This article proposes a novel distribution-aware cross-modal distillation framework to address the distillation instability in camera-based 3D object detection...
Nuclear reactor safety assessment relies on the ability to predict the structural response of critical components under extreme conditions. One such component is...
Biobased biodegradable plastics are emerging as eco-friendly alternatives to traditional petroleum-based packaging, offering renewability, biodegradability, and...
Chengdu, China, 24 April 2026 — The Canadian Journal of Urology (CJU) was invited to participate in the 3rd “Dong’an Lake” Innovation and Translation Forum and the 2026 Huaxi...
Zhengzhou, China, 19 April 2026 — The Canadian Journal of Urology (CJU) was invited to participate in the 2026 Annual Meeting of the Urological Ageing Disease Prevention...
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...
Generative Artificial Intelligence (AI) is reshaping digital marketing by creating automated content, personalizing campaigns, and offering new ways to...
Purpose of this novel review article is to unfold the current scientific worth of high performance polymer nanocomposite nanofibers, owing to growing...
A comparative analysis was performed on poly(lactic acid) (PLA), poly(caprolactone) (PCL), basalt fiber (BF) composites produced using two distinct approaches:...
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...
This special issue, “Applied Artificial Intelligence: Advanced Solutions for Engineering Real-World Challenges,” showcases the transformative power of...
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...
Generative Artificial Intelligence (AI) is reshaping digital marketing by creating automated content, personalizing campaigns, and offering new ways to...
Purpose of this novel review article is to unfold the current scientific worth of high performance polymer nanocomposite nanofibers, owing to growing...
A comparative analysis was performed on poly(lactic acid) (PLA), poly(caprolactone) (PCL), basalt fiber (BF) composites produced using two distinct approaches:...
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...
This special issue, “Applied Artificial Intelligence: Advanced Solutions for Engineering Real-World Challenges,” showcases the transformative power of...
The rapid emergence of sophisticated, dynamic, and rare or previously unseen attack pattern exposes fundamental limitations of conventional intrusion detection systems (IDS) based on static learning architectures. While deep learning (DL) models have demonstrated strong performance by capturing complex spatial and temporal traffic patterns, existing DL-based IDS largely rely on fixed decision structures, restricting adaptability to evolving threats. Furthermore, current hybrid DL-metaheuristic approaches typically use such metaheuristics as offline or auxiliary optimizers, without interacting with…
Accurate grasp detection is fundamental to successful robotic manipulation. Existing methods achieve reliable performance under good light conditions. However, their performance in low-light environments suffers from severe degradation due to the diminishing discriminative ability of visual features. In this paper, a novel low-light aware hybrid network LAH-Net is proposed. It comprises an alternating transformer-CNN module (ATCM) between the encoder and decoder, and a knowledge distillation-guided low-light enhancement module (KDLEM) before the encoder, which is activated…
Refactoring improves maintainability without altering externally observable behavior, yet it remains costly and error-prone when applied manually at scale. While large language models (LLMs) can generate plausible refactorings, practical adoption is limited by uncontrolled edit scope, inconsistent outputs under stochastic decoding, and weak traceability of why a change was produced. This paper proposes a smell-targeted, scope-bound refactoring framework for JavaScript that couples deterministic AST-based smell detection with constrained LLM transformation. The key design principle is…
Accurate malware identification and family categorization remain significant challenges in large-scale Android software analysis. Although deep learning has surpassed traditional machine learning in performance, its widespread adoption is hindered by the computational overhead stemming from feature redundancy and the lack of interpretability inherent in its black-box nature. To address these issues, this paper proposes DroidNTA, a DL-based detection model that fuses network traffic and API features. The model first constructs a simplified API Call Graph…
The integration of Internet of Things (IoT) infrastructures with Distributed Ledger Technologies (DLT) remains challenging due to the reliance on complex, tightly coupled back-end systems or centralized oracle services that hinder scalability, maintainability, and trust. This paper introduces a lightweight middleware architecture based on a Low-Code Development Platform (LCDP) that enables flexible and secure IoT-to-blockchain orchestration. We develop a custom workflow extension for the n8n platform that supports direct interaction with smart contracts, thereby removing…
Recent advancements in AI-synthesized speech have resulted in highly realistic deepfake audio, posing severe threats to authentication systems and digital media trust. Existing detection models struggle to generalize across diverse synthesis methods, especially those involving neural codec-based Audio Language Models (ALMs). In this work, we propose UniTector++, a novel prosody-aware, multi-stream detection architecture that generalizes across vocoder- and codec-based synthesis. UniTector++ incorporates three complementary streams—Whisper-based semantic embeddings, high-level prosodic features, and codec artifact representations—fused through…
Printed circuit boards (PCBs) are essential components that strongly influence the performance and reliability of modern electronic systems. However, minor and visually subtle manufacturing defects can degrade product quality and pose serious challenges for automated inspection systems. Existing deep learning–based methods often struggle to simultaneously achieve high detection accuracy, real-time processing speed, and compact model size. This study proposes an enhanced approach for real-time PCB defect detection using advanced object detection models. A dedicated dataset…
The deployment of specialized language models in resource-constrained edge environments (
Unmanned Aerial Vehicle (UAV) target tracking is one of the key technologies in aerial intelligent perception systems, playing a vital role in applications such as traffic monitoring, border patrol, disaster response, search and rescue, environmental monitoring, and military reconnaissance. Compared with generic object tracking tasks, UAV platforms exhibit significant differences in imaging perspectives, target scales, motion patterns, and onboard computing capabilities, which pose unique challenges for UAV target tracking, including small targets and drastic scale…
Mobile fog computing must support latency-sensitive applications under dynamic user mobility and time-varying network conditions. Existing mobility-aware scheduling approaches are largely reactive and often ignore prediction uncertainty, resulting in service disruptions and inefficient task migration. This paper proposes an uncertainty-aware digital twin-based orchestration framework for proactive mobility-aware fog computing. The framework maintains real-time synchronized digital twins of users and fog nodes and integrates a hybrid Gated Recurrent Unit-Exponentially Weighted Moving Average (GRU-EWMA) mobility prediction model…
Robots perform diverse tasks in real-world scenarios. In safety-critical applications, robot control must prioritize satisfying safety constraints in addition to achieving high performance. Offline safe reinforcement learning avoids risky online exploration by training from a given dataset. However, most existing methods overlook two issues in offline data. First, non-zero cost signals are typically sparse, which leads to inaccurate cost value estimates and makes it difficult to impose effective safety constraints on the policy. Second, an…
Advanced Persistent Threats (APTs) are stealthy cyberattacks that can evade detection in system-level audit logs. Provenance graphs encode these logs as interacting entities and events, exposing a causal and dependency structure that is often obscured in linear representations. Prior provenance-based detectors typically apply anomaly detection over such graphs, yet they frequently incur high false-positive rates and produce coarse grained alerts; moreover, approaches that heavily depend on node-specific identifiers (e.g., file paths) can learn spurious correlations,…
To ensure an effective disturbance response and maintain continuous production in hybrid flow shops, this paper focuses on the design of a rescheduling method. A rescheduling model is constructed that minimizes the makespan, total tardiness, and scheme deviation degree. A hybrid rescheduling driving mechanism based on the latest completion time is designed to effectively trigger rescheduling. The Whale Optimization Algorithm (WOA) is improved by integrating the good point set theory, nonlinear control parameter strategy, and…
Path planning for unmanned systems in complex environments must simultaneously satisfy safety, kinematic feasibility, and real-time performance requirements. Monte Carlo Tree Search (MCTS) offers advantages such as model-free operation, strong interpretability, and anytime planning capability, but it suffers from large branching factors, excessive search depths, and poor convergence under sparse reward conditions in high-dimensional state spaces. To address these challenges, this paper proposes a Heuristic Rolling Monte Carlo Tree Search (HRMCTS) framework. First, the path…
Accurate short-term load forecasting is essential for reliable power system operation, particularly under the increasing uncertainty caused by abnormal weather and socio-economic fluctuations. This study presents a month-conditioned boosting framework that integrates SHapley Additive Explanations (SHAPs) into model refinement. A baseline XGBoost model was first compared with linear and tree-based regressors, followed by enhancements through lagged and rolling-window features as well as loss weighting for vulnerable months. To further improve the performance, SHAP analysis was…
The plastic strain accumulation results of the multi-layered wrapped pressure vessel liner during long-term service are an important basis for its safety performance evaluation. However, the complex welds distributed on the liner bring challenges to the calculation of plastic cumulative strain. To this end, a novel hybrid deep learning framework is proposed for the efficient and precise prediction of ratcheting behavior in the liner welds of multilayered pressure vessels. By employing a BiLSTM network to…
Guest Editors: Qi Chen; Yunhui Tang Deadline: 31 December 2026
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