Search Results (333)

The intensification of olive growing has raised environmental concerns, particularly regarding nutrient loss from excessive fertiliser use. In line with the European Union’s Farm to Fork strategy, which aims to halve the soil nutrient losses by 2030, this study evaluates the effectiveness of two sensor-based approaches—proximal sensing with a FLAME spectrometer and remote sensing via UAV-mounted multispectral imaging—compared with foliar chemical analyses as the reference standard, for diagnosing the nutritional status of olive trees. The research was conducted in Elvas, Portugal, between 2022 and 2023, across three olive cultivars (‘Azeiteira’, ‘Arbequina’, and ‘Koroneiki’) subjected to different fertilisation regimes. Machine learning (ML) models showed strong correlations between sensor data and nutrient levels: the multispectral sensor performed best for phosphorus (P) (determination coefficient [$R^2$] = 0.75) and potassium (K) ($R^2$ = 0.73), while the FLAME spectrometer was more accurate for nitrogen (N) ($R^2$ = 0.64). These findings underscore the potential of sensor-based technologies for non-destructive, real-time nutrient monitoring, with each sensor offering specific strengths depending on the target nutrient. This work contributes to more sustainable and data-driven fertilisation strategies in precision agriculture. Full article

Pollen allergies represent a growing public health concern that necessitates enhancements to the network of instruments and modeling calculations in order to facilitate a more profound comprehension of pollen transportation. The Beenose instrument quantifies the light scattered by particles that traverse a laser beam at four angles. This methodology enables the differentiation of pollen particles from other particulate matter, predominantly mineral and carbonaceous in nature, thereby facilitating the retrieval of pollen concentrations. The Beenose instrument has been installed on the tourist balloon known as “Ballon de Paris” in a large park situated in the southwest of Paris, France. The measurement period is from April to November 2024, coinciding with the pollen seasons of trees and grasses. The balloon conducts numerous flights per day, reaching an altitude of 150 m when weather conditions are conducive, which occurs approximately 58% of the time during this period. The data are averaged to produce vertical profiles with a resolution of 30 m. Concentrations of the substance decrease with altitude, although a secondary layer is observed in spring. This phenomenon may be attributed to the presence of emissions from a proximate forest situated at a higher altitude. The average decrease in concentration of 11 ± 8% per 10 m is consistent with the findings of previous studies. The long-term implementation of Beenose measurements on this tourist balloon is intended to enhance the precision of the results and facilitate the differentiation of the various parameters that can influence the vertical transportation of pollen. Full article

Background: Artificial Intelligence (AI) is revolutionizing healthcare by enhancing diagnostic precision and risk assessment. In dentistry, AI has been increasingly integrated into Cone Beam Computed Tomography (CBCT) to improve image interpretation and pre-surgical planning. The lingual foramen (LF), a vital anatomical structure that transmits neurovascular elements, requires accurate evaluation during implant procedures. Traditional CBCT studies describe LF variations but lack a standardized risk classification. This study introduces a novel AI-based model for stratifying the surgical risk associated with LF using machine learning. Objectives: This study aimed to (1) assess the prevalence and anatomical variations of the lingual foramen (LF) using CBCT, (2) develop an AI-driven risk classification model based on LF characteristics, and (3) compare the AI model’s performance with that of traditional statistical methods. Materials and Methods: A retrospective analysis of 166 CBCT scans was conducted. K-means clustering and decision tree algorithms classified foramina into Low, Moderate, and High-Risk groups based on count, size, and proximity to the alveolar crest. The model performance was evaluated using confusion matrix analysis, heatmap correlations, and the elbow method. Traditional analyses (chi-square and logistic regression) were also performed. Results: The AI model categorized foramina into low (60%), moderate (30%), and high (10%) risk groups. The decision tree achieved a classification accuracy of 92.6 %, with 89.4% agreement with expert manual classification, confirming the model’s reliability. Conclusions: This study presents a validated AI-driven model for the risk assessment of the lingual foramen. Integrating AI into CBCT workflows offers a structured, objective, and automated method for enhancing surgical safety and precision in dental implant planning. Full article

Dothistroma needle blight (DNB) is one of the most significant diseases of conifers, causing premature defoliation, growth reduction, and, in extreme cases, mortality. Histological analysis was undertaken on inoculated seedlings of three different seed sources of Pinus sylvestris L. to investigate the process of infection and degradation of needle tissue on this host species. Seedlings were inoculated using a single spore isolate of Dothistroma septosporum (Doroguine) M. Morelet (D636) from northern Scotland. Mesophyll degradation in the needles occurred by four weeks after inoculation; collapse of mesophyll, bundle sheath tissues, and tracheids by five weeks; and eruption of fruiting bodies in near proximity to stomatal openings by six weeks. Significantly greater collapse of mesophyll during the early stages of infection occurred in the Austrian provenance compared with the United Kingdom provenance, although in the later stages of infection, this difference disappeared. Furthermore, disease severity, assessed as the proportion of needles with D. septosporum conidiomata on each tree, was not significantly different between seed sources. Full article

Examining the relationship between built environment (BE) and urban vitality (UV) is beneficial for promoting urban planning, as it deepens the understanding of how spatial design shapes urban life and activity patterns. However, the nonlinear effects of BE on UV from a spatiotemporal perspective have not been fully explored. In this study, the central urban area of Chongqing at the block scale is selected as a research case. The Gradient Boosting Decision Tree with SHapley Additive exPlanations (GBDT-SHAP) model is used to examine the nonlinear impacts of BE on UV. The results show the following: (1) The BE has a stronger overall impact on UV during holidays. Road intersection density (RID) has the greatest impact on UV on weekdays and holidays, building density (BD) has the greatest impact on weekend mornings, cultural and leisure accessibility (CLA) has the greatest impact on weekend afternoons, and commercial accessibility (CA) has the most significant impact on weekend evenings; (2) the impacts of the BE on UV exhibit significant nonlinear characteristics, with BD and park and square accessibility (PSA) showing a first increasing and then inhibiting effect on UV; lower CA, CLA, and MSA have inhibitory effects on UV, with higher normalized difference vegetation index (NDVI) values similarly demonstrating such effects; building height (BH), bus stop density (BSD), road network density (RD), and RID have enhancing effects on UV; functional mix degree (FMD) and water proximity index (WPI) show different trends in different time periods; (3) there are significant interactive effects among BE such as BD and BH, CA; RD and WPI, MSA; FMD and BH, PSA; PSA and CLA. A comprehensive understanding of these interactive relationships is crucial for optimizing the BE to enhance UV. This study provides a theoretical basis for urban planners to develop more effective, time-sensitive strategies. Future research should explore these nonlinear and interactive effects across different cities and scales to further generalize the findings. Full article

Proximal plant-based monitoring provides high-resolution data about trees, leading to more precise orchard management and in-depth knowledge about tree physiology. The present work focuses on continuous real-time monitoring of olive cv. ‘Ascolana tenera’ over hourly intervals during the third stage of fruit growth (mesocarp cell expansion) under mild water stress conditions (ψStem above −2 MPa). This is achieved by mounting dendrometers on the root, trunk, branch, and fruit to assess and model the behavior of each organ. The diameter variation in each organ over different time intervals (daily, two-weeks, and throughout the entire experiment), as well as their hysteretic patterns relative to each other and vapor pressure deficit, are demonstrated. The results show different correlations between various organs, ranging from very weak to strongly positive. However, the trend of fruit versus root consistently shows a strong positive relationship throughout the entire experiment (R² = 0.83) and a good one across various two-week intervals (R² ranging from 0.54 to 0.93). Additionally, different time lags in dehydration and rehydration between organs were observed, suggesting that the branch is the most reactive organ, regulating dehydration and rehydration in the tree. Regarding the hysteretic pattern, different rotational patterns and characteristics (shape) were observed among the organs and in relation to vapor pressure deficit. This research provides valuable insight into flow dynamics within a tree, models plant water relations and time lags in terms of water storage and transport, and could be implemented for precise olive tree water status detection. Full article

Climate change amplifies heat wave effects on outdoor thermal comfort by increasing their frequency, duration, and intensity. The urban heat island effect worsens heat risks in cities and impacts resilience. Nature-based solution (NBS) with tree plantation was reported as an effective mitigation measure. This simulation study, by the well-validated ENVI-met model, aimed to investigate the impact of different tree planting strategies and building parameters on urban heat risk mitigation and microclimate during a typical hot summer day. Hypothetical skyscrapers and super high-rise buildings were assumed in the study site located in southern China. Adopting meteorological inputs from a typical year, the simulation results revealed that both mean radiant temperature (T_mrt) and physiological equivalent temperature (PET) were elevated (T_mrt > 60 °C and PET > 50 °C) in early afternoon in sunlit areas. Three mitigation approaches with different tree planting locations were investigated. While all approaches demonstrated effective cooling (PET down to <35 °C) in the proximity of trees, a superior approach for mitigating the heat risks was not evident. Within the building array, the shade of bulky structures also lowered T_mrt and PET to a thermally comfortable level in the late afternoon. Combining open-space tree planting with optimized building designs is recommended to mitigate heat risks and enhance urban resilience while promoting outdoor activities and their health benefits. Full article

Background: Thermal ablation of centrally located liver tumors carries an increased risk of bile duct injury due to their proximity to the biliary tree. We aim to evaluate whether biliary cooling using a nasobiliary tube can effectively mitigate bile duct injury during the ablation process. Methods: We retrospectively analyzed the data of 322 patients who underwent thermal ablation at Dalin Tzu Chi Hospital from July 2020 to June 2023 and identified those who received prophylactic biliary cooling during thermal ablation for central liver tumors. Data including demographics, tumor characteristics, procedural details, and clinical outcomes were analyzed. Results: Among the 322 patients who underwent thermal ablation, 9 with central liver tumors received prophylactic biliary cooling. The median distance between the tumor and the central bile duct was 1 mm (range: 0–4 mm), the temperature of the cold normal saline was 4 °C, and the mean volume of normal saline infused was 150 mL (range: 100–200 mL). Complete ablation was achieved in all patients in a single session without any biliary injury. One patient developed acute cholangitis after ENBD placement, which resolved with antibiotic therapy. Conclusions: Biliary cooling with 4 °C cold saline through a nasobiliary tube can improve the safety and effectiveness of thermal ablation for central liver tumors. Full article

In dynamic environments with continuous variability, such as those affected by typhoons, ship path planning must account for both navigational safety and the maneuvering characteristics of the vessel. However, current methods often struggle to accurately capture the continuous evolution of dynamic obstacles and generally lack adaptive exploration mechanisms. Consequently, the planned routes tend to be suboptimal or incompatible with the ship’s maneuvering constraints. To address this challenge, this study proposes a Space–Time Integrated Q-Learning (STIQ-Learning) algorithm for dynamic path planning under typhoon conditions. The algorithm is built upon the following key innovations: (1) Spatio-Temporal Environment Modeling: The hazardous area affected by the typhoon is decomposed into temporally and spatially dynamic obstacles. A grid-based spatio-temporal environment model is constructed by integrating forecast data on typhoon wind radii and wave heights. This enables a precise representation of the typhoon’s dynamic evolution process and the surrounding maritime risk environment. (2) Optimization of State Space and Reward Mechanism: A time dimension is incorporated to expand the state space, while a composite reward function is designed by combining three sub-reward terms: target proximity, trajectory smoothness, and heading correction. These components jointly guide the learning agent to generate navigation paths that are both safe and consistent with the maneuverability characteristics of the vessel. (3) Priority-Based Adaptive Exploration Strategy: A prioritized action selection mechanism is introduced based on collision feedback, and the exploration factor $ϵ$ is dynamically adjusted throughout the learning process. This strategy enhances the efficiency of early exploration and effectively balances the trade-off between exploration and exploitation. Simulation experiments were conducted using real-world scenarios derived from Typhoons Pulasan and Gamei in 2024. The results demonstrate that in open-sea environments, the proposed STIQ-Learning algorithm achieves reductions in path length of 14.4% and 22.3% compared to the D* and Rapidly exploring Random Trees (RRT) algorithms, respectively. In more complex maritime environments featuring geographic constraints such as islands, STIQ-Learning reductions of 2.1%, 20.7%, and 10.6% relative to the DFQL, D*, and RRT algorithms, respectively. Furthermore, the proposed method consistently avoids the hazardous wind zones associated with typhoons throughout the entire planning process, while maintaining wave heights along the generated routes within the vessel’s safety limits. Full article

Habitat degradation poses a critical threat to the Malabar slender loris (Loris lydekkerianus malabaricus), yet little is known about its microhabitat requirements in intact forest. In Aralam Wildlife Sanctuary, we combined nocturnal trail surveys (337 loris sightings) with plotless sampling of 2830 trees (86 species from 35 families) to characterize both vegetation structure and loris presence. Our results show that lorises occur almost exclusively in mildly degraded wet evergreen and secondary moist deciduous subcanopies, where understory trees and climber networks provide continuous pathways. Individuals are most often encountered at heights of 5–15 m—ascending into higher strata as the night progresses—reflecting a balance between foraging access and predator avoidance. Substrate analysis revealed strong preferences for twigs ≤ 1 cm (36.98%) and small branches 2–5 cm in diameter, oriented obliquely to minimize energetic costs and maintain stability during slow, deliberate arboreal locomotion. Day-sleeping sites were overwhelmingly located within dense tangles of lianas on large-girth trees, where intertwined stems and thorny undergrowth offer concealment from both mammalian and avian predators. Vegetation surveys documented a near-equal mix of evergreen (50.6%) and deciduous (49.4%) species—including 26 endemics (18 restricted to the Western Ghats)—with Aporosa cardiosperma emerging as the most abundant riparian pioneer, suggesting both ecological resilience and potential simplification in fragmented patches. Complementing field observations, our recent habitat-suitability modeling in Aralam indicates that broad-scale climatic and anthropogenic factors—precipitation patterns, elevation, and proximity to roads—are the strongest predictors of loris occupancy, underscoring the interplay between landscape-level processes and microhabitat structure. Together, these findings highlight the imperative of multi-strata forest restoration—planting insect-hosting native trees, maintaining continuous canopy and climber networks, and integrating small “mini-forest” modules—to recreate the structural complexity vital for slender loris conservation and the broader resilience of Western Ghats biodiversity. Full article

Urban regeneration is pivotal to sustainable development, requiring innovative strategies that align social dynamics with spatial configurations. Traditional paradigms increasingly fail to tackle systemic challenges—neighborhood alienation, social fragmentation, and resource inequality—due to their inability to integrate human-centered spatial governance. This study addresses these shortcomings with a novel multidimensional framework that merges social perception (life satisfaction) analytics with spatial quality (GIS-based) assessment. At its core, we utilize geospatial and machine learning models, deploying an ensemble of Gradient Boosted Decision Trees (GBDT), Random Forest (RF), and multiscale geographically weighted regression (MGWR) to decode nonlinear socio-spatial interactions within Suzhou’s community environmental matrix. Our findings reveal critical intersections where residential density thresholds interact with commercial accessibility patterns and transport network configurations. Notably, we highlight the scale-dependent influence of educational proximity and healthcare distribution on community satisfaction, challenging conventional planning doctrines that rely on static buffer-zone models. Through rigorous spatial econometric modeling, this research uncovers three transformative insights: (1) Urban environment exerts a dominant influence on life satisfaction, accounting for 52.61% of the variance. Air quality emerges as a critical determinant, while factors such as proximity to educational institutions, healthcare facilities, and public landmarks exhibit nonlinear effects across spatial scales. (2) Housing price growth in Suzhou displays significant spatial clustering, with a Moran’s I of 0.130. Green space coverage positively correlates with price appreciation (β = 21.6919 ***), whereas floor area ratio exerts a negative impact (β = −4.1197 ***), highlighting the trade-offs between density and property value. (3) The MGWR model outperforms OLS in explaining housing price dynamics, achieving an R² of 0.5564 and an AICc of 11,601.1674. This suggests that MGWR captures 55.64% of pre- and post-pandemic price variations while better reflecting spatial heterogeneity. By merging community-expressed sentiment mapping with morphometric urban analysis, this interdisciplinary research pioneers a protocol for socio-spatial integrated urban transitions—one where algorithmic urbanism meets human-scale needs, not technological determinism. These findings recalibrate urban regeneration paradigms, demonstrating that data-driven socio-spatial integration is not a theoretical aspiration but an achievable governance reality. Full article

Mangrove ecosystems are renowned for their rich fungal diversity, housing a plethora of multicellular fungi and yeasts. In this investigation, we examined the yeast diversity associated with various compartments (rhizospheric soil, stems, roots, leaves, barks, and flowers) of the widely distributed mangrove tree, Avicennia officinalis, from the Kumbalam and Puthuvype mangroves in central Kerala, India. Our study revealed that the yeast strains were not uniformly distributed in various compartments. The highest abundance of yeasts was found in leaves (42%), followed by sediment (21%), and the lowest in flowers (5%). Among the 45 isolates, 27% comprised red yeasts. Dominant genera included Rhodotorula (27.5%), Debaryomyces (17.6%), Kluyveromyces (5.9%), Cryptococcus (9.8%), and Candida (7.8%), while genera such as Geotrichum, Lodderomyces, Ogataea, Galactomyces, and Saitozyma were represented by single isolates. Certain yeast species, such as C. tropicalis and Rhodotorula paludegina, exhibited a cosmopolitan distribution in various plant compartments of A. officinalis. An analysis of the proximate composition of different plant compartments of A. officinalis revealed variations in C, N, S, H, Ca, K, and the C/N ratio. Interestingly, these variations were positively correlated with the yeast community composition, suggesting a potential role of the elemental composition of plants in shaping the yeast biome of A. officinalis. However, our understanding of the inter-relationships among yeast communities in different plant compartments remains limited, highlighting the need for further comprehensive investigations in this field. Full article

Ecoacoustics examines the interactions between soundscapes, ecological processes, and anthropogenic disturbance. Acoustic communication is crucial for wildlife, making noise pollution a key factor in shaping biodiversity, though its effects are also modulated by habitat characteristics. In this work, we assess the influence of highway noise and vegetation structure on the soundscape and avian distribution of the Moriano oxbow lake (Bereguardo, PV, Italy), a Site of Community Importance in the Ticino Valley Regional Park. A two-week monitoring campaign (April 2022) used eight recorders arranged in a grid to analyze soundscape dynamics through eight ecoacoustic indices (ACI, ADI, AEI, BI, NDSI, H, DSC, ZCR). Vegetation surveys quantified tree diversity and structural parameters such as basal area, height, stem density, biomass, and leaf cover. Correlation analyses revealed that Quercus robur abundance and tree diversity significantly influenced the acoustic environment, while bird richness correlated positively with vegetation biomass and Quercus robur presence. Highway proximity was a key structuring factor, with indices (ADI, H, NDSI, ACI) increasing with distance. These findings underscore the dual role of noise and vegetation in shaping soundscapes and highlight the importance of incorporating habitat features into ecoacoustic assessments to better understand biodiversity patterns in anthropized landscapes. Full article

Since its first detection in 2013, Xylella fastidiosa subsp. pauca (Xfp) has caused a devastating Olive Quick Decline Syndrome (OQDS) outbreak in Southern Italy. Effective disease surveillance and treatment strategies are urgently needed to mitigate its impact. This study investigates the short-term (1.5 years) effects of thymol-based treatments on infected olive trees of the susceptible cultivar Cellina di Nardò in two orchards in Salento, Apulia region. Twenty trees per trial received a 3% thymol solution either alone or encapsulated in a cellulose nanoparticle carrier. Over two years, sap flux density and canopy-transmitted solar radiation were monitored using TreeTalker sensors, and spectral greenness indices were calculated. Xfp cell concentrations in plant tissues were quantified via qPCR. Neither thymol treatment halted disease progression nor significantly reduced bacterial load, though the Xfp cell concentration reduction increased over time in the preventive trial. Symptomatic trees exhibited increased sap flux density, though the treatment mitigated this effect in the curative trial. Greenness indices remained lower in infected trees, but the response to symptom severity was delayed. These findings underscore the need for longer-term studies, investigation of synergistic effects with other phytocompounds, and integration of real-time sensor data into adaptive disease management protocols. Full article

The SARS-CoV-2 virus, which causes COVID-19, has rapidly evolved, producing highly transmissible variants like Omicron. Non-structural protein 6 (NSP6) is essential for viral replication and immune evasion. This study analyzed the NSP6 protein of the Omicron variant, focusing on conserved motifs, mutations, and residual properties to better understand its structure, function, and potential for immune evasion. Sequences from humans in South America were obtained from GISAID and aligned using Clustal Omega 1.2.4, with mutations identified by a Python 3 algorithm and conserved motifs detected using the MEME tool. Sequence diversity was assessed with Shannon’s entropy, while hydrophilicity, flexibility, accessibility, and antigenicity were analyzed using EMBOSS PEPSTATS and Expasy’s ProtScale tools. Phylogenetic analysis was performed with IQ-TREE software. Analysis of 161 NSP6 protein sequences revealed significant divergence from the reference sequence, with mutations proximal to conserved regions indicating potential functional and structural changes. The analysis also identified distinct hydrophobic and hydrophilic regions, with specific amino acid positions showing high flexibility and antigenicity. Phylogenetic analysis identified three clades with varying degrees of similarity to the reference sequence. This comprehensive study of the NSP6 protein in the Omicron variant provides insights into its role in viral replication and immune evasion, contributing to the development of targeted interventions against COVID-19. Full article