Search Results (70)

3D (three-dimensional) property volume is an important data carrier for 3D land administration by using 3D cadastral technology, which can be used to express the legal space (property rights) scope matching with physical entities such as buildings and land. A 3D property volume is represented by a dense set of 3D coordinate points arranged in a predefined order and is displayed alongside the parcel map for reference and utilization by readers. To store a 3D property volume in the database, it is essential to record the connectivity relationships among the original 3D coordinate points, the associations between points and lines for representing boundary lines, and the relationships between lines for defining surfaces. Only by preserving the data structure that represents the relationships among points, lines, and surfaces can the 3D property volume in a parcel map be fully reconstructed. This approach inevitably results in the database storage volume significantly exceeding the original size of the point set, thereby causing storage redundancy. Consequently, this paper introduces a reversible 3D property volume compression coding method (called 3DPV-CC) to address this issue. By analyzing the distribution characteristics of the coordinate points of the 3D property volume, a specific rule for sorting the coordinate points is designed, enabling the database to have the ability of data storage and recovery by merely storing a reordered point set. The experimental results show that the 3DPV-CC method has excellent support capabilities for 3D property volumes of the vertical and slopped types, and can compress and restore the coordinate point set of the 3D property volume for drawing 3D parcel maps. The compression capacity of our method in the test is between 23.66% and 38.42%, higher than the general data compression methods (ZIP/7Z/RAR: 8.37–10.32%). By means of this method, land or real estate administrators from government departments can store 3D property volume data at a lower cost. This is conducive to enhancing the informatization level of land management. Full article

Unmanned Aerial Vehicles (UAVs) are being increasingly implemented in parcel delivery applications. The scientific progress in this field is progressing exponentially. However, there is a notable gap in synthesizing recent research progress in UAV applications for last-mile delivery. This review study addresses this gap and conducts an in-depth review of UAV research for last-mile delivery across seven domains: environmental performance, economic impacts, social impacts, policy and regulations, routing and scheduling, charging infrastructure, and energy consumption. The review indicates that UAVs promise to reduce last-mile delivery emissions by 71% and costs by 96.5% compared to truck delivery. Saturated knowledge analysis is conducted across the seven domains to identify potential research gaps. Additionally, this review identifies key knowledge gaps, including variability in environmental and cost data, limitations associated with 2D modelling, and a lack of experimental validation. Future research interventions aimed at advancing UAV adoption in last-mile delivery applications are discussed. Full article

To duly and correctly deliver parcels, both the capacity and the delivery route of a delivery vehicle need to be considered. Thus, the delivery process of a delivery vehicle can be characterized as a capacitated vehicle routing problem with three-dimensional loading constraints (3L-CVRP), which is an NP-hard problem. To solve the problem, a mathematical model is established in this paper to minimize the total delivery distance and maximize the loading rate, simultaneously. Additionally, a hybrid algorithm that combines a three-dimensional (3D) packing algorithm based on the residual space optimized (RSO) strategy and an improved genetic algorithm (IGA) is proposed. Initially, the proposed hybrid algorithm employs a modified Clarke–Wright savings algorithm to generate a feasible set of route solutions. Furthermore, building upon the traditional genetic algorithm, an elite retention strategy is introduced, and an enhanced order crossover method is utilized to improve the stability of the hybrid algorithm and its global search capability for optimal solutions. Finally, during each iteration of the algorithm, the RSO algorithm is integrated to verify the feasibility of 3D packing scheme. Two comparative experiments are conducted on 22 modified benchmark instances and actual logistics data of a university against two other algorithms, demonstrating that the proposed RSO-IGA algorithm achieves superior solutions in delivery efficiency. Full article

Understanding human cortical neurodynamics is increasingly important, as highlighted by the European Innovation Council, which prioritises tools for measuring and stimulating brain activity. Unravelling how cytoarchitecture, morphology, and connectivity shape neurodynamics is essential for developing technologies that target specific brain regions. Given the dynamic and non-stationary nature of neural interactions, there is an urgent need for non-linear signal analysis methods, in addition to the linear ones, to track local neurodynamics and differentiate cortical parcels. Here, we explore linear and non-linear methods using data from a public stereotactic intracranial EEG (sEEG) dataset, focusing on the superior temporal gyrus (STG), postcentral gyrus (postCG), and precentral gyrus (preCG) in 55 subjects during resting-state wakefulness. For this study, we used a linear Power Spectral Density (PSD) estimate and three non-linear measures: the Higuchi fractal dimension (HFD), a one-dimensional convolutional neural network (1D-CNN), and a one-shot learning model. The PSD was able to distinguish the regions in α, β, and γ frequency bands. The HFD showed a tendency of a higher value in the preCG than in the postCG, and both were higher in the STG. The 1D-CNN showed promise in identifying cortical parcels, with an 85% accuracy for the training set, although performance in the test phase indicates that further refinement is needed to integrate dynamic neural electrical activity patterns into neural networks for suitable classification. Full article

This study investigates the formation and triggering mechanisms of precipitation processes. Given the substantial effort required to construct a 3D model, we developed an idealized 2D precipitation scenario, using a simplified dynamical framework with vortex wind fields as the background atmospheric flow field. By modeling the transport, uplift, and subsidence of water vapor and liquid water, a condensation model was developed to simulate air parcel uplift and high-altitude water vapor condensation. Further, a cloud microphysics precipitation scheme was incorporated to simulate precipitation triggering and falling processes following water vapor condensation. Model results demonstrate that the approach accurately reproduces key processes of water vapor transport, condensation, and precipitation formation. With a time step of 15 s and a total of 120 steps, the simulation of a 30-min scenario was completed in just 158.5 s, indicating the high computational efficiency of the model. This paper introduces an innovative research scheme for a diagnostic model. Upon technological maturity, the model will utilize radar wind field data as its input to evaluate and enhance the performance of precipitation diagnostic models in real weather processes. This research lays a solid foundation for the further refinement and optimization of precipitation forecasting models, thereby advancing the accuracy of weather prediction. Full article

Pluvial flooding, driven by increasingly impervious surfaces and intense storm events, presents a growing challenge for urban areas worldwide. In Baltimore City, MD, USA, climate change, rapid urbanization, and aging stormwater infrastructure are exacerbating flooding impacts, resulting in significant socio-economic consequences. This study evaluated the effectiveness of a soil profile rehabilitation scenario using a 2D hydrodynamic modeling approach for the Tiffany Run watershed, Baltimore City. This study utilized different extreme storm events, a high-resolution (1 m) LiDAR Digital Terrain Model (DTM), building footprints, and hydrological soil data. These datasets were integrated into a fully coupled 2D hydrodynamic model, the City Catchment Analysis Tool (CityCAT), to simulate urban flood dynamics. The pre-soil rehabilitation simulation revealed a maximum water depth of 3.00 m in most areas, with hydrologic soil groups C and D, especially downstream of the study area. The post-soil rehabilitation simulation was targeted at vacant lots and public parcels, accounting for 33.20% of the total area of the watershed. This resulted in a reduced water depth of 2.50 m. Additionally, the baseline runoff coefficient of 0.49 decreased to 0.47 following the rehabilitation, and the model consistently recorded a peak runoff reduction rate of 4.10 across varying rainfall intensities. The validation using a contingency matrix demonstrated true-positive rates of 0.75, 0.50, 0.64, and 0 for the selected events, confirming the model’s capability at capturing real-world flood occurrences. Full article

Pediatric lead poisoning remains a persistent public health problem. Children in the US spend the preponderance of their time at home; thus, housing is an important social determinant of health. Improving health outcomes derived from housing-based sources involves differentiating the risks posed by the existing housing stock. In this paper, we developed a parcel-level lead risk index (LRI) based on external housing conditions and the year of home construction. The purpose of this study was to introduce a housing-based lead risk index (LRI), developed using retrospective data, to estimate parcel-by-parcel variation in housing-based lead risk. We described how the LRI is constructed, relate it to the likelihood of a pediatric occupant’s blood lead level (BLL) > 3.5 µg/dL using Lasso regression (n = 6589), visualized this relationship graphically, and mapped the outcome. We found that mapping the LRI provided more information at a more precise geographic level than was possible using other public health surveillance methods. Full article

Winter catch crops are promoted in the European Union under the Common Agricultural Policy to improve soil health and reduce nitrate leaching from agricultural fields. Currently, Member States often monitor farmers’ adoption through on-site inspections for a limited subset of parcels. Because of its potential for region-wide coverage, this study investigates the potential of Sentinel-2 satellite time series to classify catch crops at the field level in Flanders (Belgium). The first objective was to classify catch crops and identify the optimal model and time-series input for this task. The second objective was to apply these findings in a real-world scenario, aiming to provide reliable early-season predictions in a separate target year, testing early-season performance and temporal transferability. The following three models were compared: Random Forest (RF), Time Series Forest (TSF), and a One-Dimensional Convolutional Neural Network (1D-CNN). The results showed that, with a limited field-based training dataset, RF produced the most robust results across different time-series inputs, achieving a median F1-score of >88% on the best dataset. Additionally, the early-season performance of the models was delayed in the target year, reaching the F1-score threshold of 85% at least one month later in the season compared to the training years, with large timing differences between the models. Full article

The Delottococcus aberiae is a mealybug pest known as Cotonet de les Valls in the province of Castellón (Spain). This tiny insect is causing large economic losses in the Spanish agricultural sector, especially in the citrus industry. The European Copernicus program encourages the progress of Earth observation (EO) in relation to the development of agricultural monitoring tools. In this context, this work is based on the analysis of the temporal evolution of spectral surface reflectance data from Sen2Like, analyzing healthy and fields affected by the mealybug. The study area is focused on the surroundings of Vall d’Uixó (Castellón, Spain), involving an approximate area of 25 ha distributed in a total of 21 fields of citrus trees with different mealybug incidence, classified as healthy or unhealthy, during the 2020–2021 season. The relationship between the mealybug infestation level and the Normalized Difference Vegetation Index (NDVI) and other optical bands (Red, NIR, SWIR, derived from Sen2Like) were analyzed by studying the time-series evolution of each parameter across the time period 2017–2022. In this study, we also demonstrate that evergreen fruit trees such as citrus, show a seasonality across the EO-based time series, which is linked to directional effects caused by the sensor–sun geometry. This can be mitigated by using a Bidirectional Reflectance Distribution Function (BRDF) model such as the High-Resolution Adjusted BRDF Algorithm (HABA). To study the infested fields separately from healthy ones and avoid mixing fields with very different spectral responses caused by field type, separation between rows, or age, we studied the evolution of each parcel separately using monthly linear regressions, considering the 2017–2018 seasons as a reference when the pest had not developed yet. The observations indicate the feasibility of the distinction between affected and healthy plots during a year utilizing specific spectral ranges, with SWIR proving a notably effective channel, enabling separability from mid-summer to the fall. Furthermore, the anomaly inspection demonstrates an increase in the effects of the pest from 2020 to 2022 in all spectral regions and enables a first approximation for identifying healthy and affected fields based on negative anomalies in the red and SWIR channels and positive anomalies in the NIR and NDVI. This work contributes to the development of new monitoring tools for efficient and sustainable action in pest control. Full article

Atrial wall thickness (AWT) is a significant factor in understanding the pathological physiological substrate of atrial fibrillation, with a potentially substantial impact on the outcomes of catheter ablation procedures. Precise measurements of the AWT may provide valuable insights for categorising patients with AF and planning targeted interventions. Objectives: The purpose of this study was to evaluate the characteristics of the left atrium (LA) using non-invasive multidetector computed tomography (MDCT) scans and subsequent three-dimensional (3D) image post-processing using novel software designed to calculate atrial thickness dimensions and mass. Methods: We retrospectively analysed 128 consecutive patients (33.6% females; mean age 55.6 ± 11.2 years) referred for AF ablation (37 with persistent AF and 91 with paroxysmal AF) who underwent preprocedural MDCT. The images were post-processed and analysed using the ADAS software (Galgo Medical), automatically calculating the LA volume and regional wall thickness. In addition, the software employed a regional semi-automatic LA parcellation feature that divided the atrial wall into 12 segments, generating atrial wall thickness (AWT) maps per segment for each patient. Results: This study demonstrated considerable variability in the average thickness of LA walls, with the anterior segments being the thickest across the cohort. Distinct sex-specific differences were observed, with males exhibiting greater anterior and septal wall thickness than females. No significant associations were identified between the average AWT and body mass index, LA volume, or sphericity. Survival analysis conducted over 24 months revealed a meaningful relationship between mean anterior wall thickness and recurrence-free survival, with increased thickness associated with a lower likelihood of AF-free survival. No such relationship was observed for the indexed LA volume. Conclusions: The variability in AWT and its association with recurrence-free survival following AF ablation suggest that AWT should be considered when stratifying patients for AF management and ablation strategies. These findings underscore the need for personalised treatment approaches and further research on the interplay of the structural properties of the left atrium as factors that can serve as important prognostic markers in AF treatment. Full article

Background: The purpose of the study was to investigate the behavior of hard dental structures of the teeth with abfraction lesions when experimental occlusal loads were applied. Methods: A 65-year-old patient came to the dentist because she had painful sensitivity in the temporomandibular joints and the lower right premolars. The patient was examined, and cone-beam computed tomography (CBCT) of the orofacial area was indicated. The data provided from the CBCT were processed with Mimics Innovation Suite 17 software to create the desired anatomical area in 3D format. Then, the structural calculation module was used in order to perform a finite element analysis of the lower right premolar teeth. A focused review of articles published between 2014 and 2023 from specialty literature regarding the FEA of premolars with abfraction lesions was also conducted. Results: The parcel area and the cervical third of the analyzed premolars proved to be the most vulnerable areas under the inclined direction of occlusal loads. The inclined application of experimental loads induced 3–4 times higher maximum shears, stresses, and deformations than the axial application of the same forces. Conclusions: FEA can be used to identify structural deficiencies in teeth with abfractions, a fact that is particularly important during dental treatments to correct occlusal imbalances. Full article

Detecting parcels accurately and efficiently has always been a challenging task when unloading from trucks onto conveyor belts because of the diverse and complex ways in which parcels are stacked. Conventional methods struggle to quickly and accurately classify the various shapes and surface patterns of unordered parcels. In this paper, we propose a parcel-picking surface detection method based on deep learning and image processing for the efficient unloading of diverse and unordered parcels. Our goal is to develop a systematic image processing algorithm that emphasises the boundaries of parcels regardless of their shape, pattern, or layout. The core of the algorithm is the utilisation of RGB-D technology for detecting the primary boundary lines regardless of obstacles such as adhesive labels, tapes, or parcel surface patterns. For cases where detecting the boundary lines is difficult owing to narrow gaps between parcels, we propose using deep learning-based boundary line detection through the You Only Look at Coefficients (YOLACT) model. Using image segmentation techniques, the algorithm efficiently predicts boundary lines, enabling the accurate detection of irregularly sized parcels with complex surface patterns. Furthermore, even for rotated parcels, we can extract their edges through complex mathematical operations using the depth values of the specified position, enabling the detection of the wider surfaces of the rotated parcels. Finally, we validate the accuracy and real-time performance of our proposed method through various case studies, achieving mAP (50) values of 93.8% and 90.8% for randomly sized and rotationally covered boxes with diverse colours and patterns, respectively. Full article

Landfalling atmospheric rivers (LARs) are important drivers of mid-latitude climate; however, our understanding of the water vapour sources, storm trajectories, and receiving waters of ARs is limited. This study aims to characterize LARs in southwest British Columbia by their isotopic composition and storm track trajectories and to better understand how AR-derived precipitation is manifested in watershed waters. ARs were depleted (−11.71‰ δ¹⁸O, −85.80‰ δ²H, n = 19) compared to non-ARs (−9.47‰ δ¹⁸O, −69.58‰ δ²H, n = 32) (p = 0.03); however, the difference is minimal. LAR storm tracks did not show any obvious correlation to their isotopic composition, despite the large variability in their source regions across the Pacific Ocean. The lack of correlation is attributed to mixing air parcels, thereby incorporating moisture with different isotopic compositions into the main transport mechanism. D-excess values for ARs and non-ARs were statistically similar, although seasonal differences were observed. ARs with higher d-excess were sourced from the central Pacific, whereas ARs with lower d-excess had storm tracks through the northern Pacific. Watershed water d-excess values (mean = 8.58 ± 2.97‰) were more similar to winter precipitation (mean = 10.1 ± 5.1‰), compared to summer (mean = 2.8 ± 4.3‰), likely due to their source of winter precipitation at high elevation. A greater range in AR d-excess winter values relative to summer values (3.6–16.6‰, −0.3–6.0‰, respectively) is attributed to storm track variability. Full article

Urban expansion is leading to the loss and fragmentation of habitats, which poses a threat to wildlife. People are hopeful that, through scientific urban planning and the adoption of innovative models for human communities, such a situation can be improved. Thus, a case study was carried out in Nanning City, China, to extract habitats, build an ecological resistance surface, and construct a habitat connectivity network (HCN). To simulate changes to unused land in the future, we put forth the A (the parcel is divided into strips), B (the parcel is divided into two strips), C (the central area of the parcel is planned as a quadrangle), and D (opposite to Scenario C, the peripheral area is green space) scenarios of human communities that guarantee a 30% ratio of green space, and established the corresponding HCNs. The results indicate that: (1) Currently, the habitats cover approximately 153.24 km² (34.08%) of the entire study area. The ecological corridors in this region amount to a total of 5337, and the topological indicators and robustness indicate a strong stability of the current HCN. (2) With urban expansion, once continuous habitats are being fragmented into smaller green spaces, it is estimated that the habitats will shrink by 64.60 km². The topological indicators and robustness reveal that the stability of the HCNs becomes lower as well. Multiple scenario simulations demonstrated that Scenario D is better than Scenarios B and C, while Scenario A performed the worst. (3) Furthermore, we observed a stronger negative impact of urban expansion on local connectivity. This indicates that the influence of urban expansion on the local HCNs is often more pronounced and may even be destructive. Our findings can advise urban planners on decisions to minimize the impact of urban expansion on wildlife. Full article

Vacant land in residual urban areas is a crucial resource to tackle the current climate and housing crises. In this study, we present the development of a geodatabase to determine the occurrence of vacant land in the urban core of Oklahoma City, USA (OKC), and assess its potential for infill housing. As a starting point, we define urban vacant land through a literature review. We present a description of the case study’s social and urbanistic context by highlighting its relevance to this study. We explain the methodology for the development of the geodatabase to quantify residual urban land in OKC’s urban core. We examine the spatial distribution and recurring characteristics of vacant parcels using QGIS, Python scripting for Rhinoceros 3D, and aerial imagery. We find that small parcels have higher vacancy rates than average-sized parcels and there is a correlation between higher vacancy rates and proximity to downtown and brownfields. Finally, we discuss the implications of the findings by assessing the urban vacant land potential for residential development and its contribution to OKC’s housing provision. Under all the proposed scenarios, the considered developable vacant land in the urban core could entirely fulfill the need for new housing units for the entire city. Full article