Biohydrogen Production from Organic Waste – A Review

Hydrogen fuel is a promising alternative to fossil fuels because of its energy content, clean nature, and fuel efficiency. However, it is not readily available. Most current producion processes are very energy intensive and emit carbon dioxide. Therefore, this article reviews technological options for hydrogen production that are eco-friendly and generate clean hydrogen fuel. Biological methods, such different fermentation processes and photolysis are discussed together with the required substrates and the process efficiency.     

Electrocatalytic Reduction of CO2 to Ethylene by Molecular Cu‐Complex Immobilized on Graphitized Mesoporous Carbon

The electrochemical reduction of carbon dioxide (CO₂) to hydrocarbons is a challenging task because of the issues in controlling the efficiency and selectivity of the products. Among the various transition metals, copper has attracted attention as it yields more reduced and C2 products even while using mononuclear copper center as catalysts. In addition, it is found that reversible formation of copper nanoparticle acts as the real catalytically active site for the conversion of CO₂ to reduced products. Here, it is demonstrated that the dinuclear molecular copper complex immobilized over graphitized mesoporous carbon can act as catalysts for the conversion of CO₂ to hydrocarbons (methane and ethylene) up to 60%. Interestingly, high selectivity toward C2 product (40% faradaic efficiency) is achieved by a molecular complex based hybrid material from CO₂ in 0.1 m KCl. In addition, the role of local pH, porous structure, and carbon support in limiting the mass transport to achieve the highly reduced products is demonstrated. Although the spectroscopic analysis of the catalysts exhibits molecular nature of the complex after 2 h bulk electrolysis, morphological study reveals that the newly generated copper cluster is the real active site during the catalytic reactions.     

Photo-Chemically-Deposited and Industrial Cu/ZnO/Al2O3 Catalyst Material Surface Structures During CO2 Hydrogenation to Methanol: EXAFS,XANES and XPS Analyses of Phases After Oxidation,Reduction, and Reaction

Catalysis Letters - Industrial Cu/ZnO/Al2O3 or novel rate catalysts, prepared with a photochemical deposition method, were studied under functional CH3OH synthesis conditions at the set temperature...     

Membrane degumming of crude rice bran oil: Pilot plant study

The procedure for the classical chemical refining of vegetable oils consists of degumming, alkali neutralization, bleaching, and deodorization. Conventional refining of rice bran oil using alkali gives oil of acceptable quality, but the refining losses are very high. A critical work has been carried out to study the application of membrane technology in the pretreatment of crude rice bran oil. Oils intended for physical refining should have a low phosphorus content, and this is not readily achievable by the conventional acid/water degumming process. The application of membrane technology for the pretreatment of rice bran oil has been investigated. The process has already been successfully applied to other vegetable oils. Ceramic membranes, which are important from the commercial point of view, were examined for this purpose. The results showed that the membrane‐filtered oils met the requirements of physical refining, with a substantial reduction in color. It was observed that most of the waxy material was also rejected. Experiments were carried out to establish the relationship between permeate flux and rejection with membrane pore size, trans‐membrane pressure and micellar solute concentration.     

Pyramid coding based rate control for constant bit rate video streaming

In this paper, a novel pyramid coding based rate control scheme is proposed for video streaming applications constrained by a constant channel bandwidth. To achieve the target bit rate with the best quality, the initial quantization parameter (QP) is determined by the average spatio-temporal complexity of the sequence, its resolution and the target bit rate. Simple linear estimation models are then used to predict the number of bits that would be necessary to encode a frame for a given complexity and QP. The experimental results demonstrate that the proposed rate control scheme significantly outperforms the existing rate control scheme in the Joint Model (JM) reference software in terms of Peak Signal to Noise Ratio (PSNR) and consistent perceptual visual quality while achieving the target bit rate. Finally, the proposed scheme is validated through experimental evaluation over a miniature test-bed.

     

Language independent unsupervised learning of short message service dialect

Noise in textual data such as those introduced by multilinguality, misspellings, abbreviations, deletions, phonetic spellings, non-standard transliteration, etc. pose considerable problems for text-mining. Such corruptions are very common in instant messenger and short message service data and they adversely affect off-the-shelf text mining methods. Most techniques address this problem by supervised methods by making use of hand labeled corrections. But they require human generated labels and corrections that are very expensive and time consuming to obtain because of multilinguality and complexity of the corruptions. While we do not champion unsupervised methods over supervised when quality of results is the singular concern, we demonstrate that unsupervised methods can provide cost effective results without the need for expensive human intervention that is necessary to generate a parallel labeled corpora. A generative model based unsupervised technique is presented that maps non-standard words to their corresponding conventional frequent form. A hidden Markov model (HMM) over a “subsequencized” representation of words is used, where a word is represented as a bag of weighted subsequences. The approximate maximum likelihood inference algorithm used is such that the training phase involves clustering over vectors and not the customary and expensive dynamic programming (Baum–Welch algorithm) over sequences that is necessary for HMMs. A principled transformation of maximum likelihood based “central clustering” cost function of Baum–Welch into a “pairwise similarity” based clustering is proposed. This transformation makes it possible to apply “subsequence kernel” based methods that model delete and insert corruptions well. The novelty of this approach lies in that the expensive (Baum–Welch) iterations required for HMM, can be avoided through an approximation of the loglikelihood function and by establishing a connection between the loglikelihood and a pairwise distance. Anecdotal evidence of efficacy is provided on public and proprietary data.     

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.     

Lanthanide Doped Near Infrared Active Upconversion Nanophosphors: Fundamental Concepts,Synthesis Strategies,and Technological Applications

Near infrared (NIR) light utilization in a range of current technologies has gained huge significance due to its abundance in nature and nondestructive properties. NIR active lanthanide (Ln) doped upconversion nanomaterials synthesized in controlled shape, size, and surface functionality can be combined with various pertinent materials for extensive applications in diverse fields. Upconversion nanophosphors (UCNP) possess unique abilities, such as deep tissue penetration, enhanced photostability, low toxicity, sharp emission peaks, long anti‐Stokes shift, etc., which have bestowed them with prodigious advantages over other conventional luminescent materials. As new generation fluorophores, UCNP have found a wide range of applications in various fields. In this Review, a comprehensive overview of lanthanide doped NIR active UCNP is provided by discussing the fundamental concepts including the different mechanisms proposed for explaining the upconversion processes, followed by the different strategies employed for the synthesis of these materials, and finally the technological applications of UCNP, mainly in the fields of bioimaging, drug delivery, sensing, and photocatalysis by highlighting the recent works in these areas. In addition, a brief note on the applications of UCNP in other fields is also provided along with the summary and future perspectives of these materials.     

A learning automata-based fault-tolerant routing algorithm for mobile ad hoc networks

Reliable routing of packets in a Mobile Ad Hoc Network (MANET) has always been a major concern. The open medium and the susceptibility of the nodes of being fault-prone make the design of protocols for these networks a challenging task. The faults in these networks, which occur either due to the failure of nodes or due to reorganization, can eventuate to packet loss. Such losses degrade the performance of the routing protocols running on them. In this paper, we propose a routing algorithm, named as learning automata based fault-tolerant routing algorithm (LAFTRA), which is capable of routing in the presence of faulty nodes in MANETs using multipath routing. We have used the theory of Learning Automata (LA) for optimizing the selection of paths, reducing the overhead in the network, and for learning about the faulty nodes present in the network. The proposed algorithm can be juxtaposed to any existing routing protocol in a MANET. The results of simulation of our protocol using network simulator 2 (ns-2) shows the increase in packet delivery ratio and decrease in overhead compared to the existing protocols. The proposed protocol gains an edge over FTAR, E2FT by nearly 2% and by more than 10% when compared with AODV in terms of packet delivery ratio with nearly 30% faulty nodes in the network. The overhead generated by our protocol is lesser by 1% as compared to FTAR and by nearly 17% as compared to E2FT when there are nearly 30% faulty nodes.