Search Results (394)

The stream of onions entering a self-propelled onion collector varies with field conditions, feeding density, and the transient lifting behavior of the onion–soil mass, whereas the collection conveyor is conventionally operated at a fixed speed, wasting hydraulic energy. This study proposes and evaluates, through field-calibrated simulation, a vision-based feedforward conveyor speed control framework that couples reel-synchronous onion counting with variable-displacement pump control. To mitigate the periodic occlusion caused by the compact dual-conveyor structure, a frame-selection method synchronized with the detected conveyor position was implemented, and a YOLOv8n detector was trained and evaluated on 314 images extracted from 32 indoor and field source videos partitioned at the source-video level. On the independent test subset, the model achieved precision, recall, and mAP@0.5 of 0.952, 0.944, and 0.959, and the proposed counting method maintained count recovery ratios above 95% across all engine speeds in 45 independent field trials, outperforming fixed-period sampling and tracking-based baselines by 6.6 and 3.8 percentage points, respectively. An AMESim model of the fixed-displacement hydraulic system was calibrated and shown to be consistent with field measurements and was then used to evaluate the variable-displacement configuration. Net conveyor-related fuel savings (engine no-load consumption subtracted) were estimated at 14.3–18.7% under the field-identified constant transmission efficiency, with conservative estimates of 11.5–16.9% when partial-displacement efficiency losses were accounted for through an anchored loss model. These results demonstrate the feasibility of vision-based feedforward conveyor speed control and its potential for energy savings in bulb-crop collection. Full article

This study presents the development and validation of a predictive 0D/1D combustion model for a high-performance direct injection hydrogen spark-ignition engine, explicitly incorporating thermo-diffusive instability (TDI) effects through a physics-based formulation derived from linear flame stability theory and DNS-informed scaling laws. A laminar flame speed correlation obtained from detailed chemical kinetics is enhanced via an instability growth-rate model and embedded within an entrainment-based turbulent combustion framework, coupled with an Extended Zeldovich mechanism for NO_x prediction. The model is validated against experiments covering 2000–7500 rpm, loads up to 28 bar IMEP-H, and relative air-to-fuel ratio between 1.2 and 3.0. The results demonstrate that instability-aware modeling is essential for accurate ultra-lean combustion prediction, where conventional approaches severely underpredict burning rates. Across the full operating map, combustion phasing is predicted within ±3 CAD and NO_x with an average error of 20%, enabling robust system-level simulation and virtual calibration of high-performance hydrogen engines. Full article

Rail stations have non-traction electricity and heat demands, requiring coordinated renewable generation, storage, and conversion. We develop a cloud–edge–terminal energy-management framework for an electricity–hydrogen–thermal system. A 24 h mixed-integer linear programming model schedules grid import, photovoltaic (PV) generation, battery and thermal storage, electrolysis, hydrogen compression and storage, and fuel-cell and heat-pump operation. Hydrogen inventory is represented by stored hydrogen mass, with tank pressure linked to hydrogen density through a pressure–density relation. We compare three systems: hydrogen-free, hydrogen-integrated, and hydrogen-free with electrically equivalent battery storage. Hourly schedules map 150 capacity-equivalent virtual terminals for minute-level verification under operational and communication disturbances. Hydrogen integration reduces objective value by 1.06%, operating cost by 0.34%, and peak grid import by 6.89% relative to hydrogen-free operation but increases daily grid electricity purchase and CO₂ emissions by approximately 3.09%. Sensitivity analyses show stronger benefits at higher PV penetration, negligible gains from tank-volume expansion beyond the storage range, and electrolyzer power as the constraint on renewable absorption at high PV capacities. Verification achieves a 98.503% average command–delivery success rate, a steady-state grid import error below 1 kW, and 2 min to self-heal faults. Hydrogen improves peak shaving and intertemporal energy shifting, whereas total electricity use and emissions depend on renewable availability and conversion limits. Full article

Fault risks in marine diesel engines (MDEs) propagate across coupled subsystems and evolve with component degradation, but existing methods rarely integrate accident narratives, causal structure, and time-varying reliability. This study develops a novel framework that integrates large language models (LLMs), rough–fuzzy DEMATEL, interpretive structural modeling (ISM), and Bayesian networks (BNs) with service-time-dependent priors for time-varying failure analysis. First, the risk-influencing factors (RIFs) are extracted from accident and maintenance records using LLMs, text embeddings, semantic clustering, and expert consolidation. Rough–fuzzy DEMATEL and ISM are used to identify causal relationships and the hierarchical structure. The RIFs, bottom-level components, and target failure are then mapped into a multilayer BN parameterized using Noisy-OR relationships and Weibull-derived time-varying priors. In a case study of MDE hard starting, 338 cause descriptions from 35 records yielded 12 RIFs, with semantic coverage above 93% across four evaluation models. When hard starting was observed, the posterior probability of mechanical failure of the fuel injection system reached 60.92%, compared with 36.91% for governor and mechanical actuation system failure. Over 0–10,000 h of cumulative service, the model-inferred probability of hard starting during a single starting attempt increased from 38.09% to 75.23% under the specified model parameterization. The framework supports causal interpretation, troubleshooting prioritization, and service-time-dependent maintenance prioritization. Full article

Approximately 2.3 billion people still cook over open fires or on basic stoves using polluting fuels, generating indoor air pollution responsible for 3.7 million premature deaths annually. Progress toward real-world health impact has been constrained by a persistent disconnect between computational fluid dynamics (CFD) modeling, standardized experimental evaluation, and sociocultural adoption research. This scoping review maps the current state of evidence across these three domains, analyzing 143 peer-reviewed studies published between 2007 and 2025 using predefined inclusion criteria and bibliometric analysis with VOSviewer v.1.6.20. Thirteen cookstove technologies were characterized by compiling heterogeneous evidence from Water Boiling Tests (WBTs), CFD simulations with k-ε turbulence closure, and CO and PM_2.5 emission protocols. Direct combustion stoves achieve thermal efficiencies of 10–21% under real-world conditions, while TLUD gasifiers and forced-draft systems with densified fuels reach 30–47%. Bibliometric analysis reveals that engineering, epidemiology, and social sciences operate as isolated research communities. None of the technology reviewed simultaneously integrated computational validation, field emissions assessment, and clinical impact evaluation; this a gap remains the central barrier to translating laboratory performance into measurable public health outcomes. These findings point toward integrated research designs connecting fluid dynamic optimization with exposure modeling, clinical follow-up, and the sociocultural needs of communities. Full article

The steep radial temperature gradients developed in UO₂ fuels during reactor operation can drive pore migration, making pore–grain boundary (GB) coupled migration an important mechanism governing microstructural evolution. Although pores are generally regarded as pinning features that hinder GB migration, the conditions under which mobile pores retard, co-migrate with, or promote GB migration remain poorly understood. In this study, we develop a phase field model coupling vapor-transport-driven pore migration and curvature-driven grain growth to investigate the coupled migration behavior between intergranular mobile pores and GBs. The simulations first focus on an idealized source-term-free system to isolate the effect of pore–GB migration coupling from irradiation-induced pore generation and growth. The results show that the effect of pores on GB migration depends on the relative migration rate of pores and GBs, which is determined by both the pore-to-GB mobility ratio and the corresponding driving-force ratio. When pores migrate more slowly than GBs, they retard GB migration and exhibit an effective pinning effect. In contrast, sufficiently mobile pores can co-migrate with GBs and promote apparent GB migration when the pore migration rate exceeds that of the GBs. Furthermore, to illustrate the regulating effect of pores on GB migration in UO₂ under a temperature gradient, we perform additional simulations under continuous irradiation, in which pores nucleate spontaneously, migrate along the temperature gradient, and interact with GBs, in agreement with experimental observations. Based on the simulated GB migration behavior, we construct a regime map that distinguishes pinning/retardation, weak interaction, and pore-assisted migration regimes. This work provides a mechanistic phase field interpretation of pore–GB coupled migration and offers insight into microstructural evolution in porous oxide fuel materials. Full article

Turbocharger compressor geometry affects the pressure ratio, flow range, and efficiency distribution of the compressor map, but the influence pathway from compressor geometry to engine fuel economy and emissions trade-off has not been sufficiently quantified. This study develops a surrogate-assisted multi-objective optimization framework that couples compressor and engine models. A calibrated one-dimensional compressor model generated a geometry-specific map for each design, which was imported into a validated one-dimensional engine model to calculate brake-specific fuel consumption and brake-specific NOx emissions at four loads. The results for the four loads were aggregated into weighted brake-specific fuel consumption (BSFC_w) and weighted brake-specific NOx emissions (BSNOx_w). Extreme gradient boosting (XGBoost) surrogate models are trained to predict these weighted responses. The non-dominated sorting genetic algorithm II (NSGA-II) identified Pareto-optimal compressor geometries. Representative solutions were re-evaluated using the original coupled models. The Pareto front reveals a clear trade-off between BSFC_w and BSNOx_w, with the minimum-BSFC_w and minimum-BSNOx_w solutions defining the respective lower bounds within the investigated design space. For the selected trade-off solution, BSNOx_w was 1.18 g/(kW·h) lower than the baseline value, while BSFC_w was 0.82 g/(kW·h) higher. Compressor geometry alters the distribution of engine operating points on the compressor map under different loads, thereby affecting the trade-off between fuel economy and NOx emissions. Full article

Accurate fleet electrification assessment requires microscopic traffic simulation grounded in real-world demand, physics-based vehicle models, and routing that respects the lane-connection topology of urban networks. We present ISTVEL (Istanbul Simulation Tool for Vehicle Electrification), an open-source framework that ingests hourly Istanbul Metropolitan Municipality (IMM) loop-detector data, snaps detectors to OpenStreetMap edges, synthesises SUMO demand via a connection-graph Breadth-First Search (BFS) algorithm eliminating teleportation artifacts, and post-processes tripinfo.xml output to compute per-trip energy, use-phase CO₂, and energy operating cost (ECO₁₀₀), correctly distinguishing gross battery draw, regenerative recovery, and net grid consumption. Applied to the Kadıköy district of Istanbul ($3.2{\mathrm{km}}^2$, 08:00–09:00, January 2025, $\approx 2950$ vehicles), ISTVEL demonstrates that a full battery-electric vehicle (BEV) fleet reduces use-phase (operational) CO₂ by 80.1% and energy operating cost by 66.5% versus the internal-combustion-engine vehicle (ICEV) baseline at current Turkish grid intensity ($\gamma =0.45\mathrm{kg}{\mathrm{CO}}_2/\mathrm{kWh}$). However, these figures reflect use-phase emissions only (tailpipe combustion for ICEV; upstream grid emissions $\gamma \times E_{\mathrm{net}}$ for BEV) and exclude vehicle manufacturing, battery production, and upstream fuel extraction. Opportunistic in-transit dynamic wireless power transfer (DWPT) charging at 0.5 km spacing reduces post-trip battery replenishment demand by a further 67.1%, shifting grid supply from post-trip charging to in-transit delivery; total system electricity demand (including DWPT supply) is 895.7 kWh, marginally above the plain-BEV baseline of 848.1 kWh due to charging losses at ${\eta }_{\mathrm{cs}}=0.95$. Framework transferability is further demonstrated on the Fatih district under an identical protocol. Full article

Forest fire prevention increasingly depends on translating ecological monitoring into earlier, more reliable decisions about ignition risk, fuel condition, spread potential, and management intervention. This critical review evaluates artificial intelligence (AI) for forest fire prevention through full-text extraction of core studies and contextual synthesis of foundational fire-science literature. The evidence base contains 179 unique references, including an AI-focused corpus, classical deterministic and probabilistic fire-danger and spread models, global ignition and lightning studies, remote-sensing and fuel-moisture foundations, decision-support tools, and governance literature. We define prevention-facing AI as systems that support pre-ignition or pre-escalation decisions and compare studies by data source, model design, validation protocol, forecast horizon, transferability, interpretability, and management action. The synthesis shows that AI is most mature for multimodal sensing, smoke/fire detection, susceptibility mapping, and short-horizon forecasting, but less mature for prospective decision-support validation, cross-ecosystem transfer, and operational accountability. AI is therefore most useful when it is hybrid, interpretable, and deployment-aware: it should complement established fire-weather and spread-model baselines while converting ecological observations into timely and actionable prevention judgments. Full article

Forest fire susceptibility mapping is an important component of disaster risk reduction, particularly in transitional climatic zones such as Anhui Province, China. Traditional approaches often rely on expert weighting (AHP) or linear assumptions, which may be insufficient for capturing the complex, non-linear interactions of fire drivers. This study develops a data-driven framework integrating 816 field-surveyed fuel plots with MODIS active fire data (2000–2025). We applied a systematic preprocessing pipeline, including 1–99% Winsorization to reduce the influence of sensor outliers, Non-Linear Gamma Curvature Normalization to represent asymmetrical risk responses, and a spatial buffer-based pseudo-absence protocol combined with semantic land-cover masking to reduce label ambiguity and macro-environmental bias. Benchmarking against seven machine learning algorithms on a naturally balanced dataset showed that the Random Forest (RF) model achieved the highest test-set performance among the evaluated models (Test AUC = 0.831). Youden’s J statistic was used to define a data-driven risk threshold. The results suggest that topographic configuration and forest stand density act as important baseline constraints and interact with physiological moisture stress indicators to influence fire susceptibility. The species-level risk analysis was broadly consistent with ecological expectations: coniferous forests showed the highest predicted high-risk proportion (79.10%), whereas soft broadleaves showed a substantially lower predicted high-risk proportion (4.29%). Spatial mapping indicated a “South-High, North-Low” pattern associated with topographic forcing and fuel continuity, which may provide useful information for regional fire management and the planning of green firebreaks. Full article

Wildfires in California increasingly threaten communities and ecosystems. However, comprehensive estimation of fire dynamics and fuel structure remains limited. Recent advances in Uncrewed Aerial Vehicle (UAV) technology and high-spatial-resolution mapping have provided increasingly important tools for estimating wildfire fuel-height loss across fuel types. This study used a one-year Uncrewed Aerial Vehicle (UAV) time series to quantify fuel-height loss and vegetation regrowth associated with a prescribed upslope canyon fire near Salinas, California, USA. Multispectral, infrared, and visible UAV imagery collected before, during, and after burning was used to generate orthomosaic, digital surface models (DSMs), fuel-type classifications, and surface-volume estimates. To enable reliable pre- and post-fire comparison, ground control points and tie points were used to train linear regression calibrations that corrected angular discrepancies and elevation offsets among time-series DSMs. Calibrated DSMs were then integrated with ecological field measurements to map fuel-height consumption and post-fire recovery at the individual-plant scale. UAV-derived fuel-height change was associated with in situ twig-diameter measurements, which provide field-based indicators of fire effects in chaparral vegetation, while the maximum recorded temperature explained only a small proportion of variation in fuel-height loss. This workflow can support integrated fire ecology and remote-sensing studies by providing repeatable measurements of post-fire changes in vegetation structure. Full article

Airport turnaround is an important operational process that directly affects flight punctuality, airport capacity, and ground-handling efficiency. However, many turnaround activities are still monitored manually or through fragmented operational records, which can limit real-time visibility and delay identification. This study proposes a computer vision-based airport turnaround monitoring pipeline that integrates YOLOv11 object detection, Norfair multi-object tracking, and frame differencing-based motion analysis to extract key operational events from airport video footage. Publicly available turnaround footage from Shinshu Matsumoto Airport, Japan, was collected under different environmental conditions, including daytime, nighttime, rainy, after-rain, and transition lighting conditions. From selected videos, 1446 images were labeled into 11 airport turnaround object classes, including tow tug, aerobridge, airplane, baggage container, belt loader, belt loader roof, fuel line, fuel tanker, fuel tube, tractor, and window. The dataset was divided into training, validation, and testing sets using a 70:20:10 ratio. The trained YOLOv11 model achieved strong detection performance, with overall test an precision of 0.9609, recall of 0.9445, and mAP50 of 0.9617. To support activity-level interpretation beyond object detection, the proposed pipeline applies frame differencing within specific regions of interest, including the aerobridge window region for passenger deboarding and boarding detection, and the belt loader roof region for baggage unloading and loading detection. The extracted object detections, motion spikes, and temporal logs are then converted into a Gantt chart that summarizes major turnaround activities, including airplane parking, deboarding, baggage unloading, refueling, baggage loading, boarding, and pushback. The results demonstrate that the proposed modified YOLO-based pipeline can transform ordinary airport video footage into structured operational timelines, supporting more transparent, data-driven, and automated monitoring of airport turnaround processes. Full article

Predicting forest fire occurrence is essential for proactive disaster preparedness and environmental protection. We introduce a machine learning-based system that forecasts next-day fire probability at high spatial resolution using satellite-derived, multi-modal geospatial data. In contrast to existing reactive systems that rely on thermal anomaly detection (e.g., MODIS or VIIRS-SNPP), our approach is fully predictive, generating pixel-wise fire risk maps a day in advance. Our study focuses on Uttarakhand, India, which is an ecologically sensitive region that experiences frequent and severe forest fires. We curated a domain-specific geospatial dataset spanning 1 April to 29 May 2016. It includes daily 30 m GeoTIFF images with 10 bands comprising weather (e.g., temperature, wind, precipitation), topography (slope, aspect), fuel map, and fire mask. We constructed this dataset from diverse sources and aligned all bands spatially and temporally. To demonstrate the usefulness of this dataset, we implement a deep convolutional neural network (CNN) using the ResUNet-A architecture, chosen for its robust performance in the semantic segmentation of high-resolution remote sensing data. Our model is trained from scratch to produce high-resolution fire probability maps and classify fire/no-fire pixels. Our solution helps with planning and decision-making for early intervention, especially in areas with high risk. It supports the UN’s SDG 13 (Climate Action) and SDG 15 (Life on Land) by enhancing resilience and conserving ecosystems. The presented dataset and methodology can serve as a benchmark for future research on wildfire risk prediction using Earth observation data. Full article

This study develops novel mathematical models to capture the complexities of international land logistics by extending the classical Traveling Salesman Problem (TSP) within a symmetry-aware optimization framework. A focused review of literature provides the theoretical basis for model formulation and highlights the limitations of conventional distance-only approaches. In international transport, shorter routes are often assumed to reduce energy use; however, this assumption overlooks the decisive influence of travel time and traffic variability. In this context, symmetry offers a useful analytical lens, as balanced relationships among distance, time, and fuel consumption can reveal more efficient logistics structures. Accordingly, two models are proposed: the Traditional Traveling Salesman Problem in terms of Distance Concentration (TTSPD), which minimizes route length, and the Extended Traveling Salesman Problem in terms of Distance and Time Concentration (ETSPDT), which jointly considers distance, travel time, and fuel consumption. Furthermore, TTSPD was employed to validate ETSPDT, since it is based on the traditional TSP. Both models are solved exactly using the Solver Add-in in Microsoft Excel 2024 with data derived from Google Maps. The results show that ETSPDT achieves superior energy efficiency and average speed, demonstrating the practical value of multidimensional, symmetry-informed optimization for sustainable supply chain and logistics management. Full article

Machine learning approaches to wildfire spread prediction are constrained by the lack of standardized, multi-source, spatiotemporal datasets that fuse terrain, weather, and fire-state information into a single ML-ready format. We present WildfireCube, a reproducible event-centric pipeline and methodology for constructing dense fourth-order spatiotemporal tensors of shape (T, C, H, W) at 30 m spatial and 3 h temporal resolution. Following the analysis-ready data convention established in the Earth Observation community, the pipeline fuses four open data sources: the Copernicus GLO-30 Digital Elevation Model for static terrain derivatives, ERA5-Land reanalysis for hourly weather forcing, Sentinel-2 Level-2A imagery for spectral vegetation and burn-severity indices, and NASA FIRMS active-fire hotspot detections for fire-state reconstruction via ordinary kriging. The resulting 13-channel normalized tensor separates causal drivers into three physically motivated groups: static landscape controls (elevation, slope, aspect, fuel load), dynamic atmospheric forcings (wind components, temperature, precipitation), and evolving fire state (fire-front mask, burn severity, fractional burn, observation confidence). A physics-informed normalization framework maps all channels to bounded ranges using fixed physical constants rather than sample statistics, ensuring cross-event comparability and exact invertibility. We demonstrate the pipeline on 13 wildfire events across the United States, Canada, and Greece (2017–2023), producing a processed catalog exceeding 300 GB compressed and spanning a 14-fold range in burned area, a 27 °C range in mean temperature, and different fire regimes. Event tensors are stored in chunked Zarr archives with Zstandard compression, achieving a 2.58× compression ratio. As future work, the pipeline will be applied to a 40-event target catalog projected to exceed 2 TB of raw data, providing the multi-regime diversity and scale required for training robust deep learning models for spatiotemporal wildfire prediction. Full article