The development of advanced thermoelectric materials has attracted significant attention due to their potential for efficient energy conversion and sustainable...
Structural optimization is a fundamental step in density functional theory (DFT) calculations, typically driven by the Broyden–Fletcher–Goldfarb–Shanno (BFGS) optimizer....
This work introduces an autonomous maintenance strategy for desert photovoltaic installations, addressing efficiency losses caused by dust accumulation and hotspot...
The 4th International Conference on Energy Engineering (ICEE2026) will be held in Chiang Mai, Thailand from October 16 to 18, 2026. Jointly organized by Chiang Mai University and Tech...
The Canadian Journal of Urology (CJU) will attend the American Urological Association Annual Meeting (AUA 2026), taking place from May 14–18, 2026, in Washington, D.C.Representing the journal...
Knowledge distillation bridges the performance gap between camera-based and LiDAR-based 3D detectors by leveraging the precise geometric information from...
This paper provides a thorough examination of Genetic Algorithms (GAs), a category of evolutionary computation methods derived from the concepts of natural...
Topology optimization (TO) has become a core computational paradigm for structural design by defining optimality through physics-based objectives and...
Real-time prediction of temperature distribution in the pressurizer walls of Pressurized Water Reactors (PWRs) during severe accidents, such as Station...
To investigate the mechanism governing the continuous decline in fracture conductivity of unconsolidated sandstone reservoirs post-hydraulic fracturing,...
Addressing global climate challenges necessitates urgent low carbon transitions in high energy consuming enterprises (HECEs). This study proposes a comprehensive...
The advent of immune checkpoint inhibitors (ICIs) targeting PD-1, PD-L1, and CTLA-4 has transformed the therapeutic landscape of advanced non-small cell...
The rapid growth and accessibility of artificial intelligence (AI) and machine learning (ML) have opened many avenues to revolutionize biomedical research,...
Chronic myeloid leukemia (CML) is a hematopoietic malignancy originating from hematopoietic stem cells. It is characterized by the Philadelphia chromosome,...
Pancreatic ductal adenocarcinoma (PDAC) is currently the third leading cancer-related cause of death worldwide and is forecasted to become the second...
This paper provides a comprehensive review of recent advances in multi-scale modeling for simulating dynamic damage and fracture in metallic materials,...
This review examines current approaches to real-time decision-making and task optimization in Internet of Things systems through the application of machine...
3D single object tracking (SOT) based on point clouds is a fundamental task for environmental perception in autonomous driving and dynamic scene understanding...
This paper reviews the research progress and application prospects of machine learning technologies in the field of polymer materials. Currently, machine...
The direct conversion of solid-state heat to electricity using thermoelectric materials has attracted attention; however, their effective application...
In facial landmark detection, shape deviations induced by large poses and exaggerated expressions often prevent existing algorithms from simultaneously...
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...
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 rapid evolution of next-generation intelligent networks and cyber-physical systems has transformed industries by enabling seamless connectivity, real-time...
Knowledge distillation bridges the performance gap between camera-based and LiDAR-based 3D detectors by leveraging the precise geometric information from...
This paper provides a thorough examination of Genetic Algorithms (GAs), a category of evolutionary computation methods derived from the concepts of natural...
Topology optimization (TO) has become a core computational paradigm for structural design by defining optimality through physics-based objectives and...
Real-time prediction of temperature distribution in the pressurizer walls of Pressurized Water Reactors (PWRs) during severe accidents, such as Station...
To investigate the mechanism governing the continuous decline in fracture conductivity of unconsolidated sandstone reservoirs post-hydraulic fracturing,...
Addressing global climate challenges necessitates urgent low carbon transitions in high energy consuming enterprises (HECEs). This study proposes a comprehensive...
The advent of immune checkpoint inhibitors (ICIs) targeting PD-1, PD-L1, and CTLA-4 has transformed the therapeutic landscape of advanced non-small cell...
The rapid growth and accessibility of artificial intelligence (AI) and machine learning (ML) have opened many avenues to revolutionize biomedical research,...
Chronic myeloid leukemia (CML) is a hematopoietic malignancy originating from hematopoietic stem cells. It is characterized by the Philadelphia chromosome,...
Pancreatic ductal adenocarcinoma (PDAC) is currently the third leading cancer-related cause of death worldwide and is forecasted to become the second...
This paper provides a comprehensive review of recent advances in multi-scale modeling for simulating dynamic damage and fracture in metallic materials,...
This review examines current approaches to real-time decision-making and task optimization in Internet of Things systems through the application of machine...
3D single object tracking (SOT) based on point clouds is a fundamental task for environmental perception in autonomous driving and dynamic scene understanding...
This paper reviews the research progress and application prospects of machine learning technologies in the field of polymer materials. Currently, machine...
The direct conversion of solid-state heat to electricity using thermoelectric materials has attracted attention; however, their effective application...
In facial landmark detection, shape deviations induced by large poses and exaggerated expressions often prevent existing algorithms from simultaneously...
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...
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 rapid evolution of next-generation intelligent networks and cyber-physical systems has transformed industries by enabling seamless connectivity, real-time...
The construction industry’s substantial carbon footprint, primarily attributed to the production of Ordinary Portland Cement, necessitates a transition toward more sustainable alternatives. Geopolymer concrete (GPC), an innovative binder synthesized from industrial by-products like fly ash (FA), offers a promising low-carbon solution but is hindered by performance variability and a lack of standardized design protocols. This research addresses this critical barrier by developing robust predictive models for the compressive strength of FA-based GPC. Six machine learning…
Geospatial health risk signals, characterized by associations with high magnitude statistical significance, may frequently originate from circumscribed observational data streams. When these signals are fueled by massive N-size datasets, the large dimensional scale of the sample can induce a misleading interpretation of local evidence as a statistically significant risk inflation. The objective of this study is to verify whether such health risk configurations constitute geospatial structural artifacts: namely, stochastic distortions generated by the spatial information…
The introduction of the Open Radio Access Network (O-RAN) architecture enhances network flexibility but introduces novel security threats targeting open interfaces and the RAN Intelligent Controller (RIC). Particularly in the Near-RT RIC environment, an effective Intrusion Detection System (IDS) that satisfies strict near-real-time constraints of within 1 s is essential to defend against cyber attacks. This paper proposes an Artificial Intelligence (AI)-based IDS xApp designed for real-time cyber attack monitoring in the O-RAN Near-RT RIC…
Geopolymer concrete has attracted increasing attention as a lower-carbon alternative to ordinary Portland cement concrete because it can utilize aluminosilicate-rich industrial by-products while still achieving satisfactory mechanical performance. However, the 28-day compressive strength of steel fiber-reinforced geopolymer concrete (SFGPC) is governed by multiple interacting mixture variables, which makes reliable prediction difficult, especially for medium-sized experimental datasets. This study developed an interpretable deep-learning framework to predict the 28-day compressive strength (CS28) of SFGPC using an original…
Sinter quality prediction in iron ore sintering is a challenging computational modeling problem because of highly nonlinear process behavior, strong cross-variable interactions, and disturbances caused by changing operating conditions. This study develops a data-driven multi-index soft-sensing framework for sinter quality prediction by combining feature selection and hierarchical model optimization. An improved binary Greylag Goose Optimization algorithm is first employed to identify a compact subset of informative variables, reducing redundancy and multicollinearity in the original process…
This study examines the variable thermal conductivity and electroosmotic performance of Sutterby hybrid nanofluid (SBHNF) thin film flow over a stretched inclined sheet using an artificial neural network (ANN)-based on NARX (Multilayer Nonlinear Autoregressive Networks with Exogenous Inputs) multiple-layer backpropagation simulation with the Levenberg-Marquardt algorithm (LMA). AA7075 and AA7072 nanoparticles suspended in sodium alginate (SA) base fluid make up the hybrid nanofluid (HNF), which was selected due to its improved heat transfer properties and superior…
Hybrid fiber reinforced plastic (HFRP) composites, especially intra-layer carbon/glass hybrids, offer a promising balance of specific strength, impact resistance, and cost efficiency for thin-walled energy-absorbing structures. This study investigates the low-velocity impact response and energy absorption of intra-layer carbon/glass hybrid hat-shaped beams. Tensile and impact tests evaluated the effects of hybrid ratio and fiber orientation. A multiscale damage model based on micromechanical damage and failure criteria was established via Abaqus/VUMAT, integrating stress amplification factors to…
Metasurface design often requires solving field distributions across varying structural parameters and frequencies, where neural operators offer a promising avenue for fast prediction. However, conventional neural operators have problems with degradation of the accuracy in multi-scale structural analysis. In this work, we propose a Generative Residual Enhanced Neural Operator (GRE-NO) framework that introduces a generative residual network to model the systematic bias of the main predictor. The core model retains the DeepONet architecture with both…
Computer vision has been widely adopted in intelligent construction monitoring; however, existing studies primarily focus on identifying individual construction elements or isolated activities, with limited capability for integrated monitoring of complete construction workflows. Such workflow-level automation is a prerequisite for intelligent construction and unmanned job sites. To address the challenge of reliable visual recognition in drill-and-blast tunnel environments characterized by uneven illumination, localized glare, and dust interference, this study proposes a methodological framework for construction…
This study conducts a comprehensive numerical investigation of magnetohydrodynamic (MHD) mixed convection and entropy generation in a two-dimensional square cavity filled with a ternary hybrid nanofluid. The working fluid consists of Multi-Walled Carbon Nanotubes (MWCNT), Copper (Cu), and Ferric Oxide (Fe3O4) nanoparticles dispersed in water, selected for their superior thermal properties. Two vertically aligned, saw-tooth-shaped cooling structures are embedded along the left and right walls of the cavity, with four distinct configurations considered based on their…
The current study comprehensively investigates Williamson nanofluid flow and transport in an asymmetric porous tapered channel under varying slip conditions, using both analytical and supervised machine learning approaches. This mathematical model integrates thermophoresis, Brownian motion, the Soret and Dufour effects, thermal radiation, and a transverse magnetic field to accurately describe thermosoluble transport phenomena relevant to biomedical contexts. The non-Newtonian Williamson formulation is used to explain how fluids, such as blood, dilute when sheared. Darcy resistance…
In smart healthcare systems, Image data of critical patients is essential in controlling and diagnosing the disease development. To acquire the medical images, traditional methods encountered the difficulty of generating cost-effective data. This research work introduces a novel and innovative approach to collect high-quality image data from individuals with atypical clinical presentations. Initially, a new Internet of Medical Things (IoMT) image collection architecture is introduced. This design uses edge intelligence and motion-static synergy to make…
As large language models (LLMs) become increasingly integrated into enterprise decision-making processes, structural pressures such as version drift, cross-source evidence integration, and regulatory accountability have shifted the primary challenge from isolated generative performance to system-level consistency, traceability, and governability. This paper systematically reviews key technological developments relevant to enterprise requirements, including document perception, retrieval-augmented generation (RAG), hybrid RAG-KG architectures, fine-grained attribution evaluation, and multi-agent coordination. The analysis demonstrates that the main obstacle to enterprise LLM…
Vision Transformers (ViTs) have recently achieved high performance in retinal Optical Coherence Tomography (OCT) classification studies. However, ViT models continue to face significant challenges, including high computational cost, vulnerability to adversarial attacks, and pronounced sensitivity to preprocessing techniques. This study introduces GreenShield, a unified framework designed to produce an efficient and robust ViT model, referred to as GreenShield-ViT, which outperforms existing lightweight ViT variants in terms of adversarial robustness for retinal OCT classification. The framework…
Magnetic resonance imaging (MRI) is widely utilized for brain tumor segmentation, yet significant challenges persist due to intensity variations, irregular boundaries, and substantial morphological heterogeneity. Current state-of-the-art deep learning methods often struggle to capture long-range spatial dependencies, delineate fine boundary details, and efficiently process 3D volumetric data. This study introduces a novel hybrid framework that integrates state-space models with frequency-domain learning to address these limitations. The proposed model offers four primary contributions: (1) incorporation of…
Solving differential equations (DEs), including ordinary differential equations (ODEs) and partial differential equations (PDEs), is fundamental to scientific computing and engineering. The development of deep learning has led to Physics-Informed Neural Networks (PINNs), in which physical laws are embedded directly into the loss function. However, PINNs inherit the intrinsic instability of deep neural networks (DNNs) and lack an effective mechanism for Uncertainty Quantification (UQ). This paper proposes a stochastic ensemble framework to address these limitations.…
Guest Editors: Carlos Vargas-Salgado; Dácil Diaz-Bello Deadline: 30 April 2027
Guest Editors: Jungpil Shin; Yong Seok Hwang Deadline: 31 January 2027
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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