Dyeable polypropylene fibers via nanotechnology

Polypropylene (PP) nanocomposites with three different clay loadings were prepared by solution mixing technique. The solution‐mixed nanocomposites were extruded to fibers using a single screw extruder. The PP nanocomposite fibers obtained were uniformly dyed with three distinct disperse dyes at different levels of shade. The enhanced dyeability of PP nanocomposite fibers was characterized by spectrophotometric measurements. Satisfactory wash, light and crock fastness results were also achieved. Mechanical properties and degree of crystallinity of nanocomposite fibers with different clay loadings were also investigated. © 2007 Wiley Periodicals, Inc. J Appl Polym Sci 2007     

A data mining approach to characterize road accident locations

Data mining has been proven as a reliable technique to analyze road accidents and provide productive results. Most of the road accident data analysis use data mining techniques, focusing on identifying factors that affect the severity of an accident. However, any damage resulting from road accidents is always unacceptable in terms of health, property damage and other economic factors. Sometimes, it is found that road accident occurrences are more frequent at certain specific locations. The analysis of these locations can help in identifying certain road accident features that make a road accident to occur frequently in these locations. Association rule mining is one of the popular data mining techniques that identify the correlation in various attributes of road accident. In this paper, we first applied k-means algorithm to group the accident locations into three categories, high-frequency, moderate-frequency and low-frequency accident locations. k-means algorithm takes accident frequency count as a parameter to cluster the locations. Then we used association rule mining to characterize these locations. The rules revealed different factors associated with road accidents at different locations with varying accident frequencies. The association rules for high-frequency accident location disclosed that intersections on highways are more dangerous for every type of accidents. High-frequency accident locations mostly involved two-wheeler accidents at hilly regions. In moderate-frequency accident locations, colonies near local roads and intersection on highway roads are found dangerous for pedestrian hit accidents. Low-frequency accident locations are scattered throughout the district and the most of the accidents at these locations were not critical. Although the data set was limited to some selected attributes, our approach extracted some useful hidden information from the data which can be utilized to take some preventive efforts in these locations.

     

Acquired gastric outlet obstruction during infancy and childhood: a report of five unusual cases

We present a patient with iliac vein stenosis and thrombosis, which occurred 2 weeks after renal transplantation and were secondary to a perivascular hematic collection. The vein stenosis was identified by color Doppler ultrasonography and venography. Computed tomography demonstrated a perivascular collection. One week later, control venography identified iliac vein thrombosis. The patient underwent pharmacological thrombolysis with local infusion of urokinase for 4 hr, percutaneous transluminal angioplasty, and percutaneous installation of a metallic stent (Wallstent), with immediate favorable results. The patient remains in stable condition at 2 years after the procedure.     

Privacy preserving association rule mining over distributed databases using genetic algorithm

Privacy preservation in distributed database is an active area of research. With the advancement of technology, massive amounts of data are continuously being collected and stored in distributed database applications. Indeed, temporal associations and correlations among items in large transactional datasets of distributed database can help in many business decision-making processes. One among them is mining frequent itemset and computing their association rules, which is a nontrivial issue. In a typical situation, multiple parties may wish to collaborate for extracting interesting global information such as frequent association, without revealing their respective data to each other. This may be particularly useful in applications such as retail market basket analysis, medical research, academic, etc. In the proposed work, we aim to find frequent items and to develop a global association rules model based on the genetic algorithm (GA). The GA is used due to its inherent features like robustness with respect to local maxima/minima and domain-independent nature for large space search technique to find exact or approximate solutions for optimization and search problems. For privacy preservation of the data, the concept of trusted third party with two offsets has been used. The data are first anonymized at local party end, and then, the aggregation and global association is done by the trusted third party. The proposed algorithms address various types of partitions such as horizontal, vertical, and arbitrary.

     

Heritability of electronically recorded daily body weight and correlations with yield, dry matter intake, and body condition score

The objectives of this study were to estimate heritability for daily body weight (BW) and genetic correlations of daily BW with daily milk yield (MY), body condition score (BCS), dry matter intake, fat yield, and protein yield. The Afiweigh cow body weighing system records BW of every cow exiting the milking parlor. The Afiweigh system was installed at the Pennsylvania State University dairy herd in August 2001 and in July 2004 at the Virginia Tech dairy herd. The edited data included 202,143 daily BW and 290,930 daily MY observations from 575 Pennsylvania State University and 120 Virginia Tech Holstein cows. Data were initially analyzed with a series of 4-trait animal models, followed by random regression models. The models included fixed effects for age within lactation group, week of lactation, and herd-date. Random effects included animal, permanent environment, and error. The order of the polynomials for random animal and permanent environmental effects with the random regression model for daily BW was 4 and 6, respectively. Heritability estimates for daily BW ranged from 0.48 to 0.57 and were largest between 200 and 230 and smallest at 305 d of lactation. Genetic correlations were large between BW and BCS (0.60). The genetic correlation between daily BW and MY was −0.14 but was positive (0.24) after adjusting for BCS. Electronically recorded daily BW is highly heritable, and genetic evaluations of daily BW and BW change across the lactation could be used to select for less early lactation BW loss.     

Prediction of places of visit using tweets

We study the problem of predicting likely places of visit of users using their past tweets. What people write on their microblogs reflects their intent and desire relating to most of their common day interests. Taking this as a strong evidence, we hypothesize that tweets of the person can also be treated as source of strong indicator signals for predicting their places of visits. In this paper, we propose a novel approach for predicting place of visit within a given geospatial range considering the past tweets and the time of visit. These predictions can be used for generating places recommendation or for promotions. In this approach, we analyze use of various features that can be extracted from the historical tweets—for example, personality traits estimated from the past tweets and the actual words mentioned in the tweets. We performed extensive empirical experiments involving, real data derived from twitter timelines of 4600 persons with multi-label classification as predictive model. The performances of proposed approach outperform the four baselines with accuracy reaching 90 % for top five predictions. Based on our experimental study, we come up with general guidelines on building the prediction model in terms of the type of features extracted from historical tweets, window size of historical tweets and on the optimal radius of query around the place of visit at a given time.