Abstract: Based on the scientific and technological revolutions that have characterized Structural Engineering during the last two centuries, we can acknowledge how the historical scale doubling of suspension bridges has occurred for at least four times, from the beginning of the XIX to the end of the XX Century. These four revolutions represent the tangible mankind’s challenge against natural forces: gravity, wind, earthquake. In this context, the prospective scale doubling requested by next-generation bridges (e.g., the Messina Straits Bridge) could take place only if significant scientific and/or technological innovations occur. This opportunity is presently offered by Fracture Mechanics, since any appropriate advanced design approach should take into account also brittleness size-scale effects, in addition to loading-capacity size-scale effects, which are well-known since Galileo’s studies. From the technological point of view, the open problem of physical similitude could be effectively solved by using fibre-reinforcement.

Abstract: Copper-containing steel is widely used in ship plates and other marine engineering fields due to its excellent mechanical properties and good weldability. However, in hydrogen-containing media environments, ship plate steel is prone to hydrogen embrittlement during service. Existing research primarily focuses on steel grades with copper content below 3 wt.%, while the diffusion and trapping behavior of hydrogen in ultra-high copper steel with copper content exceeding 3 wt.% remains unclear. Therefore, this study designed an ultra-high copper content steel with a copper content of 6.01%, and investigated the diffusion behavior of hydrogen in the test steel under different hydrogen charging current densities through microstructure characterization, slow strain rate tensile testing, electrochemical hydrogen permeation, and internal friction tests. The results indicate that with an increase in hydrogen charging current density, the anti hydrogen embrittlement performance of the test steel is significantly improved without deteriorating its mechanical properties. At the same time, the hydrogen trap density increased by 167%, with the irreversible hydrogen trap density increasing by 76.3%, and the reversible hydrogen trap density increased significantly by 537.9%. A large number of microstructures, such as phase boundaries, grain boundaries, and dislocations, have formed inside the material, which have reversible trapping effects on hydrogen, effectively suppressing the migration of hydrogen in the crystal structure and reducing the embrittlement phenomenon caused by hydrogen. This study expands the application potential of copper containing steel in the field of ocean engineering, providing important reference for the future development of high-strength hydrogen embrittlement resistant copper steel with ultra-high copper content.

Abstract: This study aimed at producing galactooligosaccharides (GOS) from Porungo cheese whey in immobi-lized enzyme bioreactors. The β-galactosidase was produced, concentrated, and immobilized on chi-tosan-genipin supports. Initially, GOS production was conducted in conical flasks, investigating three different variables: enzyme concentration (50 U/mL - 150 U/mL), Porungo cheese whey concentration (200 g/L - 400 g/L), and temperature (37 – 43 ºC). The highest GOS yields (15.24 %) occurred under intermediate process conditions (100 U/mL, 300 g/L, 40 ºC), reaching a GOS concentration of 27.04 g/L. These conditions were then used in a packed-bed column bioreactor operated in batch mode, achieving yields of 19.72 %. Repeated batches were carried out, and the system was stable until the fifth cycle, with enzyme activity remaining at 83.56 % of the initial level. Continuous bioreactors were conducted, varying feed flow rates (1 mL/h - 3 mL/h), with the highest yields and lactose conversion occurred for the longest residence time (24.63 % and 68.38 %), respectively, with high GOS concentra-tion (44.14 g/L). Microorganisms isolated from Porungo cheese showed the ability to metabolize the GOS produced, demonstrating its prebiotic potential. This work can contribute to optimizing the production of GOS, an important product for pharmaceuticals and food industries.

Abstract: The long-term degradation of photovoltaic (PV) modules under real operating conditions remains a critical factor for accurately assessing lifetime energy yield and economic performance. In this study, the field performance of thin-film cadmium telluride (CdTe), polycrystalline silicon (poly-Si), and monocrystalline silicon (mono-Si) photovoltaic modules was evaluated after 15 years of continuous outdoor operation in a temperate continental climate in Southeastern Europe (Sliven region, Bulgaria). All modules were installed at the same site and operated under identical mounting and environmental conditions, enabling a direct comparison between technologies. The results after electrical characterization reveal a total efficiency reduction of 12.3% for CdTe modules, corresponding to an average degradation rate of approximately 0.82% per year. In contrast, polycrystalline silicon modules exhibit a significantly lower efficiency loss of 1.2% (≈0.08% per year), while monocrystalline silicon modules show the lowest degradation, with only a 0.4% decrease over the 15-year period (≈0.03% per year). Visual inspection identified localized degradation features, including surface alterations and defects, which serve as qualitative indicators of long-term material aging. The findings indicate that crystalline silicon technologies demonstrate substantially higher long-term stability than thin-film CdTe modules under moderate continental climatic conditions. The presented long-term field data contribute to improved understanding of climate-specific degradation behavior and support more accurate lifetime performance modeling for photovoltaic systems in similar environments.

Abstract: This study addresses the challenge of consistently transferring atomistic parameters of the C–C bond into phenomenological material characteristics within framework of continuum mechanics. Particular attention is given to determining the effective transverse diameter of the covalent C–C bond in carbon nanostructures. The dependence of this diameter on the Poisson’s ratio ν is examined, and the influence of the interatomic stiffness constants k_r,k_θ,and k_τ is systematically analyzed. Classical representative-volume models of the C–C bond based on the Euler–Bernoulli beam hypothesis violate thermodynamic stability conditions and lead to nonphysical Poisson’s ratio values exceeding 0.5, due to the neglect of shear deformation effects. To overcome this limitation, an approach based on Timoshenko beam theory is proposed, accounting for both bending and shear deformations. This approach enables estimation of energetically equivalent states between the phenomenological representative volume and the corresponding atomistic C–C bond model. As a result, a sixth-order algebraic equation is derived linking the effective bond diameter, the Poisson’s ratio, and the molecular mechanics force constants. Analysis of this equation reveals a narrow range of effective bond diameters and Poisson’s ratios for which thermodynamic stability conditions are satisfied. Within this range, physically consistent macroscopic material parameters can be directly expressed in terms of atomistic force constants.

Abstract: High-voltage circuit breakers (HVCBs) are critical switching devices whose mechanical reliability directly affects the safe and stable operation of power systems. However, accurate fault diagnosis of HVCBs remains challenging due to complex mechanical structures, nonlinear vibration characteristics, and sensitivity to parameter selection in data-driven models. To address these issues, this paper proposes an enhanced me-chanical fault diagnosis method based on a multi-strategy improved dung beetle op-timization–support vector machine (MIDBO-SVM) framework. First, mechanical vi-bration signals under four typical operating conditions of HVCBs are collected, and discriminative frequency-domain features are extracted using the fast Fourier trans-form. To overcome the limitations of conventional SVMs in parameter tuning, a mul-ti-strategy improved dung beetle optimization (MIDBO) algorithm is developed by integrating adaptive search mechanisms to enhance global exploration and conver-gence efficiency. The proposed MIDBO is then employed to optimize the penalty and kernel parameters of the SVM, yielding a robust and well-generalized fault diagnosis model. Experimental results demonstrate that the MIDBO-SVM model exhibits supe-rior convergence behavior and a stronger ability to escape local optima compared with standard optimization strategies. The proposed method achieves the highest diagnostic accuracy of 96.67% across multiple fault categories. Moreover, under imbalanced sample conditions, the MIDBO-SVM maintains high diagnostic accuracy and stability, effectively distinguishing different operating states of HVCBs. These results confirm the effectiveness and robustness of the proposed approach for mechanical fault diag-nosis of HVCBs.

Abstract: Floating involute splines are widely used in aviation power transmission systems to transmit torque. In this paper, by establishing a finite element model of the dynamic deformation of the floating involute spline shaft, the influence of the dynamic deformation of the spline shaft on the misalignment state of the spline pair under various typical dynamic overloads was analyzed. And a contact simulation model of the floating spline pair with the actual tooth profile was established to study the influence of the spline misalignment caused by dynamic deformation on the contact pressure distribution on the tooth surface. The contact stress fatigue strength of the spline pair under dynamic loads such as limit loads and ultimate loads was evaluated. The results show that the axial overload can lead to the axial movement of the mating surface of the floating spline, reducing the effective axial contact length; the radial overload and gyroscopic moment can lead to the parallel misalignment and angular misalignment of the spline. When the overloads are superimposed, the angular misalignment of the spline is the most significant under the limit load, and the parallel misalignment is the most significant under the ultimate load. There are obvious stress concentrations and uneven load-bearing in the contact stress distribution of the spline under the limit and ultimate loads. According to the infinite life and static strength design methods, the evaluation shows that the long-term working contact fatigue strength of the floating spline of a certain type of engine under the ultimate load does not meet the design requirements, and the spline parameters need to be optimized. The quantitative analysis method for the misalignment of the floating spline under the superposition of various dynamic loads formed in this paper provides an important theoretical reference for the design of the misalignment of the aviation floating spline and the improvement of its long-term working ability.

Abstract: Accurate quantification of the crack tip driving force (ΔK) is fundamental to predicting the fatigue life of engineering structures. Analytical formulations of ΔK are rarely available for components with complex geometries. In such cases, finite element (FE) analysis has become a widely accepted approach for determining ΔK. In this study, an FE-based solution for the crack-tip driving force of a fatigue crack in an asymmetric L-shaped bell crank geometry, a representative complex structure, is established. The structure is fabricated from AISI 410 martensitic stainless steel. The FE-predicted ΔK_I for crack growth in the Paris regime has been independently validated using the multifractal stress-intensity-factor model. Results show that the fatigue crack in the bell crank structure is driven by a combined Mode-I (opening) and Mode-II (shearing) crack tip loading along a curved crack path trajectory, as dictated by the asymmetric stress distribution. The fatigue crack edge exhibits fractality with fractal dimensions ranging from 1.00 (Euclidean) to 1.18 over the crack length, (a-a_o) up to 9.947 mm. The FE-calculated crack tip driving forces of the bell crank structure are comparable with those computed based on the corrected crack edge fractal dimensions, thus validating the FE simulation outcomes. The resulting fatigue crack growth rates, determined from crack-tip driving forces based on validated FE-computed contour integrals, are comparable to those obtained from the ASTM standard tests.

Abstract: Background: This study investigates the biomechanical and physiological demands of the pumping maneuver in Laser-class sailing, a dynamic technique requiring coordinated upper and lower body oscillations to generate propulsion in marginal wind conditions. The proposed framework utilizes a mixed-methods approach combining musculoskeletal simulation, kinematic analysis, ergonomic assessment, and subjective evaluation. Methods: Thirty-six experienced Laser sailors completed a questionnaire quantifying perceived discomfort using the Borg CR-10 scale across three temporal phases: during pumping, immediately post-sailing, and the following day. The pumping motion was replicated on land by an experienced sailor and analyzed using marker-based motion capture and Delmia® musculoskeletal simulation software. REBA ergonomic assessment was performed to evaluate postural risk. Results: Musculoskeletal simulation revealed maximal normalized activation (100.0%) in seven deep trunk stabilizers and left latissimus dorsi. Pronounced lateral asymmetry was observed, with right-sided trunk dominance. Lower extremity activation was moderate on the right and minimal on the left. Kinematic analysis identified substantial lumbar excursions (45.3° flexion-extension, 38.7° lateral flexion, 42.1° axial rotation). REBA assessment yielded a score of 11 (Very High Risk). Questionnaire data revealed a paradoxical relationship between objective activation and subjective fatigue: maximal trunk activation corresponded to lower perceived fatigue, while moderate lower limb activation corresponded to higher perceived fatigue. Musculoskeletal discomfort prevalence was 72.2%, concentrated in the lower back, shoulders, and knees. Conclusions: Findings highlight the deep trunk stabilizers, latissimus dorsi, and lower extremities as primary contributors to pumping execution, while emphasizing pronounced lateral asymmetry and high ergonomic risk. The activation-fatigue paradox suggests differential physiological mechanisms between trunk stabilizers and lower limb muscles. These insights can guide training interventions, injury prevention strategies, and ergonomic modifications to optimize performance and reduce injury risk in competitive sailing.

Abstract: Ultrasound patches represent a transformative advancement beyond conventional ultrasonography, evolving into intelligent theranostic systems for personalized healthcare. This evolution is propelled by synergistic innovations in flexible piezoelectric materials and integrated designs. The development of piezoelectric polymers, lead-free ceramics, and bio-composite materials has laid the foundation for long-term, conformal, and biosafe interfacing with the human body. Structurally, miniaturized transducer arrays, multimodal integration, and bioinspired interfaces have enabled high-precision deep-tissue sensing and spatiotemporally controlled energy delivery. These capabilities are converging to create closed-loop platforms, as demonstrated in continuous cardiovascular monitoring, image-guided neuromodulation for neurological disorders, on-demand drug delivery, and integrated tumor therapy with real-time feedback. Despite persistent challenges in material biocompatibility, energy efficiency, and clinical standardization, the future of ultrasound patches lies in their deep integration with multimodal sensing, machine learning, and adaptive control algorithms. This path will ultimately realize their potential for intelligent, closed-loop theranostics in chronic disease management, telemedicine, and personalized therapy.

Abstract: Urban last-mile logistics is increasingly served by light electric micro-mobility vehicles, whose high-variability duty cycles accelerate heterogeneous degradation and intermittent faults. This study presents a reproducible, data-centric workflow for predictive maintenance using public sensor datasets and addresses 3 complementary tasks: (i) battery ageing prognostics via State-of-Health (SoH) trajectory forecasting, (ii) multi-class fault diagnosis from propulsion/thermal telemetry, and (iii) tyre-pressure–aware energy-consumption modelling. Battery ageing was analysed using the NASA Prognostics Center of Excellence dataset of 4 Li-ion 18650 cells (B0005, B0006, B0007, B0018). After cleaning and smoothing charge-phase records, an LSTM model (50 hidden units, 35 epochs, batch size 256, shuffle=False) tracked measured capacity fade with stable convergence; removing normalization produced a small reported degradation (average error increase of 0.3%). A degree-2 polynomial regression baseline captured the global decay trend but generalized less effectively (test R² = 0.6861, test MAE = 0.0274). For fault diagnosis on the “New Energy Vehicles Diagnosis” dataset, Random Forest achieved the highest test performance (accuracy 0.899, macro-F1 0.900, macro-AUC 0.985), followed by SVM (RBF) and logistic regression. For energy consumption regression including tyre pressure, linear regression showed consistent generalization (test R² = 0.9474, test MSE = 0.2528) under nominal pressure conditions (≈ 28–35 psi). Overall, the results indicate that task-appropriate model selection and disciplined preprocessing can yield reliable, interpretable predictive signals for maintenance planning in micro-mobility contexts.

Abstract: The interoperability of electronic health records in Colombia faces a critical gap between the regulatory mandates established by Law 2015 of 2020, Resolution 866 of 2021, and Resolution 1888 of 2025, and the actual technical capacity of healthcare institutions to implement them. This article presents PIRE (Electronic Records Interoperability Platform), an open-source architecture that demonstrates the viability of end-to-end FHIR systems in the Colombian context. The main objective was to develop a platform capable of integrating health data from biomedical devices into an FHIR server, preserving clinical semantics through LOINC terminologies. The methodology followed an iterative development approach, implementing a HAPI FHIR server on AWS, a normalization application in Flask, and clinical visualization modules aligned with the FHIR Core CO Implementation Guide. The Bioharness-3 device was used to capture metrics on heart rate, respiratory rate, activity, and posture. The results demonstrate that the architecture enables the semantically preserved exchange of biosignals in real time, validating compliance with Colombian IHCE specifications. It is concluded that PIRE constitutes a reproducible reference model for healthcare institutions that wish to implement interoperability without relying on costly enterprise solutions.

Abstract: Frequency-response analysis (FRA) is widely used as a method for the offline diagnosis of winding deformations in power transformers. To apply this method to a transformer in operation, new network functions must be established. These should be suitable for the excitation and response signals of online measurements, and they should eliminate the influence of external equipment that is directly connected to the high-voltage outgoing lines of the transformers. In this paper, based on circuit network analysis, series of transfer functions for online FRA are proposed. These include comprehensive consideration of the transformers and the external electric power grid, including the connection types of the three-phase windings, whether or not the neutral point is grounded, and the mutual coupling between the high- and low-voltage windings. The suitable conditions, appropriate configurations of application, feasibility, and sensitivity of each network function were analyzed. Among them, four functions involve the parameters of the outside network but are easy to measure. Two of the functions are not affected by the outside network. These network functions will help promote online applications of FRA.

Abstract: The increasing penetration of converter-interfaced renewable energy sources has led to a reduction in system inertia and has intensified frequency stability challenges in modern power systems. Battery energy storage systems (BESSs) can provide fast active power support. However, their effectiveness depends on installation location, power rating and network operating conditions. This paper proposes a power flow informed sensitivity based method for the placement and sizing of distributed BESSs to improve frequency nadir performance in low-inertia power systems. The proposed method combines marginal frequency sensitivity obtained from time domain screening simulations with network coupling information derived from power flow. These components are integrated into an optimization formulation subject to practical installation constraints and solved using particle swarm optimization. The method is evaluated using time domain simulations on the IEEE 39-bus New England test system under multiple generator outage contingencies. The results show that BESS locations exhibit non-uniform and nonlinear contributions to frequency nadir and rate of change of frequency improvement. The proposed optimal placement and sizing method distributes BESS capacity across multiple buses based on frequency impact and network coupling. Compared with the baseline case and a benchmark metaheuristic optimal placement and sizing method, the proposed method achieves higher frequency nadirs and lower RoCoF values across all evaluated contingencies. The performance is maintained under load variation scenarios and reduced system inertia due to renewable energy integration. The proposed method provides a physically meaningful and computationally efficient approach for allocating distributed BESSs to support frequency stability in low-inertia power systems.

Abstract:

This study investigates the critical interplay between cement grade (32.5, 42.5, 52.5) and fiber/cement ratio (1/2 to 1/5) in determining the performance of cement-bonded fiberboards. Experimental results highlighted a fundamental trade-off: while reducing the fiber content significantly enhanced mechanical strength and moisture resistance, it naturally diminished thermal insulation capabilities. The analysis identified the 42.5 cement at a 1/4 ratio as the optimal formulation, offering the most effective balance between structural integrity and physical stability. To understand the mechanism behind this performance, the study employed multi-scale characterization using FTIR, XRD, and SEM. These analyses revealed that the superior properties of the optimal formulation stem from a denser hydration product network and improved fiber encapsulation. Specifically, the 42.5 cement facilitated a more robust Calcium-Silicate-Hydrate (C-S-H) gel formation compared to the 32.5 types, creating a stronger fiber-matrix interface. These findings provide a scientific basis for tailoring fiberboard production, demonstrating that material properties can be precisely engineered for either load-bearing or insulating applications.

Abstract: This study experimentally investigates a novel hybrid system integrating thermoelectric generators (TEGs) with direct contact membrane distillation (DCMD) for simultaneous low-grade heat recovery, electricity generation, and water desalination. Commercial TEG modules were sandwiched between heat spreaders to transfer thermal energy from a source (approx. 140°C) to a cooling sink, driving saline water evaporation through a hydrophobic membrane. A validated mathematical model showed strong agreement with experimental results. The system achieved freshwater mass fluxes of 8–9.5 kg/m²/h and electrical power outputs density of 25–35 W/m². Increasing heat input (450–700 W) significantly enhanced freshwater production and electrical output, improving the gain output ratio (GOR) and reducing specific energy consumption (SEC). While higher feed salinity (up to 35,000 ppm) measurably declined mass flux and thermal efficiency, thermoelectric generation and thermal resistance remained largely unaffected. Energy and exergy efficiencies showed moderate sensitivity to operating conditions, while the Water–Electrical Energy Cogeneration Index (WEeCI) increased at high salinity, highlighting the robust contribution of electricity generation. These results demonstrate the potential of the TEG–DCMD system for sustainable co-generation of water and power from industrial waste heat or renewable thermal sources.

Abstract: To address the challenges of separating roots from soil and the high soil carryover during Ophiopogon japonicus harvesting in heavy clay soils, a variable-gap tooth roller chain rod-and-slat separation device was designed, integrating variable-gap tooth roller soil crushing with vibrating chain rod-and-slat conveying and separation functions. A coupled "soil–plant–equipment" model was established using the discrete element method. Conveying speed, vibration frequency, and amplitude were selected as key operational parameters. Interaction effects were analyzed, and dual-objective optimization was performed using response surface methodology. The contact number was used to characterize soil–plant particle adhesion, while D80 (the distance corresponding to 80% soil fallout) represented spatial distribution characteristics of soil fallout. Optimization results indicate that, within the experimental parameter range, a combination yielding low contact number and low D80 can be achieved. The simulations predicted a D80 of 563.25 mm and a contact number of approximately 6. Conversion of particle-mass data indicated the average soil mass adhering to plants is about 0.0096 kg. Field validation tests conducted at a conveying speed of 0.80 m/s, vibration frequency of 12.00 Hz, and amplitude of 15.00 mm yielded an average soil mass carried by separated plants of 0.012 kg. These results demonstrated that the constructed discrete element model and response surface optimization can be applied to parameter matching for Ophiopogon japonicus root–soil separation equipment, providing a reference for optimizing root–soil separation machinery in hilly and mountainous regions for Chinese medicinal herbs.

Abstract: This article introduces a new multi functional DC-DC converter-based smart energy management system utilizing Solar PV and Wind sources for Electric Vehicle applications. To promote efficient battery charging, discharging, and enhanced protection from faults, an artificial neural network (ANN) approach is incorporated. The primary feature of the ANN controller is to detect faults in the EV battery for timely intervention. In comparison with existing topologies, the proposed converter can efficiently operate under dynamic conditions and promotes better stability. In addition, the operating principle, modes of operation, design analysis, and control strategy have also been incorporated. The performance of the proposed system is evaluated through MATLAB Simulink software. Furthermore, to validate the system’s performance, a 1kW hardware prototype was built, developed, and tested to verify the effectiveness and feasibility of the system.

Abstract: Space heating remains a consequential component of residential energy demand across many climates and persists as a seasonal load even in regions where cooling dominates annual consumption. This study examines the extent to which AI-guided passive design optimization can reduce residential heating demand when envelope and solar-responsive parameters are considered in isolation. A standardized single-story residential prototype is simulated across three climatic contexts: (a) Riyadh, representing a hot-dry environment; (b) Barcelona, representing a temperate environment; and (c) Toronto, representing a cold-humid environment. The analysis combines dynamic building energy simulation with multi-generation parametric optimization based on evolutionary search. The research objective is to minimize annual space heating demand under fixed comfort conditions. Cooling is intentionally excluded, and heating demand is modeled through an ideal loads approach to focus on effects related to the building's envelope and solar gains. Under these controlled assumptions, the optimization leads to substantial reductions in heating demand across all climates, ranging from approximately 43% in cold conditions to high relative reductions in the hot and dry case. The resulting optimal solutions demonstrate how passive design strategies vary by climate. The findings support AI-guided passive optimization as a transparent decision-support approach in the residential early design stage.

Abstract: Ferroresonance is a nonlinear phenomenon in power systems that can produce irregular oscillations and severe overvoltages, leading to insulation stress and damage to transformers, voltage transformers, cables, and associated equipment. The increasing penetration of renewable energy sources, inverter-based distributed generation, underground cables, and complex grounding configurations has expanded the operating conditions under which ferroresonance may occur in modern grids. Conventional analytical methods and protection schemes often exhibit limitations in representing nonlinear magnetization, hysteresis effects, chaotic behavior, and multimodal resonance responses. This paper provides a comprehensive review of ferroresonance detection and mitigation techniques reported till 2025 with a focus on last five years. Numerical modeling approaches, electromagnetic transient simulation tools, ferroresonance modes, and conventional mitigation strategies are systematically examined. Particular emphasis is placed on recent applications of artificial intelligence, including machine learning, deep learning, fuzzy logic systems, evolutionary algorithms, expert systems, and hybrid intelligent frameworks. A comparative analysis is presented to evaluate these methods in terms of detection accuracy, computational complexity, interpretability, and suitability for real-time protection. The review highlights the complementary roles of data-driven intelligence and physics-based modeling and emphasizes integrated approaches as a practical pathway toward improving reliability, protection performance, and resilience in evolving smart grid architectures.