This cover illustrates the transformation of bio-derived tannin from quebracho wood into porous carbon cryogels for sustainable supercapacitors. Freeze-drying preserves...
A one-year longitudinal study of 285 Chinese mother-adolescent dyads reveals that different dimensions of overparenting have distinct effects on adolescent internalizing...
This study applies the three-dimensional Ripley’s L function to quantitatively assess solid-liquid mixing homogeneity in a stirred tank. The figure presents the...
June 2026 — Tech Science Press (TSP) is pleased to announce that 17 TSP journals have received updated 2025 Scopus CiteScore and SNIP metrics.The latest results reflect the growing visibility...
Washington, DC, USA, 18 May 2026 — Canadian Journal of Urology (CJU) Managing Editor Dany Xiang attended the 2026 Annual Meeting of the American Urological Association (AUA), one of the...
Cerebral palsy is a prevalent neurodevelopmental syndrome that disrupts motor development in children, making early detection vital for effective intervention....
Current automated lesion segmentation methods have limited success, particularly for segmenting small, irregular, or heterogeneous lesions. Moreover,...
This paper presents a dynamic energy management strategy for a community-scale campus hybrid microgrid integrating photovoltaic (PV) generation, aggregated...
This study investigates the problem of prioritizing rooftop renewable energy (RE) system configurations for a multi-family residential building in Mediterranean...
This paper addresses the challenge of efficiently calculating dynamic carbon emission factors (CEFs) in large-scale power systems. Traditional methods...
Acral lentiginous melanoma (ALM) is characterized by a low mutational burden, frequent chromosomal rearrangements, and profound epigenetic dysregulation,...
Checkpoint kinase 1 (CHK1), a key regulator of cell cycle checkpoints, plays a central role in the DNA damage response network, serving as a critical...
Glioblastoma (GB) is the most common primary malignant brain tumor of adulthood, and despite optimal safe resection and chemoradiation, it is still lethal....
Objectives: Circulating tumor cells (CTCs) drive metastasis and exhibit resistance to conventional therapies, making them crucial therapeutic targets....
Molecular glue degraders (MGDs) are an emerging class of small molecules that promote selective protein degradation by inducing neomorphic interactions...
Obesity is a complex chronic condition characterized by an excess of body fat that manifests in various clinical pathophenotypes, each affecting liver...
Unmanned Aerial Vehicle (UAV) target tracking is one of the key technologies in aerial intelligent perception systems, playing a vital role in applications...
With the rapid development of artificial intelligence and data-driven modeling, deep learning has become an effective tool for analyzing scientific discovery...
Structural optimization is a fundamental step in density functional theory (DFT) calculations, typically driven by the Broyden–Fletcher–Goldfarb–Shanno...
Reliable vehicle detection in urban traffic environments remains challenging, particularly for fixed-view CCTV systems deployed in Southeast Asian cities,...
The rapid evolution of chemical biology, medicinal chemistry, and molecular pharmacology continues to drive the discovery of innovative anticancer compounds...
Cancer development and therapeutic response are governed not only by genetic alterations but also by dynamic regulatory mechanisms at the epigenetic and...
The transition toward decentralized smart grids is reshaping the way energy is generated, exchanged, controlled, and consumed. The increasing penetration...
Cancer remains one of the leading causes of morbidity and mortality worldwide, and its onset and progression are closely linked to dysregulated epigenetic...
We propose a special issue titled "Innovative Smart Polymeric Materials for Sustainable Energy Solutions: Bridging Advances in Energy and Biomedical...
Artificial Intelligence of Things (AIoT) is considered a collaborative application of artificial intelligence (AI) and the Internet of Things (IoT). The...
Further advancements in the exploitation of unconventional resources, such as tight gas, shale gas, shale oil, coalbed methane, and natural gas hydrate,...
In today’s digital era, patterns are omnipresent, shaping many aspects of our lives. These patterns can be physically observed or computationally identified...
Cerebral palsy is a prevalent neurodevelopmental syndrome that disrupts motor development in children, making early detection vital for effective intervention....
Current automated lesion segmentation methods have limited success, particularly for segmenting small, irregular, or heterogeneous lesions. Moreover,...
This paper presents a dynamic energy management strategy for a community-scale campus hybrid microgrid integrating photovoltaic (PV) generation, aggregated...
This study investigates the problem of prioritizing rooftop renewable energy (RE) system configurations for a multi-family residential building in Mediterranean...
This paper addresses the challenge of efficiently calculating dynamic carbon emission factors (CEFs) in large-scale power systems. Traditional methods...
Acral lentiginous melanoma (ALM) is characterized by a low mutational burden, frequent chromosomal rearrangements, and profound epigenetic dysregulation,...
Checkpoint kinase 1 (CHK1), a key regulator of cell cycle checkpoints, plays a central role in the DNA damage response network, serving as a critical...
Glioblastoma (GB) is the most common primary malignant brain tumor of adulthood, and despite optimal safe resection and chemoradiation, it is still lethal....
Objectives: Circulating tumor cells (CTCs) drive metastasis and exhibit resistance to conventional therapies, making them crucial therapeutic targets....
Molecular glue degraders (MGDs) are an emerging class of small molecules that promote selective protein degradation by inducing neomorphic interactions...
Obesity is a complex chronic condition characterized by an excess of body fat that manifests in various clinical pathophenotypes, each affecting liver...
Unmanned Aerial Vehicle (UAV) target tracking is one of the key technologies in aerial intelligent perception systems, playing a vital role in applications...
With the rapid development of artificial intelligence and data-driven modeling, deep learning has become an effective tool for analyzing scientific discovery...
Structural optimization is a fundamental step in density functional theory (DFT) calculations, typically driven by the Broyden–Fletcher–Goldfarb–Shanno...
Reliable vehicle detection in urban traffic environments remains challenging, particularly for fixed-view CCTV systems deployed in Southeast Asian cities,...
The rapid evolution of chemical biology, medicinal chemistry, and molecular pharmacology continues to drive the discovery of innovative anticancer compounds...
Cancer development and therapeutic response are governed not only by genetic alterations but also by dynamic regulatory mechanisms at the epigenetic and...
The transition toward decentralized smart grids is reshaping the way energy is generated, exchanged, controlled, and consumed. The increasing penetration...
Cancer remains one of the leading causes of morbidity and mortality worldwide, and its onset and progression are closely linked to dysregulated epigenetic...
We propose a special issue titled "Innovative Smart Polymeric Materials for Sustainable Energy Solutions: Bridging Advances in Energy and Biomedical...
Artificial Intelligence of Things (AIoT) is considered a collaborative application of artificial intelligence (AI) and the Internet of Things (IoT). The...
Further advancements in the exploitation of unconventional resources, such as tight gas, shale gas, shale oil, coalbed methane, and natural gas hydrate,...
In today’s digital era, patterns are omnipresent, shaping many aspects of our lives. These patterns can be physically observed or computationally identified...
The advancement of communication technology has made traffic engineering a critical issue in network systems. The traffic matrix is essential data that supports traffic engineering. The functionality of routing planning, network monitoring, and other modules within intelligent network management systems relies heavily on the network traffic matrix. However, real-time measurement of the network traffic matrix is costly and often suffers from missing or anomalous values. Consequently, long-term network traffic prediction presents significant challenges. Existing methods…
With the proliferation of network users, traffic engineering has become increasingly important for the management and optimization of networks. As a crucial component of traffic engineering, the traffic matrix can assist network managers in making informed decisions to optimize resource utilization. However, in the current complex and heterogeneous space-ground integrated network, the cost of direct real-time measurement of traffic matrix is high and the delay is high. To address this challenge, we propose a network…
The Internet of Vehicular Agents (IoVA) interconnects distributed AI agents across vehicular networks to deliver real-time intelligent services for vehicular users. Due to the limited computing capacity of vehicles, AI agents are deployed on nearby RoadSide Units (RSUs) to perform computation-intensive inference. As vehicles traverse RSU coverage boundaries, AI agents must migrate to target RSUs to maintain service continuity. However, the communication and computing resources at each RSU are shared among multiple co-served vehicles, creating…
Software-Defined Vehicles (SDVs) increase cybersecurity complexity through the combination of external connectivity, software-intensive functions, and distributed development across vehicle manufacturers and suppliers. Although United Nations (UN) Regulation No. 155 and ISO/SAE 21434 require Threat Analysis and Risk Assessment (TARA) throughout the vehicle lifecycle, conventional TARA methodologies remain largely system-focused and often provide limited procedural guidance for coordinating supplier-derived TARA results at the vehicle level. This paper proposes an orchestration model for TARA across vehicle manufacturers…
The rapid advancement of edge intelligence in Industrial Internet of Things (IIoT) is transforming human–computer interaction from conventional “command execution” to complex “human–AI deep collaboration”. Within such safety-critical industrial environments, establishing robust mutual understanding and trust mechanisms becomes a significant prerequisite for decision reliability and efficiency. However, existing industrial interaction systems predominantly focus on task progression and explicit command responses, lacking fine-grained, dynamic tracking of operators’ trust states, cognitive evolution, and behavioral dynamics. Moreover, current…
The modern sports streaming market is severely fragmented, forcing fans into costly, siloed platforms. While blockchain-based decentralized architectures offer a unified, interoperable sport streaming ecosystem, securely delivering commercial video over untrusted infrastructure remains a profound cryptographic challenge. Existing schemes fail to simultaneously support highly granular on-demand highlights and large scale dynamic live subscriptions. To resolve this, we propose a novel decentralized authorization architecture that systematically integrates existing cryptographic primitives into a decoupled three-layer protocol. By
Missing data remain a persistent challenge in statistical analysis and machine learning because many predictive methods require complete observations. Generative Adversarial Imputation Networks (GAIN) offer a flexible deep-learning approach for missing value imputation, but their practical use is limited by convergence instability, sensitivity to hyperparameter selection, and dependence on outdated software implementations. To address these limitations, we propose Enhanced Generative Adversarial Imputation Networks (EGAIN), a modernized extension of GAIN implemented in TensorFlow 2.x. EGAIN incorporates…
Keeping customers engaged remains a major challenge in appointment-based services, where user behavior continuously shifts due to seasonal, market, and social factors. These dynamic changes often cause concept drift, rendering traditional deep clustering models unreliable because they assume stable data distributions. Most existing approaches handle representation learning, parameter optimization, and model updating as separate components, limiting their adaptability in real-world streaming environments. This study proposes Hybrid-RL, a novel adaptive clustering framework that unifies incremental deep…
With the proliferation of Internet of Things (IoT) devices, accurate device fingerprinting of highly encrypted traffic has emerged as a critical challenge for ensuring network security. Existing deep learning models are either difficult to deploy in real-time due to excessive computational complexity (e.g., Transformers) or are limited in performance because their structure does not match the inherent hierarchy of traffic data (e.g., flattened state space models). Furthermore, a general lack of transparency in their decision-making…
Power enterprise inspection and supervision require greater intelligence, efficiency, and standardization; however, existing approaches are limited by inefficient knowledge retrieval, inaccurate issue identification, and insufficient support for standardized reporting and rectification tracking. This study proposes a lightweight, domain-adaptive large language model (LLM) framework based on Low-Rank Adaptation (LoRA), integrating Retrieval-Augmented Generation (RAG) and structured prompt engineering to enable evidence-grounded inspection tasks. The framework achieves parameter-efficient adaptation through low-rank decomposition and constructs a domain-specific multimodal knowledge…
Accurate identification of heart murmurs from auscultation recordings is essential for early cardiovascular screening and diagnosis. While deep learning offers strong potential for automated heart murmur classification, existing models often exhibit overconfident, incorrect predictions and limited generalization due to dataset bias and class imbalance. To address these challenges, this study proposes a two-stage confidence-regulated learning framework that jointly optimizes feature representation and decision reliability. Rather than focusing solely on improving classification performance, this work emphasizes…
The Enhanced Graph Neural Network Autoencoder (Enhanced GNN-AE), recently proposed for unsupervised cybersecurity monitoring in battery energy storage systems (BESSs), builds a multiscale
To address trust-score drift and unsafe online adaptation under cross-domain attack-contaminated streams in Industrial Internet of Things (IIoT) edge environments, this paper proposes a risk-aware lightweight test-time adaptation (TTA) framework, named RaL-TTA, for dynamic trust evaluation of edge nodes. RaL-TTA constructs a low-dimensional robust feature space and a source-domain normal-entropy reference baseline, and performs selective online maintenance in the target domain through Kolmogorov–Smirnov (KS) drift detection, SafeBrake risk gating, Adaptive Batch Normalization (AdaBN) anchor protection,…
Despite significant advances in object detection technology, vulnerable pedestrian detection in intelligent transportation systems remains highly challenging under complex weather conditions. Environmental factors such as fog, rain, and snow often lead to occlusion, motion blur, and low-contrast images, making small-scale or weak-featured vulnerable pedestrians difficult to accurately identify. Therefore, improving the detection accuracy and robustness of vulnerable pedestrians in complex weather scenarios has become an urgent research problem. To address this issue, this paper proposes…
Satellite remote sensing images pose significant challenges for object detection due to their high resolution, complex scenes, and large variations in target scales. To address the insufficient detection accuracy of the YOLOv11n model in remote sensing imagery, this paper proposes two improvement strategies. Method 1: (a) a Large Separable Kernel Attention (LSKA) mechanism is introduced into the backbone network to enhance feature extraction for small objects; (b) a Gold-YOLO structure is incorporated into the neck…
Underwater imaging facilitates the exploration of the underwater environment. However, irregular optical absorption and light scattering in water, ranging from clear to highly turbid conditions, often result in low visibility, color distortion, and blurriness in underwater images (UWIs). Conventional UWI enhancement methods are limited by inefficient physical modeling, while deep learning-based approaches are constrained by the scarcity of paired training datasets. In this work, we propose a hybrid learning framework for UWI enhancement that leverages…
Guest Editors: Carlos Vargas-Salgado; Dácil Diaz-Bello Deadline: 30 April 2027
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Guest Editors: Qi Chen; Yunhui Tang Deadline: 31 December 2026
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Guest Editors: Changhong LINGHU; Hoon Eui Jeong; Ying Jiang; Jiangtao Su; Yangchengyi... Deadline: 30 December 2026
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