The dynamic impact of uncertainty in causing and forecasting the distribution of oil returns and risk

The aim of this study is to analyze the relevance of recently developed news-based measures of economic policy and equity market uncertainty in causing and predicting the conditional quantiles of crude oil returns and risk. For this purpose, we studied both the causality relationships in quantiles through a non-parametric testing method and, building on a collection of quantiles forecasts, we estimated the conditional density of oil returns and volatility, the out-of-sample performance of which was evaluated by using suitable tests. A dynamic analysis shows that the uncertainty indexes are not always relevant in causing and forecasting oil movements. Nevertheless, the informative content of the uncertainty indexes turns out to be relevant during periods of market distress, when the role of oil risk is the predominant interest, with heterogeneous effects over the different quantiles levels.http://www.elsevier.com/locate/physa2019-10-01hj2018Economic

Asymptotic Performance of Linear Receivers in MIMO Fading Channels

Linear receivers are an attractive low-complexity alternative to optimal processing for multi-antenna MIMO communications. In this paper we characterize the information-theoretic performance of MIMO linear receivers in two different asymptotic regimes. For fixed number of antennas, we investigate the limit of error probability in the high-SNR regime in terms of the Diversity-Multiplexing Tradeoff (DMT). Following this, we characterize the error probability for fixed SNR in the regime of large (but finite) number of antennas. As far as the DMT is concerned, we report a negative result: we show that both linear Zero-Forcing (ZF) and linear Minimum Mean-Square Error (MMSE) receivers achieve the same DMT, which is largely suboptimal even in the case where outer coding and decoding is performed across the antennas. We also provide an approximate quantitative analysis of the markedly different behavior of the MMSE and ZF receivers at finite rate and non-asymptotic SNR, and show that while the ZF receiver achieves poor diversity at any finite rate, the MMSE receiver error curve slope flattens out progressively, as the coding rate increases. When SNR is fixed and the number of antennas becomes large, we show that the mutual information at the output of a MMSE or ZF linear receiver has fluctuations that converge in distribution to a Gaussian random variable, whose mean and variance can be characterized in closed form. This analysis extends to the linear receiver case a well-known result previously obtained for the optimal receiver. Simulations reveal that the asymptotic analysis captures accurately the outage behavior of systems even with a moderate number of antennas.Comment: 48 pages, Submitted to IEEE Transactions on Information Theor

Barrier modification in sub-barrier fusion reactions using Wong formula with Skyrme forces in semiclassical formalism

We obtain the nuclear proximity potential by using semiclassical extended Thomas Fermi (ETF) approach in Skyrme energy density formalism (SEDF), and use it in the extended $\ell$-summed Wong formula under frozen density approximation. This method has the advantage of allowing the use of different Skyrme forces, giving different barriers. Thus, for a given reaction, we could choose a Skyrme force with proper barrier characteristics, not-requiring extra ``barrier lowering" or ``barrier narrowing" for a best fit to data. For the $^{64}$Ni+$^{100}$Mo reaction, the $\ell$-summed Wong formula, with effects of deformations and orientations of nuclei included, fits the fusion-evaporation cross section data exactly for the force GSkI, requiring additional barrier modifications for forces SIII and SV. However, the same for other similar reactions, like $^{58,64}$Ni+$^{58,64}$Ni, fits the data best for SIII force. Hence, the barrier modification effects in $\ell$-summed Wong expression depends on the choice of Skyrme force in extended ETF method.Comment: INPC2010, Vancouver, CANAD

Analysis for Symptoms of Human Fall using Pre-Processing and Segmentation based on Deep Learning Architectures

By building sensor-based alert systems, physical therapists can not only decrease the after-fall repercussions but even save lives.Older people are prone to several diseases, and falling is a regular occurrence for them during this period.Various fall detection systems have recently been developed, with computer vision-based approaches being one of the most promising and effective. Here, the sensor-based data has been analysed for a patient's human fall symptoms. This data has been pre-processed using Gaussian filtering with kernel neural network in which the data has been normalized and trained based on neural network. The trained normalized data has been segmented using encoded Stacked Deconvolutional Network (EnSt-DeConvNet). We found that the suggested method predicts such fall symptoms with the highest accuracy from sensor data. Other algorithms' accuracy results, on the other hand, are also fairly close. Experiments reveal that the suggested technique, when compared to other generally utilized techniques based on multiple cameras fall dataset, produced reliable findings and that our dataset, which consists of more training samples, produced even better results. Experimental results showaccuracy of 96%, Precision of 94%, Recall of 88% and F-1 score of 82%, computational time of 69%

Time-Varying Priority Queuing Models for Human Dynamics

Queuing models provide insight into the temporal inhomogeneity of human dynamics, characterized by the broad distribution of waiting times of individuals performing tasks. We study the queuing model of an agent trying to execute a task of interest, the priority of which may vary with time due to the agent's "state of mind." However, its execution is disrupted by other tasks of random priorities. By considering the priority of the task of interest either decreasing or increasing algebraically in time, we analytically obtain and numerically confirm the bimodal and unimodal waiting time distributions with power-law decaying tails, respectively. These results are also compared to the updating time distribution of papers in the arXiv.org and the processing time distribution of papers in Physical Review journals. Our analysis helps to understand human task execution in a more realistic scenario.Comment: 8 pages, 6 figure

Distraction Osteogenesis In The Management Of Temporomandibular Joint Ankylosis; Series of cases.

 Patients with temporomandibular joint ankylosis commonly present with mandibular hypoplasia as a result of trauma to the temporomandibular joint, middle ear infection or due to various syndromes. There is a wide acceptance of the conventional osteotomies for treating temporomandibular joint ankylosis, but there are certain limitations pertaining to them. In order to overcome these limitations several new approaches with modifications have been introduced. One among these is the method of gradual bone elongation known as distraction osteogenesis.  This process induces new bone formation along the vector of distraction force without requiring the use of a bone graft.   This study was conducted on four patients (2 females and 3 males within the age group of 16-30 years) in which 3 patients had bilateral temporomandibular joint ankylosis and one patient with unilateral temporomandibular joint ankylosis.. These patients underwent surgical correction of temporomandibular joint ankylosis and mandibular hypoplasia using distraction osteogenesis with extra-oral  distraction device under general anesthesia.In this study we have used extraoral device to achieve distraction more than 20 mm and to overcome the limitations of intra oral devices. This study concluded that distraction osteogenesis is the treatment of choice for the temporomandibular joint reconstruction and anterior linear advancement of the hypoplastic mandible in whom the mandibular advancement is highly difficult to be achieved by the conventional osteotomy procedures. The relapse rate over a period of 5 year is very minimal

Synthesis and Structural Analysis of Nanocrystalline MnFe2O4

AbstractNanocrystalline form of manganese ferrite (MnFe2O4) has been synthesized by simple sol-gel auto combustion method using citric acid as chelating agent. The obtained nanocrystalline powders of manganese ferrite were subjected to structural and magnetic measurements. Temperature dependent magnetization was also carried out for the single phase nanocrystalline manganese ferrite and the results have been discussed in detail

An Efficient Information Extraction Mechanism with Page Ranking and a Classification Strategy based on Similarity Learning of Web Text Documents

Users have recently had more access to information thanks to the growth of the www information system. In these situations, search engines have developed into an essential tool for consumers to find information in a big space. The difficulty of handling this wealth of knowledge grows more difficult every day. Although search engines are crucial for information gathering, many of the results they offer are not required by the user because they are ranked according on user string matches. As a result, there were semantic disparities between the terms used in the user inquiry and the importance of catch phrases in the results. The problem of grouping relevant information into categories of related topics hasn't been solved. A Ranking Based Similarity Learning Approach and SVM based classification frame work of web text to estimate the semantic comparison between words to improve extraction of information is proposed in the work. The results of the experiment suggest improvisation in order to obtain better results by retrieving more relevant results

A brief review of the impact of silver nanoparticles on agriculture and certain biological properties: A case study

Nanotechnology is progressively becoming a popular field of research because it has been successful in changing our agricultural and food systems. According to research published by the UNFAO, agriculture as well as its derivatives would be in high demand sooner or later, owing to nutritional changes. Nanoparticles have been reported to be used in an agricultural sector, because of its capacity to encourage crop growth and yield. Among metal nanoparticles, Silver Nanoparticles (AgNPs) are attracting a lot of attention. We have highlighted some of the agricultural uses of AgNPs, which include pest management, plant disease detection, crop enhancement, and crop production