Search

Abstract Background Family-based selective breeding programs typically employ both between-family and within-family selection in aquaculture. However, these programs may exhibit a reduced genetic gain in the presence of a genotype by environment interactions (G × E) when employing biosecurity-based breeding schemes (BS), compared to non-biosecurity-based breeding schemes (NBS). Fortunately, genomic selection shows promise in improving genetic gain by taking within-family variance into account. Stochastic simulation was employed to evaluate genetic gain and G × E trends in BS for improving the body weight of L. vannamei, considering selective genotyping strategies for test group (TG) at a commercial farm environment (CE), the number individuals of the selection group (SG) genotyped at nucleus breeding center (NE), and varying levels of G × E. Results The loss of genetic gain in BS ranged from 9.4 to 38.9% in pedigree-based selection and was more pronounced when G × E was stronger, as quantified by a lower genetic correlation for body weight between NE and CE. Genomic selection, particularly with selective genotyping of TG individuals with extreme performance, effectively offset the loss of genetic gain. With a genetic correlation of 0.8, genotyping 20 SG individuals in each candidate family achieved 93.2% of the genetic gain observed for NBS. However, when the genetic correlation fell below 0.5, the number of genotyped SG individuals per family had to be increased to 50 or more. Genetic gain improved by on average 9.4% when the number of genotyped SG individuals rose from 20 to 50, but the increase in genetic gain averaged only 2.4% when expanding from 50 to 80 individuals genotyped. In addition, the genetic correlation decreased by on average 0.13 over 30 generations of selection when performing BS and the genetic correlation fluctuated across generations. Conclusions Genomic selection can effectively compensate for the loss of genetic gain in BS due to G × E. However, the number of genotyped SG individuals and the level of G × E significantly affected the extra genetic gain from genomic selection. A family-based BS selective breeding program should monitor the level of G × E and genotyping 50 SG individuals per candidate family to minimize the loss of genetic gain due to G × E, unless the level of G × E is confirmed to be low.

With the development of modern society and economy, modern business models have changed. Enterprise management is no longer the main function, which requires new management concepts to coordinate production and operation and create new management models. Corporate economic management is one of the main tasks of corporate governance. In order to improve corporate governance, the effective corporate economic evaluation model must be established. The advantage of fuzzy logic algorithm is that it can effectively express fuzzy data and is easy to understand, which is more transparent. The application of fuzzy logic algorithm in the research of enterprise economic management can fully reflect the advantages of fuzzy logic algorithm and improve enterprise governance. In this paper, the fuzzy logic algorithm was used to study the economic management model of enterprises. This paper first introduced the enterprise economic management model, including the definition, characteristics and shortcomings of the enterprise economic management model. The performance evaluation methods of enterprise economic management were introduced, including the clarity of performance evaluation objectives, the optimization of performance evaluation means and the feedback of performance evaluation results. Finally, the fuzzy logic algorithm was used to evaluate the performance of enterprise economic management, and the indicators and performance evaluation model of enterprise economic management were established. In the experimental part, the fuzzy logic algorithm was used to evaluate the economic management of four enterprises. The experimental results showed that Enterprise A had the highest ability to evaluate the economic management of enterprises, and the comprehensive evaluation result of economic management was above 0.9, so it had a very high enterprise economic management ability. The application of fuzzy logic to the study of enterprise economic management could directly show the advantages and disadvantages of the enterprise economic management model through quantitative analysis of the enterprise economic management model. This enabled enterprises to understand the current economic management situation and improve it based on the facts, which helped enterprises to increase their economic management ability.

Although the influence of microplastics (MPs) in different soil environments has been investigated, their effects on the physiochemical properties and chemical speciation of heavy metals in yellow-brown soil remains unknown. This study aimed to determine the effects of various concentrations of linear low-density polyethylene (LLDPE), polyamide (PA), polyurethane (PU), polystyrene (PS), and low-density polyethylene (LDPE) MPs on the yellow-brown soil environment and chemical speciation of the heavy metals cadmium (Cd), copper (Cu), lead (Pb), and zinc (Zn). MPs influenced the physicochemical properties and chemical speciation of heavy metals in yellow-brown soil. The physicochemical properties of yellow-brown soil can be altered by changing the concentrations of LDPE MP. The relationship between changes in field capacity (FC) and LDPE concentrations was approximately linear. The physiochemical properties of yellow-brown soil containing added PA, PU, and LDPE MPs were substantially improved (control vs. MPs): FC, 39 % vs. 42.50 % for PU, cation exchange capacity (CEC) 45.77, 56.65, and 57.44 cmol.kg−1 for PA, PU, and LDPE respectively, and organic matter (OM) content, 40.16 vs. 51.68 g.kg−1 for PA. The LLDPE and PU MPs also simultaneously affected the chemical speciation of heavy metals in yellow-brown soil. The LLDPE MPs increased the acid-soluble (45.17–54.67 % (Cd-F1), 7.24–11.30 % (Cu-F1), 4.20–7.23 % (Pb-F1), 21.21–31.47 % (Zn-F1)) and reducible (24.02–29.41 % (Cd-F2), 25.69–34.95 % (Cu-F2), 74.29–81.07 % (Pb-F2), 28.77–34.19 % (Zn-F2)) fractions of heavy metals, which increased their bioavailability. However, PU MPs reduced the ecological risk of heavy metals in yellow-brown soil by increasing the content of the residual fraction (26.11–40.21 % (Cd-F4), 47.63–59.67 % (Cu-F4), 17.25–26.76 % (Pb-F4), 32.63–50.46 % (Zn-F4)). Changes in the properties of yellow-brown soil and the impact of MPs on heavy metals, might change the chemical speciation of heavy metals. The impact of MPs on heavy metals in yellow-brown soil requires further investigation.

With the in-depth research on hypersonic aerodynamics and hypersonic propulsion technology, humans are growing closer to space travel. Recent studies have shown that the pre-cooled air-turborocket (ATR) or turboexpander engines are some of the potential propulsion methods for reusable space vehicles and single stage-to-orbit (SSTO) missions because they have a high specific impulse at low Mach numbers, which can overcome the problem of the “thrust gap” in turbine-based combined-cycle (TBCC) engines. The ATR engine needs an additional oxidizing agent and the turboexpander engine usually uses hydrogen as fuel, which has low energy density and poor safety. To address this problem, this paper proposed a high-energy-density (HED) hydrocarbon-fueled turboexpander engine, and its feasibility has been proven through a simplified thermodynamic model. Through detailed thermodynamic analysis based on the energy and pressure balance, this paper analyzed the performance characteristics of the engine to evaluate its capacity to work in a wide speed range at low Mach numbers. The results show that the endothermic hydrocarbon-fueled turboexpander engine has good specific impulse in Mach 0∼4 at an equivalence ratio of 0.7∼1.3, and the turboexpander engine can be combined with the dual-mode scramjet and become an efficient acceleration method for SSTO missions and the reusable spacecraft.

As a potential air control measure, RF-based surveillance is one of the most commonly used unmanned aerial vehicles (UAV) surveillance methods that exploits specific emitter identification (SEI) technology to identify captured RF signal from ground controllers to UAVs. Recently many SEI algorithms based on deep convolution neural network (DCNN) have emerged. However, there is a lack of the implementation of specific hardware. This paper proposes a high-accuracy and power-efficient hardware accelerator using an algorithm–hardware co-design for UAV surveillance. For the algorithm, we propose a scalable SEI neural network with SNR-aware adaptive precision computation. With SNR awareness and precision reconfiguration, it can adaptively switch between DCNN and binary DCNN to cope with low SNR and high SNR tasks, respectively. In addition, a short-time Fourier transform (STFT) reusing DCNN method is proposed to pre-extract feature of UAV signal. For hardware, we designed a SNR sensing engine, denoising engine, and specialized DCNN engine with hybrid-precision convolution and memory access, aiming at SEI acceleration. Finally, we validate the effectiveness of our design on a FPGA, using a public UAV dataset. Compared with a state-of-the-art algorithm, our method can achieve the highest accuracy of 99.3% and an F1 score of 99.3%. Compared with other hardware designs, our accelerator can achieve the highest power efficiency of 40.12 Gops/W and 96.52 Gops/W with INT16 precision and binary precision.

Rice (Oryza sativa L.) seedlings grown under nitrogen (N) deficiency conditions show a foraging response characterized by increased root length. However, the mechanism underlying this developmental plasticity is still poorly understood. In this study, the mechanism by which N deficiency influences rice seminal root growth was investigated. The results demonstrated that compared with the control (1 mM N) treatment, N deficiency treatments strongly promoted seminal root growth. However, the N deficiency-induced growth was negated by the application of zeatin, which is a type of cytokinin (CK). Moreover, the promotion of rice seminal root growth was correlated with a decrease in CK content, which was due to the N deficiency-mediated inhibition of CK biosynthesis through the down-regulation of CK biosynthesis genes and an enhancement of CK degradation through the up-regulation of CK degradation genes. In addition, the N deficiency-induced decrease in CK content not only enhanced the root meristem cell proliferation rate by increasing the meristem cell number via the down-regulation of OsIAA3 and up-regulation of root-expressed OsPLTs, but also promoted root cell elongation by up-regulating cell elongation-related genes, including root-specific OsXTHs and OsEXPs. Taken together, our data suggest that an N deficiency-induced decrease in CK content promotes the seminal root growth of rice seedlings by promoting root meristem cell proliferation and cell elongation.