The boundary fitted coordinate method to improve the numerical analysis of electron optical systems

This paper illustrates some of the advantages of using the boundary fitted coordinate technique in the numerical simulation of electron-optical systems. The boundary fitted coordinate technique can be used as an automatic mesh generator for the finite-element solution of field distributions, while at the same time allowing for the possibility of high order inter-polation on the mesh, typically required for the accurate plotting of electron trajectory paths. For the finite difference method of solving field distributions, the boundary fitted coordinate method can generate regular meshes, which fit curved shaped boundaries.     

Economic production quantity model with a shifting production rate

Typical models for determining the economic production quantity (EPQ) assume perfect product quality and perfect production processes. Deteriorating processes may affect production systems in several ways. They may decrease the quality of the items produced, cause production stoppage and breakdowns and/or reduce the production rate due to production process inefficiency. The purpose of this paper is to present an EPQ model that incorporates the effect of shifts in production rate on lot sizing decisions due to speed losses. The cycle starts with a certain production rate and after a random time, the production rate shifts to a lower value. A mathematical model to determine the optimal production policy under these conditions is developed and analyzed. Numerical examples are presented for illustrative purposes.     

A spectroscopic scanning electron microscope design

This paper describes a proposal to improve the design of scanning electron microscopes (SEMs). The design is based upon using an SEM column similar to the conventional one, magnetic sector plates and a mixed field immersion objective lens. The optical axis of the SEM column lies in the horizontal direction and the primary beam is turned through 90 degrees before it reaches the specimen. This arrangement allows for the efficient collection, detection and spectral analysis of the scattered electrons on a hemispherical surface that is located well away from the rest of the SEM column. The proposed SEM design can also be easily extended to incorporate time multiplexed columns and multi-column arrays.     

Time-based DDoS attack detection through hybrid LSTM-CNN model architectures: An investigation of many-to-one and many-to-many approaches

Internet data thefts, intrusions and DDoS attacks are some of the big concerns for the network security today. Detection of these anomalies, is gaining tremendous impetus with the development of machine learning and artificial intelligence. Even now researchers are shifting the base from machine learning to the deep neural architectures with auto-feature selection capabilities. We in this paper propose multiple deep neural network architectures which can select, co-learn and teach the gradients of the neural network by itself with no human intervention. This is what we call as meta-learning. The models are configured in both many to one and many to many design architectures. We combine long short-term memory (LSTM), bi-directional long short-term memory (BiLSTM), convolutional neural network (CNN) layers along with attention mechanism to achieve the higher accuracy values among all the available deep learning model architectures. LSTMs overcomes the vanishing and exploding gradient problem of RNN and attention mechanism mimics the human cognitive attention that screens the network flow to obtain the key features for network traffic classification. In addition, we also add multiple convolutional layers to get the key features for network traffic classification. We get the time series analysis of the traffic done for the possibility of a DDoS attack without using any feature selection techniques and without balancing the dataset. The performance analysis is done based on confusion matrix scores, that is, accuracy, false alarm rate (FAR), sensitivity, specificity, false-positive rate (FPR), F1 score, area under curve (AUC) analysis and loss functions on well-known public benchmark KDD Cup'99 data set. The results of our experiments reveal that our models outperform existing techniques, showing their superiority in performance.     

Integrating quality and maintenance decisions in a production-inventory model for deteriorating items

In typical production-inventory models of deteriorating items, deterioration of the production process has not been considered. In this paper, a model is proposed in which both the produced items and the production equipment deteriorate. When the production system deteriorates, it shifts to an out-of-control state and begins to produce a proportion of defective items, necessitating corrective maintenance action. A model is formulated to integrate several realistic aspects, including item and process deterioration, varying demand and production rates, quality, inspection, and maintenance. A heuristic solution algorithm is developed to determine the production and inspection schedules, and a numerical example is solved.     

Gastric Tract Disease Recognition Using Optimized Deep Learning Features

Artificial intelligence aids for healthcare have received a great deal of attention. Approximately one million patients with gastrointestinal diseases have been diagnosed via wireless capsule endoscopy (WCE). Early diagnosis facilitates appropriate treatment and saves lives. Deep learning-based techniques have been used to identify gastrointestinal ulcers, bleeding sites, and polyps. However, small lesions may be misclassified. We developed a deep learning-based best-feature method to classify various stomach diseases evident in WCE images. Initially, we use hybrid contrast enhancement to distinguish diseased from normal regions. Then, a pretrained model is fine-tuned, and further training is done via transfer learning. Deep features are extracted from the last two layers and fused using a vector length-based approach. We improve the genetic algorithm using a fitness function and kurtosis to select optimal features that are graded by a classifier. We evaluate a database containing 24,000 WCE images of ulcers, bleeding sites, polyps, and healthy tissue. The cubic support vector machine classifier was optimal; the average accuracy was 99%.     

Lifestyle genomics and the metabolic syndrome: A review of genetic variants that influence response to diet and exercise interventions

Metabolic syndrome (MetS) comprises a cluster of risk factors that includes central obesity, dyslipidemia, impaired glucose homeostasis and hypertension. Individuals with MetS have elevated risk of type 2 diabetes and cardiovascular disease; thus placing significant burdens on social and healthcare systems. Lifestyle interventions (comprised of diet, exercise or a combination of both) are routinely recommended as the first line of treatment for MetS. Only a proportion of people respond, and it has been assumed that psychological and social aspects primarily account for these differences. However, the etiology of MetS is multifactorial and stems, in part, on a person's genetic make-up. Numerous single nucleotide polymorphisms (SNPs) are associated with the various components of MetS, and several of these SNPs have been shown to modify a person's response to lifestyle interventions. Consequently, genetic variants can influence the extent to which a person responds to changes in diet and/or exercise. The goal of this review is to highlight SNPs reported to influence the magnitude of change in body weight, dyslipidemia, glucose homeostasis and blood pressure during lifestyle interventions aimed at improving MetS components. Knowledge regarding these genetic variants and their ability to modulate a person's response will provide additional context for improving the effectiveness of personalized lifestyle interventions that aim to reduce the risks associated with MetS.     

Curvilinear finite element mesh generation for electron gun simulation

Mesh generation is an integral part of the finite element simulation of electron guns. This paper shows that curvilinear meshes are the ideal type of mesh to use, both in terms of accuracy and speed of computation, and presents a numerical method to generate quasi-curvilinear meshes for a wide variety of electron gun structures.     

Scanning electron microscope design for quantitative multicontrast

Conceptual designs of scanning electron microscopes (SEMs) using a time-of-flight electron spectrometer are presented. The procedure for making quantitative measurements with such SEMs is shown to be much simpler and versatile than using conventional SEMs. SEMs which use an electron time-of-flight spectrometer are able to operate as multicontrast analytical probes, capable of simultaneously quantifying surface topography, voltage, and material type. In addition, it is demonstrated that these SEMs can be designed to have high spatial resolution, good signal-to-noise characteristics, and to be of compact table-top size.