The Bivariate Transmuted Family of Distributions: Theory and Applications

The bivariate distributions are useful in simultaneous modeling of two random variables. These distributions provide a way to model models. The bivariate families of distributions are not much widely explored and in this article a new family of bivariate distributions is proposed. The new family will extend the univariate transmuted family of distributions and will be helpful in modeling complex joint phenomenon. Statistical properties of the new family of distributions are explored which include marginal and conditional distributions, conditional moments, product and ratio moments, bivariate reliability and bivariate hazard rate functions. The maximum likelihood estimation (MLE) for parameters of the family is also carried out. The proposed bivariate family of distributions is studied for the Weibull baseline distributions giving rise to bivariate transmuted Weibull (BTW) distribution. The new bivariate transmuted Weibull distribution is explored in detail. Statistical properties of the new BTW distribution are studied which include the marginal and conditional distributions, product, ratio and conditional momenst. The hazard rate function of the BTW distribution is obtained. Parameter estimation of the BTW distribution is also done. Finally, real data application of the BTW distribution is given. It is observed that the proposed BTW distribution is a suitable fit for the data used.     

Mechanisms of Nausea and Vomiting: Current Knowledge and Recent Advances in Intracellular Emetic Signaling Systems

Nausea and vomiting are common gastrointestinal complaints that can be triggered by diverse emetic stimuli through central and/or peripheral nervous systems. Both nausea and vomiting are considered as defense mechanisms when threatening toxins/drugs/bacteria/viruses/fungi enter the body either via the enteral (e.g., the gastrointestinal tract) or parenteral routes, including the blood, skin, and respiratory systems. While vomiting is the act of forceful removal of gastrointestinal contents, nausea is believed to be a subjective sensation that is more difficult to study in nonhuman species. In this review, the authors discuss the anatomical structures, neurotransmitters/mediators, and corresponding receptors, as well as intracellular emetic signaling pathways involved in the processes of nausea and vomiting in diverse animal models as well as humans. While blockade of emetic receptors in the prevention of vomiting is fairly well understood, the potential of new classes of antiemetics altering postreceptor signal transduction mechanisms is currently evolving, which is also reviewed. Finally, future directions within the field will be discussed in terms of important questions that remain to be resolved and advances in technology that may help provide potential answers.     

Painting-91: a large scale database for computational painting categorization

Computer analysis of visual art, especially paintings, is an interesting cross-disciplinary research domain. Most of the research in the analysis of paintings involve medium to small range datasets with own specific settings. Interestingly, significant progress has been made in the field of object and scene recognition lately. A key factor in this success is the introduction and availability of benchmark datasets for evaluation. Surprisingly, such a benchmark setup is still missing in the area of computational painting categorization. In this work, we propose a novel large scale dataset of digital paintings. The dataset consists of paintings from 91 different painters. We further show three applications of our dataset namely: artist categorization, style classification and saliency detection. We investigate how local and global features popular in image classification perform for the tasks of artist and style categorization. For both categorization tasks, our experimental results suggest that combining multiple features significantly improves the final performance. We show that state-of-the-art computer vision methods can correctly classify 50 % of unseen paintings to its painter in a large dataset and correctly attribute its artistic style in over 60 % of the cases. Additionally, we explore the task of saliency detection on paintings and show experimental findings using state-of-the-art saliency estimation algorithms.     

Three Dimensional Conceptualisation of Hydrogeological Environment to Underpin Groundwater Management in Irrigation Area

Australian irrigated agriculture utilises about 70?% of all water used in the country, 21?% of which is derived from groundwater. Sustainability for irrigated agriculture also depends on keeping the watertables at a safe level below the rootzone to avoid salinisation and reduction in crop yields. There is a vital need to understand groundwater and aquifer systems and their roles in the sustainability of irrigated agriculture in order to manage groundwater properly. This study builds on the previous hydrogeological and groundwater investigations of the Coleambally Irrigation Area (CIA) in New South Wales of Australia. It presents a new approach which systematically characterises regional hydrogeological environment using a three-dimensional (3-D) conceptual framework developed in ArcGIS. The 3-D hydrological conceptualisation of the CIA has integrated disparate sources of data into a coherent knowledge base for a better visualisation of hydrogeological characteristics and a comprehensive analysis of groundwater flow and aquifers. As an application example, the model was used to develop cross-sectional models of the area and to estimate regional-scale net recharge. The results have provided a basis for the numerical modelling and added values to procedures which underpin irrigation system management investment decisions through improving the understanding of hydrogeology underlying the area and creating an action-oriented dialogue among stakeholders.     

Compact Deep Color Features for Remote Sensing Scene Classification

Neural Processing Letters - Aerial scene classification is a challenging problem in understanding high-resolution remote sensing images. Most recent aerial scene classification approaches are based...     

UNICORE 6 — Recent and Future Advancements

UNICORE is a European Grid Technology with more than 10 years of history. Originating from the Supercomputing domain, the latest version UNICORE 6 has turned into a general-purpose Grid technology that follows established standards and offers a rich set of features to its users. The paper starts with an architectural insight into UNICORE 6, highlighting the workflow features, standards and the different clients. Next, the current state of advancement is presented by describing recent developments. The paper closes with an outlook on future planned developments.     

A new mathematical approach to improve residual stress measurement in ring-core method

Journal of Mechanical Science and Technology - III-conditioning at the inverse equation of integral method in ring-core residual stress measurement leads to high stress sensitivity to strain...     

A SAR-based ΣΔ modulator using shared DAC

In this paper a new successive approximation (SA) quantizer based on the elimination of the digital to analog converter (DAC) from the quantizer structure is presented. Instead; the feedback DAC block of the ΣΔ modulator is shared by SA quantizer. Using an efficient decoding algorithm in the proposed structure in conjunction with the above SA quantizer DAC elimination method, results in a reduction of the level number of the feedback DAC, and hence, a significant drop in power and area consumption is achieved. In order to study the performance of the proposed structure, a third order discrete-time ΣΔ modulator is designed and simulated in 0.18 μm CMOS technology with the following performance characteristics; a signal to noise ratio of 79.2 dB, dynamic range of 84.8 dB, power consumption of 3.75 mW and a figure of merit of 0.66 pJ/conv from a 1.8 V supply with an input signal of 200 kHz bandwidth.