Reliability growth models for NASA applications

The objective of any reliability growth study is prediction of reliability at some future instant. Another objective is statistical inference, estimation of reliability for reliability demonstration. A cause of concern for the development engineer and management is that reliability demands an excessive number of tests for reliability demonstration. For example, the Space Transportation Main Engine (STME) program requirements call for .99 reliability at 90 pct. confidence for demonstration. This requires running 230 tests with zero failure if a classical binomial model is used. It is therefore also an objective to explore the reliability growth models for reliability demonstration and tracking and their applicability to NASA programs. A reliability growth model is an analytical tool used to monitor the reliability progress during the development program and to establish a test plan to demonstrate an acceptable system reliability

Reliability evaluation methodology for NASA applications

Liquid rocket engine technology has been characterized by the development of complex systems containing large number of subsystems, components, and parts. The trend to even larger and more complex system is continuing. The liquid rocket engineers have been focusing mainly on performance driven designs to increase payload delivery of a launch vehicle for a given mission. In otherwords, although the failure of a single inexpensive part or component may cause the failure of the system, reliability in general has not been considered as one of the system parameters like cost or performance. Up till now, quantification of reliability has not been a consideration during system design and development in the liquid rocket industry. Engineers and managers have long been aware of the fact that the reliability of the system increases during development, but no serious attempts have been made to quantify reliability. As a result, a method to quantify reliability during design and development is needed. This includes application of probabilistic models which utilize both engineering analysis and test data. Classical methods require the use of operating data for reliability demonstration. In contrast, the method described in this paper is based on similarity, analysis, and testing combined with Bayesian statistical analysis

Olfactory Neuroblastoma: Diagnostic Difficulty

Olfactory neuroblastoma is an uncommon malignant tumor of sinonasal tract arising from the olfactory neuro epithelium. The olfactory neuroblastomas presenting with divergent histomorphologies like, epithelial appearance of cells, lacking a neuro fibrillary background and absence of rosettes are difficult to diagnose. Such cases require immunohistochemistry to establish the diagnosis. We describe the clinical features, pathological and immunohistochemical findings of grade IV Olfactory neuroblastoma in a 57 year old ma

Effect of some volatile compound on some deuteromycetean fungi

Fungi respond to various volatile compounds in their ability to grow and reproduce. However, this aspect of fungal metabolism has been relatively less attended to. In the present investigation effects of certain volatile compounds on the growth and sporulation of four species of Curvularia, three species of Fusarium, two species of Phoma and Botryodiplodia sp. have been studied. C. lunata. & C. senegalensis showed poor growth in acetic acid, ethyl alcohol, n-butanol, propionic acid, phenol, toluene, formaldehyde; C. prasadii & C. clavata showed good to moderate growth except in acetic acid & phenol. Three species of Fusarium showed good to moderate growth in acetone, ethyl alcohol, phenol & toluene, formaldehyde & inhibited in acetic acid, n-butanol, propionic acid. In Phoma excellent growth was seen in acetone, good in ethyl alcohol, moderate to poor in toluene, formaldehyde, acetic acid, phenol while inhibited in propionic acid & butanol. In B. theobromae excellent growth was seen in acetone, good in ethyl alcohol, moderate in acetic acid, toluene & inhibited in propionic acid, butanol, phenol & formaldehyde

Efficiency Predictor: Predicting the Consumption Efficiency of Humans by Machine Learning Technique

As computer science advances and integrates with statistics in the field of machine learning, the predictability of future events is increasing. Our project focuses on leveraging this domain to forecast human performance using a minimal set of attributes, thereby reducing the need for extensive labels. As present solutions in machine learning helped humanity to predict natural events there is no accurate existing solution to predict the same for human beings. Human efficiency may include the development of an individual or the development of a team or collaboration. Making progress in a work without knowing the success rate might be a challenge as the final output may or may not give the expected results. The amount of hard work engaged in work that may fail in the future causes a great loss of time and energy. The involvement of computers integrated with the statistical models motivates and helps to predict the final output. So, we have taken the initiative to predict the future performance of a person in a more accurate and precise manner. This project aims to predict the consumption efficiency performance of a person using a machine learning algorithm by Ensemble-based Progressive Prediction. &nbsp

Cloud-Based Integrated Cross-Platform Regional Medical Image Cooperative Storage System

With the increasing volume of medical image data, efficient storage and retrieval of these images have become critical in the healthcare industry. This paper proposes an integrated cross-platform regional medical image cooperative storage system based on cloud computing. The system comprises a storage layer, a basic management layer, an application interface layer, and an access layer. The storage layer consists of medical image storage devices and a storage device management system. The basic management layer facilitates the collaborative operation of multiple storage devices within the medical image cloud storage. The application interface layer assigns permissions to users based on their needs. The access layer allows users to access the medical image cloud storage system. This system aims to provide a cost-effective, high-availability solution for storing and accessing a large volume of medical images. The research objective is to realize rapid storage and retrieval of medical images, satisfying the needs of healthcare professionals. The findings demonstrate the practical significance of the system and its potential to enhance medical image management

Technique for Automatic Algorithm Selection for Computational Intelligence in Cloud-Based Computing Environments

This research presents an Auto-Algorithm selection technique for Computational Intelligence in Cloud-Based computing environment. By leveraging the capabilities of a network information processing platform, users can effortlessly and intelligently build Computational Intelligence models tailored to their specific problems without the need for manually configuring the Computational Intelligence environment, selecting algorithms, or adjusting complex Computational Intelligence functions and parameters. The proposed procedure allows users to simply upload sample data through a web interface, freeing Computational Intelligence applications from environmental constraints and taking advantage of the network information processing platform's capabilities. This approach transparently handles the model building process, significantly reducing the barriers to entry for utilizing Computational Intelligence. By addressing the issues of unpredictable model selection, manual parameter adjustment, and the challenges faced by common users, this auto selection procedure empowers the practical application of Computational Intelligence in various domains