Electrical and magnetic properties of lithium borate glass containing iron

Journal of Materials Science Letters -     

Effect of the addition of Fe2O3 and heat treatment duration on the magnetic susceptibility of V2O5-P2O5-B2O3 glass system

The effect of the addition of Fe₂O₃ and heat treatment duration on the magnetic susceptibility of vanadium borophosphate glass were studied. The magnetic susceptibility of glass samples was found to increase with increasing Fe₂O₃ content, which may be explained by the formation of the FeO₆ group and the change of Fe²⁺ to Fe³⁺ which has higher paramagnetic properties. No detectable changes in the magnetic susceptibility with heat treatment for the samples containing 0.0, 0.5 and 1.0 mol% Fe₂O₃ was observed. The magnetic susceptibility for the heat treated samples containing 2.5, 5.0 and 7.5 mol% Fe₂O₃ decreases sharply with increasing duration of heat treatment up to 6 h and then remains almost constant. The sharp decrease in magnetic susceptibility of 2.5 mol% Fe₂O₃ is attributed to the increase in the number of ferrous ions. The sharp decrease for samples containing 5.0 and 7.5 mol% Fe₂O₃ is attributed to the increase in the number of Fe³⁺ in tetrahedral co-ordination. The rate of crystallization owing to the heat treatment was calculated and was found to increase with increasing iron oxide content. The geometry of crystallization was found to be in three-, two-and one-dimension(s) for samples containing 2.5, 5.0 and 7.5 mol% Fe₂O₃, respectively.     

Potential Chemical Markers for the Identification of Irradiated Sausages

Abstract: Hydrocarbons, gas compounds, and off‐odor volatiles were determined for irradiated (0 or 5 kGy) commercial sausages with different fat contents (16% and 29%) during a 60‐d storage period at 4 °C. Total of 4 hydrocarbons (C14:1, C15:0, C16:2, and C17:1) were detected only in irradiated sausages: the amount of C16:2 was the highest, followed by C17:1, C14:1, and C15:0. The concentrations of hydrocarbons decreased significantly (P < 0.05) with storage, but were still detectable at the end of 60‐d storage. Irradiated sausages produced significantly higher amounts of CO than the nonirradiated ones. CH₄ was detected only in irradiated sausages. Dimethyl disulfide was detected only in irradiated sausages and its concentration decreased significantly (P < 0.05) with storage. Fat content of sausages showed a significant effect on the production and retention of hydrocarbons, gas compounds, and sulfur volatiles in irradiated sausages during storage. Some hydrocarbons (C16:2, C17:1, C14:1, and C15:0), CH₄, and dimethyl disulfide were only found in irradiated sausages indicating that these compounds can be used as potential markers for irradiated sausages.     

Influence of metal‐oxide‐supported bentonites on the pyrolysis behavior of polypropylene and high‐density polyethylene

In this article, we report on the pyrolysis of polypropylene (PP) and high‐density polyethylene (HDPE) in the absence and presence of plain and metal‐oxide‐impregnated bentonite clays [BCs; acid‐washed bentonite clay (AWBC), Zn/AWBC, Ni/AWBC, Co/AWBC, Fe/AWBC, and Mn/AWBC] into useful products. Thermal and catalytic runs were performed at 300°C in the case of PP and at 350°C in the case of HDPE for a contact time of 30 min. The effects of different catalysts and their concentrations on the overall yields and the yields of liquid, gas, and residue were studied. The efficacy of each catalyst is reported on the basis of the highest liquid yields (in weight percentage). The derived liquid products were analyzed by Fourier transform infrared spectroscopy and gas chromatography–mass spectroscopy; this confirmed the presence of paraffins, olefins, and naphthenes. The results indicate the catalytic role of impregnated BCs compared to plain BC with the optimum efficiency shown by Co/AWBC in the case of PP and Zn/AWBC in the case of HDPE toward the formation of liquid products in a desirable C range with the enrichment of olefins and naphthenes in the case of PP and paraffins and olefins in the case of HDPE compared to the thermal run. © 2014 Wiley Periodicals, Inc. J. Appl. Polym. Sci. 2015 , 132, 41221.     

Bacterial inactivation in water,DNA strand breaking,and membrane damage induced by ultraviolet-assisted titanium dioxide photocatalysis

The effects of UV-assisted TiO₂-photocatalytic oxidation (PCO) inactivation of pathogenic bacteria (Escherichia coli O157:H7, Listeria monocytogenes, Salmonella typhimurium) in a liquid culture using different domains of UV irradiation (A, B and C) were evaluated. Structural changes in super-coiled plasmid DNA (pUC19) and genomic DNA of E. coli were observed using gel electrophoresis to demonstrate the photodynamic DNA strand breaking activity of UV-assisted TiO₂-PCO. Membrane damage in bacterial cells was observed using both a scanning electron microscope (SEM) and a confocal laser scanning microscope (CLSM). Both UVC-TiO₂-PCO and UVC alone resulted in an earlier bactericidal phase (initial counts of approximately 6 log CFU/mL) in 60 s and 90 s, respectively, in liquid culture. UVC-TiO₂-PCO treatment for 6 min converted all plasmid DNA to the linear form; however, under UVC irradiation alone, super-coiled DNA remained. Prolonged UVC-TiO₂-PCO treatment resulted in structural changes in genomic DNA from E. coli. SEM observations revealed that bacteria suffered severe visible cell damage after UVC-TiO₂-PCO treatment for 30–60 min. S. typhimurium cells showed visible damage after 30 min, which was confirmed using CLSM. All treated cells were stained red using propidium iodide under a fluorescent light.     

Deep Learning and Machine Learning-Based Model for Conversational Sentiment Classification

In the current era of the internet, people use online media for conversation, discussion, chatting, and other similar purposes. Analysis of such material where more than one person is involved has a spate challenge as compared to other text analysis tasks. There are several approaches to identify users’ emotions from the conversational text for the English language, however regional or low resource languages have been neglected. The Urdu language is one of them and despite being used by millions of users across the globe, with the best of our knowledge there exists no work on dialogue analysis in the Urdu language. Therefore, in this paper, we have proposed a model which utilizes deep learning and machine learning approaches for the classification of users’ emotions from the text. To accomplish this task, we have first created a dataset for the Urdu language with the help of existing English language datasets for dialogue analysis. After that, we have preprocessed the data and selected dialogues with common emotions. Once the dataset is prepared, we have used different deep learning and machine learning techniques for the classification of emotion. We have tuned the algorithms according to the Urdu language datasets. The experimental evaluation has shown encouraging results with 67% accuracy for the Urdu dialogue datasets, more than 10, 000 dialogues are classified into five emotions i.e., joy, fear, anger, sadness, and neutral. We believe that this is the first effort for emotion detection from the conversational text in the Urdu language domain.     

Model-based design verification for embedded systems through SVOCL: an OCL extension for SystemVerilog

Model Based System Engineering (MBSE) is a renowned approach in the context of embedded systems development. It is frequently used to deal with the structural and behavioral aspects of system design. However, the verification of system design is generally performed in isolation. It is particularly true in the context of assertion based verification. Consequently, there is a huge gap between system design and its verification that seriously effects the productivity and time-to market objectives. Therefore, in this research, we target to reduce this gap by exploiting the features of MBSE and SystemVerilog assertions (SVA’s). This article introduces a novel MBSE approach to model the design verification aspects of embedded systems, along with the system design (structural and behavioral aspects). We propose SystemVerilog in Object Constraint Language (SVOCL), an OCL temporal extension for SystemVerilog, to represent the design verification requirements by means of SVA’s. As a part of research, SVOCL transformation engine has been developed to generate SVA’s code in order to automate the design verification of embedded systems. The application of SVOCL has been validated through four case studies.     

Space Efficient Quantization for Deep Convolutional Neural Networks

Deep convolutional neural networks (DCNNs) have shown outstanding performance in the fields of computer vision, natural language processing, and complex system analysis. With the improvement of performance with deeper layers, DCNNs incur higher computational complexity and larger storage requirement, making it extremely difficult to deploy DCNNs on resource-limited embedded systems (such as mobile devices or Internet of Things devices). Network quantization efficiently reduces storage space required by DCNNs. However, the performance of DCNNs often drops rapidly as the quantization bit reduces. In this article, we propose a space efficient quantization scheme which uses eight or less bits to represent the original 32-bit weights. We adopt singular value decomposition (SVD) method to decrease the parameter size of fully-connected layers for further compression. Additionally, we propose a weight clipping method based on dynamic boundary to improve the performance when using lower precision. Experimental results demonstrate that our approach can achieve up to approximately 14x compression while preserving almost the same accuracy compared with the full-precision models. The proposed weight clipping method can also significantly improve the performance of DCNNs when lower precision is required.