Search

Effects of alkanolamine molecules on the corrosion inhibition of L80-1Cr steel were studied in a CO2-saturated 1 wt% NaCl solution at well downhole temperatures of 20 and 80°C. The electrochemical results showed lower corrosion rates at 20°C, for which corrosion rates were more influenced by the alkanolamine injection. The experimental results and molecular modelling calculations using DFT revealed that alkanolamine adsorption/desorption played a determining role in the kinetics and characteristics of FeCO3 formation. Additionally, the dependency of inhibitor efficiency on both chemical structures and adsorption energy on Fe(110), FeCO3(104) and Fe3C(001) was demonstrated, resulting in the highest efficiency provided by ethanolamine.

Drowsiness can be dangerous when performing tasks that require constant attention, such as driving a vehicle. Sleepiness is correlated with a variety of physiological variables, such as eye closing, head movements, pulse rate, eye twitch rate, etc. Also, the yawn can be considered as an accurate indicator of drowsiness and fatigue. Yawning detection is very important for the safety purpose of drivers as it will let the driver know if he/she is getting drowsy. Driving at that moment may not be safe. Several automatic yawning detection techniques have been developed for driver's drowsiness monitoring system. Nevertheless, correctly detecting the yawning of the driver and predicting exhaustion in real-time situations is still a crucial challenge. In this paper, we will be reviewing various existing machine learning approaches for driver's yawning detection. In previous approaches, various classical machine learning algorithms such as viola-Jones, contour activation algorithm and SVM have been used for yawning detection, but these approaches failed to predict yawning in realtime situations. Using Deep learning techniques, we can make a real-time yawn detection system with high accuracy. We find that some precious Deep learning algorithms like CNN, RNN, LSTM, Bi-LSTM can detect the patterns with high accuracy. After the comparison of various algorithms and techniques, we find that with the help of Deep learning algorithms the yawning can be detected in real time with high accuracy.

An epidemiological and biological rhythm study was conducted in bovines naturally infected with Trypanosoma evansi in Braj Bhoomi region of Uttar Pradesh, India. A total of 2312 blood samples of bo...

Corrosion reliability of hearing aid (HA) devices is a critical issue due to their exposure to harsh climatic conditions like high humidity and temperature, along with the combination of high level of salt contamination from human sweat and environmental pollutants. Statistical analysis of corrosion failure data can provide a better understanding of the failure sequence and cause, which is important as the issue is due to multiple parameter effects on a complex device consisting of many components. In this study, root cause failure analysis of the failed hearing aid devices used in the tropical regions was performed using scanning electron microscopy (SEM) and energy dispersive spectroscopy (EDS). Analysis was used for understanding the failure mechanisms, while the data was used for statistical analysis in order to elucidate the device degradation rate and failure probability. Potassium hydroxide (KOH) electrolyte leakage from faulty Zn-air batteries (ZABs) and human sweat were prominent causes for the corrosion failure of hearing aid components. The rate of corrosion failures was found to accelerate during the summer season due to an increase in human perspiration rate and the release of KOH electrolyte from the batteries.

It is very important to consider the factors like blurred boundaries and external noise while analyzing the brain tumor structures. It is difficult to segment accurately the brain MRI. Many studies in both developing and developed countries indicate that an inactive diagnosis has led to the death of the majority of people who suffer from brain tumor. The proposed novel method is a filter mechanism based on texture Gabor filter, which is followed by a genetic algorithm proposed to improve segmentation accuracy. A texture-based Gabor filter has been used to detect irregularities and to extract statistical properties further used in segmentation and classification. In order to improve segmentation effectiveness, a better separation of different clusters of the features from Gabor filter is studied. An objective function has been also formalized to adjust filter parameters with gradient descent and genetic algorithm. This document has demonstrated the effects of the segmentation of both qualitative and quantitative productivity. The findings show that the novel proposed approach works better with respect to the segmentation accuracy.

Segmentation is the most significant step in studying and assimilating medical CT and MR images. To identify the feature areas in the medical images and to clip them, segmentation is used. Owing to the continued growth in technology and research areas, it becomes more challenging to take out the best method of image reconstruction. Therefore, many technologies have been made for the identification, detection and classification of brain tumor parts in MR images. Different radiology tools are used for identification and detection of the inner components of the human anatomy by physicians and radiologists without any surgery. In this paper, the authors paid attention on the segmentation analysis of brain tumor MR images. For the comparison purpose, the authors consider the parameters like run-time complexity, sensitivity and segmentation accuracy.

Mistakes in healthcare systems such as a mix-up of records or confusing medical charts lead to the wrong medications to patients. Major tasks such as administrative costs, legal expenses, and liabilities incur high cost to the healthcare industry using traditional, inaccurate patient identification processes. This can be resolved by biometric technology. Only physiological features can be used for patient identification to eliminate need of SSN, insurance card, or date of birth during registration. A biometric template can be directly mapped to an electronic health record to accurately authenticate individuals on subsequent visits. This technology ensures no medical records can be mimicked and the right care is provided to the right patient. Deep learning provides a platform to solve identification and diagnostic problems arising in medicine and can be used in healthcare biometrics to analyze clinical parameters and their combinations for disease prognosis (e.g., prediction of disease, extracting medical knowledge, therapy planning, and support).

Segmentation is the important step in the analysis and interpretation of the medical CT and MR images. Segmentation is used to detect and extract the feature areas in the medical images. As per technology grows rapidly, it is always challenging to find the best medical image reconstruction technique. So, associated developments in the analysis and diagnosis have boosted medical imaging. Doctors and radiologist use Ultrasound, MRI, CT-Scan etc. for visualization and examination of internal human body structure without any surgery. In this paper, researchers focus on the review of segmentation of CT and MR images contained tumor. While doing the comparison, we consider the various parameters such as segmentation time, accuracy and sensitivity.

In this study, authors present an enhanced approach of face detection using bacteria foraging technique. This approach is based on chemotexis, reproduction and elimination and dispersal step. In this study the authors analysed face detection algorithm based on human skin color and fitting the ellipse as human face can be approximate by ellipse. Their approach for face detection requires no initial pre-processing of the image. A number of Bacteria agents with evolutionary behaviours are uniformly distributed in the 2-D image environment to search the skin-like pixels and locate each face-like region by evaluating the local color distribution. This approach has the advantage of very fast face detection by reducing pre-processing time of the image. This approach significantly improves face detection rate.

The polymorphic nature of Theileria annulata merozoite surface antigen (TAMS 1) attributes to limitation in PCR based detection of various T. annulata genotypes present in different geographical domains across the globe. Multiple reports of failure of detection of tropical theileriosis using classical N516/517 primer set in the studied area were noticed. Hence, three single PCR protocols using N516/517, TAMS F/R and NTA F/R primer sets encoding different portions of TAMS 1 gene and two nested protocols, using combinations of these three primers, were compared to find out the most suitable primer set for diagnosis of calf theileriosis in studied area. The studied area constitutes the semi-arid theileriosis endemic area of Northern India. The various PCR protocols were tested on 75 clinically confirmed cases of calf theileriosis. Alongside, 25 confirmed theileriosis negative blood samples and DNA of other haemoprotozoa were also tested for specificity of these primer sets. Results revealed that the primer set NTA F/R to be more suitable in detecting the circulating T. annulata genotypes in the studied area in comparison to the classical N516/517 primer set. None of the primers gave false positive amplification with negative samples and/or DNA of other haemoprotozoa.

The present study was designed to assess the deleterious effects of bovine tropical theileriosis on the cardiovascular system and the consequent myocardial involvement in young calves. Myocardial effects in parasitic diseases are often neglected. Hemolytic anemia, associated secondary hypoxia, and vasculitis are cardinal features of bovine theileriosis. In the present study, electrocardiogram (ECG) alongside serum cardiac troponin I (cTnI) and creatinine phosphokinase-myocardial band (CPK-MB) concentrations were analyzed in infected, treated, and control groups of young calves. Non-significant alterations were noticed in ECG. However, certain signs like sinus tachycardia, first-degree AV block, atrial premature complex, left atrial hypertrophy, and right atrial hypertrophy were found on consistent basis in infected calves. A significant increase in the serum concentration levels of cTnI and CPK-MB was noticed in infected calves followed by significant fall in both these biomarkers post treatment. cTnI and CPK-MB can definitely be used as myocardial markers in theileriosis-affected animals.

In this study, we propose an optical character recognition technique using Intro Sort. Main feature of this proposed technique is that we segment images using intro sort. It reduces the comparison time for matching the pixels of an image. It reflects reduction in OCR time. Intro sort algorithm begins with quick sort and when recursion depth exceeds a level it switches to heap sort, based on the number of pixels being sorted. This approach also has advantage of recognizing number plates and text documents in very nominal time. Our approach is able to extract characters of different font sizes. Our technique is performed well in noisy images too. General Terms Optical character recognition

Inventories are idle resources which are maintained by every organisation for efficient and smooth running of its operations. So here we are going to control the inventory cost by minimizing the inventory cost. For maintaining optimum inventory, one must consider the appropriate reorder point as well as the economic order quantity. We propose an efficient genetic algorithm approach to the find economic order quantity at a proper reorder point. Genetic algorithm is an optimization method used for solving multi-variable optimization problems. Job-shops handle a variety of jobs, where each job is different and different inventory levels are to be maintained. A case study of a power plant where different types of raw materials are used. The proposed approach is tested on C-programme.

In this study we present a novel approach on a color image refocusing and defocusing. In detail, the proposed method can accomplish the tasks of color image refocusing and defocusing. Our proposed method is thus a process of recovering the original color image from the degraded color image observations. This approach has the advantage of canny edge detection process for preserving the sharp Edges and lines of color images for restoring the blurred color image. Blind deconvolution algorithm is used to blurred images where image recovery process has been performed with little or no prior knowledge of the degrading point spread function.

Intrusion detection faces a number of challenges; an intrusion detection system must reliably detect malicious activities in a network and must perform efficiently to cope with the large amount of network traffic. In this paper, we address these two issues of Accuracy and Efficiency using Conditional Random Fields and Layered Approach. We demonstrate that high attack detection accuracy can be achieved by using Conditional Random Fields and high efficiency by implementing the Layered Approach. Experimental results on the benchmark KDD '99 intrusion data set show that our proposed system based on Layered Conditional Random Fields outperforms other well-known methods such as the decision trees and the naive Bayes. The improvement in attack detection accuracy is very high, particularly, for the U2R attacks (34.8 percent improvement) and the R2L attacks (34.5 percent improvement). Statistical Tests also demonstrate higher confidence in detection accuracy for our method. Finally, we show that our system is robust and is able to handle noisy data without compromising performance.

Cloud computing is an emerging technology that allows users to utilize on-demand computation, storage, data and services from around the world. However, Cloud service providers charge users for these services. Specifically, to access data from their globally distributed storage edge servers, providers charge users depending on the user’s location and the amount of data transferred. When deploying data-intensive applications in a Cloud computing environment, optimizing the cost of transferring data to and from these edge servers is a priority, as data play the dominant role in the application’s execution. In this paper, we formulate a non-linear programming model to minimize the data retrieval and execution cost of data-intensive workflows in Clouds. Our model retrieves data from Cloud storage resources such that the amount of data transferred is inversely proportional to the communication cost. We take an example of an ‘intrusion detection’ application workflow, where the data logs are made available from globally distributed Cloud storage servers. We construct the application as a workflow and experiment with Cloud based storage and compute resources. We compare the cost of multiple executions of the workflow given by a solution of our non-linear program against that given by Amazon CloudFront’s ‘nearest’ single data source selection. Our results show a savings of three-quarters of total cost using our model.

With the number of data breaches on a rise, effective and efficient detection of anomalous activities in applications which manages data is critical. In this paper, we introduce a novel approach to improve attack detection at application layer by modeling user sessions as a sequence of events instead of analyzing every single event in isolation.We also argue that combining application access logs and the corresponding data access logs to generate unified logs eliminates the need to analyze them separately thereby resulting in an efficient and accurate system. We evaluate various methods such as conditional random fields, support vector machines, decision trees and naive Bayes, and experimental results show that our approach based on conditional random fields is feasible and can detect attacks at an early stage even when they are disguised within normal events.

This paper describes a probabilistic mechanism for the interpretation of sentence sequences developed for a spoken dialogue system mounted on a robotic agent. The mechanism receives as input a sequence of sentences, and produces an interpretation which integrates the interpretations of individual sentences. For our evaluation, we collected a corpus of hypothetical requests to a robot. Our mechanism exhibits good performance for sentence pairs, but requires further improvements for sentence sequences.