Influence of B-site cations ordering on magnetization and dielectric relaxations in Li–Ni spinel ferrites

Ho-substituted Li–Ni ferrites with composition L i_1.2Ni_0.4Ho_xFe_2-xO₄; 0≤ x ≤ 0.15 were synthesized by a self-ignited sol-gel process. An annealing temperature of 950 °C is estimated via thermal-gravimetric (TGA) analysis. X-ray diffraction (XRD) scans have confirmed the formation of the ferrite phase with a spinel structure in all samples. Substitution of Ho ions on the B-site significantly reduced the porosity from 38 -to 23% and the crystallite size from 23.4 -to 21.7 nm. Microstructural analysis revealed a denser structure with an increase in Ho content. Dielectric results showed that both the dielectric loss and dielectric constant depict a nonlinear variation with the addition of Ho. Complex impedance behavior with a single semicircle for all samples suggests the predominant effect of the grain boundary mechanism. The substitution of Ho ions in place of Fe ions significantly decreased the electrical conductivity. The anisotropic Ho³⁺ ions reinforce the L-S coupling which consequently enhanced the coercive force from 145 -to 389 Oe, and thus the anisotropy constant.     

Robust LCMV Beamformer for Direction of Arrival Mismatch Without Beam Broadening

Wireless Personal Communications - In this work, we propose a new and efficient algorithm to mitigate the signal look direction error problem in adaptive beamforming without broadening the main...     

Turbulent flow in a machine honed rough pipe for large Reynolds numbers: General roughness scaling laws

An alternate inner wall variable, for flow over a transitional rough pipe surface, is defined as the ratio of normal coordinate measured above the mean roughness level to the wall roughness scale. The Reynolds equations for mean turbulent flow in a transitional rough pipe, in two layers (inner and outer) are considered. The predictions of the mean velocity and friction factor in fully developed turbulent flow in a rough pipe flow, presented here, covers all types of roughness. The data for a particular case of the machine honed Princeton superpipe roughness, analogous to inflectional type roughness of Nikuradse, is presented, as two expressions using our roughness scale. The velocity profile and friction factor, on a transitional rough wall, are shown to be governed by the new log laws, which are explicitly independent of the transitional wall roughness. Further, the inflectional roughness has also been connected with geometric roughness parameters; like, arithmetic mean roughness, mean peak to valley heights roughness, root mean square (rms), roughness based on texture measure; and the friction factor implicit and approximate explicit formulas have also been proposed. In entire transition region between fully smooth and fully rough wall, monotonic roughness of Colebrook (Moody Chart) over estimaton the friction factor when compared with present inlectional roughness.     

AdDroid: Rule-Based Machine Learning Framework for Android Malware Analysis

Mobile Networks and Applications - Recent years have witnessed huge growth in Android malware development. Colossal reliance on Android applications for day to day working and their massive...     

The State of the Art in Visual Analysis Approaches for Ocean and Atmospheric Datasets

The analysis of ocean and atmospheric datasets offers a unique set of challenges to scientists working in different application areas. These challenges include dealing with extremely large volumes of multidimensional data, supporting interactive visual analysis, ensembles exploration and visualization, exploring model sensitivities to inputs, mesoscale ocean features analysis, predictive analytics, heterogeneity and complexity of observational data, representing uncertainty, and many more. Researchers across disciplines collaborate to address such challenges, which led to significant research and development advances in ocean and atmospheric sciences, and also in several relevant areas such as visualization and visual analytics, big data analytics, machine learning and statistics. In this report, we perform an extensive survey of research advances in the visual analysis of ocean and atmospheric datasets. First, we survey the task requirements by conducting interviews with researchers, domain experts, and end users working with these datasets on a spectrum of analytics problems in the domain of ocean and atmospheric sciences. We then discuss existing models and frameworks related to data analysis, sense‐making, and knowledge discovery for visual analytics applications. We categorize the techniques, systems, and tools presented in the literature based on the taxonomies of task requirements, interaction methods, visualization techniques, machine learning and statistical methods, evaluation methods, data types, data dimensions and size, spatial scale and application areas. We then evaluate the task requirements identified based on our interviews with domain experts in the context of categorized research based on our taxonomies, and existing models and frameworks of visual analytics to determine the extent to which they fulfill these task requirements, and identify the gaps in current research. In the last part of this report, we summarize the trends, challenges, and opportunities for future research in this area. (see http://www.acm.org/about/class/class/2012 )     

Data-Oriented Scheduling with Dynamic-Clustering Fault-Tolerant Technique for Scientific Workflows in Clouds

Programming and Computer Software - Cloud computing is one of the most prominent parallel and distributed computing paradigm. It is used for providing solution to a huge number of scientific and...     

Integrating salient colors with rotational invariant texture features for image representation in retrieval systems

Multimedia Tools and Applications - Content based image retrieval (CBIR) systems allow searching for visually similar images in large collections based on their contents. Visual contents are...     

An optimized hybrid algorithm in term of energy and performance for mapping real time workloads on 2d based on-chip networks

In this paper, we propose an optimized, search based near-optimal mapping heuristic, named as ONMAP for mapping real time embedded application workloads on 2D based on-chip interconnection network platforms. ONMAP exploits NMAP, a well-known and fast nearest neighbor heuristic algorithm by using the modular exact optimization method. The proposed hybrid algorithm minimizes the on-chip inter-processor communication energy consumption and optimizes the interconnection network performance parameters. The algorithm inherits the constructive search based heuristic nature of the NMAP algorithm, as well as the property of exact optimization for mapping embedded applications on the target communication architecture. To verify the efficiency and effectiveness of the algorithm, we have compared the proposed algorithm with NMAP and random mapping algorithm under similar simulation environments and traffic conditions. The mapping results of the exemplary real world applications such as VOPD, PIP, MPEG4, MWD, MMS and WiFi-80211arx indicate that ONMAP algorithm is more efficient than its competitors for most of the performance parameters of the on-chip network designs. The algorithm successfully optimized the energy consumption, up to 20 % and 26% in comparison to NMAP and random algorithms, respectively. Similarly, the cost is optimized up to 10% and 60% as compared to NMAP and random mapping algorithms, respectively.     

Similarity-based prioritization of test case automation

The importance of efficient software testing procedures is driven by an ever increasing system complexity as well as global competition. In the particular case of manual test cases at the system integration level, where thousands of test cases may be executed before release, time must be well spent in order to test the system as completely and as efficiently as possible. Automating a subset of the manual test cases, i.e, translating the manual instructions to automatically executable code, is one way of decreasing the test effort. It is further common that test cases exhibit similarities, which can be exploited through reuse when automating a test suite. In this paper, we investigate the potential for reducing test effort by ordering the test cases before such automation, given that we can reuse already automated parts of test cases. In our analysis, we investigate several approaches for prioritization in a case study at a large Swedish vehicular manufacturer. The study analyzes the effects with respect to test effort, on four projects with a total of 3919 integration test cases constituting 35,180 test steps, written in natural language. The results show that for the four projects considered, the difference in expected manual effort between the best and the worst order found is on average 12 percentage points. The results also show that our proposed prioritization method is nearly as good as more resource demanding meta-heuristic approaches at a fraction of the computational time. Based on our results, we conclude that the order of automation is important when the set of test cases contain similar steps (instructions) that cannot be removed, but are possible to reuse. More precisely, the order is important with respect to how quickly the manual test execution effort decreases for a set of test cases that are being automated.     

In vitro germination and biochemical profiling of citrus reticulata in response to green synthesised zinc and copper nanoparticles

Green synthesis of nanoparticles by using plants is an emerging class of nanobiotechnology. It revolutionizes all the fields of nanobiotechnology by synthesizing chemical‐free nanoparticles for various purposes. In the present study, zinc and copper nanoparticles were synthesized by using the white leaves of Allium cepa and further characterized through Zeta analyzer and Scanning electron microscopy. Zeta analyzer elucidated that zinc nanoparticles ranged from 8‐32 nm while copper nanoparticles ranged from 15‐30 nm. Scanning electron microscopy clarified that zinc nanoparticles were irregular while copper nanoparticles were spherical in shape. The effects of green synthesized nanoparticles were evaluated on the germination frequency and biochemical parameters of plant tissues. The nucellus tissues were inoculated on Murashige and Skoog (MS) medium augmented with 30 µg/ml suspension of zinc and copper nanoparticles. Green synthesized nanoparticles enhanced the in vitro germination parameters because of their low toxicity and high efficacy. Significant results were obtained for germination parameters in response to the applications of zinc nanoparticles as compared to copper nanoparticles. These nanoparticles could also induce stress in plantlets by manipulating the endogenous mechanism as a result various defence compounds are produced which have potential in treating various human ailments. Copper nanoparticles showed higher toxicity as compared to zinc nanoparticles and triggered the production of antioxidative enzymes and non‐ enzymatic compounds.Inspec keywords: botany, zinc, copper, nanoparticles, nanofabrication, biochemistry, scanning electron microscopy, electrokinetic effects, biological tissues, toxicology, nanobiotechnology, biological techniquesOther keywords: in vitro germination, biochemical profiling, citrus reticulata, green synthesised zinc nanoparticles, green synthesised copper nanoparticles, green chemistry, secondary metabolites, nanoparticles synthesis, white leaves, Allium cepa, zeta analyser, scanning electron microscopy, onion extract, nucellus tissues, Murashige‐Skoog medium, biologically synthesised nanoparticles, toxicity, root length, shoot length, seedling vigour index, plantlets, endogenous mechanism, human ailments, antioxidative enzymes, nonenzymatic compounds, size 8 nm to 32 nm, Zn, Cu