Abstract: Conventional tunnelling, CT, in densely populated urban areas requires ensuring that both serviceability and failure limit states are satisfied throughout construction, to avoid excessive ground deformations and catastrophic failures, while maintaining optimum support and excavation lengths. To achieve both goals, continuous monitoring of the tunnel-ground system behavior and adaptation of the tunneling process through calibrated numerical models becomes critical. This paper documents the field performance and construction optimization of a 4.5 km long tunnel excavated by CT in the stiff soils of the northwestern Mexico City region. A five-step monitoring and back-analyses procedure is introduced for risk reduction and optimization of the tunneling process during CT. The monitoring information, adopted support types, and excavation lengths demonstrate that construction times can be shortened through the proposed approach, enhancing construction processes with the corresponding cost reduction. Both three-dimensional numerical models and geotechnical instrumentation, including convergences, surface topographic references, extensometers, and pressure cells, were implemented throughout construction. The numerical models were continuously calibrated against field measurements to increase their predictive capability, accounting for actual subsoil conditions, tunnel geometries, and construction procedures. From the results gathered here, the benefits of using this integral approach to ensure good tunnel performance during excavation are established, in particular when the tunnel is excavated below densely populated areas in brittle cemented fine-grained soils

Abstract: In the paper, a systematic treatment of sensitivity analysis of multivariable control systems from a perspective of the characteristic transfer functions (CTFs) method is given. The CTFs method (also called Characteristic Gain Loci method) allows one to associate with an N-dimensional multi-input multi-output (MIMO) system a set of N independent single-input single-output (SISO) characteristic systems and thereby to reduce the analysis and design of a MIMO system to analysis and design of N SISO systems. The formulas are derived determining the sensitivity functions of the CTFs and sensitivity vectors of the canonical basis axes to small variations of parameters of general type MIMO systems. The relations between the sensitivity functions of the open-loop and closed-loop MIMO systems are established. Two illustrative examples are considered. The first of them concerns the sensitivity of a two-dimensional not robust system with large degree of skewness of the canonical basis axes. In the second example, the sensitivity of the control system of a hexacopter (multirotor UAV with six rotors) to small degradations of the motors’ efficiency is analyzed.

Abstract: Short-term rider demand forecasting is a foundational operational capability for Mobility-on-Demand (MoD) systems, enabling proactive vehicle pre-positioning, dynamic pricing, and service-level optimization across ride-hailing, bike-sharing, carsharing, and demand-responsive transit platforms. Despite a rapidly growing body of literature, the field lacks a comprehensive and critically structured synthesis of methodological developments, input feature practices, evaluation standards, and unresolved research gaps. This paper presents a Systematic Literature Review (SLR) conducted in accordance with the PRISMA protocol, encompassing 291 peer-reviewed studies published between 2016 and 2025 across transportation engineering, intelligent transportation systems, and machine learning venues—the most comprehensive corpus assembled for this topic to date. The review identifies a clear five-generation methodological succession—from classical statistical and machine learning models through recurrent and convolutional deep learning architectures to Graph Neural Networks and transformer-based models—with signal decomposition methods and probabilistic architectures emerging as distinct 2023–2025 trends. Most significantly, we identify a seventeen-dimension research gap matrix that involves research gaps such as probabilistic demand forecasting, cross-city transfer, decision-focused predict-and-optimize frameworks, etc. Further, six concrete research directions grounded in these gaps are proposed, each accompanied by specific methodological proposals rather than general aspirational statements. The findings underscore the need for standardized benchmarking protocols, open dataset releases with documented preprocessing, and a fundamental reorientation of model evaluation from statistical accuracy metrics toward composite operational, probabilistic, and equity-aware performance objectives.

Abstract: 15Cr14Co12Mo5Ni2, as a new type of low-carbon high-alloy aviation gear steel, has shown significant application potential in the transmission systems of aero engines due to its excellent high-temperature performance. In this paper, the aviation gear steel 15Cr14Co12Mo5Ni2 was treated by carburizing and quenching process. The microstructure distributions of the carburized and quenched aviation gear steel at different quenching temperature were analyzed by OM, SEM and EBSD. Subsequently, the axial tension-compressive fatigue tests (stress ratio R=-1) were carried out using a high-frequency fatigue testing machine after heat treatment at different quenching temperature (1020℃, 1050℃ and 1080℃), and the stress-number of cycles (S-N) curves were obtained by fitting the number of fatigue fracture cycles. The fracture morphologies were observed by SEM and the fracture mechanisms were analyzed. The research results show that the distribution of the microstructure and carbides exhibit gradient characteristics, and the carbide content decreases and the effective carburized layer depth decreases from 0.65mm to 0.45mm with increasing quenching temperature, also the main carbide types are M₂₃C₆ and M₇C₃. The fatigue life of 15Cr14Co12Mo5Ni2 gear steel decreases as the quenching temperature increases. Their fatigue strengths at a given fatigue life of 10⁶ cycles at 1020℃, 1050℃ and 1080℃ are 192 MPa, 183 MPa and 158 MPa, respectively. The cracks propagate outward from the core and the propagation rate accelerates with the increasing quenching temperature, eventually fracturing in the carburized layer. The fracture mechanism of 15Cr14Co12Mo5Ni2 gear steel at the quenching temperatures of 1020℃ was a mixed mode of intergranular and cleavage brittle fracture, while at 1050℃and 1080℃, it is mainly brittle fracture accompanied by local ductile fracture.

Abstract: Urban construction activities are recognized as significant contributors to particulate matter (PM) emissions; however, the accurate real-time monitoring of size-resolved PM fractions presents a formidable challenge. Traditional low-cost PM sensors predominantly report cumulative concentrations, which obscures the distinct health and regulatory sig-nificance of PM1, PM2.5, and PM10. This study systematically evaluates the performance of two low-cost sensors—PMS5003 and Sniffer4D, utilizing non-cumulative measure-ments obtained under controlled laboratory conditions designed to simulate construction PM generated from concrete slab drilling. Sensor performance was rigorously analyzed using Pearson correlation coefficients, standard deviation, and mean percentage differ-ences. Six correction models—Linear Regression, Polynomial Regression, Random Forest (RF), XGBoost, Artificial Neural Network (ANN), and Kalman Filter—were independently developed for each PM size fraction to enhance measurement precision. Findings reveal that RF and ANN consistently provided the most accurate corrections, particularly for PM1 and PM2.5, with RF achieving a coefficient of determination (R²) > 0.89 for PM1 and R² > 0.87 for PM2.5 at the 50-second duration. This investigation introduces a size-resolved correction framework specifically designed for construction environments, thereby advancing the capability of low-cost sensors to enable accurate particle-specific exposure assessments.

Abstract: Automotive paint sludge (APS) is a hazardous and non-biodegradable waste generated during the painting process in the automotive sector. Approximately 40% of paint sprayed on automotive parts ends up as waste, resulting in huge amounts of APS generated every year. Its complex composition, which includes heavy metals and other toxic substances, poses significant environmental and health risks if not properly managed. Conventional disposal methods are increasingly unsustainable, necessitating for alternative approached that enable both waste reduction and resource recovery. This review explores existing literature on the thermochemical conversion of APS, with attention on combustion, incineration, pyrolysis and gasification. The paper looks at process performance, operational challenges, product distribution, and environmental implications, while identifying key knowledge gaps and emerging research directions. Thermochemical conversion technologies show potential for APS valorization via the production of syngas, liquid fuels, and char, alongside significant waste volume reduction. However, the high moisture content of APS presents serious difficulties, as it can lead to incomplete combustion, increased hazardous emissions, and the generation of heavy metal-contaminated ash requiring disposal. Mitigation strategies like pre-drying and advanced emission control systems are effective but energy-intensive and economically burdensome. Emerging approaches, particularly co-gasification and co-pyrolysis with high-calorific feedstocks, show promise in overcoming these challenges through synergistic interactions. Thermochemical conversion offers a viable route for sustainable APS management, enabling resource recovery and energy generation while lowering environmental impacts. However, technical and economic constraints associated with feedstock properties and process requirements limit its standalone application. Future research should focus on scale-up feasibility to support the transition of APS thermochemical conversion technologies from laboratory to industrial application, while considering environmental and economic requirements.

Abstract: Moisture damage in buildings has conventionally been discussed mainly in relation to winter condensation in cold climates. In hot-humid buildings, however, deterioration develops under different boundary conditions, including persistently warm and humid outdoor air, frequent rainfall, air-conditioning operation, air leakage, and limited drying after wetting. As climate change increases atmospheric moisture loading and weakens nighttime recovery, these conditions are becoming more consequential not only in established hot-humid regions but also in regions shifting toward more persistently humid climates. This review examines moisture damage in hot-humid buildings as a coupled problem linking climate change, building-envelope moisture response, risk assessment, microbial implications, and building adaptation. Representative scenarios include biological contamination on exterior surfaces, summer condensation and moisture accumulation within envelope assemblies, localized dampness at indoor surfaces and behind furniture, and moisture stagnation in semi-enclosed spaces. These phenomena are interpreted not as isolated defects, but as manifestations of drying deficit. The review discusses climatic drivers, building-physics mechanisms, and major moisture and mold risk indices, including the Fungal Index, VTT Mold Index, IBP-type approaches, MRD, and DR-SIM. It also highlights implications for envelope design, retrofit, ventilation, dehumidification, and operation. Overall, moisture damage in hot-humid buildings is best understood as the outcome of climate-driven drying deficit.

Abstract: This report describes the practical implementation of a maximum power point tracking (MPPT) system for a string of four series‑connected monocrystalline silicon photovoltaic (PV) cells. The system includes a boost DC‑DC converter that interfaces the low‑voltage PV string to a 36 V battery, and each cell has an individual MOSFET bypass switch to mitigate partial shading. We explain the operating principles, the MPPT algorithms (Perturb & Observe and Incremental Conductance), the bypass logic, and the closed‑loop control that adjusts the converter’s duty cycle to maximise power transfer. Step‑by‑step descriptions and supporting figures clarify the process.

Abstract: Laser polishing (LP) is widely employed to enhance the surface quality of additively manufactured (AM) metals; however, its behaviour within deep or confined internal geometries remains insufficiently understood. Many high-performance AM components, such as biomedical implants, turbine cooling channels, and metal microfluidic systems, incorporate narrow internal features where heat-transfer conditions differ significantly from open surfaces. In this study, laser powder bed fusion (LPBF)-fabricated 316L stainless steel specimens containing ~10 mm deep slots with widths ranging from 1 to 5 mm were subjected to laser polishing using a continuous-wave fibre laser (power: 80–120 W, scan speed: 450–750 mm/s, spot size: ~80–100 µm, ~60–70% track overlap, single-pass strategy). The influence of internal geometric confinement on microstructural evolution and mechanical response was systematically investigated. A pronounced depth-dependent microhardness gradient was observed along the slot wall, with hardness decreasing from approximately 270 HV in the lower region to ~210 HV near the slot opening, with more significant gradients in narrower geometries. Quantitative grain-size analysis revealed finer grains (~8–12 µm) in the lower region and coarser grains (~18–25 µm) toward the upper region, indicating progressive grain coarsening with increasing height. These variations are attributed to geometry-dependent thermal boundary conditions, where enhanced conductive coupling to the bulk substrate in the lower region promotes higher cooling rates, while reduced thermal extraction near the slot opening results in slower solidification. The results provide clear experimental evidence that internal geometric confinement can significantly influence microstructure–property evolution during laser polishing, even under constant processing parameters. This study highlights the importance of incorporating geometric effects into post-processing strategies for AM components and offers practical insights for achieving more predictable and uniform mechanical performance in confined internal features.

Abstract: Construction and demolition waste (CDW) is the largest single waste stream in the European Union by weight (~39% of all EU waste), yet the EU’s circular material use rate stood at only 12.2% in 2024 — less than half its 2030 target. Despite two decades of legislative ambition, the 70% recovery target under Directive 2008/98/EC has not been genuinely achieved: apparent compliance by most Member States conceals widespread downcycling and inconsistent reporting. This review identifies five persistent barrier domains — legal, technical, social, behavioural, and economic — with regulatory fragmentation and secondary material devaluation as the most structurally entrenched. A decisive paradigm shift is observed in recent research, from material characterisation towards systemic circularity, digital demolition frameworks, and governance. Emerging technologies — including AI-powered sorting, Building Information Modelling, Digital Twins, and Digital Product Passports — hold transformative potential, while Design for Deconstruction represents a critical upstream strategy the sector has yet to mainstream. The forthcoming EU Circular Economy Act will introduce legally binding obligations for Member States. The 2026 Strait of Hormuz energy crisis has reframed CDW from an environmental concern into a strategic industrial imperative: as virgin material costs surge, secondary CDW materials offer economic and geopolitical advantage. Future research must prioritise collaborative governance, longitudinal data, and scalable digital solutions.

Abstract: The rapid growth of electromobility is increasing pressure on the adequacy of charging infrastructure deployed along major transport corridors. This study presents a simulation-based framework for assessing the operational performance of electric vehicle charging infrastructure along the S19 Rzeszów–Barwinek section, a 90 km corridor forming part of the TEN-T and Via Carpathia networks. The methodology combines microscopic traffic simulation in PTV Vissim with probabilistic charging-demand modeling for passenger cars and heavy-duty vehicles, enabling the analysis of infrastructure utilization, queue formation, and unmet charging demand under realistic corridor conditions. Three electric vehicle penetration scenarios were examined: 10%, 25%, and 45% of the traffic stream. The results show that the charging system remains stable under the 10% scenario, begins to experience local overload and recurring congestion at 25%, and reaches structural insufficiency at 45%, where utilization exceeds 100% and unmet demand rises markedly. A key finding is that heavy-duty electric vehicles constitute the dominant operational bottleneck due to longer charging times, higher energy requirements, and the limited number of dedicated charging points. An additional expansion variant indicates that increasing the number of heavy-duty charging points can substantially improve system performance and restore a safer utilization range. The study demonstrates that minimum regulatory compliance should be treated as a baseline rather than a sufficient planning target and that dynamic, scenario-based simulation offers an effective decision-support tool for the adaptive development of corridor charging infrastructure.

Abstract: Spintronic crossbar arrays are emerging as a powerful hardware platform for energy-efficient computing. Unlike conventional digital processors that shuttle data between memory and processing units, these arrays perform computation directly where data is stored, a concept known as in-memory computing. This report explains, from the ground up, what spintronic crossbars are, how they operate, and the different types currently available or under development. We cover both binary (single-level) and analog (multi-level) devices, their input/output characteristics, and the physical principles that make them uniquely suited for matrix operations. The figures illustrate the architecture, switching mechanisms, and the transition from binary to multi-level behavior. This foundation is essential for understanding advanced applications such as parallel photovoltaic MPPT and neuromorphic computing.

Abstract: Existing research on quality gain-loss functions predominantly focuses on single variables or separable quality characteristics, overlooking the correlations among multiple quality attributes and the complexity of spatiotemporal factors. This paper employs the Matérn kernel to construct spatiotemporal interaction terms, incorporates Kalman filtering and smoothing algorithms to enhance computational efficiency, and establishes joint gain-loss weights using the signal-to-noise ratio method. Consequently, a multivariate multidimensional quality gain-loss function model based on the Non-Separable Gaussian Process (NSGP) is developed. The NSGP model is applied to simulation cases and dam concrete production scenarios. Comparative optimization with machine learning methods such as Gaussian processes and linear regression validates the robustness of the NSGP model. Crucially, it eliminates the computational requirement for determining covariance separability, thereby reducing computational costs. This provides robust case support for quality management in hydraulic concrete construction.

Abstract: The construction sector faces a dual challenge: meeting growing global demand while achieving deep decarbonisation in line with the European Green Deal and the EU Bioeconomy Strategy. Bio-based construction materials offer significant potential to reconcile these objectives through carbon sequestration, reduced embodied emissions, improved indoor environmental quality, and compatibility with circular economy principles. However, their transition from niche applications to mainstream specification remains limited. This paper provides a comprehensive review of bio-based construction materials and examines the systemic barriers constraining their large-scale adoption. The analysis identifies four interrelated categories of constraints—structural, economic, technical, and enabling—and emphasises the conditional relationships between them, highlighting the implications for policy prioritisation and sequencing. The strategic urgency of this transition has been reinforced by the 2026 Strait of Hormuz crisis, which triggered severe disruptions to global petrochemical supply chains and exposed the structural vulnerability of European construction to fossil-derived material inputs, reframing bio-based alternatives as a supply security imperative alongside an environmental one. The findings show that the primary obstacles to adoption are not technological, but institutional and economic, particularly regulatory fragmentation, the absence of harmonised standards, supply chain limitations, and persistent market failures that disadvantage bio-based solutions.The paper concludes that scaling bio-based construction materials requires coordinated action across governance, market design, and industrial policy. Without addressing these systemic constraints, advances in material innovation and performance are unlikely to translate into widespread adoption.

Abstract: This technical note discusses, at system level, a fully analog control architecture in which a programmable spintronic crossbar can generate rapid mismatch-handling signals and a maximum-power-control signal for a photovoltaic source operating under rapidly varying shading conditions. The note is intentionally technology-agnostic and focuses on architectural principles rather than on fabrication details, device-specific programming workflows, or implementation-specific optimization procedures. The main value of the approach is the possibility of forming protection and control signals in parallel, with very low decision latency in the fast path, while overall operating-point convergence remains governed by the source and converter dynamics. The discussion is framed as a pedagogical technical note associated with already filed patent applications. Its purpose is to explain the conceptual role of a spintronic crossbar in analog control, the relationship between crossbar decision latency and converter response time, and the practical distinction between system-level architectural advantage and device-level maturity.

Abstract: Breast cancer remains a leading cause of cancer-related mortality among women globally. This study makes one focused primary contribution: a formalized, physics-grounded preprocessing-to-fusion pipeline for multi-modal breast cancer classification that is rigorously validated under both centralized and federated learning conditions. Patient-wise stratified 5-fold cross-validation was applied across Ultrasound (BUSI, n=780), Dynamic Contrast-Enhanced MRI (DUKE, n=922), and Mammography (CBIS-DDSM, n=400). Per-modality models achieved 92.50±1.2%, 90.63±1.5%, and 92.00±1.3% accuracy (McNemar’s p<0.05 vs. baselines). Weighted late-fusion achieved 93.10±1.1% (p=0.031 vs. best individual modality). A five-algorithm FL comparison (FedAvg, FedProx, SCAFFOLD, FedNova, FP16-FedAvg) under IID and non-IID (Dirichlet α=0.5) conditions is provided with per-round training time, communication time, per-round latency, and cumulative bandwidth. FP16 transmission reduced bandwidth from 8.14 GB to 1.23 GB (−84.9%, p=0.74 vs. FP32). SCAFFOLD achieved the best non-IID accuracy (90.50%). All design choices are validated by ablation experiments with McNemar’s test and Cohen’s h effect sizes.

Abstract: Aircraft flyover measurements are used to record the acoustic pressure signals generated by large civil aircraft as they fly over a large-scale microphone array deployed on the ground, thereby obtaining the spatial distribution of aircraft airframe noise and providing technical support for aircraft noise reduction. Aircraft flyover measurements have been widely applied in the research and development of numerous large civil aircraft in Europe and North America since the 1990s. In recent years, aircraft flyover measurements have also been extensively adopted in China, particularly with the rapid development of C919, China's large civil aircraft. Computer vision techniques have also been applied to microphone position calibration and aircraft trajectory determination in measurements, which has effectively improved measurement efficiency and accuracy. This paper presents an integrated procedure for aircraft flyover measurements of large civil aircraft in China, including microphone array design, installation, and calibration, noise acquisition system setup and data acquisition, aircraft trajectory determination, and data processing.

Abstract: Reducing residual drifts and maintaining post–earthquake functionality have become key requirements in performance-based seismic design. In this study, the seismic behavior of three steel frame models 2, 3, and 6-story configurations equipped with eight-shaped mega-braces with and without Shape Memory Alloy (SMA) components was evaluated using nonlinear explicit dynamic analysis in ABAQUS. The main innovation of this research lies in the simultaneous integration of SMA elements into the mega-brace system and in analyzing their effects on residual drift, plastic strain, and response modification factor (R) through detailed three-dimensional numerical modeling. Results show that the use of SMA reduces peak von Mises stress by 2–3% and decreases plastic strain by 7% in the 2-story model, 50% in the 3-story model, and 55% in the 6-story model. The most significant finding is the 99.99% reduction in residual drift across all models, demonstrating highly effective self-centering behavior. The evaluation of the response modification factor revealed that SMA increases R by 42% in the 2-story model, decreases it by 23% in the 3-story model, and in the 6-story frame results in a 5% increase for the two-story bracing configuration and a 34% decrease for the three-story bracing configuration. These results indicate that although SMA reduces hysteretic energy dissipation in some cases, it significantly enhances the seismic performance of the mega-brace system by virtually eliminating permanent deformation, reducing stress concentration, improving cyclic stability, and effectively controlling inelastic displacements. Therefore, incorporating SMA into mega-brace systems can serve as an efficient approach for structures where maintaining functionality and usability after an earthquake is a critical priority.

Abstract: This report presents a hybrid control architecture for a series-connected string of four photovoltaic cells as a pedagogical exercise. A spintronic 4×4 crossbar is used exclusively to monitor per-cell voltages and activate the corresponding bypass MOSFETs in nanoseconds, while a conventional digital microcontroller runs a classical MPPT algorithm for string-level optimization. The note uses this hybrid “crossbar bypass + digital MPPT” architecture as an educational case study for PV shadow management.

Abstract: This study examines a cold-press manufacturing method for laminated bamboo and bamboo-timber composites, together with a cradle-to-gate carbon footprint analysis of the produced materials. The proposed material systems are assessed as alternatives to conventional engineered bamboo and to widely used construction materials such as structural steel, concrete, and aluminum. Existing engineered bamboo products are typically manufactured using hot pressing and formaldehyde-based adhesives, both of which contribute to their environmental burden. The present work, therefore, considers a more practical and environmentally responsible route based on lower-energy processing and lower-emission adhesive systems. Following a cradle-to-gate carbon footprint analysis of the produced materials, the embodied carbon values obtained for the four systems are 404.8, 310.8, 264.8, and 197.5 kg CO₂e/m³ for BBE, BPA, CBE, and CPA specimens, respectively. Relative to conventional hot-pressed laminated bamboo, these values, respectively, correspond to embodied-carbon reductions of 32.0%, 47.8%, 55.5%, and 66.8%. When the biogenic carbon stored in the bamboo and pine biomass is included, the net carbon balances become −484.0, −619.0, −646.1, and −631.2 kg CO₂e/m³, respectively. These results show that the proposed engineered bamboo and bamboo-timber composites offer a feasible low-carbon option for construction applications.