Wie privat darf die Scheidung sein?

In Syrien kann der Mann die Ehe mit seiner Frau ohne gerichtliches Verfahren beenden. Nach welchen Regeln ist eine solche Privatscheidung in der EU anzuerkennen? Der Generalanwalt beim EuGH schlägt dem Gerichtshof dazu eine sehr restriktive Linie vor – mit zweifelhaften Argumenten

Handwritten Recognition System Based on Machine Learning

مقدمة: يعد التعرف على خط اليد قضية مهمة في الوقت الحاضر ، حيث يمكن أن تكون الكتابة اليدوية صورة أو مستندًا وما إلى ذلك ، تعد قدرة الكمبيوتر على التعرف على الأرقام المكتوبة بخط اليد مهمة جدًا في أكثر من تطبيق مثل تطبيقات الترجمة والقراءة والتعرف على الأرقام. يوفر المشروع المقترح نظامًا يتعرف على الأرقام الإنجليزية المكتوبة بخط اليد ، ويتم تنزيل بيانات الإدخال من مجموعة بيانات عالمية. يتكون النظام المقترح من عدد من المراحل. المرحلة الأولى هي المعالجة المسبقة ، والتي تتضمن تغيير حجم الصور لتكون بحجم واحد (28 * 28) ، ثم يتم تطبيق خطوة (تعيين البيانات). أما بالنسبة لمرحلة التصنيف ، فقد اعتمدت على استخدام خوارزميتين ، خوارزمية KNN والشبكة العصبية (خطأ backpropagation). لبدء عملية تدريب الخوارزميات المختارة ، تم تقسيم البيانات إلى مجموعتين ، مجموعة التدريب ومجموعة الاختبار. تم استخدام خوارزميتين لغرض اختيار أفضلها من خلال تقييم أدائها باستخدام عدد من مقاييس التقييم. تم استخدام الدقة والدقة لغرض تقييم أداء الخوارزميات. كان أداء خوارزمية KNN 0.94 و 0.942 على التوالي عند k = 4. بينما كان أفضل أداء وصلت إليه آلية الشبكة العصبية 0.98673333 و 0.9698 على التوالي ، في العصر = 15. تظهر الشبكة العصبية (خطأ backpropagation) أفضل نتيجة  في مرحلة الاعتراف. طرق العمل: لا تقدم تقنية (KNN) أي افتراضات حول مجموعة البيانات الأساسية. إنه معروف بفعاليته وسهولة استخدامه. إنها خوارزمية تعلم خاضعة للإشراف. لتقدير فئة البيانات غير المسماة ، يتم توفير مجموعة تدريب معنونة تحتوي على نقاط بيانات مقسمة إلى مجموعات عديدة. الاستنتاجات: توضح مؤشرات الدقة والدقة وصفًا دقيقًا لأداء الخوارزميات المستخدمة في النظام المقترح. وصف المؤشرين أداء الخوارزمية (KNN) والتي أعطت النتائج (0.94 و 0.942) على التوالي.Background: Handwriting recognition is an important issue nowadays, where handwriting can be a image, document, etc., the ability of a computer to recognize handwritten numbers is very important in more than one application such as translation, reading and number recognition applications. The proposed project provides a system that recognizes handwritten English numbers, the input data being images downloaded from a global dataset. The proposed system consists of a number of stages. The first stage is the preprocessing, which includes resizing of the images to be one size (28 * 28), and then a step (data mapping) is applied. As for the classification stage, it relied on the use of two algorithms, the KNN algorithm and the neural network (error backpropagation). To start the process of training the selected algorithms, the data was divided into two sets, the training setand the test set. Two algorithms were used for the purpose of choosing the best of them, by evaluating their performance using a number of evaluation metrics. Accuracy and Precision were used for the purpose of evaluating the performance of the algorithms. The performance of the KNN algorithm was 0.94 and 0.942 respectively when k = 4. While the best performance reached by the neural network mechanism was 0.98673333 and 0.9698, respectively, at epoch = 15. The neural network (error backpropagation) is shows the best result in the recognation stage Materials and Methods: K-Nearest Neighbors (KNN) technique makes no assumptions about the basic dataset. It is recognized for its effectiveness and ease of use. It is a supervised learning algorithm. To estimate the category of the unlabeled data, a labeled training set containing data points separated into many groups is supplied. Results: The performance of the KNN model with various values for "K." Since the high value of model accuracy was "0.94", the "4" parameter value is the one that provides the best results and precision was "0.94". Conclusion: The problem of handwritten recognition needs high accuracy and precision indicators show an accurate description of the performance of the algorithms that were employed in the proposed system. The two indicators described the performance of the algorithm (KNN), which gave results (0.94 and 0.942)

Study the pathogenicity of Enterobacter cloacae in rats that isolated from diarrheatic buffalos calves in Babylon Province

The study was aimed to isolate Enterobacter cloacae from feces of buffalo calves suffering from diarrhea and shows its pathogenicity in rats, 150 fecal samples were collected and cultured directly on MacConky agar then tested biochemically and with EPi 20 test to confirm a diagnosis of Enterobacter cloacae. After that injected 4 groups of rat with (106,107 and 108 CFU/ml) respectively, while the fourth group not treated and considered as a control group, also extracted the cell wall from Enterobacter and used four groups of rat to injected with different concentration (150, 250 and 350 μ/ml) of extracted cell wall respectively, while the fourth group considered as a control group. Results show that 10 isolates of Enterobacter were obtained from a stool and out of 10 isolates 7 isolates belong to Enterobacter cloacae. Bacterial isolation from internal organs shows the very heavy isolation of bacteria in dose 108 CFU/ml as compared with other doses, histopathological changes in organs (liver and spleen) of animals which injected with live bacteria and extracted cell wall reveal severe changes as compared with control groups

Unrealistic pessimism and obsessive‐compulsive symptoms during the COVID‐19 pandemic: two longitudinal studies

OBJECTIVE: Unrealistic pessimism (UP) is an aspect of overestimation of threat (OET) that has been associated with obsessive‐compulsive disorder/symptoms (OCD/OCS). During the COVID‐19 pandemic, UP may have played an important role in the course of OCD. To investigate the relationship, we conducted two longitudinal studies assuming that higher UP predicts an increase in OCS. METHOD: In Study 1, we investigated UP in the general population (N = 1,184) at the start of the pandemic asking about overall vulnerability to infection with SARS‐CoV‐2 and UP regarding infection and outcome of severe illness. Further, OCS status (OCS+/−) was assessed at the start of the pandemic and 3 months later. In Study 2, we investigated UP in individuals with OCD (N = 268) regarding the likelihood of getting infected, recovering, or dying from an infection with SARS‐CoV‐2 at the start of the pandemic and re‐assessed OCS 3 months later. RESULTS: In Study 1, UP was higher in the OCS+ compared to the OCS− group, and estimates of a higher overall vulnerability for an infection predicted a decrease in OCS over time. UP regarding severe illness predicted an increase in symptoms over time. In Study 2, UP was found for a recovery and death after an infection with SARS‐CoV‐2, but not for infection itself. CONCLUSIONS: Exaggeration of one’s personal vulnerability rather than OET per se seems pivotal in OCD, with UP being associated with OCD/OCS+ as well as a more negative course of symptomatology over the pandemic in a nonclinical sample. PRACTITIONER POINTS: Unrealistic optimism, a bias common in healthy individuals, is thought to be a coping mechanism promoting well‐being in the face of danger or uncertainty. The current study extends findings that its inversion, unrealistic pessimism, may play an important role in obsessive‐compulsive disorder and may also be involved in the development of the disorder. This study highlights the importance that prevention programs during a pandemic should include targeting unrealistic pessimism

Evaluation of the Fouling Phenomenon During Membrane Clarification of Apple Juice Using Scraped Surface Membrane Unit

Introduction As a useful fruit for humans, apple (Malus domestica) is a good sourceof antioxidants, minerals, ascorbic acid, vitamins, polyphenols, fibers and other essential elements with medicinal properties. Improving the storage time of apple juice and maintaining the stability of extracts with high Brix value (during transportation and storage) and its marketability by removing the remaining water as well as reducing the turbidity, viscosity and brown color caused by colloidal suspended solids. Large (pectin, protopectin, pigments, polymeric carbohydrates, tannin, starch, cellulose, hemicellulose, fibers, etc.) is of great importance. Due to the presence of colloidal suspended solid particles and compounds that settle over time (mold, bacteria, plant cell fragments, pectin-tannin complex), apple juice must be clarified before concentration. Due to the high-energy consumption, time-consuming, degradation of thermo-sensitive components, and reduction of nutritional value in traditional methods, recently, the use of membrane concentration in food and beverage production holds great potential.. Despite all the benefits of membrane processes, one of the critical problems is permeate flux decline due to the concentration polarization and membrane fouling. In this study, an innovative mechanical motion was developed to remove the cake deposits on the membrane surface towards mitigating adverse effects of polarization and fouling.   Materials and Methods Membrane scraped surface module was designed and made with polyethylene material. The membrane was enclosed between the lower and upper parts of the module. These two parts are connected with screws and create a cylindrical part. Also, two caps are pressed axially to this cylindrical part by a metal frame to eliminate any unwanted leakage. The rotor shaft was coupled with an electric motor and the rotation of the output shaft was regulated by an inverter. A pump transferred the fresh fruit juice to the module through the inlet port and then it was divided into two output streams, permeate and retentate. The permeate was collected from the bottom of the module for further investigation and the retentate was returned to the juice tank. A polyethersulfone (PES) membrane with molecular weight cut-off (MWCO) of 4 kDa was used to clarify apple juice. Effects of the blade rotation speed (0, 600, 1400 and 2200 rpm), transmembrane pressure (TMP) (0.5, 1 and 1.5 bar), feed flow rate (FFR) (10, 15 and 20 ml/s) and the distance of the blade from the membrane surface (2 and 5 mm) on volumetric concentration factor (VCF) and fouling phenomenon were evaluated. Hermia model was used to study the main fouling mechanism and it was verified by scanning electron microscopy (SEM) images.   Results and Discussion  Results showed that rotating the blade with speed of 600 rpm at TMP of 0.5 bar, FFR of 10 ml/s and 2 mm distance from the membrane surface had the best performance in VCF and reducing fouling. The main mechanism of fouling was cake formation. Rotation of the blade decreases the intensity of cake formation and its thickness on the membrane surface and enhances the standard pore blocking. Also increasing the blade rotation speed changes the main fouling mechanism to the standard pore blocking due to the cake disintegration on the membrane surface and the penetration of fine particles into the membrane pores. As a result, the rotation of blade had a significant positive effect on increasing the VCF. On the other hand, the total resistance decreased with the rotation of the blade and by increasing the distance of blade from the membrane surface, the intensity of cake formation reduced. Also, the SEM images showed that in without blade rotation mode, the accumulation of cake particles on the membrane surface is thicker and denser than in with blade rotation mode. On the other hand, the low thickness of the cake layer formed on the membrane surface in the process of blade rotation is due to the turbulences resulting from the rotating blade. These observations confirm the results of the Hermia model in the previous sections.   Conclusion  In conclusion, the TMP 0.5 bar, FFR of 10 ml/s, blade rotation speed of 600 rpm with a distance of 2 mm from membrane surface were considered as the best conditions for ultrafiltration of apple juice using scraped-surface membrane unit

Automatic Spike Neural Technique for Slicing Bandwidth Estimated Virtual Buffer-Size in Network Environment

The Next-generation networks, such as 5G and 6G, need capacity and requirements for low latency, and high dependability. According to experts, one of the most important features of (5 and 6) G networks is network slicing. To enhance the Quality of Service (QoS), network operators may now operate many instances on the same infrastructure due to configuring able slicing QoS. Each virtualized network resource, such as connection bandwidth, buffer size, and computing functions, may have a varied number of virtualized network resources. Because network resources are limited, virtual resources of the slices must be carefully coordinated to meet the different QoS requirements of users and services. These networks may be modified to achieve QoS using Artificial Intelligence (AI) and machine learning (ML). Developing an intelligent decision-making system for network management and reducing network slice failures requires reconfigurable wireless network solutions with machine learning capabilities. Using Spiking Neural Network (SNN) and prediction, we have developed a 'Buffer-Size Management' model for controlling network load efficiency by managing the slice's buffer size. To analyze incoming traffic and predict the network slice buffer size; our proposed Buffer-Size Management model can intelligently choose the best amount of buffer size for each slice to reduce packet loss ratio, increase throughput to 95% and reduce network failure by about 97%

Is the rotation of the femural head a potential initiation for cutting out? A theoretical and experimental approach

We conclude the center-center position in the head of femur of any kind of lag screw or blade is to be achieved to minimize rotation of the femoral head and to prevent further mechanical complications