Smart prediction of liquefaction-induced lateral spreading

The prediction of liquefaction-induced lateral spreading/displacement(Dh)is a challenging task for civil/geotechnical engineers.In this study,a new approach is proposed to predict Dh using gene expression programming(GEP).Based on statistical reasoning,individual models were developed for two topographies:free-face and gently sloping ground.Along with a comparison with conventional approaches for predicting the Dh,four additional regression-based soft computing models,i.e.Gaussian process regression(GPR),relevance vector machine(RVM),sequential minimal optimization regression(SMOR),and M5-tree,were developed and compared with the GEP model.The results indicate that the GEP models predict Dh with less bias,as evidenced by the root mean square error(RMSE)and mean absolute error(MAE)for training(i.e.1.092 and 0.815;and 0.643 and 0.526)and for testing(i.e.0.89 and 0.705;and 0.773 and 0.573)in free-face and gently sloping ground topographies,respectively.The overall performance for the free-face topology was ranked as follows:GEP>RVM>M5-tree>GPR>SMOR,with a total score of 40,32,24,15,and 10,respectively.For the gently sloping condition,the performance was ranked as follows:GEP>RVM>GPR>M5-tree>SMOR with a total score of 40,32,21,19,and 8,respectively.Finally,the results of the sensitivity analysis showed that for both free-face and gently sloping ground,the liquefiable layer thickness(T_(15))was the major parameter with percentage deterioration(%D)value of 99.15 and 90.72,respectively.

Individual dynamics and local heterogeneity provide a microscopic view of the epidemic spreading

The COVID-19 pandemic has caused severe global disasters,highlighting the importance of understanding the details and trends of epidemic transmission in order to introduce efficient intervention measures.While the widely used deterministic compartmental models have qualitatively presented continuous “analytical” insight and captured some transmission features,their treatment usually lacks spatiotemporal variation.Here,we propose a stochastic individual dynamical(SID)model to mimic the random and heterogeneous nature of epidemic propagation.The SID model provides a unifying framework for representing the spatiotemporal variations of epidemic development by tracking the movements of each individual.Using this model,we reproduce the infection curves for COVID-19 cases in different areas globally and find the local dynamics and heterogeneity at the individual level that affect the disease outbreak.The macroscopic trend of virus spreading is clearly illustrated from the microscopic perspective,enabling a quantitative assessment of different interventions.Seemingly,this model is also applicable to studying stochastic processes at the “meter scale”,e.g.,human society’s collective dynamics.

Influence of network structure on spreading dynamics via tie range

There are various phenomena of malicious information spreading in the real society, which cause many negative impacts on the society. In order to better control the spreading, it is crucial to reveal the influence of network structure on network spreading. Motifs, as fundamental structures within a network, play a significant role in spreading. Therefore, it is of interest to investigate the influence of the structural characteristics of basic network motifs on spreading dynamics.Considering the edges of the basic network motifs in an undirected network correspond to different tie ranges, two edge removal strategies are proposed, short ties priority removal strategy and long ties priority removal strategy. The tie range represents the second shortest path length between two connected nodes. The study focuses on analyzing how the proposed strategies impact network spreading and network structure, as well as examining the influence of network structure on network spreading. Our findings indicate that the long ties priority removal strategy is most effective in controlling network spreading, especially in terms of spread range and spread velocity. In terms of network structure, the clustering coefficient and the diameter of network also have an effect on the network spreading, and the triangular structure as an important motif structure effectively inhibits the spreading.

Protected simultaneous quantum remote state preparation scheme by weak and reversal measurements in noisy environments

We discuss a quantum remote state preparation protocol by which two parties, Alice and Candy, prepare a single-qubit and a two-qubit state, respectively, at the site of the receiver Bob. The single-qubit state is known to Alice while the two-qubit state which is a non-maximally entangled Bell state is known to Candy. The three parties are connected through a single entangled state which acts as a quantum channel. We first describe the protocol in the ideal case when the entangled channel under use is in a pure state. After that, we consider the effect of amplitude damping(AD) noise on the quantum channel and describe the protocol executed through the noisy channel. The decrement of the fidelity is shown to occur with the increment in the noise parameter. This is shown by numerical computation in specific examples of the states to be created. Finally, we show that it is possible to maintain the label of fidelity to some extent and hence to decrease the effect of noise by the application of weak and reversal measurements. We also present a scheme for the generation of the five-qubit entangled resource which we require as a quantum channel. The generation scheme is run on the IBMQ platform.

A Robust Framework for Multimodal Sentiment Analysis with Noisy Labels Generated from Distributed Data Annotation

Multimodal sentiment analysis utilizes multimodal data such as text,facial expressions and voice to detect people’s attitudes.With the advent of distributed data collection and annotation,we can easily obtain and share such multimodal data.However,due to professional discrepancies among annotators and lax quality control,noisy labels might be introduced.Recent research suggests that deep neural networks(DNNs)will overfit noisy labels,leading to the poor performance of the DNNs.To address this challenging problem,we present a Multimodal Robust Meta Learning framework(MRML)for multimodal sentiment analysis to resist noisy labels and correlate distinct modalities simultaneously.Specifically,we propose a two-layer fusion net to deeply fuse different modalities and improve the quality of the multimodal data features for label correction and network training.Besides,a multiple meta-learner(label corrector)strategy is proposed to enhance the label correction approach and prevent models from overfitting to noisy labels.We conducted experiments on three popular multimodal datasets to verify the superiority of ourmethod by comparing it with four baselines.

Possible Cortical Spreading Depression Recorded Intraoperatively Following a Generalized Seizure: Illustrative Case

Background: We present a compelling case fitting the phenomenon of cortical spreading depression detected by intraoperative neurophysiological monitoring (IONM) following an intraoperative seizure during a craniotomy for revascularization. Cortical spreading depression (CSD, also called cortical spreading depolarization) is a pathophysiological phenomenon whereby a wave of depolarization is thought to propagate across the cerebral cortex, creating a brief period of relative neuronal inactivity. The relationship between CSD and seizures is unclear, although some literature has made a correlation between seizures and a cortical environment conducive to CSD. Methods: Intraoperative somatosensory evoked potentials (SSEPs) and electroencephalography (EEG) were monitored continuously during the craniotomy procedure utilizing standard montages. Electrophysiological data from pre-ictal, ictal, and post-ictal periods were recorded. Results: During the procedure, intraoperative EEG captured a generalized seizure followed by a stepwise decrease in somatosensory evoked potential cortical amplitudes, compelling for the phenomenon of CSD. The subsequent partial recovery of neuronal function was also captured electrophysiologically. Discussion: While CSD is considered controversial in some aspects, intraoperative neurophysiological monitoring allowed for the unique analysis of a case demonstrating a CSD-like phenomenon. To our knowledge, this is the first published example of this phenomenon in which intraoperative neurophysiological monitoring captured a seizure, along with a stepwise subsequent reduction in SSEP cortical amplitudes not explained by other variables.

DiriNet:An Estimation Network for Spectral Response Function and Point Spread Function

Hyper-and multi-spectral image fusion is an important technology to produce hyper-spectral and hyper-resolution images,which always depends on the spectral response function andthe point spread function.However,few works have been payed on the estimation of the two degra-dation functions.To learn the two functions from image pairs to be fused,we propose a Dirichletnetwork,where both functions are properly constrained.Specifically,the spatial response function isconstrained with positivity,while the Dirichlet distribution along with a total variation is imposedon the point spread function.To the best of our knowledge,the neural network and the Dirichlet regularization are exclusively investigated,for the first time,to estimate the degradation functions.Both image degradation and fusion experiments demonstrate the effectiveness and superiority of theproposed Dirichlet network.

Bearing Capacity Analysis of Spread Footing on Massif in the “Corniche Ouest” of the Dakar Peninsula (Senegal, West Africa)

This study presents various approaches to calculating the bearing capacity of spread footings applied to the rock mass of the western corniche at the tip of the Dakar peninsula. The bearing capacity was estimated using empirical, analytical and numerical approaches based on the parameters of the rock mass and the foundation. Laboratory tests were carried out on basanite, as well as on the other facies detected. The results of these studies give a range of allowable bearing capacity values varying between 1.92 and 11.39 MPa for the empirical methods and from 7.13 to 25.50 MPa for the analytical methods. A wide dispersion of results was observed according to the different approaches. This dispersion of results is explained by the use of different rock parameters depending on the method used. The allowable bearing capacity results obtained with varying approaches of calculation remain admissible to support the loads. On the other hand, the foundation calculations show acceptable settlement of the order of a millimeter for all the layers, especially in the thin clay layers resting on the bedrock at shallow depths, where the rigidity of the rock reduces settlement.

基于Leaky Noisy-or Gate和贝叶斯网络的光伏发电项目施工风险评估方法

针对复杂多变的光伏发电项目施工风险评估问题,利用Leaky Noisy-or Gate模型整合历史经验和专家知识来确定贝叶斯网络参数并建立光伏施工风险的贝叶斯网络模型,同时采用Netica仿真分析,对施工风险进行有效预测并诊断识别出关键性风险和敏感性风险,以便管理者精准施策、管控风险。为验证该方法的有效性,以Z项目为例进行建模仿真。结果表明,其施工风险等级为中等,与实际相符。

Channel Estimation for OTFS System over Doubly Spread Sparse Acoustic Channels

This paper addresses sparse channels estimation problem for the generalized linear models(GLM)in the orthogonal time frequency space(OTFS)underwater acoustic(UWA)system.OTFS works in the delay-Doppler domain,where timevarying channels are characterized as delay-Doppler impulse responses.In fact,a typical doubly spread UWA channel is associated with several resolvable paths,which exhibits a structured sparsity in the delayDoppler domain.To leverage the structured sparsity of the doubly spread UWA channel,we develop a structured sparsity-based generalized approximated message passing(GAMP)algorithm for reliable channel estimation in quantized OTFS systems.The proposed algorithm has a lower computational complexity compared to the conventional Bayesian algorithm.In addition,the expectation maximum algorithm is employed to learn the sparsity ratio and the noise variance.Simulation and experimental results show that the proposed algorithm has superior performance and low computational complexity for quantized OTFS systems.

Functionalized alginate-based bioinks for microscale electrohydrodynamic bioprinting of living tissue constructs with improved cellular spreading and alignment

Bioprinting has been widely investigated for tissue engineering and regenerative medicine applications.However,it is still difficult to reconstruct the complex native cell arrangement due to the limited printing resolution of conventional bioprinting techniques such as extrusion-and inkjet-based printing.Recently,an electrohydrodynamic(EHD)bioprinting strategy was reported for the precise deposition of well-organized cell-laden constructs with microscale filament size,whereas few studies have been devoted to developing bioinks that can be applied for EHD bioprinting and simultaneously support cell spreading.This study describes functionalized alginate-based bioinks for microscale EHD bioprinting using peptide grafting and fibrin incorporation,which leads to high cell viability(>90%)and cell spreading.The printed filaments can be further refined to as small as 30μm by incorporating polyoxyethylene and remained stable over one week when exposed to an aqueous environment.By utilizing the presented alginate-based bioinks,layer-specific cell alignment along the printing struts could be observed inside the EHD-printed microscale filaments,which allows fabricating living constructs with cell-scale filament resolution for guided cellular orientation.

Topological phase transition in network spreading

This paper investigates information spreading from the perspective of topological phase transition.Firstly,a new hybrid network is constructed based on the small-world networks and scale-free networks.Secondly,the attention mechanism of online users in information spreading is studied from four aspects:social distance,individual influence,content richness,and individual activity,and a dynamic evolution model of connecting with spreading is designed.Eventually,numerical simulations are conducted in three types of networks to verify the validity of the proposed dynamic evolution model.The simulation results show that topological structure and node influence in different networks have undergone phase transition,which is consistent with the phenomenon that followers and individual influence in real social networks experience phase transition within a short period.The infection density of networks with the dynamic evolution rule changes faster and reaches higher values than that of networks without the dynamic evolution rule.Furthermore,the simulation results are compared with the real data,which shows that the infection density curve of the hybrid networks is closer to that of the real data than that of the small-world networks and scale-free networks,verifying the validity of the model proposed in this paper.

Boosting image watermarking authenticity spreading secrecy from counting-based secret-sharing

This study presents enhancing images authentication by securing watermarking hidden data via shares generated from counting-based secret sharing.The trustfulness of shares utilised secret-sharing as an applicable privacy creation tool for the authentication of real-life complex platforms.This research adjusts embedding the watermarking data over the images by innovative redistribution of shares to be embedded spread over all the images.The anticipated watermarking technique guaranteed to scatter the share bits implanting at different least significant bits of image pixels as boosting up the trust overall authentication practicality.The paper experimentation performance analysis shows that this improved image watermarking authentication(capacity)is averagely better by 33%–67%than other related exclusive-OR oriented and octagon approaches.Interestingly,these measurement improvements did not degrade the robustness and security of the system,inspiring our research for opening novel track of related future counting-based secret-sharing authentication progresses to come.

Nonpharmaceutical interventions eff ectively reduced infl uenza spread during the COVID-19 pandemic

Seasonal infl uenza,which is transmitted by droplets and direct contact,is a global public health issue that causes an average of 2.5 excess infl uenza-like illness(ILI)consultations per 1,000 person-years in China.[1]Nonpharmaceutical interventions(NPIs)against droplet and direct contact transmission,including social distancing measures and personal protective measures,are recommended to reduce the spread of disease.Social distancing measures comprised isolating ill persons,quarantining exposed persons,school and workplace closures,and avoiding crowds.Personal protective measures included hygiene,respiratory etiquette,and face masks.[2]In December 2019,coronavirus disease 2019(COVID-19)started to spread,and China introduced NPIs to address the pandemic in January 2020.These NPIs could also help reduce the spread of other respiratory diseases,such as seasonal influenza.[3]In the second half of 2020,the COVID-19 pandemic was gradually brought under control.Production,transport,and schools were returning to normal,while citizens were still required to wear masks in public.Cases of influenza also reappeared.This study compared the ILI percentage(ILI%),laboratory(LAB)-positive rate,incidence rate,and effective reproductive number(Rt)of infl uenzas from 2019 to 2022 to clarify the change in seasonal infl uenza spread after the COVID-19 outbreak.

Simulation and Experimental Research on Liquid Spreading in a Wire-Sawn Kerf

The significance of liquids in abrasive wire sawing has been demonstrated in several studies.However,the perfor-mance of its spreading behavior is limited by the current development trend,where the wafer has a larger area and the kerf is narrower.Moreover,there are very few studies on the liquid spreading behavior in wire-sawn kerfs.Therefore,a 3D CFD(computational fluid dynamics)model is presented in this paper and used to simulate the liquid spreading behavior in a kerf based on a VOF(volume of fluid)method with a CSF(continuum surface force)model,which is used to simulate multiphase flow,and an empirical correlation for characterizing the liquid dynamic contact angle using UDF(user defined functions).Subsequently,parametric simulations are performed on the kerf area,kerf width,liquid viscosity,liquid surface tension,and liquid velocity at the inlet area of the kerf,and verification experi-ments are conducted to determine the validity of the simulation model.From the simulation and experimental results,three typical liquid spreading regimes that exhibit different effects on wire sawing in the kerfs are found,and their limiting conditions are identified using non-dimensional analysis.Subsequently,a prediction model is pro-posed for the liquid spreading regime based on a set of Weber and Capillary numbers.For wire sawing,an increase in the wafer area does not change the liquid spreading regime in the kerf;however,a reduction in the kerf width sig-nificantly hinders the liquid spreading behavior.Thereby,the spreading regime can be effectively converted to facili-tate wire sawing by adjusting the physical properties and supply conditions of the liquid.

BIFURCATION CONTROL FOR A FRACTIONAL-ORDER DELAYED SEIR RUMOR SPREADING MODEL WITH INCOMMENSURATE ORDERS

A fractional-order delayed SEIR rumor spreading model with a nonlinear incidence function is established in this paper,and a novel strategy to control the bifurcation of this model is proposed.First,Hopf bifurcation is investigated by considering time delay as bifurcation parameter for the system without a feedback controller.Then,a state feedback controller is designed to control the occurrence of bifurcation in advance or to delay it by changing the parameters of the controller.Finally,in order to verify the theoretical results,some numerical simulations are given.

Optical encryption scheme based on spread spectrum ghost imaging

An optical encryption(OE) scheme based on the spread spectrum ghost imaging(SSGI), named as SSGI-OE, is proposed to obtain a high security with a smaller key. In the scheme, the randomly selected row number of a Hadamard matrix of order N is used as the secure key, and shared with the authorized user, Bob, through a private channel. Each corresponding row vector of the order-N Hadamard matrix is then used as the direct sequence code to modulate a speckle pattern for the ghost imaging system, and an image is encrypted with the help of the SSGI. The measurement results from the bucket detector, named as ciphertext, are then transmitted to Bob through a public channel. The illuminating speckle patterns are also shared with Bob by the public channel. With the correct secure key, Bob could reconstruct the image with the aid of the SSGI system, whereas the unauthorized user, Eve, could not obtain any useful information of the encrypted image. The numerical simulations and experimental results show that the proposed scheme is feasible with a higher security and a smaller key. For the 32 × 32 pixels image, the number of bits sent from Alice to Bob by using SSGIOE(M = 1024, N = 2048) scheme is only 0.0107 times over a computational ghost imaging optical encryption scheme.When the eavesdropping ratio(ER) is less than 40%, the eavesdropper cannot acquire any information of the encrypted image. The extreme circumstance for the proposed SSGI-OE scheme is also discussed, where the eavesdropper begins to extract the information when ER is up to 15%.

Spreading speed of a food-limited population model with delay

This paper is concerned with the spreading speed of a food-limited population model with delay.First,the existence of the solution of Cauchy problem is proved.Then,the spreading speed of solutions with compactly supported initial data is investigated by using the general Harnack inequality.Finally,we present some numerical simulations and investigate the dynamical behavior of the solution.

Mineralogy and geochemistry of hydrothermal sulphide from a submarine volcanic high at 18°36.4'S Central Lau Spreading Center, Southwest Pacific

We report the mineralogy and geochemistry of hydrothermal sulphide from the crater of a volcanic high near 18°36.4′S of the Central Lau Spreading Center.During 1990s,that volcanic structure was reported active and sulphide samples were collected by MIR submersible.A section of a chimney-like structure from the crater-floor was studied here.The Fe-depleted sphalerites,and Co-depleted pyrites in that chimney were similar to those commonly found in low to moderate temperature(<300℃)sulphides from sediment-starved hydrothermal systems.Bulk analyses of three parts of that chimney section showed substantial enrichment of Zn(18%–20%)and Fe(14%–27%)but depletion of Cu(0.8%–1.3%).In chondrite-normalized rare earth element-patterns,the significant negative Ce-anomalies(Ce/Ce*=0.27–0.39)and weakly positive Eu-anomalies(Eu/Eu*=1.60–1.68)suggested sulphide mineralisation took place from reduced low-temperature fluid.The depleted concentration of lithophiles in this sulphide indicates restricted contribution of sub-ducting plate in genesis of source fluid as compared to those from other parts of Lau Spreading Centre.Uniform mineralogy and bulk composition of subsamples across the chimney section suggests barely any alteration of fluid composition and/or mode of mineralisation occurred during its growth.

A New Model for Spreading Malware over SMS Using Network Automata

By the year 2026, it is estimated that the number of smartphone users in Mexico will be approximately 118.1 million. Each smartphone has the functionality of sending and receiving SMS (Short Message Service) messages, which pose a significant threat to all users, as it makes any device vulnerable to a malware attack. In particular, worm-type malware takes advantage of this means of communication in order to spread. Studying the dynamics of malware propagation can help understand and prevent massive contagion between mobile devices. In this work, a model based on Network Automata and compartmental epidemiological models is presented, aiming to simulate, analyze and study the spread of worm-like malware through sending SMS on smartphones.