Search Results (381)

Accurate indoor localization is essential for navigation, monitoring, and industrial applications, especially in environments with Non-line of sight (NLOS) conditions. An indoor positioning system consists of fixed physical nodes, referred to as anchors, which serve as reference nodes with known locations, and entities that could be persons or objects that are also equipped with a node, referred to as targets, whose positions are estimated based on signal measurements exchanged with the surrounding anchors. Although RSSI is widely used due to hardware simplicity, its performance is often affected by signal degradation, multipath propagation, and environmental interference. To address this limitation, this work aims to develop an indoor positioning system, especially in wide areas with a minimal number of physical anchors, while maintaining high positioning accuracy and low latency. The proposed approach integrates VA, RSSI-based multilateration, and ML as a tool to refine and improve positioning accuracy, where ML models are used to predict the VA features and subsequently predict the corresponding distances. In addition, the system relies on energy-efficient WuRx nodes, which ensure a low power consumption and support on-demand communication. The study area covers two distinct floors with a total area of 366.9 m², covered using only four physical anchors. Two studies were performed, the offline and the online, in order to evaluate the proposed system under both the theoretical performance and real implementation conditions. In the offline phase, hexagonal and rectangular grid architectures were compared using multiple machine learning models under varying numbers of virtual anchors. By comparing different architectures and machine learning models, the rectangular grid with 10 virtual anchors combined with the XGBoost model achieved the best performance, resulting in an RMSE of $1.49\mathrm{m}$ with a processing time of approximately $0.15\mathrm{s}$. The online evaluation confirmed the performance of the proposed system, achieving an RMSE of $2.48\mathrm{m}$. Full article

As a high-fidelity digital mapping of the physical built environment, the Building Digital Twin (BDT) relies on physical–virtual interaction as a core enabler for lifecycle management. However, existing BDT conceptual models predominantly focus on unidirectional or single-threaded physical–virtual interactions, neglecting the dynamic, concurrent exchanges among multiple digital twins and human users. To overcome this limitation, the Multi-Interactive-Object BDT (MIO-BDT) framework is proposed. The central hypothesis is that explicitly modeling concurrent, multi-party interactions within a formalized conceptual structure can address a key representational gap in current BDT paradigms. The work pursues two testable objectives: (1) to formally define the components, relationships, and rules of the MIO-BDT framework and (2) to validate through a representative use case that the framework can model complex interaction scenarios that are inadequately supported by existing approaches. A systematic analysis of the state of the art is first conducted to ground the framework’s design. The MIO-BDT is then elaborated at both the system level (supporting dynamic interactions among twins, users, and physical entities) and the component level (integrating visual, physical, and interaction sub-models). Formal modeling and verification demonstrate that the framework is logically consistent and deadlock-free and effectively coordinates multi-entity data flows. These findings confirm that the MIO-BDT framework provides enhanced representational capacity, structural clarity for system design, and a unified model for diverse interaction types, thereby establishing a validated conceptual foundation for next-generation, interaction-aware BDT systems. Full article

With the increasing penetration of renewable energy, power systems are facing greater uncertainty and volatility, which poses significant challenges for Virtual Power Plant scheduling. Existing research mainly focuses on optimizing economic efficiency but often overlooks system reliability and the impact of forecasting deviations on scheduling, leading to suboptimal performance. Thus, this paper presents a reliability-cost bi-objective cooperative optimization model based on a dual-swarm particle swarm algorithm: it introduces positive and negative imbalance price penalty factors to explicitly describe the economic costs of forecast deviations, constructs a reliability evaluation system covering PV, EVs, air-conditioning loads, electrolytic aluminum loads, and energy storage, and solves the multi-objective model via algorithm design of “sub-swarms specializing in single objectives + periodic information exchange”. Simulation results show that the method ensures stable intraday operation of VPPs, achieving 6.8% total cost reduction, 12.5% system reliability improvement, and 14.8% power deviation reduction, verifying its practical value and application prospects. Full article

After nearly two decades of developments in Historic/Heritage Building Information Modeling (HBIM), the field has reached a stage of maturity that calls for a critical reassessment of its evolution, achievements, and remaining challenges. Digital representation has become a central component of contemporary heritage conservation, enabling advanced methods for analysis, management, and communication. This review examines the maturation of HBIM as a comprehensive framework that integrates extended reality (XR), artificial intelligence (AI), machine learning (ML), semantic segmentation and Digital Twin (DT). Six major research domains that have shaped recent progress are outlined: (1) the application of HBIM to restoration and conservation workflows; (2) the expansion of public engagement through XR, virtual museums, and serious games; (3) the stratigraphic documentation of building archaeology, historical phases, and material decay; (4) data-exchange mechanisms and interoperability with open formats and Common Data Environments (CDEs); (5) strategies for modeling geometric and semantic complexity using traditional, applied, and AI-driven approaches; and (6) the emergence of heritage DT as dynamic, semantically enriched systems integrating real-time and lifecycle data. A comparative assessment of international case studies and bibliometric trends (2015–2025) illustrates how HBIM is transforming proactive and data-informed conservation practice. The review concludes by identifying persistent gaps and outlining strategic directions for the next phase of research and implementation. Full article

The piston pin operates under severe mechanical and thermal conditions, making accurate lubrication prediction essential for engine durability. This study presents a comprehensive digital engineering framework for piston pin bearings, built upon a fully coupled thermo-elasto-hydrodynamic (TEHD) formulation. The framework integrates: (1) a Reynolds-equation hydrodynamic solver with temperature-/pressure-dependent viscosity and cavitation; (2) elastic deformation obtained from FEA (finite element analysis)-based compliance matrices; (3) a break-in module that iteratively adjusts surface profiles before steady-state simulation; (4) a three-body heat transfer model resolving heat conduction, convection, and solid–liquid interfacial heat exchange. Applied to a heavy-duty diesel engine, the framework reproduces experimentally observed behaviors, including bottom-edge rounding at the small end and the slow unidirectional drift of the floating pin. By integrating multi-physics modeling with design-level flexibility, this work aims to provide a robust digital twin for the piston-pin system, enabling virtual diagnostics, early-stage failure prediction, and data-driven design optimization for engine development. Full article

This mixed-methods research study focuses on the efficacy of virtual exchange (VE) in promoting authentic cross-cultural immersion, critical awareness of social issues, and collective engagement in local and global communities among undergraduate students. The partner institutions in this VE project were a large public US university and a small private university in Hong Kong. Discussions focused on access and opportunity issues in the US and Hong Kong for various communities, leading to a deeper analysis of the distribution of power and privilege in both countries. I analyzed the impact of VE on the US students (n = 45) through pre- and post-test surveys using the Intercultural Sensitivity Scale (ISS), which measures cross-cultural competence and thematic analysis of student artifacts. VE students’ competence significantly increased from pre-test to post-test on the ISS, while the students in a similar course without VE (n = 28) showed no change. Analysis of student artifacts revealed a shift in global awareness, an appreciation of authentic insights about another the culture, a critical understanding of social structures, and a need for collaboration concerning global issues among youth. Overall, VE offered powerful and enriching experiences for students by integrating international immersion into college education courses. Full article

Academic conferences have been pivotal in scholarly communications, facilitating the exchange of ideas and fostering collaborations among attendees by using advanced sensing, networking, and control technologies. Traditionally held in physical venues, the landscape of academic conferences has been revolutionised by the advent of virtual and hybrid formats as supported by the Internet of Things, Artificial Intelligence, and virtual reality tools. Despite the burgeoning literature on smart conferences, there exists a gap in comprehensive reviews that consolidate the various advancements and methodologies in this domain. This article aims to fill this gap by providing a thorough review of the latest developments in smart conference technologies and practices. It offers a multidimensional analysis, including predictive analytics, smart content delivery, networking improvements, and data-driven assessments. Fundamentally, we frame conference activities as a complex process involving multi-stage planning, real-time dynamic execution, and post-event analysis and refinement. This review specifically highlights how smart technologies are transforming this end-to-end process. Additionally, the concept of parallel intelligence is introduced, exploring its potential to transform future conferences. The significance of this article lies in its holistic perspective, offering valuable insights for enhancing conference planning, attendee engagement, and overall conference experiences. Full article

Although the literature is rich in studies of indoor thermal comfort, there is a lack of research on outdoor thermal comfort, despite its importance in response to global warming and the rise of urban heat islands. Physics models addressing spatial (urban energy form, green areas) and temporal (climate variability) factors are urgently needed. This study proposes a useful method for outdoor comfort evaluation at a district scale, based on the energy form of built-up areas and hyperlocal climatic conditions. It enables the determination of distributed Physiological Environmental Temperature values at a district scale, assessing the greenery effect and mutual radiative exchanges. Applied to a case study in Florence, Italy, it integrates multiple measurement techniques. The main results highlight the model’s ability to evaluate outdoor thermal perception through the new identified indicator of Virtual Physiological Environmental Temperature (PET*) spread, ranging from 23.5 to 101.0 °C, specifically referring to the worst climatic conditions inside an urban canyon in relation to different real scenarios. The results confirm the method’s effectiveness as a tool for thermodynamics and planning for the well-being of an urban built-up environment. It offers useful support for sustainability and human-centric design, oriented to UHI mitigation and climate change adaptation strategies. Full article

As artificial intelligence (AI) technologies and Virtual Exchange (VE) become increasingly embedded in higher education, AI-driven digital humans have begun to feature in design-oriented virtual international workshops, providing a novel context for examining learner behaviour. This study develops a structural model to examine the links between system support, interaction processes, self-efficacy, satisfaction, and international learning intention. Specifically, it investigates how perceived AI support, system ease of use, and interaction intensity influence students’ continuous participation in international learning through the mediating roles of learning self-efficacy, interaction quality, and satisfaction. Data were collected through an online questionnaire administered to undergraduate and postgraduate students who had participated in an AI-driven digital human–supported online international design workshop, yielding 611 valid responses. Reliability and validity analyses, as well as structural equation modelling, were conducted using SPSS 22 and AMOS v.22.0. The results show that perceived AI support, system ease of use, and interaction intensity each have a significant positive effect on learning self-efficacy and interaction quality. Both self-efficacy and interaction quality, in turn, significantly enhance learning satisfaction, which subsequently increases students’ intentions for sustained participation in international learning. Overall, the findings reveal a coherent causal chain: AI-driven digital human system characteristics → learning process experience → learning satisfaction → sustained participation intention. This study demonstrates that integrating AI-driven digital humans can meaningfully improve learners’ process experiences in virtual international design workshops. The results provide empirical guidance for curriculum design, pedagogical strategies, and platform optimization in AI-supported, design-oriented virtual international learning environments. Full article

The spread of internet infrastructure has created a virtual social system of interactions. It facilitates communication. It also brings new challenges to state governance. Political stability relies on citizen trust in the government. Therefore, political trust is a key issue in public administration. However, this virtual system of social exchange influences the formation of trust in government. This study uses social exchange theory. Based on 5547 survey responses, it employs an ordinal regression model and an instrumental variables approach. It examines the relationship between internet use, social organization participation, and government trust. Empirical results show two main findings. First, internet use has a significant negative impact on government trust. Second, participation in social organizations reduces this negative impact. This mitigating effect is more pronounced in central and western provinces, among rural residents, the Han ethnic group, and women. Therefore, future internet governance should enhance citizen participation in social organizations. It should also develop diverse channels for public affairs management. These findings offer insights for improving state governance in the digital era. Full article

Heavy metals accumulate in forest soils worldwide, yet their effects on greenhouse gas dynamics remain poorly understood. Mycorrhizal fungi lie at the heart of this issue. These symbiotic organisms regulate carbon and nutrient flow between trees and soil, positioning them to influence fluxes of CO₂, N₂O, and CH₄. However, research on mycorrhizal ecology, metal toxicology, and greenhouse gas biogeochemistry has proceeded largely in isolation. This review bridges these fields through a conceptual framework built on three contamination scenarios and four mechanistic pathways. Our confidence in these mechanisms varies by gas: well-established for CO₂, developing for N₂O, and mostly inferential for CH₄. Critical gaps remain. Studies measuring mycorrhizal communities, metal availability, and gas emissions simultaneously are rare. Comparisons between ectomycorrhizal and arbuscular mycorrhizal systems are virtually absent. This framework establishes a basis for understanding how metal-contaminated forests regulate greenhouse gas exchange and identifies priority areas for future investigation. Full article

Digital Twin Systems (DTSs) are increasingly recognized as enablers of data-driven manufacturing, yet many implementations remain limited to monitoring or visualization without closed-loop control. This study presents a fully integrated DTS for CNC milling that emphasizes real-time bidirectional coupling between a real machine and a virtual counterpart as well as the use of machine-native signals. The architecture comprises a physical space defined by a five-axis machining center, a virtual space implemented via a dexel-based technological simulation environment, and a digital thread for continuous data exchange between those. A full-factorial simulation study investigated the influence of dexel density and cycle time on engagement accuracy and runtime, yielding an optimal configuration that minimizes discretization errors while maintaining real-time feasibility. Latency measurements confirmed a mean response time of 34.2 ms, supporting process-parallel decision-making. Two application scenarios in orthopedic implant milling validated the DTS: process force monitoring enabled an automatic machine halt within 28 ms of anomaly detection, while adaptive feed rate control reduced predicted form error by 20 µm. These findings demonstrate that the DTS extends beyond passive monitoring by actively intervening in machining processes; enhancing process reliability and part quality; and establishing a foundation for scalable, interpretable digital twins in regulated manufacturing. Full article

Advancements in Non-Terrestrial Network (NTN) technology facilitate ubiquitous network access for users, whereas satellite-based Quantum Key Distribution (QKD) offers a viable solution for long-distance quantum key exchange in scenarios lacking terrestrial network infrastructure. This study explores the feasibility and practical utility of integrating NTN technology with satellite-based QKD and proposes a novel quantum-enhanced security framework for next-generation space–terrestrial networks. We have developed and deployed the first-of-its-kind 5G-enabled (fifth generation mobile communication) NTN prototype system leveraging satellite-based QKD key encryption. This system comprises a quantum satellite system, a communication satellite system, a 5G network infrastructure, and end-to-end encryption/decryption modules, aiming to validate the feasibility and usability of the proposed quantum-encrypted NTN security framework. Comprehensive tests and performance evaluations were carried out on the testbed constructed based on this prototype system, which collected critical Quality of Experience (QoE) metrics, including Round-Trip Time (RTT) and jitter, during user-plane ping measurements. Experimental results demonstrate that the integration of quantum encryption capabilities incurs an RTT overhead of 5 ms (0.75%), a necessary trade-off for systems incorporating supplementary quantum-encrypted transmission. Concurrently, the deployment of Virtual Private Network (VPN) infrastructure mitigates network jitter by 50%. These results hold critical theoretical and practical implications for the development of next-generation NTN security frameworks enabled by satellite-based QKD. Full article

In recent years, cloud computing and edge computing have flourished, establishing data centers as pivotal hubs for information exchange. However, the security for the interconnection of these data centers faces increasingly severe challenges. Traditional cryptographic techniques face potential risks of being compromised under the threat of quantum computing, whereas quantum key distribution (QKD), which possesses information-theoretic security, provides an effective foundation for secure data center interconnection. This paper focuses on the QKD-enabled cross-domain data center interconnection network, delving into the multi-dimensional resource (i.e., computing, wavelength, and key resources) allocation problem. By constructing a QKD-enabled cross-domain data center interconnection network model, it integrates key resources with traditional computing and wavelength resources, forming a multi-dimensional resource allocation framework. Furthermore, we design two heuristic algorithms, i.e., the local balancing factor-based multi-dimensional resource allocation (LBF-MDRA) and the global balancing factor-based multi-dimensional resource allocation (GBF-MDRA) algorithms, which rationally perform virtual network function (VNF) node selection and efficiently allocate multi-dimensional resources. Simulation results indicate that the LBF-MDRA and GBF-MDRA algorithms can increase the success probability of cross-domain service requests by 24.53% and 30.91% compared to the benchmark algorithm, respectively. Full article

Digital evidence custody requires consensus protocols that guarantee immediate and deterministic finality. Legal admissibility depends on proof that no party can alter or delay confirmation of evidence transfers. Conventional Byzantine fault tolerance protocols scale poorly because of quadratic communication overhead, while probabilistic ledger systems such as IOTA and SPECTRE produce confirmation uncertainty that weakens custody verification. This paper introduces SelectVote Byzantine Fault Tolerance, a deterministic consensus protocol that infers virtual votes from graph structure instead of exchanging explicit messages. The protocol operates in permissioned forensic networks and assigns validation witnesses through a fixed, hash-based selection process. Empirical evaluation demonstrates sub-quadratic communication scaling ($O\left(n^{1.7}\right)$) compared to traditional $O\left(n^2\right)$ Byzantine protocols and maintains Byzantine resilience. To ensure physical integrity, the paper also presents an environmental compensation framework for precision weight verification. The framework models temperature, humidity, and pressure effects on load cells and corrects measurement drift to preserve sub-gram accuracy across normal storage conditions. Experimental evaluation confirms that the integrated system sustains high throughput with deterministic finality and maintains consistent measurement precision under environmental variation. The combined result supports reliable, legally defensible custody of digital evidence across distributed institutions. Full article