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Introduction: Overweight and obesity are explained as excessive fat depositions in human body. They have become one of the serious public health problems of the twenty-first century in low income countries. The prevalence of overweight and obesity in children is increasing worldwide, and currently 1 child in 10 is overweight or obese. The aim of this study was to assess overweight and obesity and associated factors in high school students in Erbil.Method: It was a cross-sectional study in which 1,000 students in the age range 16-18 years of Erbil, Iraq in 2016-2017 were randomly selected. Data were gathered by a questionnaire. Data were analyzed using SPSS version 22 by appropriate statistical tests.Results: our results showed that some effective factors such as Computer games, watching TV, and irregular sleep (P-value

A simple, sensitive and reliable HPLC-MS/MS and HPLC-UV methods were developed and validated for Cis- and Trans-Cefprozil diastereoisomers determination in human plasma. The plasma samples subjected to protein precipitation after the addition of cephalexin as internal standard. Chromatographic separation was achieved using Waters Xbridge C18 column 5µm, 4.6×150mm maintained at 25° C. The HPLC-MS/MS method utilized MRM transitions; 390.1 to 208.1 and 348.1 to 158.2 for Cefprozil and cephalexin detection respectively, while the wavelength 292 nm was used for the UV detection. Both methods provided good linearity for Cis- and Trans- diastereoisomers within the ranges of 0.05-10.00 and 0.02-1.00 μg/ml respectively. Methods were validated and applied successively to study the bioequivalence of two Cefprozil pharmaceutical products. The maximum plasma levels detected (Cmax) of Cefprozil for the brand and generic products were, respectively; 10.0 and 9.9 μg/ml using the HPLC-MS/MS method compared to 10.5 and 10.6 μg/ml using the HPLC-UV method. The pharmaceutical products were found to be bioequivalent after analysis using both methods. The reference product pharmacokinetics data were statistically compared over the two methods and insignificant P-values were obtained. This comparison considered as extra prove for both methods reproducibility, reliability and ability to quantify Cefprozil diastereoisomers in human plasma.

Background: Although diabetes has been associated with COVID-19 mortality, the absolute and relative risks for Type 1 and Type 2 diabetes are unknown brbrMe

The European Green Deal 2020 goals for reducing emissions are enforcing rules on the energy performance of buildings. Therefore thermally insulating materials used as coatings are researched to reduce the energy emissions of buildings. An essential field of interest are nanomaterials. Traditional aerogel is a nanomaterial used for insulating applications due to its high porosity and large surface area, resulting in a longer path for heat to travel. However the cost and manufacturing process are highly energy demanding. Svenska Aerogel AB produces Quartzene® (Qz), a silica-based nanomaterial with similar properties as traditional aerogel. Qz can be incorporated into different paint systems to improve their thermal insulating properties. The aim of this project was to investigate the thermal insulating effects of Qz on three different painting systems (A, B, and C). Samples were moulded and their thermal properties were measured with TPS (Transient Plane Source). The thermal conductivity decreased as the wt% of Qz increased, up until around 10 wt% for system C. It became apparent that at higher wt%, it became harder to properly mix the samples into a good dispersion. The thermal conductivity started to increase above 10 wt%. Experiments showed that bigger particles were easier to mix into the paint than smaller.

Local scour around structures is an important hydraulic phenomenon that involves multiple coupled physical processes, including turbulent flow, sediment transport, and the evolution of erodible bed. This paper presents a new three-dimensional (3D) scour model, ibScourFoam, which is based on an immersed boundary method. This model is capable of dealing with scour around complex structures and thus it paves the way for practical use of 3D scour models. A special wall function has been developed to overcome the non-smoothness problem of wall shear stress in previous immersed boundary methods. Smooth and accurate wall shear is of critical importance for successful simulation of scour. The model was validated against cases reported in the literature and one flume experiment conducted in this work. The validation case of flow and scour around a vertical pile was reproduced well by our model. The flume experiment is for scour around a partially-buried, horizontal cylinder placed on a foundation. The horizontal cylinder is of finite length and the scour is initiated by flow contraction. The model revealed the distinct stages of scour in the experiment. The complex process of undermining and exposure of initially buried cylinder and foundation was captured well by the model.

Background: the third stage of labor is defined as the period of time between delivery of the fetus and delivery of the placenta. The most common complication accompanying this stage is postpartum hemorrhage (PPH) and prolonged third stage of labor owing to placenta retention and uterine atony are among the underlying cause of most cases of PPH. The duration of the third stage of labor is 5-15 min. Aim: to assess the efficacy of intraumbilical vein oxytocin in reducing duration of third stage of labour and the amount of blood loss. Patients and Methods: this study included 150 women attending the delivery room of Department of Obstetrics and Gynecology which was divided according to the inclusion and exclusion criteria into two groups: Group A (study group): include 75 cases which received 10 IU (1ml) of oxytocin in umbilical vein. Group B (control group): include 75 cases which received 1ml of saline in umbilical vein. Results: the time of third stage took seconds in each group with no statistically significant difference detected (p>0.05). Conclusion: intra-umbilical oxytocin is a useful alternative in patients where methylergometrine is contraindicated or in cases where intravenous fluids need to be restricted. For optimum effect, rapid injection immediately after clamping of the cord is essential.

Patient: Female, 57-year-old Final Diagnosis: Mediastinal diffuse large B cell lymphoma infiltrating the heart as a right atrial cardiac mass Symptoms: Cough • dyspnea • fatigue • weight loss Medication: — Clinical Procedure: — Specialty: Oncology Objective: Rare disease Background: Primary mediastinal diffuse large B cell lymphoma (DLBCL) presenting as a large intracardiac tumor is extremely rare and has not been significantly reported in the literature. Cardiac lymphoma consists of 2 subtypes: mediastinal DLBCL invading the heart and primary cardiac lymphoma. Both subtypes have a poor prognosis and are treated similarly. Mediastinal DLBCL is a life-threatening condition that, if diagnosed early, has a better survival rate. This is a rare case of a mediastinal DLBCL invading the right atrium as a large intracardiac mass, causing partial obstruction of the tricuspid valve without hemodynamic compromise. Case Report: A 57-year-old female presented with unintentional weight loss, fatigue, exertional dyspnea, and cough for 8 weeks. Transesophageal echocardiogram showed a mass (3.5×3.5 cm) in the posterior wall of the right atrium partially obstructing the tricuspid valve. Biopsy revealed DLBCL. Given new-onset lymphoma, a human immunodeficiency virus (HIV) test was done and came back positive. CD4 count was 100 cells/mm3. Chemotherapy was initiated with rituximab, cyclophosphamide, epirubicin, vincristine, and prednisone (R-CHOP). Highly active anti-retroviral (HAART) therapy was started for HIV. After treatment with R-CHOP and HAART, the patient had complete resolution of the mass and symptoms on follow-up imaging and evaluation at 6 months. Conclusions: Mediastinal DLBCL invading the heart is a life-threatening form of non-Hodgkin’s lymphoma (NHL) and early diagnosis and treatment is critical as prognosis is poor especially if diagnosed in later stages of the disease. Testing for HIV is important as 5% of HIV patients are susceptible to developing NHL.

In this paper, we describe the development of moving mesh adaptation framework and its application to charge transport simulation of semiconductor devices, with emphasis on its relevance to power semiconductor devices. Mesh adaptivity in the context of semiconductor device simulation is an important problem and can help deal with the convergence and numerical stability issues, as well as automate the meshing process. We demonstrate the efficacy of our proposed meshing scheme through the simulation of a GaN-based power diode, as well as a Si diode with a non-rectangular doping profile, by externally coupling our framework to Sentaurus Device TCAD. We perform error analysis and compare our results with simulations based on high-resolution uniform structured meshes as well as manually refined axis-aligned meshes. In addition to the benefits in terms of accuracy, automation, and generality, our method can be regarded as a stepping stone toward computationally scalable and adaptive semiconductor device simulations.

Objective: of this study is to evaluate the relationship between ADC map values of MR imaging and local aggressiveness of the prostate Cancer via comparing the ADC values and Gleason score in prostate Cancer. Methodology: this study carried out in Radiology Department of Ain Shams University Hospitals. 21 patients with pathologically proven prostate cancer underwent pelvic MRI examination including diffusion weighted imaging (DWI) and apparent diffusion coefficient (ADC). Result: The mean ADC value is inversely related to Gleason Score.

This article uses computational models to evaluate the potential of copper-indium-gallium-diselenide (CIGS) thin film solar cells. The use of cadmium sulphide (CdS) renders the solar cell environmentally hazardous. A zinc sulphide (ZnS) that is non-toxic and has a large bandgap is studied as a potential replacement for cadmium sulphide in CIGS-based solar cells. The present research focuses on the impact of the CIGS-based solar cell bandgap absorber layer by increasing the absorber layer thickness (0.1-2 μm) using the solar cell simulator simulation tool SCAPS. The basic simulation produces 18.2 % efficiency with a CdS buffer layer, which is 9.95% better than the previously published work. The Simulated efficiency is 22.16% for the CIGS solar cell using ZnS. The simulation of solar cell characteristics of how the thickness of the absorber layer, the gallium grading (efficiency ranges up to 22.25 %) is demonstrated, showing the effect of buffer layer (ZnS) on the current of short-circuit density (JSC), open-circuit voltage (Voc), fill factor (FF), and efficiency (η) of the solar cell.

In this paper, heuristic query-driven clustering-based vertical fragmentation technique is efficiently developed. The intrinsic idea is to meet the idealistic case of DDBS design which comes to happen as each query attune “closely match” its relevant fragment. The proposed technique is mainly sought to breed clusters of queries in the first place. Consequently, these clusters would be tacitly used to generate intended disjoint fragments. Moreover, the allocation process has been considered so that replicated and non-replicated scenarios of data are applied. This technique basically meant to be efficaciously applicable at the initial stage of DDBS design without the need for data statistics or empirical results, in either dynamic or static DDBS environment. Many existing design-related techniques are being incorporated to make a promising work, particularly as communication costs being the foremost design objective. Throughout this work, the experimental results and internal evaluation are extensively illustrated to demonstrate the effectiveness and validity of proposed technique.

International audience; This paper brings deep learning at the forefront of research into Time Series Classification (TSC). TSC is the area of machine learning tasked with the categorization (or labelling) of time series. The last few decades of work in this area have led to significant progress in the accuracy of classifiers, with the state of the art now represented by the HIVE-COTE algorithm. While extremely accurate, HIVE-COTE cannot be applied to many real-world datasets because of its high training time complexity in O(N 2 • T 4) for a dataset with N time series of length T. For example, it takes HIVE-COTE more than 8 days to learn from a small dataset with N = 1500 time series of short length T = 46. Meanwhile deep learning has received enormous attention because of its high accuracy and scalability. Recent approaches to deep learning for TSC have been scalable, but less accurate than HIVE-COTE. We introduce InceptionTime-an ensemble of deep Convolutional Neural Network (CNN) models, inspired by the Inception-v4 architecture. Our experiments show that InceptionTime is on par with HIVE-COTE in terms of accuracy while being much more scalable: not only can it learn from 1,500 time series in one hour but it can also learn from 8M time series in 13 hours, a quantity of data that is fully out of reach of HIVE-COTE.

Purpose: Manual feedback from senior surgeons observing less experienced trainees is a laborious task that is very expensive, time-consuming and prone to subjectivity. With the number of surgical procedures increasing annually, there is an unprecedented need to provide an accurate, objective and automatic evaluation of trainees' surgical skills in order to improve surgical practice. Methods: In this paper, we designed a convolutional neural network (CNN) to classify surgical skills by extracting latent patterns in the trainees' motions performed during robotic surgery. The method is validated on the JIGSAWS dataset for two surgical skills evaluation tasks: classification and regression. Results: Our results show that deep neural networks constitute robust machine learning models that are able to reach new competitive state-of-the-art performance on the JIGSAWS dataset. While we leveraged from CNNs' efficiency, we were able to minimize its black-box effect using the class activation map technique. Conclusions: This characteristic allowed our method to automatically pinpoint which parts of the surgery influenced the skill evaluation the most, thus allowing us to explain a surgical skill classification and provide surgeons with a novel personalized feedback technique. We believe this type of interpretable machine learning model could integrate within "Operation Room 2.0" and support novice surgeons in improving their skills to eventually become experts., Accepted at IJCARS Special Issue for MICCAI 2018

Alternative irrigation between saline water and groundwater can alleviate shortages of available agricultural water while effectively slowing the adverse effects of saline water on the soil-crop system when compared with continuous irrigation with saline water and blending irrigation between saline water and groundwater. In 2018, we tested the effect on soil CO2 and N2O emissions by two types of irrigation regimes (alternating groundwater and saline water (GW-SW), and alternating groundwater, followed by two cycles of saline water (GW-SW-SW)) between groundwater and three levels of salinity of irrigation water (mineralization of 2 g/L, 3.5 g/L, and 5 g/L), analyzed the correlation between gas emissions and soil properties, calculated comprehensive global warming potential (GWP), and investigated the maize yield. The results show that, with the same alternate irrigation regime, cumulative CO2 emissions decreased with increasing irrigation water salinity, and cumulative N2O emissions increased. Cumulative CO2 emissions were higher in the GW-SW regime for the same irrigation water salinity, and cumulative N2O emissions were higher in the GW-SW-SW regime. The GW-SW-SW regime had less comprehensive GWP and maize yield as compared to the GW-SW regime. The 2 g/L salinity in both regimes showed larger comprehensive GWP and maize yield. The 3.5 g/L salinity under the GW-SW regime will be the best choice while considering that the smaller comprehensive GWP and the larger maize yield are appropriate for agricultural implication. Fertilizer type and irrigation amount can be taken into consideration in future research direction.

Alternative irrigation between saline water and groundwater can alleviate shortages of available agricultural water while effectively slowing the adverse effects of saline water on the soil-crop system when compared with continuous irrigation with saline water and blending irrigation between saline water and groundwater. In 2018, we tested the effect on soil CO2 and N2O emissions by two types of irrigation regimes (alternating groundwater and saline water (GW-SW), and alternating groundwater, followed by two cycles of saline water (GW-SW-SW)) between groundwater and three levels of salinity of irrigation water (mineralization of 2 g/L, 3.5 g/L, and 5 g/L), analyzed the correlation between gas emissions and soil properties, calculated comprehensive global warming potential (GWP), and investigated the maize yield. The results show that, with the same alternate irrigation regime, cumulative CO2 emissions decreased with increasing irrigation water salinity, and cumulative N2O emissions increased. Cumulative CO2 emissions were higher in the GW-SW regime for the same irrigation water salinity, and cumulative N2O emissions were higher in the GW-SW-SW regime. The GW-SW-SW regime had less comprehensive GWP and maize yield as compared to the GW-SW regime. The 2 g/L salinity in both regimes showed larger comprehensive GWP and maize yield. The 3.5 g/L salinity under the GW-SW regime will be the best choice while considering that the smaller comprehensive GWP and the larger maize yield are appropriate for agricultural implication. Fertilizer type and irrigation amount can be taken into consideration in future research direction.

Time Series Classification (TSC) problems are encountered in many real life data mining tasks ranging from medicine and security to human activity recognition and food safety. With the recent success of deep neural networks in various domains such as computer vision and natural language processing, researchers started adopting these techniques for solving time series data mining problems. However, to the best of our knowledge, no previous work has considered the vulnerability of deep learning models to adversarial time series examples, which could potentially make them unreliable in situations where the decision taken by the classifier is crucial such as in medicine and security. For computer vision problems, such attacks have been shown to be very easy to perform by altering the image and adding an imperceptible amount of noise to trick the network into wrongly classifying the input image. Following this line of work, we propose to leverage existing adversarial attack mechanisms to add a special noise to the input time series in order to decrease the network's confidence when classifying instances at test time. Our results reveal that current state-of-the-art deep learning time series classifiers are vulnerable to adversarial attacks which can have major consequences in multiple domains such as food safety and quality assurance., Accepted at IJCNN 2019

Deep neural networks have revolutionized many fields such as computer vision and natural language processing. Inspired by this recent success, deep learning started to show promising results for Time Series Classification (TSC). However, neural networks are still behind the state-of-the-art TSC algorithms, that are currently composed of ensembles of 37 non deep learning based classifiers. We attribute this gap in performance due to the lack of neural network ensembles for TSC. Therefore in this paper, we show how an ensemble of 60 deep learning models can significantly improve upon the current state-of-the-art performance of neural networks for TSC, when evaluated over the UCR/UEA archive: the largest publicly available benchmark for time series analysis. Finally, we show how our proposed Neural Network Ensemble (NNE) is the first time series classifier to outperform COTE while reaching similar performance to the current state-of-the-art ensemble HIVE-COTE., Accepted at IJCNN 2019

Over the past one hundred years, the classic teaching methodology of “see one, do one, teach one” has governed the surgical education systems worldwide. With the advent of Operation Room 2.0, recording video, kinematic and many other types of data during the surgery became an easy task, thus allowing artificial intelligence systems to be deployed and used in surgical and medical practice. Recently, surgical videos has been shown to provide a structure for peer coaching enabling novice trainees to learn from experienced surgeons by replaying those videos. However, the high inter-operator variability in surgical gesture duration and execution renders learning from comparing novice to expert surgical videos a very difficult task. In this paper, we propose a novel technique to align multiple videos based on the alignment of their corresponding kinematic multivariate time series data. By leveraging the Dynamic Time Warping measure, our algorithm synchronizes a set of videos in order to show the same gesture being performed at different speed. We believe that the proposed approach is a valuable addition to the existing learning tools for surgery.

Kalkınma çalışmalarında yoksulluğa karşı en önemli mücadele aracının `sosyal koruma` olduğu görülmektedir. Latin Amerika ülkelerinde yoksulluğa karşı bir politika girişimi olarak popüler hale gelen sosyal koruma araçları zaman içinde Sahara Altı Afrika'da da yayılmıştır. Sosyal korumanın önemi Birleşmiş Milletlerin kabul ettiği Sürdürelebilir Kalkınma Hedeflerinde de dile getirilmektedir. Bu genel durumdan hareket ederek, bu çalışmada Somali'deki çocuk yoksulluğu bağlamında yoksul hanelerde yaşayan çocuklara yönelik bir sosyal koruma programı önerilmektedir. Çocuğa duyarlı sosyal koruma ve yaşam döngüsü yaklaşımlarından hareket ederek çocukların en kırılgan olduğu dönemlere ve durumlara müdale eden sosyal koruma araçların üzerinde durulmaktadır. Çalışmanın sonuç bölümünde ise çocuk-odaklı bir sosyal koruma programının Somali bağlamında karşılaşacağı sorunlar ve çözümler genişçe ele alınmaktadır. Social protection is currently seen as one of the most important mechanisms against poverty in development-related studies. Social protection tools, which has became popular as a policy intervention against poverty in Latin American countries, gradually spread over in Sub-Saharan African countries. Taking into account the vast experience about social protection generated in the context of poverty reduction, the importance of social protection is clearly expressed as a goal in the Sustainable Development Goals adopted by the United Nations. Based on this general situation, this study proposes a social protection program for the children living in poor households in the context of Somalia. Drawing on child-sensitive and life cycle-based social protection approaches, the work focuses on social protection tools targetting children's most vulnerable period. At the same time, the study addresses the context-specific problems emerging in the implementation of child-focused social protection tools in the context of Somalia, and henceforth extensively analyses the solutions that could be employed. 159

In Recommendation Systems (RS) and Collaborative Filtering (CF), the similarity measures have been the operating component upon which CF performance is essentially reliant. A dozen of similarity measures have been proposed to reach the desired performance particularly under the circumstances of data sparsity (the cold-start problem). Nevertheless, these measures still suffer the cold-start problem, and have a complex design. Moreover, a comprehensive experimental work to study the impact of the cold-start problem on CF performance is still missing. To these ends, therefore, this paper introduces three simply-designed similarity measures, namely, difference-based similarity measure (SMD), hybrid difference-based similarity measure (HSMD), and, triangle-based cosine measure (TA). Along with proposing these measures, a comprehensive experimental guide for CF measures using the K-fold cross validation is also presented. In contrary to all previous CF studies, the evaluation process is split into two sub-processes: the estimation process and recommendation process to accurately obtain the desired appropriateness in the evaluation. In addition, a new formula to calculate the dynamic recommendation count is developed depending on both the dataset and rating vectors. To draw a comprehensive experimental analysis, a dozen state-of-the-art similarity measures (30 similarity measures) including the proposed and the most widely-used traditional measures are comparatively tested. The experimental study has critically been made on three datasets with five-fold cross-validation grounded on the K nearest neighbor algorithm (KNN). The obtained results on both estimation and recommendation processes prove unquestionably that SMD and TA are preeminent measures with the lowest computational complexity outperforming all state-of-the-art CF measures.

Recent studies have found BCL10 can localize to the nucleus and that this is linked to tumor aggression and poorer prognosis. These studies suggest that BCL10 localization plays a novel role in the nucleus that may contribute to cellular transformation and carcinogenesis. In this study, we show that BCL10 functions as part of the DNA damage response (DDR). We found that BCL10 facilitates the rapid recruitment of RPA, BRCA1 and RAD51 to sites of DNA damage. Furthermore, we also found that ATM phosphorylates BCL10 in response to DNA damage. Functionally, BCL10 promoted DNA double-strand breaks repair, enhancing cell survival after DNA damage. Taken together our results suggest a novel role for BCL10 in the repair of DNA lesions.

Background and objective: Evidence-based medicine, by guiding medical care towards meaningful outcomes to patients, has a significant worldwide impact on medical care and education. It is important that medical students, whom are the future physicians, keep themselves updated with recent advancement in medical knowledge and health care. The aim of this cross-sectional study was to determine the attitude, knowledge and barriers among students towards evidence-based medicine and making them familiar with the concept and advantage of evidence based medicine. Methods: A cross-sectional study was conducted on 163 sixth year students of the College of Medicine at Hawler Medical University during the period from October 2012 to May 2013. The response rate was 86.5%. Knowledge towards evidence-based medicine, accessing and interpreting evidence and perceived barriers to practice evidence-based medicine among participants were the main outcome measures. Results: Of 141 of participants that filled the questionnaire, only 69.5% had heard about evidence-based medicine. Around 82% stated that they had known evidence-based medicine and only 23.5% had known steps of evidence-based medicine. Around 65% of respondents welcomed the promotion of evidence-based medicine and 74.4% agreed with that finding from the current study in which it would be helpful in daily management of patients. Furthermore, 31.6% of students reported that standard textbook as a frequent source used for medical knowledge. Around 91% of students did not know the strongest evidence in the hierarchy of evidence and only small proportion of students reported that they understand the common epidemiological concepts that are used in evidence-based medicine. Conclusion: Evidence-based medicine is relatively a new concept among students at the College of Medicine in Erbil. Although the students appeared interested in learning and implementing evidence-based medicine in clinical practice, they need more guidance and training to ensure the correct use of evidence-based medicine ideals.

The purpose of this study is to explore the impact of high involvement management (power, information, rewards, and knowledge) on employee retention in Syrian Private Financial Institutions. A sample of 238 employees participated in this study. The research concluded that there is a significant positive impact of rewards and knowledge practices on employee's retention, without any significant impact from power and information practices on employee's retention.