DiaMe: IoMT deep predictive model based on threshold aware region growing technique

Medical images magnetic resonance imaging (MRI) analysis is a very challenging domain especially in the segmentation process for predicting tumefactions with high accuracy. Although deep learning techniques achieve remarkable success in classification and segmentation phases, it remains a rich area to investigate, due to the variance of tumefactions sizes, locations and shapes. Moreover, the high fusion between tumors and their anatomical appearance causes an imprecise detection for tumor boundaries. So, using hybrid segmentation technique will strengthen the reliability and generality of the diagnostic model. This paper presents an automated hybrid segmentation approach combined with convolution neural network (CNN) model for brain tumor detection and prediction, as one of many offered functions by the previously introduced IoMT medical service “DiaMe”. The developed model aims to improve extracting region of interest (ROI), especially with the variation sizes of tumor and its locations; and hence improve the overall performance of detecting the tumor. The MRI brain tumor dataset obtained from Kaggle, where all needed augmentation, edge detection, contouring and binarization are presented. The results showed 97.32% accuracy for detection, 96.5% Sensitivity, and 94.8% for specificity

Deep Learning-aided Brain Tumor Detection: An Initial ‎Experience based Cloud Framework ‎

Lately, the uncertainty of diagnosing diseases increased and spread due to the huge intertwined and ambiguity of symptoms, that leads to overwhelming and hindering the reliability of the diagnosis ‎process. Since tumor detection from ‎MRI scans depends mainly on the specialist experience, ‎misdetection will result an inaccurate curing that might cause ‎critical harm consequent results. In this paper, detection service for brain tumors is introduced as ‎an aiding function for both patients and specialist. The ‎paper focuses on automatic MRI brain tumor detection under a cloud based framework for multi-medical diagnosed services. The proposed CNN-aided deep architecture contains two phases: the features extraction phase followed by a detection phase. The contour ‎detection and binary segmentation were applied to extract the region ‎of interest and reduce the unnecessary information before injecting the data into the model for training. The brain tumor ‎data was obtained from Kaggle datasets, it contains 2062 cases, ‎‎1083 tumorous and 979 non-tumorous after preprocessing and ‎augmentation phases. The training and validation phases have been ‎done using different images’ sizes varied between (16, 16) to ‎‎ (128,128). The experimental results show 97.3% for detection ‎accuracy, 96.9% for Sensitivity, and 96.1% specificity. Moreover, ‎using small filters with such type of images ensures better and faster ‎performance with more deep learning.

Solvability of an Infinite System of Singular Integral Equations

2000 Mathematics Subject Classification: 45G15, 26A33, 32A55, 46E15.Schauder's fixed point theorem is used to establish an existence result for an infinite system of singular integral equations in the form: (1) xi(t) = ai(t)+ ∫t0 (t − s)− α (s, x1(s), x2(s), …) ds, where i = 1,2,…, α ∈ (0,1) and t ∈ I = [0,T]. The result obtained is applied to show the solvability of an infinite system of differential equation of fractional orders

Spontaneous triplet pregnancy with twin fetuses papyraeci: a rare case report and review of the literature

A fetal death in a multiple pregnancy with one or more normally surviving fetus is unusual. Fetus papyraceous (FP) is a rare obstetric complication in multiple gestations. It is defined as retention of a mummified parchment like remains of a dead fetus in multiple pregnancy associated with a viable twin. It is important to reassure the patient of the normal outcome expected in most of the cases. Herein, we report a rare case of twin FP in a spontaneous triplet pregnancy with a literature review of maternal and neonatal outcomes and management of similar cases

Study the Optical and Spectral Properties of the Acridine Dye As an Effective Medium in Dye Lasers

    تم في هذا البحث دراسة اطياف الامتصاص والفلورة لمحلول صبغة الاكريدين المذابة في الايثانول وبتراكيز مختلفة، اذ تم حساب الخصائص البصرية (معامل الامتصاص ومعامل الانكسار الخطيين) والخصائص الطيفية (العمر الزمني للفلورة والنتاج الكمي للفلورة). اذ لوحظ ان زيادة التركيز يؤدي الى زيادة قيم الامتصاصية ونقصان قيم النفاذية ومن ثم زيادة  قيم معامل الامتصاص ومعامل الانكسار، كذلك تزداد قيم شدة الفلورة وتزاح قمة طيف انبعاث الفلورة نحو الاطوال الموجية الاطول(red shift)  وبالتالي يزداد العمر الزمني للفلورة ويقل النتاج الكمي بزيادة تركيز المحلول.         In this study, the absorption and fluorescence spectra of the dissolved Acridine solution were studied in ethanol and different concentrations. The optical properties (absorption coefficient, linear refraction) and spectral characteristics (Fluorescence time and quantitative fluoridation efficiency) were calculated. It is observed that increasing the concentration increases the absorbance values and decreases the permeability values, thus increasing the values of the absorption coefficient and the refractive index. Also, the values of the fluorine intensity increase. The peak of the emission spectrum is transferred to the longer wavelengths.                                                                                                                   &nbsp

Application of Allelopathy in Crop Production

Need for food production has been increasing greatly in recent years throughout the world. The interest on the supply of quality of food has also increased, but a significant loss of crop production was observed annually, especially the main cereal crops, including rice, wheat and maize, due to the presence of weeds accompanying them in the growing season. Allelopathy has emerged as an alternative approach to solve problem agriculture that including: crop rotations, intercropping, crop residue incorporation and aqueous extracts all that used to explore allelopathy for pest management, enhancement of growth and crop production. As will allelopathic consider as weeds, insect and diseases natural control. Secondary metabolites biosynthesis of at high rates have a great role in provides defense against abiotic stresses. In plant rhizosphere allelochemicals exuded improve nutrient acquisition through the processes of solublization; biological nitrification; chelation and selected retention. In this chapter, application of the allelopathic phenomenon in crop production is discussed and his roller in managing agricultural pests and improving the productivity of agricultural systems. It was found that allelochemicals promote plant growth and production at low concentration; however it can suppress the growth if applied at high concentrations, for that can be used allelopathic compounds for weed control by used high concentrations of plant residues or aqueous extracts of plant

Capability of the Invasive Tree Prosopis glandulosa Torr. to Remediate Soil Treated with Sewage Sludge

Sewage sludge improves agricultural soil and plant growth, but there are hazards associated with its use, including high metal(loid) contents. An experimental study was conducted under greenhouse conditions to examine the effects of sewage sludge on growth of the invasive tree Prosopis glandulosa, as well as to determine its phytoremediation capacity. Plants were established and grown for seven months along a gradient of sewage sludge content. Plant traits, soil properties, and plant and soil concentrations of N, P, K, Cd, Pb, Cu, Ni, Zn, Cr, Co, As, and Fe were recorded. The addition of sewage sludge led to a significant decrease in soil pH, and Ni, Co, and As concentrations, as well as an increase in soil organic matter and the concentrations of N, P, Cu, Zn, and Cr. Increasing sewage sludge content in the growth medium raised the total uptake of most metals by P. glandulosa plants due to higher biomass accumulation (taller plants with more leaves) and higher metal concentrations in the plant tissues. P. glandulosa concentrated more Cd, Pb, Cu, Zn, and Fe in its below-ground biomass (BGB) than in its above-ground biomass (AGB). P. glandulosa concentrated Ni, Co, and As in both BGB and AGB. P. glandulosa has potential as a biotool for the phytoremediation of sewage sludges and sewage-amended soils in arid and semi-arid environments, with a potential accumulation capability for As in plant leaves

Prevalence of cesarean section on demand in Assiut Governorate, Egypt

Background: The current study aims to evaluate the prevalence of CS on demand in Women's health hospital, Assiut University and Abnob Central Hospital in Assiut Governorate, Egypt.Methods: A cross sectional study conducted in Assiut Women Health Hospital and Abnob central hospital from January 2017 to December 2017. The total number of cesarean section done was 180 cases and the number of CS on demand was 64 (35.6%). The demographic data were collected by one of the study investigators. Women were asked about the causes of requesting CS before surgery.Results: The study group was 64 women with age ranging from 18-40 years old, 40 primipara and 24 multipara. Of those 24 women, 21 of them previously delivered vaginally and only 3 women delivered by emergency CS. Twenty- six women had a history of previous abortion. Fear of pain was the main cause for CS on demand in the whole study participants (57.8%). In primipara, the main cause for requesting CS is fear of pain in 62.5% of participants followed by fear on the baby in 45 % of women. On the other hand, in multipara, the main cause for CS on demand was bad history of previous experience (60%) followed by fear of pain in 50% of cases. There was statistical significant difference between both groups in only two causes; fear of pelvic floor injuries (50% in multipara vs. 20% in primipara, p=0.02) and bad history of previous experience (60% in multipara vs. 0% in primipara, p=0.001). Other causes were not statistically different.Conclusions: The incidence of cesarean sections performed on request without medical indications is rising. The reasons for this are not only for perceived medical benefit, but also due to social, cultural, and psychological factors

Safe Reinforcement Learning using Data-Driven Predictive Control

Reinforcement learning (RL) algorithms can achieve state-of-the-art performance in decision-making and continuous control tasks. However, applying RL algorithms on safety-critical systems still needs to be well justified due to the exploration nature of many RL algorithms, especially when the model of the robot and the environment are unknown. To address this challenge, we propose a data-driven safety layer that acts as a filter for unsafe actions. The safety layer uses a data-driven predictive controller to enforce safety guarantees for RL policies during training and after deployment. The RL agent proposes an action that is verified by computing the data-driven reachability analysis. If there is an intersection between the reachable set of the robot using the proposed action, we call the data-driven predictive controller to find the closest safe action to the proposed unsafe action. The safety layer penalizes the RL agent if the proposed action is unsafe and replaces it with the closest safe one. In the simulation, we show that our method outperforms state-of-the-art safe RL methods on the robotics navigation problem for a Turtlebot 3 in Gazebo and a quadrotor in Unreal Engine 4 (UE4)