Evaluation of Some Genotypes of Wheat (Triticum Durum L.) Under Conditions in Al Jabal Al Akhdar - Libya.

The experiment was conducted at the research farm of the College of Agriculture during the season (2019-2020) to evaluate the performance of six genetic compositions under semi-arid conditions using the green fodder. These compositions were obtained from the International Center for Agricultural Research in Dry Areas (ICARDA) and the International Maize and Wheat Improvement Center (CIMMYT), compared to the local variety "Ain Al-Faras." The treatments were planted according to a randomized complete block design (RCBD) with three replications. Significant differences were observed among all the genetic compositions for all the studied traits. The variety "Ain Al-Faras" outperformed the other genetic compositions in plant height, spike length, number of grains per spike, grain yield, straw yield, and biological weight, with values of 86 cm, 9.65 cm, 61 grains/spike, 1.14 tons/ha, 10.68 tons/ha, and 11.82 tons/ha, respectively. Genetic composition 2020-741 also excelled in the trait of number of grains per spike (52.33), while genetic composition D2020-72 exhibited superiority in the trait of thousand-grain weight (63.35 g). Moreover, genetic composition D2020-4 showed a higher harvest index compared to the other genetic compositions, with a value of 12.91. The results indicated that the values of phenotypic variance were close to the values of genetic variance for most of the studied traits. The highest heritability ratio was observed for the traits of number of grains per spike and grain yield. High relative genetic advance values were recorded along with high values of specific combining ability for both the number of grains per spike and grain yield. Therefore, it is possible to infer the genetic composition through morphological data, and such traits can be considered as selection criteria for improving these crops

WOMEN'S LAW REJECTS REFERENCE IN THE PERSPECTIVE OF ISLAMIC JURISPRUDENCE AND THE COMPILATION OF ISLAMIC LAW

This study used library research. The techniques used in this study include; Data collection, after the required data has been collected, then several stages are carried out, namely: Data reduction, display data, concluding.  After the process of data collection and data management has been completed, the next step is to analyze the data to get a complete picture related to the problem that is the object of research. This study discusses the issue of referencing, that is, legal reference is carried out without the consent of the wife, as long as she is still in  the period of 'iddah based on the agreement of the ulama.  Meanwhile, in the Compilation of Islamic Law, the legal reference is valid if it has received approval from the wife. From this problem, there are two formulations of problems that will be studied by the author in this study, namely: 1) How women's law rejects reference in the perspective of Islamic jurisprudence. 2) How women's law rejects references in the Compilation of Islamic Law. The objectives in this study are twofold, namely: 1) To know the law of women refusing to refer in the perspective of Islamic jurisprudence. 2) to know the law women refuse to refer to in the perspective of the Compilation of Islamic Law. Based on the results of this study, it can be concluded, namely: 1) In Islamic law, scholars agree that reference is the prerogative of the husband or the absolute right of the husband, so there is no need for consent from the wife. 2) Whereas in the Compilation of Islamic Law it is stated that if a husband is going to make a reference to his ex-wife must first obtain the consent of his ex-wife, and the wife has the right to object to the will of the reference.Keywords: Law, Women, Reference, Islamic Jurisprudence, Compilation of Islamic LawPenelitian ini menggunakan penelitian riset kepustakaan (library research). Adapun teknik yang digunakan dalam penelitian ini antara lain; Pengumpulan data, setelah data yang diperlukan telah terkumpul, kemudian dilakukan beberapa tahapan yaitu: Reduksi data (data reduction), display data, concluding. Setelah proses pengumpulan data dan pengelolahan data telah selesai, maka selanjutnya adalah menganalisis data guna mendapat sebuah gambaran utuh terkait dengan permasalahan yang menjadi objek penelitian. Penelitian ini membahas tentang masalah rujuk, yaitu rujuk sah dilakukan tanpa persetujuan istri, selama dia masih dalam masa ‘iddah berdasarkan kesepakatan ulama. Sedangkan dalam Kompilasi Hukum Islam, rujuk sah hukumnya apabila sudah mendapat persetujuan dari pihak istri. Dari permasalahan ini, ada dua rumusan masalah yang akan dikaji penulis dalam penelitian ini, yaitu: 1) Bagaimana hukum wanita menolak rujuk dalam perspektif fikih Islam. 2) Bagaimana hukum wanita menolak rujuk dalam Kompilasi Hukum Islam. Tujuan dalam penelitian ini ada dua, yaitu: 1) Untuk mengetahui hukum wanita menolak rujuk dalam perspektif fikih Islam. 2) untuk mengetahui hukum wanita menolak rujuk dalam perspektif Kompilasi Hukum Islam. Berdasarkan dari hasil penelitian ini dapat disimpulkan yaitu: 1) Dalam hukum Islam, ulama sepakat bahwa rujuk merupakan hak prerogatif suami atau hak mutlak suami, sehingga tidak diperlukan adanya persetujuan dari pihak istri. 2) Sedangkan dalam Kompilasi Hukum Islam disebutkan bahwa apabila seorang suami akan melakukan rujuk terhadap mantan istrinya terlebih dahulu harus mendapat persetujuan dari mantan istrinya, serta istri berhak mengajukan keberatan atas kehendak rujuk tersebut.Kata Kunci: Hukum, Wanita, Rujuk, Fikih Islam, Kompilasi Hukum Islam 

An effort-based model for pedestrian route choice behaviour

University of Technology Sydney. Faculty of Engineering and Information Technology.This research proposes a novel effort-based theoretical framework for the pedestrian route choice problem to discover principles that pedestrians use to select their routes. A pedestrian chooses their route by optimising certain criteria, such as distance, time, and effort. Several possible criteria that could be used to predict the route choices of a pedestrian are re-assessed. In most cases, the common criteria of a pedestrian route choice are route length and travel time. Effort is proposed as an additional criterion, which indicates metabolic energy expenditure. The basic principle and a methodology are proposed for route choice based on the least effort that a pedestrian may consume during travel between destinations. The followed deterministic approach assumes that the perceived utility of a route is deterministic and that pedestrians will only choose the route that features minimum average cost. A mathematical formulation for solving the pedestrian route choice problem utilising the concept of physical effort is introduced. We compare our effort-based model against time and distance based models and validate against the Brisbane dataset. We demonstrate that our method has higher performance efficiency than the models that exist in the state-of-the-art and thereby the model justifies optimal pedestrian behaviour when choosing a route in a congested environment. Our discussion concludes with an overview of how our approach could be used by rail service providers to optimise operations and improve customer experience. It is contended that the entire behaviour of an individual is subject to effort minimization. Hence, the pedestrian route choice problem is formulated as a constrained non-linear optimization problem whose objective function is the effort consumed while moving from current position to destination over the route. This doctoral research is a part of research project entitled “Integrated Passenger Behaviour, Train Operations Diagnostics, and Vehicle Condition Monitoring System”, which aims to consolidate foundation technology for the sensing and perception functions of a system that can monitor passenger behaviour and operational characteristics of passenger trains as they arrive at crowded stations using low-cost multi-sensor network. The Brisbane Central Rail Train Station is selected for a case study for validation of the developed model

Studying the Role of Cerebrovascular Changes in Different Compartments in Human Brains in Hypertension Prediction

Hypertension is a major cause of mortality of millions of people worldwide. Cerebral vascular changes are clinically observed to precede the onset of hypertension. The early detection and quantification of these cerebral changes would help greatly in the early prediction of the disease. Hence, preparing appropriate medical plans to avoid the disease and mitigate any adverse events. This study aims to investigate whether studying the cerebral changes in specific regions of human brains (specifically, the anterior, and the posterior compartments) separately, would increase the accuracy of hypertension prediction compared to studying the vascular changes occurring over the entire brain’s vasculature. This was achieved by proposing a computer-aided diagnosis system (CAD) to predict hypertension based on cerebral vascular changes that occur at the anterior compartment, the posterior compartment, and the whole brain separately, and comparing corresponding prediction accuracy. The proposed CAD system works in the following sequence: (1) an MRA dataset of 72 subjects was preprocessed to enhance MRA image quality, increase homogeneity, and remove noise artifacts. (2) each MRA scan was then segmented using an automatic adaptive local segmentation algorithm. (3) the segmented vascular tree was then processed to extract and quantify hypertension descriptive vascular features (blood vessels’ diameters and tortuosity indices) the change of which has been recorded over the time span of the 2-year study. (4) a classification module used these descriptive features along with corresponding differences in blood pressure readings for each subject, to analyze the accuracy of predicting hypertension by examining vascular changes in the anterior, the posterior, and the whole brain separately. Experimental results presented evidence that studying the vascular changes that take place in specific regions of the brain, specifically the anterior compartment reported promising accuracy percentages of up to 90%. However, studying the vascular changes occurring over the entire brain still achieve the best accuracy (of up to 100%) in hypertension prediction compared to studying specific compartments

Predicting the Level of Respiratory Support in COVID-19 Patients Using Machine Learning

In this paper, a machine learning-based system for the prediction of the required level of respiratory support in COVID-19 patients is proposed. The level of respiratory support is divided into three classes: class 0 which refers to minimal support, class 1 which refers to non-invasive support, and class 2 which refers to invasive support. A two-stage classification system is built. First, the classification between class 0 and others is performed. Then, the classification between class 1 and class 2 is performed. The system is built using a dataset collected retrospectively from 3491 patients admitted to tertiary care hospitals at the University of Louisville Medical Center. The use of the feature selection method based on analysis of variance is demonstrated in the paper. Furthermore, a dimensionality reduction method called principal component analysis is used. XGBoost classifier achieves the best classification accuracy (84%) in the first stage. It also achieved optimal performance in the second stage, with a classification accuracy of 83%

Atténuation de l'impact du déficit hydrique sur une culture de petit pois par la combinaison d'amendements organiques et de systèmes d'irrigation localisés

Cette recherche visait à évaluer l’impact de deux systèmes d'irrigation combinés à un amendement organique du sol, sur les paramètres hydrodynamiques et chimiques du sol et sur la productivité d'une culture de petit pois "Pisum sativum L. " soumise à des conditions de stress hydrique. Les résultats obtenus ont montré que les rampes poreuses permettent une meilleure stabilité du stock d'eau avec une amplitude de 30 mm contre 50,7 mm pour le système au goutte à goutte et un développement racinaire de petit pois plus intéressant avec une différence d'environ 2,5 cm par rapport au système de goutte à goutte. Le rendement n'a pas été significativement affecté et nous avons enregistré une différence de 3,43% en faveur des rampes poreuses. En revanche, la nodulation des racines et la fixation symbiotique de l'azote dépendaient du système d'irrigation, et nous avons trouvé une teneur en azote total plus élevée pour les sols irrigués par des rampes poreuses qui a atteint 1,4 g/kg. Les amendements organiques ont augmenté la teneur en humidité du sol à 24 et 25% pour la tourbe et le biochar respectivement par rapport au témoin. La croissance végétative de la plante a également été améliorée avec les amendements

A deep learning-based approach for automatic segmentation and quantification of the left ventricle from cardiac cine MR images

© 2020 Elsevier Ltd Cardiac MRI has been widely used for noninvasive assessment of cardiac anatomy and function as well as heart diagnosis. The estimation of physiological heart parameters for heart diagnosis essentially require accurate segmentation of the Left ventricle (LV) from cardiac MRI. Therefore, we propose a novel deep learning approach for the automated segmentation and quantification of the LV from cardiac cine MR images. We aim to achieve lower errors for the estimated heart parameters compared to the previous studies by proposing a novel deep learning segmentation method. Our framework starts by an accurate localization of the LV blood pool center-point using a fully convolutional neural network (FCN) architecture called FCN1. Then, a region of interest (ROI) that contains the LV is extracted from all heart sections. The extracted ROIs are used for the segmentation of LV cavity and myocardium via a novel FCN architecture called FCN2. The FCN2 network has several bottleneck layers and uses less memory footprint than conventional architectures such as U-net. Furthermore, a new loss function called radial loss that minimizes the distance between the predicted and true contours of the LV is introduced into our model. Following myocardial segmentation, functional and mass parameters of the LV are estimated. Automated Cardiac Diagnosis Challenge (ACDC-2017) dataset was used to validate our framework, which gave better segmentation, accurate estimation of cardiac parameters, and produced less error compared to other methods applied on the same dataset. Furthermore, we showed that our segmentation approach generalizes well across different datasets by testing its performance on a locally acquired dataset. To sum up, we propose a deep learning approach that can be translated into a clinical tool for heart diagnosis

Segmentation of Infant Brain Using Nonnegative Matrix Factorization

This study develops an atlas-based automated framework for segmenting infants\u27 brains from magnetic resonance imaging (MRI). For the accurate segmentation of different structures of an infant\u27s brain at the isointense age (6-12 months), our framework integrates features of diffusion tensor imaging (DTI) (e.g., the fractional anisotropy (FA)). A brain diffusion tensor (DT) image and its region map are considered samples of a Markov-Gibbs random field (MGRF) that jointly models visual appearance, shape, and spatial homogeneity of a goal structure. The visual appearance is modeled with an empirical distribution of the probability of the DTI features, fused by their nonnegative matrix factorization (NMF) and allocation to data clusters. Projecting an initial high-dimensional feature space onto a low-dimensional space of the significant fused features with the NMF allows for better separation of the goal structure and its background. The cluster centers in the latter space are determined at the training stage by the K-means clustering. In order to adapt to large infant brain inhomogeneities and segment the brain images more accurately, appearance descriptors of both the first-order and second-order are taken into account in the fused NMF feature space. Additionally, a second-order MGRF model is used to describe the appearance based on the voxel intensities and their pairwise spatial dependencies. An adaptive shape prior that is spatially variant is constructed from a training set of co-aligned images, forming an atlas database. Moreover, the spatial homogeneity of the shape is described with a spatially uniform 3D MGRF of the second-order for region labels. In vivo experiments on nine infant datasets showed promising results in terms of the accuracy, which was computed using three metrics: the 95-percentile modified Hausdorff distance (MHD), the Dice similarity coefficient (DSC), and the absolute volume difference (AVD). Both the quantitative and visual assessments confirm that integrating the proposed NMF-fused DTI feature and intensity MGRF models of visual appearance, the adaptive shape prior, and the shape homogeneity MGRF model is promising in segmenting the infant brain DTI

The Role of Medical Image Modalities and AI in the Early Detection, Diagnosis and Grading of Retinal Diseases: A Survey.

Traditional dilated ophthalmoscopy can reveal diseases, such as age-related macular degeneration (AMD), diabetic retinopathy (DR), diabetic macular edema (DME), retinal tear, epiretinal membrane, macular hole, retinal detachment, retinitis pigmentosa, retinal vein occlusion (RVO), and retinal artery occlusion (RAO). Among these diseases, AMD and DR are the major causes of progressive vision loss, while the latter is recognized as a world-wide epidemic. Advances in retinal imaging have improved the diagnosis and management of DR and AMD. In this review article, we focus on the variable imaging modalities for accurate diagnosis, early detection, and staging of both AMD and DR. In addition, the role of artificial intelligence (AI) in providing automated detection, diagnosis, and staging of these diseases will be surveyed. Furthermore, current works are summarized and discussed. Finally, projected future trends are outlined. The work done on this survey indicates the effective role of AI in the early detection, diagnosis, and staging of DR and/or AMD. In the future, more AI solutions will be presented that hold promise for clinical applications