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.

A Prediction Method of Electromagnetic Environment Effects for UAV LiDAR Detection System

With the rapid development of science and technology, UAVs (Unmanned Aerial Vehicles) have become a new type of weapon in the informatization battlefield by their advantages of low loss and zero casualty rate. In recent years, UAV navigation electromagnetic decoy and electromagnetic interference crashes have activated widespread international attention. The UAV LiDAR detection system is susceptible to electromagnetic interference in a complex electromagnetic environment, which results in inaccurate detection and causes the mission to fail. Therefore, it is very necessary to predict the effects of the electromagnetic environment. Traditional electromagnetic environment effect prediction methods mostly use a single model of mathematical model and machine learning, but the traditional prediction method has poor processing nonlinear ability and weak generalization ability. Therefore, this paper uses the Stacking fusion model algorithm in machine learning to study the electromagnetic environment effect prediction. This paper proposes a Stacking fusion model based on machine learning to predict electromagnetic environment effects. The method consists of Extreme Gradient Boosting algorithm (XGB), Gradient Boosting Decision Tree algorithm (GBDT), K Nearest Neighbor algorithm (KNN), and Decision Tree algorithm (DT). Experimental results show that, comprising with the other seven machine learning algorithms, the Stacking fusion model has a better classification prediction accuracy of 0.9762, a lower Hamming code distance of 0.0336, and a higher Kappa coefficient of 0.955. The fusion model proposed in this paper has a better predictive effect on electromagnetic environment effects and is of great significance for improving the accuracy and safety of UAV LiDAR detection systems under the complex electromagnetic environment on the battlefield.

Environment Effect Treatments in PWR Whole-Core Pin-by-Pin Calculation

The environment effect arises when pin-cell homogenized parameters are generated with reflective boundary conditions. To treat it in whore-core pin-by-pin calculation, two works are summarized in this article. Firstly, by analyzing the relative errors of pin-cell homogenized group constants and the relative importance of pin-cell discontinuity factors (PDF) in each group, the importance of correcting the PDF of the thermal group is recognized. Secondly, the least-squares method for a multivariate polynomial is utilized to functionalize the relation of the thermal group PDF and the core parameters, including diffusion coefficient, removal cross-section, neutron source, and normalized surface flux. The C5G7 and KAIST benchmarks are employed to evaluate the performance of the PDF predication. Numerical results indicate its effectiveness in reducing the errors of eigenvalue and pin power, especially for the cases with the fuel pins located near the interface between different assemblies.

Implementasi Pengelolaan Sampah Rumah Tangga Melalui Pelatihan Pembuatan Pupuk Kompos Metode Takakura Pada Masyarakat Di Tangerang Selatan

Berdasarkan data dari Dinas Lingkungan Hidup Tangerang Selatan pada tahun 2018, Tangerang Selatan Tangerang selatan menghasilkan sampah hingga 1.000 ton per hari. Jumlah sampah tertinggi yaitu 43,39% sampah organik dari dapur rumah tangga. Kegiatan pengabdian masyarakat yang dilakukan oleh tim Kesehatan Masyarakat UIN Syarif Hidayatullah bertujuan untuk meningkatkan pengetahuan, keterampilan, kesadaran untuk menerapkan pengelolaan limbah rumah tangga dengan Pelatihan Pupuk Kompos Takakura di Tangerang Selatan. Kegiatan ini dilakukan kepada Majelis Ta’lim dan kader kesehatan RW 04 Lengkong Gudang. Hasil dari kegiatan ini menunjukkan 78,6% peserta pelatihan melakukan pengolahan sampah organik di rumah mereka dan 71,4% peserta pelatihan merekomendasikan metode Takakura kepada orang-orang di sekitar, 67,9% masyarakat yang berpartisipasi merespons positif pengelolaan limbah rumah tangga dengan metode Takakura. Kegiatan ini diharapkan mampu mendorong masyarakat untuk menerapkan pengelolaan sampah rumah tangga. Dengan demikian, ini akan mengurangi limbah rumah tangga dan risiko dampak lingkungan, sehingga secara tidak langsung juga akan meminimalkan dampak kesehatan.---Based on data from the South Tangerang Environment Office in 2018, South Tangerang produces up to 1.000 tons of waste per day. The highest amount of waste is 43.39% organic waste from household kitchens. Community service activities undertaken by the Public Health team of UIN Syarif Hidayatullah aimed to to increase the knowledge, skills, awareness to implement household waste management by Training Takakura Compost Fertilizer in South Tangerang. This activity was carried out for the religious study group and health cadres of RW 04 Lengkong Gudang. The results of this activity showed 78.6% of trainees doing organic waste processing in their homes and 71.4% of trainees recommending the Takakura method to people around, 67.9% participants responded positively to manage household waste by the Takakura method. It was expected to be able to encourage the community to implement household waste management. Thus, it will reduce household waste and the risk of environment effect , so it also will indidirectly minimize health effect.

Analysis of Modern Aviation Fuels: A Review of Environment Effect

In our developing and changing world, the use of energy resources is becoming important. With the development of the aviation industry all over the world, the need to use more innovative, more sustainable resources emerges. When the demands in the transportation sector from past to present are examined, the transformation in energy resources will be inevitable. As stated, all studies have focused on the energy resources that reduce the environmental impact, sustainability, environmental friendliness, costs and dependence on oil producing states. In this study, the study of aviation fuels from past to present, its production, environmental impact, sustainability and the returns of alternative aviation fuels are discussed in detail. It is concluded that the world's developing and changing aviation sector should switch to sustainable alternative fuels as soon as possible.

Redefining Bean Iron Biofortification: A Review of the Evidence for Moving to a High Fe Bioavailability Approach

Iron biofortification of the common bean (Phaseolus vulgaris) commenced in earnest ~18 years ago. Based on knowledge at the time, the biofortification approach for beans was simply to breed for increased Fe concentration based on 3 major assumptions: (1) The average bean Fe concentration is ~50 μg/g; (2) Higher Fe concentration results in more bioavailable Fe delivered for absorption; (3) Breeding for high Fe concentration is a trait that can be achieved through traditional breeding and is sustainable once a high Fe bean sample is released to farmers. Current research indicates that the assumptions of the high Fe breeding approach are not met in countries of East Africa, a major focus area of bean Fe biofortification. Thus, there is a need to redefine bean Fe biofortification. For assumption 1, recent research indicates that the average bean Fe concentration in East Africa is 71 μg/g, thus about 20 μg/g higher than the assumed value. For assumption 2, recent studies demonstrate that for beans higher Fe concentration does not always equate to more Fe absorption. Finally, for assumption 3, studies show a strong environment and genotype by environment effect on Fe concentration, thus making it difficult to develop and sustain high Fe concentrations. This paper provides an examination of the available evidence related to the above assumptions, and offers an alternative approach utilizing tools that focus on Fe bioavailability to redefine Fe biofortification of the common bean.