Determination of residual organic matter in extracted oil sands using a low temperature ashing method

The application of a low temperature ashing method for estimating total residual organic matter (toluene insolubles) in oil sands is described. A linear correlation exists between organic carbon content and loss on ignition at 400 ± 10 °C of solvent extracted oil sands. The ratio between total organic carbon and the weight loss on ignition (CT/LOl) owing to the removal of residual organic matter is much lower than that obtained for toluene soluble bitumen fractions, indicating very different chemical composition for the residual organic matter. The measured carbon content of the residual organic matter in oil sands suggests that this material could be a mixture of various fractions contained in resins, asphaltenes, asphaltic acids and humic acids.     

Effect of scraped-surface tube cooler temperatures on the physical properties of palm oil margarine

The effects of scraped-surface tube cooler temperatures on the isothermal solid fat content (SFC) of palm oil margarine during processing and on margarine consistency (yield value, g/cm²), SFC, and polymorphic changes in storage were studied. SFC was measured in the mixing tank after leaving the tube cooler and the pin worker. The SFC at the tube cooler exit was proportional to the amount of cooling; a higher SFC was produced by more extreme cooling treatment. The SFC of all margarines were reduced in the pin worker, and the reduction was related to the initial SFC profile of palm oil. Margarine samples were stored at 28°C for 28 d and tested daily. Margarine processed at 25°C in the tube cooler had the highest consistency and the least change in SFC, but by the second week crystals had transformed into the β form. Uniform product consistency and SFC were observed in margarines processed at 20 and 15°C. These margarines retained the β′ crystal form for 3 and 4 wk, respectively. The best palm oil margarine was obtained with a tube cooler temperature of 15°C and a residence time of 1.8 min.     

Effect of Cation Substitution and Non-Stoichiometry on the Microwave Dielectric Properties of Sr(B'0.5Ta0.5)O3 [B'=Lanthanides] Perovskites

The Sr(B'_0.5Ta_0.5)O₃ ceramics where B'=La, Pr, Nd, Sm, Eu, Gd, Tb, Dy, Ho, Y, Er, and Yb have been prepared by the conventional solid-state ceramic route and their microwave dielectric properties have been investigated. The structure and microstructure of the ceramics have been characterized by X-ray diffraction and scanning electron microscope techniques. The relative permittiviy (ɛ_r) varies linearly with B'-site ionic radii, except for La, and the temperature coefficient of resonant frequency (τ_f) varies linearly with the tolerance factor. The Sr(B'_0.5Ta_0.5)O₃ ceramics have ɛ_r in the range 25.9–32, Q _u× f =4500–54 300 GHz, and τ_f=−79 to −42 ppm/°C. A slight deviation from stoichiometry affects the dielectric properties of these double perovskites. Partial substitution of Ba for Sr could tune the dielectric properties. Addition of rutile (TiO₂) lowered the sintering temperature and improved the dielectric properties of Sr(B'_0.5Ta_0.5)O₃ ceramics.     

KP-Rank: a semantic-based unsupervised approach for keyphrase extraction from text data

Automatic key concept identification from text is the main challenging task in information extraction, information retrieval, digital libraries, ontology learning, and text analysis. The main difficulty lies in the issues with the text data itself, such as noise in text, diversity, scale of data, context dependency and word sense ambiguity. To cope with this challenge, numerous supervised and unsupervised approaches have been devised. The existing topical clustering-based approaches for keyphrase extraction are domain dependent and overlooks semantic similarity between candidate features while extracting the topical phrases. In this paper, a semantic based unsupervised approach (KP-Rank) is proposed for keyphrase extraction. In the proposed approach, we exploited Latent Semantic Analysis (LSA) and clustering techniques and a novel frequency-based algorithm for candidate ranking is introduced which considers locality-based sentence, paragraph and section frequencies. To evaluate the performance of the proposed method, three benchmark datasets (i.e. Inspec, 500N-KPCrowed and SemEval-2010) from different domains are used. The experimental results show that overall, the KP-Rank achieved significant improvements over the existing approaches on the selected performance measures.

     

Progress and challenges in sustainability,compatibility, and production of eco-composites: A state-of-art review

Owing to economic and environmental benefits, new generations of materials/commodities follow “from waste to wealth” strategy. Recently, there has been a huge upsurge in research on the development of eco-composites using recycled plastic polymers and agro-residues because the eco-composites satisfy the stringent environment regulations and are cost-effective. Herein, we present a detailed review on the potential use of several types of natural fillers as an efficient reinforcement for recycled plastic polymers. In particular, the characterization of different categories of eco-composites according to their morphological, physical, thermal, and mechanical properties is extensively reviewed and their results are analyzed, compared, and highlighted. Furthermore, a framework to produce functional eco-composites, which includes functionalization of ingredients, critical issues on microstructural parameters, processing, and fabrication methods, is outlined and supported with sufficient data from the literature. Finally, the review outlines the emerging challenges and future prospects of eco-composites to be addressed by interested researchers to bridge the gap between research and commercialization of such a class of material. Overall, the acquired knowledge will guide researchers, scientists, and manufacturers to plan, select, and develop various forms of eco-composites with enhanced properties and optimized production processes.     

Fourier transform infrared (FTIR) authentication and batch-to-batch consistency for different types of paints using benchtop and handheld FTIR spectrophotometers for oil and gas industry

Standardization of Fourier transform infrared (FTIR) fingerprint region for paints and assessment on the reproducibility using different spectrophotometers were investigated. While selective fingerprint regions may be confusing for technicians/analysts who are non-chemists, we attempt to generalize these regions (e.g., 1300–1000 cm⁻¹ for Epoxy part A and 1400–1000 cm⁻¹ for Epoxy part B) by choosing a universal region (2000–900 cm⁻¹) that works for different paints. Comparison result using a paired student t-test shows that the degree of similarity (r) values from the studied regions are not statistically different. The paint fails the screening analysis occasionally on-site when analyzed using handheld FTIR due to the higher level of noise that gives low r values (r < 0.900 ± 0.002). The same samples were analyzed using a benchtop FTIR and the r values are above 0.900 ± 0.002. While the screening may lead to a false rejection of the sample on-site, there could be occurrence of false acceptance. The on-site screening of EPZ part A with different formulations, for instance, shows that the r values over the entire IR spectrum are above 0.900 ± 0.002 when analyzed using handheld FTIR. After the samples were analyzed using the benchtop, the r values fall below 0.900 ± 0.002.     

What can possibly go wrong? Anticipatory work in space operations

This paper explores how different forms of anticipatory work contribute to reliability in high-risk space operations. It is based on ethnographic field work, participant observation and interviews supplemented with video recordings from a control room responsible for operating a microgravity greenhouse at the International Space Station (ISS). Drawing on examples from different stages of a biological experiment on the ISS, we demonstrate how engineers, researchers and technicians work to anticipate and proactively mitigate possible problems. Space research is expensive and risky. The experiments are planned over the course of many years by a globally distributed network of organizations. Owing to the inaccessibility of the ISS, every trivial detail that could possibly cause a problem is subject to scrutiny. We discuss what we label anticipatory work: practices constituted of an entanglement of cognitive, social and technical elements involved in anticipating and proactively mitigating everything that might go wrong. We show how the nature of anticipatory work changes between planning and the operational phases of an experiment. In the planning phase, operators inscribe their anticipation into technology and procedures. In the operational phase, we show how troubleshooting involves the ability to look ahead in the evolving temporal trajectory of the ISS operations and to juggle pre-planned fixes along these trajectories. A key objective of this paper is to illustrate how anticipation is shared between humans and different forms of technology. Moreover, it illustrates the importance of including considerations of temporality in safety and reliability research.     

Objective evaluation of speech dysfluencies using wavelet packet transform with sample entropy

Dysfluency and stuttering are a break or interruption of normal speech such as repetition, prolongation, interjection of syllables, sounds, words or phrases and involuntary silent pauses or blocks in communication. Stuttering assessment through manual classification of speech dysfluencies is subjective, inconsistent, time consuming and prone to error. This paper proposes an objective evaluation of speech dysfluencies based on the wavelet packet transform with sample entropy features. Dysfluent speech signals are decomposed into six levels by using wavelet packet transform. Sample entropy (SampEn) features are extracted at every level of decomposition and they are used as features to characterize the speech dysfluencies (stuttered events). Three different classifiers such as k-nearest neighbor (kNN), linear discriminant analysis (LDA) based classifier and support vector machine (SVM) are used to investigate the performance of the sample entropy features for the classification of speech dysfluencies. 10-fold cross validation method is used for testing the reliability of the classifier results. The effect of different wavelet families on the classification performance is also performed. Experimental results demonstrate that the proposed features and classification algorithms give very promising classification accuracy of 96.67% with the standard deviation of 0.37 and also that the proposed method can be used to help speech language pathologist in classifying speech dysfluencies.