Occlusion remains a key challenge in computer vision, particularly for autonomous driving and robotics, where it degrades 2D/3D detection accuracy. This paper reviews...
This study offers a new insight into heat-transfer passive techniques, focusing on a relaxation of the mixture with a lower critical solution temperature in the...
This study evaluates the feasibility of incorporating alternative sustainable energy sources, specifically bioenergy and green hydrogen, into The Gambia's energy...
June 2025 — Tech Science Press (TSP) is pleased to announce that several of its SCI-indexed journals have demonstrated steady and significant growth in their impact indicators, as reported in...
Berlin (Germany) and Henderson (USA), 12 June 2025 – ResearchGate, the professional network for researchers, and Tech Science Press, a publisher of fully open access journals in science...
Recent advances in wearable devices have enabled large-scale collection of sensor data across healthcare, sports, and other domains but this has also...
Data curation is vital for selecting effective demonstration examples in graph-to-text generation. However, evaluating the quality of Knowledge Graphs...
AI-driven materials databases are transforming research by integrating experimental and computational data to enhance discovery and optimization. Platforms...
Legged robots have always been a focal point of research for scholars domestically and internationally. Compared to other types of robots, quadruped robots...
Response analysis of structures involving non-probabilistic uncertain parameters can be closely related to optimization. This paper provides a review...
The evaporation of micrometer and millimeter liquid drops, involving a liquid-to-vapor phase transition accompanied by mass and energy transfer through...
Resource allocation remains a challenging issue in communication networks, and its complexity is continuously increasing with the densification of the...
Finding materials with specific properties is a hot topic in materials science. Traditional materials design relies on empirical and trial-and-error methods,...
Accurate medical diagnosis, which involves identifying diseases based on patient symptoms, is often hindered by uncertainties in data interpretation and...
Customer segmentation according to load-shape profiles using smart meter data is an increasingly important application to vital the planning and operation...
Conventional fault diagnosis systems have constrained the automotive industry to damage vehicle maintenance and component longevity critically. Hence,...
Magneto-electro-elastic (MEE) materials are a specific class of advanced smart materials that simultaneously manifest the coupling behavior under electric,...
The emergence of various technologies such as terahertz communications, Reconfigurable Intelligent Surfaces (RIS), and AI-powered communication services...
As Natural Language Processing (NLP) continues to advance, driven by the emergence of sophisticated large language models such as ChatGPT, there has been...
Time series segmentation has attracted more interests in recent years, which aims to segment time series into different segments, each reflects a state...
The high performance of IoT technology in transportation networks has led to the increasing adoption of Internet of Vehicles (IoV) technology. The functional...
The tumor microenvironment encompasses not only the tumor cells themselves, but also the surrounding fibroblasts, immunological and inflammatory cells,...
Machine vision detection and intelligent recognition are important research areas in computer vision with wide-ranging applications in manufacturing,...
Object detection and tracking in videos has become an increasingly important area of research due to its potential applications in a variety of domains...
With the progress of science and technology, more and more complex engineering structures are serving in extreme environments. In the service life of...
Extracellular vesicles (EVs) are phospholipid bilayer vesicles released from tumor and non-tumor cells for intercellular communication. EVs contain...
The peridynamics proposed by Silling [1] is a non-local theory of solid mechanics. It redefines the problems by using integral equations rather than partial...
In modern time, experts started to use interdisciplinary properties with the developing of technology and science. Thus, these disciplines provide more...
More than half of the world population is living in cities. It requires extended infrastructure and various services to support the densely concentrated...
Recent advances in wearable devices have enabled large-scale collection of sensor data across healthcare, sports, and other domains but this has also...
Data curation is vital for selecting effective demonstration examples in graph-to-text generation. However, evaluating the quality of Knowledge Graphs...
AI-driven materials databases are transforming research by integrating experimental and computational data to enhance discovery and optimization. Platforms...
Legged robots have always been a focal point of research for scholars domestically and internationally. Compared to other types of robots, quadruped robots...
Response analysis of structures involving non-probabilistic uncertain parameters can be closely related to optimization. This paper provides a review...
The evaporation of micrometer and millimeter liquid drops, involving a liquid-to-vapor phase transition accompanied by mass and energy transfer through...
Resource allocation remains a challenging issue in communication networks, and its complexity is continuously increasing with the densification of the...
Finding materials with specific properties is a hot topic in materials science. Traditional materials design relies on empirical and trial-and-error methods,...
Accurate medical diagnosis, which involves identifying diseases based on patient symptoms, is often hindered by uncertainties in data interpretation and...
Customer segmentation according to load-shape profiles using smart meter data is an increasingly important application to vital the planning and operation...
Conventional fault diagnosis systems have constrained the automotive industry to damage vehicle maintenance and component longevity critically. Hence,...
Magneto-electro-elastic (MEE) materials are a specific class of advanced smart materials that simultaneously manifest the coupling behavior under electric,...
The emergence of various technologies such as terahertz communications, Reconfigurable Intelligent Surfaces (RIS), and AI-powered communication services...
As Natural Language Processing (NLP) continues to advance, driven by the emergence of sophisticated large language models such as ChatGPT, there has been...
Time series segmentation has attracted more interests in recent years, which aims to segment time series into different segments, each reflects a state...
The high performance of IoT technology in transportation networks has led to the increasing adoption of Internet of Vehicles (IoV) technology. The functional...
The tumor microenvironment encompasses not only the tumor cells themselves, but also the surrounding fibroblasts, immunological and inflammatory cells,...
Machine vision detection and intelligent recognition are important research areas in computer vision with wide-ranging applications in manufacturing,...
Object detection and tracking in videos has become an increasingly important area of research due to its potential applications in a variety of domains...
With the progress of science and technology, more and more complex engineering structures are serving in extreme environments. In the service life of...
Extracellular vesicles (EVs) are phospholipid bilayer vesicles released from tumor and non-tumor cells for intercellular communication. EVs contain...
The peridynamics proposed by Silling [1] is a non-local theory of solid mechanics. It redefines the problems by using integral equations rather than partial...
In modern time, experts started to use interdisciplinary properties with the developing of technology and science. Thus, these disciplines provide more...
More than half of the world population is living in cities. It requires extended infrastructure and various services to support the densely concentrated...
Currently, network intrusion detection systems (NIDS) face significant challenges in feature redundancy and high computational complexity, which hinder the improvement of detection performance and significantly reduce operational efficiency. To address these issues, this paper proposes an innovative weighted feature selection method combining mutual information and Extreme Gradient Boosting (XGBoost). This method aims to leverage their strengths to identify crucial feature subsets for intrusion detection accurately. Specifically, it first calculates the mutual information scores between features…
Pseudorandom number generators (PRNGs) are foundational to modern cryptography, yet existing approaches face critical trade-offs between cryptographic security, computational efficiency, and adaptability to emerging threats. Traditional PRNGs (e.g., Mersenne Twister, LCG) remain widely used in low-security applications despite vulnerabilities to predictability attacks, while machine learning (ML)-driven and chaos-based alternatives struggle to balance statistical robustness with practical deployability. This study systematically evaluates traditional, chaos-based, and ML-driven PRNGs to identify design principles for next-generation systems capable of…
This review presents a comprehensive and forward-looking analysis of how Large Language Models (LLMs) are transforming knowledge discovery in the rational design of advanced micro/nano electrocatalyst materials. Electrocatalysis is central to sustainable energy and environmental technologies, but traditional catalyst discovery is often hindered by high complexity, fragmented knowledge, and inefficiencies. LLMs, particularly those based on Transformer architectures, offer unprecedented capabilities in extracting, synthesizing, and generating scientific knowledge from vast unstructured textual corpora. This work provides…
Food waste presents a major global environmental challenge, contributing to resource depletion, greenhouse gas emissions, and climate change. Black Soldier Fly Larvae (BSFL) offer an eco-friendly solution due to their exceptional ability to decompose organic matter. However, accurately identifying larval instars is critical for optimizing feeding efficiency and downstream applications, as different stages exhibit only subtle visual differences. This study proposes a real-time mobile application for automatic classification of BSFL larval stages. The system distinguishes…
Computed Tomography (CT) reconstruction is essential in medical imaging and other engineering fields. However, blurring of the projection during CT imaging can lead to artifacts in the reconstructed images. Projection blur combines factors such as larger ray sources, scattering and imaging system vibration. To address the problem, we propose DeblurTomo, a novel self-supervised learning-based deblurring and reconstruction algorithm that efficiently reconstructs sharp CT images from blurry input without needing external data and blur measurement. Specifically,…
Few-shot learning has emerged as a crucial technique for coral species classification, addressing the challenge of limited labeled data in underwater environments. This study introduces an optimized few-shot learning model that enhances classification accuracy while minimizing reliance on extensive data collection. The proposed model integrates a hybrid similarity measure combining Euclidean distance and cosine similarity, effectively capturing both feature magnitude and directional relationships. This approach achieves a notable accuracy of 71.8% under a 5-way 5-shot…
Edge computing (EC) combined with the Internet of Things (IoT) provides a scalable and efficient solution for smart homes. The rapid proliferation of IoT devices poses real-time data processing and security challenges. EC has become a transformative paradigm for addressing these challenges, particularly in intrusion detection and anomaly mitigation. The widespread connectivity of IoT edge networks has exposed them to various security threats, necessitating robust strategies to detect malicious activities. This research presents a privacy-preserving…
In the era of exponential growth of digital information, recommender algorithms are vital for helping users navigate vast data to find relevant items. Traditional approaches such as collaborative filtering and content-based methods have limitations in capturing complex, multi-faceted relationships in large-scale, sparse datasets. Recent advances in Graph Neural Networks (GNNs) have significantly improved recommendation performance by modeling high-order connection patterns within user-item interaction networks. However, existing GNN-based models like LightGCN and NGCF focus primarily on…
The classification of respiratory sounds is crucial in diagnosing and monitoring respiratory diseases. However, auscultation is highly subjective, making it challenging to analyze respiratory sounds accurately. Although deep learning has been increasingly applied to this task, most existing approaches have primarily relied on supervised learning. Since supervised learning requires large amounts of labeled data, recent studies have explored self-supervised and semi-supervised methods to overcome this limitation. However, these approaches have largely assumed a closed-set setting,…
The blockchain trilemma—balancing decentralization, security, and scalability—remains a critical challenge in distributed ledger technology. Despite significant advancements, achieving all three attributes simultaneously continues to elude most blockchain systems, often forcing trade-offs that limit their real-world applicability. This review paper synthesizes current research efforts aimed at resolving the trilemma, focusing on innovative consensus mechanisms, sharding techniques, layer-2 protocols, and hybrid architectural models. We critically analyze recent breakthroughs, including Directed Acyclic Graph (DAG)-based structures, cross-chain interoperability frameworks,…
As vehicular networks become increasingly pervasive, enhancing connectivity and reliability has emerged as a critical objective. Among the enabling technologies for advanced wireless communication, particularly those targeting low latency and high reliability, time synchronization is critical, especially in vehicular networks. However, due to the inherent mobility of vehicular environments, consistently exchanging synchronization packets with a fixed base station or access point is challenging. This issue is further exacerbated in signal shadowed areas such as urban…
Recent advances in wearable devices have enabled large-scale collection of sensor data across healthcare, sports, and other domains but this has also raised critical privacy concerns, especially under tightening regulations such as the General Data Protection Regulation (GDPR), which explicitly restrict the processing of data that can re-identify individuals. Although existing anonymization approaches such as the Anonymizing AutoEncoder (AAE) can reduce the risk of re-identification, they often introduce substantial waveform distortions and fail to preserve…
It is well known that the kit completeness of parts processed in the previous stage is crucial for the subsequent manufacturing stage. This paper studies the flexible job shop scheduling problem (FJSP) with the objective of material kitting, where a material kit is a collection of components that ensures that a batch of components can be ready at the same time during the product assembly process. In this study, we consider completion time variance and…
The directional explosion behavior of finite volume water confined within nanochannels holds considerable potential for applications in precision nanofabrication and bioengineering. However, precise control of nanoscale mass transfer remains challenging in nanofluidics. This study examined the dynamic evolution of water clusters confined within a single-end-opened carbon nanotube (CNT) under pulsed electric field (EF) excitation, with a particular focus on the structural reorganization of hydrogen bond (H-bond) networks and dipole orientation realignment. Molecular dynamics simulations reveal…
As the group-buying model shows significant progress in attracting new users, enhancing user engagement, and increasing platform profitability, providing personalized recommendations for group-buying users has emerged as a new challenge in the field of recommendation systems. This paper introduces a group-buying recommendation model based on multi-head attention mechanisms and multi-task learning, termed the Multi-head Attention Mechanisms and Multi-task Learning Group-Buying Recommendation (MAMGBR) model, specifically designed to optimize group-buying recommendations on e-commerce platforms. The core dataset…
The capacity to diagnose faults in rolling bearings is of significant practical importance to ensure the normal operation of the equipment. Frequency-domain features can effectively enhance the identification of fault modes. However, existing methods often suffer from insufficient frequency-domain representation in practical applications, which greatly affects diagnostic performance. Therefore, this paper proposes a rolling bearing fault diagnosis method based on a Multi-Scale Fusion Network (MSFN) using the Time-Division Fourier Transform (TDFT). The method constructs multi-scale…
Guest Editors: A. Dounis; I. Kalatzis Deadline: 28 February 2026
Guest Editors: Chih-Lung Lin Deadline: 31 December 2025
Guest Editors: Fateh Mebarek-Oudina; Ioannis E. Sarris Deadline: 31 December 2025
Guest Editors: Hwa-Young Jeong; Jason C. Hung; Yen Neil Yuwen Deadline: 30 November 2025
Guest Editors: S. M. Anas; Rayeh Nasr Al-Dala'ien Deadline: 30 November 2025
Guest Editors: Sukhjit Singh Sehra Deadline: 15 November 2025
Guest Editors: José Braga de Vasconcelos; Hugo Barbosa Deadline: 10 November 2025
Guest Editors: Boyun Guo; Yin Feng Deadline: 01 November 2025
Guest Editors: Marcin Kamiński; Wojciech Sumelka Deadline: 01 October 2025
Guest Editors: Haitao Yu; Eva Binder; Qiushi Chen; Hui Wang; Xizhuo Chen Deadline: 01 October 2025
Guest Editors: Leong Hien Poh; Jie Zhi; Qixun Li; Yifeng Zhong Deadline: 30 September 2025
Guest Editors: Wei-Chiang Hong; Yi Liang Deadline: 30 September 2025
Guest Editors: Ilsun You; Gaurav Choudhary; Philip Virgil Astillo Deadline: 01 September 2025
Guest Editors: Man-Fai Leung; Athanasios V. Vasilakos Deadline: 01 September 2025
Guest Editors: Viktor Gribniak; Zdeněk Kala; Constantin E. Chalioris Deadline: 31 August 2025
Guest Editors: Zhiqiang Yang; Zifeng Yuan; Yuhang Jing; Xia Tian Deadline: 31 August 2025