Simplifying Deep-Learning-Based Model for Code Search

To accelerate software development, developers frequently search and reuse existing code snippets from a large-scale codebase, e.g., GitHub. Over the years, researchers proposed many information retrieval (IR) based models for code search, which match keywords in query with code text. But they fail to connect the semantic gap between query and code. To conquer this challenge, Gu et al. proposed a deep-learning-based model named DeepCS. It jointly embeds method code and natural language description into a shared vector space, where methods related to a natural language query are retrieved according to their vector similarities. However, DeepCS' working process is complicated and time-consuming. To overcome this issue, we proposed a simplified model CodeMatcher that leverages the IR technique but maintains many features in DeepCS. Generally, CodeMatcher combines query keywords with the original order, performs a fuzzy search on name and body strings of methods, and returned the best-matched methods with the longer sequence of used keywords. We verified its effectiveness on a large-scale codebase with about 41k repositories. Experimental results showed the simplified model CodeMatcher outperforms DeepCS by 97% in terms of MRR (a widely used accuracy measure for code search), and it is over 66 times faster than DeepCS. Besides, comparing with the state-of-the-art IR-based model CodeHow, CodeMatcher also improves the MRR by 73%. We also observed that: fusing the advantages of IR-based and deep-learning-based models is promising because they compensate with each other by nature; improving the quality of method naming helps code search, since method name plays an important role in connecting query and code

Interpolation of the GNSS Wet Troposphere Delay

Troposphere delay is one of the main distance-dependent errors in Global Navigation Satellite Systems (GNSS) observations. Precise estimation of the troposphere wet delay is necessary to aid ambiguity resolution and for positioning in network Real-Time Kinematic (RTK) and Precise Point Positioning. Wet tropospheric estimates can also serve as a source of atmospheric information to facilitate weather forecasting. Interpolation of the troposphere wet delay is thus required when its estimation is interrupted for short periods or when data are processed at higher intervals from that of available data. The objective of this research is to compare the performance of several interpolation methods that can be used in order to suggest the most appropriate technique. Six interpolation models were considered. The models ranged from the easy-to-implement linear model, to the more sophisticated Kriging model. Other models considered are the cubic spline interpolation, cubic Hermite polynomial interpolation, Lagrange polynomial interpolation, and Fast Fourier transform interpolation. The performance of these methods was assessed by comparing their results with actual troposphere wet delay data collected at the station Onsala (ONSA) in Sweden. As the number of observations used to generate the interpolation process affects the determination of the model coefficients; the use of different lengths of observations was investigated. The number of missing wet delay values considered for interpolation during testing ranged from one to four in a row.Test results showed that the linear interpolation, the cubic Hermite polynomial and fast Fourier transform models produce better estimates than splines and ordinary Kriging. The Lagrange polynomials method was the poorest performer. The paper provides explanation of the interpolation results achieved by linking them with autocorrelation of the troposphere wet delays

Dynamic Modelling of GNSS Troposphere Wet delay for Estimation of Precipitable Water Vapour

Proper dynamic modelling of the troposphere wet delay using the Global Navigation Satellite Systems (GNSS) measurements is important in precise point positioning and in estimation of the Perceptible Water Vapour (PWV) for weather forecast. The random walk (RW) and the first-order Gauss-Markov (GM) autocorrelation models are commonly used for this purpose. However, it was found that these models consistently underestimate the temporal correlations that exist among the ZWD estimates. Therefore, a new dynamic model is proposed. The performance of the proposed model in following the autocorrelation of actual data is demonstrated and its impact on the near-real time estimation of the ZWD was tested and compared to that of the GM and RW models. Results showed that the proposed model outperformed these models. When the computed ZWD were used to compute PWV, their estimated values were very close to actual PWV data measured by radiosonde with differences less than 1 mm

Body Dysmorphic-Induced Androgenic Anabolic Steroids Usage and Its Association with Mental Health Outcomes: A Systematic Review

This systematic review on body dysmorphic disorder (BDD), anabolic-androgenic steroid (AAS) use, and mental health outcomes aim to examine the relationship between them. While looking at prior research, it has been suggested that individuals with BDD may be more likely to abuse AAS due to their preoccupation with appearance but, unfortunately, AAS use may worsen mental health issues in this population and exacerbate the issue. This systematic review will also discuss treatments that will help mitigate the effects of AAS on individuals with BDD. The review will include only published literature from a variety of databases such as PubMed, Scopus, Embase, and PsycINFO from 1992-2022. Two reviewers independently screened studies for inclusion criteria, extracted pertinent data, and assessed the quality of the evidence. The findings of this review will provide important insights into the complex relationship between BDD, AAS use, and mental health outcomes, and will inform future researchers and health practitioners on effective interventions and treatments for individuals with BDD who may be considering AAS use

Impact of Heat and Mass Transfer on MHD Oscillatory Flow of Jeffery Fluid in a Porous Channel with Thermal Conductivity, Dufour and Soret

The objective of this paper is to study Dufour, Soret and thermal conductivity on unsteady heat and mass transfer of magneto hydrodynamic (MHD) oscillatory flow of Jeffery fluid through a porous medium in a channel. The partial differential equations governing the flow have been solved numerically using semi-implicit finite-difference scheme with the aid of MATLAB software. The results obtained are displayed graphically and in tabular form to illustrate the effect of various parameters on the dimensionless velocity, temperature and concentration profiles, to show the effects of different parameters entering in the problem. Results from these study shows that velocity and temperature increases with the increase of Soret and Dufour why the thermal conductivity increases as temperature profile increasing. Also, it is observed that soret number increases as concentration profile decreases.Keywords: Heat and Mass Transfer, Dufour, Soret, Jeffery Fluid and Thermal Conductivit

Dynamic Modelling of Zenith Wet Delay in GNSS Measurements

Proper modelling of the temporal correlations of the zenith wet delay (ZWD) is important in some of the Global Navigation Satellite Systems (GNSS) applications such as estimation of the Perceptible Water Vapour (PWV), and methods such as Precise Point Positioning (PPP). The random walk (RW) and the first-order Gauss- Markov (GM) autocorrelation model are commonly used for the dynamic modelling of ZWD in Kalman filtering of GNSS measurements. However, it was found that the GM model consistently underestimates the temporal correlations that exist among the ZWD estimates. Therefore, a new autocorrelation dynamic model is proposed in a form similar to that of a hyperbolic function. The impact of the proposed dynamic model on the near-real time estimation of the ZWD was tested and its results were compared to that of the GM model as well as the RW model. In this test, GPS dual-frequency data collected on the 25th Jan 2010 at two Western Australian IGS stations, namely, Yarragadee and Karratha, were used. Results showed that the proposed model outperformed the GM model, and when added to hydrostatic models were able to provide near real-time (with 30 seconds intervals) ZTD estimates to within a few cm accuracy

The effect of ruminal incubation of bioactive yeast (Saccharomyces cerevisiae) on potential rumen degradability of Panicum maximum and Centrosema pubescens in West African dwarf sheep

The rising interest in the use of organic and inorganic substances in manipulating rumen function for improved fermentative activity has provided avenues for the inclusion of various species of yeast cultures in ruminant diets. In this study, we investigated the effect of bioactive yeast (Saccharomyces cerevisiae), on rumen degradative function of the West African Dwarf Sheep (WADS) in terms of fermentable organic matter, crude protein and crude fiber of Panicum maximum and Centrosema pubescens. Three inclusion levels of Saccharomyces cerevisiae, 200, 500 and 800 milligrams were infused into the rumen of three groups (A, B and C), of three WAD sheep each. Another group (D) of same animal number served as the control. In vivo rumen potential degradability studies, using the nylon bag technique was performed using Panicum maximum and Centrosema pubescens in all the groups. The result of the study showed  that bioactive yeast improved the potential rumen degradability of crude protein, crude fibre and organic matter fractions of Panicum maximum and Centrosema pubescens in a rather dose dependent manner compared to the control. There was a significant correlation (r = 0.56) between degradability, dose and time of incubation for crude fibre and organic  matter fractions but not for crude protein. These observations suggest that regulated dietary inclusion of bioactive yeast can be used to bioengineer the rumen towards efficient fibre breakdown, particularly forages of poor protein quality and high fibre content, for efficient production of volatile fatty acids as well as probably enhancing other aspects of rumen functions.Keywords: Bioactive Yeast, Degradability, Forages, Rumen, WADS

A new monoclinic polymorph of 3-diethyl­amino-4-(4-meth­oxy­phen­yl)-1,1-dioxo-4H-1λ6,2-thia­zete-4-carbonitrile

A new monoclinic form of the title compound, C14H17N3O3S, has been found upon slow crystallization from water. Another monoclinic form of the compound was obtained previously from a mixture of dichloro­methane and diethyl ether [Clerici et al. (2002 ▶). Tetra­hedron, 58, 5173–5178]. Both phases crystallize in space group P21/n with one mol­ecule in the asymmetric unit. The formally single exocyclic C—N bond that connects the –NEt2 unit with the thia­zete ring is considerably shorter than the adjacent, formally double, endocyclic C=N bond. This is likely to be due to the extended conjugated system between the electron-donor diethyl­ammine fragment and the electron-withdrawing sulfonyl group. In the newly discovered polymorph, the meth­oxy group is rotated by almost 180° around the phen­yl–OCH3 bond, resulting in a different mol­ecular conformation

Impact of stochastic modelling on GPS height and zenith wet delay estimation

Most stochastic modelling techniques assume the physical correlations among the raw observations to be negligible when forming the variance covariance matrix of the GPS observations. Such an assumption may, however, lead to significantly biased solutions. The Minimum Norm Quadratic Unbiased Estimation (MINQUE) method is an iterative technique that can be used to estimate spatial correlation among GPS measurements. Studies by previous authors have shown that MINQUE improves the accuracy and the reliability of the ambiguity resolution, and ultimately, the geodetic solution. However, its effect on the estimation of zenith wet delay (ZWD) is somewhat unknown. In this paper, an investigation into its impact on ZWD, as well as heighting, is carried out using simulated data. The results obtained from MINQUE for an observation window of five-days in static mode indicate an average improvement of 51% and 71% in the station height precision when compared against elevation-angle dependent and equal weighting models, respectively. This development, however, did not translate into better ZWD estimation, for which the differences between each respective stochastic model are generally at the sub-millimetre level