Search Results (7,729)

This single-site industrial case study compares a leased diesel shunting locomotive with a battery-electric shunting locomotive used for the same class of in-plant railway tasks at the Seza Cement Plant. The evidence base comprises plant leasing and fuel records, equipment specifications, site-reported electricity indicators, operator-reported operational observations, direct CO₂ calculations, and documented occupational safety and health (OSH) functions; it is not a controlled or statistically replicated time–motion experiment. The diesel system incurred a monthly lease cost of USD 10,000 and consumed approximately 1800 L/month, equivalent to 21,600 L/year. Cross-checking the direct CO₂ calculation with 2.692 and 2.683 kg CO₂/L factors gives 58.1 and 58.0 t CO₂/year, respectively. The approximately 24-month payback is treated as a plant-reported investment indicator and evaluated through a normalized sensitivity model because disaggregated costs for locomotive purchase, charging infrastructure, battery replacement, and historical maintenance are not available in the case-study dataset. Operational evidence is reported descriptively: the 20–40% reduction in task time is an operator-reported range rather than a statistical mean; the 7–9 min value refers to the complete 10-wagon weighing maneuver; and 25 loaded wagons (approximately 1450 t) represents the maximum documented field movement rather than a manufacturer-rated capacity. A force-balance check shows that this maximum movement is feasible only if total equivalent resistance remains below approximately 5.41 N/kN, using the 77 kN catalog tractive effort as an upper bound. The battery-electric locomotive produces no local exhaust emissions at the point of use and incorporates SIL 2 remote-control functions, a deadman function, emergency-stop controls, camera support, lighting, and warning systems; these features indicate risk-control capability but do not constitute a measured accident-rate reduction. The study therefore contributes facility-scale, evidence-bounded information for low-speed, repetitive industrial shunting within a defined operating area rather than a general proof of battery-electric superiority across railway applications. Full article

This study presents a sensor-fused field investigation and simulation-based analysis of four horizontal and vertical traffic-calming devices—two raised speed tables, a speed hump, and a raised crosswalk—installed along a 5250-ft two-lane residential collector in Nashville, TN, USA. A dual-sensor architecture combining a Miovision Scout video-based vehicle counter and WAAS/EGNOS-augmented GPS probe-vehicle logging (5 m 3-D RMS horizontal accuracy, 1 Hz sampling) was used to reconstruct 30 quality-controlled free-flow vehicle trajectories and 12-h per-lane volume counts. A spatial kinematic transform (a = v·dv/dx) was applied to extract device-specific approach-deceleration and post-device recovery-acceleration rates, and a three-parameter log-logistic cumulative-distribution function was fitted to the field-observed desired-speed percentiles (root-mean-square error below 0.043 for both speed-table devices). The camera- and GPS-derived observations were used to calibrate and statistically validate a PTV VISSIM microsimulation replica of the corridor, achieving a mean-speed calibration error of 0.71% or better at every device, a GEH statistic below 1.5 at all four analysis turning movements, and independent travel-time validation errors of 5.7–12.1%, within the accepted 15% threshold. The validated model was then used to reconstruct device- and spacing-specific May–Keller macroscopic speed–density–flow relationships, calibrated against simulated capacities of 650–775 vehicles per hour per lane at 350-, 700-, and 1050-ft device spacing. Results show capacity reductions of 20–33% relative to free-flow conditions and yield kinematically derived maximum recommended spacings of 265–630 ft to maintain crossing speeds at or below 15 mph, depending on device geometry. The findings demonstrate a reproducible, low-cost sensor-fusion workflow for quantifying the safety–capacity trade-off of traffic-calming corridors and for informing the design of sensor-in-the-loop adaptive-calming infrastructure. Full article

The dynamics of aboveground biomass (AGB) in rubber plantations (RPs) provide an important basis for evaluating carbon stocks and environmental adaptability in tropical plantations. However, continuous monitoring of AGB of RPs at the regional scale is lacking, and its nonlinear responses to hydrothermal conditions remain insufficiently understood. This study focused on RPs in western Hainan Island (WHI), including Danzhou, Baisha, Lingao, and Chengmai, and integrated field plot data with multi-source remote sensing datasets. A framework for mapping RPs combining rule-based constraints and phenology-based random forest (RF) classification was developed. After key variable screening, extreme gradient boosting (XGBoost), Shapley additive explanations (SHAP), and generalized additive model (GAM) were used for AGB estimation and identification of climatic responses. The results showed that mapping of RPs achieved an overall accuracy of 92.89% and a Kappa coefficient of 0.854. The XGBoost-derived estimates showed that AGB of RPs in the study area increased by approximately 1.43 × 10⁶ Mg from 2017 to 2025, with growth areas mainly concentrated in the Danzhou–Baisha and western Chengmai. AGB exhibited significant nonlinear responses to climatic factors. Specifically, the effect of precipitation (PRE) shifted to negative after approximately 1945 mm yr⁻¹, whereas annual mean maximum temperature (TMAX) shifted to a positive effect after about 29.72 °C, although this effect gradually weakened as temperature continued to rise. Combinations such as PRE × annual mean temperature (PRE × TMP), PRE × TMAX, and PRE × potential evapotranspiration (PRE × PET) exhibited significant nonlinear interactions, indicating that the direction and magnitude of the effect of PRE shifted with changes in temperature and PET levels. These findings link the spatiotemporal changes in AGB of RPs in WHI with hydrothermal thresholds and their interacting effects, deepening our understanding of the climatic response characteristics of AGB in RPs in this region. They also provide a scientific basis for RP monitoring, carbon stock assessment, and climate-adaptive management in WHI. Full article

Image-level monitoring and variety-level detection of three specialty tomato cultivars, Kiss, Millennium, and White Jade, remain challenging in unstructured orchards because of foliage occlusion, overlapping fruit clusters, and variable illumination. Conventional downsampling may weaken fine spatial details of small targets, whereas larger detectors can impose computational demands that are unsuitable for mobile or edge-based agricultural platforms. To address these limitations, we propose WCAF-YOLO, a lightweight two-dimensional tomato detector based on a modified YOLOv26n architecture. The model replaces the P3 backbone downsampling operation with space-to-depth convolution (SPD-Conv) to retain fine-grained spatial information. Its weighted channel-aware fusion (WCAF) neck combines learnable branch weighting with parameter-free three-dimensional attention to refine fused features. Bounding-box regression uses focaler-minimum point distance intersection over union (Focaler-MPDIoU). Across five random seed runs on the internal held-out test subset of a custom single-site orchard dataset, WCAF-YOLO obtained a mean ${\mathrm{mAP}}_{50–95}$ of $0.9048\pm 0.0013$ and a mean recall of $0.9280\pm 0.0019$. The corresponding mean improvements over the YOLOv26n baseline were 2.14 and 3.42 percentage points, respectively. The model contained 2.36 M parameters and required 6.36 GFLOPs. Under the evaluated protocol, the model combined a compact parameter count with higher mean detection metrics than the YOLOv26n baseline. The detector outputs two-dimensional bounding boxes and variety labels for image-level orchard monitoring and variety-level assessment. Integration into agricultural field platforms remains to be validated. Full article

Large mineral flotation workshops in severe cold regions represent special industrial indoor environments characterized by the coexistence of limited ventilation and intense heat release. Such conditions generate pronounced spatial thermal stratification and localized heat accumulation within the workshop, leading to uneven worker thermal exposure and increased thermal discomfort and heat stress risk. However, conventional thermal comfort models were primarily developed for ordinary buildings with relatively stable thermal environments. Their applicability to large industrial workshops remains insufficiently validated. Nine representative monitoring points were arranged in the summer operating areas of the workshop, and thermal comfort surveys were conducted among 35 workers who had adapted to the local climate and working environment. The predicted mean vote (PMV) model was used as the baseline assessment framework, while an adaptive predicted mean vote (aPMV) model was further calibrated using field-based thermal sensation information. Monte Carlo simulation was employed to evaluate uncertainty propagation under field-data constraints, and Sobol sensitivity analysis was conducted to identify the dominant factors affecting thermal comfort predictions. The results demonstrated that the conventional PMV model exhibited a clear warm prediction bias under the investigated industrial conditions. After adaptive correction, the deviation from the field-based TSV was reduced by 82.93%, indicating improved agreement with workers’ actual thermal perception. Sensitivity analysis identified metabolic rate as the dominant contributor to aPMV output variance, with first-order and total-effect Sobol indices of 0.530 and 0.535. The proposed framework provides a scenario-specific approach for thermal comfort assessment in the investigated flotation workshop and offers preliminary methodological references for similar large-scale flotation workshops. Full article

To address the limited cooling range of ventilation in deep, high-temperature development headings and the lack of coordinated design between coil-based cooling and the ventilation system, this study proposes a coupled “twin-forcing–coil” cooling scheme. Building on conventional overlap (forcing–exhausting) ventilation, a rear-mounted second forcing duct is added to the conventional overlap (force–exhaust combined) auxiliary ventilation system, forming a dual-duct forcing, single-exhausting configuration—hereafter termed the “twin-forcing–single-exhausting” (TFSE) system—that provides a booster (relay) air supply to mitigate the along-path attenuation of cooling capacity and the short-circuiting of cold air; an in situ heat-exchange coil wall further provides supplementary cooling where ventilation-based temperature control weakens. Using a development heading at the 790 m level of a metal mine in Yunnan as the engineering background, a three-dimensional numerical model coupling the roadway, ventilation system, and coil wall was established and validated against nine field monitoring points, showing average relative errors of approximately 1% for temperature and 2–3% for humidity, comparable to the measurement uncertainty of the field instrumentation. Because the numerical model does not account for evaporative and condensation phase-change processes, two supplementary development headings with standing water at the face were used for validation; results showed that model error increases with water accumulation and heading length, indicating the model’s applicability is limited to conditions with intact surrounding rock and minimal seepage. Six operating cases were designed with duct placement and coil spacing as variables. Results show that single-duct ventilation cooling decays markedly beyond 30 m from the face, whereas twin-forcing booster (relay) air supply effectively extends the cooling range, reducing the 30–70 m section temperature by 2.7–2.9 K; the second duct should be positioned where the first duct’s cooling capacity begins to attenuate but is not yet depleted. Based on only two spacing configurations tested (10 m and 15 m), coil-staggered spacing showed limited effect on cooling performance under the field conditions examined; this preliminary finding requires validation across a broader range of spacings. Among the chilled-water conditions tested, an inlet temperature of 280.65 K and a flow velocity of 0.5 m/s offered a reasonable trade-off between cooling uniformity and economic efficiency. Under the boundary conditions and equipment parameters of this case, energy consumption estimates further indicate that the cooling effect per unit electricity consumption of twin-forcing ventilation is roughly 6–8 times that of coil-based cooling, primarily due to pumping losses over the ~240 m chilled-water delivery distance. This energy penalty indicates that coil-based cooling is better suited as a localized, short-distance supplementary measure rather than as a means of extending the cooling range over long distances. Full article

To improve trajectory estimation for highly maneuvering aerial targets under time-varying noise, an adaptive multiple-model filtering framework is developed based on Kalman filtering. The framework integrates online model-probability updating, higher-order error-propagation correction, and adaptive noise adjustment to accommodate motion-mode transitions, nonlinear estimation errors, and measurement uncertainty. Simulation results show that, at a relative velocity of $800\mathrm{m}/\mathrm{s}$, the proposed method reduces the position root-mean-square error (RMSE) by $57.73\%$ compared with the raw measurements and outperforms the individual motion-model filters. A field-programmable gate array (FPGA)-based laboratory validation platform is further developed, and the experimental results are consistent with the simulation results. The measured single-frame processing latency is $152\mathsf{\mu }\mathrm{s}$, demonstrating the effectiveness and real-time feasibility of the proposed framework for maneuvering-target trajectory estimation. Full article

In the testing of aircraft cargo smoke detectors, surrogate smoke is often used in place of fire-generated smoke to avoid the hazards of live-fire experiments. Reproducing the time-varying concentration profile of real fire smoke requires feedback control of the surrogate smoke concentration. Two obstacles arise: the turbulent smoke flow is difficult to model accurately, and the distance between the generator and detector introduces a substantial control-loop delay. This study proposes a smoke simulation method based on model-free adaptive predictive control (MFAPC). The MFAPC scheme was tested in a full-scale aircraft cargo compartment simulator, where it drove the surrogate smoke concentration to track the profile recorded from a real cargo fire. Particle image velocimetry (PIV) was used concurrently with concentration control to capture the corresponding smoke velocity field. Across all conditions, MFAPC reduced the root-mean-square error by up to 38% compared with model-free adaptive control alone. With a control-loop delay longer than 10 s, the light transmission deviation remained within 2% of the target. The PIV data show that the controlled surrogate smoke velocity field reproduces the dominant structures and evolution patterns of actual fire-generated smoke, providing fluid-mechanistic evidence that a recreated dynamic smoke environment is physically meaningful. Full article

Antenna selection is investigated as a means of reducing radio-frequency (RF) chain power in massive multiple-input multiple-output (MIMO) base stations under realistic channel state information (CSI) impairments. The study is motivated by the mismatch between conventional selection objectives and multi-cell operation with estimation error, pilot contamination, spatial correlation and inter-cell interference. APCS-Boost-R is introduced as the primary contribution. An interference-whitened D-optimal seed is combined with projected rank-one exchanges and a calibrated surrogate that incorporates a user-side interference-plus-noise report and a closed-form estimation-error correction. APCS-Boost-RG is retained as an optional graph neural network (GNN) refinement in which residual exchanges are ranked after the algebraic solution has been formed, while feasibility and non-degradation of the calibrated surrogate are verified deterministically. In a three-cell urban macro configuration derived from Third Generation Partnership Project (3GPP) TR 38.901 with 64 antennas, 16 active RF chains and eight users per cell, APCS-Boost-R achieves 19.364 bit/s/Hz over 200 paired realizations. Improvements of 2.58 percent over APCS-Boost, 6.76 percent over greedy search and 10.16 percent over a genetic algorithm are obtained. APCS-Boost-RG adds 0.019 bit/s/Hz but is treated as an optional refinement because it requires a second-stage neighborhood evaluation and offline model maintenance. In the archived common timing record, APCS-Boost-R requires 20.376 ms per three-cell realization, compared with 12.728 ms for APCS-Boost, 57.775 ms for norm-initialized greedy search and 41.302 ms for the genetic algorithm, while APCS-Boost-RG requires 24.0 ms versus 20.4 ms for APCS-Boost-R in the separate archived learned-stage record. Separate reconstructions on the documented reproducibility host require $55.3\pm 14.5$ ms for APCS-Boost-R and $592.2\pm 181.9$ ms for a complete APCS-Boost-RG rebuild. Additional paired examinations confirm robustness across stronger search budgets, report imperfections, regularized precoding, coordination, near-field sensitivity, hardware perturbations, and configurations ranging from 32 to 128 antennas and one to seven cells. Full article

Soil organic carbon (SOC) stocks are strongly influenced by land use, vegetation condition and climate, particularly in heterogeneous mountain catchments. This study quantified SOC stocks and simulated long-term SOC dynamics in the Çapakçur micro-catchment, eastern Türkiye, by integrating field assessment, geostatistical prediction, uncertainty analysis, inverse RothC calibration and scenario modelling. A total of 428 soil samples were collected from the 0–30 cm layer across forest, degraded forest, and pasture areas. SOC stocks were calculated from SOC concentration, bulk density and soil depth, and spatially predicted using ordinary kriging of log-transformed SOC stocks. RothC was calibrated for each land-use class to estimate the annual carbon inputs required to maintain observed SOC stocks, followed by 50-year restoration and climate-sensitivity simulations. SOC stocks ranged from 7.69 to 247.68 Mg C ha⁻¹, averaging 55.52 Mg C ha⁻¹. Forest had the highest mean SOC stock (78.5 Mg C ha⁻¹), followed by pasture (55.9) and degraded forest (50.2 Mg C ha⁻¹). Required annual carbon inputs were 5.17, 4.58 and 3.29 Mg C ha⁻¹ yr⁻¹, respectively. Increasing degraded forest carbon inputs to forest-equivalent levels increased SOC by 14.76 Mg C ha⁻¹ over 50 years, equivalent to 37.77 Gg C or 138.49 Gg CO₂eq at the catchment scale. A stronger restoration scenario increased this potential to 63.77 Gg C. Warming caused SOC losses, with +2 °C reducing catchment SOC by 56.78 Gg C. These findings demonstrate the potential of degraded forest restoration for SOC sequestration while highlighting the vulnerability of long-term SOC gains to climate warming. Full article

Shoreline determination is fundamental to coastal research, spatial planning, and legal boundary delineation but remains challenging in low-relief coastal areas where small sea-level variations can produce substantial horizontal shoreline displacements. This study compares shoreline determination methods based on tide gauge (TG) observations, LiDAR data, Sentinel-1 synthetic aperture radar (SAR) and Sentinel-2 optical satellite imagery using the low-relief coast of Pärnu Bay, Estonia, as a case study. The comparison was based on shorelines derived from Sentinel-1 and Sentinel-2 imagery acquired on selected common acquisition dates within the 2015–2025 study period, rather than on a temporally continuous annual dataset, and compared with temporally matched LiDAR-derived shorelines extracted from a Digital Terrain Model (DTM) generated from a 2021 LiDAR survey. The LiDAR-derived shorelines were extracted using the mean sea level (MSL) observed at the Pärnu and Häädemeeste TGs at the satellite overpass time, while the satellite-derived shorelines were additionally validated against RTK GNSS measurements. The results demonstrate that the evaluated methods produce substantially different shoreline positions. Sentinel-2-derived shorelines generally corresponded more closely to the temporally matched LiDAR-derived shorelines than Sentinel-1-derived shorelines and most accurately represented the instantaneous land–water boundary during field validation. These findings demonstrate that different shoreline determination methods represent different shoreline definitions. Consequently, shoreline datasets should be interpreted according to their intended purpose rather than treated as directly interchangeable. Full article

In physical model inversion of leaf area index (LAI) over complex vegetation canopies, variations in canopy structure, background moisture, and background brightness can cause spectrally similar lookup table (LUT) candidates to represent different combinations of canopy and background parameters, reducing retrieval stability. This study proposes a PROSAIL lookup table inversion method for GaoFen-5A (GF-5A) hyperspectral imagery based on physically informed parameter range refinement. A small set of key GF-5A feature bands was selected to preserve information sensitive to canopy structure and background conditions while reducing spectral redundancy. Vegetation type information was used to determine the LIDFa range, while the normalized difference infrared index (NDII) and brightness index (BI) were used to guide the refinement of psoil and rsoil ranges according to moisture related and brightness related spectral responses, respectively. Combining compact spectral inputs with refined PROSAIL parameter ranges improved LUT candidate selection under variable canopy and background conditions. Validation using 62 field LAI values aggregated at the GF-5A pixel scale yielded a coefficient of determination (R²) of 0.6000, a root mean square error (RMSE) of 1.1461, a mean absolute error (MAE) of 0.8712, and a Bias of −0.0092, outperforming the comparison schemes. These results indicate improved physical consistency and stability in LAI inversion under complex canopy and background conditions. Full article

In Deep Learning Remote Sensing, data quantity is rarely the limiting factor. A single high-resolution satellite image can yield thousands of training patches. What determines model performance, yet remains largely overlooked, is the quality of those patches. To date, the choice of sampling method has rarely been treated as a methodological decision. Conventional approaches, namely sliding-window and random sampling, introduce two compounding data-quality problems: severe class imbalance caused by the overproduction of background-only patches and negative learning arising from incomplete annotations, where unlabeled objects are implicitly treated as negative examples during training. To address these limitations at the data construction stage, we propose object-centric patch sampling, a model-independent strategy that anchors each training patch to the geometric centroid of an annotated object. This design ensures that every object-anchored patch contains at least one target instance and substantially reduces exposure to unlabeled regions that generate false-negative supervision signals; only a small, deliberately controlled proportion of background-only patches is retained to preserve contextual variety without reinstating background dominance. The method is evaluated on three heterogeneous remote sensing datasets spanning satellite (Sentinel-2, 10 m), aerial (NAIP, 1 m), and UAV (0.25 m) imagery, covering cotton field segmentation, rural building extraction, and water body delineation, respectively. Using a U-Net architecture under identical training conditions, the proposed approach achieves IoU scores of 0.929, 0.896, and 0.912 on the three datasets, respectively, outperforming sliding-window sampling by up to 19.6 percentage points in IoU and consistently delivering higher F1-scores across all experimental configurations. Evaluation under DeepLabV3+ gives a mean IoU of 0.9504 and a mean F1-score of 0.9772 in a multi-class segmentation task, indicating that the gains are not specific to a single architecture. Unlike model-level solutions such as focal loss or class reweighting, the proposed method improves training data quality at its source and integrates seamlessly into any deep learning pipeline without architectural modifications. Full article

Natural gas production from mature fields is progressively shifting toward low-rate wells operating near their economic limit, creating challenges for long-term asset management. This study proposes an integrated stochastic decision-support framework combining Arps decline curve analysis, a calibrated Schwartz Type-I mean-reverting jump-diffusion model, Monte Carlo simulation, and Bellman dynamic programming to optimize marginal dry gas well management. The framework evaluates pipeline commercialization and a hybrid strategy integrating on-site electricity generation, while incorporating monetized environmental externalities associated with CO₂ emissions from gas combustion and potential post-abandonment CH₄ emissions. Application to the Mareógrafo 100 well in Mexico shows that the environmentally adjusted Bellman policy yields a mean NPV of USD 34.84 thousand, exceeding the comparable pipeline-only and hybrid strategies. Internalizing environmental costs reduces the mean optimal NPV by 46.6% relative to the economic-only formulation, while the mean abandonment time is approximately 200 days. Sensitivity analysis identifies electricity price, natural gas price, and pipeline distance as the dominant profitability drivers. The proposed framework provides a transferable methodology for jointly evaluating commercialization, environmental externalities, and abandonment decisions in mature dry gas fields under uncertainty. Full article

As the scaling of silicon-based devices approaches physical limits, two-dimensional transition-metal dichalcogenide field-effect transistors (2D TMD FETs) have emerged as promising candidates for logic devices in the post-Moore era. However, their design optimization relies heavily on computationally intensive TCAD simulations, thereby limiting efficient exploration of multidimensional parameter spaces. This paper proposes an efficient automated design framework for 2D TMD FETs under small-sample conditions and validates it using a monolayer MoS₂ FET as a case study. The framework integrates device design, physics-based simulation, performance prediction, and inverse design, establishing a bidirectional mapping between device parameters and electrical performance. Target-driven closed-loop optimization is achieved through TCAD-based feedback validation. Results demonstrate that, using a dataset comprising 300 TCAD samples, the forward model achieves an average coefficient of determination (${\mathrm{R}}^2$) of 0.9503. TCAD revalidation of the inverse-designed devices yields an average mean absolute error (MAE) of 0.0464 and an average mean absolute percentage error (MAPE) of 5.46% for performance metrics. Regarding computational efficiency, while a single TCAD simulation takes approximately 25 to 50 min, the trained model performs inference in under 50 ms, achieving a speedup of at least $3\times {10}^4$ during the inference phase. Accounting for the generation of the 300 TCAD samples and the training of both forward and inverse models, the framework’s one-time computational cost ranges from 160.27 to 285.27 h. Once the cumulative number of design tasks exceeds approximately 342 to 385, the total computational cost falls below that of direct TCAD simulation, with the computational advantage becoming increasingly significant as the number of tasks grows. Consequently, this method is highly suitable for large-scale parameter sweeps, device screening, and multi-objective, high-frequency design iterations. It drastically reduces repetitive TCAD calls, offering a scalable solution for the efficient, automated design of 2D TMD FETs. Full article