Search Results (883)

This paper presents a validated computational workflow for simulating the fire-induced collapse of steel moment-resisting frames, comparing static general and dynamic explicit analysis procedures. Whereas most existing studies employ dynamic explicit analysis for collapse validation, this work evaluates the capability of the static general approach as a viable alternative. Finite element models developed with beam and shell elements capture both global instability and local failure modes. The results show that the static general procedure effectively reproduces quasi-static post-buckling behaviour and predicts the critical failure temperature within 2–3% of experimental results, similar to the dynamic explicit method. For the dynamic explicit procedure, sensitivity analyses are conducted to optimise time scaling, mesh refining, and ensure realistic physical response while maintaining computational efficiency. The study demonstrates that, along with dynamic explicit analysis, static general procedure also offers a practical and reliable alternative for simulating fire-induced structural collapse, reducing computational time by up to eighteen times for beam models and around six times for shell models, while maintaining reliable accuracy. Full article

Aiming at the problems of fixed trigger patterns that are prone to detection in existing invisible backdoor attacks, this paper proposes a backdoor attack method that integrates local binary pattern (LBP) with dynamic randomized least significant bit (LSB) steganography. The multi-bit coding characteristic of LBP is leveraged to enrich the representational expressiveness of trigger information within the embedding budget, combined with LSB steganography to maintain visual imperceptibility, and a pseudo-random number generator (PRNG) is introduced to randomize embedding locations to mitigate detectors that rely on fixed-position patterns. Experiments show that the proposed method demonstrates potential advantages in terms of steganography, attack success rate, and anti-detection capability on both CIFAR-10 and Tiny-ImageNet datasets. Among them, the structural similarity index (SSIM) and peak signal-to-noise ratio (PSNR) reach up to 0.98 and above 36 dB in terms of covertness, respectively. In anti-detection experiments, the attack method maintains high attack success rates under D-BR defense (CIFAR-10: Test_ASR > 85%; Tiny-ImageNet: Test_ASR > 95%), while under SPECTRE defense—a spectral-based statistical method—the defender’s leakage detection rate of poisoned samples remains low (CIFAR-10: 5.96%; Tiny-ImageNet: 10.56%). This clearly validates the proposed attack’s robustness against mainstream defense mechanisms. Full article

This study analysed how tactical systems and match outcomes influence micro-level passing network metrics across playing positions in a professional football team competing in the Portuguese First Division during the 2020–2021 season. It examined how structural variation affects Degree Centrality, Degree Prestige, and Proximity Prestige across tactical systems (1-4-1-4-1, 1-4-3-3, 1-3-4-3) and outcomes (win, loss, draw) in different positions. Data from 28 league matches were used, with adjacency matrices constructed from teammate interactions. Players were grouped into six positions: goalkeepers, fullbacks, central defenders, central midfielders, wingers, and strikers. One-way ANOVA revealed significant differences (p < 0.05) across positions, tactical systems, and match outcomes. Central defenders consistently showed higher values of Degree Centrality and Degree Prestige across most systems and outcomes, highlighting their structural importance. In contrast, strikers and wingers displayed greater Proximity Prestige in the 1-4-3-3 and 1-3-4-3, reflecting their offensive positioning. Match outcome analysis indicated that wingers had significantly higher Degree Prestige in won matches compared to losses. Overall, results show that micro-level network metrics vary meaningfully by position and context, underscoring the importance of interpreting them cautiously. Despite the novelty of this study, focusing on the initial tactical systems without capturing within-match adjustments may condition the generality of the results. Coaches and practitioners should account for tactical and outcome-related variations when applying network analysis to optimise team dynamics. Full article

This study focuses on the use of eye-tracking technology to analyse the rider’s visual attention during racing on a Ducati Panigale V2 motorcycle. Using the TOBII Pro Glasses 2 system, the rider’s gaze dynamics were recorded, including fixations, eye movements (saccades) and gaze distribution on key sections of the track. The results revealed a link between gaze stability and cornering efficiency, particularly in optimising braking points and selecting the ideal trajectory. Identifying unstable visual behavior—such as frequent gaze deviations or constant switching between reference points—provides valuable insights for improving driving technique. This approach confirms the importance of eye-tracking as a tool for objective evaluation and optimization of rider performance in motorsport. Full article

The lack of detailed design information in legacy hydropower plants creates challenges for modernising their ageing turbine components. This research advances a digitalisation approach which combines inverse design methodology (IDM) with multi-objective genetic algorithms (MOGA) and computational fluid dynamics (CFD) to digitally reconstruct and optimise the Bérchules Francis turbine runner and guide vane geometries using limited available legacy data, avoiding invasive techniques. A two-stage optimisation process was conducted. The first stage of runner blade optimisation achieved a 22.7% reduction in profile loss and a 16.8% decrease in secondary flow factor while raising minimum pressure from −877,325.5 Pa to −132,703.4 Pa. Guide vane optimisation during Stage 2 produced additional performance gains through a 9.3% reduction in profile loss and a 20% decrease in secondary flow factor and a minimum pressure increase to +247,452.1 Pa which represented an 183% improvement. The CFD validation results showed that the final turbine efficiency reached 93.7% while producing more power than the plant’s rated 942 kW. The sensitivity analysis revealed that leading edge loading at mid-span and normal chord proved to be the most significant design parameters affecting pressure loss and flow behaviour metrics. The research proves that legacy turbines can be digitally restored through hybrid optimisation and CFD workflows, which enables data-driven refurbishment design without needing complete component replacement. Full article

Turbulence and wind gusts pose significant risks to the safety and efficiency of UAVs (uncrewed aerial vehicles) in urban environments. In these settings, wind dynamics are strongly influenced by interactions with buildings and terrain, giving rise to small-scale phenomena such as vortex shedding and gusts. These wind speed oscillations generate unsteady forces that can destabilise UAV flight, particularly for small vehicles. Additionally, predicting their formation requires high-resolution Computational Fluid Dynamics (CFD) models, as current weather forecasting tools lack the resolution to capture these phenomena. However, such models require 3D representations of study areas with high geometric consistency and detail, which are not available for most cities. To address this issue, this work introduces an automated methodology for urban CFD mesh generation using open-source data. The proposed method generates error-free meshes compatible with OpenFOAM and includes tools for geometry modification, enhancing solver convergence and enabling adjustments to mesh complexity based on computational resources. Using this approach, CFD simulations are conducted for the city of Ourense, followed by an analysis of their impact on UAV operations and the integration of the system into a trajectory optimisation framework. The CFD model is also validated using experimental anemometer measurements. Full article

Offshore vessels are nowadays equipped with dynamic positioning systems, meaning they have additional thrusters dedicated to the station keeping of the unit. However, there is no rational criterion on the placement of these devices to increment station keeping capabilities. This is true both in case of a vessel retrofitting or for the design of a new unit. The present work proposes investigating a methodology for the optimal placement of thrusters along the hull of an offshore unit. This implies the adoption of a suitable optimisation algorithm capable of handling all the constraints of the optimisation problem. As the target is the optimal capability, the optimisation should handle multiple dynamic positioning capability calculations, meaning (in a quasi-static approach) that it is capable of solving multiple thrust allocation problems at each optimisation step. As thruster allocation is another optimisation problem, the process should handle two nested optimisations. Here, the global location problem is solved with a differential evolution algorithm, while the thrust allocation employs non-linear programming. As the capability calculations imply the adoption of a specific wind–wave correlation, the present work compares the effect of different correlations on the optimised location of the thrusters. The results presented on a reference Pipe Lay Crane Vessel highlight the differences in the final optimum as a function of the environmental modelling. Full article

Enhancing productivity while reducing environmental impact presents a major engineering challenge in sustainable dairy farming. This study proposes an engineering-oriented explainable machine learning and digital twin framework for multi-objective optimisation of milk yield and nitrogen-related emissions. Using the CowNflow dataset, which integrates individual-level nitrogen balance, feeding, and production data collected under controlled experimental conditions, the framework combines data analytics, feature selection, predictive modelling, and SHAP-based explainability to support decision-making in dairy production. The stacking ensemble model achieved the best predictive performance ($R^2$ = 0.85 for milk yield and $R^2$ = 0.794 for milk urea), providing reliable surrogates for downstream optimisation. Predicted milk urea values were further transformed using empirical equations to estimate urinary urea nitrogen (UUN) and ammonia (NH₃) emissions, offering an indirect yet practical approach to assess environmental sustainability. Furthermore, the predictive models are integrated into a digital twin platform that provides a dynamic, real-time simulation environment for scenario testing, continuous optimisation, and data-driven decision support, effectively bridging data analytics with sustainable dairy system management. This research demonstrates how explainable AI, machine learning, and digital twin engineering can jointly drive sustainable dairy production, offering actionable insights for improving productivity while minimising environmental impact. Full article

A biomimetic adaptive façade applies natural principles to building design using shading devices that dynamically respond to environmental changes, enhancing daylight, thermal comfort, and energy efficiency. While motorised systems offer precision through sensors and mechanical actuation, they consume energy and are complex. In contrast, passively actuated systems use smart materials that respond to environmental stimuli, offering simpler and more sustainable operation, but often lack responsiveness to dynamic conditions. This study explores a sequential approach by initially developing motorised shading concepts before transitioning to a passive actuation strategy. In the first phase, nine mechanically actuated shading device concepts were designed, inspired by the opening and closing behaviour of plant stomata, and evaluated on structural robustness, actuation efficiency, ease of installation, and visual integration. One concept was selected for further development. In the second phase, a biocomposite made of polylactic acid (PLA) and regenerated cellulose fibres was used for Fused Deposition Modelling (FDM) to fabricate 3D-printed modules with passive, moisture-responsive actuation. The modules underwent environmental testing, demonstrating repeatable shape changes in response to heat and moisture. Moisture application increased the range of motion, and heating led to flap closure as water evaporated. Reinforcement and layering strategies were also explored to optimise movement and minimise unwanted deformation, highlighting the material’s potential for sustainable, responsive façade systems. Full article

Against the backdrop of global climate change and resource-environmental constraints, land ecological security is paramount to regional sustainable development. This study innovatively integrates the DPSIRM system framework with a CNN-LSTM hybrid neural network model to establish a land ecological security early warning system for China’s top 100 counties, enabling scientific diagnosis and dynamic early warning of security incidents. Findings indicate: (1) From 2010 to 2023, land ecological security conditions across counties showed continuous improvement, with the proportion of counties classified as ‘relatively safe’ or higher rising from 2% in 2010 to 68% in 2023. (2) The comprehensive early warning index exhibited a ‘stepwise leap’ trend, progressing through four stages from ‘relatively unsafe’ to ‘relatively safe’. (3) The six subsystems exhibited markedly divergent evolutionary trajectories, characterised by dual-core leadership from ‘driving-management’, fluctuating improvements in ‘pressure-impact’, and low-amplitude oscillations in ‘state-response’. (4) Over the next five years, the comprehensive early warning index will exhibit a ‘gradual stabilisation and upward trend’, yet subsystems will display a polarised pattern of ‘three rising, two stagnant, and one declining’. The early warning system developed in this study provides local decision-makers with critical leading indicators, supporting differentiated management and source-level interventions. These findings hold significant implications for refining county-level ecological governance and optimising territorial spatial patterns. Full article

Background/Objectives: Respiratory syncytial virus (RSV) is a leading cause of severe respiratory disease in older adults. Despite growing recognition of RSV as a public health concern, vaccination options remain limited. This study assessed the potential long-term public health impact of extended mRNA-1345 RSV vaccination campaigns. Methods: A dynamic transmission model, stratified by age, was developed to evaluate the epidemiological and clinical impact of RSV vaccination in the UK over a 20-year time horizon. Eight vaccination strategies were assessed: two reflecting the JCVI recommendation for the 2024–2025 season and its recent extension, and six extended strategies considering broader eligible age groups, higher coverage, and/or revaccination every 2 or 3 years. Two exploratory analyses and extensive model validation versus reported data were also conducted. Results: Strategies combining broader age eligibility (≥60 years), higher coverage (80%), and 2-year revaccination achieved the greatest impact, preventing 310,000 hospitalisations over 20 years in the total UK population. Exploratory analyses showed that the expected public health impact might exceed the estimates presented in this analysis, if an alternative vaccine efficacy profile or the projected demographic shift would be confirmed. Conclusions: Extended RSV vaccination strategies including broader age eligibility and routine revaccination could offer substantial public health benefits in the UK. Targeting adults aged ≥60 years is expected to be particularly efficient in achieving a sustainable reduction in RSV burden. These findings could provide valuable support for national policy discussions on optimising RSV vaccination strategies in older adults, particularly regarding target age groups, revaccination schedules, and long-term programme planning. Full article

This paper presents an innovative primary control strategy for a modular multilevel converter aimed at enhancing reliability and dynamic performance for power electronics applications. The proposed method utilises interactive modelling tools, including MATLAB Simulink (2022b) for algorithm design and Typhoon HIL (2023.2) for real-time validation. The circuit design and component analysis were carried out using Proteus Design Suite (v8.17) and LTSpice (v17) to optimise the hardware implementation. A power hardware-in-the-loop experimental test setup was built to demonstrate the robustness and adaptability of the control algorithm under fixed load conditions. The simulation results were compared and verified against the experimental data. Additionally, the proposed control strategy was successfully validated through experiments, demonstrating its effectiveness in simplifying control development through efficient co-simulation. Full article

The dynamic growth in the dependence of numerous industrial sectors, businesses, and critical infrastructure on infocommunication technologies necessitates the enhancement of their resilience to cyberattacks and radio-frequency threats. This article addresses a relevant scientific and applied issue, which is to formulate prospective directions for improving the effectiveness of cybersecurity approaches for infocommunication networks through a comparative analysis and logical synthesis of the state-of-the-art of applied research on cyber threats to the information security of mobile and satellite networks, including those related to the rapid development of quantum computing technologies. The article presents results on the systematisation of cyberattacks at the physical, signalling and cryptographic levels, as well as threats to cryptographic protocols and authentication systems. Particular attention is given to the prospects for implementing post-quantum cryptography, hybrid cryptographic models and the integration of threat detection mechanisms based on machine learning and artificial intelligence algorithms. The article proposes a classification of current threats according to architectural levels, analyses typical protocol vulnerabilities in next-generation mobile networks and satellite communications, and identifies key research gaps in existing cybersecurity approaches. Based on a critical analysis of scientific and applied literature, this article identifies key areas for future research. These include developing lightweight cryptographic algorithms, standardising post-quantum cryptographic models, creating adaptive cybersecurity frameworks and optimising protection mechanisms for resource-constrained devices within information and digital networks. Full article

The aim of this study is to investigate the impact of machining parameters on the deflection of a cutting tool (i.e., end mill) in the milling of a surface with a curvilinear profile. Test samples were made of aluminium alloy EN AW-7075 T651. Experiments were conducted using the Gocator 2530 laser line profile sensor for real-time measurement of dynamic tool displacement with an inspection speed up to 10 kHz at resolution ranging from 0.028 to 0.054 mm. Response surface methodology was used. Five main technological factors were analysed: cutting speed, feed per tooth (cutting parameters), amplitude, term (curvilinear profile parameters), and the number of flutes (end mill parameter). Obtained data were filtered and visualised as 3D plots. The results showed that cutting speed and amplitude had the greatest impact on tool deflection, while feed per tooth also played a significant role in process stability. In particular, the use of tools with a higher number of flutes led to a considerable reduction in tool deflection, confirming their positive effect on the stability of the machining process. These findings may serve as a basis for the optimisation of machining parameters by taking into account the dynamic deformation of cutting tools. Full article

Given the projection of the impact of climate change and the uncertainty caused by geopolitical volatility, minimising emissions has become an urgent priority for the shipping industry. In this context, the aim of the present study is the calculation and estimation of emissions generated by ship operations within a maritime transportation network, as well as the identification of the optimal route that minimises both emissions and travel time. Emission estimation is carried out using methodologies and assumptions from the Fourth IMO GHG Study. The decision-making, along with the optimisation process, is performed through backward dynamic programming, following a multi-objective optimisation framework. Specifically, the analysis is carried out on both a theoretical and a realistic network. In both cases, various scenarios are examined, including different approaches to vessel speed, some of which incorporate probabilistic speed distributions, as well as scenarios involving uncertainty regarding port availability. Additionally, the resilience of the network is examined, focusing on the additional burden in terms of emissions and travel time when a port is unexpectedly unavailable and a route adjustment is required. The calculations and optimisation are carried out using Excel and the @Risk software by Palisade, with the latter enabling the incorporation of probability distributions and the execution of Monte Carlo simulations. Full article