Search Results (4,351)

Metal oxide semiconductor (MOS)-based chemiresistive gas sensors, attributable to their low cost, compact structure, and long operational lifetime, have been widely employed for the detection and monitoring of trace ozone (O₃) in environmental air. Moreover, as ozone is a highly reactive oxidizing species extensively used in medical device sterilization, hospital disinfection, and food processing and preservation, accurate monitoring of ozone concentration is also essential in medical sanitation and food safety inspection. However, their practical applications are often limited by insufficient sensitivity and the requirement for elevated operating temperatures. In this study, Au-modified indium oxide (Au-In₂O₃) nanocomposite sensing materials were synthesized via a hydrothermal route followed by surface modification. Structural and morphological characterizations confirmed the uniform dispersion of Au nanoparticles on the In₂O₃ surface, which is expected to enhance the interaction between the sensor and target gas molecules. The resulting Au-In₂O₃ sensor exhibited excellent O₃ sensing performance under room-temperature conditions. Compared with pristine In₂O₃, the Au-In₂O₃ sensor with 1.0 wt% Au modification demonstrated a remarkably enhanced response of 1398.4 toward 1 ppm O₃ at room temperature. Moreover, the corresponding response/recovery times were shortened to 102/358 s for Au-In₂O₃. The outstanding O₃ sensing performance can be attributed to the synergistic effects of Au nanoparticles, including the spillover effect and the formation of a Schottky junction at the Au-In₂O₃ interface. These results suggest that Au-modified In₂O₃ cauliflower represents a highly promising candidate material for high performance O₃ sensing at low operating temperatures. Full article

This study analyzes the relationship between fires and climate extremes in the Caatinga biome from 2012 to 2023 by integrating Fire Radiative Power (FRP) from VIIRS (S-NPP and NOAA-20), Vapor Pressure Deficit (VPD) and air temperature from ERA5, drought indices (SPI-1 and SPI-6), and heatwave events from the Xavier database. Daily percentiles of maximum (CTX90pct) and minimum (CTN90pct) temperatures were used to characterize heatwaves. Spatial and temporal dynamics of fire patterns were identified using the HDBSCAN algorithm, an unsupervised Machine Learning clustering method applied in three-dimensional space (latitude, longitude, and time). A marked seasonality was observed, with fire activity peaking from August to November, especially in October, when FRP reached ~1000 MW/h. The years 2015, 2019, 2021, and 2023 exhibited the highest fire intensities. A statistically significant upward trend in cluster frequency was detected (+1094.96 events/year; p < 0.001). Cross-correlations revealed that precipitation deficits (SPI) preceded FRP peaks by about four months, while VPD and air temperature exerted immediate positive effects. FRP correlated positively with heatwave frequency (r = 0.62) and negatively with SPI (r = −0.69). These findings highlight the high vulnerability of the Caatinga to compound drought and heat events, indicating that fire management strategies should account for both antecedent drought conditions, monitored through SPI, and real-time atmospheric dryness, measured by VPD, to effectively mitigate fire risks. Full article

The growing threat of unauthorized drones to ground-based critical infrastructure necessitates efficient ground-to-air surveillance systems. This paper proposes a lightweight framework for UAV detection and altitude measurement from a fixed ground perspective. We introduce Ghost-YOLOv11n, an optimized detector that integrates GhostConv modules into YOLOv11n, reducing computational complexity by 12.7% while achieving 98.8% mAP_0.5 on a comprehensive dataset of 8795 images. Deployed on a LuBanCat4 edge device with Rockchip RK3588S NPU acceleration, the model achieves 20 FPS. For stable altitude estimation, we employ an Extended Kalman Filter to refine measurements from a monocular ranging method based on similar-triangle geometry. Experimental results under ground monitoring scenarios show height measurement errors remain within 10% up to 30 m. This work provides a cost-effective, edge-deployable solution specifically for ground-based anti-drone applications. Full article

Background/Objectives: This study aimed to evaluate the prognostic effects of tumor spread through air spaces (STAS) and other clinical and pathological risk factors on disease-free survival (DFS) in patients with non-small cell lung cancer (NSCLC) who underwent curative lobectomy and had tumors measuring 4 cm or less. Methods: NSCLC patients who underwent surgery between March 2015 and May 2024 and had at least 12 months of follow-up were retrospectively analyzed. Patients with tumors measuring 4 cm or less who underwent R0 resection, lobectomy, and STAS assessment on intraoperative frozen sections were included in the study. Clinicopathological features of all patients were restaged according to the 9th edition of the TNM staging system. The Kaplan–Meier method, log-rank test, and univariate Cox regression analysis were used to determine the factors affecting DFS. Results: 88 patients were included in the study. The median age of the patients was 61 years, 77.3% were male, and 72.7% had adenocarcinoma histology. According to TNM 9, 23.9% of the cases were staged T1b, 18.2% T1c, and 58.0% T2a. STAS positivity was detected in 45 patients (51.1%). The rates of lymphovascular invasion (LVI) (40.0% vs. 18.6%; p = 0.028) and visceral pleural invasion (VPI) (57.8% vs. 27.9%; p = 0.005) were significantly higher in the STAS-positive group than in the STAS-negative group. Recurrence was observed in a total of 31 patients (35.2%) during a median follow-up period of 68.1 months. In Kaplan–Meier analysis, the median DFS was not reached for the entire cohort. The estimated median DFS in STAS-positive patients was 52.7 months, while the median was not reached in the STAS-negative group (p = 0.001). The median DFS was 52.3 months in those with lymph node positivity, while the median was not reached in those with lymph node negativity (p = 0.031). According to TNM 9, the difference in DFS between stage IA/IB and stage IIAB groups was not statistically significant (p = 0.080). In univariate Cox analysis, STAS positivity (HR = 3.79; 95% CI: 1.69–8.51; p = 0.001), lymph node positivity (HR = 2.58; 95% CI: 1.05–6.31; p = 0.038) and VPI (HR = 2.28; 95% CI: 1.07–4.86; p = 0.032) were found to be significant prognostic factors adversely affecting DFS. Age, gender, histological type, tumor location, T stage, LVI, perineural invasion (PNI), and adjuvant chemotherapy had no significant effect on DFS. Conclusions: STAS is a strong negative prognostic indicator for recurrence in patients with operated NSCLC with tumor size ≤ 4 cm. It is believed that STAS should be integrated into risk-based staging and adjuvant treatment decision-making processes in early-stage NSCLC, particularly when evaluated in conjunction with VPI and lymph node positivity. Full article

This study investigated the bacterial community composition and diversity in air and exercise yard bedding samples from large-scale donkey farms in Liaocheng, China, during summer using 16S rRNA high-throughput sequencing. Air samples were collected from five functional areas of donkey barns, while bedding samples were obtained from eight farms housing Dezhou donkeys. Sequencing analysis revealed 894 operational taxonomic units (OTUs) in air samples and 3127 OTUs in bedding samples. Alpha diversity indices indicated that the mare barn exhibited the highest microbial diversity in air, while the foal barn showed the lowest. Actinobacteriota, Proteobacteria, and Firmicutes were the dominant phyla across different functional areas. Rhodococcus was identified as the predominant airborne genus, representing a potential pneumonia risk in foals. In bedding materials, Firmicutes, Actinobacteriota, and Proteobacteria predominated, with Corynebacterium, Salinicoccus, and Solibacillus as dominant genera. Several potentially pathogenic bacteria were detected, including Rhodococcus, Corynebacterium, Clostridium, Streptococcus, and Escherichia-Shigella. These findings provide critical insights into the microbial ecology of intensive donkey farming environments and offer scientific evidence for developing targeted biosecurity strategies to safeguard animal health and promote sustainable livestock production. Full article

Street trees are vital components of urban ecosystems, contributing to air purification, microclimate regulation, and visual landscape enhancement. Thus, accurate segmentation of individual trees from point clouds is an essential task for effective urban green space management. However, existing methods often struggle with noise, crown overlap, and the complexity of street environments. To address these challenges, this paper introduces a multi-constraint and shortest path optimization method for individual urban street tree segmentation from point clouds. In this paper, object primitives are first generated using multi-constraints based on graph segmentation. Subsequently, trunk points are identified and associated with their corresponding crowns through structural cues. To further improve the robustness of the proposed method under dense and cluttered conditions, the shortest-path optimization and stem-axis distance analysis techniques are proposed to further refine the individual tree extraction results. To evaluate the performance of the proposed method, the WHU-STree benchmark dataset is utilized for testing. Experimental results demonstrate that the proposed method achieves an average F1-score of 0.768 and coverage of 0.803, outperforming superpoint graph structure single-tree classification (SSSC) and nyström spectral clustering (NSC) methods by 17.4% and 43.0%, respectively. The comparison of visual individual tree segmentation results also indicates that the proposed framework offers a reliable solution for street tree detection in complex urban scenes and holds practical value for advancing smart city ecological management. Full article

Low-temperature plasma treatments were applied to barley seeds using a dielectric barrier-stabilized corona discharge operated in ambient air enriched with oxygen or nitrogen to quantify surface chemical modifications and seed wettability. X-ray photoelectron spectroscopy showed that oxygen-enriched plasma produced the strongest oxidation, increasing surface oxygen from 9 ± 5 at% (control) to 24 ± 5 at%, while reducing carbon from 88 ± 5 at% to 76 ± 5 at%. Nitrogen-enriched plasma induced more moderate changes (O: 13 ± 5 at%, C: 85 ± 5 at%) but resulted in clear nitrogen incorporation, with an enhanced N 1s amine/amide component at ~400.8 eV. The hydroxyl O 1s contribution increased from 70% (control) to 82% (oxygen) and 90% (nitrogen), indicating substantial surface hydroxylation. SEM-EDX showed only minor micrometer-scale composition changes and no detectable morphological damage. Raman and ATR-FTIR spectra confirmed that polysaccharide, protein, and lipid structures remained intact, with intensity variations reflecting increased hydrophilicity. Water imbibition kinetics fitted with the Peleg model demonstrated faster initial hydration after plasma exposure, with 1/k₁ increasing from 20.25 ± 1.90 h⁻¹ (control) to 36.70 ± 6.56 h⁻¹ (oxygen) and 38.87 ± 7.57 h⁻¹ (nitrogen), while 1/k₂ remained nearly unchanged. Full article

Indoor air quality (IAQ) in high-rise residential buildings is an increasing concern, especially in hot and humid climates where prolonged indoor exposure elevates health risks. This study evaluates the performance of Fresh Air Handling Units (FAHUs) using two complementary approaches: (1) real-time sensor data to quantify IAQ conditions and (2) occupant survey responses to capture perceived comfort and pollution indicators. The results show that floor level did not predict satisfaction, even though AQI data revealed clear differences between flats, suggesting perceptions are driven more by sensory cues than by actual pollutant levels. Longer weekday exposure emerged as a stronger predictor of dissatisfaction. These gaps between perceived and measured IAQ highlight the need for improved ventilation scheduling and greater occupant awareness. FAHUs were found to be inefficient, consuming 21–26% of total building energy while lacking pollutant-specific monitoring capabilities. To address these issues, the study recommends the integration of IoT-enabled sensors for real-time pollutant detection, enhanced facade sealing to minimize external infiltration, and the upgrade of filtration systems with HEPA filters and UV purification. Additionally, AI-driven predictive maintenance and automated ventilation optimization through Building Management Systems (BMS) are suggested. These findings offer valuable insights for improving IAQ management in high-rise buildings, with future research focusing on AI-based predictive modeling for dynamic air quality control. Full article

Urban Air Mobility (UAM) promises to reduce ground traffic and journey times by using electric vertical take-off and landing (eVTOL) aircraft for short, low-altitude flights, especially in urban environments. However, low-flying aircraft are at particularly high risk of collisions with wildlife, such as birds. This study builds on previous research into UAM collision avoidance systems (UAM-CAS) by implementing one such system in the BlueSky open-source air traffic simulator and evaluating its efficacy in reducing bird strikes. Several modifications were made to the original UAM-CAS framework to improve performance. Realistic UAM flight plans were developed and combined with real-world bird movement datasets representing typical birds in sustained flight from all seasons, recorded by an avian radar at Leeuwarden Air Base. Fast-time simulations were conducted in the BlueSky Open Air Traffic Simulator using the UAM flight plan, the bird datasets, and the UAM-CAS algorithm. Results demonstrated that, under modelling assumptions, the UAM-CAS reduced bird strikes by 62%, with an average delay per flight of 15 s, whereas 27% of the remaining strikes occurred with birds outside the system’s design scope. A small number of flights faced substantially longer delays, indicating some operational impacts. Based on the findings, specific avenues for future research to improve UAM-CAS performance are suggested. Full article

Air pollution from ship operations can pose a significant challenge for coastal cities, particularly where ports are closely integrated into the urban fabric. This study examines the influence of ship docking on PM_2.5 concentrations in Chios, Greece, a medium size island city where the port directly borders densely populated neighbourhoods. Calibrated PurpleAir sensors were installed at urban and suburban sites to measure PM_2.5, with data analysed alongside ship call records and meteorological observations. An event-based concentration enhancement metric (%ΔC) was estimated to compare PM_2.5 during docking with the preceding 3 h background for 170 ship arrivals in February and August 2022. The results showed that under prevailing northerly winds in August, PM_2.5 at the downwind urban site increased on average by 5.0 µg m⁻³ (48%), whereas winter increments were smaller (6.1%) due to higher background variability. When both seasons and all wind directions were pooled, the urban site exhibited a mean enhancement of 1.7 µg m⁻³ (19%), while impacts at the suburban site remained minor (3%). Median-based uncertainty analysis confirmed robust enhancements under northerly winds only. Wind direction and wind speed were the primary controls on %ΔC, whereas ship engine power and time at berth had limited influence. The results suggest that ship-related PM_2.5 impacts are detectable but remain spatially and temporally limited in coastal urban environments, including medium-sized islands characterised by relatively low shipping activity. Full article

Urban towns and smart city governments face increasing challenges in maintaining ecological balance as urbanization, industrial activity, and climate dynamics evolve. The degradation of ecological gardens, biodiversity parks, and waterways adversely affects ecosystem stability, air and water quality, and community well-being. Conventional urban ecological systems rely on reactive assessment methods that detect damage only after it occurs, leading to delayed interventions, higher maintenance costs, and irreversible environmental harm. This study introduces a Graph–Transformer Neural Network (GTNet) as a data-driven and predictive framework for sustainable urban ecological management. GTNet provides real-time estimation of smart city garden health, addressing the gap in proactive environmental monitoring. The model captures spatial relationships and contextual dependencies among multimodal environmental features using Dynamic Graph Convolutional Neural Network (DGCNN) and Vision Transformer (ViT) layers. The preprocessing pipeline integrates Principal Component Aggregation with Orthogonal Constraints (PCAOC) for dimensionality reduction, Weighted Cross-Variance Selection (WCVS) for feature relevance, and Selective Equilibrium Resampling (SER) for class balancing, ensuring robustness and interpretability across complex ecological datasets. Two new metrics, Contextual Consistency Score (CCS) and Complexity-Weighted Accuracy (CWA), are introduced to evaluate model reliability and performance under diverse environmental conditions. Experimental results on Melbourne’s multi-year urban garden datasets demonstrate that GTNet outperforms baseline models such as Predictive Clustering Trees, LSTM networks, and Random Forests, achieving an AUC of 98.9%, CCS of 0.94, and CWA of 0.96. GTNet’s scalability, predictive accuracy, and computational efficiency establish it as a powerful framework for AI-driven ecological governance. This research supports the transition of future smart cities from reactive to proactive, transparent, and sustainable environmental management. Full article

The large number of UAVs under supervision at low altitudes have brought serious security risks to the field of air defense. Accurately analyzing the characteristics of UAVs’ echo signals is of great research significance for the detection and recognition of UAVs. Based on the principle of radar detection, the echo spatial correlation in the distributed radar detection mode is studied. According to the influence of different movement speeds on the micro-motion characteristics of UAVs, the echo signal models of UAVs in two flight states are established. Combined with the instantaneous micro-Doppler frequency model of the ideal motion state of UAVs, micro-Doppler frequency calculation functions of UAVs at different attitude angles are constructed. Through simulation calculation, the variation curves between the observation angle and the echo spatial correlation using different detection distances are given. Based on time–frequency images of UAVs in their ideal motion state, changes in the time–frequency images at different motion speeds and attitude angles are analyzed. These research results will help radar detection systems to accurately recognize UAVs in an uncertain motion state and can also provide a basis for predicting the next motion action of UAVs in subsequent target tracking. Full article

Flash drought (FD) is an extreme climate event that intensifies within days and exerts severe socio-environmental impacts. Its onset and evolution remain difficult to predict. Here, we quantify the spatio-temporal dynamics of FD across northern Xinjiang from 1961 to 2023 and identify the dominant driving factors. We apply linear trend detection, wavelet analysis, change-point detection, random forest (RF) modeling, and Pearson correlation. Results show that winter is becoming significantly wetter, whereas the annual signal and the other three seasons exhibit drying trends. After 1980, both FD frequency and FD duration increased; the longest single event lasted 40 days. Spatially, FD is concentrated in the Ili River Valley and the Altay region; the Akdala station recorded the highest count (nine events). Duration, rather than frequency, peaks on the northern slope of the Tianshan Mountains, where the maximum length reaches 40 days. RF importance ranks the Pacific Decadal Oscillation (PDO) as the leading driver (20.9%), followed by air temperature (17.8%); the sunspot index contributes only 6.1%. Full article

Ensuring good air quality in indoor environments of historical and artistic significance is essential not only for protecting valuable artworks but also for safeguarding human health. While many studies in this field tend to focus on the preservation of cultural heritage, fewer have addressed the impact on visitors and worshippers. Yet, places such as museums, galleries, churches, and other religious sites attract large numbers of people, making indoor air quality a key factor for their well-being. This study focused on evaluating air quality within the Santuario della Beata Vergine dei Miracoli in Saronno, Italy, a religious site that welcomes large numbers of visitors and worshippers each year. A detailed analysis of particulate matter was conducted, including chemical characterization by ICP-MS for metals, ion chromatography for water-soluble ions, and thermal–optical analysis for the carbonaceous fraction, as well as assessments of size distribution and radiometric properties. The results indicated overall good air quality conditions: concentrations of heavy metals were below levels of concern (<35 ng m⁻³), and gross alpha, beta, and ¹³⁷Cs activity concentrations remained below the minimum detectable thresholds. Hence, no significant health risks were identified. Full article

This study presents a comparative analysis of two rare thundersnow events accompanied by snowfall that occurred on 11 November 2023 and 10 December 2023 in Shaanxi province, China. Multiple data sources were integrated, including MICAPS surface and upper-air conventional detection observations, hourly meteorological records from Yanliang Airport, lightning location data, and ERA5 reanalysis, to examine and contrast the synoptic conditions, moisture transport mechanisms, and convective characteristics underlying these two events. The results indicate that the large-scale circulation patterns were characterized by a “high in the west and low in the east” configuration and a “two troughs-one ridge” pattern for the November and December cases, respectively. In both episodes, Shaanxi Province was located on the rear side of a high-pressure ridge, where a strong pressure gradient induced pronounced northerly winds that advected cold air southward, forming a distinct near-surface cold pool. During the November event, the convective cloud system developed east of the Tibetan plateau, guided by a westerly flow, and propagated eastward while gradually weakening, with a minimum brightness temperature of −42 °C. Conversely, in December, the convective activity initiated over southwestern Shaanxi and moved northeastward under a southwesterly flow, reaching a lower minimum brightness temperature of −55 °C, indicative of stronger vertical development. In both events, the principal water vapor transport occurred near the 700 hPa height level and was primarily sourced from the Bay of Bengal via a southwesterly flow. The November event featured a stronger northwesterly cold-air intrusion, whereas the December case exhibited a broader moisture channel. The CAPE values peaked during the afternoon and nighttime periods in both cases. The cold-pool and inversion-layer thickness were approximately 2 km/45 hPa in November and 0.8 km/150 hPa in December. Full article