Abstract: Bird strike events on rotating jet engine fan blades pose significant risks to aviation safety, yet high-fidelity numerical simulations remain computationally expensive, limiting their use in parametric design studies. This study develops a physics-informed machine learning surrogate framework for predicting bird strike response on rotating Ti-6Al-4V fan blades, systematically comparing Lagrangian (gelatin-based, Mooney–Rivlin) and Smoothed Particle Hydrodynamics (SPH, water-like) formulations. A total of 100 explicit dynamic simulations were conducted in ANSYS LS-DYNA (50 per formulation), varying bird impact velocity and blade angular speed. Random Forest, Support Vector Regression, Polynomial Regression, and XGBoost regression models were trained and evaluated using five-fold cross-validation. Results demonstrate that SPH-based surrogates achieve superior predictive accuracy, with Random Forest yielding R² = 0.9938 for maximum deformation and R² = 0.9962 for total energy dissipation. In contrast, Lagrangian-based stress surrogates exhibited severe performance degradation (R² = 0.24) due to mesh-dependent numerical noise. The trained surrogates achieved computational speed-up factors of 10⁴–10⁵ relative to direct simulation. These findings establish that surrogate model reliability is fundamentally governed by the numerical quality of the training data, providing guidance for integrating machine learning with impact simulation workflows in aero-engine blade design.

Abstract: Conventional concrete faces limitations such as brittleness, low energy absorption, and a significant environmental impact due to its reliance on natural resources. Integrating natural fibers (NF) and recycled coarse aggregate (RCA) into concrete presents a promising avenue for enhancing both performance and sustainability. However, accurately predicting the strength of these innovative concrete mixtures remains challenging. This study investigates the predictive capabilities of two machine learning (ML) models: Classification and Regression Trees (CART) and Stepwise Polynomial Regression (SPR), in forecasting the compressive and splitting tensile strength of NF-reinforced concrete incorporating RCA. Results unequivocally demonstrate the superior predictive accuracy of the CART model. CART exhibited significantly higher R-squared values and lower error metrics (RMSE, MAD, MAPE, MSE) for both compressive and splitting tensile strength. For compressive strength CART achieved R² = 0.91, RMSE = 5.5686, MSE = 31.0098, MAD = 4.1076, and MAPE = 0.1055, while for splitting tensile strength, it achieved R² = 0.89, RMSE = 0.3954, MSE = 0.1563, MAD = 0.2996, and MAPE = 0.0939. These findings underscore the significant potential of ML, particularly CART, in optimizing the design of sustainable concrete structures by enabling more precise and efficient strength predictions, ultimately contributing to more sustainable and resilient infrastructure.

Abstract: In this paper, a short-term active power curtailment (ST-APC) strategy for doubly fed induction generator (DFIG) wind farms is proposed to enhance first-swing rotor angle stability under fault disturbances. While wind power is a clean renewable resource that is widely deployed, its large-scale integration heightens concerns about transient stability. After analysing DFIG operating principles, this study advocates for using short-horizon active power control to mitigate the adverse stability impacts of wind farms. Using the Western System Coordinating Council (WSCC) three-machine nine-bus test system, the effectiveness of the ST-APC strategy across diverse operating conditions is verified. Simulation results show that, following a fault, modulating the DFIG’s active output effectively suppresses the first swing, postpones loss of synchronism, and increases the critical clearing time (CCT). The scheme yields notable benefits regarding improvements in overall stability, reductions in the frequency nadir, and acceleration of frequency recovery. Sensitivity analyses further examine the effects of activation time, control duration, and curtailment depth on CCT and offer tuning recommendations. The findings indicate that the proposed strategy is practical and adaptable, making it suitable for power systems with high wind power penetration.

Abstract: End-stage lung disease represents a major challenge in modern medicine. Lung transplantation remains the most effective treatment; however, donor shortage and rejection significantly limit its clinical impact. The engineering of bioartificial lung grafts using patient-derived cells may lead to new therapeutic strategies. Advanced culture conditions enable the formation of functional three-dimensional tissues from lineage-committed cells. Currently, bioartificial grafts capable of gas exchange have been created and transplanted in animal models. Ongoing challenges in tissue engineering include the development of ideal scaffolds and the full maturation of engineered structures to ensure graft longevity after in vivo implantation. With collaborative efforts, the goal is to design patient-derived lung grafts and achieve clinically relevant translational milestones such as airway grafts and disease models.

Abstract: Marine Spatial Planning (MSP) and Terrestrial Spatial Planning (TSP) have traditionally evolved as disconnected systems, limiting the capacity to address coastal dynamics under climate change. This article proposes Integrated Spatial Planning (ISP) as a governance and planning framework that links marine and terrestrial domains through a multi-level zoning structure operating from municipal to international scales. The approach explicitly incorporates climate change adaptation by aligning spatial planning instruments with marine climate drivers, hydrological processes, and environmental dynamics that shape coastal resilience.The methodology is applied to the Region of Murcia, Spain, a Mediterranean coastal system highly exposed to climate variability, sea level rise, and extreme runoff events. Despite the existence of multiple regulatory and strategic instruments, including urban plans, regional spatial law, basin-scale hydrological planning, climate strategies, and coastal management guidelines, planning remains fragmented across land and sea. The case study reveals critical gaps in the integration of climate projections, runoff and sediment dynamics, infrastructure planning, renewable energy deployment, and ecosystem-based adaptation, particularly in sensitive areas such as the Mar Menor lagoon.ISP addresses these challenges by establishing governance mechanisms that connect marine climate models, environmental dynamics, and spatial decision-making across administrative levels. The results demonstrate how ISP can improve coherence between climate adaptation strategies, ecosystem protection, and socio-economic development, offering a transferable framework for climate-informed coastal and marine spatial planning in vulnerable regions.

Abstract: Electric vehicles (EVs) have fundamentally changed the noise, vibration, and harshness (NVH) landscape of automotive powertrains. In the absence of masking inter-nal-combustion-engine noise, harmonic components such as gear whine, electric-motor orders, and inverter-related tones become more perceptible and more critical to vehicle re-finement. This review synthesizes the current state of the art in harmonic NVH engineer-ing for electric drivetrains, focusing on the interactions between gear geometry, manufac-turing variability, electromechanical coupling, structural transfer, and human sound per-ception. Classical mechanisms of gear-mesh excitation are revisited together with emerg-ing EV-specific challenges, including long-wavelength flank deviations, ghost orders, lightweight housing dynamics, and psychoacoustic sound-quality requirements. The re-view further examines recent progress in predictive and data-driven approaches, includ-ing machine-learning-based gear-noise modeling, digital-twin concepts, and virtual NVH assessment workflows. Overall, the literature shows that harmonic NVH engineering in EVs is evolving from a conventional gear-noise problem into a multidisciplinary sys-tem-level task integrating gear dynamics, manufacturing science, structural acoustics, electric-drive control, psychoacoustics, and data-driven optimization. This review pro-vides a structured synthesis of these developments and identifies key research gaps and future directions for the next generation of refined electric drivetrains.

Abstract: This study investigates the structural performance of a redesigned AAR Type E knuckle coupler using finite element analysis (FEA). The modified knuckle incorporates geometric reinforcement in critical load-bearing regions together with a hollow internal structure aimed at reducing component weight while maintaining structural integrity. Two numerical models were developed: a component level model, in which the knuckle was analyzed independently, and an assembly-level model that integrates the knuckle with the coupler body to capture realistic load transfer through contact interactions. Both models were subjected to a tensile draft load of 650,000 lbs (2670 kN) in accordance with Association of American Railroads (AAR) standards. The component-level analysis predicted peak von Mises stresses of approximately 1050 MPa, primarily concentrated near the pivot pin hole and curved pulling face regions. When contact interactions between the knuckle and coupler body were included in the assembly model, the representative peak stress decreased to approximately 950 MPa, corresponding to a stress reduction of about 10% due to load redistribution across the assembly interfaces. Highly localized stress peaks at sharp geometric edges were identified as numerical stress singularities and were excluded from engineering interpretation. The results demonstrate that assembly-level finite element modeling provides a more realistic representation of load transfer mechanisms in railway coupler systems and is essential for accurately predicting stress distribution and identifying critical fatigue-prone regions. These findings provide valuable insights for improving the structural reliability and design optimization of freight rail coupler components.

Abstract: The paper presents the cutting tool-life of uncoated and DLC-coated inserts used for machining of aluminum-lithium components used in the structure of Airbus A350 aircraft. Based on the collected data, a feed-forward artificial neural network with two hidden layers was created, trained using the Bayesian Regularization (trainbr) algorithm in MATLAB. The obtained results indicate a high performance of the model, with a low mean square error (MSE) and a correlation coefficient R > 0.98, which reflects an excellent generalization capacity and a close correlation between the actual and estimated values. The regression plot and error analysis confirmed the accuracy of the predictions made by the network. The internal parameters of the algorithm, such as the gradient and μ, provided additional information regarding the optimization process.

Abstract: Chronic wounds, including diabetic foot ulcers, pressure ulcers, and venous leg ulcers, remain a global healthcare burden due to persistent inflammation, impaired tissue repair, and high susceptibility to infection. The rise of antibiotic-resistant pathogens and the prevalence of biofilms in these wounds have limited the effectiveness of conventional therapies, highlighting the need for advanced strategies that simultaneously control infection and promote healing. Biomaterial-based approaches have emerged as promising solutions, offering multifunctional platforms that combine antimicrobial activity with regenerative support. Natural and synthetic polymers, antimicrobial peptide-loaded scaffolds, metal oxide nanoparticles, bacteriophages-loaded biomaterials and hybrid composites have demonstrated the ability to disrupt biofilms, deliver targeted therapeutics, and create environments favorable for cell proliferation and tissue repair. Recent innovations emphasize “smart” biomaterials that respond to wound-specific stimuli, controlled-release systems for sustained drug delivery, and bioinspired materials that mimic native tissue architecture. The integration of electrospinning, 3D bioprinting, and surface functionalization has further advanced the design of next-generation wound dressings. This comprehensive review explores how biomaterials combat infection in chronic wounds, evaluates their clinical translation, and discusses barriers such as cytotoxicity, scalability, and regulatory challenges. Finally, it outlines future directions for personalized, biomaterial-based wound care that supports antimicrobial stewardship and improved patient outcomes.

Abstract: Reinforcement learning (RL) agents are increasingly deployed for voltage control in power distribution networks. However, their opaque decision-making creates a significant trust barrier, limiting their adoption in safety-sensitive operational settings. This paper presents XRL-LLM, a novel framework that generates natural language explanations for RL control decisions by combining game-theoretic feature attribution (KernelSHAP) with large language model (LLM) reasoning grounded in power systems domain knowledge.We deployed a Proximal Policy Optimization (PPO) agent on an IEEE 33-bus network to coordinate capacitor banks, tap changers, and shunt regulators, successfully reducing voltage violations by 90.5% across diverse loading conditions. To make these decisions interpretable, KernelSHAP identifies the most influential state features. These features are then processed by a domain-context-engineered LLM prompt that explicitly encodes network topology, device specifications, and ANSI C84.1 voltage limits.Evaluated via G-Eval across 30 scenarios, XRL-LLM achieves an explanation quality score of 4.13/5. This represents a 33.7% improvement over template-based generation and a 67.9% improvement over raw SHAP outputs, delivering statistically significant gains in accuracy, actionability, and completeness (p< 0.001, Cohen’s d values up to 4.07). Additionally, a physics-grounded counterfactual verification procedure which perturbs the underlying power flow model, confirms a causal faithfulness of 0.81 under critical loading.

Abstract: (1) Background: Privacy usability in IoT smart home companion applications remains an underexplored domain despite mounting regulatory requirements and accelerating user adoption. Heuristic evaluation offers a scalable pathway to privacy usability assessment, yet validated frameworks for applying such methods in real industrial settings are scarce. This study presents the first empirical application of the ABCDE Privacy Framework, a ten-heuristic instrument grounded in Nielsen’s usability principles and Privacy by Design, to an IoT companion application developed with a major European home appliance manufacturer. (2) Methods: A structured workshop was conducted with a multidisciplinary team of seven participants (five industry professionals and two researchers) following a two-round protocol: a qualitative heuristic discussion phase (Round 1) and an individual scoring phase (Round 2). Data were analysed through MAXQDA. (3) Results: Average heuristic scores ranged from 3.6 (H9: error recovery) to 4.8 (H6: recognition; H10: documentation), with an overall mean of 4.32. Six second-order themes were identified, including Transparency Asymmetry, Centralised but Decontextualised Privacy, and Shared Household Complexity. (4) Conclusions: The ABCDE Privacy Framework is feasible, time-efficient, and analytically productive in real industrial contexts, generating design-relevant insights and enabling cross-role team alignment within a two-hour session. These findings support its potential as a scalable tool for Privacy by Design practice in IoT product development.

Abstract: The main objective of the study is to reduce the dwell time of wagons at stations and to improve the efficiency of shunting locomotive utilization. The problem has a combinatorial nature, since an increase in the number of loading and unloading fronts leads to a sharp growth in the number of feasible service variants. During the research, a mathematical model describing the servicing process of industrial sidings was developed. The study addressed the problem of determining the optimal sequence of wagon deliveries and the optimal distribution of workload among shunting locomotives. Under conditions where two or more shunting locomotives are used, an optimization method based on the indicator of wagon-hours reduction (σ) was proposed for allocating loading and unloading fronts. Using combinatorial properties, it was shown that many possible allocation variants are symmetric, which allowed the development of a mathematical solution that simplifies the search for an optimal solution. Computational results demonstrated that, at the hypothetical railway station “N-1”, applying the optimal service sequence reduces wagon dwell time by 21% compared with an arbitrary sequence. At the hypothetical station “N-2”, distributing wagon groups between two shunting locomotives improves the efficiency of the servicing process by 26% compared with using a single locomotive. Based on the proposed mathematical model and algorithm, a practical software tool was developed that enables the automatic determination of service sequences for loading and unloading fronts. The software allows the identification of optimal servicing orders, analysis of alternative variants, and evaluation of the efficiency of shunting locomotive utilization.

Abstract: The CO₂-emissions are rapidly rising with new records and the transport sector is considerably contributing to GHG emissions. The critical transition towards electrification and sustainable development demands a radical change in the transport industry. One of many solutions is to analyze the environmental benefits of optimized vehicle production and recycling of the vehicle components after its usable life to reduce dependency on limited raw materials. Electric motor is one of the most crucial powertrain components, yet studies on the overall ecological profile of production and end of its useable life is limited. This study examines the life cycle assessment (LCA) impacts of electric motors used in passenger cars and potential recycling of its materials. The analysis covers production and recycling of components, crucial elements, and permanent magnets. The results show that housing and rotor production have the highest impacts mainly due to steel, aluminum and permanent magnets. The findings discuss e-motor recycling innovations, state-of-the-art methods and emission reduction potentials of recycling. This paper also covers the understanding that a significant transformation to optimize the resource consumption in manufacturing of crucial vehicle powertrain component and reduce waste after end-of-life could bring combined ecological advantages.

Abstract: Rising energy costs and strict CO₂ traceability regulations create demand for monitoring energy and CO₂ emissions in manufacturing. This paper presents a framework for modelling component-wise energy models with deployable accuracy. In many factories, power meters log data at a sampling rate of 1–2 Hz, so short start-up peaks of components are underestimated. Manufacturers want to exploit this information to support operational decisions, such as peak shaving and optimising energy contract costs. To enable data-driven decisions with limited measurement infrastructure, energy models must extrapolate component behavior from sparse data. The framework is based on power measurements in accordance with ISO 14955-3, ensuring that the load characteristics required for subsequent modelling are known. The measurements are then segmented, and regressions are fitted for each segment. As a case study considering the mist extractors of two different machine tools, the proposed segmentation achieved determination coefficients (R²) of up to 0.94 in the complex ramp-up phase. The resulting models are compact, interpretable, and suited for energy monitoring on edge devices. The contribution is a reproducible framework for delivering peak-aware, component-level energy models from low-frequency industrial power meter data.

Abstract: Metal forming processes play a key role in modern manufacturing, but they are characterized by high energy consumption and low overall efficiency. In this context, precise methods for monitoring the operational state and cycle-dependent metrics of manufactured parts are essential to implement energy optimization strategies. This article presents a data-driven and non-intrusive methodology to identify, in real time, the part under production and to estimate both cycle time and energy consumption per part. The method relies exclusively on electrical measurements taken at the main switchboard and at the first process-stage switchboard. These signals are used to calculate electrical quantities such as root mean square (RMS) current and active power, and a machine-learning (ML) approach is proposed to automatically identify the part in production. To this end, time-domain features are extracted directly from the signals, while time-frequency features are extracted using Continuous Wavelet Transform (CWT). These features are employed to train Support Vector Machine (SVM) classifiers optimized via grid search. Experimental results show that the model achieves a test accuracy of 99.9%. Once the production state is identified, the system estimates cycle time and energy per cycle in real time. Approximately 58,000 production cycles, corresponding to several part types were characterized.

Abstract: Namibia’s rural communities continue to experience limited and unreliable electricity access despite the country’s exceptional solar, wind, and biomass renewable energy re-sources potential. Conventional grid extension remains financially and technically impractical for dispersed off-grid settlements, underscoring the need for cost-effective, re-renewable based alternatives. This paper presents a resource-driven design and multi objective optimization framework for Hybrid Renewable Energy Systems (HRESs) tailored to Namibia’s off-grid communities. The proposed model integrates solar PV, wind turbines, biomass generators, and hydrogen-based fuel cells with hybridized energy storage consisting of batteries, supercapacitors, and hydrogen tanks. Using the Non-dominated sorting Genetic Algorithm-II (NSGA-II), the system simultaneously minimizes Total Life Cycle Cost (TLCC), Levelized Cost of Electricity (LCOE), Loss of Power Supply Probability (LPSP), Carbon dioxide (CO₂) emissions, and Wasted Renewable Energy (WRE). The framework is applied to three rural villages, Oluundje, Ombudiya, and Onguati using high-resolution, site-specific renewable resource datasets and community-level load forecasts. Results demonstrate that resource-aligned configurations substantially improve system reliability (up to 99.28%), reduce LCOE (0.0023–0.0811 USD/kWh), and optimize dispatch behavior across seasonal variations. Storage hybridization further enhances stability by balancing transient and long-duration deficits. Com-pared to existing diesel mini-grids, the optimized HRESs achieve markedly superior techno-economic and environmental performance. The proposed framework offers a scalable, adaptable, and policy-ready tool for accelerating sustainable rural electrification in Namibia.

Abstract: This study examines the aerodynamic behaviour of thin perforated plates through a combined experimental and numerical methodology integrating wind-tunnel measurements, fully resolved CFD of the test section, and computationally efficient periodic ``modulus'' simulations. The objective is to provide reliable and transferable drag coefficients for porous plates employed in façade engineering and flow-control applications.The three standard approaches for estimating aerodynamic drag (force balance, total-pressure drop, and static-pressure difference across the plate) are systematically compared under imposed flow-rate conditions. Although often treated as equivalent, the methods yield non-coincident results. High-resolution CFD demonstrates that the static-pressure field on the windward face of the plate is intrinsically non-uniform, leading to a systematic overestimation of drag when pointwise static-pressure measurements are used. This motivates the introduction of a physically based correction factor, γ ≈ 5%, which is experimentally validated and enables static-pressure estimates to be aligned with force-balance data.Once validated, simulations in cyclic ``modulus'' configuration (where only the smallest repeating unit of the perforated plate is simulated) accurately reproduce the global aerodynamic response of the plates at a greatly reduced computational cost, enabling extensive parametric analyses. Results show that porosity is the dominant parameter governing drag, whereas the hole pattern mainly affects local flow structures with limited influence on the integrated force.

Abstract: Cavitation in rotating hydraulic machinery -- such as industrial pumps and hydropower turbines -- can cause blade and casing erosion, excessive vibration, noise and efficiency loss, posing significant operational and economic risks across industrial sectors. Reliable and scalable monitoring strategies are therefore essential, particularly under variable operating conditions in real-world environments. Recent advances in machine learning (ML) and deep learning (DL) have enabled data-driven approaches for cavitation detection based on operational sensor signals, yet a structured synthesis of these developments is lacking. This scoping review systematically analyzes measurement-based ML and DL approaches for cavitation monitoring, with the aim of identifying key trends, challenges and future research directions. Following PRISMA-ScR and JBI guidelines, 52 peer-reviewed studies published between 1996 and 2025 were evaluated, covering laboratory and field investigations across pumps and turbines and a wide range of model architectures. The analysis reveals that most studies are laboratory-based (∼ 80%), focus on pumps (∼ 70%) and rely on single-machine datasets (> 80%), limiting generalization across machines and operating conditions. Classical ML approaches remain relevant due to interpretability and robustness with limited data, while DL enables end-to-end learning from raw or time-frequency transformed signals, frequently achieving diagnostic accuracy above 95%. Hybrid frameworks combining DL-based feature extraction with classical classifiers are increasingly adopted. Key limitations across the literature include domain shifts between laboratory and field data, scarce or inconsistent labeling and a predominant focus on categorical cavitation severity levels.

Abstract: Hydrodynamic cavitation is attracting increasing interest in food processing as a non-thermal approach for preserving product quality and supporting the recovery of valuable bioactive compounds. Conventional Venturi devices are usually designed for fixed operating conditions, whereas real process streams may vary in temperature, viscosity, and gas or solid content. This can make it difficult to maintain stable and effective operating conditions when a fixed geometry is used. In this work, an adjustable circular Venturi is presented as a simple conceptual device for hydrodynamic cavitation in food applications. The external body and pipeline connections remain unchanged, while the throat section can be adjusted to adapt the device to different process requirements. In this sense, the proposed concept may also serve as an adjustable platform for exploring different operating conditions and identifying suitable throat configurations for specific food matrices and process targets. Once identified, such conditions may support the definition of a dedicated final Venturi configuration for the intended application. The proposed concept may be of interest for applications such as green extraction, food by-product valorization, and mild processing strategies aimed at preserving or enhancing bioactive compounds. This study is presented as a conceptual design contribution for food applications.

Abstract: Digital Twin (DT) systems combining physics-based simulation with hardware execution are critical for Industry 4.0 manufacturing, yet proprietary software solutions remain expensive and platform-dependent. This work addresses three technical challenges: maintaining geometric and kinematic fidelity across CAD-to-simulation conversion pipelines, synchronizing dual physics engines (Unity and ROS middleware) under hardware latency constraints, and optimizing motion planning while preserving trajectory quality and interactive responsiveness. We developed an integrated framework for a 7‑Degree of Freedom manipulator using CAD modeling, URDF/SRDF semantic representation, and bidirectional Unity-ROS (Robot Operating System) communication via WebSocket connectors. Motion planning uses RRTConnect from OMPL with collision-aware optimization through the Flexible Collision Library. Validation across 12 manipulation trials demonstrated positional synchronization accuracy of ±2.0 degrees, motion planning performance of 0.064 ± 0.020 seconds. Latency analysis reveals that hardware execution to be the dominant system bottleneck, significantly exceeding network communication delays. The system achieves performance metrics comparable to proprietary industrial solutions. This work establishes a replicable, cost-effective Industry 4.0 framework, demonstrating that modern game engine technology combined with open-source robotics middleware can deliver DT systems matching proprietary solutions. The architecture and validated implementation enable adaptation to alternative robotic platforms and support broader adoption of simulation-validated automation in manufacturing contexts.