Prediction of Shear Bond Strength of Asphalt Concrete Pavement Using Machine Learning Models and Grid Search Optimization Technique

Determination of Shear Bond strength(SBS)at interlayer of double-layer asphalt concrete is crucial in flexible pavement structures.The study used three Machine Learning(ML)models,including K-Nearest Neighbors(KNN),Extra Trees(ET),and Light Gradient Boosting Machine(LGBM),to predict SBS based on easily determinable input parameters.Also,the Grid Search technique was employed for hyper-parameter tuning of the ML models,and cross-validation and learning curve analysis were used for training the models.The models were built on a database of 240 experimental results and three input variables:temperature,normal pressure,and tack coat rate.Model validation was performed using three statistical criteria:the coefficient of determination(R2),the Root Mean Square Error(RMSE),and the mean absolute error(MAE).Additionally,SHAP analysis was also used to validate the importance of the input variables in the prediction of the SBS.Results show that these models accurately predict SBS,with LGBM providing outstanding performance.SHAP(Shapley Additive explanation)analysis for LGBM indicates that temperature is the most influential factor on SBS.Consequently,the proposed ML models can quickly and accurately predict SBS between two layers of asphalt concrete,serving practical applications in flexible pavement structure design.

Method for Estimating the State of Health of Lithium-ion Batteries Based on Differential Thermal Voltammetry and Sparrow Search Algorithm-Elman Neural Network

Precisely estimating the state of health(SOH)of lithium-ion batteries is essential for battery management systems(BMS),as it plays a key role in ensuring the safe and reliable operation of battery systems.However,current SOH estimation methods often overlook the valuable temperature information that can effectively characterize battery aging during capacity degradation.Additionally,the Elman neural network,which is commonly employed for SOH estimation,exhibits several drawbacks,including slow training speed,a tendency to become trapped in local minima,and the initialization of weights and thresholds using pseudo-random numbers,leading to unstable model performance.To address these issues,this study addresses the challenge of precise and effective SOH detection by proposing a method for estimating the SOH of lithium-ion batteries based on differential thermal voltammetry(DTV)and an SSA-Elman neural network.Firstly,two health features(HFs)considering temperature factors and battery voltage are extracted fromthe differential thermal voltammetry curves and incremental capacity curves.Next,the Sparrow Search Algorithm(SSA)is employed to optimize the initial weights and thresholds of the Elman neural network,forming the SSA-Elman neural network model.To validate the performance,various neural networks,including the proposed SSA-Elman network,are tested using the Oxford battery aging dataset.The experimental results demonstrate that the method developed in this study achieves superior accuracy and robustness,with a mean absolute error(MAE)of less than 0.9%and a rootmean square error(RMSE)below 1.4%.

Unveiling Effective Heuristic Strategies: A Review of Cross-Domain Heuristic Search Challenge Algorithms

The Cross-domain Heuristic Search Challenge(CHeSC)is a competition focused on creating efficient search algorithms adaptable to diverse problem domains.Selection hyper-heuristics are a class of algorithms that dynamically choose heuristics during the search process.Numerous selection hyper-heuristics have different imple-mentation strategies.However,comparisons between them are lacking in the literature,and previous works have not highlighted the beneficial and detrimental implementation methods of different components.The question is how to effectively employ them to produce an efficient search heuristic.Furthermore,the algorithms that competed in the inaugural CHeSC have not been collectively reviewed.This work conducts a review analysis of the top twenty competitors from this competition to identify effective and ineffective strategies influencing algorithmic performance.A summary of the main characteristics and classification of the algorithms is presented.The analysis underlines efficient and inefficient methods in eight key components,including search points,search phases,heuristic selection,move acceptance,feedback,Tabu mechanism,restart mechanism,and low-level heuristic parameter control.This review analyzes the components referencing the competition’s final leaderboard and discusses future research directions for these components.The effective approaches,identified as having the highest quality index,are mixed search point,iterated search phases,relay hybridization selection,threshold acceptance,mixed learning,Tabu heuristics,stochastic restart,and dynamic parameters.Findings are also compared with recent trends in hyper-heuristics.This work enhances the understanding of selection hyper-heuristics,offering valuable insights for researchers and practitioners aiming to develop effective search algorithms for diverse problem domains.

Reasons and Solutions of the Time-consuming College English Teaching with Low Efficiency

College English teaching has been regarded as time- consuming with low efficiency for a long time. This thesis proceeds with the entity of English education in China, deals with the main problems lying in the current college English teaching from 3 perspectives, including the nation and society, teachers, and students, and provides 5 suggestions accordingly.

Evolutionary Neural Architecture Search and Its Applications in Healthcare

Most of the neural network architectures are based on human experience,which requires a long and tedious trial-and-error process.Neural architecture search(NAS)attempts to detect effective architectures without human intervention.Evolutionary algorithms(EAs)for NAS can find better solutions than human-designed architectures by exploring a large search space for possible architectures.Using multiobjective EAs for NAS,optimal neural architectures that meet various performance criteria can be explored and discovered efficiently.Furthermore,hardware-accelerated NAS methods can improve the efficiency of the NAS.While existing reviews have mainly focused on different strategies to complete NAS,a few studies have explored the use of EAs for NAS.In this paper,we summarize and explore the use of EAs for NAS,as well as large-scale multiobjective optimization strategies and hardware-accelerated NAS methods.NAS performs well in healthcare applications,such as medical image analysis,classification of disease diagnosis,and health monitoring.EAs for NAS can automate the search process and optimize multiple objectives simultaneously in a given healthcare task.Deep neural network has been successfully used in healthcare,but it lacks interpretability.Medical data is highly sensitive,and privacy leaks are frequently reported in the healthcare industry.To solve these problems,in healthcare,we propose an interpretable neuroevolution framework based on federated learning to address search efficiency and privacy protection.Moreover,we also point out future research directions for evolutionary NAS.Overall,for researchers who want to use EAs to optimize NNs in healthcare,we analyze the advantages and disadvantages of doing so to provide detailed guidance,and propose an interpretable privacy-preserving framework for healthcare applications.

Cooperative UAV search strategy based on DMPC-AACO algorithm in restricted communication scenarios

Improvement of integrated battlefield situational awareness in complex environments involving dynamic factors such as restricted communications and electromagnetic interference(EMI)has become a contentious research problem.In certain mission environments,due to the impact of many interference sources on real-time communication or mission requirements such as the need to implement communication regulations,the mission stages are represented as a dynamic combination of several communication-available and communication-unavailable stages.Furthermore,the data interaction between unmanned aerial vehicles(UAVs)can only be performed in specific communication-available stages.Traditional cooperative search algorithms cannot handle such situations well.To solve this problem,this study constructed a distributed model predictive control(DMPC)architecture for a collaborative control of UAVs and used the Voronoi diagram generation method to re-plan the search areas of all UAVs in real time to avoid repetition of search areas and UAV collisions while improving the search efficiency and safety factor.An attention mechanism ant-colony optimization(AACO)algorithm is proposed for UAV search-control decision planning.The search strategy is adaptively updated by introducing an attention mechanism for regular instruction information,a priori information,and emergent information of the mission to satisfy different search expectations to the maximum extent.Simulation results show that the proposed algorithm achieves better search performance than traditional algorithms in restricted communication constraint scenarios.

基于ElasticSearch的医疗数据检索系统的设计与实现

随着医疗技术的发展,医疗业务场景越加复杂,由此产生的医疗数据也越来越多,其来源复杂,结构多变,信息冗余,数据不完整。这些特性使得系统在进行检索时,无法快速、有效、精确地查询数据。为了解决这个问题,设计并实现了基于ElasticSearch的医疗数据检索系统。该系统将医疗数据进行标准化,填补缺失值,选取合适的分词算法进行分词,将处理后的数据存入ElasticSearch中,同时使用SpringBoot构建系统应用,消耗多个医疗基础业务系统产生的数据,最终形成统一的医疗数据检索系统,给用户提供便捷、精确的查询服务。

Optimal search path planning of UUV in battlefeld ambush scene

Aiming at the practical application of Unmanned Underwater Vehicle(UUV)in underwater combat,this paper proposes a battlefield ambush scene with UUV considering ocean current.Firstly,by establishing these mathematical models of ocean current environment,target movement,and sonar detection,the probability calculation methods of single UUV searching target and multiple UUV cooperatively searching target are given respectively.Then,based on the Hybrid Quantum-behaved Particle Swarm Optimization(HQPSO)algorithm,the path with the highest target search probability is found.Finally,through simulation calculations,the influence of different UUV parameters and target parameters on the target search probability is analyzed,and the minimum number of UUVs that need to be deployed to complete the ambush task is demonstrated,and the optimal search path scheme is obtained.The method proposed in this paper provides a theoretical basis for the practical application of UUV in the future combat.

Enhanced Differentiable Architecture Search Based on Asymptotic Regularization

In differentiable search architecture search methods,a more efficient search space design can significantly improve the performance of the searched architecture,thus requiring people to carefully define the search space with different complexity according to various operations.Meanwhile rationalizing the search strategies to explore the well-defined search space will further improve the speed and efficiency of architecture search.With this in mind,we propose a faster and more efficient differentiable architecture search method,AllegroNAS.Firstly,we introduce a more efficient search space enriched by the introduction of two redefined convolution modules.Secondly,we utilize a more efficient architectural parameter regularization method,mitigating the overfitting problem during the search process and reducing the error brought about by gradient approximation.Meanwhile,we introduce a natural exponential cosine annealing method to make the learning rate of the neural network training process more suitable for the search procedure.Moreover,group convolution and data augmentation are employed to reduce the computational cost.Finally,through extensive experiments on several public datasets,we demonstrate that our method can more swiftly search for better-performing neural network architectures in a more efficient search space,thus validating the effectiveness of our approach.

Bridge Bidding via Deep Reinforcement Learning and Belief Monte Carlo Search

Contract Bridge,a four-player imperfect information game,comprises two phases:bidding and playing.While computer programs excel at playing,bidding presents a challenging aspect due to the need for information exchange with partners and interference with communication of opponents.In this work,we introduce a Bridge bidding agent that combines supervised learning,deep reinforcement learning via self-play,and a test-time search approach.Our experiments demonstrate that our agent outperforms WBridge5,a highly regarded computer Bridge software that has won multiple world championships,by a performance of 0.98 IMPs(international match points)per deal over 10000 deals,with a much cost-effective approach.The performance significantly surpasses previous state-of-the-art(0.85 IMPs per deal).Note 0.1 IMPs per deal is a significant improvement in Bridge bidding.

Walking to Work Isn’t As Time-consuming As You Think

词数:474阅读难度:建议阅读时间:5分钟Barely 3 percent of the American work force bikes or walks to work with any frequency,despite the obvious virtues:decreased risks for obesity and diabetes, environmental benefits and lower transportation costs. Ask people why they eschew(避开)what’s known as active commiting,as many surveys have,and the primary reason cited is time.

A Lightweight, Searchable, and Controllable EMR Sharing Scheme

Electronic medical records (EMR) facilitate the sharing of medical data, but existing sharing schemes suffer fromprivacy leakage and inefficiency. This article proposes a lightweight, searchable, and controllable EMR sharingscheme, which employs a large attribute domain and a linear secret sharing structure (LSSS), the computationaloverhead of encryption and decryption reaches a lightweight constant level, and supports keyword search andpolicy hiding, which improves the high efficiency of medical data sharing. The dynamic accumulator technologyis utilized to enable data owners to flexibly authorize or revoke the access rights of data visitors to the datato achieve controllability of the data. Meanwhile, the data is re-encrypted by Intel Software Guard Extensions(SGX) technology to realize resistance to offline dictionary guessing attacks. In addition, blockchain technology isutilized to achieve credible accountability for abnormal behaviors in the sharing process. The experiments reflectthe obvious advantages of the scheme in terms of encryption and decryption computation overhead and storageoverhead, and theoretically prove the security and controllability in the sharing process, providing a feasible solutionfor the safe and efficient sharing of EMR.

Privacy-Preserving Multi-Keyword Fuzzy Adjacency Search Strategy for Encrypted Graph in Cloud Environment

In a cloud environment,outsourced graph data is widely used in companies,enterprises,medical institutions,and so on.Data owners and users can save costs and improve efficiency by storing large amounts of graph data on cloud servers.Servers on cloud platforms usually have some subjective or objective attacks,which make the outsourced graph data in an insecure state.The issue of privacy data protection has become an important obstacle to data sharing and usage.How to query outsourcing graph data safely and effectively has become the focus of research.Adjacency query is a basic and frequently used operation in graph,and it will effectively promote the query range and query ability if multi-keyword fuzzy search can be supported at the same time.This work proposes to protect the privacy information of outsourcing graph data by encryption,mainly studies the problem of multi-keyword fuzzy adjacency query,and puts forward a solution.In our scheme,we use the Bloom filter and encryption mechanism to build a secure index and query token,and adjacency queries are implemented through indexes and query tokens on the cloud server.Our proposed scheme is proved by formal analysis,and the performance and effectiveness of the scheme are illustrated by experimental analysis.The research results of this work will provide solid theoretical and technical support for the further popularization and application of encrypted graph data processing technology.

Spatial search weighting information contained in cell velocity distribution

Cell migration plays a significant role in physiological and pathological processes.Understanding the characteristics of cell movement is crucial for comprehending biological processes such as cell functionality,cell migration,and cell–cell interactions.One of the fundamental characteristics of cell movement is the specific distribution of cell speed,containing valuable information that still requires comprehensive understanding.This article investigates the distribution of mean velocities along cell trajectories,with a focus on optimizing the efficiency of cell food search in the context of the entire colony.We confirm that the specific velocity distribution in the experiments corresponds to an optimal search efficiency when spatial weighting is considered.The simulation results indicate that the distribution of average velocity does not align with the optimal search efficiency when employing average spatial weighting.However,when considering the distribution of central spatial weighting,the specific velocity distribution in the experiment is shown to correspond to the optimal search efficiency.Our simulations reveal that for any given distribution of average velocity,a specific central spatial weighting can be identified among the possible central spatial weighting that aligns with the optimal search strategy.Additionally,our work presents a method for determining the spatial weights embedded in the velocity distribution of cell movement.Our results have provided new avenues for further investigation of significant topics,such as relationship between cell behavior and environmental conditions throughout their evolutionary history,and how cells achieve collective cooperation through cell-cell communication.

Does e-hailing perform better than on-street searching?An investigation based on the temporal-spatial distributions of idle vehicles

This paper investigates whether e-hailing performs better than on-street searching for taxi services.By adopting the Poission point process to model the temporal-spatial distributions of idle vehicles,passengers’waiting time distributions of on-street searching and ehailing are explicitly modeled,and closed-form results of their expected waiting time are given.It is proved that whether e-hailing performs better than on-street searching mainly depends on the density of idle vehicles within the matching area and the matching period.It is proved that given the advantage of e-hailing in rapidly pairing passengers and idle vehicles,the expected waiting time for onstreet searching is always longer than that of e-hailing as long as the number of idle vehicles within a passenger’s dominant temporal-spatial area is lower than 4/π.Moreover,we extend our analysis to explore the market equilibria for both e-hailing and on-street searching,and present the equilibrium conditions for a taxi market operating under ehailing versus on-street searching.With a special reciprocal passenger demand function,it is shown that the performance difference between e-hailing and on-street searching is mainly determined by the ratio of fleet size to maximum potential passenger demand.It suggests that e-hailing can achieve a higher capacity utilization rate of vehicles than on-street searching when vehicle density is relatively low.Furthermore,it is shown that an extended average trip duration improves the chance that e-hailing performs better than on-street searching.The optimal vehicle fleet sizes to maximize the total social welfare/profit are then analyzed,and the corresponding maximization problems are formulated.

Study on Joint Method of 3D Acoustic Emission Source Localization Simplex and Grid Search Scanning

Acoustic emission(AE)source localization is a fundamental element of rock fracture damage imaging.To improve the efficiency and accuracy of AE source localization,this paper proposes a joint method comprising a three-dimensional(3D)AE source localization simplex method and grid search scanning.Using the concept of the geometry of simplexes,tetrahedral iterations were first conducted to narrow down the suspected source region.This is followed by a process of meshing the region and node searching to scan for optimal solutions,until the source location is determined.The resulting algorithm was tested using the artificial excitation source localization and uniaxial compression tests,after which the localization results were compared with the simplex and exhaustive methods.The results revealed that the localization obtained using the proposed method is more stable and can be effectively avoided compared with the simplex localization method.Furthermore,compared with the global scanning method,the proposed method is more efficient,with an average time of 10%–20%of the global scanning localization algorithm.Thus,the proposed algorithm is of great significance for laboratory research focused on locating rupture damages sustained by large-sized rock masses or test blocks.

Even Search in a Promising Region for Constrained Multi-Objective Optimization

In recent years, a large number of approaches to constrained multi-objective optimization problems(CMOPs) have been proposed, focusing on developing tweaked strategies and techniques for handling constraints. However, an overly finetuned strategy or technique might overfit some problem types,resulting in a lack of versatility. In this article, we propose a generic search strategy that performs an even search in a promising region. The promising region, determined by obtained feasible non-dominated solutions, possesses two general properties.First, the constrained Pareto front(CPF) is included in the promising region. Second, as the number of feasible solutions increases or the convergence performance(i.e., approximation to the CPF) of these solutions improves, the promising region shrinks. Then we develop a new strategy named even search,which utilizes the non-dominated solutions to accelerate convergence and escape from local optima, and the feasible solutions under a constraint relaxation condition to exploit and detect feasible regions. Finally, a diversity measure is adopted to make sure that the individuals in the population evenly cover the valuable areas in the promising region. Experimental results on 45 instances from four benchmark test suites and 14 real-world CMOPs have demonstrated that searching evenly in the promising region can achieve competitive performance and excellent versatility compared to 11 most state-of-the-art methods tailored for CMOPs.

Database Search Behaviors: Insight from a Survey of Information Retrieval Practices

This study examines the database search behaviors of individuals, focusing on gender differences and the impact of planning habits on information retrieval. Data were collected from a survey of 198 respondents, categorized by their discipline, schooling background, internet usage, and information retrieval preferences. Key findings indicate that females are more likely to plan their searches in advance and prefer structured methods of information retrieval, such as using library portals and leading university websites. Males, however, tend to use web search engines and self-archiving methods more frequently. This analysis provides valuable insights for educational institutions and libraries to optimize their resources and services based on user behavior patterns.

Cuckoo Search-Optimized Deep CNN for Enhanced Cyber Security in IoT Networks

Phishing attacks seriously threaten information privacy and security within the Internet of Things(IoT)ecosystem.Numerous phishing attack detection solutions have been developed for IoT;however,many of these are either not optimally efficient or lack the lightweight characteristics needed for practical application.This paper proposes and optimizes a lightweight deep-learning model for phishing attack detection.Our model employs a two-fold optimization approach:first,it utilizes the analysis of the variance(ANOVA)F-test to select the optimal features for phishing detection,and second,it applies the Cuckoo Search algorithm to tune the hyperparameters(learning rate and dropout rate)of the deep learning model.Additionally,our model is trained in only five epochs,making it more lightweight than other deep learning(DL)and machine learning(ML)models.The proposed model achieved a phishing detection accuracy of 91%,with a precision of 92%for the’normal’class and 91%for the‘attack’class.Moreover,the model’s recall and F1-score are 91%for both classes.We also compared our approach with traditional DL/ML models and past literature,demonstrating that our model is more accurate.This study enhances the security of sensitive information and IoT devices by offering a novel and effective approach to phishing detection.

Research on Evacuation Path Planning Based on Improved Sparrow Search Algorithm

Reducing casualties and property losses through effective evacuation route planning has been a key focus for researchers in recent years.As part of this effort,an enhanced sparrow search algorithm(MSSA)was proposed.Firstly,the Golden Sine algorithm and a nonlinear weight factor optimization strategy were added in the discoverer position update stage of the SSA algorithm.Secondly,the Cauchy-Gaussian perturbation was applied to the optimal position of the SSA algorithm to improve its ability to jump out of local optima.Finally,the local search mechanism based on the mountain climbing method was incorporated into the local search stage of the SSA algorithm,improving its local search ability.To evaluate the effectiveness of the proposed algorithm,the Whale Algorithm,Gray Wolf Algorithm,Improved Gray Wolf Algorithm,Sparrow Search Algorithm,and MSSA Algorithm were employed to solve various test functions.The accuracy and convergence speed of each algorithm were then compared and analyzed.The results indicate that the MSSA algorithm has superior solving ability and stability compared to other algorithms.To further validate the enhanced algorithm’s capabilities for path planning,evacuation experiments were conducted using different maps featuring various obstacle types.Additionally,a multi-exit evacuation scenario was constructed according to the actual building environment of a teaching building.Both the sparrow search algorithm and MSSA algorithm were employed in the simulation experiment for multiexit evacuation path planning.The findings demonstrate that the MSSA algorithm outperforms the comparison algorithm,showcasing its greater advantages and higher application potential.