Search Results (1,388)

This review examines the transformative interplay between computing chips and Artificial Intelligence (AI), driving a revolution across various industries. First, the broader artificial intelligence and semiconductor ecosystem is analyzed, including hardware manufacturers, software frameworks, and system integration. Next, the development prospects are examined, revealing current challenges such as power consumption, manufacturing complexity, supply chain constraints, and ethical considerations. Further discussion focuses on cloud-edge collaboration in relation to system architecture and workload allocation strategies. Then, cutting-edge AI technologies are analyzed, and key insights are summarized. Finally, the overall trends in artificial intelligence and the chip industry are summarized, clearly presenting the findings for the future and making a unique contribution to this review. Full article

Background/Objectives: Renal cell carcinoma (RCC) with venous tumor thrombus (VTT) extending into the inferior vena cava (IVC) occurs in 4–10% of patients and represents one of the most technically demanding scenarios in urologic surgery. Open radical nephrectomy with en bloc thrombectomy remains the gold standard for infradiaphragmatic disease (Mayo Levels 0–III), offering the only realistic prospect for long-term cure. This narrative review provides a technically oriented, evidence-based guide for surgical urologists managing these complex cases. Methods: PubMed/MEDLINE, Scopus, and Web of Science were searched (1970–March 2025) using terms related to RCC, venous tumor thrombus, IVC thrombectomy, and perioperative management. Priority was given to prospective studies, systematic reviews, large retrospective cohorts, and current guidelines (EAU 2025, NCCN v2.2024). Original intraoperative photographs supplement procedural descriptions. Results: We detail the complete perioperative pathway: VTT classification (Mayo/AJCC), multimodal imaging, patient optimization, and level-specific open surgical techniques—ranging from Satinsky clamping for Level 0–I thrombi to full piggyback liver mobilization with hepatic vascular exclusion for Level III disease. Contemporary perioperative mortality is <2% at high-volume centers (reported in single and multicenter retrospective series from high-volume institutions), with 5-year cancer-specific survival of approximately 50–60% in non-metastatic cases. Adjuvant pembrolizumab is now a standard of care following the KEYNOTE-564 trial. Neoadjuvant immune checkpoint inhibitor plus tyrosine kinase inhibitor combinations show promising VTT downstaging rates (44–100%), though their role remains investigational. Robotic-assisted thrombectomy demonstrates favorable perioperative outcomes for Level I–II thrombi at experienced centers. Conclusions: Open surgery remains the cornerstone of curative treatment for RCC with infradiaphragmatic VTT, requiring meticulous preoperative planning and multidisciplinary collaboration at high-volume centers. Integration of perioperative systemic therapies and robotic-assisted approaches holds promise for further improving outcomes in this challenging patient population. Full article

In recent years, applications with robots collaborating actively with humans have been increasing. The transition from Industry 4.0 to 5.0 rearranges the focus of fully automated processes to a human-centered system that allows more customization and flexibility. In human-centered systems, the robot is expected to safely assist or provide support to the human operator, avoiding any unintentional harm, while the latter is focused on tasks that require human reasoning, since current decision-making systems still have some limitations. This survey reviews all the main functionalities required to make a robot (collaborative or not) act as an assistant for human operators, analyzing and comparing solutions proposed by the authors (based on previous works) and/or the ones available in the literature. In this way, it is possible to combine those functionalities and build a complete framework enabling safe mobile manipulation while interacting with humans. In particular, a mobile manipulator is used to receive requests from a user, navigate in a human-shared environment, identify the requested object, and grasp and safely deliver such an object to the user. The framework, which is completed by a user interface designed using Android Studio, is developed in ROS1, tested, and validated on a real mobile manipulator in real-world conditions. Full article

This paper presents the H-CoRE (Heterogeneous Cooperative Multi-Robot Execution) framework designed to enable autonomous multi-robot operations in GNSS-denied environments. Built on an ROS 2-based architecture, H-CoRE enables collaborative, structured task execution through standardized software stacks. Each robot’s stack combines a high-level executive system with an agent-specific motion layer and leverages multi-sensor fusion for localization and mapping. The framework is inherently reconfigurable, allowing individual agents to operate autonomously or as part of a multi-robot team for collaborative missions. In the considered scenario, the system integrates aerial and ground vehicles, a fixed pan–tilt–zoom camera, and a human supervisory interface within a unified, modular infrastructure. The proposed system has been deployed in indoor, GNSS-denied environments, demonstrating autonomous navigation, cooperative area coverage, and real-time information sharing across multiple agents. Experimental results confirm the effectiveness of H-CoRE in maintaining general awareness and mission continuity, paving the way for future applications in search-and-rescue, inspection, and exploration tasks. Full article

HRC is increasingly adopted in industrial and logistics environments, while workforce preparation often remains constrained by instructional approaches that provide limited embodied understanding of safety and ergonomics. This study examines the architectural design and system integration of a modular, human-centred XR platform intended to support safe and ergonomics-aware collaboration within smart logistics contexts. The proposed system integrates XR training scenarios deployed on consumer-grade hardware and follows a structured pedagogical progression from conceptual familiarisation through experiential task execution to reflective ergonomic evaluation. Multimodal feedback mechanisms based on posture-oriented guidance, attention-aware interaction design, and context-sensitive safety cues are incorporated without reliance on intrusive sensing technologies. A structured evaluation framework is defined to examine usability, task performance, and ergonomics-aligned posture indicators using standardised instruments and system-generated telemetry. The architectural design indicates that the framework supports scalable deployment, consistent interaction fidelity, and privacy-conscious data handling across educational and vocational settings. The proposed framework suggests that human-centred XR architectures can strengthen safety-oriented and ergonomically informed HRC within Industry 4.0 logistics environments. Full article

Construction and demolition waste (CDW) represents a critical environmental challenge in the building sector, with global generation exceeding 3.57 billion tonnes annually. The circular economy (CE) framework offers a transformative pathway through selective deconstruction and material recovery, yet implementation faces significant barriers including information asymmetry, supply chain fragmentation, and regulatory uncertainty. This study conducts a systematic literature review using the Context–Mechanism–Outcome (CMO) framework to analyze how computational methods, specifically Digital Twins (DT), Building Information Modeling (BIM), Internet of Things (IoT), blockchain, artificial intelligence, and robotics, act as enablers for resilience in CDW management. Following PRISMA 2020 guidelines and realist synthesis principles, we analyzed 42 high-quality empirical studies from Web of Science and Scopus (2015–2025). Our analysis identifies seven primary mechanisms: traceability (M1), simulation (M2), classification (M3), tracking (M4), collaboration (M5), analytics (M6) and robotics (M7). These mechanisms interact with four critical contexts (information asymmetry, supply chain fragmentation, economic uncertainty, operational risks) to generate outcomes at two levels: resilience capabilities (visibility, monitoring, collaboration, flexibility, anticipation) and performance indicators (recovery rates, cost reduction, CO₂ emissions mitigation, occupational safety). Key findings from the CMO analysis reveal that blockchain-enabled traceability increases material recovery rates by 15–25%, DT simulation reduces deconstruction costs by 20–30%, and computer vision automation improves sorting accuracy to 85–95%. The study contributes middle-range theories explaining how digital technologies enable circular transitions under specific contextual conditions, offering actionable strategic implications for researchers, project managers, technology developers, and policymakers committed to advancing computational economics in sustainable construction. Full article

Most camera view planning algorithms are employed in exploration tasks that maximise information gain, but few address the specific challenge of observing targeted surface areas with optimal image quality. This paper presents a novel camera view planning algorithm designed for dermoscopic mole mapping, which is crucial for early melanoma detection. Traditional full-body scanners, though beneficial, suffer from fixed camera positions that can compromise image quality due to varying body contours and patient sizes. Our algorithm addresses this limitation by dynamically optimizing the camera position on a set of collaborative robot (cobot) arms to enhance image resolution, safety, and viewing angles during skin examinations. The proposed method formulates the problem as a non-linear least-squares optimisation that ensures no camera occlusion and a safe distance from the end effector encapsulating the camera to the patient while adjusting the pose of the camera based on the topography of the body. This approach not only maintains optimal imaging conditions by considering resolution and angle of incidence but also prioritises patient safety by preventing physical contact between the camera and the patient. Extensive testing demonstrates that our algorithm adapts effectively to different body shapes and sizes, ensuring high-resolution images across various patient demographics. Moreover, the integration of our camera view planning algorithm into an intelligent dermoscopy system has shown promising results in improving the efficiency and geometric quality of dermoscopic image acquisition, which could lead to more reliable and faster diagnoses. This technology holds significant potential to transform melanoma screening and diagnosis, providing a scalable, safer, and more precise approach to dermatological imaging. Full article

Background: The role of minimally invasive surgery (MIS) for hepatocellular carcinoma (HCC) remains controversial because of concerns regarding oncologic adequacy, particularly margin status. While robotic-assisted hepatectomy has gained adoption, its true perioperative advantages over conventional laparoscopy and open surgery remain unclear. SIMMILR-5 was designed to evaluate the short-term outcomes of open, laparoscopic, and tele-robotic laparoscopic hepatectomy for HCC using rigorous adjustment for confounding. Methods: A retrospective international multicenter study was conducted including patients undergoing liver resection for HCC between June 2004 and November 2024 at five high-volume hepatobiliary centers. Surgical approaches included open (O), conventional laparoscopy (L), and tele-robotic laparoscopy (TRL). Propensity score matching was performed using demographic, clinical, and tumor-related variables. The primary endpoint was short-term mortality (30 and 90 days). Secondary outcomes included estimated blood loss (EBL), operative time, length of stay (LOS), R0 resection status, and major complications. Results: A total of 904 patients were identified (302 O, 568 L, 34 TRL). After matching, conventional laparoscopy was associated with significantly lower EBL, shorter operative time, and shorter LOS compared with open surgery (all p < 0.00001). Compared with open surgery, TRL was associated with lower EBL but no improvement in operative time or LOS. Compared with laparoscopy, TRL was associated with longer operative time and longer LOS. Short-term oncologic surrogates were comparable across approaches. Conclusions: Minimally invasive hepatectomy offers perioperative advantages over open surgery for selected patients with HCC, driven primarily by conventional laparoscopy. Tele-robotic hepatectomy is feasible and safe in experienced centers but does not demonstrate superiority over advanced laparoscopic techniques. Full article

The growing demand for noise reduction in multi-joint long-reach robotic arms necessitates the development of precise noise measurement methodologies. However, accurate characterization remains challenging due to the robot’s complex kinematics. Specifically, dynamic joint positions and motion trajectories can lead to acoustic occlusion, while the inherent directivity of sound sources further compromises measurement reliability. To address these issues, this study proposes a component-based hybrid measurement approach. First, the noise generated by a single joint was characterized using a simplified 4-point method, with Green’s function applied to correct for variable propagation distances. Subsequently, the total sound power level of the entire robotic arm was synthesized in a virtual environment by integrating the single-joint acoustic data with the arm’s operational kinematic program. Validation results demonstrate that the proposed method achieves a measurement error of only 0.8 dB relative to the reverberation chamber benchmark—an accuracy superior to that of direct measurements of the full robotic arm cycle (2.6 dB). Furthermore, a comparison with the ISO 3744:2025 9-point standard method reveals that while the proposed 4-point approach yields a slightly larger error (0.8 dB vs. 0.2 dB), it significantly reduces experimental complexity. Consequently, this method offers a sufficiently accurate and operationally efficient solution for practical engineering applications. Full article

The accelerated expansion of advanced technologies—particularly artificial intelligence, intelligent systems, and interactive digital environments—is influencing contemporary media ecosystems and contributing to changes in educational practices. This study provides a systematic and descriptive bibliometric mapping of recent scientific production on artificial intelligence in education, algorithmic ethics, and digital inequality. A total of 229 Scopus-indexed documents published between 2021 and 2026 were analyzed using Biblioshiny and VOSviewer to examine publication patterns, influential authors and sources, and the conceptual structure of the field. Results indicate a marked increase in research output since 2024, with an annual growth rate of 47.58%, an average of 8.68 citations per document, and an international co-authorship rate of 24.45%. These indicators reflect an expanding and increasingly collaborative research landscape, accompanied by a diversification of thematic priorities within the field. The analysis identifies five thematic clusters: (1) the technical foundations of AI and digital transformation; (2) intelligent and immersive learning environments; (3) personalized and adaptive learning systems; (4) AI literacy and pedagogical integration; and (5) ethical considerations, including algorithmic bias and educational robotics. The findings highlight the need for explicit justification of database selection, strengthened critical AI literacy, and context-sensitive strategies that address disparities in access, skills, and institutional capacity. Overall, this study offers a coherent overview of a research area that is currently expanding and undergoing conceptual reorganization, providing evidence-informed insights for future research, policy development, and the design of equitable AI-driven educational technologies. Full article

Widespread acceptance of collaborative robots in human-involved scenarios requires accessible and intuitive interfaces for lay workers and non-expert users. Existing interfaces often rely on users to plan and issue low-level commands, necessitating extensive knowledge of robot control. This study proposes a multimodal agentic AI framework integrating natural user interfaces (NUIs) to foster effortless human-like partnerships in human–robot collaboration (HRC), which enhance intuitiveness and operational efficiency. First, it allows users to instruct robots using plain language verbally, coupled with gaze, revealing objects precisely. Second, it offloads users’ workload for robot motion planning by understanding context and reasoning task decomposition. Third, coordinating with AI agents built on large language models (LLMs), the system interprets users’ requests effectively and provides feedback to establish transparent communication. This proof-of-concept study included experiments to demonstrate a practical implementation of the agentic AI framework on a mobile manipulation robot in the collaborative task of human–robot wood assembly. Seven participants were recruited to interact with this AI-integrated agentic robotic system. Task performance and user experience metrics were measured in terms of completion time, intervention rate, NASA TLX survey for workload, and valuable insights of practical applications were summarized through a qualitative analysis. This study highlights the potential of NUIs and agentic AI-embodied robots to overcome existing HRC barriers and contributes to improving HRC intuitiveness and efficiency. Full article

In complex ground environments, conventional RRT* often suffers from poor path quality and slow expansion during robot path planning. To address these issues, this paper proposes GEAR-RRT* (Goal-guided, adaptive informed-Ellipse sampling, layered obstacle-Avoidance expansion, and cost-driven Rewiring), which constructs a collaborative optimization mechanism across the three stages of sampling, expansion, and rewiring. First, the proposed method employs an adaptive informed ellipse to concentrate sampling within feasible regions while dynamically adjusting the informed-ellipse sampling domain, and further integrates Halton-directional hybrid sampling to generate high-quality candidate samples within that domain. Meanwhile, a layered expansion strategy is adopted: the planner first performs direct goal connection for rapid progress toward the goal; when this expansion is blocked by obstacles, it switches to local multi-directional offset to search for feasible expansion directions; if this still fails, an adaptive Artificial Potential Field is introduced to guide subsequent expansions until a feasible path is found. Next, a multi-factor rewiring parent selection strategy is used to optimize path length, safety clearance, and turning angle, while cubic B-spline smoothing is applied to improve path continuity. Finally, GEAR-RRT* is evaluated in five simulation environments as well as in joint ROS and physical-robot validation and is compared with five improved RRT* variants. The results demonstrate that the proposed method achieves superior overall performance in planning time, path length, and safety clearance. Full article

Robotic pipe welding represents a key and rapidly evolving technology for the automation of pipe and pipe-joint welding processes with standard, intersecting, and complex geometries. This review analyses 84 studies published over the past three decades, categorising them into four primary research areas: general pipe welding, intersecting pipes, boiler and tube-to-tubesheet welding, and control and modelling. Two separate comparative analyses were conducted: one within intersecting pipe research and another within the control and modelling category. The aggregated findings reveal consistent, complementary patterns: simulation and laboratory experiments clearly dominate validation methods, while industrial-scale evaluations remain scarce. The results further demonstrate that control strategies, sensor integration, and validation levels are strongly interconnected, collectively determining system performance, reliability, and practical applicability. Despite significant progress, challenges remain, including system integration complexity, limited robustness in variable industrial environments, insufficient real-time adaptive control, and inconsistent quantitative performance evaluation. Further research should prioritise the development of digital twins, human–robot collaboration, multi-sensor fusion, reinforcement learning-based adaptive control, and scalable industrial deployment. This review provides an overview of current progress and outlines key directions for developing intelligent and reliable robotic pipe welding systems. Full article

Background and Objectives: Diagnostics for hematologic diseases rely on integrated assessment of clinical manifestation, morphology, flow cytometry, and molecular testing. Current classification systems, including the WHO HAEM5 and the International Consensus Classification, highlight the central role of genomics in defining disease entities and risk. Simultaneously, laboratories face growing case complexity and staffing challenges. Automation, collaborative robots (cobots), digital morphology platforms, and artificial intelligence (AI) have begun to address these issues. Here we examine the application of these technologies in hematopathology and molecular diagnostics and consider their translational potential to improve diagnostic accuracy and, ultimately, patient care. Methods: A review of peer-reviewed literature and technical reports published through December 2025 was performed, focusing on digital morphology platforms, AI for peripheral blood and marrow interpretation, AI-enabled flow cytometry, automated and robotic deployments in clinical laboratories, and machine learning applications in molecular hematopathology. Results: Digital morphology analyzers show strong concordance with manual microscopy and now serve as efficient platforms for AI-assisted differentials, cell classification, and fibrosis quantification. Deep learning applied to multiparameter flow cytometry achieves performance comparable to expert review in distinguishing mature B-cell neoplasms and acute leukemias. Automated solutions, cobot systems and robotic-arm-based slide-scanning clusters have demonstrated substantial gains in throughput and pre-analytic consistency. AI models in molecular hematopathology increasingly assist with variant interpretation, genetic risk stratification, and linking morphologic and genomic findings. Conclusions: AI is beginning to change how hematopathology and molecular diagnostics are practiced. Successful translation will depend on disease-specific validation, the development of multi-modal models aligned with ICC and WHO frameworks, and laboratory governance that maintains expert oversight. Full article

Scarce global resources and reliance on non-renewable materials demand ecological, technology-integrated solutions. In Brazil, abundant wood resources remain underused in architectural education and practice. Introducing skills in curricula is essential for change and future adoption. This study developed a computational and digital fabrication methodology to rethink wood, exploring collaborative robotic assembly to build an embodied understanding of construction constraints. The Wood Innovation for Architecture in Brazil (WI-FAB) unites LAMO UFRJ and SDU CREATE robotics expertise and frames a pedagogical experiment in sustainable wood-structure design. The semester-long course tested whether the design framework could link computation, material behaviour, and assembly constraints as a pedagogical tool; the intensive workshop investigated how robotic assembly can enhance physical–digital workflows and inform future integration. The research-through-teaching methodology consisted of three phases: preliminary research, course testing, and a robotics workshop testing assembly workflows. Preliminary research developed a pedagogical framework comprising a kit of parts, joint types and string grammars tested within the semester-long course to support parametric rules and assembly sequencing. Participants assembled component “letters” that combined into “words” and then into “phrases”, developing computational and constructional understanding and converting parametric rules into tangible prototypes through iterative design-build-test cycles. Key outcomes include validation of parametric assembly rules through string grammars in the course; analysis of the robotics workshop applied four criteria (Assembly Movement; Component Geometry and Dimensions; Component Number and Slot Number; Complexity and Assembly Time) to evaluate assembly performance and workflow integration. Robotics stimulated physical–digital loops, accelerating design-to-assembly learning and informing full-scale developments. WI-FAB promotes reversible assembly, material reuse and circular-economy principles and contributes to the development of the forthcoming Sabiá parametric plugin for wooden joint design. Full article