Genetic and non-genetic effects associated with ewe productivity in Harnali sheep

Summary The objective of the current study was to estimate the genetic parameters for ewe productivity traits of Harnali sheep by examining non-genetic effects. The data records of 440 animals born to 85 sires and 259 dams were collected with respect to various traits such as litter size at birth (LSB), litter weight at birth (LWB), litter size at weaning (LSW), litter weight at weaning (LWW) and age at first lambing (AFL) for the period of 2001 to 2020. Genetic parameters were estimated by fitting a series of animal models using an average information restricted maximum likelihood (REML) algorithm in WOMBAT software. Least-squares analysis revealed significant (P < 0.05) influences of period of lambing, age and weight of ewe at lambing on the studied traits. These results indicated that heavier ewes had significantly higher (P < 0.05) values of litter weight traits than their counterparts. On the basis of likelihood ratio test, the estimates of direct heritability under best model for AFL, LSB, LWB, LSW and LWW were 0.06, 0.18, 0.09, 0.07 and 0.16, respectively. Maternal permanent environment effect made a significant contribution to the LSB trait (0.20). The genetic correlation between litter size and LWW was negative, while the remaining correlations were positive. The present results suggest that selection based on ewe productivity traits will result in low genetic progress and therefore the management role is more important for better gains.

Research on an Improved MB-LBP 3D Face Recognition Method

In order to improve the accuracy and speed of 3D face recognition, this paper proposes an improved MB-LBP 3D face recognition method. First, the MB-LBP algorithm is used to extract the features of 3D face depth image, then the average information entropy algorithm is used to extract the effective feature information of the image, and finallythe Support Vector Machine algorithm is used to identify the extracted effective information. The recognition rate on the Texas 3DFRD database is 96.88%, and the recognition time is 0.025s. The recognition rate in the self-made depth library is 96.36%, and the recognition time is 0.02s.It can be seen from the experimental results that the algorithm in this paper has better performance in terms of accuracy and speed.

Introduction to Set Shaping Theory

In this article, we define the Set Shaping Theory whose goal is the study of the bijection functions that transform a set of strings into a set of equal size made up of strings of greater length. The functions that meet this condition are many but since the goal of this theory is the transmission of data, we have analyzed the function that minimizes the average information content. The results obtained show how this type of function can be useful in data compression.

Design of Sports Training System and Motion Monitoring and Recognition under Wireless Sensor Network

In conventional sports training, coaches record and observe athletes' sports data and judge whether it is reasonable based on their own experience. This qualitative analysis method is highly subjective, has large errors, and is susceptible to interference. To solve the above problems, the design of the sports training system under the wireless sensor network and the research of movement monitoring and recognition become very important. This article aims to study the design of sports training system and the monitoring and recognition of actions under the wireless sensor network technology. This paper simulates the implementation of the proposed data collection protocol and the two basic protocols, the direct transfer algorithm and the flooding algorithm, and compares the protocol proposed in this paper with the other two algorithms in terms of average information transmission success rate and average network overhead. Among them, the average information transmission success rate represents the ratio of the number of messages successfully arriving at the base station to the total amount of information generated by all nodes, and the average network overhead represents the average number of messages sent by each node. Experimental results show that the data collection protocol proposed in this paper can dynamically provide different transmission qualities for information of different importance levels, effectively reducing network overhead, and the reduced overhead is 11% of the original.

Devising a Selection Strategy for the Jamunapari Goat for Improving Lactation Performance

This study aims to evaluate the genetic potential of the Jamunapari goat and formulate a new selection strategy for improving the lactation traits. The data set included 4049 phenotypic records for lifetime milk yield at 90 days (MY90) and 140 days (MY140), total milk yield (TMY), and lactation length (LL) obtained from the progeny of 83 sires and 1643 dams between 1990 and 2019. Animal model employing average information restricted maximum likelihood (AIREML) was used to estimate genetic parameters for milk yield traits and LL. The direct additive heritability estimates for lifetime lactation traits, that used repeatability model were 0.10 ± 0.03, 0.08 ± 0.03 and 0.12 ± 0.02 for MY90, MY140 and TMY, respectively, while it was low for LL (0.06 ± 0.02). The repeatability estimates were moderate ranging from 0.17 to 0.22 for milk yield traits and LL, indicating persistent performance over the parities. Animal permanent environment influence (c2) was significant in milk yield attributes, whereas additive maternal genetic effects were absent. As the early selection criteria based on first parity records is essential, we analysed the data for the first parity separately and obtained moderate h2 estimates viz. 0.26 ± 0.05, 0.26 ± 0.06 and 0.25 ± 0.06 for MY90, MY140 and TMY, respectively. These estimates augurs further positive scope of selection in Jamunapari goats for higher milk yield. High and positive genetic correlation of MY90 with MY140 (0.97 ± 0.01) and TMY (0.91 ± 0.05) revealed the scope of using MY90 as the selection criterion.Based on these results, we recommend use of MY90 as a single trait selection criterion for genetic improvement of all lactation traits in Jamunapari goat.

Enhancement of medical images using fuzzy logic

Image enhancement is one of the most critical subjects in computer vision and image processing fields. It can be considered as means to enrich the perception of images for human viewers. All kinds of images typically suffer from different problems such as weak contrast and noise. The primary purpose of image enhancement is to change an image's visual appearance. Many algorithms have recently been proposed for enhancing medical images. Image enhancement is still deemed a challenging task. In this paper, the fuzzy c-means clustering (FCM) technique is utilized to enhance the medical images. The method of enhancement consists of two stages. The proposed algorithm conducts a cluster test on the image pixels. It then increases the difference of gray level between the diverse objects to accomplish the enhancement purpose of the medical images. The experimental results have been tested using various images. The algorithm enhanced the small target of the image to a reasonable limit and revealed favorable performance. The results of image enhancement techniques were evaluated by using terms of different criteria such as peak signal to noise ratio (PSNR), mean square error (MSE) and average information contents (AIC), showing promising performance.

Information literacy: a desideratum of the 21st century

PurposeThe research surveyed the competency of information literacy of senior high school students in Fiji. This is to evaluate the strong predictors of information literacy.Design/methodology/approachThe study adopted a survey research design whereby a five-point Likert scale self-reporting questionnaire was administered to Year 12 and Year 13 secondary school students. The data were analysed using the Statistical Package for the Social Sciences (SPSS) software-descriptive statistics of calculating the mean and standard deviation, a correlation and linear regression analysis to deduce the strong predictors of information literacy.FindingsThe study showed that 81% of the students surveyed were average to above average information literate. The strong predictors of information literacy were the ability of an individual to collaborate and share safely online, the ability to share files securely and the ability to access the credibility of any resource assessed on the digital platform.Research limitations/implicationsThe current study evaluates information literacy of a cohort – stating how information literate the participants are, comprehending the strong predicators of information literacy so that there is an appropriate and effective implementation of interventions for the desired improvements.Practical implicationsThe results can be used to improve information literacy of students at all levels of education in the Fiji Islands.Social implicationsIf the youths are information literate they will be able to effectively contribute towards the development of their economy. Since the work environment today is technology oriented and involves a lot of information, being information literate means knowing how to use the information and differentiate between good and bad information. Hence, contributing effectively towards whatever task is performed.Originality/valueThis research if the first ever research done on evaluating the information literacy of individuals in Fiji.

A Review of Shannon and Differential Entropy Rate Estimation

In this paper, we present a review of Shannon and differential entropy rate estimation techniques. Entropy rate, which measures the average information gain from a stochastic process, is a measure of uncertainty and complexity of a stochastic process. We discuss the estimation of entropy rate from empirical data, and review both parametric and non-parametric techniques. We look at many different assumptions on properties of the processes for parametric processes, in particular focussing on Markov and Gaussian assumptions. Non-parametric estimation relies on limit theorems which involve the entropy rate from observations, and to discuss these, we introduce some theory and the practical implementations of estimators of this type.

Genetic parameters and genetic trends of conformation and management traits in Dairy Gir cattle

ABSTRACT The objective of this study was to estimate genetic parameters and genetic trends of different conformation and management traits regularly measured within the context of the National Dairy Gir Breeding Program (PNMGL). The estimation of genetic and residual variances for each trait was performed using average information restricted maximum likelihood (AI-REML) procedure in AIREMLF90 program software. The population was divided into three subpopulations constituted by measured females (with phenotype records), all females, and males. Linear regressions were applied for each trait, considering two periods of birth (1st period: 1938-1996; 2nd period: 1997-2012). The estimated heritability of conformation and management traits varied from 0.01 to 0.53, denoting a perspective of genetic improvement through selection and corrective matings for purebred Dairy Gir populations. The average genetic changes in conformation and management traits were, in general, variable and inexpressive, showing that the selection of Dairy Gir may have had been directed essentially to increase milk yield. The analysis of the two periods of birth indicated that some linear traits present progress (although inexpressive) in the 2nd period (more recent period).

Information Theory Based Evaluation of the RC4 Stream Cipher Outputs

This paper presents a criterion, based on information theory, to measure the amount of average information provided by the sequences of outputs of the RC4 on the internal state. The test statistic used is the sum of the maximum plausible estimates of the entropies H(jt|zt), corresponding to the probability distributions P(jt|zt) of the sequences of random variables (jt)t∈T and (zt)t∈T, independent, but not identically distributed, where zt are the known values of the outputs, while jt is one of the unknown elements of the internal state of the RC4. It is experimentally demonstrated that the test statistic allows for determining the most vulnerable RC4 outputs, and it is proposed to be used as a vulnerability metric for each RC4 output sequence concerning the iterative probabilistic attack.