Comparison of broiler performance, carcass yields and intestinal microflora when fed diets containing transgenic (Mon-40-3-2) and conventional soybean meal

This study was conducted to analyze the effects of transgenic glyphosate-tolerant soybeans on the performance, carcass yields and intestinal microflora of broiler chickens. Three hundred and sixty oneday- old Abor Aerec broilers were randomly divided into two dietary treatments, adding genetically modified (GM) glyphosate-tolerant soybean meal or conventional soybean meal, respectively. Broiler body weight and feed intake were recorded at regular intervals (day 0, 21 and 42). Chickens were slaughtered at day 42 for carcass yield measurement and sampling. Diversity of the ileum and cecum microflora was determined by denaturing gradient gel electrophoresis (DGGE) technique and DNA sequencing. No treatment differences (P > 0.05) were detected among dietary treatments for any measured performance and carcass parameters. The microbial population in ileum and cecum also had no significant difference between the two treatments (P>0.05). The similarity of the total ileum and cecum microflora between the two treatments was about 62 and 58%, respectively. The DNA-DGGE electrophoresis pattern bands of intestine microbe were divided into two groups because of the different diet. Fifteen DGGE DNA bands were identified, of which five of them were identified as known bacteria. The current study showed that there were no adverse effects of the transgenic soybean meal on the intestinal microflora of broilers.Key words: Broiler, glyphosate-tolerant soybean meal, intestinal microbiota, feed safety

Fine-Grained Face Swapping via Regional GAN Inversion

We present a novel paradigm for high-fidelity face swapping that faithfully preserves the desired subtle geometry and texture details. We rethink face swapping from the perspective of fine-grained face editing, \textit{i.e., ``editing for swapping'' (E4S)}, and propose a framework that is based on the explicit disentanglement of the shape and texture of facial components. Following the E4S principle, our framework enables both global and local swapping of facial features, as well as controlling the amount of partial swapping specified by the user. Furthermore, the E4S paradigm is inherently capable of handling facial occlusions by means of facial masks. At the core of our system lies a novel Regional GAN Inversion (RGI) method, which allows the explicit disentanglement of shape and texture. It also allows face swapping to be performed in the latent space of StyleGAN. Specifically, we design a multi-scale mask-guided encoder to project the texture of each facial component into regional style codes. We also design a mask-guided injection module to manipulate the feature maps with the style codes. Based on the disentanglement, face swapping is reformulated as a simplified problem of style and mask swapping. Extensive experiments and comparisons with current state-of-the-art methods demonstrate the superiority of our approach in preserving texture and shape details, as well as working with high resolution images at 1024$\times$1024.Comment: Project page, see http://e4s2022.github.i

A Transmissive X-ray Polarimeter Design For Hard X-ray Focusing Telescopes

The X-ray Timing and Polarization (XTP) is a mission concept for a future space borne X-ray observatory and is currently selected for early phase study. We present a new design of X-ray polarimeter based on the time projection gas chamber. The polarimeter, placed above the focal plane, has an additional rear window that allows hard X-rays to penetrate (a transmission of nearly 80% at 6 keV) through it and reach the detector on the focal plane. Such a design is to compensate the low detection efficiency of gas detectors, at a low cost of sensitivity, and can maximize the science return of multilayer hard X-ray telescopes without the risk of moving focal plane instruments. The sensitivity in terms of minimum detectable polarization, based on current instrument configuration, is expected to be 3% for a 1mCrab source given an observing time of 10^5 s. We present preliminary test results, including photoelectron tracks and modulation curves, using a test chamber and polarized X-ray sources in the lab

Quantitative Method for Network Security Situation Based on Attack Prediction

Multistep attack prediction and security situation awareness are two big challenges for network administrators because future is generally unknown. In recent years, many investigations have been made. However, they are not sufficient. To improve the comprehensiveness of prediction, in this paper, we quantitatively convert attack threat into security situation. Actually, two algorithms are proposed, namely, attack prediction algorithm using dynamic Bayesian attack graph and security situation quantification algorithm based on attack prediction. The first algorithm aims to provide more abundant information of future attack behaviors by simulating incremental network penetration. Through timely evaluating the attack capacity of intruder and defense strategies of defender, the likely attack goal, path, and probability and time-cost are predicted dynamically along with the ongoing security events. Furthermore, in combination with the common vulnerability scoring system (CVSS) metric and network assets information, the second algorithm quantifies the concealed attack threat into the surfaced security risk from two levels: host and network. Examples show that our method is feasible and flexible for the attack-defense adversarial network environment, which benefits the administrator to infer the security situation in advance and prerepair the critical compromised hosts to maintain normal network communication

Data-driven Virtual Inertia Control Method of Doubly Fed Wind Turbine

This paper presents a data-driven virtual inertia control method for doubly fed induction generator (DFIG)-based wind turbine to provide inertia support in the presence of frequency events. The Markov parameters of the system are first obtained by monitoring the grid frequency and system operation state. Then, a data-driven state observer is developed to evaluate the state vector of the optimal controller. Furthermore, the optimal controller of the inertia emulation system is developed through the closed solution of the differential Riccati equation. Moreover, a differential Riccati equation with self-correction capability is developed to enhance the anti-noise ability to reject noise interference in frequency measurement process. Finally, the simulation verification was performed in Matlab/Simulink to validate the effectiveness of the proposed control strategy. Simulation results showed that the proposed virtual inertia controller can adaptively tune control parameters online to provide transient inertia supports for the power grid by releasing the kinetic energy, so as to improve the robustness and anti-interference ability of the control system of the wind power system

Red Alarm for Pre-trained Models: Universal Vulnerability to Neuron-Level Backdoor Attacks

Pre-trained models (PTMs) have been widely used in various downstream tasks. The parameters of PTMs are distributed on the Internet and may suffer backdoor attacks. In this work, we demonstrate the universal vulnerability of PTMs, where fine-tuned PTMs can be easily controlled by backdoor attacks in arbitrary downstream tasks. Specifically, attackers can add a simple pre-training task, which restricts the output representations of trigger instances to pre-defined vectors, namely neuron-level backdoor attack (NeuBA). If the backdoor functionality is not eliminated during fine-tuning, the triggers can make the fine-tuned model predict fixed labels by pre-defined vectors. In the experiments of both natural language processing (NLP) and computer vision (CV), we show that NeuBA absolutely controls the predictions for trigger instances without any knowledge of downstream tasks. Finally, we apply several defense methods to NeuBA and find that model pruning is a promising direction to resist NeuBA by excluding backdoored neurons. Our findings sound a red alarm for the wide use of PTMs. Our source code and models are available at \url{https://github.com/thunlp/NeuBA}

A Magnetically and Thermally Controlled Liquid Metal Variable Stiffness Material

Smart materials that can actively tune their stiffness are of great interest to many fields, including the construction industry, medical devices, industrial machines, and soft robotics. However, developing a material that can offer a large range of stiffness change and rapid tuning remains a challenge. Herein, a liquid metal variable stiffness material (LMVSM) that can actively and rapidly tune its stiffness by applying an external magnetic field or by changing the temperature is developed. The LMVSM is composed of three layers: a gallium–iron magnetorheological fluid (Ga–Fe MRF) layer for providing variable stiffness, a nickel–chromium wire layer for Joule heating, and a soft heat dissipation layer for accelerating heating and rapid cooling. The stiffness can be rapidly increased by 4 times upon the application of a magnetic field or 10 times by solidifying the Ga–Fe MRF. Finally, the LMVSM is attached to a pneumatically controlled soft robotic gripper to actively tune its load capacity, demonstrating its potential to be further developed into smart components that can be widely adopted by smart devices

Pricing the permission of pollution:Optimal control-based simulation of payments for the initial emission allowance in China

China has modified its pollution control policy system with such price tools as the pollution charge (PC) policy and the payment for initial emission allowance (PIEA) policy. The aim of PC policy is to compensate for the environment damage caused by pollutants, while PIEA is in charge of the initial emission allowance (IEA) within the emission trading system (ETS). However, since the implementation of PIEA, it has been criticized as redundant because of the similar pricing scheme with the PC. In addition, the existing PIEA pricing approaches have ignored interactions with other policies, such as PC and total emission control (TEC) policies. In this research, we established an optimal control-based model with chemical oxygen demand (COD) and ammoniacal nitrogen (NH3-N), two independent pollutants variables, to simulate the water pollutants' PIEA price. Simulation results indicated that emission quantity and optimal social benefit in the PC–PIEA combination scenario was equal to the situation in the PIEA scenario. Under this design, PC compensated for the emission damage, and PIEA paid for the scarcity rent, while PIEA does not duplicate the PC policy. In addition, the PIEA policy has a complex effect on pollutant emission. Because PIEA policy increases the enterprises' discharging cost, most regions' COD emissions are less than the baseline, excepting Beijing, Shanghai, Jiangsu, Zhejiang, Fujian, Shandong, and Guangdong, in which emission quantities are greater than the baseline. The NH3-N emission shows an opposite trend. The simulation result is that excluding Inner Mongolia, Hubei, Hunan, Tibet, Gansu, Qinghai, Ningxia, and Xinjiang, the NH3-N emissions in the rest of regions are increased. TEC policy has a significant effect on pollutant emissions and the PIEA price. The COD emission quantity with TEC is lower than that without the TEC policy, therefore, the TEC policy will be effective for pollutant emission control. The pollutant beyond the restricted target will be charged a payment for IEA at a higher price than without the TEC policy