Numerical parametric study on the influence of location and inclination of large-scale asperities on the shear strength of concreterock interfaces of small buttress dams

When assessing the sliding stability of a concrete dam,the influence of large-scale asperities in the sliding plane is often ignored due to limitations of the analytical rigid body assessment methods provided by current dam assessment guidelines.However,these asperities can potentially improve the load capacity of a concrete dam in terms of sliding stability.Although their influence in a sliding plane has been thoroughly studied for direct shear,their influence under eccentric loading,as in the case of dams,is unknown.This paper presents the results of a parametric study that used finite element analysis(FEA)to investigate the influence of large-scale asperities on the load capacity of small buttress dams.By varying the inclination and location of an asperity located in the concrete-rock interface along with the strength of the rock foundation material,transitions between different failure modes and correlations between the load capacity and the varied parameters were observed.The results indicated that the inclination of the asperity had a significant impact on the failure mode.When the inclinationwas 30and greater,interlocking occurred between the dam and foundation and the governing failure modes were either rupture of the dam body or asperity.When the asperity inclination was significant enough to provide interlocking,the load capacity of the dam was impacted by the strength of the rock in the foundation through influencing the load capacity of the asperity.The location of the asperity along the concrete-rock interface did not affect the failure mode,except for when the asperity was located at the toe of the dam,but had an influence on the load capacity when the failure occurred by rupture of the buttress or by sliding.By accounting for a single large-scale asperity in the concrete-rock interface of the analysed dam,a horizontal load capacity increase of 30%e160%was obtained,depending on the inclination and location of the asperity and the strength of the foundation material.

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.

Microseismic source location using deep learning:A coal mine case study in China

Microseismic source location is crucial for the early warning of rockburst risks.However,the conventional methods face challenges in terms of the microseismic wave velocity and arrival time accuracy.Intelligent techniques,such as the full convolutional neural network(FCNN),can capture spatial information but struggle with complex microseismic sequence.Combining the FCNN with the long shortterm memory(LSTM)network enables better time-series signal classification by integrating multiscale information and is therefore suitable for waveform location.The LSTM-FCNN model does not require extensive data preprocessing and it simplifies the microseismic source location through feature extraction.In this study,we utilized the LSTM-FCNN as a regression learning model to locate the seismic focus.Initially,the method of short-time-average/long-time-average(STA/LTA)arrival time picking was employed to augment spatiotemporal information.Subsequently,oversampling the on-site data was performed to address the issue of data imbalance,and finally,the performance of LSTM-FCNN was tested.Meanwhile,we compared the LSTM-FCNN model with previous deep-learning models.Our results demonstrated remarkable location capabilities with a mean absolute error(MAE)of only 7.16 m.The model can realize swift training and high accuracy,thereby significantly improving risk warning of rockbursts.

A Secure Device Management Scheme with Audio-Based Location Distinction in IoT

Identifying a device and detecting a change in its position is critical for secure devices management in the Internet of Things(IoT).In this paper,a device management system is proposed to track the devices by using audio-based location distinction techniques.In the proposed scheme,traditional cryptographic techniques,such as symmetric encryption algorithm,RSA-based signcryption scheme,and audio-based secure transmission,are utilized to provide authentication,non-repudiation,and confidentiality in the information interaction of the management system.Moreover,an audio-based location distinction method is designed to detect the position change of the devices.Specifically,the audio frequency response(AFR)of several frequency points is utilized as a device signature.The device signature has the features as follows.(1)Hardware Signature:different pairs of speaker and microphone have different signatures;(2)Distance Signature:in the same direction,the signatures are different at different distances;and(3)Direction Signature:at the same distance,the signatures are different in different directions.Based on the features above,amovement detection algorithmfor device identification and location distinction is designed.Moreover,a secure communication protocol is also proposed by using traditional cryptographic techniques to provide integrity,authentication,and non-repudiation in the process of information interaction between devices,Access Points(APs),and Severs.Extensive experiments are conducted to evaluate the performance of the proposed method.The experimental results show that the proposedmethod has a good performance in accuracy and energy consumption.

Using Improved Particle Swarm Optimization Algorithm for Location Problem of Drone Logistics Hub

Drone logistics is a novel method of distribution that will become prevalent.The advantageous location of the logistics hub enables quicker customer deliveries and lower fuel consumption,resulting in cost savings for the company’s transportation operations.Logistics firms must discern the ideal location for establishing a logistics hub,which is challenging due to the simplicity of existing models and the intricate delivery factors.To simulate the drone logistics environment,this study presents a new mathematical model.The model not only retains the aspects of the current models,but also considers the degree of transportation difficulty from the logistics hub to the village,the capacity of drones for transportation,and the distribution of logistics hub locations.Moreover,this paper proposes an improved particle swarm optimization(PSO)algorithm which is a diversity-based hybrid PSO(DHPSO)algorithm to solve this model.In DHPSO,the Gaussian random walk can enhance global search in the model space,while the bubble-net attacking strategy can speed convergence.Besides,Archimedes spiral strategy is employed to overcome the local optima trap in the model and improve the exploitation of the algorithm.DHPSO maintains a balance between exploration and exploitation while better defining the distribution of logistics hub locations Numerical experiments show that the newly proposed model always achieves better locations than the current model.Comparing DHPSO with other state-of-the-art intelligent algorithms,the efficiency of the scheme can be improved by 42.58%.This means that logistics companies can reduce distribution costs and consumers can enjoy a more enjoyable shopping experience by using DHPSO’s location selection.All the results show the location of the drone logistics hub is solved by DHPSO effectively.

Prognostic impact and reasons for variability by tumor location in gastric cancer

BACKGROUND Gastric cancer(GC)is a highly prevalent gastrointestinal tract tumor.Several trials have demonstrated that the location of GC can affect patient prognosis.However,the factors determining tumor location remain unclear.AIM To investigate the tumor location of patients,we went on to study the influencing factors that lead to changes in the location of GC.METHODS A retrospective evaluation was carried out on 3287 patients who underwent gastrectomy for GC in Zhejiang Cancer Hospital.The patients were followed up post-diagnosis and post-gastrectomy.The clinicopathological variables and overall survival of the patients were recorded.By analyzing the location of GC,the tumor location was divided into four categories:“Upper”,“middle”,“lower”,and“total”.Statistical software was utilized to analyze the relationship of each variable with the location of GC.RESULTS A total of 3287 patients were included in this study.The clinicopathological indices of gender,age,serum levels of carcinoembryonic antigen(CEA),carbohydrate antigen(CA19-9)and CA72-4 levels,were significantly associated with tumor location in patients with GC.In addition,there was a strong correlation between GC location and the prognosis of postoperative patients.Specifically,patients with“lower”and“middle”GC demonstrated a better prognosis than those with tumors in other categories.CONCLUSION The five clinicopathological indices of gender,age,CEA,CA19-9 and CA72-4 levels exhibit varying degrees of influence on the tumor location.The tumor location correlates with patient prognosis following surgery.

Dynamic Location Method for Shallow Ocean Bottom Nodes Using the Levenberg-Marquart Algorithm

Ocean bottom node(OBN)data acquisition is the main development direction of marine seismic exploration;it is widely promoted,especially in shallow sea environments.However,the OBN receivers may move several times because they are easily affected by tides,currents,and other factors in the shallow sea environment during long-term acquisition.If uncorrected,then the imaging quality of subsequent processing will be affected.The conventional secondary positioning does not consider the case of multiple movements of the receivers,and the accuracy of secondary positioning is insufficient.The first arrival wave of OBN seismic data in shallow ocean mainly comprises refracted waves.In this study,a nonlinear model is established in accordance with the propagation mechanism of a refracted wave and its relationship with the time interval curve to realize the accurate location of multiple receiver movements.In addition,the Levenberg-Marquart algorithm is used to reduce the influence of the first arrival pickup error and to automatically detect the receiver movements,identifying the accurate dynamic relocation of the receivers.The simulation and field data show that the proposed method can realize the dynamic location of multiple receiver movements,thereby improving the accuracy of seismic imaging and achieving high practical value.

Robust Space-Time Adaptive Track-Before-Detect Algorithm Based on Persymmetry and Symmetric Spectrum

Underwater monopulse space-time adaptive track-before-detect method,which combines space-time adaptive detector(STAD)and the track-before-detect algorithm based on dynamic programming(DP-TBD),denoted as STAD-DP-TBD,can effectively detect low-speed weak targets.However,due to the complexity and variability of the underwater environment,it is difficult to obtain sufficient secondary data,resulting in a serious decline in the detection and tracking performance,and leading to poor robustness of the algorithm.In this paper,based on the adaptive matched filter(AMF)test and the RAO test,underwater monopulse AMF-DP-TBD algorithm and RAO-DP-TBD algorithm which incorporate persymmetry and symmetric spectrum,denoted as PSAMF-DP-TBD and PS-RAO-DP-TBD,are proposed and compared with the AMF-DP-TBD algorithm and RAO-DP-TBD algorithm based on persymmetry array,denoted as P-AMF-DP-TBD and P-RAO-DP-TBD.The simulation results show that the four methods can work normally with sufficient secondary data and slightly insufficient secondary data,but when the secondary data is severely insufficient,the P-AMF-DP-TBD and P-RAO-DP-TBD algorithms has failed while the PSAMF-DP-TBD and PS-RAO-DP-TBD algorithms still have good detection and tracking capabilities.

Earthquake relocation using a 3D velocity model and implications on seismogenic faults in the Beijing-Tianjin-Hebei region

To enhance the understanding of the geometry and characteristics of seismogenic faults in the Beijing-Tianjin-Hebei region,we relocated 14805 out of 16063 earthquakes(113°E-120°E,36°N-43°N)that occurred between January 2008 and December 2020 using the double-difference tomography method.Based on the spatial variation in seismicity after relocation,the Beijing-Tianjin-Hebei region can be divided into three seismic zones:Xingtai-Wen'an,Zhangbei-Ninghexi,and Tangshan.(1)The Xingtai-Wen'an Seismic Zone has a northeastsouthwest strike.The depth profile of earthquakes perpendicular to the strike reveals three northeast-striking,southeast-dipping,high-angle deep faults(>10 km depth),including one below the shallow(<10 km depth)listric,northwest-dipping Xinghe fault in the Xingtai region.Two additional deep faults in the Wen'an region are suggested to be associated with the 2006 M 5.1 Wen'an Earthquake and the 1967 M 6.3 Dacheng earthquake;(2)The Zhangbei-Ninghexi Seismic Zone is oriented north-northwest.Multiple northeast-striking faults(10-20 km depth),inferred from the earthquake-intensive zones,exist beneath the shallow(<10 km depth)Xiandian Fault,Xiaotangshan Fault,Huailai-Zhuolu Basin North Fault,Yangyuan Basin Fault and Yanggao Basin North Fault;(3)In the Tangshan Seismic Zone,earthquakes are mainly concentrated near the northeast-striking Tangshan-Guye Fault,Lulong Fault,and northwest-striking Luanxian-Laoting Fault.An inferred north-south-oriented blind fault is present to the north of the Tangshan-Guye Fault.The 1976 M 7.8 Tangshan earthquake occurred at the junction of a shallow northwest-dipping fault and a deep southeast-dipping fault.This study emphasizes that earthquakes in the region are primarily associated with deep blind faults.Some deep blind faults have different geometries compared to shallow faults,suggesting a complex fault system in the region.Overall,this research provides valuable insights into the seismogenic faults in the Beijing–Tianjin–Hebei region.Further studies and monitoring of these faults are essential for earthquake mitigation efforts in this region.

Location Prediction from Social Media Contents using Location Aware Attention LSTM Network

Location prediction in social media,a growing research field,employs machine learning to identify users' locations from their online activities.This technology,useful in targeted advertising and urban planning,relies on natural language processing to analyze social media content and understand the temporal dynamics and structures of social networks.A key application is predicting a Twitter user's location from their tweets,which can be challenging due to the short and unstructured nature of tweet text.To address this challenge,the research introduces a novel machine learning model called the location-aware attention LSTM(LAA-LSTM).This hybrid model combines a Long Short-Term Memory(LSTM) network with an attention mechanism.The LSTM is trained on a dataset of tweets,and the attention network focuses on extracting features related to latitude and longitude,which are crucial for pinpointing the location of a user's tweet.The result analysis shows approx.10% improvement in accuracy over other existing machine learning approaches.

Optimal Configuration of Fault Location Measurement Points in DC Distribution Networks Based on Improved Particle Swarm Optimization Algorithm

The escalating deployment of distributed power sources and random loads in DC distribution networks hasamplified the potential consequences of faults if left uncontrolled. To expedite the process of achieving an optimalconfiguration of measurement points, this paper presents an optimal configuration scheme for fault locationmeasurement points in DC distribution networks based on an improved particle swarm optimization algorithm.Initially, a measurement point distribution optimization model is formulated, leveraging compressive sensing.The model aims to achieve the minimum number of measurement points while attaining the best compressivesensing reconstruction effect. It incorporates constraints from the compressive sensing algorithm and networkwide viewability. Subsequently, the traditional particle swarm algorithm is enhanced by utilizing the Haltonsequence for population initialization, generating uniformly distributed individuals. This enhancement reducesindividual search blindness and overlap probability, thereby promoting population diversity. Furthermore, anadaptive t-distribution perturbation strategy is introduced during the particle update process to enhance the globalsearch capability and search speed. The established model for the optimal configuration of measurement points issolved, and the results demonstrate the efficacy and practicality of the proposed method. The optimal configurationreduces the number of measurement points, enhances localization accuracy, and improves the convergence speedof the algorithm. These findings validate the effectiveness and utility of the proposed approach.

Multi-Branch Fault Line Location Method Based on Time Difference Matrix Fitting

The distribution network exhibits complex structural characteristics,which makes fault localization a challenging task.Especially when a branch of the multi-branch distribution network fails,the traditional multi-branch fault location algorithm makes it difficult to meet the demands of high-precision fault localization in the multi-branch distribution network system.In this paper,the multi-branch mainline is decomposed into single branch lines,transforming the complex multi-branch fault location problem into a double-ended fault location problem.Based on the different transmission characteristics of the fault-traveling wave in fault lines and non-fault lines,the endpoint reference time difference matrix S and the fault time difference matrix G were established.The time variation rule of the fault-traveling wave arriving at each endpoint before and after a fault was comprehensively utilized.To realize the fault segment location,the least square method was introduced.It was used to find the first-order fitting relation that satisfies the matching relationship between the corresponding row vector and the first-order function in the two matrices,to realize the fault segment location.Then,the time difference matrix is used to determine the traveling wave velocity,which,combined with the double-ended traveling wave location,enables accurate fault location.

Curved Space-Time at the Planck Scale

This paper presents a physically plausible and somewhat illuminating first step in extending the fundamental principles of mechanical stress and strain to space-time. Here the geometry of space-time, encoded in the metric tensor, is considered to be made up of a dynamic lattice of extremely small, localized fields that form a perfectly elastic Lorentz symmetric space-time at the global (macroscopic) scale. This theoretical model of space-time at the Planck scale leads to a somewhat surprising result in which matter waves in curved space-time radiate thermal gravitational energy, as well as an equally intriguing relationship for the anomalous dispersion of light in a gravitational field.

How Does a University’s Computer Science Strength and Location Impact Its Total ChatGPT News?

As AI, starting with ChatGPT has become increasingly prevalent in academic discussions, school especially, colleges have become hotspots of AI activities and debates. Colleges have the responsibility of addressing not only the academic, integrity-based concerns of students using AI for their homework, but also as the forebearers of new learning and technology, how AI will change their students’ futures and careers. In this study, we will explore the different factors, such as Computer Science Score and location, that might affect how much a college discusses AI, ChatGPT specifically. To demonstrate the validity of our research, we used self-collected data with our methods detailed below.

Virtually coupled train set control subject to space-time separation:A distributed economic MPC approach with emergency braking configuration

The emerging virtual coupling technology aims to operate multiple train units in a Virtually Coupled Train Set(VCTS)at a minimal but safe distance.To guarantee collision avoidance,the safety distance should be calculated using the state-of-the-art space-time separation principle that separates the Emergency Braking(EB)trajectories of two successive units during the whole EB process.In this case,the minimal safety distance is usually numerically calculated without an analytic formulation.Thus,the constrained VCTS control problem is hard to address with space-time separation,which is still a gap in the existing literature.To solve this problem,we propose a Distributed Economic Model Predictive Control(DEMPC)approach with computation efficiency and theoretical guarantee.Specifically,to alleviate the computation burden,we transform implicit safety constraints into explicitly linear ones,such that the optimal control problem in DEMPC is a quadratic programming problem that can be solved efficiently.For theoretical analysis,sufficient conditions are derived to guarantee the recursive feasibility and stability of DEMPC,employing compatibility constraints,tube techniques and terminal ingredient tuning.Moreover,we extend our approach with globally optimal and distributed online EB configuration methods to shorten the minimal distance among VCTS.Finally,experimental results demonstrate the performance and advantages of the proposed approaches.

Unknown DDoS Attack Detection with Fuzzy C-Means Clustering and Spatial Location Constraint Prototype Loss

Since its inception,the Internet has been rapidly evolving.With the advancement of science and technology and the explosive growth of the population,the demand for the Internet has been on the rise.Many applications in education,healthcare,entertainment,science,and more are being increasingly deployed based on the internet.Concurrently,malicious threats on the internet are on the rise as well.Distributed Denial of Service(DDoS)attacks are among the most common and dangerous threats on the internet today.The scale and complexity of DDoS attacks are constantly growing.Intrusion Detection Systems(IDS)have been deployed and have demonstrated their effectiveness in defense against those threats.In addition,the research of Machine Learning(ML)and Deep Learning(DL)in IDS has gained effective results and significant attention.However,one of the challenges when applying ML and DL techniques in intrusion detection is the identification of unknown attacks.These attacks,which are not encountered during the system’s training,can lead to misclassification with significant errors.In this research,we focused on addressing the issue of Unknown Attack Detection,combining two methods:Spatial Location Constraint Prototype Loss(SLCPL)and Fuzzy C-Means(FCM).With the proposed method,we achieved promising results compared to traditional methods.The proposed method demonstrates a very high accuracy of up to 99.8%with a low false positive rate for known attacks on the Intrusion Detection Evaluation Dataset(CICIDS2017)dataset.Particularly,the accuracy is also very high,reaching 99.7%,and the precision goes up to 99.9%for unknown DDoS attacks on the DDoS Evaluation Dataset(CICDDoS2019)dataset.The success of the proposed method is due to the combination of SLCPL,an advanced Open-Set Recognition(OSR)technique,and FCM,a traditional yet highly applicable clustering technique.This has yielded a novel method in the field of unknown attack detection.This further expands the trend of applying DL and ML techniques in the development of intrusion detection systems and cybersecurity.Finally,implementing the proposed method in real-world systems can enhance the security capabilities against increasingly complex threats on computer networks.

Performance of Cross-Layer Design with Power Allocation in Space-Time Coded MIMO Systems

A cross-layer design(CLD)scheme with combination of power allocation,adaptive modulation(AM)and automatic repeat request(ARQ)is presented for space-time coded MIMO system under imperfect feedback,and the corresponding system performance is investigated in a Rayleigh fading channel.Based on imperfect feedback information,a suboptimal power allocation(PA)scheme is derived to maximize the average spectral efficiency(SE)of the system.The scheme is based on a so-called compressed SNR criterion,and has a closed-form expression for positive power allocation,thus being computationally efficient.Moreover,it can improve SE of the presented CLD.Besides,due to better approximation,it obtains the performance close to the existing optimal approach which requires numerical search.Simulation results show that the proposed CLD with PA can achieve higher SE than the conventional CLD with equal power allocation scheme,and has almost the same performance as CLD with optimal PA.However,it has lower calculation complexity.

An Iterative Power Allocation Algorithm for Group-wise Space-Time Block Coding Systems

An iterative transmit power allocation (PA) algorithm was proposed for group-wise space-time block coding (G-STBC) systems with group-wise successive interference cancellation (GSIC) receivers. Group-wise interference suppression (GIS) filters are employed to separate each group's transmit signals from other interferences and noise. While the total power on all transmit symbols is constrained, all transmit PA coefficients are updated jointly according to the channel information at each iteration. Through PA, each detection symbol has the same post-detection signal to interference-and-noise ratio (SINR). The simulation results verify that the proposed PA algorithm converges at the equilibrium quickly after few iterations, and it achieves much lower bit error rates than the previous single symbol SIC PA and the fixed ratio PA algorithms for G-STBC systems with GSIC receivers .

Reviewer Locator模块在科技期刊中的应用实践与发展前景——以《中国肺癌杂志》为例

科技期刊作为科技传播的重要力量,其对重要科研成果的发布速度影响着科学技术的传播效力,而编辑工作效率直接决定稿件发表周期,提高编辑工作效率是科技期刊发展的推进器。为此,从同行评议审稿人储备和遴选方面入手,重点阐述如何准确高效地多维度遴选优质审稿人,归纳并探析Reviewer Locator模块在科技期刊办刊工作中的实际应用经验,助力出版业实现质量更好、效率更高的发展。

Optimization of Charging/Battery-Swap Station Location of Electric Vehicles with an Improved Genetic Algorithm-Based Model

The joint location planning of charging/battery-swap facilities for electric vehicles is a complex problem.Considering the differences between these two modes of power replenishment,we constructed a joint location-planning model to minimize construction and operation costs,user costs,and user satisfaction-related penalty costs.We designed an improved genetic algorithm that changes the crossover rate using the fitness value,memorizes,and transfers excellent genes.In addition,the present model addresses the problem of“premature convergence”in conventional genetic algorithms.A simulated example revealed that our proposed model could provide a basis for optimized location planning of charging/battery-swapping facilities at different levels under different charging modes with an improved computing efficiency.The example also proved that meeting more demand for power supply of electric vehicles does not necessarily mean increasing the sites of charging/battery-swap stations.Instead,optimizing the level and location planning of charging/battery-swap stations can maximize the investment profit.The proposed model can provide a reference for the government and enterprises to better plan the location of charging/battery-swap facilities.Hence,it is of both theoretical and practical value.