Thermo gravimetric analysis and morphological behavior of castor oil based polyurethane–polyester nonwoven fabric composites

Castor oil (CO) based polyurethane (PU)– polyester nonwoven fabric composites were fabricated by impregnating the polyester nonwoven fabric in a reactive composition containing CO and diisocyanate. Composites were fabricated with two different isocyanates such as toluene‐2, 4‐diisocyanate (TDI) and hexamethylene diisocyanate (HMDI). Thermogravimetric analysis (TGA) studies of the composites were performed to establish the thermal stability and their mode of thermal degradation. It was found that degradation of neat PU takes place in two steps and that of polyester nonwoven fabric reinforced PU composites takes place in three steps. From the TGA thermograms, a little improvement in thermal stability incase of polyester nonwoven fabric reinforced PU composites were noticed compared to unreinforced PUs. Degradation kinetic parameters were obtained for the composites using Broido, Coats and Redfern, and Horowitz and Metzger methods. Tensile fractured composite specimens were used to analyze the morphology of the composites by scanning electron microscopic technique. © 2007 Wiley Periodicals, Inc. J Appl Polym Sci, 2007     

Video sequence feature extraction and segmentation using likelihood regression model

The development of digital technology is utilized by people to capture and share video frames. At present, rather than capturing images, people are interested in recording video footage for exploring information. Here, retrieval of video from large databases is challenging due to the continuous frame count. To overcome these challenges associated with the retrieval of video from available databases, this research proposed a likelihood-based regression approach for video processing. To improve the retrieval accuracy of video sequences, the proposed method utilizes a likelihood estimation technique integrated with a regression model. The likelihood estimate measures the pixel level roughly for estimating the pixel range, after which the regression approach measures the pixel level for transforming certainly blurred and unwanted pixels. In the proposed likelihood regression approach, the video is converted into a video frame and stored in a database. Query frames are taken into account by the generated database depending on the features which are used for a given video to be retrieved. The significant video retrieval performance obtained from the simulation results for the proposed likelihood-based regression model shows that the proposed model performs well over the other state-of-the-art techniques.

     

A Randomized Linear-Work EREW PRAM Algorithm to Find a Minimum Spanning Forest

We present a randomized EREW PRAM algorithm to find a minimum spanning forest in a weighted undirected graph. On an n -vertex graph the algorithm runs in o(( log n) ^{1+ ɛ} ) expected time for any ɛ >0 and performs linear expected work. This is the first linear-work, polylog-time algorithm on the EREW PRAM for this problem. This also gives parallel algorithms that perform expected linear work on two general-purpose models of parallel computation—the QSM and the BSP.     

Anosognosia in parietal lobe syndrome

Patients with right parietal lesions often deny their paralysis (anosognosia), but do they have "tacit" knowledge of their paralysis? I devised three novel tests to explore this. First, the patients were given a choice between a bimanual task (e.g., tying shoe laces) vs a unimanual one (e.g., threading a bolt). They chose the former on 17 of 18 trials and, surprisingly, showed no frustration or learning despite repeated failed attempts. I conclude that they have no tacit knowledge of paralysis (or, if such knowledge exists, it is not available for this particular task). Second, I used a "virtual reality box" to convey the optical illusion to the patient that she was moving her paralyzed left hand up and down to the rhythm of a metronome, and yet she showed no sign of surprise. Third, I irrigated patient BM's left ear canal with cold water, a procedure that is known to shift that patient's spatial frame of reference by stimulating the vestibular system. Surprisingly, this allowed her "repressed" memory of the paralysis to come to the surface; she said she had been paralyzed continuously for several days. I suggest that the vestibular stimulation produces these remarkable effects by mimicking REM sleep. These patients also employ a whole arsenal of grossly exaggerated Freudian "defense mechanisms" to account for their paralysis. To explain this, I propose that in normal individuals the left hemisphere ordinarily deals with small, local anomalies by trying to impose consistency but, when the anomaly exceeds threshold, an interaction with the right hemisphere forces a "paradigm shift." A failure of this process, in patients with right hemisphere damage, might partially account for anosognosia. Finally, I present a new conceptual framework that may help link several psychological and neurological phenomena such as Freudian defense mechanisms, vestibular stimulation, anosognosia, memory repression, visual illusions, anterograde amnesia, REM sleep, dreaming, and humor.     

Adaptive Window Based 3-D Feature Selection for Multispectral Image Classification Using Firefly Algorithm

Feature extraction is the most critical step in classification of multispectral image. The classification accuracy is mainly influenced by the feature sets that are selected to classify the image. In the past, handcrafted feature sets are used which are not adaptive for different image domains. To overcome this, an evolutionary learning method is developed to automatically learn the spatial-spectral features for classification. A modified Firefly Algorithm (FA) which achieves maximum classification accuracy with reduced size of feature set is proposed to gain the interest of feature selection for this purpose. For extracting the most efficient features from the data set, we have used 3-D discrete wavelet transform which decompose the multispectral image in all three dimensions. For selecting spatial and spectral features we have studied three different approaches namely overlapping window (OW-3DFS), non-overlapping window (NW-3DFS) adaptive window cube (AW-3DFS) and Pixel based technique. Fivefold Multiclass Support Vector Machine (MSVM) is used for classification purpose. Experiments conducted on Madurai LISS IV multispectral image exploited that the adaptive window approach is used to increase the classification accuracy.     

Associative Zone Based Energy Balancing Routing for Expanding Energy Efficient and Routing Optimization Over the Sensor Network

Wireless Personal Communications - A channel prediction scheme with channel matrix doubling and temporal-spatial smoothing is proposed. With the assumption that the underlying channel model...     

Organometallic L-Alanine Cadmium Iodide Crystals for Optical Device Fabrication

Single crystals of L-alanine cadmium iodide (LACI) were grown by the slow evaporation technique at room temperature. A single-crystal X-ray diffraction (SXRD) model was used to evaluate the crystal structure of the as-grown LACI crystal. The energy dispersive X-ray (EDX) analysis and ultraviolet-visible-near infrared (UV-vis-NIR) transmittance studies were carried out, and the results reveal the presence of elements in the title compound. From the transmittance data, the optical bandgap as a function of photon energy was estimated, and the different optical constants were calculated. A fluorescence study was performed for the LACI crystal. Thermogravimetric and differential thermal analyses have also been studied to investigate the thermal property of the LACI crystal. The efficiency of the second harmonic generation (SHG) of the title crystal was investigated. The magnetic and electrical properties were estimated by the vibrating sample magnetometer (VSM) analysis and impedance study, respectively.     

ArRaNER: A novel named entity recognition model for biomedical literature documents

Developments in advanced innovations have prompted the generation of an immense amount of digital information. The data deluge contains hidden information that is difficult to extract. In the biomedical domain, the development of technology has caused the production of voluminous data. Processing these voluminous textual data is referred to as ‘biomedical content mining’. Emerging artificial intelligence (AI) models play a major role in the automation of Pharma 4.0. In AI, natural language processing (NLP) plays a dynamic role in extracting knowledge from biomedical documents. Research articles published by scientists and researchers contain an enormous amount of hidden information. Most of the original and peer-reviewed articles are indexed in PubMed. Extracting meaningful information from a large number of literature documents is very difficult for human beings. This research aims to extract the named entities of literature documents available in the life science domain. A high-level architecture is proposed along with a novel named entity recognition (NER) model. The model is built using rule-based machine learning (ML). The proposed ArRaNER model produced better accuracy and was also able to identify more entities. The NER model was tested on two different datasets: a PubMed dataset and a Wikipedia talk dataset. The ArRaNER model obtains an accuracy of 83.42% on the PubMed articles and 77.65% on the Wikipedia articles.

     

Extranasal Staphylococcus aureus colonization predisposes to bloodstream infections in patients on hemodialysis with noncuffed internal jugular vein catheters

Introduction: Staphylococcal infection of endogenous origin is an important cause of morbidity and mortality in patients who receive hemodialysis (HD). The risk of such infections in nasal carriers of the organism is well defined. Extranasal carriage of the organism at extranasal sites may pose similar risks. Methods: A total of 70 patients about to undergo internal jugular vein catheterization for HD were enrolled in this prospective observational study. Swab cultures were obtained from anterior nares, posterior pharynx, axillae, toe web spaces, and vascular access sites at baseline and 1 week later. A patient was defined as a persistent carrier when the same organism was grown in both samples. Staphylococcus aureus bloodstream infections were assessed by blood and catheter tip cultures over a 90‐day period. Findings: The mean age of the patients was 43.71 ± 16.2 years. Persistent S. aureus carriage at anterior nares, throat, axilla, toe web spaces, vascular access site, and all sites was documented in 27.9%, 11.4%, 40%, 32.9%, 4.3%, and 64.2% of patients, respectively. Fifteen patients developed S. aureus infections. Catheter related S. aureus infections (CRI) were more likely in persistent carriers than nonpersistent carriers with odds ratios (95% CI) of 10.2 (2.8–37.1), 8.6 (1.7–42.2), 17.3 (3.4–86.0), 3.0 (0.9–9.8), and 1.9 (0.2–22.4) for anterior nares, throat, axilla, toe web spaces, and vascular access site carriers, respectively. The probability of developing CRI in persistent S. aureus carriers was 55% compared to none in noncarriers at 90 days (P = 0.04). Discussion: Extranasal S. aureus carriage is as significant a risk factor as nasal carriage for staphylococcal infections in patients on HD through catheters. The study is limited by lack of molecular phenotyping.