The extraction of buildings from multispectral Light Detection and Ranging (LiDAR) data holds significance in various domains such as urban planning, disaster response, and environmental monitoring. State-of-the-art deep learning models, including Point Convolutional Neural Network (Point CNN) and Mask Region-based Convolutional Neural Network (Mask R-CNN), have effectively addressed this particular task. Data and application characteristics affect model performance. This research compares multispectral LiDAR building extraction models, Point CNN and Mask R-CNN. Models are tested for accuracy, efficiency, and capacity to handle irregularly spaced point clouds using multispectral LiDAR data. Point CNN extracts buildings from multispectral LiDAR data more accurately and efficiently than Mask R-CNN. CNN-based point cloud feature extraction avoids preprocessing like voxelization, improving accuracy and processing speed over Mask R-CNN. CNNs can handle LiDAR point clouds with variable spacing. Mask R-CNN outperforms Point CNN in some cases. Mask R-CNN uses image-like data instead of point clouds, making it better at detecting and categorizing objects from different angles. The study emphasizes selecting the right deep learning model for building extraction from multispectral LiDAR data. Point CNN or Mask R-CNN for accurate building extraction depends on the application. For building extraction from multispectral LiDAR data, two approaches were compared utilizing precision, recall, and F1 score. The point-CNN model outperformed Mask R-CNN. The point-CNN model had 93.40% precision, 92.34% recall, and 92.72% F1 score. Mask R-CNN has moderate precision, recall, and F1.
The experiments were conducted to evaluate the effect of two different irrigation treatments, Irrigation levels and Irrigation methods on yield, quality and chemical composion of potato plant (Spunta cultivar), during the seasons of 2020 and 2021. The experiments were conducted at Baloza Research Station, Desert Research Center, North Sinai Governorate, Egypt. The experimental design was a split plot design with three replicates, every replicate included 8 treatments which were the combinations between two drip irrigation mthods (subsurfac drip irrgation SSD and surface drip irigation SD ) and four irrigation levels (40%, 60%, 80% and100% of ETo) . The results in the two experimental seasons showed that application of different irrigation levels and irrigation methods treatments affected significantly potato yield, quality and chemical composion. The use of irrigation level 100% of ETo resulted in significantly higher values of potato yield quality and tuber chemical composition, also using subsurface drip irrigation method treatment increase the potato tuber yield quality and tuber chemical composition. Regarding the interaction between irrigation levels and, the highest results of potato tuber yield, quality and tuber chemical composition were obtained by the irrigation level 100% combined with subsurface drip irrigation. The irrigation level 80% treatment was obtained the highest WUE followed by irrigation level 100% treatment, on the other side the sub-surface drip irrigation method SSD was obtained the highest WUE than surface drip irrigation method SD. Regarding the interaction between irrigation levels and irrigation methods treatments, the highest WUE were obtained by SSD with 80% followed by SSD with 60 % treatments.
4 p-chlorophenyl] ethane) will increase preterm birth and decrease gestational-age in human beings. INTRODUCTlONMany chemical compounds introduced into the environment by human activity are known to influence the endocrine system of various animals and humans (Snedeker, 2001).
This study was performed throughout consecutive seasons of 2020 and 2021 on 8 years age trees of Manfalouty pomegranate trees grown in a sandy soil at special farm situated at Banu Uday, Manfalout city, Assiut Governorate, for studying the impact of three levels irrigation: 60, 80 and 100% of water requirement (WR) via drip irrigation on growth, yield, fruit quality and irrigation water efficiency (WUE).The current results declared that all studied vegetative traits significantly decreased due to irrigate with 60% of water requirement compared to 100% of water requirement (control).No significant differences were recorded for studied parameters due to reducing water from 100 to 80%.Also, using 80 or 100% of WR significantly increased yield and marketable yield as well as improved the fruit quality.Using 60% of WR gave the maximum water use efficiency (WUE).It is evident from the obtained data that using 80% of water requirement via drip irrigation induce an improve the trees growth traits and increasing the market value of resulting crop.Moreover, raising efficiency and reduce water used by about 20% of water requirement.
Methods and tools for automatic or semi-automatic generation of 3D city models are developing rapidly, but the quality assessment of these models and spatial data are rarely addressed. A comprehensive evaluation in 3D is not trivial. Our goal is to provide a standard multidimensional approach for assessing the quality of 3D models of buildings in 1D, 2D and 3D. Two methods are applied. The first one is done by computing Root Mean Square Errors (RMSE) based on the deviations between both models (reference and test), in X, Y and Z directions. Second method is performed by applying the French legal text (arrete sur les classes de precision) that is based on the instructions published in the Official Journal from October 30, 2003. These indices pass through the space discretization in pixels or voxels for measuring the degree of superposition of 2D or 3D objects. The originality of this approach is built on the fact that the models used as input are not only limited to raster format, but also extended to vector format. The results of statistics of the quality indices calculated for assessing the building models show that the 3D building models extracted from stereo-pairs are close from each other. Also, the models reconstructed from LiDAR are less accurate than the models reconstructed from aerial images alone. In conclusion, the quality evaluation of 3D building models has been achieved by applying the proposed multi-dimensional approach. This approach is suitable for simplified 3D building vector models created from aerial images and/or LiDAR datasets.
Abstract Aim The goal of handover is the accurate reliable communication of task-relevant information across shift changes or between teams. Effective and safe transfer of clinical information is critical for patient safety. The aim of this study to assess Surgical handover in Royal Sussex County Hospital comparing it to the RCS guidelines as standard as well as improving the current practice. Method We undertook two cycles each cycle lasted for 6 weeks. These cycles aimed to assess the surgical handover and comparing them with RCS guidelines using checklists. A teaching session was conducted between the two cycles. In addition, RCS guidelines were handed over to all surgical doctors. Results we discussed a total of 2348 patients in morning surgical handover, over a period of 12 weeks (first cycle: 1203, second cycle:1145). Following conduction of teaching sessions and handing over guidelines, there was a significant improvement in documentation of patients age (from 73% to 97%), responsible consultant (from 92% to 98%), significant investigations (from 60% to 93%) and management plan (from 64% to 96%). Conclusions Education about proper handover was proven to be successful in improving the quality of surgical handover in a major trauma center.
Soil salinity is a major issue that causes land degradation, especially in arid regions which affects negatively on soil properties and reduces agricultural productivity.Remote sensing is an essential tool for detecting and monitoring salinity changes upon time.In this research an assessment was carried out to determine the best representative salinity index based on filed measurements of soil salinity in El-Sharqiyah governorate in the northeast of Egypt.The index was validated in accord with other field soil salinity data in term of total dissolved salts (TDS) measured through 3-years intervals in 2015, 2018 and 2021.Landsat-8 satellite images were used to calculate NDSI (Normalized Difference Salinity Index).Seven indices were used to determine soil salinity, where the best correlated index was based on 2015 field work and used to produce salinity map of the study area.The best correlated index on 2015 was validated upon data of 2018 and 2021.Finally, this approach led to the sensitivity of remote sensing to soil salinity and the ability of its indices for soil salinity predication.
We analyze the Guruswami--Sudan list decoding algorithm for Reed--Solomon codes over the complex field for sparse recovery in Compressed Sensing. We propose methods of stabilizing both the interpolation and the root-finding steps against numerical instabilities, where the latter is the most sensitive. For this purpose, we modify the Roth--Ruckenstein algorithm and propose a method to refine its result using Newton's method. The overall decoding performance is then further improved using Generalized Minimum Distance decoding based on intrinsic soft information. This method also allows to obtain a unique solution of the recovery problem. The approach is numerically evaluated and shown to improve upon recently proposed decoding techniques.
Background In the past 3 decades, the arterial switch procedure has replaced the atrial switch procedure as treatment of choice for transposition of the great arteries. Although survival is superior after the arterial switch procedure, data on pregnancy outcomes are scarce and transposition of the great arteries after arterial switch is not yet included in the modified World Health Organization classification of maternal cardiovascular risk. Methods and Results The ROPAC (Registry of Pregnancy and Cardiac disease) is an international prospective registry of pregnant women with cardiac disease, part of the European Society of Cardiology EURObservational Research Programme. Pregnancy outcomes in all women after an arterial switch procedure for transposition of the great arteries are described. The primary end point was a major adverse cardiovascular event, defined as combined end point of maternal death, supraventricular or ventricular arrhythmias requiring treatment, heart failure, aortic dissection, endocarditis, ischemic coronary events, and thromboembolic events. Altogether, 41 pregnant women (mean age, 26.7±3.9 years) were included, and there was no maternal mortality. A major adverse cardiovascular event occurred in 2 women (4.9%): heart failure in one (2.4%) and ventricular tachycardia in another (2.4%). One woman experienced fetal loss, whereas no neonatal mortality was observed. Conclusions Women after an arterial switch procedure for transposition of the great arteries tolerate pregnancy well, with a favorable maternal and fetal outcome. During counseling, most women should be reassured that the risk of pregnancy is low. Classification as modified World Health Organization risk class II seems appropriate.
Abstract Soil salinity is a significant challenge in numerous regions across the globe, including Egypt. The potential consequences encompass negative impacts on crop yield, human well-being, and eco-logical systems. The utilization of remote sensing and machine learning techniques is increasingly becoming recognized as cost-effective methodologies for the cartographic representation of soil salinity. The present work employed Landsat 8 satellite imaging data and sophisticated machine learning techniques to delineate and assess soil salinity levels in Sharkia Governorate, Egypt. In this work, several machine learning techniques were employed to forecast the salinity values of Total Dissolved Solids (TDS) in the designated geographical region. These algorithms encompassed support vector machines (SVM), regression trees, Gaussian linear regression, and tree-based ensemble in addition to linear regression analysis. A variety of instances were generated to develop an optimal model that accurately characterizes the salinity TDS values within the study area. This was achieved by utilizing the band values extracted from the Landsat 8 satellite imagery. The approach that demonstrated the highest performance was observed when employing the Blue, Red, and shortwave infrared bands in conjunction with the SVM-Quadratic SVM model. This particular configuration yielded an R2 value of 0.86 and an RMSE value of 175.98. The findings of this work demonstrate the feasibility of precisely mapping soil salinity through the utilization of Landsat 8 satellite imaging data and machine learning techniques. The provided data can be utilized to identify regions characterized by elevated levels of soil salinity, as well as for the formulation of effective approaches aimed at addressing this issue.
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