Search Results (12,885)

Wildfires increasingly contribute to urban particulate matter (PM) exposure, particularly fine particles (PM_2.5), through atmospheric transport processes influenced by meteorological conditions and terrain complexity. This study investigated wildfire impacts on PM₁₀ and PM_2.5 concentrations in Split, Solin, and Kaštela (Croatia) using a terrain-aware wildfire transport framework combined with statistical and machine learning (ML) approaches. Daily PM observations (2016–2024) from three air quality monitoring stations were integrated with meteorological data from six stations, wildfire polygons, and a digital elevation model (DEM). A wildfire influence index accounting for fire size, transport distance, wind conditions, and terrain-modified airflow was evaluated using Ordinary Least Squares (OLSs) regression, Random Forest (RF) modelling, and SHAP (SHapley Additive exPlanations) analysis. Results showed stronger wildfire-related effects for PM_2.5 than for PM₁₀, while meteorological variables remained the dominant predictors of PM variability. RF models improved predictive performance relative to OLS, achieving $R^2$ = 0.474 for PM_2.5 and $R^2$ = 0.416 for PM₁₀. SHAP analysis identified precipitation, temperature, and lagged wildfire transport variables as important predictors. A total of 84 wildfire events were classified as effective wildfires, with most measurable impacts occurring within approximately 30–70 km of monitoring stations, indicating that wildfire impacts on urban air quality in Mediterranean coastal environments are strongly mediated by atmospheric transport and meteorological conditions. The proposed framework demonstrates the potential of explainable and geospatially informed ML for environmental monitoring and wildfire-related urban air quality risk assessment. Full article

In satellite-denied environments such as urban canyons, tunnels, and underground parking facilities, achieving high-precision autonomous positioning for vehicles remains a critical challenge. Although high-precision inertial measurement units (IMUs) can provide accurate dead reckoning, their deployment is limited by cost, size, and power consumption, making low-cost, microelectromechanical systems IMUs (MIMUs) an attractive alternative solution. However, the single MIMU suffers from substantial measurement noise and bias instability, leading to rapid error divergence that cannot sustain long-term autonomous navigation. To address the above issues, this paper proposes an autonomous positioning system based on a wheel-mounted MIMU array (Wheel-AINS). The system adopts a differential layout in which multiple low-cost MIMU chips are installed at the center of each of the left and right rear wheels, forming redundant sensor arrays. By differentially fusing symmetrically mounted chips, common-mode noise and zero bias are effectively canceled while the wheel rotation provides natural rotational modulation. The fused gyroscope outputs and known wheel radius are then used to estimate the vehicle forward speed, replacing traditional odometers. The estimated wheel speed and vehicle kinematic constraints are then integrated within a Kalman filter framework to suppress the error divergence of the inertial navigation system. A dedicated embedded hardware prototype with multi-chip synchronous acquisition and wireless transmission was developed. Three groups of urban road tests with total distances of 0.85 km, 2.14 km, and 2.49 km were conducted. The results indicate that the average position drift rate of the Wheel-AINS is 0.50%, and the average heading RMSE is 12.2°. The closure error of the 2.49 km trajectory is 10.43 m, reduced by approximately 80% compared with a single MIMU. The ablation experiment reveals that the MIMU array fusion module is the primary source of accuracy improvement, reducing the position RMSE from 155.0 m to 10.1 m, while the dual-wheel distance constraint further optimizes the position RMSE to 8.2 m, but increases the heading RMSE from 13.3° to 13.6°. This demonstrates that the proposed method can substantially improve autonomous positioning accuracy while maintaining a notably low system cost, providing a viable technical pathway for long-endurance vehicle navigation in satellite-denied environments. Full article

Background: No preferred treatments for melasma are known, owing to its underlying complicated pathomechanisms; microneedle radiofrequency (MRF) has recently been used to treat melasma. Objectives: We aimed to investigate the effects and pathomechanisms of melasma treated by MRF and identify the possible determining factors for good response. Methods: Therapeutic outcomes were measured using the Melasma Area and Severity Index (MASI) and artificial intelligence-assisted optical coherence tomography (OCT) evaluation for collagen and pigmentation at baseline and 2 months after each treatment. Participants were divided into good- (≥25% reduction in MASI) and poor-response (<25% reduction in MASI) groups after the last MRF treatment. Results: Two patients achieved fair response and three patients achieved poor response. Overall OCT analysis showed that the confetti/granular melanin ratios (melanin aggregation index) decreased, the distance between melanosomes increased, and the size of melanin decreased. The number of dendritic cells (DCs) decreased. In subgroup analysis, the continuity of the basement membrane was improved in the fair-response group, and the melanin aggregation index and the number of DCs were decreased in the poor-response group. A higher baseline confetti/granular melanin ratio trended towards poorer therapeutic response. Conclusions: This pilot study used OCT to assess the therapeutic efficacy of MRF for melasma and identify the characteristics of individuals for whom MRF is effective. The statistical results were exploratory and descriptive. Further large-scale, randomized controlled studies are required to prove the efficacy of MRF in treating melasma and the feasibility of OCT in investigating the treatment response of melasma. Full article

Traditional acupoint localization methods rely heavily on manual operation, resulting in high subjectivity and limited accuracy. To improve the precision and stability of acupoint detection, this study integrates machine vision technology with in situ projection to achieve automated recognition and real-time visualization of human acupoints. First, an automatic calibration method based on image processing is proposed for back acupoints. Spinal features are extracted from the blue channel, enhanced using adaptive histogram equalization, and processed through region of interest extraction, minimum-threshold binarization, and morphological operations. Key spinal curve points are then fitted using Bézier functions. Canny edge detection is used to extract the human silhouette, locate the acromion, and derive the pixel scale of the “cun” measurement, enabling coordinate computation for 141 back acupoints. In the deep learning component, an improved YOLOv8-Pose model is developed for acupoint localization. Unlike existing methods that use local attention or the original Object Keypoint Similarity (OKS) loss, we introduce two innovations: a non-local attention module for global dependency modeling, and a novel Efficient Object Keypoint Similarity (EOKS) loss function that incorporates geometric constraints—namely, width, height, and center distance—in addition to Euclidean distance. A non-local attention mechanism is incorporated into the backbone to enhance global feature extraction, and the EOKS loss function is designed to improve spatiogeometric regression accuracy. An inference mechanism is further introduced to derive the remaining acupoints from 49 detected keypoints; experiments demonstrate that the improved model achieves 95.0% detection accuracy, outperforming the baseline by 2.62%, with an inference time of 14.5 ms. Finally, an in situ projection platform is constructed, combining camera calibration, four-point proportional scaling, and an OpenCV 4.5.4-based interactive interface. The system supports real-time translation, rotation, and scaling, enabling accurate projection of detected acupoints onto the human body. Full article

This paper extends a previous classification study by examining clustering methods on the same synthetic datasets and comparing their behavior with the previously obtained classification results. This study investigates the performance of selected time series clustering methods under controlled changes in noise level and class complexity. Six clustering methods representing distance-based, feature-based, and deep learning approaches were evaluated on 82 balanced synthetic datasets. The datasets contained from two to six classes, different levels of additive Gaussian noise, 200 time series per dataset, and 1000 observations per time series. The analysis focused on clustering quality, comparative behavior with classification models, and computational cost in terms of training time and peak memory usage. Clustering quality was assessed mainly using Adjusted Rand Index and V-measure, while accuracy after Hungarian label matching was used as an auxiliary measure for comparison with classification models. The results show that distance-based methods, and particularly TimeSeriesKMedoids, achieved the most robust and consistent clustering performance across the considered settings. Clustering quality decreased with both the number of classes and the noise level, but the effect of noise was clearly stronger. Feature-based and deep learning-based clustering methods were generally more sensitive to noise, while deep models were also associated with substantially higher computational cost. In terms of memory usage, classical clustering methods remained below 50 MiB, whereas deep learning-based clustering methods required substantially more memory. This study further shows that accuracy computed after Hungarian label matching may provide an overly optimistic view of clustering quality. Accuracy after Hungarian label matching is reported only as an auxiliary metric, while the main interpretation of clustering quality is based on structure-sensitive measures such as Adjusted Rand Index and V-measure. Overall, the findings highlight the importance of robust distance-based approaches and of using structure-sensitive evaluation measures when analyzing time series clustering. Full article

Radio-frequency identification (RFID)-based asset tracking in cabinet environments often encounters unpredictable detection caused by multipath fading, metal-induced interference, and tag placement sensitivity, which can render single-band systems unreliable under real-world conditions. This paper proposes a dual-band detection approach combining 915 MHz and 2.45 GHz to address these challenges through frequency diversity. Unlike designs confined to closely spaced UHF bands, this method uses a larger spectral gap to benefit from uncorrelated fading and distinct propagation properties. Theoretical analysis shows that dual-band detection significantly reduces joint failure probability under independent fading. The proposed framework is implemented using commercially available passive UHF tags at 915 MHz and an active RFID tag/reader at 2.45 GHz. The two systems are operated sequentially along the same guided scan path, and their detected tag-ID sets are combined offline using an OR-fusion rule without hardware-level synchronization. Across trials with varied scan speeds, power levels, reader distances, and tag placements, single-band detection fell below 50% under double-speed scanning at 200 cm, while the dual-band method remained above 70% and, in many cases, reached 100% reliability. Performance trends are further analyzed across individual scenarios, showing that 2.45 GHz links are less affected by metallic shadowing at close range, whereas 915 MHz links maintain more stable detection at longer distances. These findings are discussed in terms of deployment feasibility, indicating that the additional hardware and configuration requirements are offset by the measurable improvement in detection consistency, making the approach applicable for inventory tracking in logistics, warehousing, and industrial automation. Full article

The Abruzzo (AB) donkey is a mountain-adapted Italian population listed as a genetic resource at risk of extinction (census ≈ 600 animals; no studbook). We aimed to provide the first comprehensive morphometric description of the breed and to compare it with the Martina Franca (MF) donkey, its main progenitor. Sixty-nine adult donkeys (56 females, 13 males) from six farms were measured in 2024. Twenty-three linear traits plus body weight and body condition score were recorded three times by a single operator. Descriptive statistics, Welch’s t-test or Mann–Whitney U test with Benjamini–Hochberg correction, PCA and LDA with leave-one-out cross-validation were performed in R; comparison with MF was based on published summary statistics. Coefficients of variation for the three studbook-admission parameters were ≤0.10 in both sexes. Sixteen of 26 traits showed significant sex dimorphism, with the largest effect sizes for rump height, medial canthal distance and wither height. LDA correctly classified 94% of animals by sex. AB females were significantly smaller than MF in 22 of 23 shared traits but had a wider thorax (p = 0.012). The sexual dimorphism observed in the Abruzzo donkey is male-biased and predominantly size-based, with a minor and well-localised shape component in the head region. Males are significantly larger than females for all axial measurements (wither height A: +6.5 cm, +5.3%; rump height B: +6.4 cm, +5.0%; trunk length D: +12.1 cm, +9.5%), for thoracic circumference (M: +7.1 cm, +5.0%), for body weight (+49.9 kg, +20.6%) and for the main head traits (CM: +4.3 cm, +20.0%; G: +3.6 cm, +12.5%; H: +1.3 cm, +11.1%; E: +2.0 cm, +3.8%); no trait shows a significant female bias after BH-FDR correction. The AB donkey shows a uniform mesomorphic phenotype, smaller and stockier than MF, supporting the establishment of an official studbook. Full article

Background/Objectives: This study investigates the hypothesis that transient oxygen depletion is the mechanism of the skin sparing effect of ultra-high dose-rate irradiation, commonly referred to as FLASH irradiation. Methods: Two skin tattoo dots were placed approximately 1.0 cm apart on the thigh of FVB/N mice. The area overlapping the dots was irradiated with a single dose of 27 Gy protons delivered with either FLASH (~120 Gy/s) or 0.5 Gy/s conventional dose-rate (CDR) irradiation. Skin contraction was assessed by measuring the distance between the tattoo dots and complemented by histopathological skin analyses. Mice were placed in a 1.4 L chamber flushed with 5%, 7%, 20.9% or 100% oxygen (balance nitrogen, where applicable) prior to and during irradiation. Skin oxygenation was measured non-invasively using the phosphorescence quenching method. Results: Compared to air-breathing mice, skin contraction increases in mice breathing 100% oxygen and decreases when breathing 7% and 5% oxygen following CDR irradiation, showing that skin is neither fully oxygenated nor hypoxic. FLASH irradiation reduced skin contraction, epidermal thickening, and fibrosis in air-breathing mice compared to CDR irradiation. The difference between FLASH and CDR skin contraction decreases as the inspired gas oxygen content is reduced from 20.9% to 7%. Under 5% oxygen breathing conditions, the FLASH sparing effect is eliminated. Conclusions: Mean normal tissue pO₂ does not reveal the presence of cells at low pO₂ that could become susceptible to FLASH-induced radiobiological hypoxia at doses lower than would be predicted from the mean tissue pO₂ value. In the absence of oxygen, FLASH skin sparing for the late normal tissue effect, skin contraction, is eliminated. Full article

In artificial breeding of forest musk deer (Moschus berezovskii), common diseases such as abscess, enteritis, pneumonia, and parasitic infections exhibit persistently high morbidity rates. The early symptoms of certain diseases are often insidious and difficult to discern. Conventional manual inspection routines not only fail to achieve accurate diagnosis but also frequently disturb the animals, induce stress responses, and consequently delay optimal treatment windows. To address this practical challenge, this study employs an improved BRW-GPLinker joint entity-relationship extraction approach to perform integrated extraction and structural organization of disease entities, symptom manifestations, etiological associations, and preventive and therapeutic measures from farming literature and clinical records, thereby constructing a disease knowledge graph for forest musk deer. Through the introduction of a Boundary-Aware Module for refined entity boundary detection, a Relative Distance Bias Module to mitigate pairing errors in dense contexts, and a Weighted Sparse Multi-label Cross-Entropy loss function to enhance recall for infrequent relations, the proposed model achieves an F1 score of 0.887 on a self-constructed dataset and demonstrates favorable generalization capability on medical-domain datasets. By transforming fragmented clinical logs and manuals into structured medical associations, this knowledge graph facilitates rapid retrieval of forest musk deer disease information, thereby enhancing veterinary decision-making efficiency and assisting forest musk deer health management. Full article

This study evaluates the ecological impacts of seawater desalination discharge on coastal marine ecosystems through a sequential analytical framework linking systematic literature synthesis, field-monitoring evidence, spatial analysis, and predictive ecological modeling. The novelty of the study lies in combining multi-regional evidence from Mediterranean coastal zones, Persian Gulf waters, and Pacific coastal environments with threshold-based ecological risk assessment, thereby linking discharge-related environmental stressors with biological responses and ecosystem-function alterations. The systematic review first retained 750 studies published between 2004 and 2024 for qualitative synthesis. On this basis, 59 high-quality references with sufficient numerical information were selected for the main quantitative meta-analysis, while field-monitoring data were used to support the interpretation of distance-based discharge gradients. Spatial interpolation and hierarchical modeling were then applied to evaluate exposure–response patterns and ecological threshold behavior. The results showed that desalination facilities generated measurable ecological impacts mainly within 50–200 m of discharge points, with a critical transition distance of approximately 127 m where hypersaline conditions, typically 1.5–2.0 times ambient seawater levels, were associated with marked changes in marine community structure. Benthic assemblages showed taxon-specific responses, with mollusks and echinoderms exhibiting greater sensitivity than polychaetes and small crustaceans. Marine vegetation declined strongly under combined salinity, thermal, and chemical stress, while phosphonate-based antiscalants accumulated in filter-feeding organisms and produced bioaccumulation factors up to 42.1 times ambient levels. Ecosystem-function indicators, including microbial community composition and sediment organic matter processing, remained altered up to 300 m from discharge points, indicating that functional impacts may extend beyond the primary hypersaline plume. The predictive modeling framework further demonstrated that ecological risk decreased nonlinearly with distance and varied according to discharge intensity, local hydrodynamics, and biological sensitivity. These findings indicate that conventional uniform buffer-based assessment may underestimate the ecological footprint of desalination discharge. Sustainable desalination management should therefore adopt site-specific monitoring, species-sensitive protection thresholds, improved brine-management technologies, and adaptive mitigation strategies based on real-time environmental feedback. Full article

The Chishui River Basin, a core production area for Chinese sauce-aroma Baijiu (exemplified by Moutai), supports sorghum cultivation critical to the liquor’s distinctive quality. The soil environment quality within this region, therefore, directly impacts the safety and quality of both raw material and the final distilled spirit. To underpin the safe production and sustainable development of this iconic beverage, it is essential to assess soil heavy metal contamination in the soils and quantify the contributions from various sources. In this study, 172 surface soil samples were collected from typical sorghum planting bases in the Renhuai area. Concentrations of eight heavy metals (loids) (As, Cd, Cr, Cu, Hg, Ni, Pb, and Zn) were determined. The contamination status was evaluated using the geostatistical inverse distance weighting interpolation, the Nemerow pollution index (P_N), and the potential ecological risk index (RI). Source identification and quantification were performed using the positive matrix factorization receptor model (PMF). Results revealed significant enrichment of Cd and Hg in the soil, with mean concentrations 2.07 times and 2.54 times the soil background values for Guizhou Province, respectively. Pollution index results (P_i, P_N) indicated that soil Cd contamination is relatively severe, whereas contamination from other elements is minimal. Overall, approximately 86.5% of the study area was classified as clean or only slightly polluted. Cd poses a moderate ecological risk and was the primary contributor to the total ecological hazard. Other elements exhibited lower risk, resulting in a slight overall potential ecological risk. The soil environmental quality in certified organic sorghum bases was generally favorable. PMF analysis identified three principal sources: historic industrial emissions and traffic-related sources (contributing 46%), weathering of carbonate rocks combined with agricultural activities (37%), and natural background coupled with organic fertilizer application (17%). In conclusion, while the overall soil heavy metal pollution level in the sorghum planting areas is low, the notable enrichment and higher ecological risk of Cd necessitate enhanced dynamic monitoring and targeted risk control measures to ensure long-term soil health and product safety. Full article

Saudi Arabia currently lacks a nationally representative, multi-day National Household Travel Survey comparable to the US, UK, or New Zealand programmes; existing official data products focus on aggregate road-transport indicators or general household statistics rather than detailed day-to-day travel diaries. This study develops a benchmark-driven framework for NHTS–KSA by comparing Saudi demographic, geographic, infrastructure, climate, and mobility indicators with those of the United States, United Kingdom, and New Zealand, and by systematically assessing 15 survey-design indicators across their national household travel surveys. Context benchmarking identifies the United States as the closest for highway-oriented interurban structure and motorisation level, New Zealand for geography and demographic structure (in particular, near-identical physiological density on limited arable land), and the United Kingdom as the most aspirationally aligned benchmark for the multimodal mobility patterns Saudi Arabia aims to develop under Vision 2030. Design benchmarking shows that the three surveys are closely matched in aggregate similarity but lead on distinct elements: New Zealand on diary length and integrated passive tracking, the US on digital tools and emerging-behaviour modules, and the UK on interviewer-led recruitment and multimodal analysis, a pattern that proves robust to plausible variation in individual scores. The resulting NHTS–KSA blueprint specifies a statistically justified, stratified multistage annual household sample, a two-day diary with rolling 12-month fieldwork, interviewer-assisted recruitment, a digital-first diary with optional GPS tracking, and modules on long-distance travel, telework, e-commerce, gendered mobility, accessibility, safety, and environmental attitudes. While preserving international comparability, the framework provides the data foundation required to steer public-transport investment, demand-management measures, and land-use policies in line with Saudi Arabia’s Vision 2030 objectives for sustainable, inclusive, and smart mobility. Full article

Ultrasonic thickness resonance can be effectively used to detect and quantify the level of corrosion in steel nuclear storage containers as well as other corrosion-prone thin-walled structures, such as pipes and storage tanks. Electro-Magnetic Acoustic Transducers (EMATs) have several advantages over more traditional piezoelectric-based transducers; namely, they can be used in a non-contact fashion on robotic platforms, allowing for measurements regardless of surface conditions or temperature. The major challenge of EMAT application is the power required to counteract the low actuation efficiency, which is achieved with a high-power ultrasonic pulse generator and a transformer circuit. Resonance techniques, in which most of the energy is concentrated near structural resonance frequencies, are preferable to improve efficiency of electro-magnetic acoustic measurements. This methodology was applied to 316L stainless steel thin plates subjected to uniform corrosion as well as pitting corrosion imitating different damage scenarios in a nuclear waste container. The resonant peak frequency shift was found to be proportional to the severity of corrosion for minimally corroded samples. However, the complete disappearance of the resonance peak was observed in the samples with severe corrosion damage. The EMAT liftoff distance was studied to quantify its effect on the amplitude, spread, and frequency of resonant peaks. Recommendations for use of EMATs for assessing corrosion damage are presented. The study demonstrates the success of frequency-based detection of corrosion damage in 316L stainless steel used in fabrication of nuclear waste storage containers. Full article

Background: Research on the acute effects of β-alanine supplementation has primarily focused on performance outcomes, with limited attention to the underlying physiological mechanisms. This study aimed to investigate the acute effects of two β-alanine doses on performance, mechanical output, and acid–base balance during a 10 min cycling time trial (10’-TT), and to explore the relationship between buffering-related variables and performance. Methods: Eighty-five recreational cyclists performed a 10’-TT under indoor conditions before (control) and following the acute ingestion of β-alanine (moderate-dose β-alanine 10 g—BAM; high-dose β-alanine 20 g—BAH) or placebo (PLA), with each condition tested on separate days. Data were analyzed using two-way repeated-measures ANOVA and correlation analyses. Results: No significant differences were observed in performance variables (distance, speed, cadence, or heart rate; p ≥ 0.751). However, total external mechanical work (kJ) was significantly reduced following acute supplementation (p = 0.028). Notably, the BAM condition reduced the mechanical cost of exercise without impairing performance, and this effect was moderately associated with changes in bicarbonate levels. Conclusions: Acute β-alanine supplementation did not improve performance outcomes but may alter buffering-related physiological responses associated with reduced mechanical work during high-intensity cycling exercise. These findings highlight the relevance of buffering-related mechanisms, particularly bicarbonate dynamics, in modulating the mechanical cost (work performed relative to performance achieved) of high-intensity exercise. Full article

Extrapyramidal symptoms (EPS) are motor side effects commonly induced by antipsychotic medications and can lead to measurable changes in handwriting patterns. These symptoms affect both the spatial and temporal characteristics of writing, including stroke thickness, direction and the rate of directional change. To model these complex variations, we propose a novel Liquid Generative Adversarial Network (LiquidGAN), which combines the adaptive dynamics of liquid neural networks with the data generation capability of GANs. Handwriting data were collected from 94 patients with confirmed EPS and 30 healthy controls using Archimedean spiral patterns drawn with both hands. A total of 211 images were processed for both binary and multiclass classification using a pretrained ResNet50 model. The pretrained ResNet50 achieved 92% accuracy and 97% precision in the binary classification task; however, its performance dropped significantly to 57% accuracy in multiclass classification, indicating limited capability in capturing fine-grained EPS severity variations. In contrast, the proposed LiquidGAN demonstrated excellent performance in the binary classification task, achieving 97% accuracy and 98% precision. More importantly, LiquidGAN substantially outperformed the baseline in the more challenging multiclass setting, achieving 70% accuracy and precision across four classes (mild, moderate, severe, and control). This shows that the diverse dataset from the liquidGAN significantly improves the HOG-ANN classification and effectively captures complex and subtle handwriting variations associated with different EPS severity levels that conventional models such as ResNet50 fail to distinguish. In addition, LiquidGAN generated diverse and realistic synthetic handwriting samples, yielding improved Fréchet Inception Distance (FID), precision, and recall compared with style GAN. These findings demonstrate that handwriting biomarkers, when analyzed through dynamic generative learning, offer an effective and non-invasive approach for monitoring extrapyramidal side effects in clinical settings. Full article