Search Results (280)

Despite growing curricular emphasis on computational thinking (CT) and engineering design practice (EDP), preparing elementary teacher candidates (ETCs) for integrated, practice-based instruction remains challenging. Grounded in Communities of Practice (CoP) and design-based implementation research (DBIR) frameworks, this study examines how ETCs (pre-survey: n = 23, post-survey: n = 13) develop learning-to-teach practices for integrating coding and robotics into PreK–5 STEAM learning (STEM with Arts integration) to foster these competencies. The study is situated within an interdisciplinary, community-partnered course, Toying with Technology (TwT), where ETCs collaborate in Critical Friends teams to design, facilitate, and reflect on community partners’ need-oriented, empathy-centered STEAM experiences. Utilizing PreK–5 learning technologies and field-based insights from STEM/STEAM professionals, ETCs developed 5E lesson plans integrating science, literacy, mathematics, and robotics, culminating in both an in-person and a virtual STEAM Family Night. Data sources include pre- and post-surveys measuring ETCs’ technology integration perceptions, post-field exit tickets, family feedback, community partner reflections, and course artifacts. Pre-survey results indicated limited prior experience and neutral confidence regarding technology integration. In contrary, post-survey findings demonstrated significant increases in self-reported confidence, conceptual understanding of CT and EDP, and readiness to facilitate empathy-centered, technology-rich learning. Qualitative analysis further revealed growth in collaboration, problem-solving, and pedagogical reasoning, while parent and partner feedback highlighted high levels of engagement and increased STEAM interest. Overall, this study provides initial empirical evidence, within a community-partnered, empathy-oriented course design, showing how community-engaged teacher education, informed by CoP and DBIR frameworks, prepares ETCs to teach CT, EDP, and critical problem-solving through integrated, authentic STEAM integration. Full article

Robotics serves as a key enabler in disability support by offering new pathways to augment social work practice and enhance autonomy, safety, and inclusion for persons with disabilities (PWDs). In this study, an interdisciplinary team executed a socio-technical investigation that integrated robotics engineering, artificial intelligence, rehabilitation sciences, and social work. It was examined how assistive and socially interactive robots, including mobility robots, cognitive-assistive systems, exoskeletons, telepresence units, and socially assistive humanoids, must be embedded within disability services to improve functional independence, strengthen care continuity, and address increasing workforce demands. A comprehensive research design was adopted by combining a systematic literature review and technical benchmarking of robot capabilities with qualitative inputs gathered from co-codesign workshops involving PWDs, caregivers, and social workers. To test these applications, the team evaluated three pilot domains: home-based independent living support, community-based rehabilitation, and social-work-led remote engagement utilizing telepresence robotics. The results demonstrate that these robotic interventions improved independent task completion by 22–41% and reduced caregiver burden by 18–34%. Furthermore, the data revealed significant gains in communication and emotional engagement for individuals with cognitive or speech impairments. While robotics cannot replace social workers, these technologies meaningfully complement care delivery when practitioners develop them through ethical, participatory, and contextually sensitive frameworks. Ultimately, this paper highlights clear pathways toward scalable, inclusive robotic support systems that align engineering innovation with person-centered social work values. Full article

Artificial intelligence (AI) is entering cardiac surgery through predictive modelling, multimodal imaging, perioperative monitoring, workflow automation, and emerging computer-vision applications. The most mature evidence concerns risk prediction before and after surgery. Even in this domain, however, systematic reviews show that improvements over conventional statistical models are often modest and that routine clinical implementation remains limited. In surgical education, simulation, automated video analysis, and objective performance metrics may expand opportunities for deliberate practice and provide feedback that is less dependent on individual observers. Most of this evidence comes from general, laparoscopic, urological, and robotic surgery rather than cardiac-specific training, and its transferability should not be assumed. The same technologies also create risks. Automation bias, cognitive off-loading, reduced exposure to failure management, and displacement of mentor–trainee interaction may weaken the independent judgement on which safe cardiac surgery depends. Opaque models, dataset shift, inequitable performance, and uncertain accountability add further clinical and ethical concerns. This narrative review examines the current and emerging roles of AI across the cardiac surgical pathway and in cardiothoracic training, while distinguishing demonstrated applications from plausible but unproven uses. We propose a human-in-command framework based on external validation, local performance testing, transparent intended use, preserved manual and crisis-management competencies, simulation of technology failure, faculty oversight, competency-based credentialing, and continuous audit. AI should be judged not by technical novelty alone but by whether it improves care while preserving the ability of surgeons and teams to operate safely when the technology is unavailable or wrong. Full article

Industry 5.0 demands that engineering graduates develop competencies that integrate technical knowledge with human-centricity, sustainability and resilience. However, engineering education has not always advanced at the same pace, and effective educational designs that operationalise these principles within applied learning contexts remain scarce. This paper presents Innovation Day, a four-hour challenge-based learning experience that operationalises Industry 5.0 principles through an artificial intelligence (AI)-supported cyber–physical logistics challenge in management engineering master’s programmemes. The activity engaged 47 students organised into 14 teams across two master’s degrees at the Universitat Politècnica de València. Students progressively developed and integrated data structuring, QR-based identification, sequencing and storage assignment logic, computer vision and collaborative robotics within a single supervised automation workflow. All 14 teams completed the first three phases, while completion rates reached 71% in Phase 4 and 64% in Phase 5, reflecting the increasing demands of system integration. Results also show that generative AI acted as a pedagogical tool, enabling students with limited programming experience to develop functional Python applications while maintaining human validation and accountability. The experience demonstrates that complex Industry 5.0 scenarios can be translated into manageable, transferable educational activities through phased challenge design, functional validation gates and structured AI-supported learning. Full article

Autonomous microrobot teams have significant potential for distributed drug delivery, cooperative vascular intervention, and parallelized biomedical diagnostics. However, coordinated control in cardiovascular environments remains challenging due to partial observability, limited communication bandwidth, and hydrodynamically coupled pulsatile blood flow. This paper introduces Decentralized Hemodynamic-Aware Multi-Agent Reinforcement Learning (DH-MARL), a distributed learning and control framework in which individual microrobots learn decentralized policies from local observations while graph-based attention mechanisms model inter-agent interactions during centralized training. The proposed framework integrates turbulence-aware adaptive exploration, reduced-order hydrodynamic interaction modeling, diffusion-based local communication, and hemodynamic-aware counterfactual credit assignment to improve cooperative learning and role specialization in dynamic vascular environments. A scalable Unity-based simulator supporting coupled pulsatile flow for up to 32 agents was developed for training and evaluation. Our experimentalresults cover four therapeutic scenarios: distributed drug delivery, cooperative clot dispersion, stenosis mapping, and vessel bottleneck traversal. For 16-agent teams, DH-MARL reaches an 88.7% team success rate. This performance exceeds independent single-agent controllers and centralized MAPPO baselines, and inter-robot collision rates remain below 4%. The learned policies generalize to unseen team sizes with minimal performance degradation, highlighting the scalability and robustness of the proposed decentralized control strategy. These simulation-level results demonstrate the feasibility of distributed reinforcement learning and graph-based coordination as a control paradigm for future multi-agent microrobot systems in biomedical environments. The results also provide a foundation for the calibration of subsequent microfluidic, ex vivo, and preclinical experiments. Full article

Effective task allocation is fundamental to the success of heterogeneous multi-robot cooperative missions in smart manufacturing workshops, yet real-world operational uncertainties pose severe challenges to solution feasibility and mission robustness. Addressing these challenges, this paper focuses on the inspection and maintenance task allocation problem for heterogeneous mobile robot teams operating under fluctuating equipment maintenance time windows, variable task execution durations, and uncertain robot travel speeds caused by workshop congestion and payload variations. First, the aforementioned uncertain parameters are characterized using triangular fuzzy numbers, upon which a fuzzy chance-constrained programming model is constructed with the objective of minimizing total operational cost while ensuring constraint satisfaction under uncertainty. The proposed model simultaneously handles two types of critical constraints: the service time window constraint, which requires each task to be completed before its latest allowable service deadline, and the time sequence constraint, which enforces that each equipment inspection task must be completed prior to the corresponding maintenance task. Then, to tackle the inherent NP-hardness of this problem, a multi-strategy hybrid Harris Hawks Optimization algorithm incorporating differential evolution, termed MSHHODE, is proposed. In detail, three targeted enhancement mechanisms are introduced: a hunting enthusiasm factor that governs the dynamic balance between global exploration and local exploitation throughout the search process; an elite-assisted guidance strategy that stabilizes convergence by leveraging high-quality solutions to direct population evolution; and an adaptive differential evolution mechanism that reinforces global search diversity and mitigates premature convergence to local optima. Finally, simulation experiments conducted across multiple workshop-scale scenarios demonstrate that MSHHODE consistently outperforms benchmark algorithms across different key performance metrics under varied uncertain conditions, which validates the effectiveness and robustness of the proposed approach in solving complex, constrained allocation problems, offering a practical and reliable framework for real-world heterogeneous multi-robot task planning in smart manufacturing environments. Full article

Cooperative multi-robot exploration relies on a shared belief over teammates’ poses and maps, but bandwidth-limited radios refresh that belief only intermittently. We identify a failure mode beyond simply sending fewer messages: when robots broadcast on a common schedule, the shared belief goes stale in phase, the learned explorer acts on the same outdated information across the team, and robots herd toward the same frontiers. We call this the synchrony tax and show it is controllable at test time, without retraining. A scheduling layer over a frozen graph-attention policy fixes each robot’s broadcast rate and changes only the phase of transmissions, cutting travel by $9.8\%$ and sensing overlap by $13.2\%$ (independent replications reach $-12.9\%$). The primary contributing factor is a higher peak (not mean) team staleness, which a staleness-to-overlap bound links to redundant coverage; the benefit accordingly disappears on planners that barely use the shared belief. OW-Desync, a budget-constrained activation policy, carries even staggering to range-limited, heterogeneous channels, improving the freshness it controls while matching staggering on the task. Four-robot experiments over real WiFi reproduce the timing-to-staleness stage of the mechanism at a matched rate: across five repeated paired trials, staggering cuts the measured peak team staleness by $37\%$ (all pairs concordant, within $1.5\%$ of the predicted optimum) with the mean unchanged. Full article

Humanoid robots are emerging as flexible robotic resources for autonomous manufacturing systems, where different types of tasks must be assigned to suitable robots while shared production resources are coordinated effectively. However, realistic manufacturing environments involve dynamic task arrivals, event-driven priority changes, heterogeneous robot capabilities, and shared-resource contention. In addition, digital twin-based scheduling may rely on state information that is delayed or uncertain, which can reduce the reliability of scheduling decisions. To address this issue, this paper extends the previously proposed deep reinforcement learning-based concurrency control (DRLCC) framework for robustness-aware scheduling and shared-resource control of a multi-functional humanoid robot team. The extended framework integrates capability-aware task assignment, feasibility-based action masking, and priority ceiling protocol (PCP)-based shared-resource coordination under delayed and uncertain digital twin observations. The framework is evaluated in a humanoid-based autonomous manufacturing scenario using performance indicators including task completion, urgent-task delay, resource contention, humanoid utilization, and robustness degradation under imperfect state feedback. Compared with the greedy ceiling-based baseline, DRLCC reduces high-priority task delay by approximately 10.9%, priority inversions by 42.1%, average waiting time by 73.8%, average block count by 74.0%, and temporary infeasible events by 73.5%, while maintaining comparable humanoid utilization. The robustness analysis further shows that overall task-completion performance remains stable under imperfect digital twin feedback, although coordination-level metrics are more sensitive to observation uncertainty and delay. These results suggest that the extended DRLCC framework can support robustness-aware humanoid robot team scheduling in autonomous manufacturing environments where digital twin observations are delayed or uncertain. Full article

The introduction of robotic-assisted total hip arthroplasty (THA) has had significant effects over and above the more accurate component position it delivers. Diagnosis and templating are improved as a result of the planning CT scan. Complex cases involving abnormal anatomy are simplified surgically by a single ream for the acetabular component and feedback for hip length and offset for the femoral component. The learning curve for surgeons relates primarily to operative time rather than complications. Once planned, robotic systems may reduce variability in component positioning between surgeons with differing levels of experience. Patient Reported Outcome Measures (PROMs) will need to be supplemented with gait lab data and functional tests to prove the added value of robotic-assisted THA. As robotic-assisted surgery (RAS) becomes increasingly reliant on imaging systems and digital workflows, operating theatres face greater complexity in technology integration and intraoperative coordination. Emerging evidence links spatial organisation, equipment positioning, and workflow to operative efficiency, intraoperative safety, and surgical performance. This paper provides an evidence-informed narrative perspective on how advances in robotic-assisted THA are influencing the spatial and functional requirements of operating theatres, thereby affecting clinical performance. As surgical practice advances, there is a need to move towards future-proofed design strategies informed by clinical performance and systems-based evidence. This includes a greater focus on flexibility and integrated digital infrastructure to support evolving technologies, changing team dynamics, and increasing procedural demands. Operating theatres must therefore evolve to enable effective integration of RAS in THA, and support improved patient safety and clinical outcomes. Full article

Advances in AI are increasing the scope for “reasonable disagreements” between humans and artificial systems. Intelligent robots and virtual agents are increasingly tasked with complex, potentially open-ended assignments that require the AI to function autonomously. AI judgments and decisions can lack transparency and explainability, leading to conflict with the human operator or user, requiring compromise to resolve the conflict. This article presents a new theory of human-AI conflict and compromise, drawing on research on decision-making, conflict in human teams, trust in human-robot teaming, and the challenges for humans of interacting with AI. It is proposed that the nature of conflict depends on the human’s mental model of AI functioning. If the human sees the AI as an advanced tool, conflict arises from differences in choice and implementation of algorithms for joint decision-making. Research on multi-cue judgment within the framework of the Brunswik Lens Model illustrates this type of conflict and its resolution. By contrast, if the mental model attributes humanlike characteristics to the AI, including a Theory of Mind, conflict can arise from different person-centered narratives for framing the task or team functioning. When narratives clash, the robot may be perceived as unsupportive or in violation of team role expectancies. In this case, system design may require using AI natural language capabilities and dialogue can facilitate compromise through context-sensitive matching of the human’s mental model to robot functionality. Full article

Matrix manufacturing requires close coordination between collaborative workcells, mobile robots, and battery management resources to support the execution of heterogeneous human-robot operations in reconfigurable production environments. This paper presents a cyber-physical robotic coordination framework for human-robot teams deployed in an industrial matrix manufacturing system, integrating a collaborative workstation, a fleet of mobile programmable cobots, and an automatic battery changer through ROS/OPC UA communication. The framework coordinates task execution, intra-logistics, and energy management through a decision layer that assigns operations to human and robotic agents, relocates idle mobile robots, and triggers battery swaps. Three coordination modules—a Battery Management Module, a Task Allocation Module, and a Robot Relocation Module—implement this pipeline by computing feasible execution plans at each scheduling cycle, accounting for human and robot capabilities, workstation availability, transport times, and battery state. The approach is validated on a deployed industrial matrix manufacturing platform through a disassembly task comprising human-only, robot-only, and human–robot collaborative operations, demonstrating the feasibility of coordinating heterogeneous robotic and human resources in a physical reconfigurable manufacturing environment. Full article

Artificial intelligence (AI) tools—including audit data analytics, robotic process automation, machine-learning models, and generative AI—are changing how audit teams identify risks, select procedures, and evaluate evidence. At the same time, Regulation (EU) 2024/1689 (the EU AI Act) establishes a risk-based governance architecture built around risk management, data governance, technical documentation, logging, transparency, human oversight, robustness, cybersecurity, and post-market monitoring. The Act is not an auditing standard and does not directly regulate every tool used by audit firms. Nevertheless, its governance logic is relevant where audit firms develop, procure, or rely on AI-enabled systems that process sensitive client data, influence professional judgement, or become part of audit-relevant client systems. This conceptual study uses doctrinal requirements-to-controls mapping and design-oriented analysis to translate selected AI Act governance objectives into firm-level and engagement-level quality-management controls and into criteria for evaluating AI-enabled audit evidence. The paper specifies three modes of AI Act relevance: direct legal relevance where a regulated AI Act role is engaged; indirect relevance where AI compliance documentation becomes audit-relevant information; and benchmark relevance where the Act supplies governance objectives for quality management without creating an audit-law duty. The resulting artefacts are a traceable AI Act/IAASB standards crosswalk, an evidence-risk typology, a quality-management integration model, a documentation and review checklist, and a proportional maturity model. The framework clarifies when AI outputs remain triage or risk-assessment tools, when they provide directional or corroborative evidence, and the narrower conditions under which they may contribute to substantive evidence. It links reliance to data completeness, reconciliation, versioning, validation, false-positive and false-negative behaviour, explainability, logging, source-document corroboration, and reviewer challenge. The contribution is a scalable governance-to-controls framework that supports defensible reliance and inspection readiness without overstating the AI Act’s direct legal applicability. Empirical validation in audit firms remains a priority for future research. It further explains how quantitative risk features and anomaly-detection outputs feed into qualitative audit judgement: models can route attention to unusual transactions or documents, but evidential weight still depends on base-rate-aware error analysis, source-document corroboration, and reviewer challenge. Full article

This study presents a decentralized shared actor–critic framework for cooperative multi-robot coverage in continuous two-dimensional simulation. The method combines permutation-invariant local observations, continuous differential-drive control, and reward shaping based on stepwise Hungarian assignment distances, collision penalties, and time efficiency. Homogeneous teams of four, five, and six agents are evaluated in an obstacle-free environment using five independent training seeds. In the final training window, the full reward configuration achieved full-team success rates of 98.2 ± 2.9% for four agents, 85.1 ± 18.0% for five agents, and 96.3 ± 2.0% for six agents, with mean landmark coverage above 96% in all cases. The lower mean in the five-agent setting was associated with higher seed-level variability dominated by one low-success seed. Reward ablations without assignment shaping or collision penalties remained viable, and seed-level tests did not show a statistically significant final-window advantage of the full reward configuration. The full configuration reached the 80% rolling-success threshold earlier in median terms, with the clearest seed-level support in the four-agent setting. Within-environment comparison showed higher full-team success than MADDPG and MAPPO under the matched training horizon and final-window protocol. Deterministic arena-size transfer from $15\times 15$ to $30\times 30$ showed decreasing full-team success as arena size increased, while partial landmark coverage remained higher than strict full-team completion. The results support the method for small homogeneous teams in the tested obstacle-free simulation, while larger teams, external obstacles, aerial-robot dynamics, formal safety guarantees, and hardware deployment remain future work. Full article

Lung cancer remains the leading cause of cancer-related mortality worldwide, largely due to late-stage diagnosis, biological heterogeneity, and persistent challenges in staging and treatment selection. This narrative review summarizes current and emerging applications of AI across lung cancer screening and early detection, imaging-based staging and prognostication, tissue and liquid biopsy-based tumor characterization, treatment planning, surgical and intraoperative guidance, and drug discovery. In imaging, deep learning models have demonstrated high performance in pulmonary nodule detection, risk stratification, and prediction of molecular alterations, while also showing promise in improving screening efficiency and reducing interpretive variability. In pathology and liquid biopsy domains, AI enables prediction of driver mutations, immunotherapy response, and survival outcomes directly from histopathology slides, circulating tumor DNA, and other blood-based biomarkers, facilitating minimally invasive precision oncology approaches. In treatment planning and delivery, AI systems are being developed to support clinical decision-making, surgical planning (through advanced image segmentation and delineation of operative anatomy), and intraoperative navigation through robotic and computer vision-enabled platforms. Despite these advances, significant barriers remain, including limited real-world validation, algorithmic biases, workflow integration issues, and unresolved ethical and legal concerns. Future progress will depend on the development of transparent, clinically validated, and generalizable AI systems that augment rather than replace the expertise of clinical providers and healthcare teams. Active engagement from pulmonologists, oncologists, radiologists, and thoracic surgeons will be essential in guiding safe implementation and ensuring that AI-driven innovations translate into meaningful improvements in patient outcomes. Full article

Background/Objectives: With increasing frequency in sublobar resections, accurate intraoperative localization has become essential to ensure adequate resection margins and spare lung parenchyma. Our study evaluates the efficacy of shape-sensing robotic bronchoscopy (SS-RAB) with integrated mobile cone-beam CT (mCBCT) for intraoperative localization of lung tumors using indocyanine green (ICG). We further aimed to explore the feasibility of a single intubation-single positioning technique for bronchoscopy and surgery. Methods: We retrospectively reviewed patients who underwent SS-RAB with integrated mCBCT for ICG marking, followed by minimally invasive sublobar resection. ICG marking was deemed successful when it allowed the operative team to localize and resect the lesion with adequate pathology margins. Results: A total of 28 patients with 30 pulmonary lesions from a single institution were included. Median tumor size was 10.5 mm (IQR, 8.7–14.6 mm) and distance from pleura 7.8 mm (IQR, 2.45–13.8 mm). Twenty lesions (66.6%) were solid, 5 lesions (16.6%) semi-solid, and 5 lesions (16.6%) ground-glass. ICG localization was successful in 28 lesions (93%). Nineteen patients (68%) were intubated only with a double-lumen endotracheal tube (DL-ETT), used for bronchoscopy and surgery, and in 10 patients (36%) ICG marking and surgery were both performed in lateral decubitus. One patient developed a small pneumothorax during bronchoscopy which did not prevent ICG injection. Conclusions: SS-RAB with integrated mCBCT for ICG marking is successful and safe. Single intubation with DL-ETT and lateral decubitus positioning for both bronchoscopy and surgery are feasible. Further studies are needed to prove a potential increase in efficiency with this technique. Full article