Evaporation estimation from climatic factors

This study assessed the ability of two models, Local Linear Regression (LLR) and Artificial Neural Network (ANN) to estimate monthly potential evaporation from Pantagar, US Nagar (India) which falls under sub-humid and subtropical climatic zone. Observations of relative humidity, solar radiation, temperature, wind speed and evaporation have been used to train and test the developed models. A comparison was made between the estimates provided by the LLR model and ANN model. Results shown that the models were able to well learn the events they were trained to recognize. For ANN model the correlation coefficient for training period is 0.9311 and for testing period is 0.9236 and the value of root mean square error for training period is 1.070 and for testing period it is 0.9863. In case of LLR model the correlation coefficient for training period is 0.9746 and for testing period is 0.9273 and value of root mean square error for training period is 0.6121 and for testing period it is 1.5301.

Investigation of fluorescein stain–based tear film breakup time test reliability in dogs in a clinical setting

OBJECTIVE To determine intra- and interobserver reliability of a fluorescein stain–based tear film breakup time (TFBUT) test as performed in a clinical environment with and without administration of a topical anesthetic. ANIMALS 21 privately owned dogs. PROCEDURES A randomized study design was used. Two independent observers that commonly perform the TFBUT test in clinical practice read the same description of TFBUT. Observers performed TFBUT testing for each dog before and after topical administration of 0.5% proparacaine solution in 4 testing periods with a 1-hour interval between periods. Intraclass correlation coefficient (ICC) analysis was used to assess inter- and intraobserver test reliability. Linear mixed models were used to assess the main effects of testing period, observer, eye, and presence of ophthalmic disorders and their interactions on TFBUT. RESULTS Mean TFBUT measurements performed by observer 1 and observer 2 were 5.9 seconds and 8.6 seconds, respectively, when adjusted for other effects in the model. Intraobserver ICC was poor for one observer and moderate for the other. Interobserver ICC was poor without use of topical anesthetic and slightly lower when anesthetic was used. Observer and testing period were each significantly associated with TFBUT; the measurements decreased and were more variable after multiple applications of fluorescein stain and proparacaine. CONCLUSIONS AND CLINICAL RELEVANCE Results suggested tear film stability is negatively affected by topical administration of 0.5% proparacaine solution and repeated applications of fluorescein stain. The TFBUT test as performed in this study had poor to moderate reliability.

THERMAL BEHAVIOR IMPROVEMENT OF BIODEGRADABLE FIBER POLYMER COMPOSITES POLYLACTIC ACID (PLA)/COIR USING ACEH’S BENTONITE

Research has been conducted on the manufacture of PLA Coir Bentonite composites. This study aims to examine the effect of PLA on mechanical strength with the addition of coir and bentonite fillers from North Aceh and Central Aceh. The sample formulations used were single polymer PLA/Coir and PLA/Coir with variations of filler Bentonite Aceh Utara and Aceh Tengah with 2, 4, 6 and 8% respectively. The combination of PCa samples showed the highest bacterial colony growth rate, which was more than 100 colonies/gram during the 1 week testing period. In the PBATd filler mixture sample, the maximum bacterial test value was 65 colonies/gram and the minimum value contained in the PBAUa sample was 105 colonies/gram. The best tensile strength was obtained in the PBATc sample, namely 65 MPa. PBATd samples began to degrade at 370.15oC compared to PCa samples degraded at 280.21oC. While the PBAUa sample began to degrade at a temperature of 282.11oC. The surface structure of the PCa sample is more homogeneous because there is no bentonite filler mixture, but it is brittle and crumbles easily. For the PBATd sample, the surface structure is smoother and more homogeneous compared to the PBAUa sample.

Generative type-aware mutation for testing SMT solvers

We propose Generative Type-Aware Mutation, an effective approach for testing SMT solvers. The key idea is to realize generation through the mutation of expressions rooted with parametric operators from the SMT-LIB specification. Generative Type-Aware Mutation is a hybrid of mutation-based and grammar-based fuzzing and features an infinite mutation space—overcoming a major limitation of OpFuzz, the state-of-the-art fuzzer for SMT solvers. We have realized Generative Type-Aware Mutation in a practical SMT solver bug hunting tool, TypeFuzz. During our testing period with TypeFuzz, we reported over 237 bugs in the state-of-the-art SMT solvers Z3 and CVC4. Among these, 189 bugs were confirmed and 176 bugs were fixed. Most notably, we found 18 soundness bugs in CVC4’s default mode alone. Several of them were two years latent (7/18). CVC4 has been proved to be a very stable SMT solver and has resisted several fuzzing campaigns.

PSXV-8 The study of feeding behavior traits in connection to feed efficiency in duroc boars bred in Russia

The study of feeding behavior is of particular interest because it is directly related to efficiency of feeding. The aim of our study was to determine the genetic parameters of the feeding behavioral traits in Duroc boars (n = 766) in relation to the feed efficiency based on the analysis of variation components. Genstar and Shauer feeders were used to collect the behavioral data, including daily feed intake (DFI), total daily time spent in feeder (TPD), number of daily visits to feeder (NVD), average duration of each visit (TPV), feed intake per visit (FPV), and feed intake rate (FR). Genetic and environmental variabilities were determined by the mixed model equation using the REMLF90 program. Multiple regression analyses were used to adjust feed conversion rate (FCR) for the duration of the testing period, initial and final body weight, and average daily gain. The average initial body weight was 35.7 kg and the duration of testing period was 78.1 days. Actual FCR values reached 2.20 kg/kg with a phenotypic variability of 26.3%. Moderate values of heritability coefficients (h2) were observed for TPV (h2=0.168), FCR (h2=0.214), and DFI (h2=0.221) traits. The heritability parameters for FPV and TPD traits were higher and accounted to 0.269 and 0.290, respectively. The highest value of h2 was observed for NVD (0.494). Analyses of genetic correlations revealed several interesting findings. The boars, which more often visited feeding stations, spent more time in feeder (r2=+0.536 for NVD/TPD), herewith duration of each visit and feed intake rate were decreased (r2=–0.593 and –0.760 for NVD/TPV and NVD/FR, respectively). Feed conversion rate was positively correlated with TPD (r2=+0.530) and negatively correlated with FR (r2=–0,772). Our research results will be useful for development of artificial selection programs to select Duroc pigs for increased feeding efficiency. The study was supported by RFBR No19-316–90008.

Evaluation of Various Surface Cleaning Techniques Inside Tunnels on Pavement Skid Resistance

A request started a comprehensive investigation on the Skid Resistance (SR) inside a highway tunnel in Madrid, Spain, to determine methods that will improve or maintain the SR above the minimum required level (60), after the application of Hydroblasting, without the need of pavement surface rehabilitation. The right lane located on eight sections inside tunnel XC was used as a test section, in which three went thru the application of water pressure applied weekly, biweekly, and monthly. Three other sections for surface sweeping at the same frequencies and two untreated sections as control. Using the Microgriptester, weekly SR measurements taken before and after tracked the pavement response. Results showed that SR decreased weekly depending on the frequency of the treatment applied and for higher frequencies, SR was almost at the same level. Water pressure and sweeping applied weekly, biweekly, and monthly, during the two months testing period, maintained the SR level above the required value of 60 and the untreated sections showed that pavement surface must be treated to maintain the SR above 60.

The cooperative IGS RT-GIMs: a reliable estimation of the global ionospheric electron content distribution in real time

The Real-Time Working Group (RTWG) of the International GNSS Service (IGS) is dedicated to providing high-quality data and high-accuracy products for Global Navigation Satellite System (GNSS) positioning, navigation, timing and Earth observations. As one part of real-time products, the IGS combined Real-Time Global Ionosphere Map (RT-GIM) has been generated by the real-time weighting of the RT-GIMs from IGS real-time ionosphere centers including the Chinese Academy of Sciences (CAS), Centre National d'Etudes Spatiales (CNES), Universitat Politècnica de Catalunya (UPC) and Wuhan University (WHU). The performance of global vertical total electron content (VTEC) representation in all of the RT-GIMs has been assessed by VTEC from Jason-3 altimeter for 3 months over oceans and dSTEC-GPS technique with 2 d observations over continental regions. According to the Jason-3 VTEC and dSTEC-GPS assessment, the real-time weighting technique is sensitive to the accuracy of RT-GIMs. Compared with the performance of post-processed rapid global ionosphere maps (GIMs) and IGS combined final GIM (igsg) during the testing period, the accuracy of UPC RT-GIM (after the improvement of the interpolation technique) and IGS combined RT-GIM (IRTG) is equivalent to the rapid GIMs and reaches around 2.7 and 3.0 TECU (TEC unit, 1016 el m−2) over oceans and continental regions, respectively. The accuracy of CAS RT-GIM and CNES RT-GIM is slightly worse than the rapid GIMs, while WHU RT-GIM requires a further upgrade to obtain similar performance. In addition, a strong response to the recent geomagnetic storms has been found in the global electron content (GEC) of IGS RT-GIMs (especially UPC RT-GIM and IGS combined RT-GIM). The IGS RT-GIMs turn out to be reliable sources of real-time global VTEC information and have great potential for real-time applications including range error correction for transionospheric radio signals, the monitoring of space weather, and detection of natural hazards on a global scale. All the IGS combined RT-GIMs generated and analyzed during the testing period are available at https://doi.org/10.5281/zenodo.5042622 (Liu et al., 2021b).

Prediction of Daily Streamflow Using Various Kernel Function Based Regression: A Case Study in India

River daily discharge estimation and modeling considers an important step for scheduling and planning different water resources for sustainable socio-economic development. In the current work, four techniques of Gaussian processes regression (GPR): Polynomial Kernel, Radial Basis Function Kernel, Normalized Polynomial Kernel, and PUK Kernel, were used to model the daily discharge. Hydrological-datasets containing daily-stage (m) and discharge (m3/sec) were gathered over the period from 2004-2013. The datasets were divided into two sections: (i) models training containing 70% (2004-2010) of the total data and (ii) remaining 30% (2011- 2013) were for testing. Comparing all the four developed models, our findings show that the superlative model was the PUK-Kernel model with a correlation coefficient (r) of 0.96, MAE of 36.70 m3/s, RMSE of 90.92 m3/s, RAE of 17.50 %, RRSE of 26.05 % in the training period. Whereas, it performed equally well in the testing period with r = 0.97, MAE = 44.84 m3/s, RMSE = 95.05 m3/s, RAE = 17.98 %, RRSE = 24.94 % in the testing period. Our findings can be included that GPR-PUK was more accurate and stable than other models, and can be used to help water-users, decision-makers, development-planners for managing water resources and achieving sustainable development.

Using company-specific headlines and convolutional neural networks to predict stock fluctuations

This work presents a convolutional neural network for the prediction of next-day stock fluctuations using company-specific news headlines. Experiments to evaluate model performance using various configurations of word embeddings and convolutional filter widths are reported. The total number of convolutional filters used is far fewer than is common, reducing the dimensionality of the task without loss of accuracy. Furthermore, multiple hidden layers with decreasing dimensionality are employed. A classification accuracy of 61.7% is achieved using pre-learned embeddings, that are fine-tuned during training to represent the specific context of this task. Multiple filter widths are also implemented to detect different length phrases that are key for classification. Trading simulations are conducted using the presented classification results. Initial investments are more than tripled over an 838-day testing period using the optimal classification configuration and a simple trading strategy. Two novel methods are presented to reduce the risk of the trading simulations. Adjustment of the sigmoid class threshold and re-labelling headlines using multiple classes form the basis of these methods. A combination of these approaches is found to be more than double the Average Trade Profit achieved during baseline simulations.

ANALISIS ABNORMAL RETURN PORTOFOLIO WINNER - LOSER PADA PERUSAHAAN YANG TERDAFTAR DI INDEKS KOMPAS 100

This study aims to analyze whether there is a difference in abnormal return average of stock portfolios in winner and loser categories during two different periods namely formation and testing periods to test winner-loser anomaly occurrence. The population of this study was the companies listed in the Kompas 100 Index of Indonesia Stock Exchange from February 2015 to July 2019. A number of 44 companiesused as the samples of this study which selected by using the purposive sampling technique. Parametric paired sample t-test and nonparametric Wilcoxon signed ranks test were used to analyze the data that processed by using SPSS program. The results show that the abnormal return average of the winner stock portfolio and loser stock portfolio in the formation period has a significant difference with the abnormal return average of the winner stock portfolio and loser stock portfolio in the testing period.