Effect of vanadium and tin additives on structural and magnetic properties of Bi-YIG nano-particles

In this research work, substituted tin and vanadium garnets Y_2.5Bi_0.5Fe_2?2xSn_xFe_3?xV_x O₁₂ (0?≤?x?≤?0.2) were prepared by mechanochemical processing following with 10 h milling and post-annealing at different temperatures. Physical properties of samples were studied by X-ray diffraction analysis (XRD), Far-infrared spectroscopy (Far-IR), and field emission scanning electron microscopy (FESEM), energy-dispersive X-ray spectroscopy (EDS), and vibrating sample magnetometer (VSM). XRD patterns of prepared garnets show that the samples are all single phase with garnet structure at 900 °C. Further, the average crystallite size was calculated by Debye–Scherrer, and Williamson–Hall methods. We also studied the magnetic properties of prepared samples by using a vibrating sample magnetometer (VSM). Experiments showed that the maximum value of vanadium that can be entered in the garnet structure is 0.2.

     

Stille cross‐coupling applied to get higher molecular weight polymers: Synthesis,optoelectronic, Voc properties,and solar cell application

Conjugated polymers are highly desirable for the photovoltaic applications. We report the synthesis, characterization, optoelectronic properties, and solar cell application of two polymers, namely, poly[(9,9‐didodecylfluorene‐2,7‐diyl)‐alt‐(2,2′:5′,2″‐terthiophene‐5,5″‐diyl)] (P1) and poly[(1,4‐bis(dodecyloxy)benzene‐2,5‐diyl)‐alt‐(2,2′:5′,2″‐terthiophene‐5,5″‐diyl)] (P2). The polymers were synthesized via Stille cross‐coupling reaction, and were characterized by the gel permeation chromatography, nuclear magnetic resonance, Fourier transform infrared, UV–vis, thermogravimetric analysis, and cyclic voltammetry analyses. The two copolymers are processable due to their good solubility in organic solvents (tetrahydrofuran, CHCl₃, toluene, chlorobenzene, and o‐dichlorobenzene). The optical band gaps (UV–vis, film, and E_g^opt) of the P1 and P2 are 2.04 and 2.00 eV, respectively. The density functional theory output structures showed that S ^… O space interaction is likely responsible for the higher planarity of P2. The polymers showed low HOMO energy levels (P1: −5.33 eV, P2: −5.05 eV). The E_HOMO for P1 is close to the E_HOMO (−5.4 eV) of an ideal polymer, which is an important, rare, and main origin of the observed higher V_oc (801–808 mV). The onset decomposition temperatures (T_d) for the P1 and P2 are 418°C and 365°C, respectively. The polymer solar cell based on the P1: C₆₀ (1: 1) and P2: C₆₀ (1: 1) blend showed a power conversion efficiency (PCE) of 0.94 and 0.71%, respectively. The composite polymer : PC₆₀BM = 1 : 2 increased PCE of the P1 (1.65%) and P2 (1.09%) under AM 1.5 illumination (100 mW/cm²). The study provided important examples to design donor–donor (D–D) polymers for the photovoltaic applications. © 2015 Wiley Periodicals, Inc. J. Appl. Polym. Sci. 2015 , 132, 42147.     

Tolerance analysis of mechanical assemblies based on modal interval and small degrees of freedom (MI-SDOF) concepts

Tolerance analysis is a key analytical tool for estimation of accumulating effects of the individual part tolerances on the design specifications of a mechanical assembly. This paper presents a new feature-based approach to tolerance analysis for mechanical assemblies with geometrical and dimensional tolerances. In this approach, geometrical and dimensional tolerances are expressed by small degrees of freedom (SDOF) of geometric entities (faces, feature axes, edges, and features of size) that are described by tolerance zones. The uncertainty of dimensions and geometrical form of features due to tolerances is mathematically described using modal interval arithmetic. The two concepts of modal interval analysis and SDOF are combined to describe the tolerance specifications. The algorithm is presented which explains the steps and the procedure of tolerance analysis. The proposed method is compatible with the current GD&T standards and can incorporate GD&T concepts such as various material modifiers (maximum material condition, least material condition, and regardless of feature size), envelope requirement, and bonus tolerances. This method can take into account multidimensional effects due to geometrical tolerances in tolerance analysis. The application of the proposed method is illustrated through presenting an example problem and comparing results with tolerance charting method.     

In situ vibration enhanced pressure slip casting of submicrometer alumina powders

This paper presents a novel method for improvement of particle packing in consolidation of submicrometer alumina powders by pressure slip casting. In this method, filtration cell is subjected to a mechanical vibration field with constant frequency of 50 Hz and vibration amplitudes ranging from 0 (no vibration) to 2 mm. Filtration rate, thickness and green density of the fabricated samples were measured to investigate the influence of vibration on filtration characteristics. It was revealed that employment of vibration can significantly increase filtration rate. Furthermore, there is an optimum vibration amplitude which results in the structure with the highest packing density. This value is shifted to higher vibration amplitudes as more concentrated alumina slurries is used. As the available formulation based on Darcy's law could not predict the results of the present investigation, a “Correction Factor” was utilized in order to increase the accuracy of the prediction in the presence of a vibration field.     

Understanding the Language of ISIS: An Empirical Approach to Detect Radical Content on Twitter Using Machine Learning

The internet, particularly online social networking platforms have revolutionized the way extremist groups are influencing and radicalizing individuals. Recent research reveals that the process initiates by exposing vast audiences to extremist content and then migrating potential victims to confined platforms for intensive radicalization. Consequently, social networks have evolved as a persuasive tool for extremism aiding as recruitment platform and psychological warfare. Thus, recognizing potential radical text or material is vital to restrict the circulation of the extremist chronicle. The aim of this research work is to identify radical text in social media. Our contributions are as follows: (i) A new dataset to be employed in radicalization detection; (ii) In depth analysis of new and previous datasets so that the variation in extremist group narrative could be identified; (iii) An approach to train classifier employing religious features along with radical features to detect radicalization; (iv) Observing the use of violent and bad words in radical, neutral and random groups by employing violent, terrorism and bad words dictionaries. Our research results clearly indicate that incorporating religious text in model training improves the accuracy, precision, recall, and F1-score of the classifiers. Secondly a variation in extremist narrative has been observed implying that usage of new dataset can have substantial effect on classifier performance. In addition to this, violence and bad words are creating a differentiating factor between radical and random users but for neutral (anti-ISIS) group it needs further investigation.     

Enhancing breast pectoral muscle segmentation performance by using skip connections in fully convolutional network

The precise detection and segmentation of pectoral muscle areas in mediolateral oblique (MLO) views is an essential step in the development of a computer-aided diagnosis system to access breast malignant lesions or parenchyma. The goal of this article is to develop a robust and fully automatic algorithm for pectoral muscle segmentation from mammography images. This paper presents an image enhancement approach that improves the quality of mammogram scans and a convolutional neural network-based fully convolutional network architecture enhanced with residual connections for automatic segmentation of the pectoral muscle from the MLO views of a digital mammogram. For this purpose, the model is tested and trained on three different mammogram datasets named MIAS, INBREAST, and DDSM. The ground truth labels of the pectoral muscle were identified under the supervision of experienced radiologists. For training and testing, 10-fold cross-validation was used. The proposed model was compared with baseline U-Net-based architecture. Finally, we used a postprocessing step to find the actual boundary of the pectoral muscle. Our presented architecture generated a mean Intersection over Union (IoU) of 97%, dice similarity coefficient (DSC) of 96% and 98% accuracy on testing data. The proposed architecture for pectoral muscle segmentation from the MLO views of mammogram images with high accuracy and dice score can be quickly merged with the breast tumor segmentation problem.     

Bioenergy from rice crop residues: role in developing economies

Energy demands of industry, agriculture, transport and domestic sectors of a developing nation are primarily in terms of electricity and transportation fuel. Rice is a major crop in many developing countries. The residues of this crop, viz. rice husk, and rice straw have high potential for bioenergy generation. This review article tries to explore potential of this bio-resource and emphasizes its effective utilization for energy production through techno-economic analysis. The structure, properties, and treatment of rice crop residues have been described. A literature review in production of various biofuels through thermo-chemical and biochemical conversion of rice straw and husk has been presented. Finally, brief literature review on economic analysis of production of liquid and gaseous biofuels from rice crop residues through biochemical and thermo-chemical routes has been presented. This analysis reveals that production of different biofuels from rice crop residues is economically viable. This review emphasizes that bioenergy from rice crop residues provides simultaneous solution to issues of energy security and climate change risk in developing nations.     

Isolation and characterisation of locally isolated Gluconacetobacter xylinus BCZM sp. with nanocellulose producing potentials

Recently, attention has been given to nanocellulose produced by bacteria due to its unique properties and environmentally friendly nature when compared with plant cellulose. Bacterial nanocellulose (BNC) producing isolate was successfully isolated from rotten fruits via dilution and spread plates method. Based on the biochemical characterisation and molecular analysis of the 16S rDNA gene, the isolate was identified as Gluconacetobacter xylinus BCMZ sp. Nanocellulose productivity was confirmed by the formation of the white gelatinous layer between air/liquid surfaces when the culture was cultivated under a stationary condition at 30°C. Successful purification of nanocellulose was achieved using alkaline treatment method. The Fourier transformed infrared spectrum showed a characteristics band signature of pure nanocellulose, by displaying strong absorption peaks at 3335.36 and 2901.40 cm⁻¹ representing carbonyl and carbon–hydrogen bonding, respectively. Morphological characteristics of the BNC were determined by scanning electron microscopy (SEM). Elemental analysis of BNC was determined by energy dispersive X‐ray (SEM/EDX) analysis. The isolates BCZM showed significant nanocellulose production ability with a high degree of purity when compared with plant nanocellulose. BNC purification using 1 M NaOH solution is effective and eco‐friendly with no indication of recalcitrant formation as commonly found in plant nanocellulose purification steps.Inspec keywords: microorganisms, purification, scanning electron microscopy, X‐ray chemical analysis, Fourier transform infrared spectroscopy, biotechnologyOther keywords: locally isolated Gluconacetobacter xylinus BCZM sp, nanocellulose producing potentials, bacterial nanocellulose producing isolate, BNC producing isolate, rotten fruits, dilution, spread plates method, biochemical characterisation, molecular analysis, white gelatinous layer, air/liquid surfaces, nanocellulose purification, alkaline treatment method, Fourier transformed infrared spectrum, characteristics band signature, scanning electron microscopy, elemental analysis, energy dispersive X‐ray analysis, SEM analysis, EDX analysis, plant nanocellulose, BNC purification, recalcitrant formation