The review article delves into the intricate relationships between climate change, water quality, and phytoremediation, the natural process through which aquatic...
This issue delves into the innovative integration of lignin-based biochar particles in polymer nanocomposites, paving the way for eco-friendly and high-performance...
This study delves into the impact of chemical admixtures on the fluidity of ultra-high performance concrete (UHPC). The image as a whole illustrates the defining...
Beijing (China) and Henderson (USA) – Tsinghua University Press (TUP) and Tech Science Press (TSP) are excited to announce a new collaboration that will see 11 of TSP's esteemed STM journals...
We are pleased to announce that Tech Science Press journals have entered into a collaboration with the PubScholar platform, a publicly funded academic resource established by the Chinese Academy...
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...
Software-Defined Networking (SDN) represents a significant paradigm shift in network architecture, separating network logic from the underlying forwarding...
Since the 1950s, when the Turing Test was introduced, there has been notable progress in machine language intelligence. Language modeling, crucial for...
Steganography is a technique for hiding secret messages while sending and receiving communications through a cover item. From ancient times to the present,...
Image steganography is one of the prominent technologies in data hiding standards. Steganographic system performance mostly depends on the embedding strategy....
COVID-19 pandemic restrictions limited all social activities to curtail the spread of the virus. The foremost and most prime sector among those affected...
The International Skin Imaging Collaboration (ISIC) datasets are pivotal resources for researchers in machine learning for medical image analysis, especially...
Federated learning is an innovative machine learning technique that deals with centralized data storage issues while maintaining privacy and security....
This review describes the mechanisms of natural coagulants. It provides a good understanding of the two key processes of coagulation-flocculation: adsorption...
Bio-based cyclodextrins (CDs) are a common research object in supramolecular chemistry. The special cavity structure of CDs can form supramolecular self-assemblies...
This paper expounds upon a novel target detection methodology distinguished by its elevated discriminatory efficacy, specifically tailored for environments...
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...
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...
Software-Defined Networking (SDN) represents a significant paradigm shift in network architecture, separating network logic from the underlying forwarding...
Since the 1950s, when the Turing Test was introduced, there has been notable progress in machine language intelligence. Language modeling, crucial for...
Steganography is a technique for hiding secret messages while sending and receiving communications through a cover item. From ancient times to the present,...
Image steganography is one of the prominent technologies in data hiding standards. Steganographic system performance mostly depends on the embedding strategy....
COVID-19 pandemic restrictions limited all social activities to curtail the spread of the virus. The foremost and most prime sector among those affected...
The International Skin Imaging Collaboration (ISIC) datasets are pivotal resources for researchers in machine learning for medical image analysis, especially...
Federated learning is an innovative machine learning technique that deals with centralized data storage issues while maintaining privacy and security....
This review describes the mechanisms of natural coagulants. It provides a good understanding of the two key processes of coagulation-flocculation: adsorption...
Bio-based cyclodextrins (CDs) are a common research object in supramolecular chemistry. The special cavity structure of CDs can form supramolecular self-assemblies...
This paper expounds upon a novel target detection methodology distinguished by its elevated discriminatory efficacy, specifically tailored for environments...
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...
The Internet of Vehicles (IoV) will carry a large amount of security and privacy-related data, which makes the secure communication between the IoV terminals increasingly critical. This paper studies the joint beamforming for physical-layer security transmission in the coexistence of Vehicle-to-Infrastructure (V2I) and Vehicle-to-Vehicle (V2V) communication with Reconfigurable Intelligent Surface (RIS) assistance, taking into account hardware impairments. A communication model for physical-layer security transmission is established when the eavesdropping user is present and the base…
Multi-object tracking (MOT) has seen rapid improvements in recent years. However, frequent occlusion remains a significant challenge in MOT, as it can cause targets to become smaller or disappear entirely, resulting in low-quality targets, leading to trajectory interruptions and reduced tracking performance. Different from some existing methods, which discarded the low-quality targets or ignored low-quality target attributes. LQTTrack, with a low-quality association strategy (LQA), is proposed to pay more attention to low-quality targets. In the…
Human Interaction Recognition (HIR) was one of the challenging issues in computer vision research due to the involvement of multiple individuals and their mutual interactions within video frames generated from their movements. HIR requires more sophisticated analysis than Human Action Recognition (HAR) since HAR focuses solely on individual activities like walking or running, while HIR involves the interactions between people. This research aims to develop a robust system for recognizing five common human interactions, such…
Traditional information hiding techniques achieve information hiding by modifying carrier data, which can easily leave detectable traces that may be detected by steganalysis tools. Especially in image transmission, both geometric and non-geometric attacks can cause subtle changes in the pixels of the image during transmission. To overcome these challenges, we propose a constructive robust image steganography technique based on style transformation. Unlike traditional steganography, our algorithm does not involve any direct modifications to the carrier…
Mural paintings hold significant historical information and possess substantial artistic and cultural value. However, murals are inevitably damaged by natural environmental factors such as wind and sunlight, as well as by human activities. For this reason, the study of damaged areas is crucial for mural restoration. These damaged regions differ significantly from undamaged areas and can be considered abnormal targets. Traditional manual visual processing lacks strong characterization capabilities and is prone to omissions and false…
To address the issues of low accuracy and high false positive rate in traditional Otsu algorithm for defect detection on infrared images of wind turbine blades (WTB), this paper proposes a technique that combines morphological image enhancement with an improved Otsu algorithm. First, mathematical morphology’s differential multi-scale white and black top-hat operations are applied to enhance the image. The algorithm employs entropy as the objective function to guide the iteration process of image enhancement, selecting…
Accurate geospatial data are essential for geographic information systems (GIS), environmental monitoring, and urban planning. The deep integration of the open Internet and geographic information technology has led to increasing challenges in the integrity and security of spatial data. In this paper, we consider abnormal spatial data as missing data and focus on abnormal spatial data recovery. Existing geospatial data recovery methods require complete datasets for training, resulting in time-consuming data recovery and lack of…
Disjoint sampling is critical for rigorous and unbiased evaluation of state-of-the-art (SOTA) models e.g., Attention Graph and Vision Transformer. When training, validation, and test sets overlap or share data, it introduces a bias that inflates performance metrics and prevents accurate assessment of a model’s true ability to generalize to new examples. This paper presents an innovative disjoint sampling approach for training SOTA models for the Hyperspectral Image Classification (HSIC). By separating training, validation, and test…
With the rapid advancement of Internet of Vehicles (IoV) technology, the demands for real-time navigation, advanced driver-assistance systems (ADAS), vehicle-to-vehicle (V2V) and vehicle-to-infrastructure (V2I) communications, and multimedia entertainment systems have made in-vehicle applications increasingly computing-intensive and delay-sensitive. These applications require significant computing resources, which can overwhelm the limited computing capabilities of vehicle terminals despite advancements in computing hardware due to the complexity of tasks, energy consumption, and cost constraints. To address this issue in IoV-based…
As the penetration ratio of wind power in active distribution networks continues to increase, the system exhibits some characteristics such as randomness and volatility. Fast and accurate short-term wind power prediction is essential for algorithms like scheduling and optimization control. Based on the spatio-temporal features of Numerical Weather Prediction (NWP) data, it proposes the WVMD_DSN (Whale Optimization Algorithm, Variational Mode Decomposition, Dual Stream Network) model. The model first applies Pearson correlation coefficient (PCC) to choose…
Maritime transportation, a cornerstone of global trade, faces increasing safety challenges due to growing sea traffic volumes. This study proposes a novel approach to vessel trajectory prediction utilizing Automatic Identification System (AIS) data and advanced deep learning models, including Long Short-Term Memory (LSTM), Gated Recurrent Unit (GRU), Bidirectional LSTM (DBLSTM), Simple Recurrent Neural Network (SimpleRNN), and Kalman Filtering. The research implemented rigorous AIS data preprocessing, encompassing record deduplication, noise elimination, stationary simplification, and removal of…
Although sentiment analysis is pivotal to understanding user preferences, existing models face significant challenges in handling context-dependent sentiments, sarcasm, and nuanced emotions. This study addresses these challenges by integrating ontology-based methods with deep learning models, thereby enhancing sentiment analysis accuracy in complex domains such as film reviews and restaurant feedback. The framework comprises explicit topic recognition, followed by implicit topic identification to mitigate topic interference in subsequent sentiment analysis. In the context of sentiment analysis,…
Demand-responsive transportation (DRT) is a flexible passenger service designed to enhance road efficiency, reduce peak-hour traffic, and boost passenger satisfaction. However, existing optimization methods for initial passenger requests fall short in addressing real-time passenger needs. Consequently, there is a need to develop real-time DRT route optimization methods that integrate both initial and real-time requests. This paper presents a two-stage, multi-objective optimization model for DRT vehicle scheduling. The first stage involves an initial scheduling model aimed…
The security performance of cloud services is a key factor influencing users’ selection of Cloud Service Providers (CSPs). Continuous monitoring of the security status of cloud services is critical. However, existing research lacks a practical framework for such ongoing monitoring. To address this gap, this paper proposes the first Non-Collaborative Container-Based Cloud Service Operation State Continuous Monitoring Framework (NCCMF), based on relevant standards. NCCMF operates without the CSP’s collaboration by: 1) establishing a scalable supervisory
The strategy evolution process of game players is highly uncertain due to random emergent situations and other external disturbances. This paper investigates the issue of strategy interaction and behavioral decision-making among game players in simulated confrontation scenarios within a random interference environment. It considers the possible risks that random disturbances may pose to the autonomous decision-making of game players, as well as the impact of participants’ manipulative behaviors on the state changes of the players.…
With the rapid development of deep learning-based detection algorithms, deep learning is widely used in the field of infrared small target detection. However, well-designed adversarial samples can fool human visual perception, directly causing a serious decline in the detection quality of the recognition model. In this paper, an adversarial defense technology for small infrared targets is proposed to improve model robustness. The adversarial samples with strong migration can not only improve the generalization of defense…
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