Search Results (179)

Background/Objectives: Glaucoma affects over 70 million people worldwide and is a major cause of irreversible blindness, with elevated intraocular pressure (IOP) as the only modifiable risk factor. Conventional techniques like Goldmann applanation tonometry (GAT) are widely used but cannot provide continuous or nocturnal monitoring, limiting the detection of pressure peaks relevant to disease progression. Emerging technologies, including home-based devices, wearable sensors, such as contact lens-based sensors (CLBS), and implantable biomedical microelectromechanical systems (bioMEMS), offer more comprehensive and continuous assessment of IOP patterns. Thus, this scoping review aimed to map the available evidence on technologies for continuous IOP monitoring, summarizing their performance and agreement with traditional tonometry. Methods: A systematic search of electronic databases was conducted to identify studies published in the last 10 years evaluating self-tonometry devices, CLBS, or implantable systems designed for continuous IOP monitoring. Two reviewers independently screened articles, applied eligibility criteria, charted relevant data, including device characteristics and agreement with GAT, and reported clinical applications. Results: Self-tonometry devices demonstrated generally good agreement with GAT while enabling patients to monitor IOP outside clinical settings. These devices provided valuable information on diurnal and nocturnal IOP fluctuations, especially in individuals with rapid progression or those undergoing postoperative follow-up. BioMEMS-based wearable and implantable sensors showed promise for continuous long-term monitoring and revealed previously unrecognized fluctuation patterns, including activity-related changes. Conclusions: Emerging IOP-monitoring technologies appear to complement standard clinical methods by offering more detailed IOP profiles. Their integration into clinical practice may support individualized risk assessment and improved management of glaucoma progression. Full article

Spatiotemporal niche differentiation plays a critical role in facilitating mutual adaptation and sustaining coexistence among sympatric species. We investigated these patterns in sympatric ungulates through an infrared camera trap survey conducted in the Kazila Mountain region of southwestern China from July 2023 to May 2025. A total of seven species were recorded across 54 camera sites, with tufted deer (Elaphodus cephalophus) being the most frequently detected, while forest musk deer (Moschus berezovskii) and Chinese goral (Naemorhedus griseus) were the least. Nocturnality indices (β > 0.54 indicating nocturnal, β < 0.54 indicating diurnal, and β = 0.54 indicating no distinct diel preference) revealed significant differences in activity patterns among the five species. Tufted deer (β = 0.415), alpine musk deer (Moschus chrysogaster) (β = 0.438), and wild boar (Sus scrofa) (β = 0.234) were predominantly diurnal. In contrast, sambar (Rusa unicolor) (β = 0.571) was nocturnal, while the Chinese serow (Capricornis milneedwardsii) (β = 0.534) showed no strong diel preference. Nine of ten species pairs exhibited significant diel rhythm differences, with the exception of sambar-Chinese serow, and these rhythms showed marked seasonal variation, particularly in tufted deer, Chinese serow, and sambar. Temporal overlap was generally higher in the cold season for seven species pairs, suggesting that such overlap may be related to resource availability and increased interspecific competition under harsher conditions. Pianka’s overlap index (Oik) (ranging from 0 to 1, where 0 indicates no overlap and 1 indicates complete overlap) was used to assess spatial niche overlap, with values ranging from 0.16 (alpine musk deer–wild boar) to 0.86 (tufted deer–wild boar). Spatial autocorrelation and clustering analysis showed that tufted deer exhibited significant positive spatial autocorrelation, indicating a clustered high-value distribution, while the other species were randomly distributed. Spatial hotspot analysis revealed substantial overlap between tufted deer and wild boar, while the remaining species showed higher levels of spatial segregation. Collectively, these results suggest that seasonal variation in activity patterns, coupled with spatial segregation, mitigates interspecific competition and supports the stable sympatric coexistence of ungulates in this montane ecosystem. Full article

Accurate prediction of photosynthetic parameters is pivotal for precision viticulture, as it enables non-invasive monitoring of plant physiological status and informed management decisions. In this study, spectral reflectance data were used to predict key photosynthetic parameters such as assimilation rate (A), effective photosystem II (PSII) quantum yield (${\Phi }_{\mathrm{PSII}}$), and electron transport rate (ETR), as well as stem and leaf water potential (${\Psi }_{\mathrm{stem}}$ and ${\Psi }_{\mathrm{leaf}}$), in Vitis vinifera (cv. Müller-Thurgau) grown in an experimental vineyard in Lower Franconia (Germany). Measurements were obtained on 25 July, 7 August, and 12 August 2024 using a LI-COR LI-6800 system and a PSR+ hyperspectral spectroradiometer. Various machine learning models (SVR, Lasso, ElasticNet, Ridge, PLSR, a simple ANN, and Random Forest) were evaluated, both as standalone predictors and as base learners in a stacking ensemble regressor with a Random Forest meta-learner. First derivative reflectance (FDR) preprocessing enhanced predictive performance, particularly for ${\Phi }_{\mathrm{PSII}}$ and ETR, with the ensemble approach achieving R² values up to 0.92 for ${\Phi }_{\mathrm{PSII}}$ and 0.85 for A at 1 nm resolution. At coarser spectral resolutions, predictive accuracy declined, though FDR preprocessing provided some mitigation of the performance loss. Diurnal patterns revealed that morning to mid-morning measurements, particularly between 9:00 and 11:00, captured peak photosynthetic activity, making them optimal for assessing vine vigor, while midday water potential declines indicated favorable timing for irrigation scheduling. These findings demonstrate the potential of integrating hyperspectral data with ensemble machine learning and FDR preprocessing for accurate, scalable, and high-throughput monitoring of grapevine physiology, supporting real-time vineyard management and the use of cost-effective sensors under diverse environmental conditions. Full article

This study investigates the influence of urban greenery on microclimate conditions in Novi Sad, a city characterized by a temperate oceanic climate, by integrating high-resolution remote sensing data with in situ measurements from 12 urban climate stations. Sentinel-2 imagery was used to capture vegetation patterns, including tree lines and small green patches, while air temperature data were collected across two climatically contrasting years. Vegetation extent and structural characteristics were quantified using NDVI thresholds (0.6–0.8), capturing variability in vegetation activity and canopy density. Results indicate that high-activity vegetation, particularly dense tree canopies, exerts the strongest cooling effects, significantly influencing air temperatures up to 750 m from measurement sites, whereas total green area alone showed no significant effect. Cooling effects were most pronounced during summer and autumn, with temperature reductions of up to 2 °C in areas dominated by mature trees. Diurnal–nocturnal analyses revealed consistent spatial cooling patterns, while seasonal variability highlighted the role of evergreen and deciduous composition. Findings underscore that urban heat mitigation is driven more by vegetation structure and composition than by green area size, emphasizing the importance of preserving high-canopy trees in urban planning. This multidimensional approach provides actionable insights for optimizing urban greenery to enhance microclimate resilience. Full article

The ionospheric E layer (90–150 km altitude) significantly influences ionospheric dynamics and plays a crucial role in radio wave propagation. The Tibetan Plateau, as the “Third Pole,” affects E-layer morphology due to its unique topographical factors. Given the limited systematic studies in this high-altitude region, this study analyzes E-layer spatiotemporal characteristics and their controlling mechanisms over the Tibetan Plateau and adjacent regions. We analyzed foE (critical frequency of E-layer) data from six ionospheric observation stations across the Tibetan Plateau and neighboring areas during 2013–2023, covering a complete solar cycle from solar minimum to maximum. Combined with sunspot numbers as solar activity indicators, we systematically examined diurnal, seasonal, and solar cycle variations to understand regional E-layer behavior patterns. Daytime foE values significantly exceed nighttime values, demonstrating strong solar control. Spatially, Kunming shows the strongest daytime E-layer intensity with peak values reaching 3.12 MHz, while Urumqi exhibits the weakest at 2.94 MHz. Daytime foE values decrease with increasing latitude, whereas nighttime values show opposite latitudinal trends, indicating pronounced diurnal distribution asymmetry. Kunming displays the largest day-night foE variation amplitude, while Urumqi shows the smallest changes. Notably, most stations exhibit E-layer intensity peaks in July rather than June when solar zenith angles are minimum, differing from typical mid-low latitude seasonal behavior. These patterns may be related to complex vertical atmospheric coupling influenced by the region’s unique topography, which could affect the spatiotemporal distribution of the E-layer over the Tibetan Plateau. Full article

Based on five years of data (2017–2021) from the China National Lightning Detection Network (CNLDN), this study compares and analyzes the temporal and spatial distribution characteristics of cloud-to-ground (CG) lightning activities in the Hengduan Mountain region and its surroundings. It explores the relationship between CG lightning occurrences and altitude, topography, and various meteorological elements. Our findings reveal a stark east–west divide: high lightning density in the Sichuan Basin and the central Yungui Plateau contrasts sharply with lower densities over the eastern Tibetan Plateau and Hengduan Mountains. This geographical dichotomy extends to the diurnal cycle, where positive cloud-to-ground (PCG) lightning activities are more prevalent in the western part of the study area, while significant nocturnal activity defines the eastern basin and plateau. The study also finds that the relationship between CG lightning activities in the four sub-regions and 2 m temperature, precipitation, convective available potential energy, and Bowen ratio (the ratio of sensible heat flux to latent heat flux) exhibits similarities. Furthermore, we show that the relationship between lightning frequency and altitude is highly region-specific, with each area displaying a unique signature reflecting its underlying topography: a normal distribution over the eastern Tibetan Plateau, a bimodal pattern in the Hengduan Mountains, a sharp low-altitude peak in the Sichuan Basin, and a complex trimodal structure on the Yungui Plateau. These distinct regional patterns highlight the intricate interplay between large-scale circulation, complex terrain, and local meteorology in modulating lightning activity. Full article

Despite growing evidence of the widespread impacts of human activities on carnivores and their prey, it remains unclear how different types and intensities of human disturbance reshape predator–prey interactions. In this study, we conducted a systematic camera-trapping survey on a threatened carnivore, the small Indian civet (Viverricula indica). This species forages on prey with contrasting diel patterns, including nocturnal rats and diurnal species such as Pallas’s squirrel (Callosciurus erythraeus) and Chinese bamboo partridge (Bambusicola thoracica) in the southern Wuyi Mountains of southeastern China. Based on data from an extensive sampling effort (60,901 trap days at 180 camera stations), we used kernel density estimation and Pianka’s index to examine whether and how different types and intensities of human activity (human presence, roads, and settlements), as well as diverse altitudes and different seasons, affect the spatiotemporal interactions between small Indian civets and their potential prey. We found that all studied species adjusted their activity patterns, either advancing or delaying their peaks, to achieve temporal segregation under varying types and intensities of human disturbance and different altitudes and seasons. At the temporal scale, interactions between small Indian civets and their potential prey supported the human shield hypothesis, suggesting that increased human disturbance provides diurnal prey with refuge from predation pressure. Conversely, at both spatial and spatiotemporal scales, higher levels of human disturbance increased the overlap between small Indian civets and their prey species. These findings highlight that human impacts on wildlife interactions are scale-dependent: temporal refuge for prey does not necessarily reduce spatial or spatiotemporal overlap, which may still increase encounter rates and predation risk. Because our sampling relied on ground-level cameras, our inferences are limited to terrestrial interactions; arboreal interactions remain unquantified and require combined ground–canopy sampling in future work. Effective conservation management thus requires considering these scale-dependent effects of human activities on wildlife interactions. Full article

The aim of this experiment was to determine whether reticulorumen temperature (ReT), rumination, activity or pH captured by a rumen sensor bolus system (smaXtec animal care GmbH, Graz, Austria) can be used as an early indicator of heat load (HL) and to assess how its daily patterns are influenced by diurnal effects. Physiological and behavioral data from 70 male feedlot cattle (Uckermärker, Hereford, Simmentaler) housed in a closed barn were investigated using the calculated temperature-humidity index (THI) from remote HOBO Onset climate sensors over a period of 210 days. Using time series analysis and seasonal ARIMA modeling, it was found that ReT followed the same patterns throughout days with a THI < 74 as well as days under heat load conditions. Time series and correlation analyses were also performed for the rumen pH, rumination index and activity index. The collective mean ReT over the winter days assessed (n = 14,971) was 39.48 °C, with a minimum mean of 38.31 °C and a maximum mean of 40.69 °C. In comparison, the collective mean ReT over the summer days assessed (n = 14,030) was 39.53 °C, with a minimum mean of 38.39 °C and a maximum mean of 42.02 °C. Pearson’s correlation did not reveal a relationship between THI and ReT (r = −0.06; p < 0.001) and only minimally for rumination (r = −0.11; p < 0.001). Rumination clearly decreased with increasing ambient temperature in comparison to days with a THI < 74. A long-term effect is also visible when the monthly mean rumination from all bulls tends to decrease slightly from February to May and then increases beginning in June. The mean pH values decreased throughout the summer months. Nevertheless, the comparison between daily fluctuations in pH values under HL failed to yield significant deviations from those captured on days of winter. The Pearson correlation for rumen pH showed a weak negative linear relationship with THI (r = −0.3; p < 0.001). The monthly means of the motion activity index could also not verify that HL led to increasing activity (Pearson correlation for motion activity and THI: r = 0.04; p < 0.001). The heat load had no visible short-term effects on the ReT or rumen pH, but rumination and peak motion activity were reduced on days with high ambient temperatures. Full article

Photosynthesis drives terrestrial carbon uptake, yet its diurnal dynamics remain poorly resolved due to the sparse availability of flux towers and the coarse spatial resolution of current satellite observations. Solar-induced chlorophyll fluorescence (SIF) provides a direct proxy of carbon uptake, but the existing global monthly mean diurnal total canopy SIF product is limited to 0.5° resolution. We developed a random forest-based downscaling framework to generate a global monthly mean hourly SIF dataset (SIF_{total_01}) at 0.1° resolution for 2000–2022. When validated against eddy-covariance-based gross primary productivity (GPP) data, SIF_{total_01} showed a strong correlation (R² = 0.81) and reduced root mean square error when compared with SIF_total (2.89→2.8 mW m⁻² nm⁻¹), providing notable gains in broadleaved forests (R²: 0.80→0.88 with a root mean square error of 2.32→1.81 mW m⁻² nm⁻¹). The SIF_{total_01} dataset revealed a distinct double-peak in the SIF_{total_01}–GPP slope, reflecting widespread afternoon depression of photosynthesis, with normalized slopes declining from 1.03 in the morning to 0.98 in the afternoon. Soil moisture modulated this depression pattern, as the afternoon–morning SIF_{total_01} difference increased from 0.02 to 0.10 mW m⁻² nm⁻¹ across dry to wet years. Under water stress, SIF yield was more sensitive than absorbed photosynthetic active radiation (APAR), with a doubling of the afternoon–morning SIF yield difference (0.5→1.1 10⁻³ nm⁻¹), while the afternoon–morning APAR difference showed a smaller change (−300→−180 kJ m⁻²). This study improves the potential for bridging observational gaps and constraining models offer valuable insights for fundamental and applied research in the analysis of ecosystem productivity, climate-carbon feedbacks, and vegetation stress. Full article

Climate change continues to threaten global biodiversity, making it essential to assess how keystone species may shift their distributions and to use these findings to inform conservation planning. This study evaluated the current and future habitat suitability of D. macropodum, an important tree species within subtropical evergreen broad-leaved forests in China, using 354 occurrence records and a suite of environmental variables. A parameter-optimized MaxEnt model (calibrated with ENMeval; RM = 4, FC = QHPT) was applied to simulate the species’ present distribution and projected changes under three climate scenarios (SSP126, SSP245, SSP585). The main factors influencing distribution were determined to be moisture and temperature seasonality, with the precipitation of the coldest quarter (Bio19, 36.3%), the mean diurnal range (Bio2, 37.5%), and the precipitation of the warmest quarter (Bio18, 14.2%) jointly contributing 88.0% of the total influence. The model projections indicated a 40.1% reduction in the total number of suitable habitats under high-emission scenarios (SSP585) by the 2090s, including a loss of over 80% of highly suitable areas. Centroid movements also diverged across the scenarios: a southwestern shift under SSP126 and SSP245 contrasted with a southeastern shift under SSP585, with each accompanied by significant habitat fragmentation. Key climate refugia were identified primarily in central Taiwan Province and the mountainous zones of Zhejiang and Fujian Provinces, which should be prioritized for conservation activities. These insights offer a foundational understanding for the conservation of D. macropodum and other ecologically similar subtropical evergreen species. However, direct extrapolation to other taxa should be made cautiously, as specific responses may vary based on differing ecological tolerances and dispersal capacities. Further research is needed to test the generalizability of these patterns across diverse plant functional types. Full article

Based on GPM satellite observations during June to September from 2014 to 2023, deep convective systems (DCSs) over the Tibetan Plateau and Sichuan Basin exhibited distinct spatiotemporal and structural characteristics. Over the Plateau, DCSs were primarily concentrated in the central and eastern regions, with echo-top heights typically ranging from 15 to 17 km and 40 dBZ echo tops mostly found between 6 and 8 km. In contrast, the Basin displayed a more spatially uniform distribution of convection, characterized by lower echo-top heights (12–14 km) and higher 40 dBZ echo tops. Although both regions experienced a seasonal peak in DCS frequency in July, their diurnal variations differed significantly. The Plateau exhibited a pronounced unimodal peak between 13:00 and 16:00, which was driven by strong surface heating. In the Basin, a bimodal pattern was observed, with elevated frequencies during 23:00–02:00 and 08:00–11:00. This pattern was likely influenced by local thermodynamic and topographic conditions. The altitude of maximum corrected radar reflectivity (MaxCRF) was predominantly between 4 and 7 km over the Plateau and confined to 2–4 km over the Basin. Over the Plateau, DCS frequency increased significantly with elevation, consistent with the enhancing role of high terrain, whereas no comparable relationship was found in the Basin. Instead, convective activity in the Basin appeared to be modulated primarily by atmospheric instability and moisture availability, highlighting the contrasting environmental controls between the two regions. Full article

Urban riparian areas serve as vital blue-green infrastructure for climate adaptation, yet mechanisms governing energy exchange remain underexplored. This study aims to quantify the spatiotemporal patterns of sensible heat flux (H) and latent heat flux (LE) across riparian plant communities on daily and annual scales, and to disentangle the interactive effects of vegetation structure and water bodies on these fluxes. Using year-long field monitoring (September 2020–August 2021) across seven riparian plant communities along the Danshui River in Shanghai, environmental parameters were collected at multiple distances from the river. Interpretable machine learning models (Random Forest with SHAP analysis) were employed to identify key drivers. Results reveal significant diurnal and seasonal dynamics: LE amplitude exceeded H in summer but reversed in winter, with spatial gradients in H and LE strongly influenced by proximity to water bodies in grasslands and broadleaf forests but weakened in conifers. Meteorological factors such as photosynthetically active radiation and sunshine duration dominated daily-scale fluxes, while vegetation structures such as canopy height and leaf area index (LAI) contributed >50% to annual-scale variability. These findings underscore vegetation’s role in modulating energy partitioning, providing a theoretical basis for optimizing riparian plant configurations to enhance microclimate regulation in urban riparian. Full article

This study investigates the variability of major air pollutants, such as nitrogen oxides (NO_x, including nitric oxide (NO) and nitrogen dioxide (NO₂)), ozone (O₃), and particulate matter with a diameter ≤ 10 µm (PM10), in Thessaloniki over the period 2001–2022, highlighting their evolution in response to vehicle technology adoption and the COVID-19 pandemic. Four monitoring stations representing urban traffic, urban background, urban industrial, and suburban industrial environments were analyzed. PM10 concentrations generally decreased until 2015 but rose thereafter, mainly due to increased petrol car usage, with the highest levels recorded at the urban traffic station during colder months, influenced by domestic heating and local wind patterns. NO and NO₂ concentrations peaked at urban traffic and industrial sites, closely linked to vehicle emissions and industrial activities, respectively, with notable reductions during the 2020 COVID-19 lockdown. O₃ levels showed steady trends with diurnal and seasonal variability inversely related to NO_x concentrations and positively correlated with temperature. Despite some pollutant reductions, air quality issues persist in Thessaloniki. The findings emphasize the need for robust governmental policies promoting cleaner heating alternatives; two policy scenarios are presented in this respect with the corresponding air pollutant concentrations estimates up to 2035. Full article

The urban heat island (UHI) effect has become a critical environmental issue affecting urban livability and public health, attracting widespread attention from both academia and society. Although numerous studies have examined the influence of urban characteristics on land surface temperature (LST), most have been restricted to single variables or single time points, and the traditional “urban–rural dichotomy” approach fails to capture intra-urban thermal heterogeneity. To address this limitation, this study integrates the Local Climate Zone (LCZ) framework with machine learning techniques to systematically analyze the diurnal variation patterns of LST across different LCZ types in Beijing and explore the interactive effects of urban characteristic variables on LST. The results show the following: (1) Compact building zones (LCZ 1–3) exhibit significantly higher daytime LST than open building zones (LCZ 4–6), with reduced differences at night; high-rise buildings cool daytime surfaces through shading but increase nighttime LST due to heat storage. (2) Blue–green space variables, such as NDVI and tree coverage (T_PLAND), substantially lower daytime LST through evapotranspiration, but their nighttime cooling effect is weak; cropland coverage (C_PLAND) plays a particularly important role in lowering nighttime LST. (3) Blue–green space and urban form variables exhibit significant interaction effects on LST, with contrasting impacts between day and night. (4) Population activity variables are strongly correlated with increased LST, especially at night, when their warming effects are more prominent. This study reveals the relative importance and nonlinear relationships of different variables across diurnal cycles, providing a scientific basis for optimizing blue–green space configuration, improving urban morphology, regulating human activity, and formulating effective UHI mitigation strategies to support the development of more sustainable urban environments. Full article

Identifying reliable geochemical signals that reflect crustal stress evolution remains a major challenge in earthquake monitoring. Spring fluids, due to their deep circulation and rapid response, provide an important window into fault-zone processes. This study presents three years (May 2022–March 2025) of hourly hydrogen gas (H₂) concentration monitoring in spring gases from the Muji Basin on the northern Pamir Plateau, integrated with meteorological and seismic data. H₂ concentrations exhibited a stable diurnal pattern, positively correlated with water and air temperatures and negatively correlated with atmospheric pressure. Short-term anomalies during seismically quiet periods may reflect a combination of temperature-dependent solubility effects and transient degassing caused by localized gas accumulation and sudden release under heterogeneous fault and aquifer conditions. During seismically active phases, sustained increases in H₂ concentrations were also recorded; however, such anomalies did not consistently precede earthquakes, instead reflecting broader phases of tectonic instability and episodic fault-zone degassing. These findings highlight the potential of long-term H₂ monitoring to improve our understanding of the coupling between crustal stress, fluid transport, and degassing processes in tectonically active regions. Full article