竞争战略与美国特朗普政府对华政策调整

The Competitive Strategy, which helped the US gain great advantages in competition with its major rivals, has played an important part in US foreign policy for a long time. The full use of it in the cold war with Soviet Union is the most significant case. In recent years, under the promotion of the academic circle and the strategic circle, the concept of a competitive strategy is rapidly returning into the US foreign strategy, becoming an important theoretical basis for the adjustment of the Trump Administration's China policy. Recently, the United States has taken multi-domain actions simultaneously to strengthen the strategic competition and containment policies against China, such as mobilizing public opinion, provoking China-related issues, creating new weaponized legislation and even increasingly making use of Taiwan and Hong Kong to contain China. Under this competition situation, China must balance the whole picture and the critical point, make proper adjustments accordingly while sticking to their bottom-line priorities. Only in this way can China relieve the pressure and gain the initiative to effectively manage competition with the US. During this course, China should make more effort in improving its capacity and level of governance, strengthening the domestic foundation of competition against the US. Meanwhile, China should also actively adapt itself to the changing world and make full use of the situation to guide the direction of the China-US relationship. Key Words: Strategic competition; China-US relationship; Competition management; Counterbalancing ability 竞争战略在美国对外政策中占有重要地位，尤其在和主要对手的较量中，美国善于运用这一战略并曾收到较大成效，其中最显著的案例就是在美苏冷战中的充分运用。在美国学界和战略界的推动下，竞争战略理念在美对外战略中快速回归，也成为特朗普政府对华政策调整的重要理论依据。近来美国不断强化对华战略竞争与遏制的舆论动员，设置涉华议题，为强化对华战略竞争与遏制提供法律依据，并在等多个领域出手，全面强化对华战略竞争压力，甚至更多地利用台湾、香港局势的发展变化来牵制中国。面对咄咄逼人的竞争态势，中国既需抓住关键也需把握全局，既要坚守底线也要进退有据，有效管控中美竞争，化解战略压力拓展战略主动；而在竞争中更须切实提升国家治理能力和水平，筑牢战略竞争的国内基础，同时也要积极顺应世界变局，因势利导努力牵引中美关系的发展方向。 【关键词】战略竞争 中美关系 竞争管控 制衡能力 因势利

Experimental Study on the Deactivating Effect of KNO 3

Nanosized Ce/TiO2 is effective in selective catalytic reduction of NO with NH3. The NO conversion of Ce/TiO2 is 93% at 370°C. However, addition of potassium using KNO3, KCl, or K2SO4 as precursors effectively deactivates Ce/TiO2. NO conversion at 370°C is reduced to 45%, 24%, and 16% after addition of KNO3, KCl, and K2SO4, respectively, with a controlled K/Ce molar ration at 0.25. The deactivation may be attributed to the changes in the structural and chemical state of ceria and the degradation of surface acidity. The transformation of amorphous ceria into ceria crystals after potassium addition, together with the decrease of surface defects, is also determined. Oxygen diffusion in the process of ceria reduction is slow, and the redox cycle is slowed down. Moreover, the surface acid sites are markedly destroyed, leading to the reduced capacity of ammonia adsorption. These results may provide useful information for the application and life management of CeO2/TiO2 in potassium-rich environments such as biofuel-fired boilers

Design and experimental evaluation of a new modular underactuated multi-fingered robot hand

© IMechE 2020. In this paper, a modular underactuated multi-fingered robot hand is proposed. The robot hand can be freely configured with different number and configuration of modular fingers according to the work needs. Driving motion is achieved by the rigid structure of the screw and the connecting rod. A finger-connecting mechanism is designed on the palm of the robot hand to meet the needs of modular finger’s installation, drive, rotation, and sensor connections. The fingertips are made of hollow rubber to enhance the stability of grasping. Details about the design of the robot hand and analysis of the robot kinematics and grasping process are described. Last, a prototype is developed, and a grab test is carried out. Experimental results demonstrate that the structure of proposed modular robot hand is reasonable, which enables the adaptability and flexibility of the modular robot hand to meet the requirements of various grasping modes in practice

The cadmium–mercaptoacetic acid complex contributes to the genotoxicity of mercaptoacetic acid-coated CdSe-core quantum dots

Quantum dots (QDs) have many potential clinical and biological applications because of their advantages over traditional fluorescent dyes. However, the genotoxicity potential of QDs still remains unclear. In this paper, a plasmid-based system was designed to explore the genotoxic mechanism of QDs by detecting changes in DNA configuration and biological activities. The direct chemicobiological interactions between DNA and mercaptoacetic acid-coated CdSecore QDs (MAA–QDs) were investigated. After incubation with different concentrations of MAA–QDs (0.043, 0.13, 0.4, 1.2, and 3.6 μmol/L) in the dark, the DNA conversion of the covalently closed circular (CCC) DNA to the open circular (OC) DNA was significantly enhanced (from 13.9% ± 2.2% to 59.9% ± 12.8%) while the residual transformation activity of plasmid DNA was greatly decreased (from 80.7% ± 12.8% to 13.6% ± 0.8%), which indicated that the damages to the DNA structure and biological activities induced by MAA–QDs were concentration-dependent. The electrospray ionization mass spectrometry data suggested that the observed genotoxicity might be correlated with the cadmium–mercaptoacetic acid complex (Cd–MAA) that is formed in the solution of MAA–QDs. Circular dichroism spectroscopy and transformation assay results indicated that the Cd–MAA complex might interact with DNA through the groove-binding mode and prefer binding to DNA fragments with high adenine and thymine content. Furthermore, the plasmid transformation assay could be used as an effective method to evaluate the genotoxicities of nanoparticles

Data-Driven Dispatching Rules Mining and Real-Time Decision-Making Methodology in Intelligent Manufacturing Shop Floor with Uncertainty

From MDPI via Jisc Publications RouterHistory: accepted 2021-07-08, pub-electronic 2021-07-15Publication status: PublishedFunder: National Natural Science Foundation of China; Grant(s): 51875420, 51875421In modern manufacturing industry, the methods supporting real-time decision-making are the urgent requirement to response the uncertainty and complexity in intelligent production process. In this paper, a novel closed-loop scheduling framework is proposed to achieve real-time decision making by calling the appropriate data-driven dispatching rules at each rescheduling point. This framework contains four parts: offline training, online decision-making, data base and rules base. In the offline training part, the potential and appropriate dispatching rules with managers’ expectations are explored successfully by an improved gene expression program (IGEP) from the historical production data, not just the available or predictable information of the shop floor. In the online decision-making part, the intelligent shop floor will implement the scheduling scheme which is scheduled by the appropriate dispatching rules from rules base and store the production data into the data base. This approach is evaluated in a scenario of the intelligent job shop with random jobs arrival. Numerical experiments demonstrate that the proposed method outperformed the existing well-known single and combination dispatching rules or the discovered dispatching rules via metaheuristic algorithm in term of makespan, total flow time and tardiness

A novel multi-agent-based collaborative virtual manufacturing environment integrated with edge computing technique

This paper proposes a multi-agent-based collaborative virtual manufacturing environment (VME) to save energy consumption and improve efficiency in the manufacturing process. In order to achieve the high autonomy of the manufacturing system, a multi-agent system (MAS) is designed to build a collaborative VME. In this new VME environment, edge computing is embedded to strengthen the cyber resource utilization and system economy. Moreover, an efficient communication channel between networks is proposed. The subsequent cooperation and collaboration protocols among agents are designed to ensure flexible and process-oriented operations. Furthermore, the fuzzy resolution algorithm is employed to resolve the competition conflicts among function-similar MASs in the distributed manufacturing scenario. Lastly, a simulation and case study are performed to evaluate the performance of the proposed VME in Internet of Things (IoT)-based manufacturing. The analysis results have demonstrated the feasibility and effectiveness of the proposed VME system.The National Natural Science Foundation of Jiangsu Province, Fundamental Research project of Central Universities, Priority Academic Program Development of Jiangsu Higher Education Institutions (PAPD) and Australia ARC DECRA.http://www.mdpi.com/journal/energiesam2020Electrical, Electronic and Computer Engineerin

Machine learning classification models for fetal skeletal development performance prediction using maternal bone metabolic proteins in goats

Background: In developing countries, maternal undernutrition is the major intrauterine environmental factor contributing to fetal development and adverse pregnancy outcomes. Maternal nutrition restriction (MNR) in gestation has proven to impact overall growth, bone development, and proliferation and metabolism of mesenchymal stem cells in offspring. However, the efficient method for elucidation of fetal bone development performance through maternal bone metabolic biochemical markers remains elusive. Methods: We adapted goats to elucidate fetal bone development state with maternal serum bone metabolic proteins under malnutrition conditions in mid- and late-gestation stages. We used the experimental data to create 72 datasets by mixing different input features such as one-hot encoding of experimental conditions, metabolic original data, experimental-centered features and experimental condition probabilities. Seven Machine Learning methods have been used to predict six fetal bone parameters (weight, length, and diameter of femur/humerus). Results: The results indicated that MNR influences fetal bone development (femur and humerus) and fetal bone metabolic protein levels (C-terminal telopeptides of collagen I, CTx, in middle-gestation and N-terminal telopeptides of collagen I, NTx, in late-gestation), and maternal bone metabolites (low bone alkaline phosphatase, BALP, in middle-gestation and high BALP in late-gestation). The results show the importance of experimental conditions (ECs) encoding by mixing the information with the serum metabolic data. The best classification models obtained for femur weight (Fw) and length (FI), and humerus weight (Hw) are Support Vector Machines classifiers with the leave-one-out cross-validation accuracy of 1. The rest of the accuracies are 0.98, 0.946 and 0.696 for the diameter of femur (Fd), diameter and length of humerus (Hd, Hl), respectively. With the feature importance analysis, the moving averages mixed ECs are generally more important for the majority of the models. The moving average of parathyroid hormone (PTH) within nutritional conditions (MA-PTH-experim) is important for Fd, Hd and Hl prediction models but its removal for enhancing the Fw, Fl and Hw model performance. Further, using one feature models, it is possible to obtain even more accurate models compared with the feature importance analysis models. In conclusion, the machine learning is an efficient method to confirm the important role of PTH and BALP mixed with nutritional conditions for fetal bone growth performance of goats. All the Python scripts including results and comments are available into an open repository at https://gitlab.com/muntisa/goat-bones-machine-learning

Gastrointestinal Spatiotemporal mRNA Expression of Ghrelin vs Growth Hormone Receptor and New Growth Yield Machine Learning Model Based on Perturbation Theory

[Abstract] The management of ruminant growth yield has economic importance. The current work presents a study of the spatiotemporal dynamic expression of Ghrelin and GHR at mRNA levels throughout the gastrointestinal tract (GIT) of kid goats under housing and grazing systems. The experiments show that the feeding system and age affected the expression of either Ghrelin or GHR with different mechanisms. Furthermore, the experimental data are used to build new Machine Learning models based on the Perturbation Theory, which can predict the effects of perturbations of Ghrelin and GHR mRNA expression on the growth yield. The models consider eight longitudinal GIT segments (rumen, abomasum, duodenum, jejunum, ileum, cecum, colon and rectum), seven time points (0, 7, 14, 28, 42, 56 and 70 d) and two feeding systems (Supplemental and Grazing feeding) as perturbations from the expected values of the growth yield. The best regression model was obtained using Random Forest, with the coefficient of determination R2 of 0.781 for the test subset. The current results indicate that the non-linear regression model can accurately predict the growth yield and the key nodes during gastrointestinal development, which is helpful to optimize the feeding management strategies in ruminant production system.National Natural Science Foundation of China; 31320103917State of California; XDA05020700National Space Science Center (China); 2010T2S13National Space Science Center (China); 2012T1S0009Hunan Provincial People's Government (China); 2013TF3006Xunta de Galicia; GRC2014/04