Omani camel calves in a traditional management system

Le dromadaire Omani est une race particulière de la région du Golfe persique. C'est un animal à usages multiples et, depuis récemment, très demandé pour ses qualités de coureur. La population cameline à Oman était de 98 500 têtes en 1994 avec un taux de croissance annuel de 3,7 p. 100. Cette enquête a été réalisée entre 1992 et 1997. La reproduction a lieu en hiver (de novembre à mars). Vingt femelles adultes sur 364 ont été mises à la reproduction chaque année. En moyenne, 12 chamelons sont nés chaque année (taux de mise bas de 60 p. 100). Le taux moyen de conception a été de 4,5 p. 100, indiquant un sérieux problème dans le système d'élevage traditionnel. La plupart des chamelons (71,6 p. 100) sont nés de chamelles du groupe d'âge 11-20 ans. Les plus jeunes animaux en âge de se reproduire étaient utilisés pour la selle ou la course. Le rapport des chamelons femelles/mâles était de 1,14. Pour 53 gestations répertoriées, la durée moyenne de gestation a été de 384 jours (12,6 mois). Le taux de mortalité annuel a été de 2,66 p. 100. Tous les cas ont été répertoriés au cours du premier mois post-partum. Les causes de mortalité du chamelon comprenaient la pneumonie, les diarrhées et la sous-nutrition. Le système traditionnel d'élevage n'était pas adapté à une préparation adéquate de la mère pour l'allaitement post-partum. Trois chamelles ont mis bas deux fois avec des intervalles entre les mises bas de 3,3, 2,87 et 2,6 ans. Sept mâles reproducteurs étaient gardés séparément des femelles. Au cours des cinq années d'observation, 273 saillies ont été effectuées, conduisant à 60 naissances. Deux des mâles ont sailli 59 fois chacun, et chacun d'eux a participé à 11 conceptions. La gestion de la reproduction n'a pas permis une évaluation de la fertilité mâle ou femelle. (Résumé d'auteur

Enhancing FP-Growth Performance Using Multi-threading based on Comparative Study

The time required for generating frequent patterns plays an important role in mining association rules, especially when there exist a large number of patterns and/or long patterns. Association rule mining has been focused as a major challenge within the field of data mining in research for over a decade. Although tremendous progress has been made, algorithms still need improvements since databases are growing larger and larger. In this research we present a performance comparison between two frequent pattern extraction algorithms implemented in Java, they are the Recursive Elimination (RElim) and FP-Growth, these algorithms are used in finding frequent itemsets in the transaction database. We found that FP-growth outperformed RElim in term of execution time. In this context, multithreading is used to enhance the time efficiency of FP-growth algorithm. The results showed that multithreaded FP-growth is more efficient compared to single threaded FP-growth

Sound Visualization for Deaf Assistance Using Mobile Computing

This thesis presents a new approach to the visualization of sound for deaf assistance that simultaneously illustrates important dynamic sound properties and the recognized sound icons in an easy readable view. .In order to visualize general sounds efficiently ,the MFCC sound features was utilized to represent robust discriminant properties of the sound. The problem of visualizing MFCC vector that has 39 dimensions was simplified by visualizing one-dimensional value, which is the result of comparing one reference MFCC vector with the input MFCC vector only. New similarity measure for MFCC feature vectors comparison was proposed that outperforms existing local similarity measures due to their problem of one to one attribute value calculation that leaded to incorrect similarity decisions. Classification of input sound was performed and attached to the visualizing system to make the system more usable for users. Each time frame of sound is put under K-NN classification algorithm to detect short sound events. In addition, every one second the input sound is buffered and forwarded to Dynamic Time Warping (DTW) classification algorithm which is designed for dynamic time series classification. Both classifiers works in the same time and deliver their classification results to the visualization model. The application of the system was implemented using Java programming language to work on smartphones that run Android OS, so many considerations related to the complexity of algorithms is taken into account. The system was implemented to utilize the capabilities of the smartphones GPU to guarantee the smoothness and fastness of the rendering. The system design was built based on interviews with five deaf persons taking into account their preferred visualizing system. In addition to that, the same deaf persons tested the system and the evaluation of the system is carried out based on their interaction with the system. Our approach yields more accessible illustrations of sound and more suitable for casual and little expert users

Malware Detection for Android Operating Systems Applications

Many researches were done to find creative techniques, for Android platform, that can detect malware in easy and reliable manner. The aim is not only the effectiveness but to have less processing time, and less resources consumption. This research provide a solution for a part of this problem by finding an easy and fast way to analyze static application code and to generate its figure-print or signature to be used later in similarity measurement with available database of malwares signatures. We proposed a new method depends on SimHash algorithm which generate signature for reverse code from .apk android package kit. We compare the proposed algorithm with an existing Androguard tool, which also analyze static code and generate signatures using reverse engineering. We found that the proposed method saves 70% of time with similar results and time distribution behavior in comparison with Androguard

Comparative Analysis of the Performance of Popular Sorting Algorithms on Datasets of Different Sizes and Characteristics

Abstract: The efficiency and performance of sorting algorithms play a crucial role in various applications and industries. In this research paper, we present a comprehensive comparative analysis of popular sorting algorithms on datasets of different sizes and characteristics. The aim is to evaluate the algorithms' performance and identify their strengths and weaknesses under varying scenarios. We consider six commonly used sorting algorithms: QuickSort, TimSort, MergeSort, HeapSort, RadixSort, and ShellSort. These algorithms represent a range of approaches and techniques, including divide-and-conquer, hybrid sorting, and simple comparison-based methods. To assess their performance, we employ a diverse set of datasets, including the Iris dataset (1K), student dataset (5.8K), Wine dataset (6.5K), Uniform (10K), Normal (10K), Exponential (10K), Bimodal (10K), Yelp dataset (10K), MNIST dataset (42K), Uniform (100K), Normal (100K), Exponential (100K), Bimodal (100K), Uniform (500K), Normal (500K), Exponential (500K), Bimodal (500K), Uniform (1M), Normal (1M), Exponential (1M), and Bimodal (1M). These datasets cover a wide range of sizes and characteristics, allowing us to analyze the algorithms' performance across different dimensions. We measure and compare several key metrics, including execution time, memory usage, algorithmic complexity and stability. By analyzing these metrics, we gain insights into the efficiency and suitability of each algorithm for different dataset sizes and characteristics. We also discuss the implications of the findings in practical applications. Our results reveal important trade-offs among the sorting algorithms. While some algorithms excel in certain scenarios, others demonstrate better scalability or memory efficiency. We identify the best-performing algorithms for specific dataset characteristics and highlight their strengths and limitations. This research can assist developers and practitioners in selecting appropriate sorting algorithms based on their specific requirements and dataset characteristics. In conclusion, this comparative analysis provides a valuable contribution to the understanding of sorting algorithm performance. The findings contribute insights into the efficiency and suitability of popular sorting algorithms across datasets of different sizes and characteristics. By evaluating key metrics and discussing the implications, we offer guidance for selecting the most appropriate sorting algorithm in various practical scenarios

COVID-19 Disease Severity in Persons Infected with Omicron BA.1 and BA.2 Sublineages and Association with Vaccination Status

[No abstract available

Exposure assessment of radon in the drinking water supplies: a descriptive study in Palestine

<p>Abstract</p> <p>Background</p> <p>Radon gas is considered as a main risk factor for lung cancer and found naturally in rock, soil, and water. The objective of this study was to determine the radon level in the drinking water sources in Nablus city in order to set up a sound policy on water management in Palestine.</p> <p>Methods</p> <p>This was a descriptive study carried out in two phases with a random sampling technique in the second phase. Primarily, samples were taken from 4 wells and 5 springs that supplied Nablus city residents. For each source, 3 samples were taken and each was analyzed in 4 cycles by RAD 7 device manufactured by Durridge Company. Secondly, from the seven regions of the Nablus city, three samples were taken from the residential tap water of each region. Regarding the old city, ten samples were taken. Finally, the mean radon concentration value for each source was calculated.</p> <p>Results</p> <p>The mean (range) concentration of radon in the main sources were 6.9 (1.5-23.4) Becquerel/liter (Bq/L). Separately, springs and wells' means were 4.6 Bq/L and 9.5 Bq/L; respectively. For the residential tap water in the 7 regions, the results of the mean (range) concentration values were found to be 1.0 (0.9-1.3) Bq/L. For the old city, the mean (range) concentration values were 2.3 (0.9-3.9) Bq/L.</p> <p>Conclusions</p> <p>Except for Al-Badan well, radon concentrations in the wells and springs were below the United State Environmental Protection Agency maximum contaminated level (U.S EPA MCL). The level was much lower for tap water. Although the concentration of radon in the tap water of old city were below the MCL, it was higher than other regions in the city. Preventive measures and population awareness on radon's exposure are recommended.</p

apoA2 correlates to gestational age with decreased apolipoproteins A2, C1, C3 and E in gestational diabetes.

Pregnant women with gestational diabetes mellitus (GDM) are at risk of adverse outcomes, including gestational hypertension, pre-eclampsia, and preterm delivery. This study was undertaken to determine if apolipoprotein (apo) levels differed between pregnant women with and without GDM and if they were associated with adverse pregnancy outcome. Pregnant women (46 women with GDM and 26 women without diabetes (ND)) in their second trimester were enrolled in the study. Plasma apos were measured and correlated to demographic, biochemical, and pregnancy outcome data. apoA2, apoC1, apoC3 and apoE were lower in women with GDM compared with control women (p=0.0019, p=0.0031, p=0.0002 and p=0.015, respectively). apoA1, apoB, apoD, apoH, and apoJ levels did not differ between control women and women with GDM. Pearson bivariate analysis revealed significant correlations between gestational age at delivery and apoA2 for women with GDM and control women, and between apoA2 and apoC3 concentrations and C reactive protein (CRP) as a measure of inflammation for the whole group. Apoproteins apoA2, apoC1, apoC3 and apoE are decreased in women with GDM and may have a role in inflammation, as apoA2 and C3 correlated with CRP. The fact that apoA2 correlated with gestational age at delivery in both control women and women with GDM raises the hypothesis that apoA2 may be used as a biomarker of premature delivery, and this warrants further investigation

Corrigendum: Association of Complement-Related Proteins in Subjects With and Without Second Trimester Gestational Diabetes (Front. Endocrinol., (2021), 12, (641361), 10.3389/fendo.2021.641361)

In the original article, there was an error. One of the funders wasmissed out in the Acknowledgements. A correction has been made to the Acknowledgements section. “The authors would like to thank Qatar Metabolic Institute, Medical Research Center, Translational Research Institute, Hamad Medical Corporation, Doha, Qatar for supporting the study. And Medical Research Center, Hamad Medical Corporation for the article processing fees support”. The authors apologize for this error and state that this does not change the scientific conclusions of the article in any way. The original article has been updated