Paper mill sludge as a renewable substrate for the production of acetone-butanol-ethanol using Clostridium sporogenes NCIM 2337

In the present study, pre-treated paper mill sludge (PMS) was evaluated extensively as a substrate for production of acetone-butanol-ethanol using Clostridium sporogenes NCIM 2337. The PMS was subjected to three types of pre-treatment methods namely alkali, mechanical, and thermal treatment and was analyzed by SEM. The pre-treatment of PMS by alkali was observed to be more effective over the other pre-treatment methods. The alkali pre-treated sludge was then made to undergo fermentation, which showed the conventional process of acidogenesis followed by solventogenesis. The acetone, butanol, and ethanol concentration for 15% alkali pre-treated PMS was estimated to be maximum.     

ZnO energy transfer and enhanced photoluminescence in MEH-PPV/ZnO hybrid nanocomposite

Hybrid organic–inorganic nanocomposites are great candidates for display and illumination systems due to improved optoelectronic properties and photostability. This work endeavours towards the scientific study of the influence of defect-induced zinc oxide nanoparticles (ZnO) on the optical characteristics of poly[2-methoxy-5-(2′-ethylhexyloxy)-1,4-phenylenevinylene] (MEH-PPV). ZnO nanoparticles consist of many vacancies which facilitate light emission across the visible region. The green defective emission occurring due to the presence of oxygen vacancies in ZnO was used to re-excite MEH-PPV and hence, improve the luminescence quantum efficiency. The photostability of the nanocomposite was enhanced through charge transfer (prevents the formation of superoxides) and energy transfer (reduces the non-radiative decay) mechanisms.

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Selective fractionation of cholesterol from whole milk powder: optimisation of supercritical process conditions

Supercritical fluid extraction (SCFE) of cholesterol from whole milk powder (WMP) was investigated in this study. The combined effects of temperature (40–80 °C) and pressure (150–250 bar) on the efficacy of cholesterol extraction (mg 100 g^?1), modifications in the fat content (FC) (%) and solubility index (SI) (%) of WMP were studied and optimised by the application of response surface methodology (RSM). Variations in the free fatty acids (FFAs) (mg oleic acid per 100 g of milk fat) and lightness value (L*) were also investigated after SCFE process. About 55.8% reduction in cholesterol was achieved at the optimised condition of 68 °C, 207 bar with 40 min static time and 2 h dynamic time at flow rate of 6 L min^?1. Extraction at the optimised conditions maximised the yield of cholesterol while retaining the FC, SI, FFA and L* at moderate limits of 23.7%, 85.1%, 7.7 mg per 100 g milk fat and 95.4, respectively.     

An innovative hybrid approach for extracting named entities from unstructured text data

Named entity recognition (NER) is the core part of information extraction that facilitates the automatic detection and classification of entities in natural language text into predefined categories, such as the names of persons, organizations, locations, and so on. The output of the NER task is crucial for many applications, including relation extraction, textual entailment, machine translation, information retrieval, etc. Literature shows that machine learning and deep learning approaches are the most widely used techniques for NER. However, for entity extraction, the abovementioned approaches demand the availability of a domain‐specific annotated data set. Our goal is to develop a hybrid NER system composed of rule‐based deep learning as well as clustering‐based approaches, which facilitates the extraction of generic entities (such as person, location, and organization) out of natural language texts of domains that lack generic named entities labeled domain data sets. The proposed approach takes the advantages of both deep learning and clustering approaches but separately, in combination with a knowledge‐based approach by using a postprocessing module. We evaluated the proposed methodology on court cases (judgments) as a use case since it contains generic named entities of different forms that are poorly or not present in open‐source NER data sets. We also evaluated our hybrid models on two benchmark data sets, namely, Computational Natural Language Learning (CoNLL) 2003 and Open Knowledge Extraction (OKE) 2016. The experimental results obtained from benchmark data sets show that our hybrid models achieved substantially better performance in terms of the F‐score in comparison to other competitive systems.     

Joint beam selection and user association strategy for beam space millimetre wave downlink systems

Beam space multiple‐input multiple‐output (B‐MIMO) systems operating at mmWave frequency in downlink uses different beam selection techniques for reducing the required number of radio frequency chains without apparent performance loss. In this paper, a joint beam selection and user association scheme with the objective of sum rate maximization is proposed. The proposed work on beam selection is based on a norm and uncorrrelation based approach, which considers channel correlation among the users so as to avoid inter‐user interference. Two different methods for beam user association with one modelled as a balanced assignment problem based on average sum assignment method and the other method that associates an optimal beam to every user by considering their channel gain values are also proposed along with beam selection. The proposed beam assignment methods are less complex and efficient in finding a suitable beam to every user within the cell. Simulation results show that the proposed method accomplish comparable performance in terms of sum rate and energy efficiency with appreciably low computational complexity relative to the existing methods in a correlated environment.     

A novel whispered speaker identification system based on extreme learning machine

Whispered speech speaker identification system is one of the most demanding efforts in automatic speaker recognition applications. Due to the profound variations between neutral and whispered speech in acoustic characteristics, the performance of conventional speaker identification systems applied on neutral speech degrades drastically when compared to whisper speech. This work presents a novel speaker identification system using whispered speech based on an innovative learning algorithm which is named as extreme learning machine (ELM). The features used in this proposed system are Instantaneous frequency with probability density models. Parametric and nonparametric probability density estimation with ELM was compared with the hybrid parametric and nonparametric probability density estimation with Extreme Learning Machine (HPNP-ELM) for instantaneous frequency modeling. The experimental result shows the significant performance improvement of the proposed whisper speech speaker identification system.     

An Investigation of Secret Key Generation for Physical Layer Security Using Wavelet Packets

Wireless Personal Communications - This work investigates the effectiveness of wavelet packets in dynamic secret key generation (DSKG) for physical layer security (PLS). Preprocessing channel...     

Mass + retention time = structure: a strategy for the analysis of N-glycans by carbon LC-ESI-MS and its application to fibrin N-glycans

Analysis of the numerous possible, often isobaric structures of protein-bound oligosaccharides calls for a high-performance two-dimensional method that combines liquid chromatography's ability to separate isomers and mass spectrometry's ability to determine glycan composition. Here we investigate the usefulness of porous graphitic carbon columns coupled to ESI-MS for the separation of N-glycans with two or more sialic acids. Internal standards helped to rectify retention time fluctuations and thus allowed elution times to play an essential role in the structural assignment of peaks. For generation of a retention time library, standards representing the possible isomers of diantennary non-, mono-, and disialylated N-glycans, differing in the linkage of galactose and sialic acids as well as isobaric hybrid-type N-glycans, were produced using recombinant glycosyltransferases. Once the retention times library was established, isomers could be identified by LC-ESI-MS in the positive mode without additional MS/MS experiments. The method was applied for the detailed structural analysis of fibrin(ogen) N-glycans from various species (human, cow, pig, mouse, rat, cat, dog, Chinese hamster, horse, sheep, and chicken). All fibrins contained diantennary N-glycans. They differed in the occurrence of beta1,3-linked galactose, alpha2,3-linked sialic acids, and N-glycolylneuraminic acid, in the mono/diantennary glycan ratio, and in the O-acetylation of neuraminic acids. The separation system's potential for analyzing tri- and tetrasialylated N-glycans was demonstrated.     

Effect of Mg on Solidification Microstructures of Homogenous and Functionally Graded A390 Aluminum Alloys

Hypereutectic Al?CSi alloys are used in components that require high resistance wear and corrosion, good mechanical properties, low thermal expansion and less density. The size and morphology of hard primary silicon particles present in Al?CSi alloys greatly influences the mechanical properties. Addition of Mg leads to formation of intermetallic Mg₂Si phases, which contributes towards the properties of high silicon alloy as well as alters the nature and quantity of primary silicon formed. The high silicon alloy subjected to centrifugal casting leads to the formation of functionally gradient material, which provides variation in spatial and continuous distribution of primary phases in a definite direction exhibiting selective properties and functions within a component. The present study is to evaluate the effect of Mg on solidification microstructures of homogenous and functionally graded A390 aluminium alloys. The addition of Mg from 3 to 5?% in A390 alloy using Al?C20Mg master alloy has shown a transformation from primary silicon rich matrix to Mg₂Si rich matrix. Centrifugal casting shows the gradient distribution of primary silicon and Mg₂Si phases towards the inner periphery of the casting.