Genetic and genomic analysis of age at first insemination in Israeli dairy cattle

We performed a genetic analysis of age at first insemination, including estimation of the heritability and genetic correlations with other economic traits, and the consequences of including this trait in the Israeli selection index. The genetic factors affecting age at first insemination were determined via GWAS. Five data sets were analyzed. Data sets 1, 2, and 3 were used to compute variance components among age at first insemination, first calving age, days from first insemination to calving, and the 9 traits included in the Israel breeding index. Heritabilities for age at first insemination, calving age, and days from first insemination to calving in Israeli Holsteins as computed by REML individual animal model analyses of 273,239 Israeli Holstein cows were 0.072, 0.042, and 0.014. The estimated genetic correlation between the first 2 traits was 0.88. In addition to the fact that heritability of age at first insemination is 1.7 times the heritability for calving, the former trait has the advantage that the number of records is greater, and the records are generated earlier. Absolute values of the genetic and residual correlations between age at first insemination and the 9 traits included in the Israeli index were all less than 0.2. Data set 4 included first insemination dates of 1,181,600 calves born from 1985 through 2018. Genetic evaluations were computed by a single trait animal model. Annual phenotypic and genetic trends for age at first calving for calves born since 1985 were “positive,” that is, economically negative, at 0.320 ± 0.003 and 0.169 ± 0.005 d, respectively. Applying the GCTA-GREML software, 54% of variance in the transmitting ability of 1,585 sires could be explained by considering all 40,498 markers included in the GWAS analysis. The significant markers were mainly associated with milk production genes. The SNP UA-IFASA-8854 on chromosome 11 had the lowest probability value, 1.2 × 10^?24. This marker is located between the genes RETSAT and ELMOD3, both of which are overexpressed in human mammary glands. The gene RETSAT is reported to be essential for lipid accumulation and adipogenesis promotion. Gene enrichment analysis found that genes in the genomic region flanking significant markers are associated with vasopressin receptor activity, which was shown to mediate puberty in humans. If age at first insemination is included in the index with a weighting to account for 9% of the index, reductions of 2.8 and 2.6 d for age at first insemination and first calving age after 10 yr of selection are predicted, as compared with reductions of 1.4 and 1.1 d with the current index. Gains for the other index traits are only marginally affected. We suggest selection on age at first insemination as an alternative to selection for early calving.     

优化GD-模糊规则的滑坡预报模型研究

为了提高滑坡灾害预报准确率，改善传统的滑坡监测和预报中存在的参数选取困难及模糊控制系统作为预报模型精确度不高的问题，首先采用山体结构稳定性分析法进行滑坡灾害参数的选取，得出降雨量、含水率、土压力及岩土表面位移增量作为预报参数的结论；其次将选取的参数作为模糊系统的输入，建立滑坡灾害发生概率模型，并引入优化的GD算法修正预报模型中的动态参数，使模糊控制模型具有自适应性；同时与未优化的模糊控制模型以及单独模糊控制模型进行仿真对比，仿真结果表明，该控制算法收敛速度快，具有很好的收敛性；最后将该模型在某滑坡重点灾区实验区进行实验测试，实验结果显示该模型具有较好的收敛性，且预报精度达到90%。     

基于Apriori算法的食品抽检数据的关联规则挖掘

目的 为了发现检测数据的不合格项目之间有意义的关联规则, 并对挖掘出的关联规则进行分析解读, 进一步发掘了食品抽检数据的价值, 从而对食品安全监管具有一定的指导意义。方法 本文对利用Apriori算法对2015~2019年间山东食品药品监督管理局网站公布的食品安全抽检数据的不合格项目进行了关联规则挖掘。结果 通过挖掘得出最符合要求的10条规则。结论 利用关联规则挖掘算法对食品检验数据进行挖掘, 能够挖掘出有价值、有意义的规则, 对食品安全管理具有指导意义, 从中也可以看出数据挖掘技术在食品安全数据挖掘分析中具有广阔的应用前景。     

Critical state model for structured soil

Structure is an evident determinant for macroscopic behaviors of soils. However, this is not taken into account in most constitutive models, as structure is a rather complex issue in models. For this, it is important to develop and implement simple models that can reflect this important aspect of soil behavior. This paper tried to model structured soils based on well-established concepts, such as critical state and sub-loading. Critical state is the core of the classic Cam Clay model. The sub-loading concept implies adoption of an inner (sub-loading) yield surface, according to specific hardening rules for some internal strain-like state variables. Nakai and co-workers proposed such internal variables for controlling density (ρ) and structure (ω), using a modified stress space, called t_ij. Herein, similar variables are used in the context of the better-known invariants (p and q) of the Cam Clay model. This change requires explicit adoption of a non-associated flow rule for the sub-loading surface. This is accomplished by modifying the dilatancy ratio of the Cam Clay model, as a function of the new internal variables. These modifications are described and implemented under three-dimensional (3D) conditions. The model is then applied to simulating laboratory tests under different stress paths and the results are compared to experiments reported for different types of structured soils. The good agreements show the capacity and potential of the proposed model.     

Signature of Meyer-Neldel compensation rule in iso-conversional crystallization under the influence of γ-ray irradiation

In the current article, the influence of three different doses of gamma-rays on the thermally assisted crystal growth in a novel ternary chalcogenide glassy Se₇₈Te₂₀Sn₂ semiconductor has been reported in terms of the re-crystallization. The iso-conversional kinetic approach is used in the present study. The variation of crystal growth rate with temperature obeys the Arrhenius relation for all the doses of gamma-rays irradiation. Further analysis confirms that pre-factor K₀ of crystal growth rate and the corresponding energy ΔE involved in thermally governed crystallization follows Meyer-Neldel compensation rule (MNCR). Further, we have observed a linear correlation i.e., Further Meyer-Neldel compensation rule (FMNCR) between Meyer-Neldel pre-factor K₀₀ and Meyer-Neldel energy kT₀. The observed correlations between these significant parameters (MNCR between K₀ & ΔE and FMNCR between K₀₀ & kT₀) may open a new gateway for revealing the external effects on crystal growth rate during the iso-conversional analysis of crystallization kinetics.     

Similarities and differences between male and female novice designers on color‐concept associations for warnings,action required,and signs and equipment status messages

This research examined the male and female novice designers toward color associations for the concepts used for ‘warnings’, ‘action required’, and ‘signs and equipment status’ through a questionnaire‐based study. A total of 178 Hong Kong Chinese final year undergraduate design students (89 males and 89 females) participated in the study. The test used required the participants to indicate their choice of one of nine colors to associations with each of 38 concepts in a color‐concept table, so that any one color could be associated with any one of the concepts. For both male and female groups of novice designers, chi‐square tests revealed a strong color association for each concept tested in this study (P < .05). The results showed males and females agreed on some color‐concept association stereotypes which were therefore gender neutral. The male and female novice designers had the same color associations and similar levels of stereotype strengths for 21 concepts. The nine strongest and therefore most useful color‐concept association stereotypes for both male and female novice designers were: red‐danger, red‐fire, red‐hot, red‐stop, red‐emergency, red‐error, blue‐cold, blue‐male, and green‐exit. However, the male and female novice designers had different color association stereotypes for the standby (green vs. yellow), emergency exit (green vs. red), and toxic (purple vs. black) concepts, and the strengths of the 14 remaining associations for both groups were not at equivalent levels. Overall, it is anticipated that the findings of this study will act as a useful reference for novice designers and other design practitioners to optimize color coding in the design of ‘warnings’, ‘action required’, and ‘signs and equipment status’ messages.     

GA-based learning for rule identification in fuzzy neural networks

Employing an effective learning process is a critical topic in designing a fuzzy neural network, especially when expert knowledge is not available. This paper presents a genetic algorithm (GA) based learning approach for a specific type of fuzzy neural network. The proposed learning approach consists of three stages. In the first stage the membership functions of both input and output variables are initialized by determining their centers and widths using a self-organizing algorithm. The second stage employs the proposed GA based learning algorithm to identify the fuzzy rules while the final stage tunes the derived structure and parameters using a back-propagation learning algorithm. The capabilities of the proposed GA-based learning approach are evaluated using a well-examined benchmark example and its effectiveness is analyzed by means of a comparative study with other approaches. The usefulness of the proposed GA-based learning approach is also illustrated in a practical case study where it is used to predict the performance of road traffic control actions. Results from the benchmarking exercise and case study effectively demonstrate the ability of the proposed three stages learning approach to identify relevant fuzzy rules from a training data set with a higher prediction accuracy than alternative approaches.     

A proposed expert system for word sense disambiguation: deductive ambiguity resolution based on data mining and forward chaining

One of the major issues in the process of machine translation is the problem of choosing the proper translation for a multi‐sense word referred to as word sense disambiguation (WSD). Two commonly used approaches to this problem are statistical and example‐based methods. In statistical methods, ambiguity resolution is mostly carried out by making use of some statistics extracted from previously translated documents or dual corpora of source and target languages. Example‐based methods follow a similar approach as they also make use of bilingual corpora. However, they perform the task of matching at run‐time (i.e. online matching). In this paper, by looking at the WSD problem from a different viewpoint, we propose a system, which consists of two main parts. The first part includes a data mining algorithm, which runs offline and extracts some useful knowledge about the co‐occurrences of the words. In this algorithm, each sentence is imagined as a transaction in Market Basket Data Analysis problem, and the words included in a sentence play the role of purchased items. The second part of the system is an expert system whose knowledge base consists of the set of association rules generated by the first part. Moreover, in order to deduce the correct senses of the words, we introduce an efficient algorithm based on forward chaining in order to be used in the inference engine of the proposed expert system. The encouraging performance of the system in terms of precision and recall as well as its efficiency will be analysed and discussed through a set of experiments.     

基于改进关联规则算法的警情数据分析

针对传统Apriori算法挖掘警情数据的缺点，提出一种改进的Apriori算法。该算法首先在关联规则发现阶段引入权值参数，设计支持度阈值函数，以挖掘不常发生的重大案情发生规律；然后提出一种压缩矩阵优化算法，将数据压缩存储在只有0或1的矩阵中，并用2个数组来记录矩阵中每一行及每一列1的总数，可多次压缩矩阵，提升挖掘效率；最后将改进的算法用于实际的警情数据挖掘分析，给出关联规则挖掘结果。实验表明，改进算法不仅执行效率较传统算法有所提升，且针对警情数据的挖掘结果准确性也有所提高。