Search Results (419)

Recent advances in bionic intelligence are reshaping humanoid-robot design, demonstrating unprecedented agility, dexterity and task versatility. These breakthroughs drive an increasing need for large scale and high-quality data. Current data generation methods, however, are often expensive and time-consuming. To address this, we introduce Digital Twin Loong (DT-Loong), a digital twin system that combines a high-fidelity simulation environment with a full-scale virtual replica of the humanoid robot Loong, a bionic robot encompassing biomimetic joint design and movement mechanism. By integrating optical motion capture and human-to-humanoid motion re-targeting technologies, DT-Loong generates data for training and refining embodied AI models. We showcase the data collected from the system is of high quality. DT-Loong also proposes a Priority-Guided Quadratic Optimization algorithm for action retargeting, which achieves lower time delay and enhanced mapping accuracy. This approach enables real-time environmental feedback and anomaly detection, making it well-suited for monitoring and patrol applications. Our comprehensive framework establishes a foundation for humanoid robot training and further digital twin applications in humanoid robots to enhance their human-like behaviors through the emulation of biological systems and learning processes. Full article

The rapid evolution of sequencing technologies has profoundly advanced precision oncology. Whole-exome sequencing (WES), whole-genome sequencing (WGS), and whole-transcriptome sequencing (RNA-Seq) enable comprehensive characterization of tumor biology by detecting actionable mutations, gene fusions, splice variants, copy number alterations, and pathway dysregulation. These approaches also provide critical insights into biomarkers such as homologous recombination deficiency (HRD), tumor mutational burden (TMB), and microsatellite instability (MSI), which are increasingly essential for guiding therapeutic decisions. Importantly, comprehensive genomic profiling not only refines patient stratification for targeted therapies but also sheds light on tumor–immune interactions and the tumor microenvironment, paving the way for more effective immunotherapeutic combinations. WGS is considered the gold standard for detecting germline mutations and complex structural variants, while WES remains central for detecting somatic driver mutations that guide targeted therapies. RNA-Seq complements these methods by capturing gene expression dynamics, identifying clinically relevant fusions, and revealing mechanisms of resistance. Together with advances in bioinformatics and artificial intelligence, these tools translate molecular data into actionable strategies for patient care. This review integrates insights from WGS, WES, and RNA-Seq with an overview of FDA- and EMA-approved targeted therapies, organized by tumor type, and highlights the molecular signaling pathways that drive cancer development and treatment. By bridging genomic profiling with regulatory-approved therapies, we outline current advances and future perspectives in delivering personalized cancer care. Full article

With the rapid advancement and widespread adoption of artificial intelligence (AI), AI-powered personalized product recommendation (AI-PPR) has become a core tool for enhancing user experience and driving monetization on short-form video platforms, fundamentally reshaping consumer behavior. While prior research has largely focused on impulse buying intention (I-BI)—purchases triggered by emotional and sensory stimuli—there remains a lack of systematic exploration of spontaneous buying intention (S-BI), which emphasizes rational and cognitively driven decisions formed in unplanned contexts. Addressing this gap, this study integrates the Technology Acceptance Model (TAM) with a perceived evaluation perspective to propose and validate a dual-mediation framework: “AI-PPR → Perceived Usefulness/Perceived Trust → S-BI”. Using a large-scale survey of Generation Z users in mainland China (N = 754), data were analyzed via SPSS 26.0, including reliability and validity tests, regression analysis, and Bootstrap-based mediation analysis. The results indicate that AI-PPR not only has a significant positive direct effect on S-BI but also exerts strong indirect effects through perceived usefulness and perceived trust. Specifically, perceived usefulness accounts for 35.17% and perceived trust for 31.18% of the mediation, jointly constituting 66.35% of the total effect. The findings contribute theoretically by extending the boundary of purchase intention research, differentiating rational S-BI from emotion-driven impulse buying, and enriching the application of TAM in consumption contexts. Practically, the study highlights the importance for short-form video platforms and brand managers to enhance recommendation transparency, interpretability, and trust-building while pursuing algorithmic precision, thereby fostering rational spontaneous buying and achieving a balance between short-term conversions and long-term user value. Full article

As an important driving force for intelligent transformation, the development and application of industrial robots have promoted the transformation of traditional production modes and the upgrading of energy utilization methods, playing a significant role in improving energy efficiency. Based on the panel data of 283 prefectural-level cities in China from 2008 to 2019, this study used a two-way fixed-effects model to examine the impact of industrial robots on urban energy efficiency. The study found that industrial robots significantly improve energy efficiency, with the mechanisms including scale effects, structural effects, and green technology effects. Heterogeneity analysis shows that this effect is more prominent in innovative cities, central and western regions, and areas with high human capital. The research provides a basis for understanding the pathways through which industrial robots promote the improvement of energy efficiency and offers policy insights for China to advance intelligent manufacturing and green development. Full article

Only about 5% of colorectal cancers are hereditary, which is due to the low carrier rate of pathogenic gene mutations. The typical pattern of these cases is intergenerational aggregation within families and early onset. But public awareness of early diagnosis and intervention of HCRC is insufficient, resulting in most patients being diagnosed only after developing cancer, thereby missing the optimal window for treatment. This article reviews the latest developments in precision screening, treatment, evaluation and prevention strategies for HCRC, including innovative uses of artificial intelligence (AI) in molecular diagnostics, imaging technology advances, and potential application prospects. Regarding precision screening, tests of genomics, transcriptomics, microbiome, etc., combined with personalised risk stratification, can, respectively, effectively detect pathogenic mutations and “cancer-promoting” intestinal environments in the preclinical stage. AI combined with endoscopic and imaging tools has improved the accuracy of polyp detection and tumor profiling. Liquid biopsy and molecular marker detection provide new non-invasive monitoring solutions. In precision treatment, beyond traditional approaches like surgery and chemotherapy, immunotherapy with checkpoint inhibitors may be considered for HCRC patients with mismatch repair deficiency (dMMR). For patients harboring somatic mutations such as KRAS or BRAF V600E, targeted therapy can be guided by these specific mutations. Regarding precision assessment, AI incorporates microsatellite instability (MSI) detection and imaging diagnostic techniques, crucial for integrating genetic, environmental, and lifestyle data in follow-up. This helps assess the risk of recurrence and adjust the long-term medication regimens, as well as provide effective nutritional support and psychological counselling. In summary, the rapid development of precision medicine is driving the clinical management of HCRC into the era of tailored care, aiming to optimise patient outcomes. Full article

In the realm of sustainable public transportation, the integration of intelligent electric bus propulsion systems represents a novel and promising approach to reducing environmental impact—particularly through the mitigation of NOx emissions and overall exhaust pollutants. This emerging technology underscores the growing need for advanced drive control architectures that ensure not only operational safety and reliability but also compliance with increasingly stringent emissions standards. The present article introduces an innovative analysis of energy-optimized dual-drive electric propulsion systems, with a specific focus on their potential for real-world application in emission-conscious urban mobility. A detailed dynamic model of a dual-drive electric bus was developed in MATLAB Simulink, incorporating a Fuzzy Logic-based decision-making algorithm embedded within the Transmission Control Unit (TCU). The proposed control architecture includes a torque-limiting safety strategy designed to prevent motor overspeed conditions, thereby enhancing both efficiency and mechanical integrity. Furthermore, the system architecture enables supervisory override of the Fuzzy Inference System (FIS) during critical scenarios, such as gear-shifting transitions, allowing adaptive control refinement. The study addresses the unique control and coordination challenges inherent in dual-drive systems, particularly in relation to optimizing gear selection for reduced energy consumption and emissions. Key areas of investigation include maximizing efficiency along the motor torque–speed characteristic, maintaining vehicular dynamic stability, and minimizing thermally induced performance degradation. The thermal modeling approach is grounded in integral formulations capturing major loss contributors including copper, iron, and mechanical losses while also evaluating convective heat transfer mechanisms to improve cooling effectiveness. These insights confirm that advanced thermal management is not only vital for performance optimization but also plays a central role in supporting long-term strategies for emission reduction and clean, efficient public transportation. Full article

With the rapid advancement in autonomous driving technology, vehicle object detection has become a crucial component of perception systems, where accuracy and inference speed directly influence driving safety. To address the limitations of existing lightweight detection models in small-object perception and deployment efficiency, this study proposes an enhanced YOLOv8n-based framework, termed YOLOv8n-ALM. The proposed model integrates Mixed Local Channel Attention (MLCA), a Task-Aligned Dynamic Detection Head (TADDH), and Layer-Adaptive Magnitude-based Pruning (LAMP). Specifically, MLCA enhances the representation of salient regions, TADDH aligns classification and regression tasks while leveraging DCNv2 for improved spatial adaptability, and LAMP compresses the network to accelerate inference. Experiments conducted on the KITTI dataset demonstrate that YOLOv8n-ALM improves mAP@0.5 by 2.2% and precision by 5.8%, while reducing parameters by 65.33% and computational load by 29.63%. These results underscore the proposed method’s capability to achieve real-time, compact, and accurate vehicle detection, demonstrating strong potential for deployment in intelligent vehicles and embedded systems. Full article

The rapid advancement of artificial intelligence in automotive applications, particularly in Advanced Driver-Assistance Systems (ADAS) and smart battery management on electric vehicles, increases the demand for efficient near-sensor processing. While the problem of linear algebra in machine learning is well-addressed by existing accelerators, the computation of nonlinear activation functions is usually delegated to the host CPU, resulting in energy inefficiency and high computational costs. This paper introduces TOXOS, a RISC-V-compliant coprocessor designed to address this challenge. TOXOS implements the COordinateRotation DIgital Computer (CORDIC) algorithm to efficiently compute nonlinear functions. Taking advantage of RISC-V modularity and extendability, TOXOS seamlessly integrates with existing computing architectures. The coprocessor’s configurability enables fine-tuning of the area-performance tradeoff by adjusting the internal parallelism, the CORDIC iteration count, and the overall latency. Our implementation on a 65nm technology demonstrates a significant improvement over CPU-based solutions, showcasing a considerable speedup compared to the glibc implementation of nonlinear functions. To validate TOXOS’s real-world impact, we integrated TOXOS in an actual RISC-V microcontroller targeting the on-vehicle execution of machine learning models. This work addresses a critical gap in transcendental function computation for AI, enabling real-time decision-making for autonomous driving systems, maintaining the power efficiency crucial for electric vehicles. Full article

Unmanned aerial vehicles (UAVs), commonly known as drones, have emerged as promising solutions to overcome the shortcomings of traditional highway-monitoring approaches. UAVs have been used extensively for highway traffic monitoring, infrastructure inspection, safety analysis, and environmental management. This review summarizes the latest applications, contributions, and challenges of UAV technology in highway systems, highlighting their transformative impacts on traffic monitoring, infrastructure inspection, and safety assessment. Several UAV-based highway traffic datasets significantly improve research in traffic behavior analysis and automated driving system validation. The integration of UAVs with advanced technologies, such as artificial intelligence (AI), the Internet of Things (IoT), and 5G, further enhances their capabilities, enabling enhanced real-time analytics and better decision-making support. Addressing ethical, regulatory, and social implications through transparent governance and privacy-preserving technologies is essential for sustainable deployment. Full article

The increasing digitization of design processes has profoundly transformed the role of sketching in industrial design, integrating it with advanced technologies such as artificial intelligence (AI). This paper presents an innovative methodology for automotive design that combines the intuitive power of sketching, both traditional and digital, with the structured approach of Stylistic Design Engineering (SDE) and the capabilities of generative AI. The study investigates how AI can enhance and accelerate key phases of the design process, including ideation, style analysis, and development, by generating design variations and optimizing the transition from initial concepts to re-fined digital models. Through case studies integrating manual sketching, digital tools, and AI, this research demonstrates how this approach not only pre-serves the designer’s creativity but also improves efficiency and precision. The core contribution of this work lies in the development of a circular and iterative framework that balances creative exploration with methodological rigor, enabling significant reductions in time and cost while fostering innovation. The results underscore the potential of this integrated approach to drive a paradigm shift in automotive design and broader industrial design practices. By bridging creative ideation and systematic development, this methodology offers valuable applications not only in aesthetic design but also in engineering design contexts, where sketching can aid in defining and optimizing functional solutions from the earliest stages. Full article

This paper presents a pioneering and novel analysis of the synergistic relationship between China’s leadership in electric vehicle (EV) adoption and the rapid advancement of autonomous driving (AD) technologies within the nation’s mobility ecosystem. Challenging the conventional view of electrification as a parallel trend, this study introduces a new perspective by demonstrating how EV infrastructure serves as a fundamental enabler of autonomy, providing the necessary high-voltage architectures for critical AD functions like real-time sensor fusion and over-the-air updates. In doing so, it addresses the central research question: How does large-scale electrification influence the architecture, deployment, and safety development of autonomous driving vehicles, particularly in the context of China’s intelligent mobility ecosystem? Through technical analysis and industry examples, the paper offers original contributions by illustrating how EV-driven platforms overcome the inherent limitations of internal combustion engine systems, enhancing autonomous execution and system reliability. Furthermore, this research provides novel insights into China’s unique public–private innovation ecosystem, highlighting the role of vertically integrated startups and cross-sector coordination in driving AD development. By analyzing these previously overlooked systemic interactions, the paper posits that China’s EV dominance strategically amplifies its autonomous vehicle ambitions, positioning the nation to lead the next generation of intelligent transportation systems. Full article

Intelligent ventilation systems can optimize airflow regulation to enhance mining safety and reduce energy consumption, driving green development in mineral resource extraction. This paper systematically elaborates on the overall architecture, cutting-edge advances, and core technologies of current intelligent mining ventilation. Building upon this foundation, a comprehensive intelligent mine ventilation solution encompassing the entire process of ventilation design, optimization, and operation is constructed based on a five-layer architecture, integrating key technologies such as intelligent sensing, real-time solving, airflow regulation, and remote control, providing an overarching framework for smart mine ventilation development. To address the computational efficiency bottleneck of traditional methods, an improved loop-solving method based on minimal independent closed loops is realized, achieving near real-time analysis of ventilation networks. Furthermore, a multi-level airflow regulation strategy is realized, including the methods of optimization control based on mixed integer linear programming and equipment-driven demand-based regulation, effectively resolving the challenges of calculating nonlinear programming models. Case studies indicate that the intelligent ventilation system significantly enhances mine safety and efficiency, leading to approximately 10–20% energy saving, a 40–60% quicker emergency response, and an average increase of about 20% in the utilization of fresh air at working faces through its remote and real-time control capabilities. Full article

People with disabilities often face challenges like moving around independently and depending on personal caregivers for daily life activities. Traditional assistive devices are universally accepted by these communities, but they are designed with one-size-fits-all approaches that cannot adjust to individual human sizes, are not easily customized, and are made from rigid materials that do not adapt as a person’s condition changes over time. This systematic review examines the integration of smart materials, sensors, actuators, and 3D/4D printing technologies in advancing assistive devices, with a particular emphasis on mobility aids. In this work, the authors conducted a comparative analysis of traditional devices with commercially available innovative prototypes and research stage assistive devices by focusing on smart adaptable materials and sustainable additive manufacturing techniques. The results demonstrate how artificial intelligence drives smart assistive devices in hospital decentralized additive manufacturing, and policy frameworks agree with the Sustainable Development Goals, representing the future direction for adaptive assistive technology. Also, by combining 3D/4D printing and AI, it is possible to produce adaptive, affordable, and patient centered rehabilitation with feedback and can also provide predictive and preventive healthcare strategies. The successful commercialization of adaptive assistive devices relies on cost effective manufacturing techniques clinically aligned development supported by cross disciplinary collaboration to ensure scalable, sustainable, and universally accessible smart solutions. Ultimately, it paves the way for smart, sustainable, and clinically viable assistive devices that outperform conventional solutions and promote equitable access for all users. Full article

With the rapid advancement of artificial intelligence technology, the widespread application of deep learning in computer vision is driving the transformation of agricultural pest detection and control toward greater intelligence and precision. This paper systematically reviews the evolution of agricultural pest detection and control technologies, with a special focus on the effectiveness of deep-learning-based image recognition methods for pest identification, as well as their integrated applications in drone-based remote sensing, spectral imaging, and Internet of Things sensor systems. Through multimodal data fusion and dynamic prediction, artificial intelligence has significantly improved the response times and accuracy of pest monitoring. On the control side, the development of intelligent prediction and early-warning systems, precision pesticide-application technologies, and smart equipment has advanced the goals of eco-friendly pest management and ecological regulation. However, challenges such as high data-annotation costs, limited model generalization, and constrained computing power on edge devices remain. Moving forward, further exploration of cutting-edge approaches such as self-supervised learning, federated learning, and digital twins will be essential to build more efficient and reliable intelligent control systems, providing robust technical support for sustainable agricultural development. Full article

The rapid advancement of Artificial Intelligence (AI) technologies is driving a profound transformation in higher education, shifting traditional learning toward digital, remote, and AI-mediated environments. This shift—accelerated by the COVID-19 pandemic—has made computer-supported collaborative learning (CSCL) a central pedagogical model for engaging students in virtual, interactive, and peer-based learning. However, while these environments enhance access and flexibility, they also introduce new emotional, social, and intercultural challenges that students must navigate without the benefit of face-to-face interaction. In this evolving context, Social and Emotional Learning (SEL) has become increasingly essential—not only for supporting student well-being but also for fostering the self-efficacy, adaptability, and interpersonal competencies required for success in AI-enhanced academic settings. Despite its importance, the role of SEL in higher education—particularly within CSCL frameworks—remains underexplored. This study investigates how SEL, and specifically cultural empathy, influences students’ learning experiences in multicultural CSCL environments. Grounded in Bandura’s social cognitive theory and Allport’s Contact Theory, this study builds on theoretical insights that position emotional stability, social competence, and cultural empathy as critical SEL dimensions for promoting equity, collaboration, and effective participation in diverse, AI-supported learning settings. A quantitative study was conducted with 258 bachelor’s and master’s students on a multicultural campus. Using the Multicultural Social and Emotional Learning (SEL CASTLE) model, the research examined the relationships among SEL competencies and self-efficacy in CSCL environments. Findings reveal that cultural empathy plays a mediating role between emotional and social competencies and academic self-efficacy, emphasizing its importance in enhancing collaborative learning experiences within AI-driven environments. The results highlight the urgent need to cultivate cultural empathy to support inclusive, effective digital learning across diverse educational settings. This study contributes to the fields of intercultural education and digital pedagogy by presenting the SEL CASTLE model and demonstrating the significance of integrating SEL into AI-supported collaborative learning. Strengthening these competencies is essential for preparing students to thrive in a globally interconnected academic and professional landscape. Full article