DYNAMICAL CHAOS IN 6 ï† -RAYLEIGH OSCILLATOR WITH THREE WELLS DRIVEN AN AMPLITUDE MODULATED FORCE.

Chaotic behavior of6 ï† -Rayleigh oscillator with three wells is investigated. The method of multiple scale method is usedto solve the system up to 3rd order approximation. Effect of parameters is studied numerically; all resonance cases arestudied numerically to obtain the worst case. Stability of the system is investigated using both phas

Pre- and in-hospital delays in the use of thrombolytic therapy for patients with acute ischemic stroke in rural and urban Egypt

BackgroundReducing pre- and in-hospital delays plays an important role in increasing the rate of intravenous thrombolysis (IVT) in patients with acute ischemic stroke. In Egypt, the IVT rate has increased steadily but is still far away from an ideal rate.AimThe study aimed to investigate the factors associated with pre- and in-hospital delays of IVT among patients with acute ischemic stroke coming from urban and rural communities.MethodsThis prospective, multicenter, observational cohort study was conducted from January 2018 to January 2019. Patients with acute ischemic stroke, who did not receive IVT, were included in the study. Patients were recruited from three large university stroke centers in Egypt, Assiut (south of Egypt), Tanta (north of Egypt), both serving urban and rural patients, and the University Hospital in Cairo (capital city), only serving an urban community. All participants underwent the National Institutes of Health Stroke Scale and full neurological assessment, urgent laboratory investigations, and computed tomography or magnetic resonance imaging to confirm the stroke diagnosis. The patients were subjected to a structured questionnaire that was designed to determine the parameters and time metrics for the pre- and in-hospital delays among patients from rural and urban regions.ResultsA total of 618 patients were included in the study, of which 364 patients (58.9%) lived in rural regions and 254 (41.1%) in urban regions. General demographic characteristics were similar between both groups. Approximately 73.3% of patients who arrived within the therapeutic time window were urban patients. The time from symptom onset till hospital arrival (onset to door time, ODT) was significantly longer among rural patients (738 ± 690 min) than urban patients (360 ± 342 min). Delayed onset to alarm time (OAT), initial misdiagnosis, and presentation to non-stroke-ready hospitals were the most common causes of pre-hospital delay and were significantly higher in rural patients. For patients arriving within the time window, the most common causes of in-hospital delays were prolonged laboratory investigations and imaging duration.ConclusionThe limited availability of stroke-ready hospitals in rural Egypt leads to delays in stroke management, with subsequent treatment inequality of rural patients with acute stroke

Artificial Intelligence Approaches for Studying the <em>pp</em> Interactions at High Energy Using Adaptive Neuro-Fuzzy Interface System

Adaptive Neuro-Fuzzy Inference System (ANFIS), a popular machine learning model, is introduced in this chapter. ANFIS has a long development history and good agreement on scientific accomplishments. The value of ANFIS has grown dramatically along with the great interest in deep learning. We will examine how machine learning and ANFIS are related. Different methods can be used to implement machine learning models. ANFIS is a Fuzzy Inference System (FIS) that works within the context of adaptive networks. It merges the ideas of Artificial Neural Networks (ANNs) and Fuzzy Logic (FL) into a single framework. This framework can learn to estimate nonlinear functions and operates as a universal estimator. This chapter aimed to investigate the behavior of D mesons ratios production cross section (D+/D0,D∗+/D0,Ds+/D0,andDs+/D+), differential production cross section of prompt (D0,D+, D∗+andDs+ mesons) as a function of PT in pp collisions at (s = 5.02 and 7 TeV) and predict the behavior for others. The ANFIS model was created through a series of trial-and-error experiments. The ANFIS-based model simulation results perfectly fit the experimental data. When tested with non-training data points, the ANFIS prediction capabilities performed well. The ANFIS offers extensive procedures for high-energy physics modeling

Impact of opioid-free analgesia on pain severity and patient satisfaction after discharge from surgery: multispecialty, prospective cohort study in 25 countries

Background: Balancing opioid stewardship and the need for adequate analgesia following discharge after surgery is challenging. This study aimed to compare the outcomes for patients discharged with opioid versus opioid-free analgesia after common surgical procedures.Methods: This international, multicentre, prospective cohort study collected data from patients undergoing common acute and elective general surgical, urological, gynaecological, and orthopaedic procedures. The primary outcomes were patient-reported time in severe pain measured on a numerical analogue scale from 0 to 100% and patient-reported satisfaction with pain relief during the first week following discharge. Data were collected by in-hospital chart review and patient telephone interview 1 week after discharge.Results: The study recruited 4273 patients from 144 centres in 25 countries; 1311 patients (30.7%) were prescribed opioid analgesia at discharge. Patients reported being in severe pain for 10 (i.q.r. 1-30)% of the first week after discharge and rated satisfaction with analgesia as 90 (i.q.r. 80-100) of 100. After adjustment for confounders, opioid analgesia on discharge was independently associated with increased pain severity (risk ratio 1.52, 95% c.i. 1.31 to 1.76; P &lt; 0.001) and re-presentation to healthcare providers owing to side-effects of medication (OR 2.38, 95% c.i. 1.36 to 4.17; P = 0.004), but not with satisfaction with analgesia (beta coefficient 0.92, 95% c.i. -1.52 to 3.36; P = 0.468) compared with opioid-free analgesia. Although opioid prescribing varied greatly between high-income and low- and middle-income countries, patient-reported outcomes did not.Conclusion: Opioid analgesia prescription on surgical discharge is associated with a higher risk of re-presentation owing to side-effects of medication and increased patient-reported pain, but not with changes in patient-reported satisfaction. Opioid-free discharge analgesia should be adopted routinely

Analysis on the AES Implementation with Various Granularities on Different GPU Architectures

The Advanced Encryption Standard (AES) is One of the most popular symmetric block cipher because it has better efficiency and security. The AES is computation intensive algorithm especially for massive transactions. The Graphics Processing Unit (GPU) is an amazing platform for accelerating AES. it has good parallel processing power. Traditional approaches for implementing AES using GPU use 16 byte per thread as a default granularity. In this paper, the AES-128 algorithm (ECB mode) is implemented on three different GPU architectures with different values of granularities (32,64 and 128 bytes/thread). Our results show that the throughput factor reaches 277 Gbps, 201 Gbps and 78 Gbps using the NVIDIA GTX 1080 (Pascal), the NVIDIA GTX TITAN X (Maxwell) and the GTX 780 (Kepler) GPU architectures

Comparative Study of Intelligent Classification Techniques for Brain Magnetic Resonance Imaging

Brain tissue classification from Magnetic Resonance Imaging (MRI) is of great importance for research and clinical studies of the normal and diseased human brain. All MRI classification methods are sensitive to overlap in the tissue intensity distributions. Such overlaps are caused by inherent limitations of the image acquisition process, such as noise, intensity non-uniformity, and partial volume effect. Several approaches have been proposed to address this limitation of intensity-based classification. The objective of this paper is to make a comparative study on the recent published classification techniques for the brain magnetic resonance images (MRI). The contribution of this study is to determine the advantages and disadvantages of each technique and develop robust classification technique capable to perform an efficient and automated MRI normal/abnormal brain images classification

A Machine Leaming Technique for MRI Brain Images

This study presents a proposed hybrid intelligent machine learning technique for Computer-Aided detection system for automatic detection of brain tumor through magnetic resonance images. The technique is based on the following computational methods; the feedback pulse-coupled neural network for image segmentation, the discrete wavelet transform for features extraction, the principal component analysis for reducing the dimensionality of the wavelet coefficients, and the feed forward backpropagation neural network to classify inputs into normal or abnormal. The experiments were carried out on 101 images consisting of 14 normal and 87 abnormal (malignant and benign tumors) from a real human brain MRI dataset. The classification accuracy on both training and test images is 99 % which was significantly good. Moreover, The proposed technique demonstrates its effectiveness compared with the other machine learning recently published techniques

Multiple myeloma in egyptian population and interleukin 10 (IL-10) and its receptor (IL-10R) polymorphism

Background: Multiple myeloma (MM) is a cancer of bone marrow (BM) that is caused by the clonal plasma cells which produce monoclonal immunoglobulin that cause manifestation such as hypercalcemia, pathological bone fracture, renal impairment,&nbsp; BM deficiency, and hyper viscosity. Interleukin 10 (IL-10) has anti-inflammatory effects by binding to IL-10R dependent signals on cell surface; if IL‐10 mutation affects cancer immunity so may be cause of many cancers including hematologic malignancies. Objective: To investigate role of IL-10 gene promoter (rs1800872) and its receptor beta (rs2834167) genotypes in (MM) patients on clinical presentation, laboratory findings, treatment response and outcome in a group of MM patients in Egyptian patients. Patients and Methods: The present study included 50 MM patients compared to 50 matched healthy individuals in age and sex with normal laboratory findings as a control group for analysis of single nucleotide polymorphisms (SNP) of IL-10 gene promoter (rs1800872), and IL‐10Rβ (rs2834167) genotypes using Real time- Polymerase Chain reaction (RT-PCR) technique. Results: The genotypes frequency distribution of IL-10 SNP (rs1800872), there was statistically significant difference between MM patient and the control groups (P- value 0.003) but not found as regard IL-10 RB (P –value 0.142).&nbsp

Analysis on the AES implementation with various granularities on different GPU architectures

The Advanced Encryption Standard (AES) is One of the most popular symmetric block cipher because it has better efficiency and security. The AES is computation intensive algorithm especially for massive transactions. The Graphics Processing Unit (GPU) is an amazing platform for accelerating AES. it has good parallel processing power. Traditional approaches for implementing AES using GPU use 16 byte per thread as a default granularity. In this paper, the AES-128 algorithm (ECB mode) is implemented on three different GPU architectures with different values of granularities (32,64 and 128 bytes/thread). Our results show that the throughput factor reaches 277 Gbps, 201 Gbps and 78 Gbps using the NVIDIA GTX 1080 (Pascal), the NVIDIA GTX TITAN X (Maxwell) and the GTX 780 (Kepler) GPU architectures