Abstract: In Europe, bitumen classification has traditionally relied on empirical tests, namely penetration and the Ring‐and‐Ball softening point, originally developed for unmodified binders and insufficient for modern modified materials. As an alternative, a rheology‐based method, the Bitumen Typisierungs Schnell Verfahren (BTSV), has been developed in Germany to characterize high service temperature performance, with performance requirements introduced in 2025. In this study, the performance of five bitumen types commonly used in Hungarian road construction was investigated using the BTSV method. During testing, the softening temperature corresponding to the rheological threshold value of G* = 15.0 kPa (TBTSV) and the phase angle (δBTSV) were determined. The results are compared with each other, with softening point values determined by the standardized Ring‐and‐Ball method, and with German bitumen classification systems. A total of 137 samples from production control were analyzed, including paving grade, SBS‐modified, and chemically stabilized rubber‐modified binders. Statistical evaluation included mean values and 95% confidence intervals. For rubber‐modified bitumens, the recoverable, insoluble rubber content was deter‐mined using Soxhlet extraction. Based on the results, it can be concluded that with increasing rubber content, the TBTSV value shows an increasing trend, while the δBTSV value decreases. A strong linear relationship was observed between the investigated parameters in the TBTSV–δBTSV diagram, with a coefficient of determination of R² = 0.99.

Abstract: Soil liquefaction is a significant geotechnical hazard that can lead to severe structural damage during seismic events. Traditional liquefaction assessment methods, such as those based on the Standard Penetration Test (SPT) and Cone Penetration Test (CPT), rely on empirical correlations but often struggle to capture the complex, nonlinear interactions between soil properties and seismic parameters. Recent advancements in machine learning (ML) offer data-driven approaches that can improve liquefaction prediction accuracy. This study evaluates and compares the performance of Random Forest (RF) and Artificial Neural Networks (ANNs) for liquefaction potential prediction using a dataset containing 480 field observations derived from CPT-based studies. The dataset was preprocessed using min-max normalization, and models were trained and optimized through hyperparameter tuning. Model performance was assessed using accuracy, precision, recall, F-measure, Cohen’s kappa, and AUC-ROC analysis. The results show that RF achieved the highest accuracy (89%), outperforming both ANN (86%) and the traditional CPT-based liquefaction assessment method (87%). Additionally, ROC-AUC values of 0.932 for RF and 0.872 for ANN indicate the superior classification capability of machine learning models. Feature importance analysis in RF revealed that cone tip resistance (qc), cyclic stress ratio (CSR), and peak ground acceleration (amax) are the most influential factors in liquefaction prediction. These findings demonstrate that machine learning techniques, particularly RF, provide more reliable liquefaction predictions compared to conventional empirical methods. The study highlights the potential of ML models in improving seismic risk assessments and guiding engineering decision-making processes.

Abstract: During the exploration drilling process, maintaining a vertical well trajectory is a critical issue. In geological formations with complex conditions that are prone to well deviation, conventional drilling tool assemblies exhibit poor anti-deviation performance. To achieve anti-deviation and accelerate drilling in exploration wells, a pre-bent drilling tool assembly is proposed. In this study, a dynamic model of the pre-bent drilling tool assembly was established. The anti-deviation mechanism of the pre-bent drilling tool assembly was investigated. The deviation-reduction effects of the drilling tool assembly under different parameter conditions were analyzed. The results indicate that the deviation-reducing force initially increases and then decreases as the pre-bend angle of the anti-deviation drilling tool increases. When the bend angle is between 1° and 1.13°, a larger deviation-reducing force is generated at the drill bit. A shorter distance (L1) between the near-bit stabilizer and the drill bit, a smaller near-bit stabilizer diameter, and a larger upper stabilizer diameter result in a greater deviation-reducing force. The relationship between the deviation-reducing force and the distance between the two stabilizers (L2) is not explicitly linear, but a decreasing trend is observed after the distance exceeds 10 m. Compared with the conventional pendulum anti-deviation drilling tool assembly, the deviation-reducing force of the pre-bent drilling tool assembly has an advantage of more than two orders of magnitude. Based on the calculation results, the optimal design of the pre-bent drilling tool assembly was carried out. The bend angle was increased to 1.15°, the diameter of the near-bit stabilizer was reduced to 305 mm, L2 was reduced to 9–11 m, and L1 was reduced to 0.9 m. Field applications in 22 exploration wells show that the pre-bent drilling tool assembly provides excellent anti-deviation effects. It can fully release the weight on bit while ensuring a vertical trajectory, achieving a 14% increase in the drilling rate. This technology effectively replaces vertical steering tools. Tool costs are significantly saved, providing an effective method for anti-deviation in complex formations.

Abstract: This work establishes that the complete set of Maxwell’s equations and the dynamics of the electromagnetic field emerge deductively as a theorem from the three primitive axioms of the Zeta-Minimizer Theorem (ZMT). Starting from the helical transfer matrix in star topology with anchor prime 19 and applying the integer gear up to its prime rule, the grand-partition function is uniquely constructed. Critical compositions in the s→0 limit fix the per-gear constants C_k, which govern the interaction parameters and the full Lyapunov spectrum. Thermodynamic continuity at interfaces of differing gear content then enforces the matching condition that recovers Maxwell’s equations and the electromagnetic field dynamics from first principles via the covariant fugacity Hessian. As the principal engineering realization, the Radial Helical Gear Condenser (RHGC) is introduced, a self-regulating cylindrical membrane whose hybrid layered polymer–metal composite architecture enables precise radial pressure-gradient tuning. This spontaneously forms a thin, controllable shell of marginal stability (λ_(k,19)=0). The results provide a thermodynamic origin for electromagnetism and a versatile, first-principles pathway to high-temperature superconductivity and advanced materials design.

Abstract: The ongoing energy crisis triggered by disruptions around the Strait of Hormuz is reshaping the economics of the global construction sector. Rising energy costs and supply chain disruptions are increasing production costs for conventional building materials such as cement, steel, and plastics, all of which are highly energy-intensive. This context intensifies the strategic relevance of materials capable of sequestering carbon over their life cycle, which offer the dual benefit of reduced energy consumption in manufacture and long-term climate benefit. Against this backdrop, this paper provides an updated overview of the accelerating global warming crisis, incorporating the most recent scientific evidence, and presents a comprehensive account of distinct classes of cementitious construction materials with CO₂ sequestration capacity. This paper also addresses the principal barriers to large-scale deployment and explores the landmark EU Energy Performance of Buildings Directive underscores the persistent lack of robust global regulations requiring real estate investors to disclose embodied carbon emissions.

Abstract: The article presents the results of research on the course of variability of planned and actual cost during the implementation of sustainable construction projects. The correlation of cost-time parameters in implementation in a group of 41 investment tasks, classified into five typologically homogeneous construction sectors in Poland, completed in the years 2006 – 2025, was examined. All of these enterprises have been designed and carried out within sustainable requirements and all fulfilled either platinum or gold LEED certification. The logistic centres have been executed in line with BREEAM certification. The course of cost volatility and the functions of the cost trend during the implementation of sustainable investment tasks were determined. Proven managerial tools were used, well described scientifically – the S-curve and the EVM method. The result of the research achievement is to confirm the correctness of the main thesis of the research: cost and time of implementation are not completely interdependent parameters and show great variability during the implementation of typologically diverse, although uniformly balanced construction projects. Finally – as regard sustainability – there is no correlation between sustainable design with execution and cost course of construction projects. Deep analysis of construction costs variability of sustainable diverse enterprises led to the main conclusion: design and construction of sustainable projects in accordance to LEED or BREEAM certification do not guarantee execution projects in line with their planned cost, no matter what sector the project belongs to.

Abstract: The construction sector plays a central role in global resource depletion and waste generation, with construction and demolition activities accounting for more than one-third of total waste produced in the European Union. Despite growing interest in circular construction, one of the major barriers to large-scale material reuse is the lack of reliable information on the type, quantity, location, and availability of secondary materials in the urban environment. Existing planning tools rarely integrate material stock information into design and policy decision-making processes. Addressing this gap is essential for implementing circular economy strategies and enabling urban mining practices. This study presents the application of a spatially explicit bottom-up Material Stock Analysis (MSA) to quantify and map the embedded materials within an urban district of Milan. The research results in the creation of a secondary material cadaster and the estimation of material stock. The adopted methodology combines municipal GIS datasets, historical cartography, building archetype classification, and literature-derived material intensity coefficients. The final dataset is re-integrated into a geospatial environment to visualize material distributions and generate material-specific spatial analyses and heat maps. The study intends to support architects, urban designers, planners, and policymakers with decision-support information to guide design strategies, demolition planning, and resource governance at the district and metropolitan scales. The outcome aims at bridging architectural design knowledge with urban-scale material information through a replicable GIS-based workflow.

Abstract: A numerical model is presented for heat-coupled continuous-wave second harmonic generation in a double-pass type-II potassium titanyl phosphate (KTP) cavity. The model solves eight coupled partial differential equations governing forward and backward ordinary and extraordinary fundamental fields at 1064 nm, forward and backward second-harmonic fields at 532 nm, three-dimensional transient heat diffusion, and thermally induced phase mismatching (TIPM). Given crystal geometry, beam parameters, pump power, and cooling boundary conditions, the solver produces spatiotemporal temperature distributions, phase-mismatch profiles, and electric-field amplitudes along the propagation axis. The implementation requires less than 8 GB of memory and runs on standard desktop hardware. Comparison with published experimental data yields agreement within 4 % in predicted conversion efficiency. The source code is available under the MIT License (v1.0.2, DOI 10.5281/zenodo.17362470).

Abstract: Robotic assistive devices, such as exoskeletons, are increasingly employed in walking rehabilitation. Therefore, the measurement of both movement kinematics and cognitive workload is important to understand this human-robot interaction in real-world contexts. To address this need this study presents the validation of a framework integrating inertial motion capture (Xsens) and eye-tracking sensor (Pupil Neon) within a Mixed Reality (Meta Quest 3) architecture. We developed an overground dual-task paradigm in which holographic numbers appear in the user’s peripheral vision. This setup actively stimulates visuospatial attention while quantifying kinematic and cognitive output. To validate the framework, the protocol has been tested on 30 healthy subjects across repeated exoskeleton training sessions. Statistical analyses revealed that the Multiple Correlation Coefficient (CMC) and Spectral Arc Length (SPARC), calculated on the shank angular velocity, together with the Step Length Variability exhibited significant time effects (p < 0.01), mapping the transition toward automated gait. Concurrently, pupillometric data demonstrated a measurable reduction in neurocognitive demand; specifically, the Task-Evoked Pupillary Response (TEPR) decreased significantly across progressive training sessions (p < 0.05). With this work, we validated a measurements protocol that aims to provide a novel methodology for objectively evaluating motor and cognitive adaptation in wearable assistive devices.

Abstract: Hybrid steel–PVA fiber-reinforced concrete offers promise for enhancing both load-bearing capacity and deformation capacity. However, the coupled effects of fiber parameters and volume-fraction combinations on compressive strength (σc) and peak strain (εc) are still not fully understood. A unified, interpretable, and engineer-ing-oriented quantitative framework is still lacking. This study compiled experimental data from 26 published literature, building a multi-source database consisting of 397 datasets for σc and 203 datasets for εc. Based on this database, a comprehensive ana-lytical framework was proposed, including model prediction, SHAP-based interpreta-tion, Monte Carlo marginalization, synergy gain window determination, and du-al-objective mix proportion optimization. For σc prediction, LightGBM achieved the highest test-set R² (0.9783), whereas CatBoost showed more robust error control (MAE = 2.7409 MPa). CatBoost was therefore selected as the base model for the subsequent interpretation analysis. For εc prediction, Bayesian-optimized CatBoost achieved the best test performance (R² = 0.9659, MAE = 0.0218, RMSE = 0.0358), while the trans-fer-learning model reached a comparable accuracy level (R² = 0.9650). SHAP analysis revealed that σc is mainly governed by matrix mix-proportion factors and steel fiber volume fraction, whereas εc is more sensitive to S/B and PVA-related variables. The mean synergy-gain maps generated via Monte Carlo marginalization and two-dimensional grid evaluation further showed clear differences between the two targets. Positive synergy in σc was highly localized. Its maximum mean synergy gain was 4.7949 MPa at (Steel, PVA) = (1.875%, 2.000%). By contrast, εc exhibited a wider positive-synergy region, with a peak value of 0.0141629 at (0.38%, 1.62%). Therefore, the engineering output of this study is not a single optimal mix point. In-stead, it is a set of candidate windows for different performance targets, together with boundary-risk identification and priorities for experimental validation.

Abstract: The structural strength requirements for timber buildings have been significantly tightened in the second generation of Eurocodes (EN 1990:2023, EN 1991-1-7), which poses a particular challenge for solid timber frames with a beam-and-column structure, where the transfer of tensile forces via dowel connections is inherently limited. This article presents an effective two-scale finite element method (FEM) modelling framework for assessing the strength of such frames during column removal. At the connection level, a continuous fracture mechanics model is used, based on a modified quadratic Hashin-type failure criterion, combining non-linear FEM up to peak load with post-peak behaviour defined in accordance with Eurocode 5. At the overall frame level, multi-fibre beam elements accounting for plasticity and damage, non-linear connection elements with six degrees of freedom interaction, and an element erosion method have been implemented. Both levels have been verified against published experimental data. Modelling at the joint level reproduces measured strength and stiffness values with an error of no more than 5% and corresponds to the characteristic values of Eurocode 5, second edition. Modelling at the frame level accurately reflects the non-linearity of the ‘load-displacement’ relationship, the sequence of joint failure, and the axial forces in the chain line under large displacements exceeding 390 mm. The proposed methodology demonstrates high potential for the practical design of structures in accordance with the current Eurocode provisions on reliability.

Abstract: Reliable and rapid assessment of meat freshness is essential for food safety and waste reduction throughout the supply chain. This study evaluates a handheld volatile‑sensing device based on a piezoelectrically driven microcantilever functionalized with a biogenic‑amine–selective binder for monitoring spoilage progression in pork cutlets and lamb fillet during refrigerated storage. Pork and lamb samples were assessed from days 1-6 using four complementary indicators: (i) handheld sensor output, (ii) total viable counts (TVC), (iii) sensory evaluation, and (iv) cadaverine concentration. In pork, TVC increased from early-stage levels to approximately 106 CFU/g by day 4 (the safety threshold), accompanied by a marked rise in volatile amines. The handheld sensor detected increasing VOC concentrations, with signals correlating strongly with log₁₀(TVC). In contrast, lamb fillet generated extremely low cadaverine levels throughout storage, insufficient to trigger a measurable sensor response despite microbial proliferation. These findings confirm that microcantilever‑based sensing is well suited for pork freshness evaluation but reveals matrix‑dependent limitations for lamb due to low headspace amine release.

Abstract: The increasing penetration of renewable generation and electrified loads introduces non-Gaussian and strongly nonlinear uncertainty in power system operation. Probabilistic power flow (PPF) methods based on Monte Carlo simulation provide accurate uncertainty propagation but remain computationally demanding, while many analytical and approximation-based approaches rely on restrictive distributional assumptions. This letter proposes a Distribution-Matching Likelihood-Free Importance Sampling (DM-LFIS) framework for PPF. The method avoids explicit likelihood construction and posterior sampling, and instead propagates uncertainty by reweighting candidate operating states according to a discrepancy between simulated and observed power injection distributions. Data-driven and physics-informed proposals guided by DC and Newton--Raphson power flow solutions are used to improve sampling efficiency. Numerical results on standard IEEE test systems show that DM-LFIS accurately captures voltage and angle uncertainty with reduced computational cost compared to conventional Monte Carlo-based PPF methods.

Abstract: With the expansion of offshore oil and gas exploration into deep-water regions, the efficient scheduling of Platform Supply Vessels (PSVs) is critical to offshore operations. The Platform Supply Vessel Routing and Scheduling Problem (PSVRSP) is an NP-hard combinatorial optimization problem, which is further complicated by uncertainty in offshore demand. Existing studies reveal a methodological gap: heuristic approaches cannot guarantee optimality, while exact algorithms often ignore demand uncertainty. To address this gap, this study proposes a Branch-and-Price (B&amp;P) method for the Platform Supply Vessel Routing and Scheduling Problem with Uncertain Demand (PSVRSP-UD). A scenario-based Mixed-Integer Linear Programming (MILP) model is formulated, in which demand uncertainty is captured using Latin Hypercube Sampling (LHS) combined with Cholesky Decomposition and Sample-Based Reduction (SBR). Based on Dantzig–Wolfe Decomposition, the proposed B&amp;P algorithm integrates NG-Route labeling and a two-level branching strategy to achieve global optimization. Computational experiments show that the B&amp;P algorithm outperforms CPLEX in both computational efficiency and solution quality. Sensitivity analyses examine the impacts of scenario number, demand fluctuation, and weight coefficients on the results. The new results in this study can provide a practical decision-support tool for offshore logistics operations.

Abstract: Predictive maintenance is becoming essential for modern U.S. manufacturing plants as unplanned machine downtime leads to significant productivity losses, supply delays, and increased operational costs. This research proposes an AI-driven predictive maintenance framework that integrates Industrial Internet of Things (IoT) sensor streams, machine learning failure prediction, and reliability-based maintenance scheduling. The model utilizes vibration, temperature, power consumption, and operational cycle data to detect early-stage degradation patterns in industrial equipment. A hybrid deep learning and survival analysis approach is introduced to estimate Remaining Useful Life (RUL) and predict the probability of failure over time. Additionally, an optimization layer is developed to automatically generate cost-effective maintenance schedules that minimize downtime while balancing labor availability and spare parts constraints. The proposed framework is highly scalable and can be implemented across diverse manufacturing sectors, including automotive, semiconductor, and aerospace production. By improving equipment reliability, reducing emergency repairs, and supporting Industry 4.0 modernization, this work directly contributes to U.S. manufacturing competitiveness and industrial resilience.

Abstract: The proliferation of unmanned aerial vehicles (UAVs) in civil, commercial, and defence domains has exposed a critical architectural gap: existing platforms optimise either communication or perception independently, leaving safety coverage incomplete under simultaneous stress in Beyond Visual Line of Sight (BVLOS) operations. This paper proposes the Risk-Aware UAV Safety Architecture (RASA), a three-layer conceptual framework integrating multi-modal sensor fusion, satellite communication (SATCOM), and AI-driven risk modelling aligned with functional safety principles such as ISO 26262. The RASA framework quantifies operational risk as R(t) = α·U_sensor(t) + β·L_c_norm(t) + γ·U_sensor(t)·L_c_norm(t) — a function of normalised sensor uncertainty and normalised communication latency, with an interaction term capturing compound degradation effects — enabling onboard risk estimation without ground-in-the-loop dependency. Building on prior validated work in multi-modal sensor fusion for safety-critical human detection [10] and SATCOM communication architectures for UAV connectivity [15], this paper extends those contributions to the BVLOS domain. Monte Carlo simulations across three representative operational scenarios validate the risk model’s behaviour and demonstrate that the interaction term produces steeper risk escalation under compound failure conditions compared to the linear baseline. This paper addresses the critical gap in BVLOS UAV safety architectures by integrating perception and communication reliability within a single, auditable, risk-aware framework.

Abstract: Miniaturization of microfluidic measurement systems offers several advantages, including reduced sample and reagent volumes, improved control over experimental conditions, and the ability to multiplex complementary measurement modalities, to enable new experimental approaches in microbial electrochemistry. We present a scalable glass-based microfluidic bioelectrochemical cell (µ-BEC) platform for multiplexed investigations of microbial extracellular electron uptake (EEU). The platform integrates eight independently addressable three-electrode cells in a 2×4 array, with transparent indium tin oxide working electrodes that support simultaneous electrochemical analysis and optical imaging. Systematic electrochemical characterization using the ferri/ferrocyanide redox couple demonstrated diffusion-controlled behavior and stable reference electrode performance, with well-to-well coefficients of variation in peak potentials of 0.6–1.5% and 0.6–1.1% for anodic and cathodic processes and device-to-device coefficients of variation of approximately 1.8% and 1.6%, respectively. Differential pulse voltammetry measurements demonstrated concentration-dependent electrochemical sensing over a three-order-of-magnitude range from 1 µM to 1 mM ferri/ferrocyanide, with peak currents exhibiting linear dependence on concentration for both anodic and cathodic processes across all tested wells. Biological compatibility was validated using the phototrophic bacteria Rhodopseudomonas palustris TIE-1, where reproducible light-dependent EEU was observed following 96 hours of incubation, and a reduction in current response after microfluidic removal of planktonic cells confirmed the contribution of surface attached cells to EEU. Together, these results establish the µ-BEC as a robust and reproducible microfluidic electrochemical platform suitable for parallelized, multimodal studies of microbial and abiotic electrochemical processes.

Abstract: Fiber-reinforced polymer composites are increasingly used in lightweight aerospace structures due to their high strength-to-weight ratio, excellent corrosion resistance, and superior mechanical performance compared with conventional metallic materials. Among these materials, glass fiber-reinforced polymer (GFRP) and carbon fiber-reinforced polymer (CFRP) composites have gained widespread attention for use in unmanned aerial vehicle (UAV) structures, where structural efficiency, durability, and cost-effectiveness are critical design considerations. Understanding the compressive behaviour and failure mechanisms of composite laminates is therefore essential for ensuring structural reliability and safe operation in aerospace applications. This study presents an experimental investigation of the compressive behaviour of woven E-glass fiber-reinforced epoxy and carbon fiber-reinforced epoxy composite laminates. Rectangular specimens were prepared from commercially manufactured composite laminate plates with approximate dimensions of 100 mm × 95 mm and a laminate thickness of approximately 1.5 mm. Compression tests were performed using a universal testing machine under displacement-controlled loading conditions until structural failure occurred. The results revealed significant differences in the mechanical response of the two composite systems. Carbon fiber-reinforced laminates exhibited considerably higher stiffness and compressive load capacity due to the higher modulus of carbon fibers. However, carbon fiber specimens exhibited brittle failure, characterized by sudden fiber fracture and a rapid loss of load-carrying capacity. In contrast, E-glass laminates exhibited lower stiffness but showed more progressive damage, including matrix cracking and fiber buckling, prior to final failure. These findings highlight the trade-off between stiffness and damage tolerance in fiber-reinforced composites and provide useful experimental insight into the compressive performance of commonly used aerospace composite materials. The results contribute to the development and optimization of lightweight composite structures for UAV structural applications.

Abstract: Microfluidic electrokinetic flows play a central role in applications such as lab-on-a-chip diagnostics, microelectronics cooling, and biomedical sample manipulation. These systems involve intricate heat transfer processes, including Joule heating from ionic currents, temperature-driven flow instabilities, and strongly coupled thermal–fluid interactions, that crucially affect device performance, reliability, and scalability. Current challenges include non-equilibrium charge dynamics, incomplete thermophysical property data for complex fluids, and thermal crosstalk in integrated platforms. This review summarizes the literature published over the past 20 years, with occasional reference to earlier work, covering the fundamental mechanisms of heat generation and dissipation in electrokinetic microflows; analytical, numerical, and experimental approaches for characterizing thermal effects; and discussion on the limitations and application-driven opportunities and limitations. It also highlights open questions and future research directions and offers a comprehensive view of design principles and guidelines for developing robust, thermally optimized electrokinetic microfluidic technologies.

Abstract: Overall Equipment Effectiveness (OEE) is the dominant metric for manufacturing productivity, computed as the multiplicative product of Availability (A), Performance (P), and Quality (Q). Despite its widespread adoption, the classical OEE formula embeds a structural limitation: the three components are treated as equally important regardless of operational context, a fixed-weight assumption that systematically distorts maintenance prioritisation in environments with asymmetric operational priorities. No published framework has formally addressed this limitation through a structured, auditable multi-criteria weighting model. This paper proposes Adaptive OEE, a FUCOM-TOPSIS framework that replaces the fixed A×P×Q product with a context-driven weighting model. FUCOM elicits context-specific weights for A, P and Q from expert judgement using only n−1 pairwise comparisons with guaranteed consistency, while TOPSIS ranks equipment assets under the weighted criteria, producing a closeness coefficient comparable across assets and contexts. Three illustrative case studies covering availability-dominant, performance-dominant, and quality-dominant contexts demonstrate that the classical OEE ranking is not preserved under any weight configuration, with Divergence Index values ranging from 0.667 to 1.333. Divergence is most severe when one component carries strongly asymmetric weight, precisely the condition equal weighting cannot accommodate. The principal contributions are the formalisation of the equal-weighting assumption as a measurement-theoretic deficiency, the replacement of multiplicative aggregation with a weighted distance measure preserving the A/P/Q decomposition, and the introduction of the Divergence Index as a quantitative measure of context-insensitive rank displacement.