Flux growth of crystals of some transition metal fluorides

The flux growth of crystals of the following complex fluorides is reported: KAlF₄, KMnF₃, RbFeF₄, Rb₂FeF₅, Rb₃FeF₆, Rb _x FeF₃ (0.18<x<0.29), CsFeF₄, CsFe₂F₇, Cs₃Fe₂F₉, Cs _x FeF₃ (0.18<x<0.29), Na₂CoFeF₇, Na₂NiFeF₇, Na₂NiAlF₇, Na₂ZnCrF₇, NaCrF₄ and Rb₂Cr₅F₁₇. Flux impurity levels, determined by electron probe microanalysis (EPMA), were low. However, attempts to produce BaNiF₄, BaCoF₄, CsCrF₄ and Cs₂Cr₅F₁₇ resulted in crystals with higher levels of substitutional impurities. X-ray powder patterns and EPMA were used to characterize the crystals.     

Synthesis ofGEM-dibromocyclopropanoid fatty esters using phenyl (tribromomethyl) mercury

Thegem-dibromocyclopropanation of naturally occurring unsaturated hydroxy fatty esters, methyl ricinoleate (I) and methyl isoricinoleate (II), has been undertaken to provide compounds which might have potential utility. Phenyl(tribromomethyl)mercury reacts only with the carbon-carbon double bond of each substrate, leaving the OH group intact. The product, methyl 9,10-dibromomethylene-12-hydroxyoctadecanoate (III) and methyl 12,13-dibromomethylene-9-hydroxyoctadecanoate (IV), obtained from I and II, respectively, were characterized by elemental, infrared (IR) and nuclear magnetic resonance (NMR) analyses.     

Physicochemical studies of hemp (Cannabis sativa) seed oil using enzyme‐assisted cold‐pressing

The effects of enzyme‐assisted cold‐pressing (EACP) on the physicochemical attributes of Cannabis sativa (hemp) seed oil were investigated using five enzyme preparations: Protex 7L, Viscozyme L, Kemzyme, Feedzyme, and Natuzyme. The oil contents (28.4–32.8%) offered by the enzyme‐treated hempseeds were found to be significantly (p <0.05) higher than that determined for the control (26.7%). The protein, fiber, and ash contents of the seeds were unaffected by the enzyme treatment. There were no significant (p >0.05) variations observed for the values of iodine number, refractive index, density, unsaponifiable matter and fatty acid composition between the enzyme‐extracted and control hempseed oils. The levels of saponification value, free fatty acids, iodine value and peroxide value were slightly varied between the oils tested. The color intensity of the enzyme‐extracted oils was also higher than that of the control oil. A relatively higher level of tocopherols (724.4–788.8 mg/kg) was observed in the enzyme‐extracted oils compared to the control (691.2 mg/kg), showing an enhancement of ca. 4.8–14.1% in the total tocopherols. The Rancimat profiles and sensory scores of the enzyme‐extracted oils were noted to be improved compared to the control. The results of the present analysis (with respect to the control) showed that the enzyme added during the hempseed cold‐pressing resulted in considerably higher oil yields, without adversely affecting the quality of the oil.     

Fabrication and characterization of Cd-enriched CdTe thin films by close spaced sublimation

Cd-enriched cadmium telluride (CdTe) polycrystalline films were grown on corning glass substrates by close spaced sublimation (CSS) technique. To our knowledge, Cd-enriched CdTe thin films by CSS have not been reported earlier. The structural investigations performed by means of X-ray diffraction (XRD) technique, scanning electron microscope (SEM), and energy dispersive X-ray spectroscopy (EDX) showed that the deposited films exhibit a polycrystalline structure with 〈111〉 as preferred orientation. The structural, optical, and electrical properties of these films were analyzed as a function of the Cd concentration. For the films having an excess of Cd, the electrical resistivity dropped several orders of magnitude. The deposited films also showed that the value of resistivity decreased with increasing temperature manifesting the semiconducting behavior of the films. The results showed that using this deposition technique, n-type Cd-enriched CdTe polycrystalline film could be produced.     

NS-2 based simulation framework for cognitive radio sensor networks

In this paper, we propose a simulation model for cognitive radio sensor networks (CRSNs) which is an attempt to combine the useful properties of wireless sensor networks and cognitive radio networks. The existing simulation models for cognitive radios cannot be extended for this purpose as they do not consider the strict energy constraint in wireless sensor networks. Our proposed model considers the limited energy available for wireless sensor nodes that constrain the spectrum sensing process—an unavoidable operation in cognitive radios. Our model has been thoroughly tested by performing experiments in different scenarios of CRSNs. The results generated by the model have been found accurate which can be considered for realization of CRSNs.     

Nonlinear convective flow with variable thermal conductivity and Cattaneo-Christov heat flux

An analysis is introduced to investigate the salient features of nonlinear convective flow of thixotropic fluid in the version of Cattaneo-Christov heat flux theory. The stagnation point flow is present. The flow phenomenon is by an impermeable stretching sheet. The energy expression is modeled through the theory of Cattaneo-Christov heat flux. Characteristics of heat transfer phenomenon are described within the frame of variable thermal conductivity. Suitable variables reduced to the nonlinear partial differential expressions to the ordinary differential expressions. Series solutions of resulting systems are acquired within the frame of homotopy theory. Convergence analysis is achieved and suitable values are determined by capturing the so-called ℏ−curves. Graphical results for velocity and temperature are displayed and argued for sundry physical variables. Expression of skin friction coefficient is calculated through numerical values. Higher values of mixed convection parameter, Prandtl number, and thermal relaxation time lead to decay the temperature and layer thickness.

     

A Secure NDN Framework for Internet of Things Enabled Healthcare

Healthcare is a binding domain for the Internet of Things (IoT) to automate healthcare services for sharing and accumulation patient records at anytime from anywhere through the Internet. The current IP-based Internet architecture suffers from latency, mobility, location dependency, and security. The Named Data Networking (NDN) has been projected as a future internet architecture to cope with the limitations of IP-based Internet. However, the NDN infrastructure does not have a secure framework for IoT healthcare information. In this paper, we proposed a secure NDN framework for IoT-enabled Healthcare (IoTEH). In the proposed work, we adopt the services of Identity-Based Signcryption (IBS) cryptography under the security hardness Hyperelliptic Curve Cryptosystem (HCC) to secure the IoTEH information in NDN. The HCC provides the corresponding level of security using minimal computational and communicational resources as compared to bilinear pairing and Elliptic Curve Cryptosystem (ECC). For the efficiency of the proposed scheme, we simulated the security of the proposed solution using Automated Validation of Internet Security Protocols and Applications (AVISPA). Besides, we deployed the proposed scheme on the IoTEH in NDN infrastructure and compared it with the recent IBS schemes in terms of computation and communication overheads. The simulation results showed the superiority and improvement of the proposed framework against contemporary related works.     

An Efficient False-Positive Reduction System for Cerebral Microbleeds Detection

Cerebral Microbleeds (CMBs) are microhemorrhages caused by certain abnormalities of brain vessels. CMBs can be found in people with Traumatic Brain Injury (TBI), Alzheimer’s disease, and in old individuals having a brain injury. Current research reveals that CMBs can be highly dangerous for individuals having dementia and stroke. The CMBs seriously impact individuals’ life which makes it crucial to recognize the CMBs in its initial phase to stop deterioration and to assist individuals to have a normal life. The existing work report good results but often ignores false-positive’s perspective for this research area. In this paper, an efficient approach is presented to detect CMBs from the Susceptibility Weighted Images (SWI). The proposed framework consists of four main phases (i) making clusters of brain Magnetic Resonance Imaging (MRI) using k-mean classifier (ii) reduce false positives for better classification results (iii) discriminative feature extraction specific to CMBs (iv) classification using a five layers convolutional neural network (CNN). The proposed method is evaluated on a public dataset available for 20 subjects. The proposed system shows an accuracy of 98.9% and a 1.1% false-positive rate value. The results show the superiority of the proposed work as compared to existing states of the art methods.     

A Framework for Systematic Classification of Assets for Security Testing

Over the last decade, a significant increase has been observed in the use of web-based Information systems that process sensitive information, e.g., personal, financial, medical. With this increased use, the security of such systems became a crucial aspect to ensure safety, integrity and authenticity of the data. To achieve the objectives of data safety, security testing is performed. However, with growth and diversity of information systems, it is challenging to apply security testing for each and every system. Therefore, it is important to classify the assets based on their required level of security using an appropriate technique. In this paper, we propose an asset security classification technique to classify the System Under Test (SUT) based on various factors such as system exposure, data criticality and security requirements. We perform an extensive evaluation of our technique on a sample of 451 information systems. Further, we use security testing on a sample extracted from the resulting prioritized systems to investigate the presence of vulnerabilities. Our technique achieved promising results of successfully assigning security levels to various assets in the tested environments and also found several vulnerabilities in them.     

A Novel Auto-Annotation Technique for Aspect Level Sentiment Analysis

In machine learning, sentiment analysis is a technique to find and analyze the sentiments hidden in the text. For sentiment analysis, annotated data is a basic requirement. Generally, this data is manually annotated. Manual annotation is time consuming, costly and laborious process. To overcome these resource constraints this research has proposed a fully automated annotation technique for aspect level sentiment analysis. Dataset is created from the reviews of ten most popular songs on YouTube. Reviews of five aspects—voice, video, music, lyrics and song, are extracted. An N-Gram based technique is proposed. Complete dataset consists of 369436 reviews that took 173.53 s to annotate using the proposed technique while this dataset might have taken approximately 2.07 million seconds (575 h) if it was annotated manually. For the validation of the proposed technique, a sub-dataset—Voice, is annotated manually as well as with the proposed technique. Cohen's Kappa statistics is used to evaluate the degree of agreement between the two annotations. The high Kappa value (i.e., 0.9571%) shows the high level of agreement between the two. This validates that the quality of annotation of the proposed technique is as good as manual annotation even with far less computational cost. This research also contributes in consolidating the guidelines for the manual annotation process.