Engineering

Abstract: Alzheimer's disease (AD), a leading cause of dementia worldwide, is a neurological disorder characterized by progressive cognitive decline. AD is also considered a significant socioeconomic burden. While definitive diagnostic tools such as positron emission tomography (PET) imaging and cerebrospinal fluid (CSF) biomarker analysis offer high sensitivity and specificity, they are limited by high cost, invasiveness, and limited accessibility. Consequently, these gold standard approaches hinder their applicability for large-scale screening and longitudinal follow-up. Recent advances in blood-based biomarkers hold promise in capturing systemic molecular changes associated with AD. In particular, transcriptomic signatures derived from RNA sequencing (RNA-seq) are promising in capturing systemic molecular changes associated with AD. Gene expression profiles in peripheral blood reveal underlying pathological processes. These pathological processes can be listed as synaptic dysfunction, neuroinflammation, and metabolic dysregulation. Together with the high-dimensional datasets and AI approaches enable the identification of robust predictive models which has the assistance of estimating AD-related biomarker status. We further discussed the integration of multiple omics data, including genomics, proteomics, and metabolomics to improve biomarker robustness. We also addressed key challenges related to reproducibility, repeatibility, cohort heterogeneity, and clinical application. And we outline future directions of standardized, scalable, and clinically applicable diagnostic machineries.

Abstract: Inhomogeneous magnetic field distributions in high-frequency planar transformers frequently cause severe localized thermal hotspots and elevated leakage inductance. Traditional interleaved winding designs rely heavily on empirical trial-and-error, which becomes computationally prohibitive for multi-layer parallel structures due to the factorial "curse of dimensionality." To address this bottleneck, this paper proposes a universal, data-driven optimization methodology. First, a quantitative one-dimensional prefix-sum model is established to correlate winding arrangements with spatial magnetomotive force (MMF) distributions, effectively simplifying the electromagnetic evaluation. Subsequently, a customized Genetic Algorithm (GA) framework, featuring physical-constraint-preserving operators such as Order Crossover (OX), is introduced to efficiently navigate the high-dimensional discrete search space. Using an extreme 26-layer complex parallel winding configuration (Np:Ns = 9:2) as a primary case study, the proposed GA method effectively bypasses over 1.5 million permutations, converging to the global optimum within 100 generations. The optimized structure achieves profound peak-shaving, drastically reducing both the peak MMF and total uncoupled magnetic energy area. This methodology provides a systematic, computationally lightweight EDA solution that fundamentally replaces empirical trial-and-error in the design of high-frequency magnetic components.

Abstract: Hero Ridge shale oil reservoirs are characterized by stacked pay boxes, strong vertical heterogeneity, rapid variations in lithology and in situ stress, and significant well-to-well interference during platform-scale three-dimensional development. Conventional fracturing design methods that focus mainly on single-well stimulation are insufficient to simultaneously address fracture propagation, reservoir contact and development economics. Taking the 1H platform and representative wells in the upper member of the Xiaganchaigou Formation (E₃², Boxes 5-6) as examples, this study establishes a workflow integrating reservoir-engineering dual-quality evaluation, single-well parameter optimization, platform-coordinated fracturing, dynamic pore-pressure-stress updating, and EUR-IRR response-surface analysis. Results show that Box 6 has better reservoir quality and fracability than Box 5, with average porosity, oil saturation and brittle-mineral content of 7.6%, 50.9% and 67.6%, respectively. Well 1H6-1, with a 1500 m lateral, penetrated Class I + II sweet spots for 90.6% of the horizontal interval, providing a geological basis for efficient volume stimulation. For conventional sweet-spot wells, the optimal single-well design includes eight clusters per stage, a pumping rate of 18 m³/min, a fluid intensity of 35 m³/m and a proppant intensity of 3.25 m³/m. For 200 m-spaced wells, the pumping rate and fluid intensity should be reduced to 16 m3/min and 32 m³/m, respectively, with 100 m³ of prepad gel to mitigate fracture overlap and stress interference. Further response-surface analysis based on actual EUR-IRR data shows that the highest EUR occurs at a lateral length of 4000 m and well spacing of 50 m (EUR = 566,261 m³), but the IRR is -27.1%. By contrast, the best IRR point is at a lateral length of 4000 m and well spacing of 600 m (IRR = 14.5%), with EUR of 377,500 m³. This demonstrates that the production-optimal and economics-optimal schemes are not coincident. The expanded pilot scheme has an after-tax IRR of 9.31%, after-tax NPV of RMB 131.38 million and payback period of 5.93 years. The results indicate that fracturing optimization in Hero Ridge should move from single-well engineering maximization to integrated decision-making that combines single-well design, platform coordination, lateral-length/well-spacing optimization and techno-economic evaluation.

Abstract: The integration of generative artificial intelligence (AI) into agricultural extension services presents a transforma- tive opportunity to address the unique challenges faced by smallholder farmers, particularly in resource-constrainedsettings. While traditional extension services often struggle with scalability and personalized support, generative AI offers potential solutions through dynamic content generation, real-time decision-making assistance, and adaptive learning tools. This systematic literature review examines the efficacy of generative AI in enhancing agricultural extension services, focusing on its applications, benefits, and limitations for smallholder farmers. We synthesize existing research across multiple dimensions, including AI-driven farmer support, IoT-enabled monitoring, andclimate-smart agriculture, to identify gaps and trends in the current knowledge landscape. A rigorous methodol- ogy was employed to select and analyze relevant studies, ensuring a comprehensive evaluation of both theoreticalframeworks and practical implementations. The findings reveal that generative AI can significantly improve access to tailored agricultural advice, optimize resource allocation, and mitigate climate-related risks; however, challengessuch as digital literacy, infrastructure limitations, and ethical concerns remain critical barriers to widespread adop- tion. The review also highlights the disproportionate focus on high-income regions, underscoring the need for moreinclusive research in low-resource agricultural systems. By consolidating these insights, we provide actionable rec- ommendations for policymakers, researchers, and practitioners to harness generative AI’s potential while addressingits socio-technical constraints, thereby fostering equitable and sustainable agricultural development.

Abstract: This study investigates how intersection-related factors affect traffic crash severity through a comparative analysis of two major U.S. cities: Chicago and New York City (NYC). Using large-scale crash datasets, the analysis applies logistic regression and machine-learning methods to assess how intersections and temporal conditions influence injury outcomes. The results indicate that intersection-related crashes significantly increase the probability of injury in both cities, though the magnitude is substantially larger in Chicago. Nighttime conditions consistently elevate crash severity across both cities. Model evaluation using ROC curves suggests moderate predictive performance, indicating the influence of additional unobserved factors. A comparative modeling framework further reveals that the relationship between intersection-related factors and crash severity is context-dependent, varying across urban environments. These findings highlight the importance of developing location-specific traffic safety strategies and demonstrate the value of integrating statistical, machine-learning, and spatial analyses in crash severity research.

Abstract: Atmospheric icing is one of the most critical meteorological hazards for unmanned aerial vehicles (UAV), whose operation under adverse conditions—high latitudes, elevated altitudes, long-endurance missions without pilot intervention—particularly exposes them to ice accumulation on aerodynamic surfaces and propellers. Unlike manned aviation, where this phenomenon has been extensively studied and regulated, a significant knowledge gap exists in the UAV domain that limits the development of effective protection systems adapted to energy constraints. This article provides an integrated review of atmospheric ice formation mechanisms, their specific effects on UAV propellers, and the two most promising mitigation approaches: electrothermal modelling for the optimisation of electric heating systems, and the development of functional surface materials, including superhydrophobic coatings (SHC), composites with conductive nanofillers (graphene, carbon nanotubes), and piezoelectric actuators. The analysis demonstrates that hybrid systems combining passive and active strategies managed by intelligent control represent the most viable solution for extending UAV operational envelopes under known icing conditions, with a potential reduction in anti-icing energy consumption exceeding 40% compared to conventional continuous heating. Key research gaps are identified, and a prioritised future research agenda is proposed to support the development of certifiable anti-icing systems for rotary-wing UAV platforms.

Abstract: This paper quantifies the spatial divergence between 128-channel Light Detection and Ranging (LiDAR) point clouds and Frequency Modulated Continuous Wave (FMCW) radar tracks in high-clutter urban environments using the TiAND dataset. Nearest-neighbor Euclidean distance between radar target centers and raw LiDAR geometry serves as the error metric, chosen because the dataset provides no semantic bounding-box annotations. Across all processed frames the system produced an RMSE of 10.083 m with a median error (P₅₀) of 1.157 m, while the 99th-percentile (P₉₉) deviation reached 43.008 m with the single worst-case ghost target exceeded 217 m. A total of 4,113 detections crossed the 15 m catastrophic threshold—a figure that must be interpreted against the full detection population reported in Section III. Critically, the top anomalies cluster across consecutive frames near fixed infrastructure suggesting persistent multi-path reflection geometry rather than isolated single-frame noise. These findings indicate that raw FMCW radar output without downstream filtering or LiDAR verification cannot be relied upon for spatial localization in unstructured urban traffic.

Abstract: Existing unmanned aerial vehicle (UAV) urban logistics planning follows a sequential paradigm—depot siting first, routing second—that embeds a structural information loss. Straight-line distance screening systematically overestimates the feasible service radius of candidate depots, creating a blindzone of depot–demand pairs that appear reachable but prove operationally infeasible under road-network distances. We term this range-feasibility blindness and derive its analytical radius Δ=Rmax(α−1)/(2α), where α is the road-to-straight-line distance ratio. Empirical measurement across three Chinese urban districts confirms α∈[1.40,1.52] and blindzone radii exceeding 2.8 km, establishing the phenomenon as a systemic property of high-density urban road geometry. To eliminate this failure by construction, we formulate a feasibility-embedded location–routing mixed-integer linear programme (MILP) that enforces road-network range constraints simultaneously with depot-opening decisions, making blindzone configurations implicitly inadmissible. A structure-aware Adaptive Large Neighbourhood Search (ALNS) solves the model at practical scales. Benchmark experiments across all three cities show consistent cost reductions of 20.6–28.2% over sequential baselines, with gains increasing monotonically with instance scale. These results position joint optimisation as a necessary methodological shift for city-scale UAV infrastructure planning.

Abstract: The rapid advancement of Machine Learning (ML) has significantly transformed polymer science by enabling efficient prediction and design of polymer properties through high‑throughput screening. However, current methods still struggle with nonlinear Structure–Property Relationships (SPRs), limited dataset standardization, and computational inefficiency, which restrict prediction accuracy and interpretability. This study proposes a comprehensive ML‑based framework for predicting polymer properties and identifying SPRs. The approach integrates data preprocessing, molecular descriptor and topological index–based feature extraction, iterative feature selection, and XGBoost predictive modeling. Model hyperparameters are optimized using the Starfish Optimization Algorithm (SOA) to enhance performance and efficiency. Model interpretability is achieved through SHapley Additive exPlanations (SHAP) and Local Interpretable Model-Agnostic Explanations (LIME), providing both global and local insights into the influence of molecular features on polymer properties. Experimental evaluation on the PolyOne dataset demonstrates strong predictive performance, with R² values exceeding 0.92, mean absolute error (MAE) below 0.08, and root mean square error (RMSE) under 0.12 for key physical and optical polymer properties. Overall, the proposed framework effectively balances accuracy, computational efficiency, and interpretability, offering a robust and practical tool for accelerating polymer design while enhancing understanding of molecular structure–property relationships.

Abstract: With the large-scale commercialization of 5G and rapid evolution of 6G wireless systems, planar interdigital bandpass filters (BPFs) have become the core passive components for low-power RF front-ends. However, state-of-the-art filter design methods either rely heavily on empirical trial-and-error with 8–10 simulation iterations, or fail to resolve the inherent trade-off between center frequency tuning and stopband performance degradation, which cannot meet the demands of rapid customized design for 5G/6G multi-band scenarios. In this paper, a symmetric five-resonator three-segment patch-type interdigital BPF is taken as the research object. Through theoretical derivation, full-wave electromagnetic simulation, parametric scanning and orthogonal experiments, the quantitative mapping between structural parameters and filter performance is established. Notably, the directional tuning mechanism of the resonator’s narrow segment width on the first stopband is first revealed, which realizes lossless stopband optimization without disturbing the center frequency. On this basis, a three-stage standardized design procedure is proposed, which reduces design iterations from 8–10 to 3, shortens the design cycle by over 70%, and achieves 100% compliance of core design indexes. This work provides an implementable, low-threshold engineering method for rapid customized design of planar interdigital BPFs for 5G/6G RF front-ends.

Abstract: Cycling is one of the most popular sports and recreational activities. Millions of new people start to integrate bicycling into their daily routines every year. Fitness and activity trackers are the most powerful motivation tools for cycling novices and serious cycling enthusiasts. For this purpose, we present LaserFit, a laser-based direct force power meter for fitness and activity tracking during cycling. We developed embedded hardware to collect the torque and the wheel rotation data, which is produced by a laser-based position sensing system mounted on the rear wheel to precisely record the power output produced by the rider during cycling. The sensor data transmits to a smartphone via Bluetooth/ANT+ for data acquisition and analysis. Our device can be produced at low costs and deliver a level of accuracy similar to that obtained with the most expensive systems available on the market. To evaluate the accuracy of our system extensive experiments were conducted. The results of the present study suggest that the LaserFit power meter provides a strong relationship (r = 0.97) across a range of trials in laboratory and field conditions when compared with the SRM power meter. The LaserFit is therefore considered a valid alternative for training and performance measurement during continuous cycling.

Abstract: The growing demand for high-efficiency, high-power-density converters in data centers, electric vehicle chargers, and renewable energy systems has accelerated the adoption of wide bandgap (WBG) devices. Gallium nitride (GaN) transistors offer superior switching speed, lower losses, and higher power density compared with Silicon (Si) devices. Accurate characterization of GaN switching dynamics is essential due to parasitic effects and transient phenomena affecting performance and reliability. The Double Pulse Test (DPT) is widely used to quantify critical parameters, including switching energy losses, dynamic RDS(on) and transient voltage and current waveforms. This paper reviews DPT techniques for GaN devices, focusing on measurement methodologies, parasitic mitigation, and reliability considerations, providing practical guidance for optimizing high-frequency GaN-based power converters.

Abstract: Expanding renewable energy capacity in sub-Saharan transmission systems is a cornerstone of sustainable development, yet weak grid infrastructure and the absence of flexible storage remain principal barriers to reliable and low-carbon energy access. This paper addresses the economic and environmental dimensions of that challenge by proposing a hierarchical multi-objective framework for the optimal siting and sizing of Battery Energy Storage Systems (BESS), applied to the 130-bus Mali transmission network within the EMERGE project. The upper level employs the NSGA-II evolutionary algorithm to simultaneously maximize daily price-arbitrage revenue—the economic sustainability indicator—and minimize active power losses—the environmental efficiency indicator. For each candidate design, the lower level solves a multi-period DC Optimal Power Flow (DC-OPF) via CasADi/IPOPT, with thermal branch constraints embedded as hard linear inequalities through the Power Transfer Distribution Factor (PTDF) matrix, and voltage-corrected loss estimates recovered via a vectorized Extended DC Power Flow (EDCPF) model. Over 500 NSGA-II generations, the framework identifies Bus 91 (SIRAKORO II, 150 kV) as the dominant storage location, achieving maximum daily revenue of approximately € 10,033 at a marginal loss increment of 6.7×10⁻³ MWh. The Pareto front provides Mali system planners with a quantitative tool for balancing private investment returns against grid-level environmental impact, demonstrating that rigorous network-constrained BESS planning is both technically tractable and economically viable in the resource-constrained context of sub-Saharan sustainable energy transitions.

Abstract: This study evaluates the feasibility of using asphalt concrete as an impermeable core material for rockfill dams under tropical conditions. Laboratory testing and numerical modeling were conducted to assess the hydraulic and mechanical performance of asphalt concrete mixtures produced with locally available aggregates in Thailand. Asphalt mixtures were designed using the Marshall method with asphalt binder contents of 6% and 7% and target air void contents between 1-4%. Laboratory testing included permeability testing, Marshall stability testing, and triaxial compression tests to determine hydraulic conductivity, shear strength parameters, and deformation characteristics. Results show that asphalt concrete mixtures with air void contents below 1% exhibit extremely low permeability, with hydraulic conductivity on the order of 10⁻¹¹–10⁻¹² m/s, satisfying requirements for impervious dam cores. Triaxial compression tests yielded cohesion values between 97-572 kPa and friction angles ranging from 31° to 52°, indicating adequate shear resistance. Numerical simulations performed using GeoStudio compared rockfill dams with asphalt concrete cores and conventional clay cores. The results demonstrate that a 0.5‑m‑thick asphalt concrete core provides comparable seepage control and slope stability while requiring significantly smaller material volume. The findings suggest that asphalt concrete cores represent a technically feasible and economically advantageous alternative to clay cores, particularly in regions where suitable clay materials are limited.

Abstract: Moisture content is a critical parameter influencing the durability, structural performance, and maintenance of timber structures. However, current building inspection practices often rely on subjective interpretation, resulting in inconsistent assessment outcomes and ineffective maintenance decision-making. Despite the availability of various moisture measurement techniques, a standardized framework for interpreting moisture levels in relation to timber condition is still lacking. This study presents a structured review and synthesis of moisture content thresholds reported in the literature and proposes a standardized classification framework for timber defect assessment. The findings indicate that moisture content levels can be systematically categorized into four condition states dry, moderate, poor, and critical for each associated with specific maintenance actions. The proposed framework provides a practical linkage between moisture measurements and condition-based maintenance strategies, enabling more consistent and reliable inspection practices. The study contributes by transforming dispersed moisture-related data into a unified and actionable classification system, serving as a decision-support tool for building inspectors and maintenance practitioners. The proposed framework enhances the implementation of condition-based maintenance by reducing subjectivity and improving the accuracy of timber condition assessment.

Abstract: Rising complexity in cyber-physical systems development exposes challenges in the consistent and reusable specification of graphical domain-specific languages (DSLs). Despite the benefits of model-based systems engineering (MBSE), the absence of a standardized, lifecycle-wide specification process results in semantic inconsistencies, tool dependence, and limited interoperability. While our previous work has addressed individual stages of DSL definition, a comprehensive, standards-based process integrating these stages remains missing. Building on these foundations, this paper introduces a unified language specification process for graphical DSLs grounded in established standards---the Meta-Object Facility (MOF), Unified Modeling Language (UML), Web Ontology Language (OWL), and Resource Description Framework (RDF). The process integrates three core artifacts: a tool-independent ontology capturing domain semantics, a MOF-conformant metamodel unifying abstract syntax, semantics, and concrete syntax, and a UML-profile-based implementation. To support and exemplify this process, a prototypical toolchain is introduced that enables automated transformations between these artifacts, thereby facilitating the consistent propagation of semantics from ontology to implementation. The applicability of the proposed process is demonstrated through both a top-down automotive case and a bottom-up cybersecurity DSL, illustrating its cross-domain generalizability. By explicitly structuring and connecting ontology, metamodel, and implementation, this work contributes a semantically consistent, machine-interpretable, and tool-independent specification process for graphical DSLs in MBSE.

Abstract: The flotation of oxidized lead–zinc ores presents a significant challenge due to the low floatability of oxidized minerals and their weak interaction with conventional reagents. This study investigates the influence of the electrochemical parameters of the pulp (redox potential, Eh, and pH) on the flotation kinetics of oxidized lead–zinc ore from the Koskuduk deposit. It was established that the use of sodium sulfide Na₂S leads to the selective activation of lead-bearing minerals (Pb recovery up to 40.74%) with low zinc recovery (~12%). The use of a polysulfide-lime system S:CaO:H₂O is proposed, providing more uniform and stable sulfidization of the mineral surface. It is shown that the application of this reagent increases recovery to 65.10% for lead and 56.89% for zinc. It was established that the maximum recoveries are achieved within an Eh range of -120 to -180 mV at pH 11-12. Kinetic studies demonstrated that the main contribution to metal recovery occurs within the first 2-6 minutes of flotation. The obtained results indicate that flotation efficiency is determined both by the type of reagent and by the electrochemical state of the pulp, and that the use of polysulfide systems represents a promising approach for the processing of oxidized lead-zinc ores.

Abstract: Highway–rail grade crossing (HRGC) safety analysis is often based on raw incident counts or site-level models that do not control for exposure and ignore spatial dependence. This limits the ability to identify where risk is structurally concentrated across the rail network. The problem is important because misidentifying high-risk environments leads to inefficient allocation of limited safety resources and weakens corridor-level intervention strategies. This study introduces accumulated incidents per crossing (AIPX), an exposure-normalized metric that measured cumulative incident burden at the county level over a 51-year period (1975–2025). The study developed an algorithmic framework that integrates data reconciliation with spatial autocorrelation analysis, distributional modeling, and nonparametric machine learning to identify and interpret high-intensity risk environments. Global Moran’s I indicates statistically significant positive spatial autocorrelation (I = 0.359, p = 0.001), confirming that incident intensity is spatially clustered rather than random. Local indicators identify coherent high and low intensity county clusters. Distributional analysis shows that AIPX in high intensity clusters follows heavy-tailed behavior best represented by lognormal and Johnson SU distributions, indicating concentrated risk in a small subset of counties. Machine learning models achieve strong classification performance (AUC ≈ 0.85), with explainability methods consistently identifying temperature, train direction, crossing warning configuration, train composition, and track class as dominant associated features. These variables function as proxies for exposure intensity and network structure rather than causal drivers. The findings demonstrate that HRGC risk is a regional, network-driven phenomenon concentrated along freight-intensive corridors. The study provides a transparent and transferable framework that supports corridor-level prioritization of safety interventions and more effective allocation of infrastructure investments.

Abstract: We present a systematic experimental investigation of the primary breakup of a planar liquid film subjected to high-speed co-flowing gas streams. A water film of thickness D_ℓ ≈ 150 μm is produced from a symmetric airfoil lip and sheared on both sides by compressed air. Interfacial dynamics were recorded with a high-speed camera and analyzed to extract transverse wavelengths, rupture modes, and their dependence on operating conditions. We find that the transverse wavelength λ_tra decreases strongly with increasing gas speed and that, for a given dynamic pressure ratio M = (ρ_gV²_g )/(ρ_ℓV²_ℓ ), different absolute combinations of V_g and V_l produce markedly different λ_tra. These observations indicate that gas-shear intensity and the gas flow instability modes (vortex shedding) control the breakup of the liquid film; the liquid inflow plays a secondary role under our conditions. The results provide experimental benchmarks for model validation and suggest routes to tune atomizer performance via gas-side control.

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. Climate change is increasing atmospheric moisture loading and weakening nighttime recovery. These changes make hot-humid moisture risks 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, moisture stagnation in semi-enclosed spaces, and material deterioration or performance loss. 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 (FI), the VTT Mold Index, isopleth-based approaches, Mold Resistance Design (MRD), and the Dose-Response Simple Isopleth for Mold (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.