This paper presents an edge-deployable vision-based framework for human–robot interaction using a xArm collaborative robot and a single RGB camera mounted on the robot wrist, and lightweight AI-based perception modules. The system enables intuitive, contact-free control by combining hand understanding and object detection within a unified perception–decision–control pipeline. Hand landmarks are extracted using MediaPipe Hands, from which continuous hand trajectories, static gestures, and dynamic gestures are derived. Task objects are detected using a YOLO-based model, and both hand and object observations are mapped into the robot workspace using ArUco-based planar calibration. To ensure stable robot motion, the hand control signal is smoothed using low-pass and Kalman filtering, while dynamic gestures such as waving are recognized using a lightweight LSTM classifier. The complete pipeline runs locally on edge hardware, specifically NVIDIA Jetson Orin Nano and Raspberry Pi 5 with a Hailo AI accelerator. Experimental evaluation includes trajectory stability, gesture recognition reliability, and runtime performance on both platforms. Results show that filtering significantly reduces hand-tracking jitter, gesture recognition provides stable command states for control, and both edge devices support real-time operation, with Jetson achieving consistently lower runtime than Raspberry Pi. The proposed system demonstrates the feasibility of low-cost edge AI solutions for responsive and practical human–robot interaction in collaborative industrial environments.

Infectious disease outbreaks continue to pose a significant threat to global health, underscoring the importance of timely detection and reliable reporting for effective interventions. Traditional reporting systems often rely on hierarchical data flows, which introduce delays, inconsistencies, and vulnerabilities, as highlighted during the COVID-19 pandemic. Blockchain, a disruptive technology, offers a promising solution. This study proposes a blockchain-based infectious disease reporting system built on Hyperledger Fabric that supports multi-level reporting and governance across national health systems. The architecture preserves hierarchical structures while enabling real-time reporting across authorized health stakeholders. It separates public test results from sensitive patient information, with private data secured via Private Data Collections and anchored using cryptographic hashes. Smart contracts enforce role-based access and validation, ensuring data integrity and controlled oversight. The system prototype was deployed within Docker containers and evaluated using illustrative COVID-19 case data. Network performance was benchmarked using Hyperledger Caliper, measuring throughput, latency, and resource utilization. The results demonstrate proper system functioning and stable transaction processing under the tested experimental conditions, supporting the feasibility of the proposed architecture for privacy-preserving multi-level infectious disease reporting systems.

Generative no-code development tools enable users to create applications directly from natural-language prompts, shifting interface design from manual construction to AI-mediated generation. However, identical prompts frequently produce substantially different user interface (UI) outcomes across tools and even across repeated executions within the same tool. This paper presents a systematic literature review examining how generative no-code systems make design and aesthetic decisions with respect to layout structure, visual consistency, usability, accessibility, and reproducibility. Twenty peer-reviewed studies (2021–2025) were analyzed following a structured review protocol. Existing research predominantly evaluates usability and accessibility in isolation while providing limited insight into aesthetic coherence, design variability, and prompt-to-output stability. Across studies, generative tools exhibit implicit design priors and stochastic behavior that lead to inconsistent visual outcomes and partial misalignment with human-centered design principles. These findings indicate that generative no-code tools do not act as deterministic translators of user intent but instead introduce their own stylistic tendencies. The paper identifies critical evaluation gaps and outlines requirements for future systems, including reproducible generation, transparent design reasoning, and user-directed control, to support reliable and predictable interface development.

Guaranteeing equitable access to computational thinking (CT) remains a persistent challenge in computing education, particularly across socioeconomically diverse school contexts. Although prior research has demonstrated the effectiveness of block-based and physical computing environments, limited empirical evidence has examined whether structured instructional mediation can compensate for contextual disparities. This quasi-experimental pre–post study addresses this gap by analyzing CT development in three socioeconomically diverse primary schools in Chile (N=88, third grade), including private urban, public urban, and rural public institutions. Students engaged in scaffolded Scratch programming and Arduino simulation activities designed to explicitly support abstraction, sequencing, and debugging processes. These activities were framed within a broader STEAM learning approach, integrating computational thinking with problem-solving, experimentation, and interdisciplinary reasoning. Statistical analysis revealed significant differences in instructional time across contexts (F(2,85)=14.62, p<0.001, η2=0.26), indicating structural disparities in pacing. However, no statistically significant differences were observed in CT gains (F(2,85)=0.31, p=0.74), suggesting that structured pedagogical scaffolding buffered contextual inequalities. These findings provide empirical evidence from a Latin American non-WEIRD context and advance the conceptualization of instructional mediation as a compensatory mechanism for equity in early computing education. This study contributes to digital equity research by demonstrating that instructional design quality may play a more decisive role than infrastructural availability in enabling computational thinking development for all learners.

LiDAR point cloud semantic segmentation is essential for autonomous driving, yet LiDAR-only methods remain constrained by sparsity and limited texture cues. We propose Cross-Modal Collaborative Manifold Distillation (CMCMD), which transfers open-world semantic priors from the DINOv3 Vision Foundation Model to a LiDAR student network. The framework combines an Adaptive Relation Convolution (ARConv) backbone with geometry-conditioned aggregation, a Unified Bidirectional Mapping Module (UBMM) for explicit 2D–3D interaction, and Manifold-Aware Topological Distillation (MATD), which aligns inter-sample affinity structures in a shared latent manifold rather than enforcing pointwise feature matching. By preserving relational topology instead of absolute feature coordinates, CMCMD mitigates negative transfer across heterogeneous modalities. Experiments on SemanticKITTI and nuScenes yield mIoU values of 72.9% and 81.2%, respectively, surpassing the compared distillation baselines and approaching the performance of multimodal fusion methods at lower inference cost. Additional evaluation on real-world campus scenes further supports the cross-domain robustness of the proposed framework.

The rapid development of quantum computing poses severe threats to traditional blockchain security mechanisms, while existing full-quantum blockchains face challenges regarding high hardware costs and limited scalability. To address these issues, this paper proposes a secure and practical semi-quantum blockchain system. Specifically, a Semi-Quantum Delegated Proof of Stake consensus mechanism is constructed by integrating an adapted semi-quantum voting protocol with the Borda count method and a malicious behavior penalty model. Furthermore, a lightweight transaction verification framework is designed based on semi-quantum key distribution, enabling classical users with limited quantum capabilities to participate securely. Theoretical analysis demonstrates that the system achieves unconditional security against quantum attacks while maintaining high throughput. These results indicate that the proposed asymmetric resource design significantly lowers hardware barriers compared to full-quantum schemes, effectively balancing security, practicality, and cost-effectiveness for post-quantum blockchain networks.

Access control systems rely increasingly on multimodal biometric and behavioral signals to enhance security and robustness against sophisticated attacks. However, when heterogeneous modalities provide conflicting evidence, such as valid biometric credentials accompanied by abnormal behavioral or acoustic patterns, traditional fusion strategies based on static thresholds or majority voting often fail, leading to false alarms or insecure authorization decisions. This paper addresses this critical limitation by proposing a contextual decision-making fusion framework designed to resolve conflicting multimodal evidence at the decision-making level. The proposed approach models access control as a decision-making problem in a context of uncertainty, where independent agents generate modality-specific evidence from authentication channels based on face, voice, and fingerprints. A centralized fusion mechanism integrates heterogeneous results using adaptive reliability weighting and contextual reasoning to resolve conflicts before operational decisions are made. Rather than treating each modality independently, the framework explicitly considers inconsistencies, uncertainties, and situational context when aggregating evidence. The framework is evaluated using public benchmarks, including VGGFace2, VoxCeleb2, and FVC2004, combined with controlled multimodal scenarios that induce conflicting evidence. Experimental results obtained under controlled contradiction scenarios show that the proposed fusion strategy reduces false alarms and improves decision consistency by approximately 18%. These results are interpreted within the scope of controlled multimodal simulations.

Hypertension is a leading global health risk and requires accurate and continuous monitoring for effective management. Although photoplethysmography (PPG) is a promising non-invasive modality for cuffless blood pressure (BP) assessment, many existing approaches (especially raw-signal deep learning) are vulnerable to data leakage, overfitting on small datasets, limited interpretability, and poor performance on minority BP stages. To address these limitations, we propose a robust and physiologically grounded framework for multi-class BP stage classification based on interpretable PPG features. Our approach centers on a comprehensive multi-domain feature engineering pipeline that extracts 124 PPG features, including demographic, morphological, functional decomposition, spectral, nonlinear dynamics, and clinical composite indices. We apply rigorous preprocessing and feature selection prior to model training. We validate the framework on two datasets: PPG-BP dataset (657 segments, 4 classes) for benchmarking and PulseDB (283,773 segments, 3 classes) to assess scalability. We evaluate the proposed framework using a segment-level train/test split, appropriate for assessing intra-subject BP tracking after initial personalization. For the PulseDB dataset, this follows the protocol established by the dataset creators, while for the PPG-BP dataset, it enables direct comparison with prior work given practical dataset constraints. On PPG-BP, LightGBM trained on the selected features achieved macro-F1 = 0.78 and accuracy = 0.74, outperforming comparable deep-learning models. On the PulseDB, a custom Residual MLP achieved accuracy = 0.81 and macro-F1 = 0.79, supporting generalization at scale. These results show that the proposed feature-based approach can outperform complex end-to-end deep-learning models on small datasets while providing improved interpretability. This work establishes a reliable and transparent pathway toward clinically viable continuous BP staging, moving beyond black-box models toward physiologically grounded decision support. Ablation analysis reveals that engineered features provide most of the predictive power (F1 = 0.911), while raw PPG features alone achieve modest performance (F1 = 0.384). For the minority hypertension stage 2 (HT-2) class, a bootstrap 95% confidence interval of [0.762, 1.000] is reported, reflecting uncertainty due to limited sample size.

When LLMs are applied in the veterinary field, they often produce serious hallucinations and logical restrictions, especially in the accurate diagnosis of bovine disease, where accuracy is crucial. To meet this challenge, this paper proposes GraphRAG-Vet, a Knowledge Graph Retrieval-Augmented Generation framework specifically designed for the dairy industry. First, we constructed a domain knowledge map comprising 2500 elements and 3000 relationships, covering high-frequency diseases in cows such as mastitis and ketosis. Second, the semantic-to-password parsing module is designed to retrieve disease symptom subgraphs from the Neo4j database accurately. Finally, the hard constraint injection mechanism is introduced to force LLMs to generate diagnoses strictly in accordance with the retrieved graph context, thereby implementing the “refuse to answer” function for foreign queries. The experimental results showed that GraphRAG-Vet achieved 100% accuracy in diagnosing core infectious diseases and had an almost-zero hallucination rate compared with baseline LLMs. This study provides a reliable, low-resource solution for automated veterinary consultation.

Current peer-to-peer (P2P) energy trading systems face important challenges in decentralised trading environments, particularly in managing participant trustworthiness, preventing dishonest behaviour, and mitigating transaction defaults. These limitations reduce transaction reliability and weaken trust among participants in community-scale energy trading markets. Although P2P energy trading enables communities to exchange locally generated renewable energy in smart environments, existing platforms often lack effective mechanisms to regulate participant behaviour and support reliable transactions. This paper proposes RepuTrade, a blockchain-based P2P energy trading platform tailored for community-scale microgrids. The proposed framework integrates a reputation-based consensus mechanism and a dynamic collateral management scheme that is directly linked to participant reputations such that trading reliability can be strengthened through behavioural incentives. In addition, a reputation-driven matching algorithm preferentially pairs highly reputable participants to improve market stability and trust. Simulation-based evaluation, involving 200 users across 8 trading rounds, shows that the RepuTrade framework consistently achieves higher trade success rates (92–99% compared to 83–95% in the baseline) and reduces defaults by more than 40% (27–44 vs. 55–72 per run). The results further reveal a strong negative correlation between user reputation and default probability, indicating that higher reputation is associated with a lower likelihood of dishonest behaviour. Overall, under the simulated settings considered in this study, the proposed framework improves transaction reliability and execution efficiency by reducing failed trades and lowering consensus validation latency. These findings contribute to the design of trust-aware decentralised energy trading mechanisms and provide simulation-based insights for developing more reliable and transparent community-scale renewable energy markets.