This study explores the performance of CP2K on a heterogeneous HPC system integrating CPU and GPDSP, aiming to optimize computational efficiency for large-scale molecular simulations. CP2K is an open-source software package designed for simulating condensed matter systems, particularly excelling in handling complex quantum chemistry and molecular dynamics workloads. We present the integration of CPU

This paper extends the Proper Generalized Decomposition framework to develop a reduced-order model parameterized by Rayleigh damping coefficients. The developed method incorporates damping modes to construct a damped surrogate model effectively. A novel method is introduced for treating the problem in space: during the offline phase, the spatial problem is initially projected onto the subspace spanned

Autoscaling technology enables cloud-native systems to adapt to dynamic workload changes by scaling outward or inward without manual intervention. However, when facing sudden and unpredictable workloads, it becomes particularly difficult to determine which services need to be scaled and to assess the amount of resources required, especially for complex time-varying service dependencies that are difficult

Contact problems are inherently non-linear and present significant computational challenges in simulations. Traditional proper orthogonal decomposition-based non-linear system reduction often proves inefficient due to the complexity of handling full-scale models. This article presents a generalized parametric model order reduction framework tailored for dynamic contact problems involving arbitrarily-shaped

Object detection algorithm in urban traffic using remote sensing images often suffers from high complexity, low real-time performance, and low accuracy. To address these challenges, we propose RTL-Net, an urban traffic object detection network structure based on You Only Look Once (YOLO) v8s. To enhance real-time performance beyond the benchmark, we implemented lightweight designs for the loss function

DNN testing evaluates the vulnerability of neural networks through adversarial test cases. The developers implement minor perturbations to the seed inputs to generate test cases, which are guided by meticulously designed testing criteria. Nevertheless, current coverage-guided testing methods rely on covering model states rather than analyzing the influence of seed inputs on inducing erroneous behaviors

This study proposes a novel time homogenization scheme designed for phase-field formulations in fatigue fracture analysis. Inspired by the methodologies for evaluating the long-term behavior of asphalt pavements, this study builds upon a phase-field formulation that accounts for material degradation due to fatigue and the Representative Crack Element formulation as an energy split. The novelty of this

In an increasingly connected world, technologies such as smartphones, 5G, drones, the Internet of Things, and Smart Cities bring new challenges and opportunities. The increase in data collected by these devices and their ease of access allows the use of machine learning techniques to provide intelligent and quality services. Considering these services’ distributed access to data, using Federated Learning

DETR (DEtection TRansformer) is a CV model for object detection that replaces traditional complex methods with a Transformer architecture, and has achieved significant improvement over previous methods, particularly in handling small and medium-sized objects. However, the attention mechanism-based detection framework of DETR exhibits limitations in small and medium-sized object detection. It struggles

With the rapid development of the Internet, the existence of fake news and its rapid spread has brought many negative effects to the society. Consequently, the fake news detection task has become increasingly important over the past few years. Existing methods are predominantly unimodal methods or the multimodal representation of unimodal fusion for fake news detection. However, the large number of

The interpretability of quantum machine learning (QML) refers to the capability to provide clear and understandable explanations for the predictions and decision-making processes of QML models. However, most quantum convolutional layers (QCLs) utilize closed-box structures that are inherently devoid of interpretability, leading to the opacity of principles and the suboptimal mapping of classical data

Anomaly detection is crucial for maintaining the stability and security of systems. However, anomaly detection systems often generate numerous false positives or irrelevant alerts, which obscure genuine security threats. To both reduce false positives in time series detection and accurately identify the source of anomalies, leveraging artificial intelligence techniques has emerged as a promising solution

Deep neural network (DNN) models have demonstrated significant success in large-scale image datasets, facilitating information exchange over networks for various purposes, including user identification, remote patient health monitoring, early disease detection, and personalized medical treatments. Given the increasing reliance on DNN models for critical applications, ensuring their copyright protection

Quantum circuit mapping is a critical process in quantum computing that involves adapting logical quantum circuits to adhere to hardware constraints, thereby generating physically executable quantum circuits. Current quantum circuit mapping techniques, such as solver-based methods, often encounter challenges related to slow solving speeds due to factors like redundant search iterations. Regarding this

Federated Learning (FL) is a distributed learning mechanism that enables multiple clients to collaboratively train a global model (e.g. a neural network) while maintaining the privacy of their data. However, FL is susceptible to adversarial attacks, especially those involving poisoned samples. Despite significant research efforts, adversarial attacks and defenses in FL remain an unresolved issue. In

The isotropic plastic hardening behavior of ductile metals is commonly characterized by the accumulated equivalent plastic strain. Advanced plasticity models further incorporate dependencies on hydrostatic pressure and Lode angle to more accurately represent material behavior under complex stress states. However, such models have rarely been applied to mild steels or structural failure analyses. This

A numerical method for the time-domain dynamic analysis of structures with viscoelastic energy dissipation dampers, modeled using fractional derivatives, is presented. Two fractional viscoelastic models are considered: the fractional Kelvin-Voigt model and another one referred to here as the fractional simplified Maxwell model, to distinguish it from the widely used fractional Maxwell model, where

In the context of disaster response and recovery within 6th Generation (6G) networks, achieving both low-latency and energy-efficient communication under compromised infrastructure remains a critical challenge. This paper introduces a novel framework that integrates a solar-powered High-Altitude Platform (HAP) with multiple Unmanned Aerial Vehicles (UAVs) equipped with Reconfigurable Intelligent Surfaces

This study introduces a three-step procedure for identifying high-resolution bridge mode shapes using responses from a limited number of test vehicles. First, contact-point displacements are retrieved from the vehicle responses using the generalized Kalman filter with unknown input algorithm. Second, the sparse bridge response matrix, populated with contact-point displacements, is completed using the

This article addresses the problem of resilient collision avoidance distributed optimal coordination (DOC) for multiple Euler-Lagrangian (EL) systems under unreliable communication topologies. Due to adverse network conditions and cyber attacks, communication between agents can be disrupted during certain time intervals. To achieve collision avoidance between agents, a barrier function is redesigned

Recently, interest in flat-type projection clustering methods has grown as they improve learner's performance by exploring multiple projection subspaces. However, solvers used in previous representative works predominantly rely on greedy search strategies, which incur high computational costs and fail to consider interdependencies between projections. Moreover, these methods do not simultaneously guarantee

As data centers and cloud environments expand, enhancing fairness in I/O queue resource scheduling has become increasingly urgent in the field of Non-Volatile Memory Express (NVMe) storage virtualization. Existing methods usually focus on metrics such as Input/Output Operations Per Second (IOPS) enhancement or latency reduction, overlooking fairness issues among virtual machines (VMs) which may lead

Multiple vessel tracking plays a vital role in maritime surveillance systems. Previous studies have typically integrated object detection and trajectory association techniques to address this problem, but they still face some significant challenges. On one hand, these methods are susceptible to losing tracked targets due to long-term occlusion by other obstacles or slow-moving vessels in inland waterways

To address the issues of low discovery probability, inefficient search, and antagonistic targets during the process of dynamic target search in the ocean, a dynamic target search path planning method based on the Glasius biologically-inspired neural network (GBNN) in combination with marine environmental information is proposed. Firstly, the motion model of the searcher and the capability model of

This paper introduces a real-time head-pose detection and eye-gaze estimation system for Automatic Driver Assistance Technology (ADAT) aimed at enhancing driver safety by accurately collecting and transmitting data on the driver’s head position and eye gaze to mitigate potential risks. Existing methods are constrained by significant limitations, including reduced accuracy under challenging conditions

Network robustness can be effectively augmented through edge safeguarding, especially when topology modification is not feasible. Although approximation algorithms are used due to the intrinsic hardness of problem, when the connectivity of the initial graph is adjusted to the desired value, the connectivity of the concealed weak graph is escalated to a maximum level. Consequently, a substantial amount

Surrogate-assisted evolutionary algorithms provide an effective means for complex and computationally expensive optimization problems. However, due to the scarcity of training samples, the prediction accuracy of frequently-used regression surrogate models can hardly be guaranteed as the difficulty of the problem increases, resulting in performance degradation of the whole algorithm. Since real-world

Knowledge tracing (KT) aims to predict students’ future performance by assessing their level of knowledge mastery from past problem-solving records. However, many existing methods fail to take full advantage of the potential relationship between questions and skills, or fail to effectively utilize students’ historical learning data, which makes it difficult to accurately capture individualized mastery

Class imbalance and high-dimensional data pose significant challenges in intrusion detection systems (IDSs), impacting model performance and interpretability. This paper introduces a novel approach, CTGAN-ENN, combining explainable Conditional Tabular generative adversarial networks (CTGAN) and Edited Nearest Neighbor (ENN) with feature selection (FS) for improving IDS interpretability. The framework

Realizing the Ocean Internet of Things (OIoT) requires diverse and comprehensive real-time marine data. This creates significant demands on spectrum resources for effective underwater communication. Coexisting users in the OIoT can generate massive amounts of data, which results in competition for underwater acoustic spectrum. To address the problem of poor utilization of this spectrum in complex and

Personal data, as an important category of data elements of a trusted data circulation, needs to be shared to others in “one-to-one” mode or “one-to-many” mode with simultaneously, parallelly or sequentially to meet all kinds of complex business scenarios. Actually, today’s application asks more personalized content and more often than ever before, it increasingly highlights the need for data owners

Unlike conventional concrete fractures, few models of 3D-printed concrete (3DPC) fractures have been reported; moreover, systematic validation across diverse tests, materials, and laboratories is lacking. This paper first reviews existing 3DPC fracture simulations against experiments, noting mixed performance in most cases. Additionally, current models often require excessive material parameters that

This paper develops an iterative solution model for the efficient and accurate simulation of the dynamics of a three-dimensional (3D) train-periodic slab track-subgrade (TPSTS) system. The entire system is divided into the train-rail subsystem and the periodic slab-subgrade subsystem. An ordinary differential equation (ODE) model of the train-rail system is established, and a step-moving strategy is

Conventional error-tolerant schemes for Neural Networks (NNs) usually require either redundancy, or changes in normal operation, leading to considerable overheads. They are not feasible for large-scale Machine Learning (ML) systems that typically employ several complex networks. This paper proposes a Perturbation-Based Error Detection and Correction (PBEDC) scheme designed to perform error detection

Remanufacturing has become a mainstream sustainable manufacturing paradigm for energy conservation and environmental protection. Disassembly and reprocessing operations are two main activities in remanufacturing. This work proposes multiobjective integrated scheduling of disassembly and reprocessing operations considering product structures and random processing time. First, a stochastic programming

n-intuitionistic polygonal fuzzy sets have significant advantages over traditional fuzzy sets in handling uncertain information. Due to the fact that information measure is an effective tool for handling uncertain information, this paper proposes distance measures, symmetric cross entropies and knowledge measures for n-intuitionistic polygonal fuzzy sets. First, this paper initially formulates distance

As internet services and Internet of Things (IoT) devices rapidly expand, Data Center Networks (DCN) have become essential for supporting online services, cloud computing, and big data analysis. These devices generate massive amounts of data continuously, leading to uneven and sudden network loads that traditional networks struggle to handle. Existing routing strategies often rely on fine-grained routing

Swarm intelligence systems are a class of distributed systems in which device nodes utilize distributed algorithms to achieve data consensus and execute complex collective tasks. These systems operate in highly dynamic environments, where unstable network conditions, often induced by environmental complexities, can significantly affect the progress and efficiency of data consensus. To tackle this challenge

Denoising Diffusion Models (DDMs) have attracted significant attention due to their capacity to generate diverse, high-quality samples in computer vision tasks, offering flexible architectures and straightforward training processes. While several studies have extended diffusion models to quantum domains, these approaches often rely on hybrid U-net architectures or mixed-state manipulations with trace-out

This paper presents numerical investigations of the abrasion behavior of concrete for hydraulic structures considering concrete structural characteristics as well as various hydraulic conditions. Three-dimensional mesoscale models of concrete composed of aggregates, mortar, and interfacial transition zones are developed using in-house Python 2 codes and the commercial finite-element software Abaqus