Corrosion Protection of Carbon Steel Oil Pipelines by Unsaturated Polyester/Clay Composite coating

The EIS measurements for coated specimens after polarization show higher values of total impedance |Z|, polarization resistance (Rp) and low capacitance (C) in comparison with the un-coated carbon steel specimen. This indicates the stability of the surface protected film of these coated specimens and their resistance against dissolution.The aim of this paper is to prepare a protective coating on API 5L (carbon steel) alloy which is the widely used materials in oil industry and study its effect on the corrosion behavior of the carbon steel in crude oil environment. Unsaturated polyester (UP) and clay were used to prepare composite consist of 75% of polyester and 25% of clay. The coated specimen was investigated in comparison to uncoated and 100% polyester coated specimens. X-ray Diffraction (XRD) and Scanning Electron Microscopy (SEM) were employed on the coated specimens to understand the phases formed on the modified surfaces. The corrosion behavior of the modified surface in comparison with untreated one was investigated by potentiodynamic cyclic polarization in 3.5 M of NaCl and crude oil solution using Solartron made electrochemical interface SI 1287 and Electrochemical Impedance Spectroscopy (EIS) measurements at OCP condition using Solartron make 1255 HF frequency response analyzer (FRA). The results show improvement in the corrosion parameters predicted from the polarization test.

Knowledge of doctors and nurses about Tichmonus Vaginalis among pregnant woman in Bent Al-Huda and Al-Rifai Hospital

Objective of Study: Assesement of the Knowledge of Doctors and nurses about Trichmonus vaginalis in pregnant woman. And A statistical study to find the difference in Assessment of the Knowledge in infection with this parasite. Methodology: This study consider descriptive study involved The main instrument that use in this study including questionnaire has been approved to see and reach the objectives of the study. The questionnaire consists of two parts; part one contain general a question about Tichmonus Vaginalis with demographic characteristics and part two consist of Nurses knowledge toward Tichmonus Vaginalis parasite. The current study including the (100) Doctor and (100) nurse distribution Between two hospital Bent Al-Huda and Al- Al-Rifai Hospital these sample involved (50) doctor and (50) nurse to each hospital ,during the period from 10/1/2021 to 20/4/2021 The questionnaire’s validity and reliability was calculated through a pilot Test. The data collection tool is well Structured interview questionnaire Ethical consideration permission was taken from the hospital administrator and consent from each nurse midwives. Data was processed using the SPSS edition (version 25.0.). (Frequency and percentage)

A novel enhanced softmax loss function for brain tumour detection using deep learning

Background and Aim: In deep learning, the sigmoid function is unsuccessfully used for the multiclass classification of the brain tumour due to its limit of binary classification. This study aims to increase the classification accuracy by reducing the risk of overfitting problem and supports multi-class classification. The proposed system consists of a convolutional neural network with modified softmax loss function and regularization. Results: Classification accuracy for the different types of tumours and the processing time were calculated based on the probability score of the labeled data and their execution time. Different accuracy values and processing time were obtained when testing the proposed system using different samples of MRI images. The result shows that the proposed solution is better compared to the other systems. Besides, the proposed solution has higher accuracy by almost 2 % and less processing time of 40∼50 ms compared to other current solutions. Conclusion: The proposed system focused on classification accuracy of the different types of tumours from the 3D MRI images. This paper solves the issues of binary classification, the processing time, and the issues of overfitting of the data

Ensuring Data Integrity Scheme Based on Digital Signature and Iris Features in Cloud

Cloud computing is a novel paradigm that allows users to remotely access their data through web- based tools and applications. Later, the users do not have the ability to monitor or arrange their data. In this case, many security challenges have been raised. One of these challenges is data integrity. Contentiously, the user cannot access his data directly and he could not know whether his data is modified or not. Therefore, the cloud service provider should provide efficient ways for the user to ascertain whether the integrity of his data is protected or compromised. In this paper, we focus on the problem of ensuring the integrity of data stored in the cloud. Additionally, we propose a method which combines biometric and cryptography techniques in a cost-effective manner for data owners to gain trust in the cloud. We present efficient and secure integrity based on the iris feature extraction and digital signature.  Iris recognition has become a new, emergent approach to individual identification in the last decade. It is one of the most accurate identity verification systems. This technique gives the cloud user more confidence in detecting any block that has been changed. Additionally, our proposed scheme employs user’s iris features to secure and integrate data in a manner difficult for any internal or external unauthorized entity to take or compromise it. Iris recognition is an internal organ that is well protected against damage and wear by a highly transparent and sensitive membrane. Extensive security and performance analysis show that our proposed scheme is highly efficient and provably secure

A novel solution of deep learning for enhanced support vector machine for predicting the onset of type 2 diabetes

Type 2 Diabetes is one of the most major and fatal diseases known to human beings, where thousands of people are subjected to the onset of Type 2 Diabetes every year. However, the diagnosis and prevention of Type 2 Diabetes are relatively costly in today’s scenario; hence, the use of machine learning and deep learning techniques is gaining momentum for predicting the onset of Type 2 Diabetes. This research aims to increase the accuracy and Area Under the Curve (AUC) metric while improving the processing time for predicting the onset of Type 2 Diabetes. The proposed system consists of a deep learning technique that uses the Support Vector Machine (SVM) algorithm along with the Radial Base Function (RBF) along with the Long Short-term Memory Layer (LSTM) for prediction of onset of Type 2 Diabetes. The proposed solution provides an average accuracy of 86.31% and an average AUC value of 0.8270 or 82.70%, with an improvement of 3.8 milliseconds in the processing. Radial Base Function (RBF) kernel and the LSTM layer enhance the prediction accuracy and AUC metric from the current industry standard, making it more feasible for practical use without compromising the processing time

Evaluating efficacy of a novel dentifrice in reducing probing depths in Stage I and II periodontitis maintenance patients: A randomized, double-blind, positive controlled clinical trial.

BackgroundCompliance to periodontal maintenance therapy (PMT) is essential for long-term periodontal health. Between PMT visits, patients must maintain good oral hygiene. A dentifrice with demonstrable clinical benefits for use between PMT visits would be highly desirable. The aim of this clinical study was to investigate the effect of a novel dental gel on probing depths (PD) and inflammation when used as a home care dentifrice in Stage I and II periodontitis patients.MethodsThis double-blind clinical study randomized 65 subjects with Stage I and II periodontitis to the novel dental gel containing 2.6% EDTA, and a commercially available anti-gingivitis dentifrice with 0.454% stannous fluoride. Primary endpoint was PD at 6 months for those sites with baseline PD ≥ 4 mm and secondary endpoints included whole mouth mean scores of modified gingival index (MGI), modified sulcus bleeding index (mSBI) and plaque index (PI). No SRP was performed at baseline.ResultsSubjects using the novel dentifrice showed significant PD reductions of 1.18 mm (from 4.27 mm at baseline to 3.09 mm at 6 months) compared to 0.93 mm (from 4.23 mm at baseline to 3.30 mm at 6 months) shown for those using the positive control dentifrice. Difference between treatments at 6 months was 0.21 mm with P-value = 0.0126. Significant improvements in MGI (P = 0.0000), mSBI (P = 0.0000), and PI (P = 0.0102) were also observed in 6 months.ConclusionThe novel dentifrice showed significant reductions in PD and gingival inflammation over 6 months solely as a home care dentifrice without baseline SRP in Stage I and II periodontitis maintenance patients