Database

In Industrial environments, huge amount of data is being generated which in turn collected indatabase anddata warehouses from all involved areas such as planning, process design, materials, assembly, production, quality, process control, scheduling, fault detection,shutdown, customer relation management, and so on. Data Mining has become auseful tool for knowledge acquisition for industrial process of Iron and steel making. Due to the rapid growth in Data Mining, various industries started using data mining technology to search the hidden patterns, which might further be used to the system with the new knowledge which might design new models to enhance the production quality, productivity optimum cost and maintenance etc. The continuous improvement of all steel production process regarding the avoidance of quality deficiencies and the related improvement of production yield is an essential task of steel producer. Therefore, zero defect strategy is popular today and to maintain it several quality assurance techniques areused. The present report explains the methods of data mining and describes its application in the industrial environment and especially, in the steel industry.

We have developed a novel three-dimensional (3D) cellular microarray platform to enable the rapid and efficient tracking of stem cell fate and quantification of specific stem cell markers. This platform consists of a miniaturized 3D cell culture array on a functionalized glass slide for spatially addressable high-throughput screening. A microarray spotter was used to deposit cells onto a modified glass surface to yield an array consisting of cells encapsulated in alginate gel spots with volumes as low as 60 nL. A method based on an immunofluorescence technique scaled down to function on a cellular microarray was also used to quantify specific cell marker protein levels in situ. Our results revealed that this platform is suitable for studying the expansion of mouse embryonic stem (ES) cells as they retain their pluripotent and undifferentiated state. We also examined neural commitment of mouse ES cells on the microarray and observed the generation of neuroectodermal precursor cells characterized by expression of the neural marker Sox-1, whose levels were also measured in situ using a GFP reporter system. In addition, the high-throughput capacity of the platform was tested using a dual-slide system that allowed rapid screening of the effects of tretinoin and fibroblast growth factor-4 (FGF-4) on the pluripotency of mouse ES cells. This high-throughput platform is a powerful new tool for investigating cellular mechanisms involved in stem cell expansion and differentiation and provides the basis for rapid identification of signals and conditions that can be used to direct cellular responses. Biotechnol. Bioeng. 2010; 106: 106–118. © 2010 Wiley Periodicals, Inc.

The National Archive of Portugal (IAN/TT) doesn't currently have the needed infrastructures to support the processes of ingestion and management of digital objects produced by the public administration (PA). The eGovernment initiatives establish the need of information and ...

Wireless Sensor Networks (WSN) is a research hotspot in recent years and has yielded many fruitful results. However, the biomedical applications of WSN haven’t received significant development due to its many unique challenges for the engineers and physicians. In this paper, a system architecture solution of body area network (BAN), our so called project “MediMesh”, is presented. A biomedical dedicated BAN node hardware platform is devised to fit the medical monitoring application. The sensor node is built up especially for healthcare monitoring while the biocompatibility and portability are considered. A light weight network protocol is designed considering the radiation effect and communication overhead. After data acquisition from sink stations, a data publishing system based on web service technology is implemented for typical hospital environment. The publishing of biomedical data is realized through 3 phases: first storing the data automatically in a database, then creating information sharing service by using Web Service technology, and finally allowing data access by the physicians and end users.

This study focuses on the surface modification of the commercial aromatic polyamide (PA) thin-film composite (TFC) reverse osmosis (RO) membranes with thermo-responsive copolymers poly(N-isopropylacrylamide-co-acrylamide) (P(NIPAM-co-Am)) for improved membrane properties. Firstly, thermo-responsive copolymers P(NIPAM-co-Am) with certain lower critical solution temperature (LCST) were synthesized by free radical copolymerization in aqueous solution. The resultant copolymers were then used to modify the commercial TFC

Background: Common measures of adherence to prescribed medications derived from administrative databases reflect both patients’ and physicians’ behavior, even if the measures are often interpreted as reflecting only the patient's adherence. Adherence to inhaled corticosteroids (ICSs) has been shown to be low among patients with asthma. Objective: To develop a new measure of patients’ adherence adjusted for prescription patterns and to evaluate the extent to which the low use of ICSs in asthma is due to patients’ nonadherent or suboptimal prescribing practices. Methods: The new measure of adherence, called the proportion of prescribed days covered (PPDC), is defined as the ratio of the total days’ supply dispensed to the total days’ supply prescribed during the study period. The PPDC is a modification of an existing adherence measure, the proportion of days covered (PDC). The PPDC and PDC for ICSs, therapy that should be prescribed for chronic daily use to patients with persisten...

Solar energy applications require readily available, site-oriented, and long-term solar data. However, the frequent unavailability of diffuse irradiation, in contrast to its need, has led to the evolution of various regression models to predict it from the more commonly available data. Estimating the diffuse component from global radiation is one such technique. The present work focuses on improvement in the accuracy of the models for predicting horizontal diffuse irradiation using hourly solar radiation database from nine sites across the globe. The influence of sunshine fraction, cloud cover, and air mass on estimation of diffuse radiation is investigated. Inclusion of these along with hourly clearness index, leads to the development of a series of models for each site. Estimated values of hourly diffuse radiation are compared with measured values in terms of error statistics and indicators like, R2, mean bias deviation, root mean square deviation, skewness, and kurtosis. A new me...

In this paper we introduce COV, a novel information retrieval (IR) algorithm for massive databases based on vector space modeling and spectral analysis of the covariance matrix, for the document vectors, to reduce the scale of the problem. Since the dimension of the covariance matrix depends on the attribute space and is independent of the number of documents, COV can be applied to databases that are too massive for methods based on the singular value decomposition of the document-attribute matrix, such as latent semantic indexing (LSI). In addition to improved scalability, theoretical considerations indicate that results from our algorithm tend to be more accurate than those from LSI, particularly in detecting subtle differences in document vectors. We demonstrate the power and accuracy of COV through an important topic in data mining, known as outlier cluster detection. We propose two new algorithms for detecting major and outlier clusters in databases—the first is based on LSI, and the second on COV. Our implementation studies indicate that our cluster detection algorithms outperform the basic LSI and COV algorithm in detecting outlier clusters.

Improving student's academic performance is not an easy task for the academic community of higher learning. The academic performance of engineering and science students during their first year at university is a turning point in their educational path and usually encroaches on their General Point Average (GPA) in a decisive manner. The students evaluation factors like class quizzes mid and final exam assignment lab - work are studied. It is recommended that all these correlated information should be conveyed to the class teacher before the conduction of final exam. This study will help the teachers to reduce the drop out ratio to a significant level and improve the performance of students. In this paper, we present a hybrid procedure based on Decision Tree of Data mining method and Data Clustering that enables academicians to predict student's GPA and based on that instructor can take necessary step to improve student academic performance Graded Point Average (GPA) is a co...

After collecting a data base of fingerprint images, we design a neural network algorithm for fingerprint recognition. When presented with a pair of fingerprint images, the algorithm outputs an estimate of the probability that the two images originate from the same finger. In one experiment, the neural network is trained using a few hundred pairs of images and its performance is subsequently tested using several thousand pairs of images originated from a subset of the database corresponding to 20 individuals. The error rate currently achieved is less than 0.5%. Additional results, extensions, and possible applications are also briefly discussed.