Evaluating the use of Drones in the area of Transportation/Construction

Drones are proving out as a valuable tool and growing quickly in the world of technological advances. The applications of these vehicles are spreading widely in the areas of remote sensing, real time monitoring, goods delivery, security, defense, surveillance, infrastructure inspection. Although, the intent behind creating this tool was remote sensing. Smart drones will be the next big innovation and modification, which would have much wider applications especially in the field of infrastructure where it can reduce risks and lower costs. Current direct evaluation techniques are tedious, and the information caught is frequently not led in a precise manner with the areas tested not being geographically correct and the resulting reports being delivered past the point of no return. These were the reasons, which have increased the demand and usage of unmanned vehicles. In this research paper, we present critical review of main advancements of Drones in the area of transportation and agriculture. We present all the research related to civil applications in those areas and challenges including traffic monitoring, Bridge condition assessment, Roadway asset detection and many other applications related to infrastructure inspection enhancement. The paper also contributes with a discussion on the opportunities, which are opened, and the challenges that need to be addressed. Findings from the case studies, it is reported that around 25% of the bridges in united states are deficient and need continuous monitoring for enhancements to prevent any hazard. Unmanned vehicles could be a great help in monitoring these bridges and other important components of transportation, which can efficiently minimize the cost as well as the time spent on inspection for each of this component, as manual inspection requires labor and time which would be subsequently reduced by incorporating the usage of drones in the area of transportation

Pronunciation as a Stumbling Block for the Saudi English Learners: An Analysis of the Problems and Some Remedies

Pronunciation is an area of L2 learning that has long been relegated to the status of secondary skill. However, it is a mistaken belief or wrong notion that correct pronunciation plays little role in communication. It is observed that in many cases, mispronunciation leads to unintelligibility of speech and/or misinterpretation of the message/information: a barrier to communication. This premise prompted the researchers to study the difficulty in pronunciation experienced by Saudi students. This paper is also an attempt at exploring the pronunciation problems faced by the Saudi students of English and aims to propound possible remedial measures.  The researchers have included students enrolled in the English departments and their teachers at two universities in Saudi Arabia. Primary data was collected from the students and their teachers using surveys, interviews, and classroom observation of students’ presentations. The study made some suggestions regarding materials that can help rectify the pronunciation of English among Saudi learners of English

Spatial risk estimation in Tweedie compound Poisson double generalized linear models

Tweedie exponential dispersion family constitutes a fairly rich sub-class of the celebrated exponential family. In particular, a member, compound Poisson gamma (CP-g) model has seen extensive use over the past decade for modeling mixed response featuring exact zeros with a continuous response from a gamma distribution. This paper proposes a framework to perform residual analysis on CP-g double generalized linear models for spatial uncertainty quantification. Approximations are introduced to proposed framework making the procedure scalable, without compromise in accuracy of estimation and model complexity; accompanied by sensitivity analysis to model mis-specification. Proposed framework is applied to modeling spatial uncertainty in insurance loss costs arising from automobile collision coverage. Scalability is demonstrated by choosing sizable spatial reference domains comprised of groups of states within the United States of America.Comment: 34 pages, 10 figures and 12 table

Causal Relationship between Foreign Institutional Investments, Exchange Rate and Stock Market Index i.e. Sensex in India: an Empirical Analysis

Since the global crisis (2008) emerged in the world economy, the inflows of foreign investors increased in developing countries and India was not the exception in terms of huge investment by foreign investors. India’s capital market recognized as an emerging market in the world and growing fast since the economic liberalization and globalization in 1991. Since 1993, when liberalization policies came in to effect and Indian market opened for foreign investment, the FIIs become the driving force for the overall development of economy as well as pose threat in the development. This paper attempts to analyze the impact of currency fluctuations on the investment by the foreign investment investors, for analyzing the impact and causal relationship, Augmented Dickey-Fuller test and Granger Causality test has been applied, and for analyzing FIIs role in the development of Indian capital market linear regression model has been used. After applying the Granger Causality test, we found that FII granger causes Exchange rate. As far as causality relationship is concerned, a unidirectional causality or one-way causality is found from FII towards exchange rate. As far as the causal relationship between the FIIs and SENSEX, FII are only responsible for up to 45.4%. This means that whatever changes have happened in the SENSEX for period under study the FI investments are responsible up to 45.4%. This implies that there are many other macro-economic factors which have indirectly affected the SENSEX in India. Keywords: FIIs, SENSEX, INRUSD, BSE, Volatility, GDP, RBI, FDICausal Relationship between Foreign Institutional Investments, Exchange Rate and Stock Market Index i.e. Sensex in India: an Empirical Analysi

Spatial Tweedie exponential dispersion models

This paper proposes a general modeling framework that allows for uncertainty quantification at the individual covariate level and spatial referencing, operating withing a double generalized linear model (DGLM). DGLMs provide a general modeling framework allowing dispersion to depend in a link-linear fashion on chosen covariates. We focus on working with Tweedie exponential dispersion models while considering DGLMs, the reason being their recent wide-spread use for modeling mixed response types. Adopting a regularization based approach, we suggest a class of flexible convex penalties derived from an un-directed graph that facilitates estimation of the unobserved spatial effect. Developments are concisely showcased by proposing a co-ordinate descent algorithm that jointly explains variation from covariates in mean and dispersion through estimation of respective model coefficients while estimating the unobserved spatial effect. Simulations performed show that proposed approach is superior to competitors like the ridge and un-penalized versions. Finally, a real data application is considered while modeling insurance losses arising from automobile collisions in the state of Connecticut, USA for the year 2008.Comment: 26 pages, 3 figures and 7 table

Enhancement of total antioxidants and flavonoid (quercetin) by methyl jasmonate elicitation in tissue cultures of onion (Allium cepa L.)

The onion (Allium cepa) is a vegetable used extensively all over the world both for culinary purposes as well as in medicine. Its medicinal values are due to the high levels of biologically-active compounds present within the bulb. There are various phytochemicals of therapeutic importance found in A. cepa. Quercetin, a flavonoid, is one of these phytochemicals and it is a potent antioxidant. Allium cepa is a dietary supplement and is beneficial for diverse ailments, thus justifying its status as a valuable medicinal plant. Due to its medicinal significance, elicitation of total antioxidants and quercetin levels have been attempted to enhance their production in tissue callus cultures. This study reports in vitro enhancement of total antioxidants and quercetin in A. cepa using methyl jasmonate as an elicitor. A reverse phase-high performance liquid chromatography (RP-HPLC) method was used with an isocratic system and a flow rate of 1.0 mL min−1 and a mobile phase of acetonitrile: 1% v/v acetic acid (60%:40% v/v). The detection wavelength was 362 nm and the retention time 8.79 minutes. Total antioxidant and quercetin contents were maximal with 100 µM of methyl jasmonate in leaf tissue callus cultures at 84.61 ±6.03% and 0.81 ±0.03 mg g−1 dry cell weight, respectively. They decreased with further increases of methyl jasmonate at 200 µM. The increase in total antioxidant and quercetin contents were 2.3- and 13.9-fold, respectively. The optimization of methyl jasmonate as an elicitor, as well as the determination of a suitable concentration in A. cepa in callus cultures, will be helpful for enhanced production of various other secondary metabolites of therapeutic significance. This could be beneficial for the pharmaceutical and neutraceutical industries for herbal drug formulations

Tumor radiogenomics in gliomas with Bayesian layered variable selection

We propose a statistical framework to analyze radiological magnetic resonance imaging (MRI) and genomic data to identify the underlying radiogenomic associations in lower grade gliomas (LGG). We devise a novel imaging phenotype by dividing the tumor region into concentric spherical layers that mimics the tumor evolution process. MRI data within each layer is represented by voxel–intensity-based probability density functions which capture the complete information about tumor heterogeneity. Under a Riemannian-geometric framework these densities are mapped to a vector of principal component scores which act as imaging phenotypes. Subsequently, we build Bayesian variable selection models for each layer with the imaging phenotypes as the response and the genomic markers as predictors. Our novel hierarchical prior formulation incorporates the interior-to-exterior structure of the layers, and the correlation between the genomic markers. We employ a computationally-efficient Expectation–Maximization-based strategy for estimation. Simulation studies demonstrate the superior performance of our approach compared to other approaches. With a focus on the cancer driver genes in LGG, we discuss some biologically relevant findings. Genes implicated with survival and oncogenesis are identified as being associated with the spherical layers, which could potentially serve as early-stage diagnostic markers for disease monitoring, prior to routine invasive approaches. We provide a R package that can be used to deploy our framework to identify radiogenomic associations

RADIOHEAD: Radiogenomic Analysis Incorporating Tumor Heterogeneity in Imaging Through Densities

Recent technological advancements have enabled detailed investigation of associations between the molecular architecture and tumor heterogeneity, through multi-source integration of radiological imaging and genomic (radiogenomic) data. In this paper, we integrate and harness radiogenomic data in patients with lower grade gliomas (LGG), a type of brain cancer, in order to develop a regression framework called RADIOHEAD (RADIOgenomic analysis incorporating tumor HEterogeneity in imAging through Densities) to identify radiogenomic associations. Imaging data is represented through voxel intensity probability density functions of tumor sub-regions obtained from multimodal magnetic resonance imaging, and genomic data through molecular signatures in the form of pathway enrichment scores corresponding to their gene expression profiles. Employing a Riemannian-geometric framework for principal component analysis on the set of probability densities functions, we map each probability density to a vector of principal component scores, which are then included as predictors in a Bayesian regression model with the pathway enrichment scores as the response. Variable selection compatible with the grouping structure amongst the predictors induced through the tumor sub-regions is carried out under a group spike-and-slab prior. A Bayesian false discovery rate mechanism is then used to infer significant associations based on the posterior distribution of the regression coefficients. Our analyses reveal several pathways relevant to LGG etiology (such as synaptic transmission, nerve impulse and neurotransmitter pathways), to have significant associations with the corresponding imaging-based predictors