Adaptive Policy Learning to Additional Tasks

This paper develops a policy learning method for tuning a pre-trained policy to adapt to additional tasks without altering the original task. A method named Adaptive Policy Gradient (APG) is proposed in this paper, which combines Bellman's principle of optimality with the policy gradient approach to improve the convergence rate. This paper provides theoretical analysis which guarantees the convergence rate and sample complexity of $\mathcal{O}(1/T)$ and $\mathcal{O}(1/\epsilon)$, respectively, where $T$ denotes the number of iterations and $\epsilon$ denotes the accuracy of the resulting stationary policy. Furthermore, several challenging numerical simulations, including cartpole, lunar lander, and robot arm, are provided to show that APG obtains similar performance compared to existing deterministic policy gradient methods while utilizing much less data and converging at a faster rate

Advanced Geological Prediction

Due to the particularity of the tunnel project, it is difficult to find out the exact geological conditions of the tunnel body during the survey stage. Once it encounters unfavorable geological bodies such as faults, fracture zones, and karst, it will bring great challenges to the construction and will easily cause major problems, economic losses, and casualties. Therefore, it is necessary to carry out geological forecast work in the tunnel construction process, which is of great significance for tunnel safety construction and avoiding major disaster accident losses. This lecture mainly introduces the commonly used methods of geological forecast in tunnel construction, the design principles, and contents of geological forecast and combines typical cases to show the implementation process of comprehensive geological forecast. Finally, the development direction of geological forecast theory, method, and technology is carried out. Prospects provide a useful reference for promoting the development of geological forecast of tunnels

Monte Carlo Method-Based Behavioral Reliability Analysis of Fully-Mechanized Coal Mining Operators in Underground Noise Environment

Sound is an important aspect in working environment, and underground strong noise environment imposes a serious impact on operators\u27 physical and mental health and easily leads to people\u27s unsafe behaviors, thus giving rise to accidents. How to quantitatively study operators\u27 behavioral reliability is a study hotspot. To mitigate the noise impact on operators and reduce the occurrence rate of accidents due to human factors, the relational models between noisy working environment and human physiological indexes were established first through a laboratory simulation, and then a reliability integral model was obtained using performance function and limit state equation. Second, the established reliability integral model was numerically simulated based on Monte Carlo numerical simulation method to obtain its numerical solution, and operators\u27 behavioral reliability values under different sound pressure levels (SPLs) on fully-mechanized coal mining face were calculated. The results show that the behavioral reliability of fully-mechanized coal mining operators is high with low accident occurrence rate under noise SPL of 50-70 dB. Under 70-90 dB, their behavioral reliability is 0.7092 with potential accident risks. The behavioral reliability is low when SPL is 90-110 dB, under which accidents may easily take place. This study manifests that operators\u27 behavioral reliability analysis under underground noise environment based on Monte Carlo method is of certain feasibility. The conclusions have a certain guiding significance for relieving human physical and mental harms incurred by noise, improving human behavioral reliability, reducing human errors and guaranteeing safety production

Supplementary document for Explaining the shift of V-O charge transfer band experimentally and the application in single-excitation ratiometric optical thermometry - 6033500.pdf

Additional Figures S1, S2, S

An Update on the Mutual Impact between SARS-CoV-2 Infection and Gut Microbiota

The gut microbiota is essential for good health. It has also been demonstrated that the gut microbiota can regulate immune responses against respiratory tract infections. Since the outbreak of the COVID-19 pandemic, accumulating evidence suggests that there is a link between the severity of COVID-19 and the alteration of one’s gut microbiota. The composition of gut microbiota can be profoundly affected by COVID-19 and vice versa. Here, we summarize the observations of the mutual impact between SARS-CoV-2 infection and gut microbiota composition. We discuss the consequences and mechanisms of the bi-directional interaction. Moreover, we also discuss the immune cross-reactivity between SARS-CoV-2 and commensal bacteria, which represents a previously overlooked connection between COVID-19 and commensal gut bacteria. Finally, we summarize the progress in managing COVID-19 by utilizing microbial interventions

Distributed algorithm for achieving finite-time minimum l1 norm solutions of linear equation

Abstract— This paper proposes a distributed algorithm for multi-agent networks to achieve a minimum l1-norm solution to a linear equation Ax = b where A has full row rank. When the underlying network is undirected and fixed, it is proved that the proposed algorithm drive all agents’ individual states to converge in finite-time to the same minimum l1-norm solution. Numerical simulations are also provided as validation of the proposed algorithms

Corona Onset Characteristics of Bundle Conductors in UHV AC Power Lines at 2200 m Altitude

The corona onset characteristic of bundle conductors is an important limiting factor for the design of UHV AC power lines in high-altitude areas. An experimental study on the corona characteristics of 8 × LGJ630, 6 × LGJ720, 8 × LGJ720 and 10 × LGJ720 bundle conductors commonly used in UHV power lines under dry and wet conductor conditions, as well as artificial moderate and heavy rain conditions, was conducted in Ping’an County, Xining City (elevation 2200 m). By using the tangent line method, the corona onset voltages and onset electric field of four types of conductors at high altitudes are obtained for the first time. In addition, the calculation model of corona onset voltage considering the outer strands’ effect on the electric field and the geometric factor in the corona cage in high altitude areas is established. The comparison of the calculation results and experimental results under dry conditions verifies the model’s correctness. Based on the results, an optimal selection scheme for high altitudes is proposed. The roughness coefficient was also calculated and analysed: the roughness coefficient of bundled conductors was between 0.59 and 0.77, and the roughness coefficient of the wet conductor was between the dry and rainy conditions. Both the experimental data and the calculation model can provide a reference for conductor selection for UHV AC power lines for use in high-altitude areas