Search Results (1,554)

The built environment contributes approximately 25% of the UK’s total greenhouse gas emissions, positioning it as a critical sector in the national net-zero strategy. This review investigates the enabling role of the domestic smart metering infrastructure combined with other IoT systems in accelerating the decarbonisation of residential buildings. Drawing from experience gained from governmental and commercially funded R&D projects, the article demonstrates how smart metering data can be leveraged to assess building energy performance, underpin cost-effective carbon reduction solutions, and enable energy flexibility services for maintaining grid stability. Unlike controlled laboratory studies, this review article focuses on real-world applications where data from publicly available infrastructure is accessed and utilised, enhancing scalability and policy relevance. The integration of smart meter data with complementary IoT data—such as indoor temperature, weather conditions, and occupancy—substantially improves built environment digital energy analytics. This capability was previously unattainable due to the absence of a nationwide digital energy infrastructure. The insights presented in this work highlight the untapped potential of the UK’s multibillion-pound investment in smart metering, offering a scalable pathway for low-carbon innovation for the built environment, thus supporting the broader transition to a net-zero future. Full article

Accurately estimating total accident costs is essential for managing construction safety budgets. While direct costs are well-documented, indirect costs—such as productivity loss, material damage, and legal expenses—are difficult to predict and often overlooked. Traditional ratio-based methods lack accuracy due to variability across projects and accident types. This study introduces a two-tiered machine learning framework for real-time indirect cost estimation. In the first tier, classification models (decision tree, random forest, k-nearest neighbor, and XGBoost) predict total cost categories; in the second, regression models (decision tree, random forest, gradient boosting, and light-gradient boosting machine) estimate indirect costs. Using a dataset of 1036 construction accidents collected over two years, the model achieved accuracies above 87% in classification and an R² of 0.95 with a training MSE of 0.21 in regression. Compared to conventional statistical and single-step models, it demonstrated superior predictive performance, reducing average deviations to $362.63 and sometimes achieving zero deviation. This framework enables more precise, real-time estimation of hidden costs, promoting better safety investment, reduced financial risk, and adaptive learning through retraining. When integrated with a national accident cost database, it supports ongoing improvement and informed risk management for construction stakeholders. Full article

Datasets with many zero outcomes are common in real-world studies and often exhibit overdispersion and strong correlations among predictors, creating challenges for standard count models. Traditional approaches such as the Zero-Inflated Poisson (ZIP), Zero-Inflated Negative Binomial (ZINB), and Hurdle models can handle extra zeros and overdispersion but struggle when multicollinearity is present. This study introduces the Ridge-Hurdle Negative Binomial model, which incorporates L₂ regularization into the truncated count component of the hurdle framework to jointly address zero inflation, overdispersion, and multicollinearity. Monte Carlo simulations under varying sample sizes, predictor correlations, and levels of overdispersion and zero inflation show that Ridge-Hurdle NB consistently achieves the lowest mean squared error (MSE) compared to ZIP, ZINB, Hurdle Poisson, Hurdle Negative Binomial, Ridge ZIP, and Ridge ZINB models. Applications to the Wildlife Fish and Medical Care datasets further confirm its superior predictive performance, highlighting RHNB as a robust and efficient solution for complex count data modeling. Full article

Operationalizing Earth Observation (EO)-based Machine Learning (ML) algorithms (or GeoML) for ingestion in environmental Digital Twins remains a challenging task due to the complexities associated with balancing real-time inference with cost, data, and infrastructure requirements. In the field of GHG monitoring, most GeoML models of land use CO₂ fluxes remain at the proof-of-concept stage, limiting their use in policy and land management for net-zero goals. In this study, we develop and demonstrate a Digital Twin-ready framework to operationalize a pre-trained Random Forest model that estimates the Net Ecosystem Exchange of CO₂ (NEE) from drained peatlands into a biweekly, field-scale CO₂ flux monitoring system using EO and weather data. The system achieves an average response time of 6.12 s, retains 98% accuracy of the underlying model, and predicts the NEE of CO₂ with an R² of 0.76 and NRMSE of 8%. It is characterized by hybrid data ingestion (combining non-time-critical and real-time retrieval), automated biweekly data updates, efficient storage, and a user-friendly front-end. The underlying framework, which is part of an operational Digital Twin under the UK Research & Innovation AI for Net Zero project consortium, is built using open source tools for data access and processing (including the Copernicus Data Space Ecosystem OpenEO API and Open-Meteo API), automation (Jenkins), and GUI development (Leaflet, NiceGIU, etc.). The applicability of the system is demonstrated through running real-world use-cases relevant to farmers and policymakers concerned with the management of arable peatlands in England. Overall, the lightweight, modular framework presented here integrates seamlessly into Digital Twins and is easily adaptable to other GeoMLs, providing a practical foundation for operational use in environmental monitoring and decision-making. Full article

With the increasing penetration of renewable energy and the rising demand for power quality, the dynamic performance and accuracy of grid-voltage detection have become crucial for the control of grid-following devices such as dynamic voltage restorers (DVRs). However, the conventional moving average filter (MAF) in grid-voltage detection suffers from inherent limitations in dynamic response. To address this issue, this paper proposes a voltage-detection method, which is based on an adaptive variable-parameter filtering algorithm termed MAF-MATCH-V. First, a cascaded filter model is constructed by integrating a zero-pole matcher (MATCH) with the MAF. Frequency-domain analysis demonstrates that the MATCH compensates for the mid- and high-frequency magnitude attenuation and reduces the phase delay of the MAF, thereby accelerating the dynamic response while preserving its harmonic-rejection capability. Second, the influence of the matching coefficient on the time-domain response is investigated, and a time-varying adaptive strategy is designed to balance rapid disturbance recognition with steady-state convergence. Finally, experimental results under various non-ideal grid conditions demonstrate that the proposed method achieves superior overall performance compared with conventional approaches. Specifically, MAF-MATCH-V realizes millisecond-level event recognition and zero steady-state error convergence, making it a practical solution for the real-time control of grid-following equipment in modern power systems. Full article

This study presents the design, modeling, and multi-platform simulation of NEPTUNE-R, a solar-powered autonomous hydro-robot developed for sustainable water purification and oxygenation. Mechanical design was performed in Fusion 360, trajectory optimization in MATLAB R2024a, and dynamic motion analysis in Roblox Studio, creating a reproducible digital twin environment. The proposed path-planning strategies—Boustrophedon and Archimedean spiral—achieved full surface coverage across various lake geometries, with an average efficiency of 97.4% ± 1.2% and a 12% reduction in energy consumption compared to conventional linear patterns. The integrated Euler-based force model ensured stability and maneuverability under ideal hydrodynamic conditions. The modular architecture of NEPTUNE-R enables scalable implementation of photovoltaic panels and microbubble-based oxygenation systems. The results confirm the feasibility of an accessible, zero-emission platform for aquatic ecosystem restoration and contribute directly to Sustainable Development Goals (SDGs) 6, 7, and 14 by promoting clean water, renewable energy, and life below water. Future work will involve prototype testing and experimental calibration to validate the numerical findings under real environmental conditions. Full article

Background/Objective: Healthcare accessibility remains a critical challenge in medically underserved regions, particularly for specialized care such as trauma treatment and hyperbaric oxygen therapy (HBOT). This study aims to develop and empirically evaluate the Dr. LINK platform, a remote collaborative care system designed to bridge healthcare gaps in geographically isolated or resource-limited areas through real-time interdisciplinary medical collaboration. Methods: Dr. LINK platform employs a SaaS-based infrastructure with Zero Trust security architecture, supporting structured data exchange, automated notifications, and dynamic consultation transfer. Patients completed a modified Telehealth Usability Questionnaire on a 7-point Likert scale, evaluating usefulness, ease of use, interface quality, interaction quality, reliability, and overall satisfaction. Results: Dr. LINK successfully facilitated real-time collaborative consultations for emergency medicine and HBOT, supporting multiple concurrent consultations while maintaining data security and system performance. Overall usability scores were high (mean 6.71–6.83/7), with HBOT patients consistently reporting higher satisfaction across all domains. The platform enabled timely, structured, and coordinated care, reducing unnecessary patient transfers and enhancing multidisciplinary decision-making. Conclusions: Dr. LINK represents a significant advancement in addressing healthcare disparities by enabling structured, secure, and scalable remote collaborative care. The platform effectively overcomes geographic and infrastructural barriers, providing a practical framework for future telemedicine implementations in specialized care domains. Continued refinement and evaluation will be essential to fully realize its potential in transforming healthcare delivery models toward greater equity and accessibility. Full article

Controllers designed on the basis of an approximate discrete time model can fail when they are applied to the real processes. This is a problem of interest in adaptive symmetry control system theory. In this paper, we introduce the certain polynomial for discretizing the $n$-th order integrator in the case of a fractional-order hold (FROH) and delta operator. Moreover, the corresponding FROH discretization pulse transfer function and sampling data model in normal form are derived. On the basis of these results, we present FROH discrete system zero dynamics and sampling zero dynamics where their asymptotic symmetry properties are obtained. In addition, the implications incorporating sampling zero dynamics in the discrete-time models used for system identification become evident as the sampling period approaches zero. It is a further extension of the corresponding results. Full article

Vibrations induced by marine environmental loads can compromise the operational performance of offshore platforms and, in severe cases, result in structural instability or overturning. This study proposes a biomimetic nonlinear X-shaped vibration isolation system (NXVIS) to suppress earthquake-induced vibration response in offshore platforms. Compared with traditional passive vibration isolators, the key innovations of the NXVIS include: (1) the proposed NXVIS can be tailored to different load requirements and resonant frequencies to accommodate diverse offshore platforms and environmental loads; (2) By adjusting isolator parameters (e.g., link length and spring stiffness, etc.), the anti-vibration system can achieve different types of nonlinear stiffness and a large-stroke quasi-zero stiffness (QZS) range, enabling ultra-low frequency (ULF) vibration control without compromising load capacity. To evaluate the effectiveness of the designed NXVIS for vibration suppression of jacket offshore platforms under seismic loads, numerical analysis was performed on a real offshore platform subjected to seismic loads. The results show that the proposed nonlinear vibration isolation solution significantly reduces the dynamic response of deck displacement and acceleration under seismic loads, demonstrating effective low-frequency vibration control. This proposed NXVIS provides a novel and effective method for manipulating beneficial nonlinearities to achieve improved anti-vibration performance. Full article

We establish multiple novel upper estimates for the numerical radius associated with off-diagonal operator matrices defined on a complex Hilbert space $\mathfrak{H}$. The operators considered have a specific structure, with zero diagonal entries and anti-diagonal entries given by a bounded linear operator $\mathcal{C}$ and the adjoint of another, ${\mathcal{D}}^{★}$. The primary contribution is a set of inequalities that connect the square of the numerical radius to expressions involving the norms of these constituent operators. As applications, we specialize our main results to obtain refined inequalities for two significant cases: when $\mathcal{D}$ is the adjoint of $\mathcal{C}$, where $\mathcal{C}$ and $\mathcal{D}$ represent the real and imaginary components of one operator $\mathcal{T}$. Full article

Malware analytics suffer from scarce, delayed, and privacy-constrained labels, limiting fully supervised detection and hampering responsiveness to zero-day threats. We propose SMAD, a Semi-supervised Android Malicious App Detector that integrates a segmentation-oriented backbone—to extract pixel-level, multi-scale features from APK imagery—with a dual-branch consistency objective that enforces predictive agreement between two parallel branches on the same image. We evaluate SMAD on CICMalDroid2020 under label budgets of 0.5, 0.25, and 0.125 and show that it achieves higher accuracy, macro-precision, macro-recall, and macro-F1 with smoother learning curves than supervised training, a recursive pseudo-labeling baseline, a FixMatch baseline, and a confidence-thresholded consistency ablation. A backbone ablation (replacing the dense encoder with WideResNet) indicates that pixel-level, multi-scale features under agreement contribute substantially to these gains. We observe a coverage–precision trade-off: hard confidence gating filters noise but lowers early-training performance, whereas enforcing consistency on dense, pixel-level representations yields sustained label-efficiency gains for image-based malware detection. Consequently, SMAD offers a practical path to high-utility detection under tight labeling budgets—a setting common in real-world security applications. Full article

Email continues to serve as a primary vector for cyber-attacks, with phishing, spoofing, and polymorphic malware evolving rapidly to evade traditional defences. Conventional email security systems, often reliant on static, signature-based detection struggle to identify zero-day exploits and protect user privacy in increasingly data-driven environments. This paper introduces TwinGuard, a privacy-preserving framework that leverages digital twin technology to enable adaptive, personalised email threat detection. TwinGuard constructs dynamic behavioural models tailored to individual email ecosystems, facilitating proactive threat simulation and anomaly detection without accessing raw message content. The system integrates a BERT–LSTM hybrid for semantic and temporal profiling, alongside federated learning, secure multi-party computation (SMPC), and differential privacy to enable collaborative intelligence while preserving confidentiality. Empirical evaluations were conducted using both synthetic AI-generated email datasets and real-world datasets sourced from Hugging Face and Kaggle. TwinGuard achieved 98% accuracy, 97% precision, and a false positive rate of 3%, outperforming conventional detection methods. The framework offers a scalable, regulation-compliant solution that balances security efficacy with strong privacy protection in modern email ecosystems. Full article

Zero-day attacks pose unprecedented challenges to modern cybersecurity frameworks, exploiting unknown vulnerabilities that evade traditional signature-based detection systems. This paper presents ZeroDay-LLM, a novel large language model framework specifically designed for real-time zero-day threat detection in IoT and cloud networks. The proposed system integrates lightweight edge encoders with centralized transformer-based reasoning engines, enabling contextual understanding of network traffic patterns and behavioral anomalies. Through comprehensive evaluation on benchmark cybersecurity datasets including CICIDS2017, NSL-KDD, and UNSW-NB15, ZeroDay-LLM demonstrates superior performance, with a 97.8% accuracy in detecting novel attack signatures, a 23% reduction in false positives compared to traditional intrusion detection systems, and enhanced resilience against adversarial evasion techniques. The framework achieves real-time processing capabilities with an average latency of 12.3 ms per packet analysis while maintaining scalability across heterogeneous network infrastructures. Experimental results across urban, rural, and mixed deployment scenarios validate the practical applicability and robustness of the proposed approach. Full article

This study presents an advanced signal analysis model for internal defect identification in bridge decks using impact echo field testing data designed to mitigate signal noise and the variability encountered during real-world inspections. Field tests were conducted on a concrete bridge deck utilizing an automated inspection system, systematically capturing impact-echo signals across multiple scanning paths. The large volume of field-acquired data poses significant challenges, particularly in identifying defects and isolating clean signals and suppressing noise under variable environmental conditions. To enhance the accuracy of defect detection, a deep learning framework was designed to refine critical signal parameters, such as signal duration and the starting point in relation to the zero-crossing. A convolutional neural network (CNN)-based classification model was developed to categorize signals into delamination, non-delamination, and insignificant classes. Through systematic parameter tuning, optimal values of 1 ms signal duration and 0.1 ms starting time were identified, resulting in a classification accuracy of 88.8%. Laboratory test results were used to validate the signal behavior trends observed during the parameter optimization process. Comparison of defect maps generated before and after applying the CNN-optimized signal parameters revealed significant enhancements in detection accuracy. The findings highlight the effectiveness of integrating advanced signal analysis and deep learning techniques with impact-echo testing, offering a robust non-destructive evaluation approach for large-scaled infrastructures such as bridge deck condition assessment. Full article

The six-degree-of-freedom (6-DoF) interaction forces/torque of the tool-end play an important role in the robotic tool manipulation using a gripper, which are usually indirectly measured by a robot wrist force/torque sensor. However, the real-time decoupling of the tool’s inertial force remains a challenge when different tools and grasping postures are involved. This paper presents a universal tool-end interaction forces estimation approach, which is capable of handling diverse grippers and tools. Firstly, to address uncertainties from varying tools and grasping postures, an online-identifiable tool dynamics model was built based on the Newton–Euler approach for the integrated gripper–tool system. Sensor zero-drift caused by factors such as the tool weight and prolonged operation is incorporated into the dynamic model and identified online in real time, enabling a coarse estimation of the interaction forces. Secondly, a spiking neural network (SNN) is specially employed to compensate for uncertainties caused by the wrist sensor creep effect, since its temporal processing and event-driven characteristics match the time-varying creep effects introduced by tool changes. The proposed method is experimentally validated on a robotic arm with a gripper, and the results show that the root mean square errors of the estimated tool-end interaction forces are below 0.5 N with $x$, $y$, and $z$ axes and 0.03 Nm with ${\tau }_x$, ${\tau }_y$, and ${\tau }_z$ axes, which has a comparable precision with the in situ measurement of the interaction forces at the tool-end. The proposed method is further applied to robotic scraper manipulation with impedance control, achieving the interaction forces feedback during compliant operation precisely and rapidly. Full article