Search Results (399)

Background: Multimodal large language models (MLLMs) show promise in medical imaging, but their performance is highly dependent on prompt engineering. This study systematically evaluates how different prompting strategies affect diagnostic accuracy in clinical laboratory image interpretation. Methods: We evaluated five MLLMs (ChatGPT-4o, Gemini 2.0 Flash, Claude 3.5 Sonnet, Grok-2, and Perplexity Pro (Claude 3.5 Sonnet)) using 177 proficiency testing images across three domains: blood smears (n = 78), urinalysis (n = 50), and parasitology (n = 49). Three prompting approaches were compared: (1) complex multi-choice prompts with 20 diagnostic options, (2) zero-shot open-ended prompts, and (3) two-step descriptive-reasoning prompts. Images were sourced from the Taiwan Society of Laboratory Medicine external quality assurance archives with expert consensus diagnoses. Results: Zero-shot prompting significantly outperformed complex multi-choice prompts across all models and domains (p < 0.001). With zero-shot prompts, Gemini achieved 78.5% overall accuracy (urinalysis: 92.0%; parasitology: 75.5%; blood smears: 64.1%), representing a 17% improvement over complex prompts. Two-step descriptive-reasoning prompts further improved blood smear accuracy by 8–12% for top-performing models, but showed minimal benefit in urinalysis and parasitology. The re-query mechanism (“please reconsider”) improved urinalysis accuracy by 7.6% but had a negligible effect on blood smears and parasitology. Conclusions: Prompting strategy critically determines MLLM diagnostic performance. Zero-shot approaches with minimal constraints consistently outperform complex multi-choice formats. The remarkable performance of general-purpose models in structured domains like urinalysis (>90% accuracy) demonstrates the considerable progress of multimodal AI. However, complex morphological tasks like blood smear interpretation require either specialized prompting techniques or domain-specific fine-tuning. These findings provide evidence-based guidance for optimizing AI integration in clinical laboratories. Full article

Unmanned Aerial Vehicles (UAVs) are complex nonlinear systems characterized by high dimensionality. They are prone to aerodynamic effects, structural dynamics, actuation constraints, and networked interactions, requiring advanced mathematical models and precise control. Their governing equations involve nonlinear rigid-body dynamics coupled with fluid and elasticity models, while modern architectures introduce redundancy that creates constrained mappings between generalized forces and actuator inputs. Coordinated UAV teams add another layer of mathematical structure through graph-based interaction models that determine consensus, formation keeping, and distributed stability. These characteristics give rise to several interconnected challenges. High-fidelity aerodynamic and aeroelastic solvers provide accurate results; however, these are computationally intensive, motivating the development of reduced-order models and data-driven approximations that preserve dominant physical behavior. Methods for quantifying uncertainty support robustness assessments by characterizing the effects of parametric variation and model form error. At the actuation level, control allocation problems rely on constrained linear algebra, convex optimization, and dynamic formulations to ensure feasible and stable realization of command forces and moments. In multi-agent systems, the spectral properties of adjacency and Laplacian matrices govern convergence and cooperative behavior. This article reviews the state of the art in these areas, highlights the mathematical foundations that relate them, and provides a coherent perspective on the methods that enable reliable modeling and control of modern UAV systems. Full article

Formation control of unmanned surface vehicles (USVs) in complex marine environments is required to contend with strongly coupled, high-dimensional disturbances. A Multi-Agent Active Disturbance Rejection Control (MAADRC) framework is developed for this purpose. The design centers on a distributed extended state observer (DESO) coupled with a dual-channel feedback structure—NEFL-GCO and LGL-FC—that collectively maintains formation geometry. Three main ideas underpin the approach. First, a bandwidth-efficient distributed observation scheme enables agents to share disturbance estimates while using substantially less communication bandwidth. Second, an adaptive consensus compensation mechanism accommodates parameter variations as formations evolve. Third, a formation-compatible obstacle avoidance algorithm enhances reliability in congested waters. To evaluate the control structure and optimize its parameters, a multi-agent reinforcement learning (MARL) method—specifically Multi-Agent Proximal Policy Optimization (MAPPO)—is employed. The MARL agent tunes two critical parameters: observer bandwidth and nonlinear feedback gain, thereby establishing a performance baseline. After ten million training steps, the MAPPO-optimized MAADRC achieves a tracking root-mean-square error (RMSE) of 1.18 m. This value lies within 3% of the manually tuned result of 1.21 m, indicating that the bandwidth parameterization is near-optimal. Extensive simulations incorporating realistic wind, wave and current disturbances demonstrate a dynamic obstacle avoidance success rate maintaining an expected level, alongside consistently low formation tracking errors. Collectively, these findings confirm the resilience and practical utility of the proposed framework in demanding maritime settings. Full article

As industries deploy more connected devices in factories, warehouses, and smart facilities, the need for artificial intelligence (AI) systems that can operate securely in distributed, data-intensive environments is growing. Traditional centralized learning and online education platforms struggle when students and systems have to process real-time streams (sensors, video, text) with strict latency and privacy requirements. To address this challenge, a blockchain-secured, edge-enabled multimodal federated learning framework tailored for Industrial IoT (IIoT) environments is proposed. The model integrates four key layers: (i) a blockchain layer that provides credentialing, transparency, and token-based incentives; (ii) a multimodal community layer that supports group formation, peer consensus, and cross-modal learning across text, images, audio, and sensor data; (iii) an edge computing layer that enables low-latency task offloading and secure training within Intel SGX enclaves; and (iv) a data layer that applies pre-processing, differential privacy, and synthetic augmentation to safeguard sensitive information. Experiments on industrial multimodal datasets demonstrate 42% faster model aggregation, 78.9% multimodal accuracy, and 1.9% accuracy loss under ε = 1.0 differential privacy. This shows a scalable and practical path for decentralized AI training in next-generation IIoT systems, confirming the possibility of technical support for educational processes. However, the conducted research requires a validation of pedagogical effectiveness. Full article

To facilitate the practical deployment and engineering implementation of multi-robot coordination for biomimetic underwater spherical robots (BUSRs), it is imperative to develop a formation tracking control method with a simple structure, a small number of tunable parameters, convenient parameter tuning and strong anti-disturbance capability. This study proposes a formation controller integrating virtual structure (VS), consensus protocol, and parallel output-velocity-type active disturbance rejection control (POV-ADRC), denoted as VS-C-POV-ADRC. A rotating global (RG) coordinate system is established to decouple robot positions from heading angles, which makes the parameter tuning more convenient. A double-loop control architecture is constructed, where the outer consensus control loop generates the desired velocity for each robot based on virtual-structure reference positions, and the inner POV-ADRC loop achieves high-precision velocity tracking. The proposed controller features a compact structure with only five adjustable parameters per motion direction, realizing easy engineering implementation and adaptation to the limited computing capacity of BUSRs. The simulation and experiment results demonstrate that the proposed algorithm enables robots to maintain a stable formation and achieve trajectory tracking accuracy within one body length, while exhibiting superior disturbance rejection. The proposed method provides a feasible and practical solution for BUSR formation control. Full article

Plant viruses from the Potyviridae family have a significant impact on crop productivity worldwide. We conducted a bioinformatic analysis of the VPg sequences from several members of the Potyviridae family. All analyzed primary structures of VPg contain an invariant arginine, which, according to the model we proposed earlier, is located in the functionally important α1–α2 hairpin of the viral protein and forms a recognition contact during the formation of its complex with the eIF4E host cell. Among the amino acid mutations observed in the sequences of VPg PVY, we separately considered those associated with adaptation to the host plant. Several strain-specific mutations were identified, the functional roles of which are currently unclear. For each of the Potyviridae species considered, a consensus VPg sequence was determined. 3D-models of the corresponding proteins were constructed by de novo molecular modelling using the consensus amino acid sequences. Cross-comparative analysis of the theoretical models and the experimental VPg PVY structure obtained by NMR showed that all these proteins share a high degree of structural homology and contain the conserved arginine within the α1–α2 hairpin. However, the spatial position of this arginine may vary across models, which apparently reflects species-specific differences in the VPg recognition module. Full article

In this work, an adaptive event-triggered-based consensus control strategy is proposed for the quadrotor unmanned aerial vehicle (QUAV) formation system in the presence of external disturbances and state constraints. Firstly, the disturbed QUAV formation system dynamic model is established. Then, to address the initial peaking explosion problem in the traditional active disturbance rejection control method, a time-varying gain extended state observer (TGESO) is designed to suppress external disturbances. Meanwhile, a novel barrier Lyapunov function (BLF) is constructed to cope with the adverse effects caused by state constraints. Furthermore, aiming to alleviate network congestion and reduce communication burden, the adaptive event-triggered mechanism (AETM) is adopted to design the formation flight controller. Finally, the stability of the developed consensus controller and the boundedness of all error signals are proved via Lyapunov theory. Comparative simulation results demonstrate the practicality of the presented control algorithm. Full article

To advance research in multi-agent reinforcement learning (MARL) for pursuit–evasion scenarios, this paper introduces a novel algorithm called Expert Knowledge and Opponent Modeling Multi-UAV Deep Deterministic Policy Gradient (EO-MADDPG). EO-MADDPG consists of two key components: the integration of expert knowledge and real-time sampled data and the prediction of evader UAV actions. The expert knowledge includes a multi-UAV formation control algorithm and an encirclement strategy, which incorporates consensus algorithms and Apollonius circle guidance. Additionally, the network-training framework is optimized by integrating information about opponent actions under a fixed policy for improved prediction accuracy. The experiments focus on three vs. one and three vs. two scenarios, where pursuer UAVs utilize EO-MADDPG and evader UAVs follow fixed policies with Gaussian perturbations. Experimental results show that EO-MADDPG achieves success rates of 99.9 ± 0.3% and 97.5 ± 1.4% (mean ± std over five seeds) in three vs. one and three vs. two pursuit–evasion simulations, respectively, outperforming the baseline MADDPG (72.7 ± 6.0% and 64.4 ± 34.4%). Ablation studies and cooperative landmark tasks further demonstrate improved training stability and interpretability. Full article

This paper investigates the consensus problem for a class of uncertain nonlinear multi-agent systems (MASs) subject to external disturbances with unknown control directions (UCDs). A novel control scheme integrating Nussbaum-type gain is proposed to actively compensate for UCDs, while fuzzy logic systems (FLSs) are embedded in a feed-forward compensator to approximate unknown nonlinear dynamics, thereby achieving global stability. The proposed distributed control laws ensure global asymptotic convergence for both first- and second-order MASs through Lyapunov stability analysis. By implementing a strategic reparameterization technique, this scheme systematically reduces computational complexity, requiring each agent to adapt only a minimal parameter set. Moreover, the framework is extended to address complex formation control tasks. Comprehensive simulations validate the efficacy of the theoretical findings. Full article

Despite consensus in the mathematics education literature regarding the mutually dependent components of math proficiency, as well as the importance of their development, most elementary-aged students in the United States demonstrate a lack of proficiency in math according to national assessment data. Whole number knowledge, which includes skills in computation and word-problem solving, is understood to be a critical foundation for the development of later math skills. This study used a multiple-baseline experimental design to evaluate the impacts of an evidence-based mathematics intervention, Accelerating Mathematics Performance with Practice Strategies (AMPPS), on third- through fifth-grade students’ skills with complex computation, as well as on their word-problem-solving performance. Furthermore, we evaluated effects on students’ math self-concept. Five students identified to have difficulties in math received AMPPS in a one-on-one, in-person format. The results of the study were mixed. For example, when using visual analyses as our primary analytic method, these analyses did not show robust intervention effects on students’ computation skills but did show at least some improvement for most students’ word-problem-solving skills. Additionally, supplemental analyses comparing student growth to national and school-based norms suggested that all participants seemed to benefit from the intervention, but these analyses were not intended to examine experimental causality. Despite study limitations and a lower than optimal number of AMPPS sessions (dosage) provided to students, the present study offers several directions for future research, as well as possible implications for practitioners regarding intervention selection, intensity, and evaluation. The findings will also be discussed in the context of conducting systematic replication studies, which are essential for understanding the generality of a given phenomenon (e.g., an effect of a school-based intervention) across a wide range of situations and conditions. Full article

This paper addresses distributed formation control for multiple unmanned aerial vehicles (UAVs) operating in obstacle-dense environments under directed switching communication topologies. A leader–follower architecture is adopted, wherein the leader performs online trajectory replanning while followers rely on delayed and intermittently available neighbor information. To simultaneously tackle collision avoidance, formation feasibility under narrow passages, and communication intermittency, we propose an integrated deformable formation navigation framework. The framework couples Safe Flight Corridor (SFC)-constrained Bézier trajectory planning with a dynamic formation scaling mechanism, allowing the swarm to adaptively shrink or expand its geometric configuration when traversing constricted spaces, thereby ensuring all agents remain within certified collision-free corridors. A nonlinear distributed consensus-based estimator is designed to propagate leader reference states under directed switching graphs with bounded delays. Using a max-min contraction analytical approach, we establish guaranteed practical convergence for both leader tracking and inter-follower agreement without requiring persistent connectivity. Extensive simulations in complex cluttered environments demonstrate that the proposed approach enables flexible and real-time formation reshaping, enhancing navigational safety and robustness while maintaining cohesive swarm behavior under challenging communication and spatial constraints. Full article

This study develops an integrated computational model to illuminate the micro-dynamics through which transient emotional contagion evolves into stable social identity within organizations, with a specific focus on hybrid work environments. Drawing on organizational psychology and employing an agent-based modeling approach, we formalize a four-stage process—Emotional Cycle, Emotional Memory Accumulation, Cognitive Formation, and Enhancement Effect—that captures how fleeting affective experiences coalesce into enduring group identification. Our simulations reveal that cognitive heterogeneity moderates this pathway, leading to slower but more robust identity formation. Gender differences emerge as significant, with females demonstrating higher susceptibility to emotional contagion, while males’ identification is more strongly influenced by issue relevance. Crucially, exploratory simulations contrasting high- and low-hybridity configurations demonstrate that dispersed, digitally mediated work attenuates the emotional feedback loop, slows consensus formation, and heightens the risk of sub-group silos, thereby fundamentally reshaping the identity formation pathway. This research provides a mechanistic explanation of the emotional foundations of organizational culture and offers managers an evidence-based, dynamic framework for strategically cultivating collective identity in an increasingly hybrid world. Full article

Blockchain technology, with special features of decentralization, immutability, consensus mechanisms, and smart contracts, has been integrated into different areas of digital applications recently. However, its abstract concepts present a steep learning curve for beginners, especially in the absence of online resources that offer dynamic, hands-on learning experiences. In response to this problem, we developed a digital interactive teaching tool using the OwlSpace platform to explain what blockchain truly is in its four core foundational concepts. Interactive operations, guided workflows, and visual simulations are applied in the system to assist the learner in interpreting decentralized architectures, immutability of data interactively, the consensus formation process, and the mechanics behind smart contract operation. The system has also put a focus on conceptual understanding and gamified experiences rather than competitive ones, providing a practical and engineering-focused tool for introductory information engineering students. Full article

Global climate policy requires constant attention due to shifting interests and alliances between national negotiators. Whether represented at global or national scales, three universal features of fused climate policy conjoin the wealthy and emerging G20 economies that are historically responsible for the most greenhouse gas emissions. The former are represented by G7 Western powers—the United States, Europe, United Kingdom, Japan, and Canada—and the latter are centered on the fast-expanding ‘BRICS’ bloc: Brazil–Russia–India–China–South Africa (2010–2023), new members Egypt, Ethiopia, Indonesia, Iran, and the United Arab Emirates, and potentially also Saudi Arabia (a member invitee), along with ten new ‘partners’ designated in 2024, many of which have carbon-intensive economies. Although conflicts regularly arise—especially over emissions-related trade policy and climate financing—and although Donald Trump’s exit from United Nations climate politics profoundly disrupted the usually coherent G7 bloc, the consensual principles uniting these diverse Western and BRICS governments at multilateral climate summits include the following: (1) not cutting corporate, state, and household emissions to the extent necessary for avoiding unmanageable planetary disasters, in the process denying effective ways of leaving fossil fuels underground (by reimbursing poor countries); (2) not pricing carbon properly or acknowledging their economies’ ‘climate debt’; and (3) instead promoting carbon trading and offset mechanisms. The implications are important for alliance-formation involving climate-victimized, low-income countries and climate justice activists, alike. In sum, there is an increasingly urgent rationale to transcend ‘Global North’ and ‘Global South’ dichotomies and instead consider climate (like many other aspects of G7-BRICS relations) with a perspective open to critique of the imperial–subimperial fusions, not only oft-assumed frictions. Full article

Background: Metastasis and recurrence account for the failure of nasopharyngeal carcinoma (NPC) treatment. Growing evidence indicates the dominant roles of cancer stem cells (CSCs) in tumor progression and therapy resistance. However, the heterogeneity of CSCs and potential stemness-related markers in NPC patients are still largely unknown. Methods: Consensus clustering was first applied to identify robust stemness subtypes for NPC patients based on the activities of stem cell gene sets. The differences in clinical outcomes, tumor immune microenvironment (TIME), and drug response were compared between subtypes. The stemness-related markers were prioritized via weighted gene correlation network analysis (WGCNA) and Cox regression, and verified through in vitro experiments. Results: NPC patients were classified into C1 and C2 subtypes. The C2 subtype exhibited higher activities of stem cell gene sets, worse prognosis, and aggressive tumor progression thus defined as stem cell-like tumor phenotype. The exclusionary relationships between tumor stemness and TIME infiltration were observed. The efficacy of several drugs and immunotherapy varied between NPC stemness subtypes. Through the WGCNA and survival analysis, we found that PSMC3IP, NABP2, CDC45, and HJURP were stemness-relevant genes. Sphere formation assays and analysis of the protein expression of stem cell markers by Western blotting revealed the roles of PSMC3IP, NABP2, CDC45, and HJURP in promoting CSC properties. Moreover, these genes were found to be related to the therapeutic effect of telomerase inhibitor in CCK8 experiments. Conclusions: This study systematically characterized two NPC subtypes with distinct stemness features, clinical outcomes, and TIME features. Novel stemness-related markers will provide valuable targets against metastatic or recurrent NPC. Full article