The role of youth in building a culture of peaceful coexistence in society

يهدف البحث الحالي إلى الكشف عن الدور الاجتماعي للشباب في  تعزيز ثقافة التعايش السلمي المجتمعي، ويهدف إلى تحديد أهم الجوانب الثقافية التي يعززها الشباب لتحقيق التعايش السلمي المجتمعي، ومعرفة الفروق ذات الدلالة الاحصائية وفق متغير النوع للشباب (ذكور - إناث) في مدى تحقيق دورهم المجتمعي في تعزيز ثقافة التعايش السلمي المجتمعي، ولتحقيق أهداف البحث قمنا ببناء أداة البحث من أجل تحقيق أهداف البحث الحالي وقد اطلعنا على الادبيات ودراسات السابقة التي تخص موضع البحث الحالي، قام الباحث ببناء أداة مكونة من أربع مجالات ولكل مجال ستة فقرات، وقد اخترنا طلبة الدراسة الاعدادية مجتمعا وعينتا للبحث الحالي، لأنهم ضمن أعمار فئة شبابية مثقفة وواعية تساهم في قيادة وبناء مستقبل المجتمع، وقد استخدمنا أساليب التحليل الاحصائي المتمثلة بالقوة التميزية وعلاقة الفقرة بالمجموع الكلي من أجل الوصول لأفضل أداة للبحث الحالي، وقد تم استخراج صدق وثبات الاداة، وتم استخدام الحقيبة الاحصائية (spss) للعلوم الانسانية لتحليل بيانات عينة البحث، وتم عرض نتائج البحث في جدأول وفق مؤشرات التحليل الاحصائي والتي اظهرت أن الدور الاجتماعي للشباب في تعزيز ثقافة التعايش السلمي المجتمعي ذات مستوى مرتفع ومؤثر في المجتمع، وقد حدد البحث الحالي أهم الجوانب الاجتماعية التي يعمل الشباب من خلالها على تعزيز وبناء ثقافة التعايش السلمي في المجتمع، واظهرت كذلك النتائج أنه لا توجد فروق ذات دلالة احصائية حول دور الشباب في تعزيز ثقافة التعايش السلمي المجتمعي حسب متغير النوع (ذكور، إناث)، إذ أصبح دور الشاب والشابة متقارب في العمل والدراسة والعمل الميداني والاجتماعي مما نعكس على دوره في التفاعل والانصهار الاجتماعي الايجابي، ووضع الباحث عدد من التوصيات والمقترحات.The present research aims at revealing the social role of youth in promoting a culture of peaceful coexistence in society and aims to identify the most important cultural aspects that youth promote to achieve peaceful coexistence in society and to know the differences of statistical significance according to gender variable. The researcher sought to build the tool of research in order to achieve the objectives of the current research. The researcher examined the literature and the previous studies concerning the current research. The researcher built a tool consisting of four fields and each field has six paragraphs. The researcher used the methods of statistical analysis represented by the power of excellence and the relation of the paragraph to the total number in order to reach the best tool for the current research. The researcher used the statistical bag (SPSS) for human sciences to analyze the data of the research sample. The results of the research were presented in tables according to statistical analysis indicators, which showed that the social role of youth in promoting a culture of peaceful co-existence The results of the study show that there are no statistically significant differences in the role of youth in promoting a culture of peaceful coexistence in society according to gender variable. , Female), as the role of young men and women became close in work and study, field work and social, which reflects on its role in interaction and social integration positive, and put the researcher a number of recommendations and proposals

Early Diagnosis System For Lung Nodules Based On The Integration Of A Higher-Order Mgrf Appearance Feature Model And 3d-Cnn

In this chapter, a new system for lung nodule diagnosis, using features extracted from one computed tomography (CT) scan, is presented. To get an accurate diagnosis of the detected lung nodules, the proposed framework integrates the following two groups of features: (i) appearance features that are modeled using higher-order Markov–Gibbs random field (MGRF)-model that has the ability to describe the spatial inhomogeneities inside the lung nodule; and (ii) local features that are extracted using 3D convolutional neural networks (3D-CNN) because of its ability to exploit the spatial correlation of input data in an efficient way. The novelty of this chapter is to accurately model the appearance of the detected lung nodules using a new developed 7th-order MGRF model that has the ability to model the existing spatial inhomogeneities for both small and large detected lung nodules, in addition to the integration with the extracted local features from 3D-CNN. Finally, a deep autoencoder (AE) classifier is fed by the above two feature groups to distinguish between the malignant and benign nodules

Differences in rates and odds for emergency caesarean section in six Palestinian hospitals: A population-based birth cohort study

Objective To assess the differences in rates and odds for emergency caesarean section among singleton pregnancies in six governmental Palestinian hospitals. Design A prospective population-based birth cohort study. Setting Obstetric departments in six governmental Palestinian hospitals. Participants 32 321 women scheduled to deliver vaginally from 1 March 2015 until 29 February 2016. Methods To assess differences in sociodemographic and antenatal obstetric characteristics by hospital, χ2 test, analysis of variance and Kruskal-Wallis test were applied. Logistic regression was used to estimate differences in odds for emergency caesarean section, and ORs with 95% CIs were assessed. Main outcome measures The primary outcome was the adjusted ORs of emergency caesarean section among singleton pregnancies for five Palestinian hospitals as compared with the reference (Hospital 1). Results The prevalence of emergency caesarean section varied across hospitals, ranging from 5.8% to 22.6% among primiparous women and between 4.8% and 13.1% among parous women. Compared with the reference hospital, the ORs for emergency caesarean section were increased in all other hospitals, crude ORs ranging from 1.95 (95% CI 1.42 to 2.67) to 4.75 (95% CI 3.49 to 6.46) among primiparous women. For parous women, these differences were less pronounced, crude ORs ranging from 1.37 (95% CI 1.13 to 1.67) to 2.99 (95% CI 2.44 to 3.65). After adjustment for potential confounders, the ORs were reduced but still statistically significant, except for one hospital among parous women. Conclusion Substantial differences in odds for emergency caesarean section between the six Palestinian governmental hospitals were observed. These could not be explained by the studied sociodemographic or antenatal obstetric characteristics.publishedVersio

Pattern of Congenital Dislocation of the Hip in Arar City, Northern Saudi Arabia

Background: Developmental dysplasia of the hip (DDH) is one of the most widely discussed abnormalities in neonates. The etiology of DDH is unknown. There are many insights, however, from epidemiologic/demographic information. Aim of the study: To determine the incidence, risk factors and treatment modalities of congenital dislocation of the hip (CDH) in Arar city, Northern Saudi Arabia. Methods: This is prospective study involve 955(19100hips) infants referred to Arar central hospital. During the period from 1 January 2014, to 31 December 2016, each infant was evaluated by history taking, clinical and sonographic examination for hip abnormality. Results: The incidence of (CDH) was 3.1% (73.3% were females), 70.0% of the affected had positive family history and in 46.7% there was consanguinity between parents. In 80.0% there was regular follow up during pregnancy. 16.7% had history of oligohydramnios. Breech presentation was found in 26.7% and 15.0% delivered by caesarian section. First born children constituted 25.0%. The left hip joint was more affected( 41.6%) , the right hip joint affected in  28.3% and bilateral CDH were involved in 13.3%. In the studied cases, 40.0% of the infants were treated surgically, 30% conservatively, 16% by both and 14% were referred to higher centers. Conclusion and recommendations: CDH in Arar, Central hospital and by inference in Northern region of Saudi Arabia was found to be 3.1%. Awareness programs, routine neonatal hip joint examination at birth and up to one year of age as well as ultrasound examination of pelvis in high-risk babies are strongly recommended

Patients with Inflammatory Bowel Disease and the Higher Incidence of Clostridium Difficile Infection

This study aimed at analyzing the patients with inflammatory bowel disease and the higher incidence of clostridium difficile infection by emphasizing the theoretical review of studies discussing the  inflammatory bowel diseases (IBD), which include Crohn’s disease and ulcerative colitis. And by discussing the treatment of CDI in IBD patients, the diagnosis of CDI in IBD, and the risk factors for CDI in IBD. The study concluded that clinicians should be cautious about the chances of CDI in patients who have an exacerbation of IBD. At times the IBD flare cannot be differentiated from CDI requiring a high degree of clinical suspicion and vouching for early stool testing for toxin assay. When CDI in IBD are established primarily within two days of hospital admission it suggests that a good number of the infection was acquired before admission. CDI should, therefore, be suspected in differentiated diagnosis for intractable IBD patients, because many such patients need not present with a history of antibiotic exposure or hospital admission and may largely be receiving outpatient treatment

Multi-species fish stock assessment by acoustic method in the South China Sea Area I: Gulf of Thailand and east coast of Peninsular Malaysia

Acoustic resource surveys were conducted by M/V SEAFDEC in the Gulf of Thailand and off the east coast of Peninsular Malaysia from September 5 to 28, 1995 for pre-NE monsoon season and from April 24 to May 17, 1996 for post-NE monsoon season, using the scientific echosounder FQ-70 (Furuno Electric Co.). Collected raw values of backscattering strength (SV) from the 200 kHz were carefully corrected and filtered to eliminate the influence of plankton. These corrected SV values were classified into pelagic and demersal fish, and were used to estimate the biomass of pelagic and demersal multispecies fish. Biomass of pelagic and demersal fish for each season was only estimated in the east coast of Peninsular Malaysia within Malaysian EEZ waters due to the availability of previous fisheries statistics and biological data. Dominant species were selected based on the fisheries statistics and landing place survey. Length (L) and weight were obtained from previous literatures. Target strength (TS) of these dominant species were calculated as TS =20 log (L) -66. The distribution of the SV values for pelagic fish showed a distinct difference between preand post- monsoon seasons. Greater concentrations of SVs were observed from offshore compared to the nearshore waters in pre-monsoon season. The distribution for the demersal fish showed that there was no clear difference between pre- and post-monsoon. The estimated biomass of multi-species fish off the east coast of Peninsular Malaysia within Malaysian EEZ for the pre-and post-monsoon seasons was 4.4x105 tonnes (2.3x105 tonnes of pelagic fish and 2.1x 105 tonnes of demersal fish) and 3.1x105 tonnes (1.9x105 tonnes of pelagic fish and 1.2x 105 tonnes of demersal fish), respectively

A Novel Technique to Determine Concentration-Dependent Solvent Dispersion in Vapex

Vapex (vapor extraction of heavy oil and bitumen) is a promising recovery technology because it consumes low energy, and is very environmentally-friendly. The dispersion of solvents into heavy oil and bitumen is a crucial transport property governing Vapex. The accurate determination of solvent dispersion in Vapex is essential to effectively predict the amount and time scale of oil recovery as well to optimize the field operations. In this work, a novel technique is developed to experimentally determine the concentration-dependent dispersion coefficient of a solvent in Vapex process. The principles of variational calculus are utilized in conjunction with a mass transfer model of the experimental Vapex process. A computational algorithm is developed to optimally compute solvent dispersion as a function of its concentration in heavy oil. The developed technique is applied to Vapex utilizing propane as a solvent. The results show that dispersion of propane is a unimodal function of its concentration in bitumen

Explainable Multi-Class Classification Based on Integrative Feature Selection for Breast Cancer Subtyping

Breast cancer subtype classification is a multi-class classification problem that can be handled using computational methods. Three main challenges need to be addressed. Consider first the high dimensionality of the available datasets relative to the extremely small number of instances. Second, the integration of different levels of data makes the dimensionality problem even more challenging. The third challenging issue is the ability to explain the predictions provided by a machine learning model. Recently, several deep learning models have been proposed for feature extraction and classification. However, due to the small size of the datasets, they were unable to achieve satisfactory results, particularly in multi-class classification. Aside from that, explaining the impact of features on classification has not been addressed in previous works. To cope with these problems, we propose a multi-stage feature selection (FS) framework with two data integration schemes. Using multi-omics data, four machine learning models, namely support vector machines, random forest, extra trees, and XGBoost, were investigated at each level. The SHAP framework was used to explain how specific features influenced classification. Experimental results demonstrated that ensemble models with early integration and two stage feature selection improved results compared to baseline experiments and to state-of-the art methods. Furthermore, more explanations regarding the implications of the main relevant features in the predictions are provided, which could serve as a baseline for future biological investigations

Explainable Multi-Class Classification Based on Integrative Feature Selection for Breast Cancer Subtyping

Breast cancer subtype classification is a multi-class classification problem that can be handled using computational methods. Three main challenges need to be addressed. Consider first the high dimensionality of the available datasets relative to the extremely small number of instances. Second, the integration of different levels of data makes the dimensionality problem even more challenging. The third challenging issue is the ability to explain the predictions provided by a machine learning model. Recently, several deep learning models have been proposed for feature extraction and classification. However, due to the small size of the datasets, they were unable to achieve satisfactory results, particularly in multi-class classification. Aside from that, explaining the impact of features on classification has not been addressed in previous works. To cope with these problems, we propose a multi-stage feature selection (FS) framework with two data integration schemes. Using multi-omics data, four machine learning models, namely support vector machines, random forest, extra trees, and XGBoost, were investigated at each level. The SHAP framework was used to explain how specific features influenced classification. Experimental results demonstrated that ensemble models with early integration and two stage feature selection improved results compared to baseline experiments and to state-of-the art methods. Furthermore, more explanations regarding the implications of the main relevant features in the predictions are provided, which could serve as a baseline for future biological investigations