This paper presents a highly efficient and accurate analytical method for the dynamic analysis of subjected to moving loads. Bridges with complex cross section and considering damping are modelled as plate built-up structures by the dynamic stiffness method with few degrees of freedom. Different moving loads with time-varying amplitudes travelling at different speeds can be modelled semi-analytically:

Industry 5.0 vision, a step toward the next industrial revolution and enhancement to Industry 4.0, conceives the new goals of resilient, sustainable, and human-centric approaches in diverse emerging applications such as factories-of-the-future and digital society. The vision seeks to leverage human intelligence and creativity in nexus with intelligent, efficient, and reliable cognitive collaborating

Lateral movement, in which a cyber attacker progresses through an enterprise network in order to compromise its most valuable assets, is a key stage of any intrusion nowadays. Therefore, being able to mitigate lateral movement, be it by slowing down attacker progress or by limiting its reach, is a top priority for enterprise cyber-defence. Due to the inherent complexity of enterprise networks, it is

Today’s experimental noisy quantum processors can compete with and surpass all known algorithms on state-of-the-art supercomputers for the computational benchmark task of Random Circuit Sampling (Boixo et al., 2018, Arute et al., 2019, Wu et al., 2021, Zhu et al., 2022, Morvan et al., 2023). Additionally, a circuit-based quantum simulation of quantum information scrambling (Mi et al., 2021), which

With the rapid development of Internet of Things(IoT) technology, it is more and more common to use IoT devices to obtain health data. Health data sharing is a crucial component of health data utilization, yet it encounters serious privacy and security challenges. How to solve the problem of data security is the key factor to encourage users to participate, and existing data sharing methods generally

Nowadays, various edge applications play an increasingly important role in our life, in which video data accounts for a dominant portion of the data traffic. Although standard compression methods, such as H.264 and MPEG4, can considerably reduce the wireless bandwidth for transmission energy savings of edge devices, the large volume of raw video streaming data is still buffered in edge device video

Over the last few years, substantial research has been conducted towards developing efficient abnormal detection techniques while considering efficiency, accuracy, high-dimensional data, distributed environments, and others. Researchers increasingly deal with “abnormalities” in clinical patient data to derive relevant clinical knowledge for making informed decisions. However, data collection for clinically

This paper introduces a novel method for shape optimization and form-finding of free-form, triangular grid shells, based on geometric deep learning. We define an architecture which consumes a 3D mesh representing the initial design of a free-form grid shell, and outputs vertex displacements to get an optimized grid shell that minimizes structural compliance, while preserving design intent. The main

A novel crack identification method is presented in this paper for cantilever beam-type structures within the scope of vibration-based damage identification. Natural frequencies are more easily to be measured yet are weakly sensitive to damage in comparison to other dynamic parameters such as mode shape and damping ratio. Therefore, this paper develops a surrogate-based crack identification method

Due to the complexity and diversity of Industrial Internet of Things (IIoT) systems, which include heterogeneous devices, legacy and new connectivity protocols and systems, and distributed networks, sophisticated attacks like ransomware will likely target these systems in the near future. Researchers have focused on studying and addressing ransomware attacks against various platforms in recent years

Target localization is an essential aspect of smart environmental monitoring, which has gained prominence thanks to the Internet of Things (IoT) paradigm. Localization is the process of determining the location of an unknown target within an area of interest (AoI) based on data gathered by IoT sensors. Existing target localization works assume full knowledge about the sensors without considering the

The security of industrial Internet of Things (IIOT) has recently attracted significant attention. As typical IIoT systems, industrial robots are suffering from lots of threats involving control, communication, and computing, which are difficult to detect IIoT attacks accurately in real-time due to resource constraints. How to efficiently and accurately identify IoT attacks on industrial robots is

Molecular dynamics (MD) simulation is a research method to simulate the change of particle systems with the help of the powerful calculating power of computers. As the number of atoms increases and the duration of the simulation system extends, current mainstream software for MD simulations has become increasingly strained. This is largely due to a surge in computing time, attributed to the architecture

With the rapid development of information technology and the increasing popularity of personalized services, the flow of users’ personal information in the Internet is inevitable between different platforms and applications, which greatly affects the trust relationship in the digital society. In recent years, an increasing number of researchers have been dedicated to exploring the applications of blockchain

In recent years, the adoption of the service mesh as a dedicated infrastructure layer to support cloud-native systems has gained significant popularity. Service meshes involve the incorporation of proxies to handle communication between microservices, thereby speeding up the development and deployment of microservice applications. However, the use of service meshes also increases the request latency

Identifying objects in aircraft monitoring systems poses significant challenges due to the presence of extreme loading conditions. Despite the presence of several sensor units, the transmission of precise data to multiple data units is hindered by an increase in time intervals. Therefore, the suggested methodology is specifically developed for the purpose of generating digital replicas for aeronautical

The switch migration (SM) issue has been widely addressed in distributed Software Defined Networks (SDN). Most of researches have focused on achieving SM with balanced loads among controllers but little efforts are devoted to equilibrate the service offered to switches when limited resources in control plane are experienced. This paper deals with the problem of dynamic switch migration in multi-controller

Next-generation smart city applications, attributed to the power of the Internet of Things (IoT) and Cyber-Physical Systems (CPS), significantly rely on sensing data quality. With an exponential increase in intelligent applications for urban development and enterprises offering sensing-as-a-service these days, it is imperative that a shared sensing infrastructure could thwart the better utilization

This article introduces a novel approach called terminal sliding-mode control for achieving time-synchronized convergence in multi-input-multi-output (MIMO) systems under disturbances. To enhance controller design, the systems are categorized into two groups: 1) input-dimension-dominant and 2) state-dimension-dominant, based on signal dimensions and their potential for achieving thorough time-synchronized

Target tracking, a critical application in the Internet of Things (IoT) and Mobile Crowd Sensing (MCS) domains, is a complex task that involves the continuous estimation of the positions of an object by using efficient and accurate algorithms. Some potential applications of target tracking include surveillance systems, asset tracking, wildlife monitoring, and cross-border security. The existing target

Modern cyber–physical systems by the most part are large-scale multilevel distributed heterogeneous systems consisting of elements of different physical nature, built using Internet of Things platforms and are characterized by a high level of structural and behavioral (architectural) variability. Managing such systems and keeping them in working condition throughout their lifetime is a difficult task

In this paper, directional maximum length scale control for both the solid and void phases is proposed for density-based topology optimization with a lower computational cost. The method introduces porosity and material rate in the locally searched domain to achieve the length scale control for the solid and void phases, respectively. To enable directional length scale control, local rectangle and

The aim of this paper is to propose a robust yet simple model to predict the variation of the tensile strength of granular materials with the degree of saturation. Because capillary phenomenon and air–water surface tension govern interparticle bonding in granular materials, they are explicitly taken into consideration. These vary with the degree of saturation as with all geotechnical structures that

Active fault detection (AFD) is the newest frontier in the field of fault detection and has drawn increasing amounts of research attention. AFD technology can enhance fault detection performance by injecting a predesigned auxiliary input signal for a specific fault. In most existing studies, system control objectives are not fully considered in the auxiliary input design of AFD. This article investigates

In this paper, we consider the market-driven workflow scheduling problem on heterogeneous cloud resources with deadline constraints. The transmission delay between dependent tasks on different virtual machines (VMs) is considered. The objective is to minimize the total monetary cost, which is computed based on the on-demand price structure. Inspired by the heuristic and meta-heuristic algorithms, a

To reduce costs and risks, service providers can purchase Infrastructure-as-a-Service (IaaS) instances from public clouds to deploy service applications and deliver job-execution services to users. However, it is challenging to make optimal instance purchasing and job scheduling decisions due to diverse instance billing methods and random service demands. In order to minimize time average service costs

In this paper, a novel algorithm is developed to generate the geometrical model of coarse aggregate based on its physical properties. The size, elongation index, and flakiness index are considered while developing the algorithm. The developed geometrical model of aggregate is further used for generating the finite element (FE) meso-model of concrete. Three distinct phases are considered in the FE model

The cost of interacting with a blockchain and the time required to search and retrieve information from it are important factors in the design of Decentralized Applications (DApps). The corresponding approaches for data management in Ethereum vary from pure on-chain solutions to hybrid architectures. In this work, we thoroughly examine a wide range of data management methods, including unconventional

Any numerical procedure in mechanics requires choosing an appropriate model for the constitutive law of the material under consideration. The most common assumptions regarding linear wave propagation in a viscoelastic material are the standard linear solid model, (generalized) Maxwell, Kelvin-Voigt models or the most recent fractional derivative models. Usually, once the frequency-dependent constitutive

Hotspot issue is ubiquitous in the in-memory key-value stores, which seriously affects its performance. The efficiency of the index structure is the key to the performance of the in-memory key-value store, and the access of hotspot data must be through the search of the index structure. However, the existing index structures do not consider the hotspot issue, they treat and manage all the data equally

Nowadays many modern and Artificial Intelligence (AI) enabled Internet of Things (IoT) applications consist of chains connecting microservices distributed across the fog and cloud layers to achieve a certain functionality. These microservices should be assigned to the available processing instances placed on the things, fog and cloud layers while considering the resource utilization, Quality of Service

Federated Learning (FL), as a novel distributed machine learning paradigm, offers infinite possibilities for collaborative use of decentralized data among distributed entities. However, the potential data heterogeneity in distributed entities poses a great challenge to deploying FL for real-world practical applications. Inspired by the observed phenomenon of data heterogeneity simulation, we propose

When processing composite service application jobs containing parallel tasks, service providers can optimize their quality of services (QoS) based on refined parallelism settings and resource allocation schemes by leveraging analytical models. However, building such analytical models is particularly challenging due to the fact that an accurate model is required to capture the dependence among sequential

The detection of Maximal Extractable Value (MEV) in blockchain is crucial for enhancing blockchain security, as it enables the evaluation of potential consensus layer risks, the effectiveness of anti-centralization solutions, and the assessment of user exploitation. However, existing MEV detection methods face limitations due to their low recall rate, reliance on pre-registered Application Binary Interfaces

User interfaces for digital twins (DTs) should provide information that allows the user to be aware of the state of the physical entity that is virtualised. Typically, this real entity is a shared space in which various human and artificial agents interact (for instance, in smart cities, various citizens and vehicles interact; in manufacturing, operators and machinery cooperate in the production, etc

Multi-access Edge Computing (MEC) is an enabling technology to leverage new network applications, such as virtual/augmented reality, by providing faster task processing at the network edge. This is done by deploying servers closer to the end users to run the network applications. These applications are often intensive in terms of task processing, memory usage, and communication; thus mobile devices

The Industrial Internet of Things (IIoT) has brought practical application value to many industries, where significant amounts of IIoT data and resources are outsourced to cloud server (CS) via diverse networks for data fusion, monitoring, sharing, and calculation analysis. Considering privacy, there is a need to execute the encryption operation on the data before outsourcing, while how to retrieve

This paper aims to formulate the multi-objective structural optimization problem to find the best member grouping of truss structures. The weight of the structure and the different number of discrete cross-sectional areas are the conflicting objective functions to be minimized simultaneously, generating a Pareto front presenting the non-dominated solutions. Sixteen multi-objective evolutionary algorithms

Cluster users expect to minimize the resource costs while ensuring target performance for different applications. It is particularly difficult to reach such a goal, because the applications are diverse with dynamic load changes, and interference exists between them. In addition, the performance of the applications depends on heterogeneous resources with different costs. However, existing works either

Metaverse is recently envisioned as the main driver for immersive multimedia in future networks. Light field video (LFV), considered an intermediate transition scheme towards the metaverse, is conducive to sensing and reconstructing realistic scenes in the digital twins. However, the research on LFV systems still lacks comprehensive investigation, impeding their practical application. This paper addresses

Mesh generation for crack simulation is often the rate-limiting step because of the rapid variations in crack shape. The classical meshing paradigm, place-nodes-and-link, relies on predefined rules and fails to generalize various crack shapes. We proposed a graph neural networks-based method for recovering the missing connection information in the crack meshes. The constrained Delaunay triangulation

Existing multiagent exploration works focus on how to explore in the fully cooperative task, which is insufficient in the environment with nonstationarity induced by agent interactions. To tackle this issue, we propose When to Explore (WToE), a simple yet effective variational exploration method to learn WToE under nonstationary environments. WToE employs an interaction-oriented adaptive exploration

This work aims at presenting a new sampled-data model-free adaptive control (SDMFAC) for continuous-time systems with the explicit use of sampling period and past input and output (I/O) data to enhance control performance. A sampled-data-based dynamical linearization model (SDDLM) is established to address the unknown nonlinearities and nonaffine structure of the continuous-time system, which all the

Federated learning is vulnerable to poisoning attacks due to the inability to verify the authenticity of local data. Existing robust federated learning methods maintain a global model by discarding potentially risky local updates. However, they generally assume that the server knows the number of potentially abnormal clients. In this paper, we propose a robust federated learning method based on voting

In this work, an effective Damage-Informed Neural Network (DINN) is first developed to pinpoint the position and extent of structural damage. Instead of resolving the damage identification problem by conventional numerical methods, a Deep Neural Network (DNN) is employed to minimize the loss function which is designed by combining multiple damage location assurance criterion and flexibility matrices