Search Results (10)

The process of urbanization has intensified the urban heat environment, with the degradation of thermal conditions closely linked to the morphological characteristics of different functional zones. This study delineated urban functional areas using a multivariate dataset and investigated the seasonal and threshold effects of landscape and architectural features on land surface temperature (LST) through boosted regression tree (BRT) modeling and Spearman correlation analysis. The key findings are as follows: (1) LST exhibits significant seasonal variation, with the strongest urban heat island effect occurring in summer, particularly within industry, business, and public service zones; residence zones experience the greatest temperature fluctuations, with a seasonal difference of 24.71 °C between spring and summer and a peak temperature of 50.18 °C in summer. (2) Fractional vegetation cover (FVC) consistently demonstrates the most pronounced cooling effect across all zones and seasons. Landscape indicators generally dominate the regulation of LST, with their relative contribution exceeding 45% in green land zones. (3) Population density (PD) exerts a significant, seasonally dependent dual effect on LST, where strategic population distribution can effectively mitigate extreme heat events. (4) Mean building height (MBH) plays a vital role in temperature regulation, showing a marked cooling influence particularly in residence and business zones. Both the perimeter-to-area ratio (LSI) and frontal area index (FAI) exhibit distinct seasonal variations in their impacts on LST. (5) This study establishes specific indicator thresholds to optimize thermal comfort across five functional zones; for instance, FVC should exceed 13% in spring and 31.6% in summer in residence zones to enhance comfort, while maintaining MBH above 24 m further aids temperature regulation. These findings offer a scientific foundation for mitigating urban heat waves and advancing sustainable urban development. Full article

Research on the aerodynamic characteristics of leaves is part of the study of wind-induced tree disasters and has relevance to plant biological processes. The frontal area, which varies with the structure of leaves, is an important physical parameter in studying the aerodynamic characteristics of leaves. In order to measure the frontal area of a leaf in a wind tunnel, a method based on improved U-Net is proposed. First, a high-speed camera was used to collect leaf images in a wind tunnel; secondly, the collected images were corrected, cut and labeled, and then the dataset was expanded by scaling transformation; thirdly, by reducing the depth of each layer of the encoder and decoder of U-Net and adding a batch normalization (BN) layer and dropout layer, the model parameters were reduced and the convergence speed was accelerated; finally, the images were segmented based on the improved U-Net to measure the frontal area of the leaf. The training set was divided into three groups in the experiment. The experimental results show that the MIoUs were 97.67%, 97.78% and 97.88% based on the improved U-Net training on the three datasets, respectively. The improved U-Net model improved the measurement accuracy significantly when the dataset was small. Compared with the manually labeled image data, the RMSEs of the frontal areas measured by the models based on the improved U-Net were 1.56%, 1.63% and 1.60%, respectively. The R² values of the three measurements were 0.9993. The frontal area of a leaf can be accurately measured based on the proposed method. Full article

The family Ciconiidae comprises 19 extant species which are highly social when nesting and foraging. All species share similar morphotypes, with long necks, a bill, and legs, and are mostly coloured in the achromatic spectrum (white, black, black, and white, or shades of grey). Storks may have, however, brightly coloured integumentary areas in, for instance, the bill, legs, or the eyes. These chromatic patches are small in surface compared with the whole body. We have analyzed the conservatism degree of colouration in 10 body areas along an all-species stork phylogeny derived from BirdTRee using Geiger models. We obtained low conservatism in frontal areas (head and neck), contrasting with a high conservatism in the rest of the body. The frontal areas tend to concentrate the chromatic spectrum whereas the rear areas, much larger in surface, are basically achromatic. These results lead us to suggest that the divergent evolution of the colouration of frontal areas is related to species recognition through visual cue assessment in the short-range, when storks form mixed-species flocks in foraging or resting areas. Full article

Electroencephalographic studies using graph-theoretic analysis have found aberrations in functional connectivity in dyslexics. How visual nonverbal training (VT) can change the functional connectivity of the reading network in developmental dyslexia is still unclear. We studied differences in the local and global topological properties of functional reading networks between controls and dyslexic children before and after VT. The minimum spanning tree method was used to construct the reading networks in multiple electroencephalogram (EEG) frequency bands. Compared to controls, pre-training dyslexics had a higher leaf fraction, tree hierarchy, kappa, and smaller diameter (θ—γ-frequency bands), and therefore, they had a less segregated neural network than controls. After training, the reading-network metrics of dyslexics became similar to controls. In β1 and γ-frequency bands, pre-training dyslexics exhibited a reduced degree and betweenness centrality of hubs in superior, middle, and inferior frontal areas in both brain hemispheres compared to the controls. Dyslexics relied on the left anterior temporal (β1, γ1) and dorsolateral prefrontal cortex (γ1), while in the right hemisphere, they relied on the occipitotemporal, parietal, (β1), motor (β2, γ1), and somatosensory cortices (γ1). After training, hubs appeared in both hemispheres at the middle occipital (β), parietal (β1), somatosensory (γ1), and dorsolateral prefrontal cortices (γ2), while in the left hemisphere, they appeared at the middle temporal, motor (β1), intermediate (γ2), and inferior frontal cortices (γ1, β2). Language-related brain regions were more active after visual training. They contribute to an understanding of lexical and sublexical representation. The same role has areas important for articulatory processes of reading. Full article

Estimation of the aerodynamic load on trees is essential for urban tree management to mitigate the risk of tree failure. To assess that in a cost-effective way, scaled down tree models and numerical simulations were utilized. Scaled down tree models reduce the cost of experimental studies and allow the studies to be conducted in a controlled environment, namely in a wind or water tunnel, but the major challenge is to construct a tree model that resembles the real tree. We constructed 3D-printed scaled down fractal tree models of major urban tree species in Singapore using procedural modelling, based on species-specific growth processes and field statistical data gathered through laser scanning of real trees. The tree crowns were modelled to match the optical porosity of real trees. We developed a methodology to model the tree crowns using porous volumes filled with randomized tetrahedral elements. The wind loads acting on the tree models were then measured in the wind tunnel and the velocity profiles from selected models were captured using particle image velocimetry (PIV). The data was then used for the validation of Large Eddy Simulations (LES), in which the trees were modelled via a discretized momentum sink with 10–20 elements in width, height, and depth, respectively. It is observed that the velocity profiles and drag of the simulations and the wind tunnel tests are in reasonable agreement. We hence established a clear relationship between the measured bulk drag on the tree models in the wind tunnel, and the local drag coefficients of the discretized elements in the simulations. Analysis on the bulk drag coefficient also shows that the effect of complex crown shape could be more dominant compared to the frontal optical porosity. Full article

Background: Gulf War Illness (GWI) and Chronic Fatigue Syndrome (CFS) are two debilitating disorders that share similar symptoms of chronic pain, fatigue, and exertional exhaustion after exercise. Many physicians continue to believe that both are psychosomatic disorders and to date no underlying etiology has been discovered. As such, uncovering objective biomarkers is important to lend credibility to criteria for diagnosis and to help differentiate the two disorders. Methods: We assessed cognitive differences in 80 subjects with GWI and 38 with CFS by comparing corresponding fMRI scans during 2-back working memory tasks before and after exercise to model brain activation during normal activity and after exertional exhaustion, respectively. Voxels were grouped by the count of total activity into the Automated Anatomical Labeling (AAL) atlas and used in an “ensemble” series of machine learning algorithms to assess if a multi-regional pattern of differences in the fMRI scans could be detected. Results: A K-Nearest Neighbor (70%/81%), Linear Support Vector Machine (SVM) (70%/77%), Decision Tree (82%/82%), Random Forest (77%/78%), AdaBoost (69%/81%), Naïve Bayes (74%/78%), Quadratic Discriminant Analysis (QDA) (73%/75%), Logistic Regression model (82%/82%), and Neural Net (76%/77%) were able to differentiate CFS from GWI before and after exercise with an average of 75% accuracy in predictions across all models before exercise and 79% after exercise. An iterative feature selection and removal process based on Recursive Feature Elimination (RFE) and Random Forest importance selected 30 regions before exercise and 33 regions after exercise that differentiated CFS from GWI across all models, and produced the ultimate best accuracies of 82% before exercise and 82% after exercise by Logistic Regression or Decision Tree by a single model, and 100% before and after exercise when selected by any six or more models. Differential activation on both days included the right anterior insula, left putamen, and bilateral orbital frontal, ventrolateral prefrontal cortex, superior, inferior, and precuneus (medial) parietal, and lateral temporal regions. Day 2 had the cerebellum, left supplementary motor area and bilateral pre- and post-central gyri. Changes between days included the right Rolandic operculum switching to the left on Day 2, and the bilateral midcingulum switching to the left anterior cingulum. Conclusion: We concluded that CFS and GWI are significantly differentiable using a pattern of fMRI activity based on an ensemble machine learning model. Full article

The presence of obstacles like a tree, buildings, or birds along the path of a drone has the ability to endanger and harm the UAV’s flight mission. Avoiding obstacles is one of the critical challenging keys to successfully achieve a UAV’s mission. The path planning needs to be adapted to make intelligent and accurate avoidance online and in time. In this paper, we propose an energy-aware grid based solution for obstacle avoidance (EAOA). Our work is based on two phases: in the first one, a trajectory path is generated offline using the area top-view. The second phase depends on the path obtained in the first phase. A camera captures a frontal view of the scene that contains the obstacle, then the algorithm determines the new position where the drone has to move to, in order to bypass the obstacle. In this paper, the obstacles are static. The results show a gain in energy and completion time using 3D scene information compared to 2D scene information. Full article

Rubber trees in southern China are often impacted by natural disturbances, and accurate rubber tree crown segmentation and property retrieval are of great significance for forest cultivation treatments and silvicultural risk management. Here, three plots of different rubber tree clones, PR107, CATAS 7-20-59 and CATAS 8-7-9, that were recently impacted by hurricanes and chilling injury were taken as the study targets. Through data collection using ground-based mobile light detection and ranging (LiDAR) technology, a weighted Rayleigh entropy method based on the scanned branch data obtained from the region growing algorithm was proposed to calculate the trunk inclination angle and crown centre of each tree. A watershed algorithm based on the extracted crown centres was then adopted for tree crown segmentation, and a variety of tree properties were successfully extracted to evaluate the susceptibility of different rubber tree clones facing natural disturbances. The results show that the angles between the first-order branches and trunk ranged from 35.1–67.7° for rubber tree clone PR107, which is larger than the angles for clone CATAS 7-20-59, which ranged from 20.2–43.2°. Clone PR107 had the maximum number of scanned leaf points, lowest tree height and a crown volume that was larger than that of CATAS 7-20-59, which generates more frontal leaf area to oppose wind flow and reduces the gaps among tree crowns, inducing strong wind loading on the tree body. These factors result in more severe hurricane damage, resulting in trunk inclination angles that are larger for PR107 than CATAS 7-20-59. In addition, the rubber tree clone CATAS 8-7-9 had the minimal number of scanned leaf points and the smallest tree crown volume, reflecting its vulnerability to both hurricanes and chilling injury. The results are verified by field measurements. The work quantitatively assesses the susceptibility of different rubber tree clones under the impacts of natural disturbances using ground-based mobile LiDAR. Full article

Surface roughness parameters, such as roughness length and displacement height, impact the estimation of surface moisture, and the frontal areas of buildings and trees are two components that contribute to surface roughness in urban areas. Research on tree frontal area has not been conducted in urban areas before, and we hope to fill that gap in the literature with this study by using Terrestrial Light Detection and Ranging (LiDAR) data to estimate tree frontal areas in Warren Township, Indianapolis, IN, USA. We first estimated the frontal areas of individual trees based on their morphology, then calibrated a regression model to estimate the tree frontal area in 30 m pixels using parameters derived from LiDAR data and tree inventory data. The parameters included tree crown base area, height, width, conditions, defects, maintenances, genera, and land use. The validation shows that R² yielded values ranging from 0.84 to 0.88, and RMSEs varied with tree category. The tree categories were identified based on the height and broadness of the canopy, which indicated the degree of resistance to air flow. This type of model can be used to empirically determine local roughness values at the tree-level for any city with a complete tree inventory. With the strong correlation between trees’ frontal area and crown base area, this model may also be used to determine local roughness value at 30 m resolution with NLCD (National Land Cover Database) tree canopy cover data as a component. A proper tree categorization according to the vertical air resistance, e.g., height and canopy density, was effective to reduce the RMSE in tree frontal area estimation. Geometric parameters, such as height, crown base height, and crown base area extracted from Airborne LiDAR, which demand less storage and computation capacity, may also be sufficient for tree frontal area estimation in the areas where Terrestrial LiDAR is not available. Full article

As convective clouds in Northeast Asia are accompanied by various hazards related with heavy rainfall and thunderstorms, it is very important to detect convective initiation (CI) in the region in order to mitigate damage by such hazards. In this study, a novel approach for CI detection using images from Meteorological Imager (MI), a payload of the Communication, Ocean, and Meteorological Satellite (COMS), was developed by improving the criteria of the interest fields of Rapidly Developing Cumulus Areas (RDCA) derivation algorithm, an official CI detection algorithm for Multi-functional Transport SATellite-2 (MTSAT-2), based on three machine learning approaches—decision trees (DT), random forest (RF), and support vector machines (SVM). CI was defined as clouds within a 16 × 16 km window with the first detection of lightning occurrence at the center. A total of nine interest fields derived from visible, water vapor, and two thermal infrared images of MI obtained 15–75 min before the lightning occurrence were used as input variables for CI detection. RF produced slightly higher performance (probability of detection (POD) of 75.5% and false alarm rate (FAR) of 46.2%) than DT (POD of 70.7% and FAR of 46.6%) for detection of CI caused by migrating frontal cyclones and unstable atmosphere. SVM resulted in relatively poor performance with very high FAR ~83.3%. The averaged lead times of CI detection based on the DT and RF models were 36.8 and 37.7 min, respectively. This implies that CI over Northeast Asia can be forecasted ~30–45 min in advance using COMS MI data. Full article