.When the in situ stress ratio(δ_(x)/δ_(y))is 1,the presence of bedding can reduce the initiation pressure and failure pressure.Nevertheless,it will cause the fracture to propagate along the bedding direction,reducing the fracture complexity.In rocks without bedding,hydraulic fracturing has the lengthening and widening effects for SC-CO_(2)induced fracture.In shale,fractures induced at the hydraulic fracturing stage are more likely to be dominated by in situ stresses and have a shorter reorientation radius.Therefore,fracture branches propagating along the maximum principal stress direction may be generated around the main fractures induced by SC-CO_(2)at the hydraulic fracturing stage.When the branches converge with the main fractures,fracture zones are easily formed,and thus the fracture complexity and damage area can be significantly increased.The results are instructive for the design and application of SC-CO_(2)compound fracturing.

Environmental Information-Aided Electromagnetic Propagation Testbed for Maritime Communication

Maritime channel modeling can be affected by some key time-varying environmental factors.The ducting effect is one of the thorniest factors since it causes anomalous propagation enhancement and severe co-channel interference.Moreover,the atmospheric attenuation is much more severe in the ocean environment,resulting in shorter coverage distance and more link outage.In this paper,we propose an environmental information-aided electromagnetic propagation testbed.It is based on complex refractivity estimation and improved parabolic equation propagation model,taking into account both ducting effect and atmospheric attenuation.A large-scale temporal and spatial propagation measurement was conducted with meteorological acquisition.We consider practical path loss and ducting conditions to verify the testbed feasibility in these long-distance radio links.The simulation results are in good agreement with the measured data,which further reveal the basic temporal and spatial distribution of ducting effect at 3.5 GHz band.

Propagation and sealing efficiency of chemical grouting in a two-dimensional fracture network with flowing water

In this study, an orthogonal array experiment is conducted by using a transparent fracture network replica. Image processing and theoretical analysis are performed to investigate the model sealing efficiency(SE), factors influencing SE, and the effect of flowing water on propagation. The results show that grout propagation can be classified into three patterns in the fracture network: sealing off, partial sealing,and major erosion. The factors controlling the SE in a descending order of the amount of influence are the initial water flow speed, fracture aperture, grout take, and gel time. An optimal value for the combination of the gel time and grout take(artificial factors) can result in a good SE. The grouting and seepage pressures are measured, and the results reveal that their variations can indicate the SE to some extent. The SE is good when the seepage pressure at each point increases overall;the frequent fluctuations in the seepage pressure indicate a moderately poor SE, and an overall decline in the seepage pressure indicates a major erosion type. The deflection effect of grouting shows an approximately elliptical propagation with the long axis expanding along the wider fracture opening, demonstrating further application in grouting design.

Gate-voltage control of alternating-current-driven skyrmion propagation in ferromagnetic nanotrack devices

Magnetic skyrmions, with topologically protected particle-like magnetization configurations, are promising information carriers for future spintronics devices with ultralow energy consumption. Generally, during motion, skyrmions suffer from the skyrmion Hall effect(Sk HE) wherein the skyrmions deflect away from the intended path of the driving force.Numerous methods have been proposed to avoid this detrimental effect. In this study, we propose controllable alternating current(AC)-driven skyrmion propagation in a ferromagnetic nanowire based on combination of gate-voltage-controlled magnetic anisotropy(VCMA) and Sk HE. Micromagnetic simulations show that a skyrmion oscillatory closed-loop-like in situ motion driven by AC can be transformed into directional ratchet-like propagation along the nanotrack by creating a VCMA-gate barrier. Additionally, we show that the skyrmion propagation conditions depend on the gate barrier potential and driving AC parameters, and they can be used for the optimal design of nanotrack devices. Moreover, this mechanism could be used to control skyrmion macroscopic propagation directions by dynamically alternating the voltage of another series of gates. We further show the dynamic control of the long-distance propagation of skyrmions along with the pinning state. The study results provide a promising route for designing future skyrmion-based spintronics logical and memory devices.

Shallow water bathymetry based on a back propagation neural network and ensemble learning using multispectral satellite imagery

The back propagation(BP)neural network method is widely used in bathymetry based on multispectral satellite imagery.However,the classical BP neural network method faces a potential problem because it easily falls into a local minimum,leading to model training failure.This study confirmed that the local minimum problem of the BP neural network method exists in the bathymetry field and cannot be ignored.Furthermore,to solve the local minimum problem of the BP neural network method,a bathymetry method based on a BP neural network and ensemble learning(BPEL)is proposed.First,the remote sensing imagery and training sample were used as input datasets,and the BP method was used as the base learner to produce multiple water depth inversion results.Then,a new ensemble strategy,namely the minimum outlying degree method,was proposed and used to integrate the water depth inversion results.Finally,an ensemble bathymetric map was acquired.Anda Reef,northeastern Jiuzhang Atoll,and Pingtan coastal zone were selected as test cases to validate the proposed method.Compared with the BP neural network method,the root-mean-square error and the average relative error of the BPEL method can reduce by 0.65–2.84 m and 16%–46%in the three test cases at most.The results showed that the proposed BPEL method could solve the local minimum problem of the BP neural network method and obtain highly robust and accurate bathymetric maps.

Wave propagation responses of porous bi-directional functionally graded magneto-electro-elastic nanoshells via nonlocal strain gradient theory

This study examines the wave propagation characteristics for a bi-directional functional grading of barium titanate(BaTiO_(3)) and cobalt ferrite(CoFe_(2)O_(4)) porous nanoshells,the porosity distribution of which is simulated by the honeycomb-shaped symmetrical and asymmetrical distribution functions.The nonlocal strain gradient theory(NSGT) and first-order shear deformation theory are used to determine the size effect and shear deformation,respectively.Nonlocal governing equations are derived for the nanoshells by Hamilton's principle.The resulting dimensionless differential equations are solved by means of an analytical solution of the combined exponential function after dimensionless treatment.Finally,extensive parametric surveys are conducted to investigate the influence of diverse parameters,such as dimensionless scale parameters,radiusto-thickness ratios,bi-directional functionally graded(FG) indices,porosity coefficients,and dimensionless electromagnetic potentials on the wave propagation characteristics.Based on the analysis results,the effect of the dimensionless scale parameters on the dispersion relationship is found to be related to the ratio of the scale parameters.The wave propagation characteristics of nanoshells in the presence of a magnetoelectric field depend on the bi-directional FG indices.

Propagation of surface magnetoplasmon polaritons in a symmetric waveguide with two-dimensional electron gas

The properties of surface magnetoplasmon polaritons(SMPPs)in a symmetric structure,composed of two semi-infinite regions of high-density two-dimensional electron gas(2DEG)separated by a thin film in Voigt configuration,are investigated.The normal and absorption dispersion relations for the transverse magnetic polarization are derived by correlating Maxwell’s equation and the boundary conditions.It is demonstrated that the features of SMPPs are greatly influenced by the external magnetic field,collision frequency of 2DEG,the dielectric constant,and the thickness of the thin film,suggesting that the locations and propagation lengths of SMPPs can be governed accordingly.It is shown that the symmetry of the physical geometry preserves the symmetry of the dispersion relations of SMPPs.Furthermore,it is discovered that as the external magnetic field increases,the penetration depth of SMPPs decreases,while their energy loss reduces,implying that plasmons can propagate for longer distances.Additionally,it is observed that SMPPs in the symmetric configuration have a longer lifetime than those in the asymmetric configuration.

A malware propagation prediction model based on representation learning and graph convolutional networks

The traditional malware research is mainly based on its recognition and detection as a breakthrough point,without focusing on its propagation trends or predicting the subsequently infected nodes.The complexity of network structure,diversity of network nodes,and sparsity of data all pose difficulties in predicting propagation.This paper proposes a malware propagation prediction model based on representation learning and Graph Convolutional Networks(GCN)to address the aforementioned problems.First,to solve the problem of the inaccuracy of infection intensity calculation caused by the sparsity of node interaction behavior data in the malware propagation network,a mechanism based on a tensor to mine the infection intensity among nodes is proposed to retain the network structure information.The influence of the relationship between nodes on the infection intensity is also analyzed.Second,given the diversity and complexity of the content and structure of infected and normal nodes in the network,considering the advantages of representation learning in data feature extraction,the corresponding representation learning method is adopted for the characteristics of infection intensity among nodes.This can efficiently calculate the relationship between entities and relationships in low dimensional space to achieve the goal of low dimensional,dense,and real-valued representation learning for the characteristics of propagation spatial data.We also design a new method,Tensor2vec,to learn the potential structural features of malware propagation.Finally,considering the convolution ability of GCN for non-Euclidean data,we propose a dynamic prediction model of malware propagation based on representation learning and GCN to solve the time effectiveness problem of the malware propagation carrier.The experimental results show that the proposed model can effectively predict the behaviors of the nodes in the network and discover the influence of different characteristics of nodes on the malware propagation situation.

Prediction of SMILE surgical cutting formula based on back propagation neural network

AIM:To predict cutting formula of small incision lenticule extraction(SMILE)surgery and assist clinicians in identifying candidates by deep learning of back propagation(BP)neural network.METHODS:A prediction program was developed by a BP neural network.There were 13188 pieces of data selected as training validation.Another 840 eye samples from 425 patients were recruited for reverse verification of training results.Precision of prediction by BP neural network and lenticule thickness error between machine learning and the actual lenticule thickness in the patient data were measured.RESULTS:After training 2313 epochs,the predictive SMILE cutting formula BP neural network models performed best.The values of mean squared error and gradient are 0.248 and 4.23,respectively.The scatterplot with linear regression analysis showed that the regression coefficient in all samples is 0.99994.The final error accuracy of the BP neural network is-0.003791±0.4221102μm.CONCLUSION:With the help of the BP neural network,the program can calculate the lenticule thickness and residual stromal thickness of SMILE surgery accurately.Combined with corneal parameters and refraction of patients,the program can intelligently and conveniently integrate medical information to identify candidates for SMILE surgery.

Novel Contiguous Cross Propagation Neural Network Built CAD for Lung Cancer

The present progress of visual-based detection of the diseased area of a malady plays an essential part in the medicalfield.In that case,the image proces-sing is performed to improve the image data,wherein it inhibits unintended dis-tortion of image features or it enhances further processing in various applications andfields.This helps to show better results especially for diagnosing diseases.Of late the early prediction of cancer is necessary to prevent disease-causing pro-blems.This work is proposed to identify lung cancer using lung computed tomo-graphy(CT)scan images.It helps to identify cancer cells’affected areas.In the present work,the original input image from Lung Image Database Consortium(LIDC)typically suffers from noise problems.To overcome this,the Gaborfilter used for image processing is highly enhanced.In the next stage,the Spherical Iterative Refinement Clustering(SIRC)algorithm identifies cancer-suspected areas on the CT scan image.This approach can help radiologists and medical experts recognize cancer diseases and syndromes so that serious progress can be avoided in the early stages.These new methods help to remove unwanted por-tions of the CT image and better utilization the image.The subspace extraction of features approach is beneficial for evaluating lung cancer.This paper introduces a novel approach called Contiguous Cross Propagation Neural Network that tends to locate regions afflicted by lung cancer using CT scan pictures(CCPNN).By using the feature values from the fourth step of the procedure,the proposed CCPNN tends to categorize the lesion in the lung nodular site.The efficiency of the suggested CCPNN approach is evaluated using classification metrics such as recall(%),precision(%),F-measure(percent),and accuracy(%).Finally,the incorrect classification ratios are determined to compare the trained networks’effectiveness,through these parameters of CCPNN,it obtains the outstanding per-formance of 98.06%and it has provided the lowest false ratio of 1.8%.

Modeling and Simulation of High Frequency Electromagnetics Wave Propagation on Vivaldi Antenna Using Finite Element Method

The simulation of the electromagnetic wave propagation plays an important role in predicting the performance of wireless transmission and communication systems. This research paper performs a numerical simulation using the finite element method (FEM) to study electromagnetic propagation through both conductive and dielectric media. The simulations are made using the COMSOL Multiphysics software which notably implements the finite element method. The microwave is produced by a Vivaldi antenna at the respective frequencies of 2.6 and 5 GHz and the propagation equation is formulated from Maxwell’s equations. The results obtained show that in the air, strong electric fields are observed in the slot and the micro-strip line for the two frequencies, they are even greater when the wave propagates in the glass and very weak for the copper. The 3D evolutions of the wave in air and glass present comparable values at equal frequencies, the curves being more regular in air (dielectric). The radiation patterns produced for air and glass are directional, with a large main lobe, which is narrower at 5 GHz. For copper, the wave propagation is quite uniform in space, and the radiation patterns show two main lobes with a much larger size at 2.6 GHz than at 5 GHz. The propagation medium would therefore influence the range of values of the gain of the antenna.

Deterministic and Stochastic Analysis of a New Rumor Propagation Model with Nonlinear Propagation Rate in Social Network

This paper presents a study on a new rumor propagation model with nonlinear propagation rate and secondary propagation rate. We divide the total population into three groups, the ignorant, the spreader and the aware. The nonlinear incidence rate describes the psychological impact of certain serious rumors on social groups when the number of individuals spreading rumors becomes larger. The main contributions of this work are the development of a new rumor propagation model and some results of deterministic and stochastic analysis of the rumor propagation model. The results show the influence of nonlinear propagation rate and stochastic fluctuation on the dynamic behavior of the rumor propagation model by using Lyapunov function method and stochastic related knowledge. Numerical examples and simulation results are given to illustrate the results obtained.

Effect of diagenetic variation on the static and dynamic mechanical behavior of coral reef limestone

Coral reef limestone at different depositional depths and facies differ remarkably on the textural and mineralogical characteristics,owing to the complex sedimentary diagenesis.To explore the effects of pore structure and mineral composition associated with diagenetic variation on the mechanical behavior of reef limestone,a series of quasi-static and dynamic compression tests along with microscopic examinations were performed on the reef limestone at shallow and deep burial depths.It is revealed that the shallow reef limestone(SRL)is classified as a porous aragonite-type carbonate rock with high porosity(55.3±3.2)%and pore connectivity.In comparison,the deep reef limestone(DRL)is mainly composed of dense calcite-type calcium carbonate with low porosity(4.9±1.6)%and pore connectivity.The DRL strengthened and stiffened by the tight grain framework consistently displays much higher values of the dynamic compressive strength,elastic modulus,brittleness index,and specific energy absorption than those of the SRL.The gap between two types of limestone further increases with an increase in strain rate.It appears that the failure pattern of SRL is dominated by the inherent defects like weak bonding interfaces and growth lines,revealed by the intricate fracturing network and mixed failure.Likewise,although the preexisting megapores in DRL may affect the crack propagation on pore tips to a certain distance,it hardly alters the axial splitting failure of DRL under impacts.The stress wave propagation and attenuation in SRL is primarily controlled by the reflection and diffusion caused by plenty mesopores,as well as an energy dissipation in layer-wise pore collapse and adjacent grain crushing,while the stress wave in DRL is highly hinged on the insulation and diffraction induced by the isolated megapores.This process is accompanied by the energy dissipation behavior of inelastic deformation resulted from the pore-emanated microcracking.

Acid-rock reaction kinetics in a two-scale model based on reaction order correction

The reaction order plays a crucial role in evaluating the response rate of acid-rock.However,the conventional two-scale model typically assumes that the reaction order is constant as one,which can lead to significant deviations from reality.To address this issue,this study proposes a novel multi-order dynamic model for acid-rock reaction by combining rotating disk experimental data with theoretical derivation.Through numerical simulations,this model allows for the investigation of the impact of acidification conditions on different orders of reaction,thereby providing valuable insights for on-site construction.The analysis reveals that higher response orders require higher optimal acid liquid flow rates,and lower optimal H+diffusion coefficients,and demonstrate no significant correlation with acid concentration.Consequently,it is recommended to increase the displacement and use high-viscosity acid for reservoirs with high calcite content,while reducing the displacement and using low-viscosity acid for reservoirs with high dolomite content.

Robust autofocusing propagation in turbulence

Turbulence in complex environments such as the atmosphere and biological media has always been a great challenge to the application of beam propagation in optical communication, optical trapping and manipulation. To overcome this challenge, this study comprehensively investigates the robust propagation of traditional Gaussian and autofocusing beams in turbulent environments. In order to select stable beams that exhibit high intensity and high field gradient at the focal position in complex environments, Kolmogorov turbulence theory is used to simulate the propagation of beams in atmospheric turbulence based on the multi-phase screen method. We systematically analyze the intensity fluctuations, the variation of the coherence factor and the change in the scintillation index with propagation distance. The analysis reveals that the intensity fluctuations of autofocusing beams are significantly smaller than those of Gaussian beams, and the coherence of autofocusing beams is better than that of Gaussian beams under turbulence. Moreover, autofocusing beams exhibit less oscillation than Gaussian beams, indicating that autofocusing beams propagate in complex environments with less distortion and intensity fluctuation. Overall, this work clearly demonstrates that autofocusing beams exhibit higher stability in propagation compared with Gaussian beams, showing great promise for applications such as optical trapping and manipulation in complex environments.

Effect mechanism of seepage force on the hydraulic fracture propagation

The flow of fluid through the porous matrix of a reservoir rock applies a seepage force to the solid rock matrix.Although the seepage force exerted by fluid flow through the porous matrix of a reservoir rock has a notable influence on rock deformation and failure,its effect on hydraulic fracture(HF)propagation remains ambiguous.Therefore,in this study,we improved a traditional fluid–solid coupling method by incorporating the role of seepage force during the fracturing fluid seepage,using the discrete element method.First,we validated the simulation results of the improved method by comparing them with an analytical solution of the seepage force and published experimental results.Next,we conducted numerical simulations in both homogeneous and heterogeneous sandstone formations to investigate the influence of seepage force on HF propagation.Our results indicate that fluid viscosity has a greater impact on the magnitude and extent of seepage force compared to injection rate,and that lower viscosity and injection rate correspond to shorter hydraulic fracture lengths.Furthermore,seepage force influences the direction of HF propagation,causing HFs to deflect towards the side of the reservoir with weaker cementation and higher permeability.

Modelling of debris-flow susceptibility and propagation: a case study from Northwest Himalaya

The geological and geographical position of the Northwest Himalayas makes it a vulnerable area for mass movements particularly landslides and debris flows. Mass movements have had a substantial impact on the study area which is extending along Karakorum Highway(KKH) from Besham to Chilas. Intense seismicity, deep gorges, steep terrain and extreme climatic events trigger multiple mountain hazards along the KKH, among which debris flow is recognized as the most destructive geohazard. This study aims to prepare a field-based debris flow inventory map at a regional scale along a 200 km stretch from Besham to Chilas. A total of 117 debris flows were identified in the field, and subsequently, a point-based debris-flow inventory and catchment delineation were performed through Arc GIS analysis. Regional scale debris flow susceptibility and propagation maps were prepared using Weighted Overlay Method(WOM) and Flow-R technique sequentially. Predisposing factors include slope, slope aspect, elevation, Topographic Roughness Index(TRI), Topographic Wetness Index(TWI), stream buffer, distance to faults, lithology rainfall, curvature, and collapsed material layer. The dataset was randomly divided into training data(75%) and validation data(25%). Results were validated through the Receiver Operator Characteristics(ROC) curve. Results show that Area Under the Curve(AUC) using WOM model is 79.2%. Flow-R propagation of debris flow shows that the 13.15%, 22.94%, and 63.91% areas are very high, high, and low susceptible to debris flow respectively. The propagation predicated by Flow-R validates the naturally occurring debris flow propagation as observed in the field surveys. The output of this research will provide valuable input to the decision makers for the site selection, designing of the prevention system, and for the protection of current infrastructure.