Search Results (855)

The exhaust gas temperature (EGT) of the gas generator is a critical indicator for the health management system of a marine gas turbine engine. Therefore, EGT prediction can not only support predictive maintenance decision-making but also serves as a reliable virtual sensor for EGT measurement. However, the engine EGT exhibits strongly nonlinear coupling relationships with other gas path variables, which causes challenges for data-driven prediction. Graph neural networks (GNNs) are particularly effective in capturing the coupling relationships among gas path sensor variables. However, conventional static graph structures fail to characterize the varying coupling strengths under different operating conditions. In this study, a thermodynamic knowledge-driven graph attention network (TKD-GAT) method is proposed for accurate and robust EGT prediction. First, a physics-guided graph topology is constructed based on the gas turbine thermodynamic equations. Subsequently, a multi-head attention mechanism is introduced to generate edge weights that capture the varying thermodynamic coupling strengths under different operation conditions. The proposed model is evaluated on a real-world LM2500 gas turbine, which is widely used in modern propulsion systems of commercial and military ships. The ablation study confirms that the thermodynamic knowledge-driven graph topology and the attention mechanism-based edge weights are both necessary to enhance the EGT prediction performance. The TKD-GAT model shows the best performance with an RMSE of 0.446% and an R² of 0.971 compared with state-of-the-art models. The paired t-test and effect size measurement (Cohen’s d) statistically confirm the significance of performance improvements. The statistical results from multiple independent experiments prove the stability of the TKD-GAT model. Additionally, the model achieves a competitive computational cost despite the integration of a physics-guided graph topology and attention mechanisms. Crucially, an interpretability analysis confirms that the learned attention weights adhere to thermodynamic principles under different operation conditions. The proposed TKD-GAT model provides an effective solution for EGT prediction in health management systems. Full article

The static magnetic field from large seafloor exploration platforms severely interferes with weak geological signals. Accurately predicting and compensating for this interference is critical for deep-sea surveys. However, traditional inversion methods using limited spatial measurements have severely ill-posed coefficient matrices, amplifying near-field noise and causing massive divergence during far-field extrapolation. To address this, we propose a reliable and data-efficient magnetic field prediction method utilizing prior-constrained boundary integrals. First, a virtual plane is constructed between the platform and the measurement plane. A differential recursive algorithm extracts the local magnetic field on this plane from limited measurements to serve as physical prior information. Incorporating this knowledge to structurally constrain the boundary integral inversion fundamentally mitigates the ill-posed problem. Simulations and scaled physical experiments demonstrate that this method prevents near-field noise overfitting, achieving enhanced far-field reliability. By maximizing the utility of limited spatial data, the maximum relative error on the far-field prediction plane is reduced from 10.5% to 8.3% in simulations, and from 13.2% to 9.8% in physical experiments. This provides a highly reliable approach for marine magnetic interference compensation. Full article

Computational chemistry has played a central role in early-stage drug discovery by accelerating target selection, hit identification, and lead optimization. This review summarizes recent developments in molecular docking, pharmacophore modeling, molecular dynamics (MD), and virtual screening (VS), with a focus on their application in practical drug discovery workflows. Advances in docking protocols, including consensus scoring, physics-based rescoring, and ensemble approaches, addressed the challenges of receptor flexibility. Both ligand-based and structure-based pharmacophore models facilitated scaffold hopping and guided library prioritization. MD simulations were used to assess binding pose stability, identify cryptic binding pockets, and characterize solvent interactions. These simulations also supported free-energy calculations using endpoint and alchemical methods. Large-scale VS campaigns employed curated compound libraries, often composed of make-on-demand molecules, and relied on high-performance computing or cloud infrastructure to screen up to 10⁹ compounds. Hits were validated using orthogonal biophysical assays and filtered by absorption, distribution, metabolism, excretion, and toxicity (ADMET) predictions. Integrated pipelines combining pharmacophore modeling, docking, MD, and free-energy calculations improved enrichment rates and reduced the number of compounds requiring synthesis. Several case studies demonstrated the identification of nanomolar-affinity leads from ultra-large screening campaigns. The review also addressed ongoing challenges, such as inconsistent scoring of binding affinity, protonation, and tautomeric errors, dataset bias, and reproducibility issues. Strategies to mitigate these limitations included standardized library preparation, adherence to FAIR (Findable, Accessible, Interoperable, and Reusable) data principles, and the use of prospective benchmarking protocols. The review discussed emerging trends, including the use of quantum chemistry for electronic structure refinement, ensemble docking guided by cryo-electron microscopy (cryo-EM) data, and the integration of computational tools with automated synthesis and high-throughput screening in closed-loop discovery systems. These approaches have the potential to accelerate the design–make–test cycle, increase hit novelty, and improve decision-making in early drug development programs. Full article

The subjective perception of safety in public space is a crucial indicator of urban participation, shaping how people experience and navigate their surroundings. Urban fear spaces highlight how physical, social, and emotional factors unequally structure access to and use of public environments, linking spatial perception to social justice. This paper addresses the question: What opportunities and limitations does a mixed-methods approach—combining immersive Virtual Reality (VR), electrodermal activity (EDA) measurement, and semi-structured interviews—offer for examining subjective perceptions of urban fear? It offers a methodological reflection on an exploratory study of potential fear spaces on the campus of Ruhr University Bochum, hypothesizing that mixed-methods integration reveals non-conscious arousal patterns inaccessible via verbal data alone. We discuss methodological potentials and limitations in integrating physiological data within qualitative frameworks. The study design comprised VR simulation, physiological signal acquisition, and qualitative interpretation and triangulation. Findings show that combining immersive VR with EDA detects non-conscious physiological arousal patterns that would remain inaccessible through verbal data alone, while simultaneously revealing substantial interpretative challenges that necessitate qualitative contextualization. Integrating interviews proved vital for linking physiological patterns to subjective meaning. The reflection concludes with implications for applying such multimodal approaches in participatory urban planning and spatial research. Full article

False data injection (FDI) cyberattacks pose a growing threat to modern power distribution systems in smart cities by manipulating state-estimation processes and provoking covert voltage violations that traditional defense mechanisms fail to detect. Recent industry data indicate that coordinated FDI attacks can distort measurement sets by as little as 3–7%, yet trigger voltage deviations exceeding 10% in vulnerable feeders, resulting in operational instability, unnecessary load curtailments, and elevated outage risk. To address these challenges, this paper proposes a quantum-accelerated digital twin (QDT) framework that integrates quantum optimization algorithms with a high-fidelity digital twin (DT) of the distribution system to detect, localize, and remediate FDI-induced cyberattacks in real time. The rationale behind the approach lies in the superior combinatorial search capability of quantum solvers, which accelerates the identification of falsified measurement vectors and optimal corrective control actions compared with classical methods. In addition, the framework introduces an innovative Bitcoin-mining-oriented virtual energy storage (BMOVES) mechanism that treats mining facilities as dynamically controllable, fast-response electrical loads within smart city demand–response programs. By modulating mining power consumption with sub-second granularity, the proposed BMOVES resource provides up to 18–45% flexible capacity during attack scenarios, enabling voltage restoration without relying on conventional energy storage assets. The unified QDT + BMOVES architecture is validated using the 136-bus Brazilian distribution system, a realistic benchmark for cyber–physical resilience studies. Simulation results demonstrate over 99% FDI detection accuracy, up to an 82% reduction in peak voltage violations, and restoration of operational limits 11 times faster than state-of-the-art classical methods. These findings highlight the transformative potential of integrating quantum computing, digital twins, and nontraditional flexible assets to enhance cyber-resilient power infrastructure in future smart cities. Full article

The growing integration of immersive technologies into education is opening new possibilities for teaching and learning, while also raising concerns about the reliability and potential distortion of knowledge in artificial intelligence-mediated environments. Understanding how users perceive and accept artificial intelligence-generated content in immersive learning systems is therefore essential. This study explores the factors that influence user acceptance of artificial intelligence-driven virtual reality educational applications and explains it through a multidimensional framework that extends the Technology Acceptance Model, the Theory of Reasoned Action, and the Theory of Planned Behavior—a new ED-SCALE model. We innovated the previous models by adding an ergonomic dimension, often overlooked in virtual reality-based education. To test the model, we developed an artificial intelligence-driven virtual reality educational escape room designed to simulate adaptive and interactive learning experiences. Data were collected from 213 participants using a questionnaire measuring subjective norms, perceived behavioral control, attitudes toward artificial intelligence-mediated instruction, perceived informational efficacy, and ergonomic quality. The findings show that ergonomic quality, intuitive interfaces, physical comfort, and social influence play an important role in shaping user trust and long-term adoption intentions. The results suggest that the success of artificial intelligence-driven immersive learning systems depends not only on technological performance but also on user experience and social context, confirming our first hypothesis regarding new variables that are conditional for virtual technology acceptance. Full article

Inherently, there exists a significant security hole in sensor networks. The majority of sensors are not high-end Internet of Things (IoT) devices with sufficient computing resources. Connected sensors (physical nodes in real networks) are allocated to logical nodes and managed remotely by a supervisor in a virtual network. Data acquired by sensors are then collected by a data center on which artificial intelligence operates. If an adversary spoofs a logical node (e.g., an account in a transport layer security (TLS) session) of a vulnerable sensor on the network, then it can manipulate data input to artificial intelligence. Artificial intelligence cannot verify the integrity of the data input for learning. It is difficult to stop data poisoning with no countermeasures against session spoofing. To avoid session spoofing, physical and logical nodes must be linked seamlessly. One might think this can be achieved by utilizing Hardware Root-of-Trust (HRoT) based on a Physically Unclonable Function (PUF). However, a PUF is based on an expensive System-on-a-Chip (SoC), which has been specifically designed for high-end devices, like expensive smartphones. Many sensors (low-end and middle-end IoT devices) can hardly be protected with existing PUFs. Since the number of IoT devices with a PUF is insufficient to cover the entirety of IoT devices, an attacker can find a vulnerable IoT device with no PUF to perform session spoofing. This is the problem of numbers. To resolve it, we propose Physical Cyber Authentication (PCA). A Blockchain account (a logical node in a TLS session) is anchored to an integrated circuit (IC) chip inside a sensor, allowing Blockchain to manage sensor networks, which provides necessary data to artificial intelligence, thus forming a Blockchain of sensors. Full article

High-frequency (HF) skywave propagation exploits ionospheric reflection for beyond-line-of-sight transmission, making time-difference-of-arrival (TDOA)-based geolocation a primary technique for localizing non-cooperative HF emitters. However, reliable TDOA estimation remains challenging due to time-varying ionospheric conditions, wideband multipath dispersion, and low signal-to-noise ratio (SNR). This paper proposes an integrated framework coupling realistic channel synthesis, deep learning-based TDOA estimation, and convex optimization-based localization. Three contributions are made. First, an improved wideband ionospheric channel model is constructed by integrating the International Reference Ionosphere (IRI) with region-specific calibration and a stochastic perturbation module, yielding time-varying multipath responses for physics-consistent waveform generation. Second, a convolutional neural network (CNN)-based TDOA estimator is designed to jointly exploit time-domain complex-baseband in-phase/quadrature (I/Q) waveforms, multi-weight generalized cross-correlation (GCC) feature maps, and channel-state information (CSI) within a unified regression network, achieving robust delay estimation under severe noise and multipath conditions. Third, the geolocation problem is formulated as a bias-regularized constrained least-squares problem with unknown ionospheric excess-delay surrogates, and a semidefinite programming (SDP) relaxation is derived to yield a tractable solution without prescribing a fixed virtual reflection height. Simulations show that the proposed estimator consistently outperforms competing algorithms across a wide SNR range and narrows the gap to the Cramér–Rao lower bound (CRLB) at high SNR. On field-recorded signals, the estimator reduces the mean absolute TDOA deviation by 51% relative to GCC with phase transform (GCC-PHAT), and the end-to-end pipeline achieves a mean geolocation error of 19.67 km across 100 field segments, outperforming all compared baselines. Full article

In smart manufacturing, the injection molding industry faces a “data scarce environment” due to prohibitive physical trial costs. Processing recycled polypropylene (rPP) exacerbates this challenge, as traditional response surface methodology (RSM) fails to capture complex non-linear rheological behaviors induced by material variability. This study proposes a “domain-knowledge guided data augmentation framework,” integrating Taguchi experimental data (L₂₅) with Moldex3D digital twin simulations to construct a 300-sample hybrid dataset. A back-propagation neural network (BPNN) with L₂ regularization was employed for small-sample learning, providing a continuous differentiable physical mapping. To rigorously prevent neighborhood data leakage, the model was evaluated via a strict nested group-based 5-fold cross-validation. Particle swarm optimization (PSO) was coupled to overcome the local minima of gradient descent. Comparative analysis demonstrates that BPNN significantly outperforms both traditional RSM and a newly introduced Random Forest (RF) baseline, achieving a testing mean squared error (MSE) of 0.001 (±0.0002) and a testing R² of 0.95. PSO minimized the shrinkage rate to 3.079%, validated via Moldex3D digital twin simulation with a 0.19% relative error. Synergizing virtual–physical integration with robust neural computing enables superior process control precision in small-data regimes, offering small and medium-sized enterprises (SMEs) a cost-effective pathway for smart optimization. Full article

Complex psychosis is a clinically relevant rehabilitation construct rather than a formal diagnostic category and refers to psychotic illness associated with treatment-resistant symptoms, functional impairment, and additional cognitive, psychiatric, neurodevelopmental, or physical health complexity. In this clinician-oriented narrative review, we synthesised current evidence on rehabilitation interventions for adults with complex psychosis, integrating direct evidence from specialist rehabilitation settings with indirect evidence from schizophrenia-spectrum studies when clinically informative. We searched major clinical databases, prioritised guidelines, systematic reviews, meta-analyses, and controlled studies, and organised the synthesis by functional domain and pathway relevance. Evidence was strongest for cognitive remediation, particularly when combined with broader psychiatric rehabilitation or vocational support, for family interventions in relapse prevention, and for individual placement and support in competitive employment. Social–cognitive and metacognitive interventions appear clinically valuable, although transfer to real-world functioning is more variable. Community-based rehabilitation, supported accommodation, illness self-management, and ecological adaptation strategies remain central to functional recovery when embedded within multidisciplinary pathways. Digital and virtual interventions are promising adjuncts, but their efficacy remains heterogeneous and implementation challenges include engagement, privacy, and service integration. Overall, rehabilitation in complex psychosis is most convincing when it is personalised, measurement-based, and delivered through integrated service models linking assessment, intervention selection, supported living, and recovery-oriented care. Full article

Air-to-air heat pumps are a key technology for improving energy efficiency and reducing carbon emissions in residential buildings, yet their optimal control remains challenging under real-world conditions. This study evaluates the performance of a hybrid physical–LSTM model for controlling an air-to-air heat pump in a residential building in Zadar, Croatia. The hybrid framework integrates a first-order energy balance model of the building envelope with LSTM-based temperature correction using adaptive weighting. The physical model was calibrated and validated against 52,128 real IoT measurements collected during the 2024/2025 heating season, achieving high accuracy (RMSE ≈ 0.076 °C). Rolling one-day and continuous multi-day closed-loop simulations (up to 15 days) show that the hybrid model yields slightly lower RMSE in long-term runs compared to the pure physical model. However, this apparent statistical improvement is accompanied by systematic underestimation of indoor temperature and significantly higher simulated energy consumption. The results indicate that the observed effect originates from an implicit virtual heat flux introduced by the LSTM correction, which affects thermodynamic consistency in closed-loop operation. The findings highlight that short-term error metrics such as RMSE alone are insufficient for evaluating hybrid models intended for model predictive control (MPC). The main contribution of this study is the explicit demonstration and quantification of an implicit virtual heat flux generated by the LSTM correction in closed-loop multi-day operation, which leads to misleading statistical improvements while causing significant thermodynamic inconsistency and energy overconsumption. In 15-day continuous simulations the hybrid model (ω = 0.05–0.10) caused an indoor temperature underestimation of 1.25–1.31 °C and increased simulated electricity consumption by more than 300% (316 kWh vs. 72 kWh) compared to the physical model. These results have direct implications for the development of reliable digital twins and model predictive control strategies in residential HVAC systems. Full article

Global decarbonization and the rapid growth of distributed energy resources increase the need for information-centric mechanisms that can support secure, scalable, cross-border coordination under heterogeneous technical and regulatory conditions. This paper proposes a Global Virtual Prosumer (GVP) framework that integrates IoT sensing, multi-agent coordination, and permissioned blockchain smart contracts to operationalize cross-border energy services as auditable service commitments rather than physical power exchange. Building on prior work that validated MAS-based power management and blockchain-secured operation within individual Virtual Prosumers, the present contribution lies in the cross-border coordination layer and its associated contractual and evaluation mechanisms, not in the constituent technologies themselves. A layered IoT–AI–blockchain architecture is introduced, where off-chain optimization produces allocations and admissibility indicators and on-chain contracts enforce identity, feasibility guards, delegation and partner-assignment rules, oracle verification, and settlement time compliance outcomes. The contractual lifecycle is formalized through four smart-contract algorithms covering trade registration, conditional delegation, cooperative fulfillment, and cross-border settlement with explicit failure semantics and event-based audit trails. The framework is evaluated on a global case study with seven Virtual Prosumers and quantified using contract-centric KPIs that capture registration time rejections, settlement success versus non-compliance, oracle-driven failure attribution, and full lifecycle traceability. The results demonstrate internal consistency of the proposed lifecycle and the practical value of KPI-driven accountability for cross-border energy service coordination. At the same time, the evaluation is based on synthetic parameterization and an emulated contract environment; realistic deployment constraints—including consensus latency, cross-region communication reliability, and regulatory overlap—are discussed as explicit limitations and directions for future empirical validation. Full article

Three-dimensional integrated circuit (3D IC) packaging and heterogeneous integration have emerged as central pillars of contemporary semiconductor scaling. Yet, the multi-physics coupling inherent to stacked architectures manifesting as thermal hot spots, warpage-induced stresses, and interconnect aging demands monitoring and control capabilities that surpass traditional offline metrology. Although Digital Twin (DT) technology provides a principled route to real-time reliability management, the existing literature remains fragmented and frequently blurs the distinction between static multi-physics simulation workflows and truly dynamic, closed-loop twins. This critical review addresses these deficiencies through three main contributions. First, we clarify the Digital Twin hierarchy to resolve terminological ambiguity between digital models, shadows, and twins. Second, we synthesize three foundational enabling technologies. We examine physics-based modeling, emphasizing the shift from finite-element analysis (FEA) to real-time surrogates. We analyze data-driven paradigms, highlighting virtual metrology (VM) for inferring latent metrics. Finally, we explore in situ sensing, which serves as the “nervous system” coupling the physical stack to its virtual counterpart. Third, beyond a descriptive survey, we outline a possible hybrid DT architecture that leverages physics-informed machine learning (e.g., PINNs) to help reconcile data scarcity with latency constraints. Finally, we outline a standards-aligned roadmap incorporating IEEE 1451 and UCIe protocols to support the transition from passive digital shadows toward more adaptive and fully coupled Digital Twin frameworks for 3D IC manufacturing and field operation. Full article

The accelerated low-carbon transition of power systems and the widespread integration of Electric Vehicles (EVs) present both severe operational challenges and substantial flexible regulation potential for Active Distribution Networks (ADNs). This paper provides an integrative review of the coordinated control and multi-market bidding mechanisms for EV-centric Technical Virtual Power Plants (TVPPs). Moving beyond descriptive surveys, this review systematically synthesizes the fragmented literature across three critical dimensions: (1) the physical-economic bidirectional mapping, which considers nonlinear power flow constraints and node voltage limits within the TVPP framework; (2) multi-market coupling mechanisms, evolving from unilateral energy bidding to coordinated participation in carbon trading and ancillary services; and (3) real-time control strategies, critically evaluating the trade-offs between optimization techniques (e.g., Model Predictive Control) and cutting-edge artificial intelligence approaches (e.g., Deep Reinforcement Learning) in mitigating battery degradation. Furthermore, a transparent review methodology is adopted to ensure literature rigor. By explicitly outlining the boundaries between TVPPs, Commercial VPPs (CVPPs), and EV aggregators, this paper identifies core unresolved trade-offs among aggregation fidelity, market complexity, and communication latency, providing evidence-backed pathways for future engineering demonstrations and V2G applications. Full article

Accurate needle insertion during epidural anesthesia is challenging due to strong dependence on clinician experience and the limited integration of guidance modalities that simultaneously provide visual feedback and physical motion constraints. Current approaches, including ultrasound guidance and augmented reality visualization, mainly offer passive assistance and do not actively regulate insertion trajectory and depth, which may lead to variability in accuracy and increased risk of complications. This work presents a multimodal human–machine assistance system that combines augmented reality guidance with virtual fixtures to support lumbar epidural needle placement. A Tuohy needle is coupled to a haptic device interacting with a patient-specific L3–L4 lumbar phantom fabricated using 3D printing and ballistic gel. A model-based force profile reproduces the mechanical response of anatomical layers during insertion. Three experimental conditions are evaluated: freehand execution, augmented reality guidance with trajectory and depth visualization, and cooperative guidance using virtual fixtures defined by a cylindrical corridor and a depth-limiting plane. Results show a progressive reduction in mean depth error from 6.82 ± 3.46 mm (freehand) to 4.96 ± 2.41 mm (augmented reality) and 2.21 ± 1.73 mm (virtual fixtures). These findings indicate that the integration of visual and haptic guidance significantly enhances insertion precision and control. The proposed approach highlights the potential of multimodal human–machine cooperation for safer training and assisted interventions. Full article