Search Results (546)

Human Activity Recognition (HAR) plays a significant role in various applications, from learning a discipline to physical rehabilitation. In older adults, activity patterns can indicate levels of frailty, which helps inform the design of physical training programs to prevent falls and maintain mobility. HAR sensing ranges from wearable sensors such as IMUs and RGB cameras to video, specialized gait laboratories, perturbation units, VR, and other modalities. This paper presents a comprehensive study of depth-based, skeleton-driven HAR and age group recognition (AGR) using data collected from real-world nursing home environments. Depth sensors offer a privacy-preserving and non-invasive alternative to wearable and RGB-based systems, enabling continuous 24-h monitoring without requiring user compliance. We systematically evaluate multiple modeling paradigms, including classical machine learning models (DT, RF, KNN, SVM, HMM, HMM+SVM), sequence-based models (LSTM, TCN, ARNN), and graph-based approaches, using skeletal joint data extracted from depth images. Experiments are conducted on two heterogeneous datasets: NTU RGB+D (younger adults) and ETAP-DID (older adults). We analyze the impact of different joint subset configurations (full-body, limb-only, leg-only, and torso-only) and compare raw joint representations with handcrafted time-series features (TSFEL) for frame-based HAR. Beyond activity recognition, we introduce an AGR pipeline to distinguish younger from older adults based on skeletal motion patterns. We investigate multiple feature representations, including absolute joint positions, root-relative coordinates, bone vectors, and joint velocities, and provide interpretability through feature importance and saliency analysis to identify age-discriminative joints and motion cues. Our study provides a comprehensive analysis of various HAR models applied to depth data, examining model performance and the contribution of joint-based features to HAR and AGR. Our study highlights the potential for personalized privacy-preserved monitoring and intervention in nursing homes. Full article

Individuals with lower-limb mobility impairment often experience fear of falling and difficulty coordinating with caregivers during transfer tasks. Therefore, safe, stable, and easy-to-operate collaborative transfer assistive equipment is urgently needed. This study proposes a Health Belief Model-driven design method for transfer assistive equipment, aiming to translate users’ risk perceptions and behavioral needs into implementable structural design strategies. First, transfer-related behavioral characteristics were analyzed according to the six constructs of the Health Belief Model. Second, the KJ method was used to classify original user requirements and establish a hierarchical requirement model, while the Fuzzy Analytic Hierarchy Process was applied to calculate requirement weights. Third, Quality Function Deployment and the House of Quality were used to transform user requirements into technical characteristics, through which four key technical contradictions were identified: anti-tipping stability versus lightweight design, status feedback versus process simplification, assistance and effort reduction versus lightweight design, and support-bearing capacity versus human–machine adaptability. Finally, structural optimization strategies were generated using TRIZ, and the proposed concept was preliminarily assessed through JACK digital human simulation and multi-stakeholder assessment at the conceptual design stage. The multi-stakeholder assessment further indicated that the proposed concept responded to requirements related to fall risk mitigation, injury protection, and perceived benefits, while adaptability for small-sized users, distal lower-limb support, and status-feedback mechanisms require further optimization. This study provides a traceable design process for incorporating users’ psychological and behavioral factors into structural innovation for assistive devices, thereby offering a methodological reference for the design of transfer assistive equipment. Full article

The rapid deployment of distributed photovoltaic (PV) systems has increased renewable energy generation while introducing operational challenges for distribution networks, particularly surplus PV export during periods of high solar production. This study proposes a measurement-driven, rule-based smart electric vehicle (EV) charging framework to enhance the local utilization of surplus PV generation using real operational measurements acquired from a Siemens PAC3200T power quality analyzer installed at the point of common coupling (PCC) of the Far’ata–Immatain distribution feeder in Palestine. The proposed framework coordinates EV charging based on representative surplus PV operating states and PCC operating conditions while considering user charging requirements and network operational limits. Four MATLAB-based simulation scenarios representing increasing EV penetration were evaluated. The results demonstrate progressive reductions in reverse active power export together with corresponding improvements in local PV utilization as EV penetration increases. Under the highest investigated charging scenario, up to 200 kW of the investigated surplus PV generation was locally utilized, and reverse active power export was eliminated under the investigated operating conditions. Overall, the proposed framework provides a practical and scalable approach for improving renewable energy utilization in data-limited distribution networks without requiring comprehensive feeder models or immediate network reinforcement. Full article

The diverse application scenarios envisioned for sixth generation (6G) are characterized by the deep integration of sensing and ubiquitous connectivity, which imposes unprecedentedly stringent security requirements. However, due to the open nature of wireless channels, mobile communications systems always face severe information security threats such as falsification, spoofing, interception, and repudiation. Cryptography-based symmetric and asymmetric encryption techniques remain mainstream solutions for information protection. Symmetric encryption is efficient and secure for legitimate users but suffers from key-distribution difficulties over open wireless channels, whereas asymmetric encryption resolves this problem but faces increasing risks from quantum computing due to its reliance on structured mathematical hardness assumptions. In response to these limitations, physical layer security (PLS) has gained a great deal of research attention as a powerful security component that leverages the features of varying wireless channels. Existing PLS schemes can be broadly classified into two categories. The first one takes advantage of the legitimate link’s opportunistic channel-quality superiority over or different spatial-domain directions from the attacking link, which still faces many practical implementation difficulties. The second category is termed physical-layer key generation (PLKG). It extracts the unique features of the legitimate link’s channel variation, which is often reciprocal, as the source of secret key generation and therefore can naturally implement secure key distribution tasks. This advantage no doubt injects new vigor to symmetric encryption as a stronger protection approach. Following this trend, we in this paper concentrate on the PLKG techniques. Specifically, we present a comprehensive overview on existing PLKG schemes, discussing diverse secret key generation and reconciliation methods. We further investigate the model-driven and deep-learning-based approaches tailored for the scenario with imperfect channel reciprocity between the sender and receiver. After comprehensively reviewing major existing schemes, we further discuss a recently proposed PLKG design based on codeword reconstruction, which makes use of the strong error-correcting capability of the forward-error-correction (FEC) codes to effectively implement secure and consistent secret key generation between the legitimate sender and receiver. Finally, we share our opinions on the unsolved challenges and potential research directions dedicated to PLKG toward meeting the security requirements of 6G. Full article

Central heating systems often rely on uniform regulation, making it difficult to meet the differentiated comfort demands of users and frequently leading to overheating. This paper proposes an end-to-end, data-driven framework that systematically couples density-adaptive clustering with dynamic response modeling for precision heating control. First, an adaptive DBSCAN (Density-Based Spatial Clustering of Applications with Noise) algorithm is developed, which automatically determines its parameters via k-distance graph initialization, differential evolution optimization, and hierarchical clustering post-processing. Without requiring a pre-set cluster number, it consistently identifies four typical daily room temperature fluctuation patterns. Validated on 120-day data from a residential community in Luoyang, the first four clusters cover over 80% of users, and the clustering quality approaches that of manually optimized conventional methods. Second, multi-input ARX (Autoregressive with Exogenous Inputs) models are built for the representative user of each cluster to characterize dynamic responses to supply water temperature, flow rate, and outdoor temperature. Rolling prediction for the entire community achieves an RMSE of 0.24 °C and an R² of 0.93. Finally, a differentiated regulation strategy combining main-cluster supply temperature control and small-cluster flow compensation is designed. Simulation results demonstrate that this strategy drives the room temperatures of all clusters significantly toward the 20 °C comfort target, with a marked reduction in standard deviation. The primary contribution of this study lies in the construction of a reproducible, closed-loop pipeline—from raw room temperature data to demand-based regulation logic—offering a quantitative basis for central heating systems transitioning from passive balancing to data-driven, classified control. Full article

The rapid development of large model-driven agent applications, such as digital assistants and robots, requires 6G radio access networks (RAN) to deliver enhanced flexibility, adaptability, and low-latency capabilities. However, the existing RAN user plane (UP) architecture suffers from coarse decoupling granularity and significant cross-layer functional redundancy. These limitations severely hinder the on-demand orchestration and dynamic reconfiguration required by heterogeneous agent services. To address these challenges, this paper proposes a ComBERT-driven service-based RAN UP decoupling method, specifically targeting the functional coupling and redundancy between the PDCP and RLC sublayers. First, we develop a domain-specific language model, ComBERT, by pre-training a BERT model on a 3GPP protocol corpus and fine-tuning it on text-matching tasks to deeply comprehend protocol semantics. Subsequently, ComBERT is utilized to extract semantic features from UP functional components, employing a sliding window mechanism to overcome truncation in lengthy protocol texts and using cosine similarity to measure functional relevance. Finally, a threshold-based fusion algorithm is designed to identify and merge cross-layer redundant functions, thereby forming independent service units with distinct responsibilities. These fused units serve as the basic building blocks for scenario-specific orchestration. Simulation results demonstrate that the proposed method reduces the number of UP components by 12.5%, 18.7%, and 18.2% in eMBB, URLLC, and mMTC scenarios, respectively. Simultaneously, it decreases average processing delays by 7.9%, 10.2%, and 11.0% across these respective scenarios. Ultimately, this approach effectively improves the lightweight deployment, processing efficiency, and reconfiguration capabilities of the service-based UP, providing a crucial foundation for on-demand service orchestration in 6G networks tailored to agent services. Full article

Context: The Agile methodology has been prevalent in the software industry for more than two decades, marking a shift from plan-driven to market-driven approaches and introducing various challenges. While the literature identifies numerous challenges in Agile development, little attention has been given to their ranking and prioritization, which are critical for effective project management and decision making. This study fills this gap by combining empirical evidence from the literature and practitioners. Objectives: This study aims to identify and hierarchically prioritize the most recent challenges faced by Agile practitioners during product development. To achieve this, a Systematic Literature Review (SLR) was conducted using 115 published studies between 2010 and 2025 followed by empirical data collection from 30 Agile experts through semi-structured interviews conducted with practitioners from Agile companies and an online survey. This study applies Cumulative Voting (100-Dollar Test) and Multi-Criteria Decision Making (MCDM) techniques to rank and prioritize these challenges. Results: The SLR identifies several recurring Agile challenges; however, limited research has focused on their ranking and prioritization. The present study reveals new challenges, such as user interface complexities, lack of pre-development and pre-operational cost information, and lack of cost scalability at the module and feature levels. The current study identifies Inadequate Architecture (22%), Lack of Standardized Framework (18%), Communication and Coordination (16%), Poor Requirement Verification (13%), and Minimum Documentation (8%) as the most significant challenges. Conclusions: This study provides valuable insight for Agile practitioners and organizations, enabling more informed project planning, resource allocation, and strategic decision making. By focusing on the most critical challenges, teams can enhance software quality, streamline processes, and improve overall productivity in Agile environments. Full article

Ambient Assisted Living (AAL) environments aim to enable older people to remain active and lead an autonomous and independent life for as long as possible. Among the technologies that can be incorporated into these settings, socially assistive robots (SAR) seek to establish a more natural and intuitive means of interaction with people, whilst helping them to carry out everyday tasks. One of the main challenges facing the design of these robots is how to enable them to undertake more complex tasks. Recent advances in Large Language Models (LLMs) have opened new avenues for flexible robot deliberation, yet their integration into real-time robotic systems remains challenging due to latency constraints, reasoning reliability, and the complexity of coordinating multi-step tasks. This paper proposes a hierarchical multi-agent architecture for robot deliberation that addresses these challenges by combining LLM-based planning with structured execution mechanisms within the ROS 2 ecosystem. The proposed architecture employs a supervisor agent that decomposes high-level natural language instructions into prioritised subtasks, enabling a priority-driven execution model that dynamically adapts to task relevance, temporal constraints, and environmental feedback. Subtasks are delegated to a set of Single-Purpose Agents (SPAs), orchestrated via LangGraph state machines and coordinated through a priority-aware scheduling mechanism. A key design principle is the use of Behaviour Trees (BTs) as high-level callable tools through the Model Context Protocol (MCP), encapsulating closed-loop control strategies while enabling preemptive and priority-consistent execution. This reduces the number of LLM inference steps required per task and improves robustness under dynamic conditions. A further contribution concerns the deployment of fine-tuned, lightweight LLMs—on the order of 0.6 billion parameters—specifically adapted for both the supervisor and the individual SPA roles through parameter-efficient low-rank adaptation (LoRA). These models are trained on role-specific tool-calling datasets to specialise in constrained reasoning patterns and task-specific decision-making, enabling efficient, low-latency inference directly on edge hardware. The combination of fine-tuning and hierarchical priority control enhances both the determinism and responsiveness of the system while mitigating error propagation across agent interactions. The paper presents the full software architecture, a formal characterisation of the system as a priority-aware hierarchical policy over a graph of agent workflows, and an experimental evaluation in an Ambient Assisted Living scenario assessing task success rate, inference efficiency, responsiveness under competing priorities, and overall user experience. Because SPA execution is decoupled from the supervisor’s own reasoning loop, the architecture is designed to keep accepting, processing, and queuing new user queries while previously dispatched SPAs are still executing their tasks. Full article

Early-stage architectural design is characterized by high decision uncertainty, ill-defined requirements, and limited opportunities to elicit reliable user feedback, despite the disproportionate impact of early decisions on downstream outcomes. While recent AI-enabled design tools increasingly support generative exploration and performance-driven optimization, they largely rely on static models trained on aggregated data, thereby producing “average-user” responses that fail to capture pronounced inter-individual variation in architectural preferences. This conceptual framework paper, developed through critical narrative synthesis of interdisciplinary literature, argues that meta-learning—i.e., learning-to-learn—offers a conceptually appropriate mechanism to address this personalization gap by enabling rapid adaptation to a new user’s preference structure from limited interactions, while leveraging transferable knowledge learned across many users. Drawing on a structured, PRISMA-informed literature identification process complemented by purposive theoretical sampling across user-centered design traditions in architecture, computational preference-elicitation methods, and contemporary meta-learning research, the paper develops a theoretically grounded conceptual framework for adaptive user preference modeling in early-stage design workflows. The framework articulates four interdependent constructs—(1) Preference Representation, (2) Adaptation Engine, (3) Design Space Navigator, and (4) Feedback Loop—describing how iterative preference refinement can co-evolve with design-space exploration without displacing architectural agency. An illustrative application scenario is also presented to demonstrate the operational logic of the framework in a realistic early-stage design context. The paper further formulates a set of testable propositions and evaluation pathways to guide future empirical investigation, alongside a discussion of implications for practice and education and key ethical considerations (bias, privacy, and digital equity). The proposed framework provides conceptual scaffolding for developing AI-augmented, user-responsive design systems that are aligned with the epistemic conditions of early-stage architectural design. Full article

Managing safety and operational efficiency in large-scale events requires decision-support tools capable of representing complex crowd dynamics while enabling rapid and evidence-based operational assessment. This paper presents a Generative AI-driven simulation-enabled digital twin prototype that integrates an agent-based crowd simulation framework, an API-based execution pipeline, and a Large Language Model (LLM)-driven conversational interface within a unified architecture. The proposed framework enables the dynamic configuration, execution, and analysis of crowd scenarios under different operational conditions, including high-demand situations and emergency evacuation contexts. Experimental results show that the system can reproduce nonlinear crowd dynamics, identify congestion patterns, and assess evacuation performance. While evaluated under TRL-4 conditions, these results demonstrate the framework’s architectural potential to provide actionable insights for planning and safety evaluation once empirically calibrated with real-world data. A central contribution of this work is the introduction of an API-based execution paradigm that exposes the complete simulation lifecycle, including configuration, validation, execution, and output retrieval, through programmatic interfaces. This design supports reproducible, modular, and scalable what-if analysis. In addition, the integration of an LLM-based conversational interface allows non-technical users to interact with complex simulation models through natural language, improving accessibility without compromising execution control. The framework is validated through a TRL-4 prototype, demonstrating stable performance and reliable interaction behavior. Scalability is strictly confirmed within the evaluated hardware configuration, model abstraction level, and tested agent scale (up to 60,000 agents), providing a foundation for localized event management. Overall, the proposed system serves as a simulation-enabled digital twin prototype, demonstrating how models can transition from static analytical representations toward executable, interactive, and user-centered platforms, laying the necessary architectural groundwork for future operational decision support in complex urban environments. Full article

Cloud Service Level Agreements (SLAs) define security obligations, remediation commitments, and compliance requirements between cloud service providers and customers. SLAs define the performance standards and expectations between service providers and users. Therefore, it is an essential element in cloud computing. However, SLA documents are typically represented in unstructured natural language and lack semantic integration with operational vulnerability management processes. Meanwhile, cloud security platforms continuously generate vulnerability intelligence containing security findings, severity levels, and remediation information. The lack of semantic interoperability between SLA-defined security obligations and vulnerability intelligence limits automated security governance, compliance assessment, and vulnerability management in cloud environments. To address these challenges, this study proposes an integrated semantic approach that enables the representation, integration, and reasoning of SLA-derived security knowledge and cloud vulnerability intelligence within a unified ontology model. The proposed solution combines automated knowledge acquisition from SLA documents, the development of an SLA Security Ontology and a Vulnerability Ontology, ontology alignment techniques, and Semantic Web Rule Language (SWRL)-based reasoning mechanisms to support automated vulnerability management, remediation commitment assignment, and consistency verification. Further, the proposed approach is evaluated using real-world SLA documents and vulnerability information. Experimental results demonstrate that the developed reasoning mechanism successfully identifies logical inconsistencies in 73% of the evaluated SLA cases. The analysis further reveals that the undetected cases correspond to contractually incorrect yet logically consistent outputs, highlighting the complementary role of human validation in ontology-driven security management. As a result, the study demonstrates that ontology engineering and rule-based semantic reasoning can effectively bridge the gap between contractual security obligations and operational vulnerability intelligence. Therefore, the proposed approach provides a foundation for automated vulnerability management, SLA compliance assessment, and semantically aware cloud security governance. Full article

This study examines how misinformation can persist when broadcast exposure and social feedback reinforce one another under stochastic platform conditions. Text classifiers and single-layer cascade models omit latent exposure, reply-driven amplification, random attention shocks, and uncertainty in intervention response. A stochastic duplex SEIR model is developed on heterogeneous networks, with an information exposure layer for broadcast and recommendation channels and a social feedback layer for replies, discussion, and amplification. The analysis combines degree-weighted mean-field equations, next-generation threshold calculations, Lyapunov stability arguments, Fokker–Planck linear noise approximation, Milstein simulation, and Wasserstein distributionally robust control. Theoretical results provide positivity, stochastic threshold conditions, extinction and persistence regimes, and sufficient conditions for stationary behavior and robust control stability. Numerical simulations show extinction–persistence transitions, cross-layer resonance, noise-induced threshold shifts, stationary bands, control cost–safety trade-offs, and sensitivity to unidentifiable stochastic parameters. A CoAID tweet–reply case study maps public interaction traces to observable duplex indicators, including tweet–reply densities, propagation elasticities, coupling proxies, and classifier features. Duplex observable features improve over a single-layer public data baseline, while model-assisted stochastic features add modest gains in the available public projection. Structural fitting of the stochastic duplex process would require time-stamped user-level multiplex trajectories, recommendation exposures, and intervention logs. The case study also clarifies the data granularity needed for future platform-level calibration and operational readiness. The framework supports data-informed platform governance by linking propagation thresholds, algorithmic down-ranking, reply thread moderation, intervention cost, and robustness bounds within a common threshold control language for practical settings. Full article

This study presents a data-driven approach, MLS-CAPS (mobile laser scanning completeness analysis for path spacing), for defining optimal path spacing (walking path) for backpack or handheld MLS surveys in ecological applications. By using an initial high-density pilot scan of the site, captured with the same MLS system intended for subsequent full surveys, MLS-CAPS quantifies how voxel occupancy completeness, digital terrain model accuracy, and canopy height model accuracy vary with lateral distance from individual walking trajectories. This provides an objective basis for translating local vegetation structure into path spacing recommendations for subsequent MLS surveys. Case study results showed that structural complexity strongly influences the rate of decay, with denser and more complex vegetation requiring closer path spacing to maintain data completeness. While this meets expectations, what has previously been lacking is a way to quantify it in a manner that directly supports data acquisition decision making. MLS-CAPS, provided as a Python tool, allows users to define thresholds aligned with their metrics of interest, recognising that no single spacing is universally sufficient across all structural attributes. The framework therefore enables MLS operators to plan surveys that balance efficiency with accuracy, while maintaining transparency in the trade-offs between path spacing and completeness. By formalising what has previously been a trial-and-error process, this method offers a practical tool to support ecological applications of MLS across diverse environments. Full article

Multistorey timber construction (MTC) is widely promoted for lowering embodied carbon, yet its uptake across Europe remains uneven. This study asks which drivers and barriers shape mid-rise wood buildings and how they differ across regions and stakeholders. We combine a structured literature review with a survey of 116 construction professionals across three deliberately contrasted national construction contexts. Belgium as an emerging market, Austria with a growing timber culture, and Norway as a mature policy-driven setting advancing timber. Respondents rated 31 literature-derived factors and 16 strategies. Across all contexts, respondents converge on a shared core: timber was perceived positively when it offered predictable project value, especially construction speed, image/marketing value, sustainability, and user-related benefits. Conversely, obstacles clustered around economic, technical, and legal uncertainty—especially building cost, fire performance, acoustics, conventional procurement, and path dependency. Country patterns suggest different priorities: Belgium may require increased ecosystem readiness, Austria standardisation and clearer regulatory interpretation, and Norway continued political support and market legitimacy. Stakeholder patterns further suggest differentiated support needs across architects, engineers, contractors, clients, suppliers, and verifiers. These findings explore MTC challenges and opportunities as contextual policy and industry priorities for empowering low-carbon construction and the circular economy transition in the built environment. Full article

The expansion of Internet-of-Things (IoT) deployments in 5G New Radio (NR) networks has made periodic measurement reporting a growing burden for low-mobility devices, which benefit little from frequent updates yet must report as often as highly mobile ones. At present, User Equipment (UE) transmits MeasurementReport messages at a fixed ReportInterval—typically 240 ms—regardless of mobility. This continuous transmission needlessly depletes UE battery energy and consumes critical uplink signaling capacity. This paper proposes a five-level adaptive ReportInterval selection scheme driven by the statistical properties of Reference Signal Received Power (RSRP) and Signal-to-Interference-plus-Noise Ratio (SINR). A low-mobility criterion combines four statistical conditions—the variance and gradient of both RSRP and SINR—through a logical AND, while a two-stage hysteresis mechanism (a 3 dB margin and a 2 s holding timer) suppresses unnecessary level transitions. The scheme is slice-agnostic: By relying on observed signal statistics rather than network-slice labels, it serves low-mobility mMTC and stationary eMBB devices while leaving URLLC and high-mobility UEs at their standard configuration. In Monte Carlo simulations over the 3GPP TR 38.901 Urban Micro (UMi) channel model (200 UEs, 300 s, 100 iterations), the algorithm attains a classification accuracy of 91.32% and a sensitivity of 98.77%. Based on the DRX energy model, it yields an estimated 10.87% reduction in average UE power (from 28.15 to 25.09 mW) together with a 51.09% reduction in the network-wide MeasurementReport count. The hysteresis mechanism cuts level transitions by a factor of 31.33 (from 6852.7 to 218.7 per iteration), substantially lowering RRC reconfiguration signaling. Operating at O(n) complexity on the gNodeB and using only conventional MeasConfig signaling, the scheme requires no protocol additions or UE-side modifications, making it directly deployable on existing 3GPP Release 17 infrastructure as a gNB-side software update. Full article