Search Results (9,443)

High-resolution PM2.5 mapping is increasingly required for exposure assessment and for assessing compliance with environmental policies. However, statistical downscaling from coarse gridded products assumes that PM2.5-predictor relationships are transferable across spatial resolutions. This study evaluated that assumption and developed a two-stage framework for 100 m PM2.5 mapping in Zhejiang Province, China. In Stage 1, the 1 km CHAP-PM2.5 product was downscaled with multi-source predictors using XGBoost and TabICL v2. In Stage 2, monitoring observations were integrated through daily bias correction and residual calibration. Scale transferability was assessed using coefficient-side similarity, output-side transfer tests, and a simulation-based recoverability experiment; calibration was evaluated using stratified and buffered site-based cross-validation. Scale transferability was only partial: predictor effects and transferred outputs became less stable as the resolution gap increased, and downscaling recovered only part of the known sub-kilometre heterogeneity. Stage 1 mainly enhanced spatial texture, whereas Stage 2 produced clearer improvements in agreement with ground observations and recovered much of the variability lost in the coarse-resolution data product. In cross-validation, the best calibrated product increased R² from 0.55 to 0.74 and reduced RMSE from 7.3 to 5.5 μg/m³. This paper provides an extensive evaluation for 2020 and provides a series of quality-assured daily 100 m resolution PM2.5 maps for Zhejiang province for 2020–2024 which can be flexibly aggregated in space and time for different applications. Full article

Cross-view geo-localization (CVGL) between unmanned aerial vehicle (UAV) imagery and satellite imagery is a key technique for autonomous UAV navigation in Global Navigation Satellite System (GNSS)-denied environments. However, most existing methods rely on energy-intensive Artificial Neural Networks (ANNs), making them difficult to deploy on resource-constrained edge computing platforms. Spiking Neural Networks (SNNs) provide a promising alternative for energy-efficient inference, but their application to CVGL still faces two challenges that remain insufficiently addressed. First, the isotropic computation used by existing SNN backbones is mismatched with the directional characteristics of spike activations. Spike activations tend to form oriented aggregation patterns along elongated geographic structures, and isotropic computation can therefore dilute directional signals. Second, the limited representational capacity of SNNs further increases the sensitivity during training optimization. However, the standard triplet loss adopts a static weighting strategy and assigns the same weight to all triplets that violate the margin constraint, which is unfavorable for learning from hard negatives. To address these challenges, we propose a framework with two core contributions. At the feature extraction level, the Directional Adaptive Convolution Module (DACM) processes spike feature maps by sequentially performing horizontal strip convolution and vertical strip convolution, thereby capturing a more complete geometric structure of directional spike clusters. At the training supervision level, we propose a Dual-dimensional Progressive Reweighting (DPR) loss, which jointly characterizes sample difficulty from pairwise difficulty and positive-pair quality difficulty. A learnable fusion parameter is used to adaptively balance these two types of difficulty information. Experimental results on the University-1652 and SUES-200 benchmarks show that the proposed framework, when equipped with the same representation learning head as its ANN counterparts, achieves competitive and, in many settings, superior performance. In terms of energy efficiency, its estimated theoretical energy consumption is over $8.8\times$ lower than that of published ANN methods under their original configurations. Under a more rigorous matched ANN control that shares the identical architecture, the estimated energy is reduced from 29.84 mJ to 6.36 mJ, an approximately $4.7\times$ reduction obtained at a cost of only 2.29 percentage points in R@1. Full article

Seed coat color is an important quality trait in mung bean (Vigna radiata) and is closely associated with seed appearance, commercial value, and phytochemical composition. To investigate the genetic basis of black seed coat formation, six F₂ populations were derived from reciprocal crosses between the black-seeded accession LZL115 and the green-seeded accession LZL156. Among 755 F₂ plants, 559 produced black-coated seeds and 196 produced green-coated seeds, conforming to a 3:1 segregation ratio (χ² = 0.37, p = 0.54). These results indicated that black seed coat color was dominant and consistent with the control by a single dominant locus, designated VrScL115, in the LZL115 × LZL156 genetic background. Bulked segregant analysis sequencing (BSA-seq) initially mapped VrScL115 to an approximately 2.90 Mb region on chromosome 4. Using newly developed KASP markers and recombinant screening in expanded F₂ populations, the locus was further delimited to a 121.79 kb interval between markers LS_K3333 and LS_K3379. Of the 11 annotated genes within this interval, LOC106758748 was the only gene containing high-confidence coding-sequence variants between the parents. This gene encodes a putative R2R3-MYB transcription factor homologous to MYB90. Comparative sequence analysis identified several allelic variants potentially associated with black seed coat color, and protein structure prediction indicated local structural differences between the parental proteins. LOC106758748 showed consistently higher expression in the developing seed coats of LZL115 than in those of LZL156 at 10, 15, and 20 days after pollination, with expression peaking at 15 days. Haplotype analysis of 246 mung bean accessions showed that the LZL115-associated allele combination at LS_K3352, LS_K3365, LS_K3367, and LS_K3370 was present in 21 of 27 black-seeded accessions (77.8%) and absent from all 219 non-black accessions, corresponding to a specificity of 100% and a false-negative rate of 22.2%. These findings support LOC106758748 as the leading candidate gene for VrScL115; however, direct in vivo functional validation is still required to confirm its causal role in black seed coat formation. The four-marker combination may be useful for identifying germplasm carrying the LZL115-associated allele, although further validation in independent germplasm populations is required. Full article

Robotic multi-seam welding path planning for box-type thin-walled structures faces significant challenges due to multiple constraints, multiple objectives, and its high-dimensional discrete combinatorial nature. To address these issues, a deformation-informed multi-objective path planning method for multi-seam welding (DMOPP) is proposed, comprising a multi-objective formulation and an optimization algorithm. At the modeling level, welding path length and maximum structural deformation are defined as the optimization objectives. Collision-free path planning is used to calculate the path length, while an XGBoost-based surrogate model is developed to establish the mapping between welding path variables and maximum deformation, enabling rapid deformation prediction without computationally expensive finite element simulations. At the optimization level, a modified discrete artificial lemming algorithm (MODALA) is proposed to improve global search capability and convergence stability. Experimental results show that MODALA outperforms the comparison algorithms on benchmark functions and discrete optimization problems, demonstrating its superior optimization performance. The XGBoost surrogate model achieves an R² of 0.8447 and an RMSE of 0.0841 mm, indicating good predictive accuracy. In welding path planning simulations, the proposed method effectively reduces the path length and welding deformation while achieving a favorable trade-off between path efficiency and structural quality. Robotic welding experiments further validate its practical effectiveness. Full article

Positioning jumps are a primary cause of trajectory discontinuities in urban canyon environments, severely hindering the widespread adoption of autonomous vehicles. This paper proposes a CNN-GRU fusion-based method for correcting such positioning jumps. A complete 15-dimensional error-state extended Kalman filter (EKF) framework is established to analyze the jump generation mechanisms from three perspectives—pseudorange distortion, inertial drift, and filter gain divergence—thereby justifying the use of inertial measurement unit (IMU) time-series data for anomaly prediction. An end-to-end mapping model is further constructed, in which a one-dimensional convolutional neural network (CNN) extracts cross-channel spatial features from multi-axis inertial data, while a gated recurrent unit (GRU) captures long-term temporal error evolution. A hysteresis navigation quality factor and a piecewise Huber loss function are incorporated to enable hierarchical adaptive optimization. Experimental results demonstrate that the proposed method reduces the positioning root mean square error (RMSE) from 1.24 m to 0.70 m, achieves a jump suppression rate of 43.5%, and maintains a single-frame inference latency of 11.8 ms, meeting the real-time requirements for future autonomous driving localization. Full article

High shrinkage-related defect rates in aluminum die casting reduce effective production capacity. They also create energy-intensive re-melting loops, which weaken production planning reliability and environmental performance. This study examines how Green Lean Six Sigma can stabilize a resource-constrained Small and Medium-Sized Enterprise (SME) die-casting process and translate quality improvement into measurable capacity recovery and Scope 2 electricity-related carbon savings. Based on a 10-month case study, the Define–Measure–Analyze–Improve–Control (DMAIC) framework was integrated with factorial ANOVA, the Response Surface Methodology (RSM), one-way analysis of variance (ANOVA) and statistical process control (SPC). These methods supported process parameter identification, operating-window development and shop-floor process stabilization. The analysis identified the filling speed and mold temperature as significant shrinkage drivers, developed a mold temperature control map, and determined the standardized filling speed at 800 mm/s. The intervention reduced the shrinkage defect rate from 7.19% to 1.46%, reduced the overall scrap rate from 7.60% to 2.71%, and improved the overall sigma level from 2.93 to 3.42. This yield improvement generated a 4.89 percentage-point yield-equivalent capacity gain, avoided 4401 kWh of re-melting electricity, reduced Scope 2 emissions by 2.36 t CO₂e, and generated gross annualized savings of RMB 249,214 (USD 35,783). Considering a one-time implementation cost of RMB 31,445 (USD 4515), the first-year net saving was RMB 217,769 (USD 31,268). The findings show that accessible statistical process control methods can provide SMEs with a resource-efficient pathway to improve process stability, capacity utilization and electricity-related environmental performance before investing in advanced digital technologies. Full article

Large-scale administrative hospital discharge data is a key resource for secondary health systems research, yet reuse is constrained by barriers of access, quality, interoperability, and semantic comparability. This paper presents and validates a reproducible, declarative, loss-aware harmonization framework for public discharge data that avoids full migration to a comprehensive common data model. The framework comprises a lightweight 14-field canonical model, versioned JSON crosswalks, a shared execution engine, a resilient file reader, schema validation, idempotency tests, value-domain checks, and an information-loss map. It was evaluated on public record-level discharge data from five jurisdictions on three continents (Korea, Brazil, Mexico, Chile, and New York State), comprising 561,966,231 harmonized records from 2001 to 2025. Validation demonstrated conformance to the declared source profiles for 82 of 99 files and full canonical conformance for 39, idempotency across all 99 files, 99.99% conformance with permitted value domains under an explicitly stated aggregation, and detection of source-level defects such as truncated files, malformed rows, and completeness anomalies. A marker-condition query for ischemic stroke (ICD-10 I63) showed that a single case definition executes consistently on the four sources retaining raw ICD-10 codes. The results show that, for heterogeneous administrative data, the key value lies not in scale alone but in the auditability of transformations, explicit loss documentation, and reproducibility of the harmonization process. Full article

Background/Objectives: Nursing involves repeated exposure to emotionally, morally, and psychologically distressing experiences, yet the mechanisms through which low-intensity occupational stressors accumulate and contribute to psychological harm remain conceptually unclear. This scoping review examined how the nursing literature conceptualises the cumulative effects of repeated occupationally distressing experiences and identified the conceptual gap created by the absence of a unifying term such as micro-trauma. Methods: A scoping review was undertaken to map concepts used to describe cumulative occupational distress in nursing and determine whether any terminology captures the cumulative impact of repeated low-intensity exposures. Searches were conducted across CINAHL, MEDLINE, and Emcare, supplemented by citation searching, Google Scholar, targeted grey literature searching and structured generative artificial intelligence-assisted source identification. Data were extracted using a priori criteria and synthesised narratively. Consistent with Joanna Briggs Institute guidance, quality appraisal was not undertaken. Results: No included publication applied the term ‘micro-trauma’ to nursing or healthcare workers. Seven publications met the inclusion criteria following expanded searching across supplemental pathways. The literature used heterogeneous and overlapping terminology, including burnout, compassion fatigue, cumulative grief, moral distress, workplace trauma, and post-traumatic stress disorder symptomatology, to describe cumulative psychological burden, but none provided a unifying conceptual frame for repeated low-intensity exposures. Conclusions: The current literature does not yet clearly define or consistently apply micro-trauma in nursing, despite evidence that repeated occupational exposures contribute to cumulative psychological burden. This review highlights a conceptual space before end-point conditions such as burnout, compassion fatigue, or post-traumatic stress disorder symptomatology are recognised. Future research should develop and test a clearer conceptual framework for micro-trauma in nursing before it is applied as a distinct unifying concept. Full article

Large-scale, multi-center projects have become common in the era of rapid technological development, but protocol standardization remains challenging. In whole-exome sequencing (WES), various exome enrichment kits exhibit variable efficiency across genomic regions, leading to systematic, non-biological batch effects, much stronger than other technical factors. We propose a workflow to minimize the effect of WES capture inconsistencies in single-nucleotide variation (SNV) data. The pipeline consists of quality control, mapping to the genome, SNV calling, joint genotyping, and imputing genotypes using reference haplotypes. SNVs are then aggregated into gene-level features measuring the burden of deleterious variants. Finally, a gene-level imputation is performed using a customized algorithm. Namely, if the detection rate of a gene is low in samples enriched with a given capture kit but high in samples enriched with other kits, missing values in the former group are imputed, as such differences are unlikely to reflect true biology. As a benchmark, we conducted a study on over a thousand breast cancer cases across 11 cohorts, using eight exome capture kits. We demonstrated that the proposed pipeline leads to a considerable decrease in the batch effect signal, potentially increasing the likelihood of finding true biological signals. Full article

A Transmission Control Protocol (TCP) throughput map represents communication quality over road networks and supports communication-aware applications in intelligent transportation systems. Maintaining such a map online is challenging because vehicular measurements are sparse and unevenly distributed, network conditions vary rapidly, and sensing-budget constraints limit the number of vehicles from which onboard communication measurements can be uploaded at each time step. This work addresses online TCP throughput map maintenance under sparse vehicular observations and sensing-budget constraints. To support budget-constrained sensing, we combine discoverability-guided vehicle selection and probabilistic map updating within a digital twin (DT)-assisted vehicular sensing architecture. The resulting sensing-and-mapping method, referred to as Discoverability-aware and Statistical Mapping (DISMAP), maintains a spatio-temporal discoverability map to characterize historical sensing coverage and select vehicles that improve the coverage of under-represented regions. It then uses Gaussian Process Regression (GPR) as a probabilistic mapping engine to estimate the mean TCP throughput and predictive standard deviation, where the standard deviation is adjusted using local vehicle density. Simulation results show that DISMAP reduces the mean absolute error (MAE) and mean standard deviation (MSTD) by up to 23.7% and 37.5%, respectively, and achieves a prediction-interval miss rate (PIMR) of 0.048, which is close to the nominal value of 0.05. These results indicate a favorable balance among prediction accuracy, interval sharpness, calibration, and spatial representativeness across different traffic-density conditions. Full article

Air pollution is a major environmental risk factor, particularly for children, who experience repeated exposure during daily school commuting. Exposure-aware routing has been proposed as a strategy to reduce contact with environmental pollutants; however, limited evidence exists regarding how optimization opportunities vary across pollutants and urban environments. This study presents a GeoAI-based framework for pollutant-aware route optimization using OpenStreetMap street networks and air quality data from the Basque Government environmental monitoring network. Approximately 2000 simulated school commuting trajectories were generated across Donostia-San Sebastián and Bilbao’s metropolitan area. For each origin–destination pair, the shortest-path route was compared with alternative routes optimized for PM₁₀ and NO_x exposure. The results revealed substantial differences between pollutants and cities. In Donostia-San Sebastián, NO_x optimization produced the greatest benefits, with more than 10% of routes achieving exposure reductions above 5% and a maximum reduction of 54.9%. In contrast, Bilbao exhibited the highest optimization potential for PM₁₀, with a maximum reduction of 32.8% and 8.2% of routes achieving reductions greater than 5%. Meaningful reductions were generally achieved with moderate increases in traveled distance. These findings suggest that exposure-aware routing may benefit a subset of school commuting trajectories and that optimization potential strongly depends on the local environmental conditions. Full article

Atmospheric deposition in industrial basins of Central Asia is strongly influenced by local emissions and wintertime dispersion conditions. This study characterized snowpack at 63 sampling stations across Ust-Kamenogorsk, Kazakhstan, including operational background station 1, on 24–26 February 2025 after a 116-day accumulation period. Major ions were determined in a spatially distributed exploratory subset of 16 samples, and trace elements were measured in samples from all 63 stations by means of inductively coupled plasma mass spectrometry and optical emission spectrometry. Mean meltwater pH and total dissolved solids were 6.55 ± 0.34 and 37.3 ± 18.0 mg L⁻¹, respectively. Charge-balance errors for the 16 hydrochemical samples ranged from −0.3% to +0.7%. Using the contamination index based on exceedances of the current Kazakhstan water-quality thresholds, 48 stations had CI < 1, seven had CI = 1–3, and eight had CI > 3; the highest value (60.21) occurred at station 26. Principal component analysis showed that the first three components explained 53.6% of the variance and separated a broad mineral/industrial aerosol association from a Pb–Cd–Zn association consistent with non-ferrous metallurgy and mixed urban sources. Cadmium was therefore interpreted as the principal contributor to the MPC-normalized index at the most affected stations, rather than as the dominant component by absolute concentration. The dissolved fraction can be mobilized during spring melt, indicating a potential pathway to soils and receiving waters, although direct ecological or human-health risk was not quantified. Station-level point mapping and projection along the NW–SE axis showed localized multi-element maxima rather than a monotonic citywide gradient. Full article

Airside vehicles operate close to aircraft, service equipment and safety-critical surface markings, yet driver-assistance functions developed for road traffic do not directly transfer to airport aprons, taxiways and service roads. This article presents a vision-based driver-assistance and warning prototype for recognizing airport surface types and horizontal markings in video recorded at Poznan Airport. Two manually annotated segmentation datasets were prepared from GoPro HERO8 video acquired from a vehicle perspective: a four-class surface dataset covering asphalt, concrete, paving blocks and grass, and a three-class marking dataset covering red, white and yellow lines. The study compares You Only Look Once (YOLO) variants YOLOv8 and YOLOv11 with U-Net, DeepLabV3 and SegFormer under a common 512-by-512 input resolution and evaluates both model-level quality and complete video-application behavior. For semantic segmentation, SegFormer achieved the highest validation results, with Intersection over Union (IoU)/Dice of 0.7657/0.8624 for surfaces and 0.8852/0.9380 for markings. Among YOLO models, YOLOv8m obtained the highest surface mean average precision at an IoU threshold of 0.50 (mAP@50) of 0.7847, whereas YOLOv8s obtained the highest marking mAP@50 of 0.8449. On video recordings, paired YOLO configurations processed approximately 15–16 frames per second (FPS) on a personal computer (PC), while U-Net, DeepLabV3 and SegFormer processed approximately 10–11 FPS. A YOLOv8n pair compiled for Raspberry Pi 5 with Raspberry Pi AI HAT+ Hailo-8 reached 10.05 detection FPS and 18.15 processing FPS without GUI rendering. Under the adopted evaluation protocol, SegFormer achieved the highest mask quality in the conducted comparison, while the paired YOLOv8n configuration demonstrated embedded throughput feasibility. Full article

Wastewater treatment plants (WWTPs) are essential infrastructure for urban water management. Their stable operation is critical to effluent quality and public safety. However, wastewater treatment involves complex biochemical processes and extensive electromechanical equipment. Hazards such as sludge flotation in secondary clarifiers, fire, electric shock, and toxic gas poisoning may occur. These hazards can threaten worker safety and reduce treatment efficiency. Traditional inspection mainly relies on manual patrols, fixed-camera monitoring, and experience-based judgment. These methods often have low efficiency, limited coverage, and delayed responses. To address these limitations, this paper investigates robot-based hazard detection for WWTPs. A multisource hazard detection dataset is constructed for secondary clarifiers and confined spaces, including images collected by an inspection robot. Object detection models are then applied to identify typical hazards. Comparative experiments are conducted using Faster R-CNN and several YOLO-series models. YOLOv12 achieves mAP@0.5 values of 0.917 and 0.819 for sludge flotation detection and confined space hazard detection, respectively. It also provides a good balance between detection performance and inference efficiency. The results demonstrate that robot vision combined with object detection can support intelligent inspection in WWTPs. Full article

This article presents a practice-oriented methodological proposal for designing inclusive multimodal learning content with Generative AI (GAI) in migrant adult literacy. It does not report an experimental intervention or a statistical evaluation of learning outcomes. Its contribution lies in formalizing a context-sensitive design pathway for early preA1–A2 literacy and language-learning provision in Italian CPIA settings, where learner profiles are highly heterogeneous, attendance may be discontinuous, and written language is both a learning goal and a barrier to participation. Unlike generic AI-supported instructional design frameworks, the proposed approach starts from recurrent communicative needs in adult migrant education and translates them into short, modular and reusable learning artifacts that coordinate textual, visual, audio-oral and interactive layers. The framework distinguishes multimodal design, understood as the pedagogical coordination of different semiotic modes, from the mere use of multiple media. It also integrates accessibility as a set of concrete design criteria, including linguistic readability, visual clarity, audio quality, layout, font size, contrast, cognitive load and usability in print or mobile formats. The article outlines a sequence of design operations: mapping learner profiles, selecting situated communicative scenarios, generating and revising textual material, developing visual and audio scaffolds, structuring guided interaction, and applying pedagogical, cultural and ethical review. An illustrative micro-unit on asking for information at a municipal office shows how this pathway can support dialog, visual glossary, audio practice, role-play and formative assessment. The proposal is intended for CPIA educators, adult literacy professionals, instructional designers and researchers in multimedia learning and educational technology. Its educational implication is that GAI can support inclusive material design only when its outputs are treated as provisional resources to be selected, adapted and validated through human pedagogical judgment. Full article