Carbon black as a dulling agent for nylon 6 filaments and the lightfastness of dyes on dulled filaments

Nylon 6 filaments containing 0–1.5% carbon-black pigment are dyed to different depths of shade. The colour on the substrate is evaluated on CIELAB a* b* diagrams at given concentrations of carbon black and dye. The carbon-black pigment acts essentially as a neutral dulling agent and only with a few dyes does it appear to alter the hue angle slightly. Thus, it is possible to use nylon 6 filaments containing carbon-black pigment to produce dull shades by the overdyeing process. The dyed samples thus produced are exposed to mercury-lamp light as well as daylight. The lightfastness is evaluated visually using the Blue Wool Scale. There is a remarkable improvement in the lightfastness of all dyes on nylon 6 containing carbon-black pigment. The carbon black in the filament appears to protect both the dye and the polymer from the degrading action of actinic radiation. However, the remarkable improvement in the lightfastness is essentially due to the optical effect.     

An Internet accessible telepresence

The US Vice President, Al Gore, in a speech on the information superhighway, suggested that it could be used to remotely control a nuclear reactor. We do not have enough confidence in computer software, hardware, or networks to attempt this experiment, but have instead built a Internet-accessible, remote-controlled model car that provides a race driver's view via a video camera mounted on the model car. The remote user can see live video from the car, and, using a mouse, control the speed and direction of the car. The challenge was to build a car that could be controlled by novice users in narrow corridors, and that would work not only with the full motion video that the car natively provides, but also with the limited size and frame rate video available over the Internet multicast backbone. We have built a car that has been driven from a site 50 miles away over a 56-kbps IP link using format video at as little as one frame per second and at as low as pixels resolution. We also built hardware to control the car, using a slightly modified voice grade channel videophone. Our experience leads us to believe that it is now possible to put together readily available hardware and software components to build a cheap and effective telepresence.     

Cloning of intestinal phospholipase A2 from intestinal epithelial RNA by differential display PCR

Differential display polymerase chain reaction (DD-PCR) is a powerful technique for comparing gene expression between cell types, or between stages of development or differentiation. Differentially expressed genes may be cloned and analysed further. Here we extend the use of DD-PCR to analyse differences in gene expression between two complex epithelia: that of the small intestine and of the large intestine. The aim of this study was to identify genes expressed preferentially in Paneth cells. Paneth cells are secretory epithelial cells putatively involved in host defense and regulation of crypt cell proliferation and are found at the base of the small intestinal crypts adjacent to the stem cell zone. Of 34 clones that were analysed, partial sequencing identified two clones related to known Paneth cell products: a homologue of secretory phospholipase A2 (clone B1) and a homologue of a neutrophil defensin (clone C5). B1 was strongly expressed in Paneth cells, as demonstrated by in-situ hybridization. B1 was also expressed at a lower level in the large intestinal epithelium. A full length B1 cDNA clone was isolated and sequenced, and shown to be highly homologous to type II secretory phospholipase A2 genes, and almost identical to the enhancing factor gene and the putative gene for the MOM-1 locus. B1 expression is limited to the intestinal tract, and we propose that it be designated intestinal phospholipase A2, or i-PLA2. The method we describe is well suited to the rapid identification of genes expressed exclusively or predominantly in Paneth cells.     

Weakly Supervised and Online Learning of Word Models for Classification to Detect Disaster Reporting Tweets

Social media has quickly established itself as an important means that people, NGOs and governments use to spread information during natural or man-made disasters, mass emergencies and crisis situations. Given this important role, real-time analysis of social media contents to locate, organize and use valuable information for disaster management is crucial. In this paper, we propose self-learning algorithms that, with minimal supervision, construct a simple bag-of-words model of information expressed in the news about various natural disasters. Such a model is human-understandable, human-modifiable and usable in a real-time scenario. Since tweets are a different category of documents than news, we next propose a model transfer algorithm, which essentially refines the model learned from news by analyzing a large unlabeled corpus of tweets. We show empirically that model transfer improves the predictive accuracy of the model. We demonstrate empirically that our model learning algorithm is better than several state of the art semi-supervised learning algorithms. Finally, we present an online algorithm that learns the weights for words in the model and demonstrate the efficacy of the model with word weights.     

Employee churn prediction

Customer churn is a notorious problem for most industries, as loss of a customer affects revenues and brand image and acquiring new customers is difficult. Reliable predictive models for customer churn could be useful in devising customer retention plans. We survey and compare some major machine learning techniques that have been used to build predictive customer churn models. Employee churn (or attrition) closely related but not identical to customer churn is similarly painful for an organization, leading to disruptions, customer dissatisfaction and time and efforts lost in finding and training replacement. We present a case study that we carried out for building and comparing predictive employee churn models. We also propose a simple value model for employees that can be used to identify how many of the churned employees were “valuable”. This work has the potential for designing better employee retention plans and improving employee satisfaction.     

Monitoring snow cover variability in an agropastoral area in the Trans Himalayan region of Nepal using MODIS data with improved cloud removal methodology

Monitoring the extent and pattern of snow cover in the dry, high altitude, Trans Himalayan region (THR) is significant to understand the local and regional impact of ongoing climate change and variability. The freely available Moderate Resolution Imaging Spectroradiometer (MODIS) snow cover images, with 500 m spatial and daily temporal resolution, can provide a basis for regional snow cover mapping, monitoring and hydrological modelling. However, high cloud obscuration remains the main limitation. In this study, we propose a five successive step approach — combining data from the Terra and Aqua satellites; adjacent temporal deduction; spatial filtering based on orthogonal neighbouring pixels; spatial filtering based on a zonal snowline approach; and temporal filtering based on zonal snow cycle — to remove cloud obscuration from MODIS daily snow products. This study also examines the spatial and temporal variability of snow cover in the THR of Nepal in the last decade. Since no ground stations measuring snow data are available in the region, the performance of the proposed methodology is evaluated by comparing the original MODIS snow cover data with least cloud cover against cloud-generated MODIS snow cover data, filled by clouds of another densely cloud-covered product. The analysis indicates that the proposed five-step method is efficient in cloud reduction (with average accuracy of > 91%). The results show very high interannual and intra-seasonal variability of average snow cover, maximum snow extent and snow cover duration over the last decade. The peak snow period has been delayed by about 6.7 days per year and the main agropastoral production areas of the region were found to experience a significant decline in snow cover duration during the last decade.     

Assessment of the Impact of Municipal Solid Waste on Groundwater Quality near the Sangamner City using GIS Approach

The present work aims at assessing the impact of MSW on the groundwater quality around dumping yard site, located near the Sangamner city by water quality index (WQI) and its integration in geographical information system (GIS). Groundwater samples (n?=?15) around the dumping yard were collected using Garmin GPS device in October 2013 and October 2014. Physico-chemical analysis of same samples was carried out for pH, EC, TDS, Na⁺, K⁺,Ca²⁺, Mg²⁺, TH, Cl^?, HCO₃ ^?, SO₄ ^2? and NO₃ ^? along with the heavy metals like Fe, Zn, Cd and Cr by using standard methods. Similarly, SAR, KRs, RSC and SSP were also calculated to know the groundwater quality into irrigation perspective. WQI for 15 samples were calculated using physico-chemical results/data of 12 parameters and its desirable limit of BIS standard. Generated WQI (z) for October 2013 and October 2014 were integrated with latitude (y) and longitude (x) values, collected using GPS during the field work. Integrated xyz data were then interpolated in Surfer-10 GIS software using inverse distance weight (IDW) method to estimate the groundwater quality of the study area. Study revealed that the groundwater quality around the dumping yard area does not confirm to drinking and domestic purposes as per the WQI and BIS standard. However, the groundwater quality is marginally suitable for irrigation as per SAR, KRs, RSC and SSP. The influence of leachate from MSW dumping site to surrounding groundwater is creating a serious concern and susceptible to potential health hazards. Thus, continuous monitoring of groundwater is desperately required in order to minimize the groundwater pollution for control the pollution-caused MSW.     

Fuzzy association rule mining approaches for enhancing prediction performance

This paper presents an investigation into two fuzzy association rule mining models for enhancing prediction performance. The first model (the FCM–Apriori model) integrates Fuzzy C-Means (FCM) and the Apriori approach for road traffic performance prediction. FCM is used to define the membership functions of fuzzy sets and the Apriori approach is employed to identify the Fuzzy Association Rules (FARs). The proposed model extracts knowledge from a database for a Fuzzy Inference System (FIS) that can be used in prediction of a future value. The knowledge extraction process and the performance of the model are demonstrated through two case studies of road traffic data sets with different sizes. The experimental results show the merits and capability of the proposed KD model in FARs based knowledge extraction. The second model (the FCM–MSapriori model) integrates FCM and a Multiple Support Apriori (MSapriori) approach to extract the FARs. These FARs provide the knowledge base to be utilized within the FIS for prediction evaluation. Experimental results have shown that the FCM–MSapriori model predicted the future values effectively and outperformed the FCM–Apriori model and other models reported in the literature.     

Spreading of liquid droplets on proton exchange membrane of a direct alcohol fuel cell

Spreading of liquid droplets over solid surfaces is a fundamental process with a number of applications including electro-chemical reactions on catalyst surface in membrane electrode assembly of proton exchange membrane (PEM) fuel cell and direct alcohol fuel cell. The spreading process of droplet on the PEM porous substrate consists of two phenomena, e.g., spreading of droplet on PEM surface and imbibition of droplet into PEM porous substrate. The shrinkage of the droplet base occurs due to the suction of the liquid from the droplet into the PEM porous substrate. As a result of these two competing processes, the radius of the drop base goes through a maximum with time. The variation of droplet base and front diameter with time on the PEM porous substrate is monitored using microscope fitted with CCD camera and a PC. It is seen that the droplet base diameter goes through a maximum with time, whereas the front diameter increases continuously with time. Further, methanol droplet spreading and wetting front movement was faster than that for ethanol and deionized water. As the PEM porous substrate is wetted and imbibed well by the methanol compared to ethanol, it is expected that the cross over of methanol would be higher than that of ethanol in direct alcohol fuel cell. It should be noted that cross over of alcohol from anode side to cathode side through membrane is detrimental to the fuel cell operation. The experimental data on the variation of droplet base and wetting front diameter with time is predicted by the model available in the literature.