High-performance photocatalysts for the selective oxidation of alcohols to carbonyl compounds

With an increase in awareness about the need for green chemistry, there is a shift in focus towards identifying eco-compatible technologies that can improve product yield and eliminate the use or generation of hazardous compounds. An immediate practical example of such an approach is the development of sustainable methods for alcohol oxidation as alternatives to the current processes that are energy intensive and rely on ecotoxic chemicals. In this regard, heterogeneous photocatalysis has been identified as a robust technique to catalyze reactions under benign conditions, which would otherwise require harsh synthesis routes. With the advent of materials sciences and nanotechnology, there has been a tremendous increase in the scope of applicability of photocatalysis in fine chemicals synthesis. Though an attractive choice, much of the fundamental information pertaining to catalyst activity, selectivity and reaction conditions for optimum conversion are still to be investigated for most of these systems. To this end, this review will encompass recent achievements in the selective photocatalytic oxidation of alcohols by harnessing solar radiation as a viable source of energy. The discussion will be arranged based on common types of photocatalysts reported in literature, namely metal oxides (eg, TiO₂ and ZnO, Nb₂O₅), sulphides (eg, CdS, CuS, and Bi₂S₃), and carbonaceous photocatalysts (eg, g-C₃N₄). Several such candidates for photocatalysts will be discussed critically with the aim of providing useful insight into developing selective photocatalysts that can oxidize alcohols via eco-friendly pathways along with high yields.     

The influence of mesoscale eddies on a commercial fishery in the coastal waters of the Andaman and Nicobar Islands,India

Mesoscale eddies enhance the productivity in a stratified coastal environment by upwelling. The seas around the Andaman and Nicobar Islands have been found to have frequent mesoscale eddy activity. Commercial fishing grounds coincide with upwelling areas associated with cyclonic and anticyclonic eddies and also with areas between two adjacent eddies. There are different eddy zones supporting different types of fishing gears and fish. The current study aims at identifying the different zones of mesoscale eddies in the Andaman Sea and compares the productivity and fishing activity in each of them. Data collected from 454 commercial fishing trips in the Andaman Sea along with maps of sea level anomaly and Moderate Resolution Imaging Spectroradiometer (MODIS) global level 3 mapped thermal infrared (IR) daytime sea surface temperature (SST) from the Aqua and Terra satellites were used for the study. Known upwelling areas such as the periphery of anticyclonic and the core of cyclonic eddies showed higher catches in longlines, ring seines among the fishing gears, and among all the fish species groups. Downwelling areas such as the periphery of cyclonic and the core of anticyclonic eddies showed lower catches with ring seines and the fish species groups. Areas in between adjacent eddies were explored in this study and the fish captures in such areas were found to be different with types of fishing and the target fish group. The study shows results that link eddy activity with the performance of a fishery.     

Influence of Ball Burnishing Process on Equal Channel Angular Pressed Mg-Zn-Si Alloy on the Evolution of Microstructure and Corrosion Properties

Silicon - In the present study, Mg-4Zn-1Si alloy was subjected to equal channel angular pressing (ECAP) up to 4 passes at 300 °C, followed by ball burnishing using 0.3 mm...     

Symbolic dynamic filtering and language measure for behavior identification of mobile robots

This paper presents a procedure for behavior identification of mobile robots, which requires limited or no domain knowledge of the underlying process. While the features of robot behavior are extracted by symbolic dynamic filtering of the observed time series, the behavior patterns are classified based on language measure theory. The behavior identification procedure has been experimentally validated on a networked robotic test bed by comparison with commonly used tools, namely, principal component analysis for feature extraction and Bayesian risk analysis for pattern classification.     

Redesigned Spatial Modulation for Spatially Correlated Fading Channels

In this paper, a new variant of Spatial Modulation (SM) Multiple-Input Multiple-Output (MIMO) transmission technique, designated as Redesigned Spatial Modulation (ReSM) has been proposed. In ReSM scheme, a dynamic mapping for antenna selection is adopted. This scheme employs both single antenna as well as double antenna combinations depending upon channel conditions to combat the effect of spatial correlation. When evaluated over spatially correlated channel conditions, for a fixed spectral efficiency and number of transmit antennas, ReSM exhibits performance improvement of at least 3 dB over all the conventional SM schemes including Trellis Coded Spatial Modulation (TCSM) scheme. Furthermore, a closed form expression for the upper bound on Pairwise Error Probability (PEP) for ReSM has been derived. This has been used to calculate the upper bound for the Average Bit Error Probability (ABEP) for spatially correlated channels. The results of Monte Carlo simulations are in good agreement with the predictions made by analytical results. The relative gains of all the comparison plots in the paper are specified at an ABER of 10^?4.     

Cytocompatible chitosan-graft-mPEG-based 5-fluorouracil-loaded polymeric nanoparticles for tumor-targeted drug delivery

Biodegradable materials like chitosan (CH) and methoxy polyethylene glycol (mPEG) are widely being used as drug delivery carriers for various therapeutic applications. In this study, copolymer (CH-g-mPEG) of CH and carboxylic acid terminated mPEG was synthesized by carbodiimide-mediated acid amine reaction. The resultant hydrophilic copolymer was characterized by Fourier transform infrared spectroscopy and ¹H NMR studies, revealing its relevant functional bands and proton peaks, respectively. Blank polymeric nanoparticles (B-PNPs) and 5-fluorouracil loaded polymeric nanoparticles (5-FU-PNPs) were formulated by ionic gelation method. Furthermore, folic acid functionalized FA-PNPs and FA-5-FU-PNPs were prepared for folate receptor-targeted drug delivery. FA-5-FU-PNPs were characterized by particle size, zeta potential, and in vitro drug release studies, resulting in 197.7?nm,?+29.9?mv, and sustained drug release of 88% in 24?h, respectively. Cytotoxicity studies were performed for FA-PNPs and FA-5-FU-PNPs in MCF-7 cell line, which exhibited a cell viability of 80 and 41%, respectively. In vitro internalization studies were carried out for 5-FU-PNPs and FA-5-FU-PNPs which demonstrated increased cellular uptake of FA-5-FU-PNPs by receptor-mediated transport. Significant (p?相似文献   

Towards ontology-based multilingual URL filtering: a big data problem

Web content filtering is one among many techniques to limit the exposure of selective content on the Internet. It has gotten trivial with time, yet filtering of multilingual web content is still a difficult task, especially while considering big data landscape. The enormity of data increases the challenge of developing an effective content filtering system that can work in real time. There are several systems which can filter the URLs based on artificial intelligence techniques to identify the site with objectionable content. Most of these systems classify the URLs only in the English language. These systems either fail to respond when multilingual URLs are processed, or over-blocking is experienced. This paper introduces a filtering system that can classify multilingual URLs based on predefined criteria for URL, title, and metadata of a web page. Ontological approaches along with local multilingual dictionaries are used as the knowledge base to facilitate the challenging task of blocking URLs not meeting the filtering criteria. The proposed work shows high accuracy in classifying multilingual URLs into two categories, white and black. Evaluation results conducted on a large dataset show that the proposed system achieves promising accuracy, which is on a par with those achieved in state-of-the-art literature on semantic-based URL filtering.     

Automatic segmentation of liver & lesion detection using H-minima transform and connecting component labeling

Automatic segmentation of the liver and the Lesion detection can be a very challenging task due to its variability in size, shape, position and the presence of other organs with similar intensities. Manual segmentation and detection of a tumor is a time-consuming task and greatly depends upon the expertise and experience of the physician. We proposed a method which consists of automatic segmentation and detection of liver and lesion using CT scan modality. H-minima transform filter, Otsu global thresholds, Morphological opening by reconstruction and modified Connected Component Labeling algorithms are applied for liver segmentation. To keep the technique simple and effective, an appropriate range of threshold values are defined to detect different types of lesions. Performance of the proposed system is evaluated and compared with the state-of-the art algorithms. The results of the comparison show that the proposed approach is robust and efficient due to its simplicity. The dice coefficient score for the hepatic segmentation is 94% while sensitivity and specificity for hepatic lesion are 93% and 87% respectively.

     

ν☆: a robot path planning algorithm based on renormalised measure of probabilistic regular languages

This article introduces a novel path planning algorithm, called ν ^☆, that reduces the problem of robot path planning to optimisation of a probabilistic finite state automaton. The ν ^☆-algorithm makes use of renormalised measure ν of regular languages to plan the optimal path for a specified goal. Although the underlying navigation model is probabilistic, the ν ^☆-algorithm yields path plans that can be executed in a deterministic setting with automated optimal trade-off between path length and robustness under dynamic uncertainties. The ν ^☆-algorithm has been experimentally validated on Segway Robotic Mobility Platforms in a laboratory environment.     

Controlled release tablet formulation containing natural Δ9-tetrahydrocannabinol

Cannabinoids are increasingly being used in the treatment of chemotherapy-induced nausea and vomiting (CINV) because of their action on the cannabinoid receptors, CB1 and CB2. The currently marketed capsule formulations (sesame oil based and crystalline powder) are required to be administered frequently to maintain therapeutic levels, which leads to non-compliance. In the present study, oral controlled release tablet formulations of Δ⁹-tetrahydrocannabinol (THC) were prepared using the lipids Precirol® and Compritol®. Release profiles using THC-lipid matrices and/or with the lipids in the external phase (blend) were evaluated. The effect of directly compressible diluents lactose mixture (Ludipress®), dicalcium phosphate anhydrous (Emcompress®) and microcrystalline cellulose (Avicel® 102) on tablet characteristics and in vitro drug release was also investigated. Further, in vitro THC release in the presence of a lipase inhibitor, Pluronic® F68, was also studied. A 24 h zero-order THC release profile was obtained with a combination of Precirol® and Compritol® in the compression blend. Addition of Pluronic® F68 did not alter THC release in vitro. These optimized tablets were chemically and physically stable for 3 months, the last time point tested, at 25?°C/60% RH. The overall results demonstrate the feasibility of preparing oral THC tablets for once a day administration which can improve CINV management.