Search Results (4,238)

This article presents AgoraAI, an open-source framework designed to enable dynamic, multi-participant conversations by integrating Multi-Persona Orchestration within a shared conversational environment. Unlike traditional single-agent Large Language Model (LLM) interactions or passive commercial meeting assistants, AgoraAI allows users to configure distinct AI personas that engage in active facilitation and simultaneous, turn-based dialogues with human participants. The system supports diverse high-stakes use cases, including formal panel discussions and interactive educational settings. Crucially, this work addresses the engineering challenge of the “Concurrency-Coherence Paradox” in real-time voice systems. Key architectural contributions include: (1) the implementation of Asynchronous Dual-Queue Processing, a thread-safe integration strategy that synchronizes real-time Speech-to-Text streams with LLM generation to resolve race conditions; and (2) Dynamic Context-Injection pipelines that ensure persona consistency. The platform’s ecological validity is demonstrated through deployment in a human-supervised Master’s thesis seminar and a corporate coordination meeting. Results from an exploratory pilot study indicate high usability, perceived utility, and strong user acceptance. These findings suggest that AgoraAI provides a flexible, empirically evaluated architecture for democratizing multi-perspective collaboration across education, research, and professional domains. Full article

The construction of traditional underground utility tunnels faces prominent challenges, including high costs, long construction cycles, and limited workspace. Although 3D printing technology offers an effective solution to these issues, its practical application is largely constrained by key performance factors such as the printability, early strength, and interlayer bonding of concrete materials. This study aims to develop a 3D-printable concrete material specifically suited for the construction of underground utility tunnels. Through collaborative optimization of parameters such as the water–binder ratio, additives, and fiber content using single-factor and orthogonal tests, the optimal mix proportion was determined: a water–binder ratio of 0.30, a 10% dosage of rapid-hardening sulphoaluminate cement (R·SAC), a sand-to-binder ratio of 1.0, 20% mineral admixtures (15% fly ash + 5% silica fume), and a 1.0% volume fraction of polypropylene fibers. The results indicate that the fresh paste achieved a flowability of 192 mm, demonstrating excellent printability. Specimens printed using a sawtooth toolpath reached a 3-day compressive strength of 37.8 MPa, with 28-day compressive and flexural strengths increasing to 56.3 MPa and 7.8 MPa, respectively, and an interlayer bond strength of 3.5 MPa. Crucially, the compressive and flexural anisotropy coefficients were as low as 0.023 and 0.066, respectively, showing a preliminary exploratory trend superior to levels reported in some literature and suggesting the potential of printed components to improve structural performance consistency. This material system not only meets the requirements of 3D printing for early strength and workability but also, by introducing R·SAC to form a low-alkalinity binder system, provides a potential pathway for enhancing long-term durability in corrosive environments. This study offers a reliable theoretical and experimental basis for the application of 3D printing technology in underground engineering. Long-term durability will remain a primary focus of subsequent research. Full article

The rapid proliferation of distributed energy resources (DERs), including photovoltaics, wind power, battery energy storage, and electric vehicles, has transformed traditional power systems into highly decentralized and data-rich environments. Virtual power plants (VPPs) have emerged as a key mechanism for aggregating these heterogeneous assets and enabling coordinated control, market participation, and grid-support functions. Recent advances in artificial intelligence (AI) have further elevated the scalability, autonomy, and responsiveness of VPP operations. This paper presents a comprehensive review of AI for VPPs, organized around a taxonomy of machine learning, deep learning, reinforcement learning, and hybrid approaches, and examines how these methods map to core VPP functions such as forecasting, scheduling, market bidding, aggregation, and ancillary services. In parallel, we analyze enabling architectural frameworks—including centralized cloud, distributed edge, hybrid cloud–edge collaboration, and emerging 5G/LEO satellite communication infrastructures—that support real-time data exchange and scalable deployment of intelligent control. By integrating methodological, functional, and architectural perspectives, this review highlights the evolution of VPPs from rule-based coordination to intelligent, autonomous energy ecosystems. Key research challenges are identified in data quality, model interpretability, multi-agent scalability, cyber-physical resilience, and the integration of AI with digital twins and edge-native computation. These findings outline promising directions for next-generation intelligent VPPs capable of delivering secure, flexible, and self-optimizing DER aggregation at scale. Full article

Learning environments are increasingly expected to enable students to develop competencies necessary for addressing complex social, environmental, and technological challenges in sustainable societies. Within this context, instructional approaches that are inclusive, flexible, and learner-centered have gained increasing importance. This study investigates the effects of project-based learning (PBL) informed by Universal Design for Learning (UDL) principles on secondary school students’ 21st-century skills. Employing a mixed-methods embedded design, the quantitative component utilized a quasi-experimental pretest–posttest control group model. The study was conducted with 60 eleventh-grade students enrolled in a public high school, with one group receiving UDL-informed PBL instruction and the other following the standard curriculum. Data were collected using the 21st Century Learner Skills Usage Scale and analyzed through paired-samples t-tests, independent-samples t-tests, and ANCOVA. The findings revealed statistically significant improvements in the experimental group’s overall 21st-century skills, particularly in cognitive skills and collaboration and flexibility, with medium to large effect sizes. In contrast, the control group showed no meaningful gains, and a decline was observed in innovation skills. The results indicate that project-based learning informed by UDL principles constitutes an effective pedagogical approach for fostering inclusive and sustainable learning environments that support the development of future-oriented learner competencies. These findings further suggest that integrating UDL principles into project-based instructional models may contribute to competency-oriented and inclusive secondary education systems aligned with sustainability frameworks. Full article

To address the task-grouping problem for air–ground integrated Unmanned Aerial Vehicle (UAV) swarm missions in three-dimensional (3D) environments, this study proposes a data-preprocessing and hybrid initialization clustering method based on 3D spatial features. A dual-modal prototype meta-heuristic optimization model, Dual-Prototype Metaheuristic K-Means (DPM-Kmeans), is constructed accordingly. First, to overcome spatial information loss in high-dimensional task allocation, a 3D spatial task data preprocessing technique and a hybrid initialization strategy based on the golden spiral distribution are designed. This ensures the diversity and environmental adaptability of the initial solutions. Second, a dual-modal prototype optimization framework incorporating row prototypes (local refinement) and column prototypes (global combination) was constructed using meta-heuristics and clustering algorithms. The prototype-driven replacement update mechanism simultaneously performs global and local search, balancing the algorithm’s exploration and exploitation capabilities while expanding the solution space. This effectively addresses premature convergence issues in complex search spaces. Simultaneously, a collaborative multi-constraint, dynamically weighted optimization model was constructed, incorporating task requirements and flight distance constraints to ensure that the grouping scheme approximates the global optimum. Simulation results demonstrate that compared to traditional K-means and mainstream meta-heuristic optimization algorithms, DPM-Kmeans achieves an overall improvement of 2–10% in Sum of Squared Errors (SSE), Silhouette Coefficient (SC), and Davies–Bouldin Index (DB) metrics. It exhibits superior convergence speed and solution quality, proving the method’s excellent scalability and robustness in multi-constraint, large-scale 3D scenarios. Full article

The digital carbon footprint denotes the environmental impact generated by digital technologies throughout their lifecycle. Industry 4.0 manufacturing environments rely extensively on data processing, information storage, and artificial intelligence, thereby increasing energy demand and associated carbon emissions. These conditions have intensified interest in Green AI, particularly in applications such as predictive maintenance and collaborative human–machine systems. This research investigates determinants of behavioural intention to adopt Green AI through an extended Unified Theory of Acceptance and Use of Technology (UTAUT) model tailored to Industry 4.0 and sustainability contexts. The framework incorporates performance expectancy, Industry 4.0 eligibility, technology influence, digital manufacturing competence, sustainability conditions, Green AI recognition, and green manufacturing concern. Data were obtained from an anonymous survey of 1003 Generation Z students enrolled in technical disciplines and preparing for manufacturing-oriented careers. Relationships among constructs were analysed using partial least squares structural equation modelling (PLS-SEM). The model demonstrates strong explanatory and predictive capability. Adoption intention is primarily associated with performance expectancy, Industry 4.0 eligibility, and digital manufacturing competence, while sustainability-oriented perceptions play a contextual rather than direct behavioural role. The study offers a domain-specific empirical extension of UTAUT within pre-workforce technical education rather than proposing a new acceptance theory. The findings reflect intention formation prior to labour-market entry and require validation in operational manufacturing settings before broader generalisation. Full article

As robotic manipulators evolve toward lightweight and long-link structures, flexibility increasingly affects dynamic response and trajectory tracking accuracy. However, existing studies often lack a consistent coupling mechanism between finite element structural models and control models, and flexible effects are typically treated as disturbances, limiting the direct use of structural parameters for control prediction and optimization. This paper proposes a structure–control collaborative co-simulation framework for a six-degree-of-freedom (6-DOF) flexible-joint manipulator. ANSYS-based finite element analysis (FEA) is integrated with the MATLAB/Simulink control environment to extract joint-level equivalent stiffness, inertia, modal frequencies, and damping parameters, which are embedded into a rigid–flexible coupled dynamic model. A regression-based representation is introduced to capture unmodeled flexible residual dynamics, and a regression-compensated adaptive PID torque controller with σ-modification and a dead-zone mechanism is developed to ensure bounded adaptation and closed-loop stability. Simulation results under no-load and payload conditions demonstrate improved oscillation suppression and tracking accuracy. By establishing a unified coupling mechanism from structural parameters to the control model, the proposed method achieves consistent co-modeling of the structural and control domains and provides an engineering-feasible co-simulation approach for dynamic prediction and control optimization of multi-DOF flexible manipulators under varying operating conditions. Full article

In massively multiplayer online games (MMOGs), complex social environments exist in which cooperation is central not only to playing the game but also to experiencing it as an individual player. The growth of multiplayer games that emphasise cooperative activities in computer-based environments has sparked academic interest in collaboration and its role in the field, engaging scholars from domains such as human–computer interaction and digital entertainment. This paper presents a systematic literature review (SLR) and bibliometric analysis of 70 peer-reviewed journal papers published between 2015 and 2024. This data is derived from the Web of Science and Scopus databases. This literature review contributes to the understanding of collaborative factors in MMOGs, which include task interdependence, communication, trust, leadership, and player behaviour. The review is in the field using bibliometrics. To present the findings, we construct an input–process–output (IPO) model that links game features (inputs) and interaction dynamics (processes) to team performance and player experience (outputs) in MMOGs. This review maps the field’s dominant factors (task interdependence, communication, trust, leadership, and player behaviour), pinpoints methodological priorities, and sets a concrete agenda for future research on team collaboration in MMOGs. Full article

Accurate perception of a diver’s position and orientation by Autonomous Underwater Vehicles (AUVs) is essential for effective human–robot collaboration in underwater environments. However, conventional position and orientation estimation methods that combine deep learning with Perspective-n-Point (PnP) algorithms are primarily designed for rigid objects. In contrast, divers exhibit highly variable postures underwater, with no fixed configuration. To address this limitation, this paper proposes a framework for estimating the six-degree-of-freedom (6-DoF) position and the orientation of a diver. In addition, a novel network architecture, termed “VideoPose5CH,” is proposed. In the proposed framework, temporal sequences of 2D joint coordinates are provided to VideoPose5CH, which then outputs the 3D joint coordinates of the current frame as well as the corresponding refined 2D joint locations. Subsequently, the diver’s 6-DoF position and orientation relative to the camera are further recovered via a PnP algorithm. To mitigate the scarcity of underwater 3D human pose datasets, a land-based 3D human pose dataset augmentation strategy tailored to underwater conditions is further proposed. With this strategy, diver pose estimation accuracy is improved and the robustness of the proposed method across diverse scenarios is enhanced. Experimental results demonstrate that the proposed method can stably estimate the 6-DoF position and orientation of the diver within a distance range of 2.643 m to 11.477 m. The average position errors along the three axes are 7.33 cm, 4.04 cm, and 27.15 cm, respectively, while the average orientation errors are 6.96°, 8.47°, and 2.62°. Full article

The decarbonization of shipping and the transformation towards digitally assisted or automated ship operation require new methods to analyze, predict, and optimize energy demand during maneuvering. The SimPleShip-SigMa sub-project of Hochschule Wismar developed and validated a comprehensive simulation-based framework combining real-time capable fast-time simulation of ship motion, detailed thermodynamic engine modeling, and hybrid data exchange via Functional Mock-up Units (FMU/FMI). The approach enables consistent coupling between navigation-related and machinery-related simulations and supports energy-optimized decision-making on the bridge. Operational relevance and validation of use cases were supported through collaboration with Carnival Maritime GmbH, providing practical feedback on large passenger-ship operations. The study presents the architecture of the simulation environment, the implementation of energy- and emission-prediction models, and the result of validation runs and simulator-based trials. The developed method was applied to a virtual cruise-ship scenario representing a confined coastal environment similar to the Geiranger Fjord. The work builds upon earlier research on simulation-augmented maneuvering and extends it toward a modular digital-twin concept linking hydrodynamic and thermodynamic models. The paper concludes with an outlook on applying the system for crew training, on-board support, and gradual automation of sustainable ship operations. Full article

Deep-sea exploration increasingly relies on Autonomous Underwater Vehicles (AUVs) to enable persistent, wide-area surveying in harsh and uncertain environments. In practice, however, deployments are constrained by tight energy budgets and bandwidth-limited, intermittent acoustic links, which complicate mission-level coordination. Moreover, many existing systems treat perception and control as loosely coupled modules, often resulting in redundant sensing, inefficient communication, and degraded overall performance—particularly under heterogeneous sensing modalities and shifting geological conditions. To address these challenges, we propose a hierarchical Federated Meta-Transfer Learning (FMTL) framework that tightly integrates collaborative perception with adaptive control for swarm optimization. The framework operates at three levels: (1) Representation Learning aligns heterogeneous sensors in a shared latent space via a physics-informed contrastive objective, substantially reducing communication overhead; (2) Meta-Learning Adaptation enables rapid transfer and convergence in new environments with minimal data exchange; and (3) Energy-Aware Control realizes closed-loop exploration by coupling Federated Explainable AI (FXAI) with decentralized multi-agent reinforcement learning (MARL) for path planning under energy constraints. Validated in high-fidelity hardware-in-the-loop simulations and a digital-twin environment, FMTL outperforms state-of-the-art baselines, achieving an AUC of 0.94 for target identification. Furthermore, an energy–intelligence Pareto analysis demonstrates a 4.5× improvement in information gain per Joule. Overall, this work provides a physically consistent and communication-efficient blueprint for the optimization and control of next-generation intelligent marine swarms. Full article

This work presents a novel soft gripper concept featuring integrated force feedback and a compact, resource-efficient geometry. The gripper is designed to provide a low-cost, adaptable, and precise solution for manipulating delicate and irregularly shaped objects. By embedding force feedback directly into the structure, the system reliably detects contact and enables controlled, gentle gripping of fragile items. The design was developed for collaborative and assistive robotic applications, where safety and human–robot interaction are prioritized. The prototype is fabricated using consumer-grade 3D-printed components and employs a simple cable-driven actuation system. The hybrid soft–rigid architecture combines compliant fingers with a rigid, sensorized thumb, preserving the adaptive grasping characteristics of soft robotics while simplifying sensing integration and construction. A motor-based control mechanism synchronizes finger motion through cable traction, ensuring reliable and repeatable performance. Experimental evaluations demonstrate secure, damage-free handling across diverse object types, highlighting the gripper’s potential in assistive robotics, cobot environments, biomedical contexts, and other domains requiring safe and delicate manipulation. Full article

Maritime dynamic observation missions, such as environmental monitoring, marine ranching inspection, and emergency response, typically require large-scale and high-efficiency operations in complex and variable maritime environments. Unmanned Surface Vehicles (USVs) and Unmanned Aerial Vehicles (UAVs) offer complementary advantages in such missions: USVs provide long endurance and stable platform support, while UAVs enable rapid, high-coverage aerial perception. However, limited UAV battery capacity and dynamic task environments pose significant challenges to autonomous collaborative operations. This study proposes a collaborative operation and energy replenishment strategy for USV–UAV systems in maritime dynamic observation missions. Under a unified framework, task allocation, collaborative path planning, and energy replenishment are jointly optimized, where the USV serves as a mobile replenishment platform to provide energy support for the UAV. The proposed method incorporates dynamic task updates, environmental disturbances, and energy constraints, achieving real-time adaptive collaboration between heterogeneous agents. Validation through both simulations and actual sea trials demonstrates that the proposed strategy significantly outperforms four baseline methods (greedy strategy, static planning, multi-objective genetic algorithm, and reinforcement learning scheduler) across five core metrics: task completion rate (91.74% in simulation/90.85% in sea trials), total energy consumption (1284.66 kJ/1298.42 kJ), mission completion time (40.28 min/41.12 min), average response time (10.21 s/10.35 s), and path redundancy (13.79%/14.03%). Furthermore, ablation experiments verify that the energy replenishment strategy enhances the task completion rate in both simulation and field tests. This method provides a feasible and scalable collaborative solution for autonomous multi-agent systems, offering significant guidance for the practical deployment of future maritime observation and monitoring missions. Full article

Design Thinking (DT) has been widely promoted as a creative, human-centred approach for engaging students with real-world problems. Yet, research consistently shows that DT in mainstream schooling often struggles with ambiguity, superficial engagement with socio-scientific issues, weak integration of disciplinary knowledge, and epistemological tensions with school learning. In this paper, we examine whether DT can become more effective and educationally meaningful when enacted through a socio-constructionist environment using digital media as both design tools and design products. Drawing on a school-based intervention with 70 students using ChoiCo—an open-source digital authoring system for creating socio-scientific games—we analysed critical incidents of student interaction to explore how constructionist digital media mediate reasoning, collaboration, and conceptual development. Our findings show that ChoiCo supports conceptual clarity, iterative refinement, and epistemic grounding by requiring students to encode ideas into rules, thresholds, and consequences. The system’s malleability and embedded feedback align with a special socio-constructionist DT model developed through a multi-organisational European Research and Innovation Project ExtenDT2, enabling rapid prototyping and collaborative meaning-making. We argue that socio-constructionist DT offers a promising way to address long-standing shortcomings of DT in education, shifting the focus from producing polished artefacts to engaging in meaningful, iterative, and epistemically rich design activity. Implications for curriculum design, teacher practice, and the integration of constructionist digital media in DT pedagogy are discussed. Full article

(1) Background: The complexity of today’s healthcare system requires the formation of highly cohesive work teams that guarantee safe and high-quality care. Clinical simulation has become established as a pedagogical strategy capable of promoting the collaborative skills of teams of students and healthcare professionals. The objective of this study was to analyze the influence of learning through clinical simulation on group cohesion in nursing student teams. (2) Methods: A pre–post quasi-experimental study without a control group was conducted with final-year nursing students using the short Spanish version of the Group Environment Questionnaire, validated for nursing students. This questionnaire was administered twice, before and after participation in clinical simulation sessions. (3) Results: Clinical simulation significantly increased group cohesion in most items and in all dimensions with moderate to large effect sizes (r > 0.5). The Group Integration-Task (GI-T) dimension showed the greatest improvement after clinical simulation. Although causal relationships cannot be established, the results suggest an association between exposure to clinical simulation and increased group cohesion. (4) Conclusions: Clinical simulation was associated with significant improvements in both task-oriented and social dimensions of group cohesion among nursing students. These findings suggest that clinical simulation may enhance collaboration, communication, and commitment to shared goals within student teams. Future studies including control groups are needed to confirm these associations and further explore the impact of clinical simulation on team performance in both student and healthcare professional contexts. Full article