Abstract:

Additive manufacturing of polymer tools represents a promising alternative to conventional steel tooling for low-force and low-volume sheet metal air bending. However, accurate prediction of sheet springback and the resulting deviation of the bending angle after elastic unloading remains a major challenge. This study presents an integrated experimental–numerical framework for the analysis of air bending with additively manufactured polymer tools, with emphasis on material characterization, springback prediction, and tool angle compensation. The methodology combines uniaxial tensile testing, controlled air-bending experiments, finite element modelling with rigid and deformable tools, and optical 3D scanning for angle measurement. Low-carbon steel DC04 sheets were modeled using an elastoplastic constitutive law, while FDM-printed ABS tools were described by experimentally calibrated material models. Numerical simulations were performed over a range of forming forces to evaluate springback behavior and elastic tool deformation. The results show very good agreement between experiments and simulations. Deviations in bending angle were below 1.5% for metallic tools and below 0.5% for springback compensation, with the smallest discrepancy obtained using a two-dimensional model with deformable tools. Experimental validation with ABS tools confirmed bending accuracy within ±1°. The proposed framework provides a reliable basis for springback prediction and rational design of additively manufactured polymer tools for air-bending applications.

Abstract:

In this study, both linear and nonlinear parametric models (M1–M6) and machine learning (ML)–based approaches were evaluated for the reliable and interpretable prediction of tunnel boring machine (TBM) penetration rate (ROP). The analyses incorporated rock hardness index (BI), uniaxial compressive strength (UCS), joint angle (α), excavation depth (DPW), and BTS as input variables. Parametric model coefficients were optimized using the Differential Evolution (DE) algorithm, and variable effects were examined via Jacobian-based elasticity analysis under both original and standardized data scenarios. Parametric results indicate that the proposed M6 model outperforms existing literature correlations in terms of prediction accuracy and represents variable contributions in a more balanced and physically meaningful manner. While the dominant influence of BI and UCS on ROP is preserved across all models, interaction terms allow the indirect contributions of variables such as DPW and BTS to be captured more clearly. Model performance systematically improves when moving from linear to nonlinear and interaction-inclusive structures, with R² increasing from 0.62 for M1 to 0.69 for M6. Machine learning–based variable importance analyses largely corroborate the parametric findings, highlighting BI and α in tree-based methods, and UCS and α in SVM and GAM models. Notably, the GAM model exhibited the highest predictive performance under both data scenarios. Overall, the combined use of parametric and ML approaches provides a robust hybrid framework for accurate and interpretable prediction of TBM penetration rates.

Abstract: A comprehensive analysis of the notch toughness of Electron Beam Powder Bed Fused (EB-PBF) Ti-6Al-4V was conducted, which focused on the influence of build orientation and correlations with key mechanical properties. Horizontal and vertical specimens were fabricated with optimized process parameters and reused powder. The microhardness and microstructure of the metal were examined and both profilometry and scanning electron microscopy were used in evaluating the fracture surfaces. Results showed that the metal with vertical build orientation absorbed ~47% higher impact energy than the horizontal orientation due to crack propagation perpendicular to the prior-β grains, lower microhardness, and greater ductility. The importance of ductility to the vertical specimens was evidenced by greater shear lip height (~35%), shear area (~50%), and roughness of the fracture surface (~15%). Overall, results show that the notch toughness of EB-PBF Ti6Al4V is dependent on the build orientation due to difference in microstructure and the bulk mechanical properties. The notch toughness is well correlated with tensile properties and fracture toughness. The relationship between the notch toughness in dynamic loading and quasi-static fracture toughness for EB-PBF Ti-6Al-4V is discussed.

Abstract: The widespread use of textual data sanitization techniques,such as identifier removal and synthetic data generation, has raised ques-tions about their effectiveness in preserving individual privacy. This studyintroduced a comprehensive evaluation framework designed to measureprivacy leakage in sanitized datasets at a semantic level. The frameworkoperated in two stages: linking auxiliary information to sanitized recordsusing sparse retrieval and evaluating semantic similarity between orig-inal and matched records using a language model. Experiments wereconducted on two real-world datasets, MedQA and WildChat, to assessthe privacy-utility trade-off across various sanitization methods. Resultsshowed that traditional PII removal methods retained significant privateinformation, with over 90% of original claims still inferable. Syntheticdata generation demonstrated improved privacy performance, especiallywhen enhanced with differential privacy, though often at the cost ofdownstream task utility. The evaluation also revealed that text coher-ence and the nature of auxiliary knowledge significantly influenced re-identification risks. These findings emphasized the limitations of currentsurface-level sanitization practices and highlighted the need for robust,context-aware privacy mechanisms that balance utility and protection insensitive textual data releases.

Abstract: In the face of accelerating climate change and urbanization, sustainable mobility infrastructure plays a critical role in reducing greenhouse gas emissions. This article assesses the Sunglider concept – an elevated, solar-powered transport system – through the New European Bauhaus (NEB) Compass, which emphasizes sustainability, inclusion, and aesthetic value. Designed by architect Peter Kuczia and collaborators, Sunglider combines photovoltaic energy generation with modular, parametrically designed wooden pylons to form a lightweight, climate-positive mobility solution. The study evaluates the system’s technological feasibility, environmental performance, and urban integration potential, drawing on existing design documentation and simulation-based estimates. While Sunglider demonstrates strong alignment with NEB principles, including zero-emission operation and material circularity, its implementation is challenged by high initial investment, political and planning complexities, and integration into dense urban environments. Mitigation strategies—such as adaptive routing, visual screening, and universal station access—are proposed to address concerns around privacy, aesthetics, and accessibility. The article positions Sunglider as a scalable and replicable model for mid-sized European cities, capable of advancing inclusive, carbon-neutral mobility while enhancing the urban experience. It concludes with policy and research recommendations, highlighting the importance of embedding infrastructure innovation within broader ecological and cultural transitions.

Abstract: The increasing penetration of renewable energy is essential for achieving sustainable development goals, yet it poses significant challenges to power grid stability, energy security, and resilience. Solar power, in particular, is variable and weather-dependent, requiring well-defined strategies to ensure reliable integration into existing power systems. This study focuses on developing strategic approaches for integrating large-scale and distributed solar power into power grids while enhancing energy security and system resilience against disruptions. The study employs a mixed-methods approach combining a comprehensive literature review, power system modeling, and scenario-based analysis. Grid integration strategies were evaluated using simulation models that assess load balancing, frequency regulation, and reliability under varying solar penetration levels. Key strategies examined include energy storage deployment, smart grid technologies, demand-side management, and policy-driven market mechanisms. The findings indicate that coordinated integration strategies significantly improve grid resilience and energy security. Energy storage and advanced grid management systems reduce intermittency impacts, while demand response mechanisms enhance system flexibility. High solar penetration scenarios showed improved resilience to fuel supply disruptions and reduced dependence on centralized generation. Strategic solar power integration, supported by technological, operational, and policy measures, can strengthen power grid resilience and energy security. The study provides a framework to guide policymakers and grid operators in planning reliable, secure, and sustainable solar-integrated power systems.

Abstract: In order to solve the dynamic analysis and interactive imaging control problems in the deformation process of bionic soft lenses, dielectric elastomer (DE) actuators are separated from the convex lenses, and data-driven eye-controlled motion technology is investigated. According to the DE properties which are consistency with the deformation characteristics of hydrogel electrodes, the motion and deformation effects of eye-controlled lenses under film prestretching, lens size and driving voltage, are studied. The results show that when the driving voltage increases to 7.8 kV, the focal length of the lens, whose prestreching λ is 4, and the diameter d is1 cm, varies in the range of 49.7 mm and 112.5 mm. And the maximum focal‒length change could reach to 58.9%. In the process of eye controlling design and experimental verification, the high DC voltage supply was programmed and the eye movement signals for controlling the lens were analyzed by MATLAB software. The eye-controlled interactive real-time motion and tunable imaging of the lens were realized. The response efficiency of soft lenses could reach over 93%. The adaptive lens system developed in this research has the potential to be applied to medical rehabilitation, exploration, augmented reality (AR) and virtual reality (VR) in the future.

Abstract: In electronic product manufacturing, quality control and production cost management pose challenges for enterprises. First, key factors for production decisions are identified: component inspection, assembly, inspection of semi-finished and finished products, and handling defective goods at different stages. Next, dynamic programming and genetic algorithms optimise sampling inspection. A production decision model covers multiple processes and component combinations. The relationship between the inspection and the final product quality is explored, showing that different decision paths affect the total cost and defect rate. Real-time monitoring and a dynamic Bayesian network guide production strategy adjustments to boost efficiency and reduce defects. This study proposes adaptable inspection and disassembly strategies that reduce costs and optimise resource use across production scenarios.

Abstract: Driven by global initiatives to mitigate climate change, the offshore wind power industry is experiencing rapid growth. Personnel transfer between service operation vessels (SOVs) and offshore wind turbines under complex sea conditions remains a critical factor governing the safety and efficiency of operation and maintenance (O&M) activities. This study establishes a fully coupled dynamic response and control simulation framework for an SOV equipped with an active motion-compensated gangway. A numerical model of the SOV is first developed using potential flow theory and frequency-domain multi-body hydrodynamics to predict realistic vessel motions, which serve as excitation inputs to a co-simulation environment (MATLAB/Simulink coupled with MSC Adams) representing the Stewart platform-based gangway. To address system nonlinearity and coupling, a composite control strategy integrating velocity and dynamic feedforward with three-loop PID feedback is proposed. Simulation results demonstrate that the composite strategy achieves an average disturbance isolation degree of 21.81 dB, significantly outperforming traditional PID control. Validation is conducted using a ship motion simulation platform and a combined wind-wave basin with a 1:10 scaled prototype. Experimental results confirm high compensation accuracy, with heave variation maintained within 1.6 cm and a relative error between simulation and experiment of approximately 18.2%.

Abstract: Efficient placement of Virtual Machines (VMs) iscritical for optimizing resource utilization and ensuring servicereliability in cloud computing infrastructures. Existing validationmethods for VM placement algorithms, such as limited in-vivoexperiments and ad hoc simulators, often fail to reflect real-worldcomplexities and provide fair comparisons. This paper introducesVMPlaceS, a simulation framework built on SimGrid, designed toaddress these shortcomings by enabling the robust evaluation andcomparison of VM placement strategies. VMPlaceS facilitateslarge-scale scenario modeling with customizable parameters torepresent dynamic workloads and realistic platform conditions.By simulating centralized, hierarchical, and distributed algo-rithms, this study highlights the framework’s capability to assessscalability, reactivity, and SLA adherence in various deploymentscenarios. VMPlaceS emerges as a valuable tool for researchersand practitioners to explore innovative VM placement solutionsand advance the field of cloud computing resource management.

Abstract:

Polyurethane-based implantable devices(PUIDs) delivered via catheter are increasingly used in structural heart interventions; however, limited in vivo data exist regarding their long-term biostability and biological safety. This study evaluated a balloon-shaped implant made of Pellethane^®, a polyether-based polyurethane, designed as a three-dimensional intracardiac spacer and deployed via percutaneous femoral vein access. The device was chronically positioned adjacent to the tricuspid valve annulus in seven pigs for 24 weeks. Explanted devices and surrounding tissues were evaluated through material characterization (SEM, GPC, FT-IR, and ¹H-NMR) and histological analysis. SEM and FT-IR confirmed preserved surface morphology and chemical bonds, GPC showed stable molecular weight, and ¹H-NMR revealed intact urethane and ether linkages. Materials characterization revealed no evidence of hydrolytic or oxidative degradation, indicating structural stability of the devices. Histological analysis showed stable device positioning with minimal thrombosis or inflammatory response. Biocompatibility was confirmed via ISO 10993-1:2018 Standard, and extractable substances were evaluated under aggressive extraction conditions specified by ISO 10993-18:2020, with no toxicologically significant findings. These findings support the long-term biostability and biological safety of the PUIDs in dynamic cardiac environments, informing future design criteria for catheter-delivered cardiovascular devices.

Abstract: This study investigates media wear in stirred media mills by varying key variables such as specific energy input and feed sizes. Specific energy input is considered a primary influencing factor in the grinding process, making it a central focus of the study. The investigation was carried out in two parts: experimental studies and DEM simulations. In the experimental phase, the specific energy input was varied at three levels (5, 10, and 20kWh/t), while feed sizes were varied at two levels (1150+850 μm and -600+425 μm). The data generated were used to calibrate the DEM mill program. The DEM was then used to assess energy spectra and particle probability breakage properties to predict the milling rate. The experimental findings revealed clear insights into the impact of varying specific energy and feed sizes on the grinding process, highlighting specific energy as a key driver of grinding efficiency. It is demonstrated that this simple scheme indicates good assessment capabilities without requiring the complexity of combining DEM with CFD. This approach is recommended for speedier evaluation of the material grinding rate, provided the fracture properties of the material to be ground are available.

Abstract: Performance based evaluation of reinforced soil retaining structures often relies on numerical analyses that demand substantial time and expert effort, largely due to the complex interactions among soils, reinforcements, facings, and seismic loading. This study introduces an efficient approach for developing seismic resisting capacity curves for geosynthetic reinforced slopes with rigid facings, using a computer program built on the Force Equilibrium based Finite Displacement Method (FFDM). Positioned between conventional, non performance based limit equilibrium methods (LEM) and the more computationally intensive finite element method (FEM), the FFDM offers a practical platform for performance based seismic assessment in engineering design. The method is demonstrated through a re examination of the Tanada Wall, a geosynthetic reinforced soil retaining wall with a full-height rigid panel facing (GRS-FHR) that experienced strong shaking during the 1995 Hyogoken Nambu earthquake (ML = 7.2). Using only parameters available in published databases, the FFDM generates realistic seis-mic resistance curves and directly computes seismic displacements. Three advantages distinguish the FFDM from traditional LEM based Newmark approaches: (1) explicit incorporation of peak soil strength and post peak degradation along the slip surface, eliminating the need for empirical “operational” strength adjustments; (2) direct use of peak ground acceleration (HPGA/g) as input, avoiding reliance on empirically selected seismic coefficients; and (3) capability for back analysis, enabling soil strength and de-formation parameters to be calibrated from small observed displacements (on the order of 10⁻³ m) during medium scale earthquakes and subsequently used to predict structural response under more severe ground shaking.

Abstract: Pulsed electrolysis is a promising technology for the clean and sustainable generation of hydrogen. Research on electrode materials with similar geometries regarding their microscopic behavior in electrolysis at the solid-liquid interface is needed to improve the efficiency of this production process. This work compares zinc and stainless-steel electrodes using different electrolyte concentrations, employing both conventional and pulsed electrolysis. Parameters such as current and voltage curves, Tafel plots, efficiencies, solution resistances, and charge transfer resistance are compared. The findings highlight the duty cycle as a key operational parameter for modifying the electric double layer, demonstrating that optimized pulsed electrolysis can enhance efficiency without changing electrolyte concentration, and instead choosing the adequate chemical composition and surface roughness of the electrode material. Conclusions are drawn regarding the best electrode and the improvement techniques for corrosive electrodes.

Abstract: Millimeter-wave (mmWave) detection is a widespread human activity recognition (HAR) method. However, due to the mmWave characteristics, it is challenging to per-form HAR in non-line-of-sight (NLOS) environments. Before the prevalence of artificial intelligence (AI) technologies, mmWave-based HAR systems mainly relied on tradi-tional signal processing and handcrafted feature extraction (e.g. time–frequency analy-sis, multi-path modeling, micro-Doppler signature analysis), combined with classical classifiers such as Support Vector Machine (SVM) or Random Forest. However, these approaches were highly sensitive to environmental variations and failed to generalize in NLOS conditions. With the advent of AI, techniques such as Convolutional Neural Network (CNN) have played a crucial role in feature extraction from two-dimensional images generated by mmWave radar signals. In this paper, for the first time, a frame-work for Federated Continual Learning (FCL) based on CNN is proposed for improving radar detection accuracy in NLOS environments while preserving personal privacy through local training without uploading radar tensors and personal data to the global server for model aggregation. Additionally, the FCL model learns from both LOS and NLOS environments for improving cross-domain adaptability and recognition.

Abstract: This research implemented management indicators to increase produc-tivity at the ice plant, seeking to align with SDG 9 through the construc-tion of resilient infrastructure, promoting sustainability and innovation. The overall objective was to improve plant productivity through the ap-plication of management indicators. The research was applied, with a quantitative approach and a pre-experimental design, analyzing produc-tion records. Results showed a 52.49% increase in production and a 107.65% increase in gross profit, with 52.50% of production capacity be-ing utilized. These improvements were attributed to the implementation of a balanced scorecard, standardization, the Kanban board, enhanced maintenance management, and staff training. Additionally, raw material productivity increased by 7.66%, labor productivity by 130.02%, and total productivity by 53.37%. These improvements were demonstrated through inferential testing, yielding a p-value of 0.006 (p < 0.05), thus confirming the hypothesis. In conclusion, the establishment of management indica-tors enabled the implementation of significant improvement actions within the organization.

Abstract: Leading-edge erosion of wind turbine blades caused by repeated raindrop impingement can significantly reduce power output and increase maintenance costs. This study develops a rain erosion atlas for Japan over 11 years from 2006 to 2016 based on CRIEPI-RCM-Era2 dataset. The NREL 5 MW, DTU 10 MW, and IEA 15 MW wind turbines were employed to evaluate the incubation time (erosion onset time) of commercial polyurethane-based coating at blade tip. Erosion progression was simulated using an empirical damage model that relates raindrop impingement and impact velocity to the incubation time. The rain erosion atlas reveals a clear correlation between wind turbine size and erosion risk: the NREL 5MW turbine shows the incubation time of 3–12 years, the DTU 10MW turbine 1–4 years, and the IEA 15MW turbine 0.5–2 years. Shorter incubation times are observed on the Pacific Ocean side, where annual precipitation is higher than on the Sea of Japan side. Additionally, the influence of coating degradation due to ultraviolet radiation was assessed using solar radiation data, revealing a further reduction in incubation time on the Pacific Ocean side. Finally, the potential of erosion-safe mode operation was examined, demonstrating its effectiveness in alleviating erosion progression.

Abstract: This paper aims to maximize the information transmission rate by eliminating channel redundancy while still enabling reliable recovery of uncoded data. It is shown that parallel message-passing decoders can recover uncoded transmitted bits by increasing only the receiver-side computational complexity. In the proposed architecture, the $k$ transmitted information bits are embedded into a higher-dimensional linear block code at the receiver, and appropriately valued log-likelihood ratios (LLRs) are assigned to the parity positions. One-shot parallel decoding is performed across all hypotheses in the codebook, and the final decision is obtained by minimizing an orthogonality-based energy criterion between the decoded vector and the complement of the code space. For a fixed $(8,24)$ linear block code, the decoding behavior is investigated as a function of the parity-bit LLR magnitude. Increasing the parity LLR magnitude introduces an artificial reliability that improves hypothesis separation in the code space and yields a sharper waterfall region in the bit-error-rate (BER) curves. This increase in parity LLR also induces a systematic rightward shift of the BER curves, which does not correspond to a physical noise reduction and must therefore be compensated for fair performance comparison. After proper compensation, it is observed that increasing the parity LLR improves decoding performance up to a point where it can surpass the performance of conventional LDPC decoding with iterative processing. In principle, arbitrarily strong decoding performance can be approached by increasing the parity LLR magnitude; however, the maximum usable value is limited by numerical instabilities in practical message-passing implementations. Overall, the results demonstrate that strong decoding performance can be achieved without transmitting redundancy or employing high-dimensional coding at the transmitter, relying instead on receiver-side processing and controlled parity reliability over an additive white Gaussian noise (AWGN) channel.

Abstract: The building sector accounts for approximately 30% of global energy use. The demand for energy-efficient, high-performance buildings is increasing given the increasing awareness of the climate crisis. The building envelope greatly influences overall building energy performance. Considering the broad shift from passive to adaptive systems, smart window technologies are attracting attention. Despite their potential, few scholars have examined occupant comfort in spaces with smart windows. This gap is addressed herein by comparatively analyzing occupants’ responses to thermal and visual environments in a room with a smart window (RoomSW) and a room with a conventional window (RoomCW) in a residential building in winter. The smart window is operated via a glare-prevention tint control strategy, whereby the tint level is adjusted stepwise when glare occurs. The results reveal that under thermal conditions comparable to those in an actual dwelling, winter-time smart window tinting for glare prevention does not decrease occupants’ thermal sensation or satisfaction. Regarding visual comfort, the conditions in both the RoomSW and RoomCW satisfy the minimum indoor illuminance requirements, but glare occurs in the RoomCW. The questionnaire results indicate greater satisfaction with the luminous environment in the RoomSW relative to the RoomCW. This difference is statistically significant (p < 0.05).

Abstract: Growing concerns over greenhouse gas (GHG) emissions have positioned hydrogen fuel cell buses (HFCBs) as a promising alternative for sustainable urban mobility. By elimi-nating tailpipe emissions and enabling significant reductions in well-to-wheel GHG in-tensities when hydrogen is sourced from renewables, HFCBs can contribute to im-proved urban air quality, energy diversification, and alignment with climate goals. De-spite these benefits, large-scale adoption faces challenges related to production costs, hy-drogen infra-structure, and efficiency improvements across the supply chain. Life Cycle Assessment (LCA) provides a valuable framework to assess these trade-offs holistically, capturing en-vironmental, economic, and social dimensions of HFCB deployment. How-ever, incon-sistencies in system boundaries, functional units, and impact categories high-light the need for more standardized and comprehensive methodologies. This paper ex-amines the potential of hydrogen buses by synthesizing evidence from peer-reviewed studies and identifying opportunities for integration into urban fleets. Findings suggest that when combined with robust LCA approaches, hydrogen buses offer a pathway to-ward decar-bonized, cleaner, and more resilient public transport systems. Strategic adop-tion could not only enhance environmental performance but also foster innovation, infra-structure de-velopment, and long-term economic viability, positioning HFCBs as a corner-stone of sus-tainable urban transportation transitions.