Novel catechol-derived phosphorus-based precursors for coating applications

Polymer Bulletin - Depletion of the petroleum resources and poor flame-retardant properties of the epoxy resins drive researchers to develop an epoxy resin with good heat stability from...     

Triple Metal Surrounding Gate Junctionless Tunnel FET Based 6T SRAM Design for Low Leakage Memory System

Silicon - The promising capability of Triple Material Surrounding Gate Junctionless Tunnel FET (TMSG – JL – TFET) based 6 T SRAM structure is demonstrated by employing...     

Utilization of flyash as filler for polybutyleneterepthalate‐toughened epoxy resin

Flyash, a waste product generated in large quantities in thermal power plants, has been posing problems of disposal. The purpose of the present work was to make a meaningful utilization of flyash as filler in neat epoxy resin matrix and 2% Polybutyleneterepthalate (PBT)/epoxy blend matrix. For this purpose, the tensile, flexural, compression, impact, chemical resistance, and water absorption properties were studied. Composites were made with varying proportion of flyash in epoxy resin and 2% PBT/epoxy blend matrix. Tensile, flexural, and compression properties were measured on a computerized universal testing machine, according to ASTM procedures. Impact strength was determined using izod impact tester for un‐notched specimens. PBT (2%)/epoxy blend matrix composites showed improved mechanical properties over neat epoxy flyash composites. All the composites were found to have good chemical resistance toward acids, solvents, and alkalies. These composites showed better water resistance over neat epoxy flyash composites. POLYM. ENG. SCI. 46:946–953, 2006. © 2006 Society of Plastics Engineers     

Positional distribution of CLA in TAG of lamb tissues

The content and positional distribution of CLA in TAG fractions of lamb tissues was examined with either preformed CLA or the linoleic acid precursor of CLA in the diet as experimental treatments. The CLA content of phospholipid (PL) from these tissues was also examined. Thirteen lambs were randomized to the following dietary treatments: (i) control diet (no supplement); (ii) CLA supplementation (0.33 g d⁻¹ for 21 d prior to weaning) to milk-replacer of pre-ruminating lambs, or (iii) feeding linoleic acid-rich oil (6% safflower oil on a dry matter basis) to weaned ruminating lambs. At slaughter, tissue samples were procured from diaphragm, rib muscle, and subcutaneous (SC) adipose tissue. Safflower oil supplementation in the diet resulted in an increase in CLA content of the TAG from diaphragm, rib muscle, and SC adipose tissue by about threefold (P<0.05) on a mol% basis. CLA was localized to the sn-1/3 positions of TAG. Animals that received pre-formed CLA, however, had increased proportions of CLA at the sn-2 position of TAG from SC adipose tissue, suggesting that there were tissue-specific dietary effects and possible age-related effects on the mode of FA incorporation into TAG. Safflower oil supplementation in the diet had no effect on the CLA content of PL from diaphragm, rib muscle, and SC adipose tissue, suggesting that CLA was preferentially incorporated into the TAG of these tissues.     

Performance evaluation of saponin over other organic acid and tap water for removal of chromium in tannery sludge by electrokinetic enhancement

The performance of Saponin over different electrolyte-mediated electrokinetic (EK) process for removal of Cr(III) from tannery sludge was assessed. Saponin exhibited maximum Cr(III) removal efficiency, i.e. 67.6–80.75% as compared to other groups of processing fluids. The possible mechanistic approach for the highest removal efficiency of saponin over other electrolytes may be attributed to synchronized effect of micelle formation and subsequent complexation and sorption of chromium ions. The highest electroosmotic flow rate of saponin indicated low electroosmotic resistance during EK process, which validates the observed trend. Therefore, saponin can be considered as a promising electrolyte for EK treatment of tannery sludge.

Abbreviation: CA: Citric acid; LA: lactic acid; OA: oxalic acid; BOD: biochemical oxygen demand; COD: chemical oxygen demand; EK: electokinetic.     

Observer-invariant histopathology using genetics-based machine learning

Prostate cancer accounts for one-third of noncutaneous cancers diagnosed in US men and is a leading cause of cancer-related death. Advances in Fourier transform infrared spectroscopic imaging now provide very large data sets describing both the structural and local chemical properties of cells within prostate tissue. Uniting spectroscopic imaging data and computer-aided diagnoses (CADx), our long term goal is to provide a new approach to pathology by automating the recognition of cancer in complex tissue. The first step toward the creation of such CADx tools requires mechanisms for automatically learning to classify tissue types—a key step on the diagnosis process. Here we demonstrate that genetics-based machine learning (GBML) can be used to approach such a problem. However, to efficiently analyze this problem there is a need to develop efficient and scalable GBML implementations that are able to process very large data sets. In this paper, we propose and validate an efficient GBML technique——based on an incremental genetics-based rule learner. exploits massive parallelisms via the message passing interface (MPI) and efficient rule-matching using hardware-implemented operations. Results demonstrate that is capable of performing prostate tissue classification efficiently, making a compelling case for using GBML implementations as efficient and powerful tools for biomedical image processing.     

An Optimized Deep-Learning-Based Low Power Approximate Multiplier Design

Approximate computing is a popular field for low power consumption that is used in several applications like image processing, video processing, multimedia and data mining. This Approximate computing is majorly performed with an arithmetic circuit particular with a multiplier. The multiplier is the most essential element used for approximate computing where the power consumption is majorly based on its performance. There are several researchers are worked on the approximate multiplier for power reduction for a few decades, but the design of low power approximate multiplier is not so easy. This seems a bigger challenge for digital industries to design an approximate multiplier with low power and minimum error rate with higher accuracy. To overcome these issues, the digital circuits are applied to the Deep Learning (DL) approaches for higher accuracy. In recent times, DL is the method that is used for higher learning and prediction accuracy in several fields. Therefore, the Long Short-Term Memory (LSTM) is a popular time series DL method is used in this work for approximate computing. To provide an optimal solution, the LSTM is combined with a meta-heuristics Jellyfish search optimisation technique to design an input aware deep learning-based approximate multiplier (DLAM). In this work, the jelly optimised LSTM model is used to enhance the error metrics performance of the Approximate multiplier. The optimal hyperparameters of the LSTM model are identified by jelly search optimisation. This fine-tuning is used to obtain an optimal solution to perform an LSTM with higher accuracy. The proposed pre-trained LSTM model is used to generate approximate design libraries for the different truncation levels as a function of area, delay, power and error metrics. The experimental results on an 8-bit multiplier with an image processing application shows that the proposed approximate computing multiplier achieved a superior area and power reduction with very good results on error rates.     

Manifold Preserving Features and Regression for Semantic Labelling in High Dimensional Images

Wireless Personal Communications - This paper proposes an combined method for manifold preservation and Subspace Eigenvectors(SE) based regression in high dimensional (HD) images. We studied...