Distribution of Camptotheca Decaisne: Endangered Status

Camptotheca Decaisne is endemic to southern China. Since 1934, C. acuminata has been widely introduced to many gardens and arboreta in North America, Asia, and Europe as living collections. Our national surveys in China between 1995 and 1998 indicated that the genus. Camptotheca spp. are severely endangered in native range. Our field surveys failed to locate any wild populations of C. acuminata although it is often cultivated as landscape trees in southern China. We could not identify any living trees of C. acuminata var. tenuifolia and var. rotundifolia. We estimated that there are approximately 500 mature trees of C. lowreyana in Guangdong and less than 50 wild mature trees of C. yunnanensis in Yunnan

The effect of geographic distance on independent directors’ performance from the perspective of inefficient investment

Geoeconomics has attracted sustained attention in recent years, but the role of independent directors’ geographic distance in investment efficiency remains unexplored. We explore the governance effects of independent directors from a geographic location perspective. Specifically, the Great Circle Distance Formula is employed to calculate the geographic distance between the independent directors and the enterprise. Then, we measure the inefficient investment. Using a detailed sample in the Chinese market from 2009 to 2018, we find that geographic distance is not conducive to the functioning of independent directors and that there is a positive relationship between independent directors’ geographic distance and inefficient investment. The coefficients are robust to multiple robustness checks. In addition, the positive effect of independent directors’ geographic distance on inefficient investment will increase (become more positive) when there is no high-speed rail and the marketisation process is low in the enterprise’s location. Mechanism tests show that geographic distance does affect inefficient investment by inhibiting independent directors’ access to information as well as their reputation. Our results have important implications for investment policy and corporate governance

Modeling and Model Predictive Power and Rate Control of Wireless Communication Networks

A novel power and rate control system model for wireless communication networks is presented, which includes uncertainties, input constraints, and time-varying delays in both state and control input. A robust delay-dependent model predictive power and rate control method is proposed, and the state feedback control law is obtained by solving an optimization problem that is derived by using linear matrix inequality (LMI) techniques. Simulation results are given to illustrate the effectiveness of the proposed method

Electric-field-induced strong enhancement of electroluminescence in multilayer molybdenum disulfide.

The layered transition metal dichalcogenides have attracted considerable interest for their unique electronic and optical properties. While the monolayer MoS2 exhibits a direct bandgap, the multilayer MoS2 is an indirect bandgap semiconductor and generally optically inactive. Here we report electric-field-induced strong electroluminescence in multilayer MoS2. We show that GaN-Al2O3-MoS2 and GaN-Al2O3-MoS2-Al2O3-graphene vertical heterojunctions can be created with excellent rectification behaviour. Electroluminescence studies demonstrate prominent direct bandgap excitonic emission in multilayer MoS2 over the entire vertical junction area. Importantly, the electroluminescence efficiency observed in multilayer MoS2 is comparable to or higher than that in monolayers. This strong electroluminescence can be attributed to electric-field-induced carrier redistribution from the lowest energy points (indirect bandgap) to higher energy points (direct bandgap) in k-space. The electric-field-induced electroluminescence is general for other layered materials including WSe2 and can open up a new pathway towards transition metal dichalcogenide-based optoelectronic devices

MCNS: Mining Causal Natural Structures Inside Time Series via A Novel Internal Causality Scheme

Causal inference permits us to discover covert relationships of various variables in time series. However, in most existing works, the variables mentioned above are the dimensions. The causality between dimensions could be cursory, which hinders the comprehension of the internal relationship and the benefit of the causal graph to the neural networks (NNs). In this paper, we find that causality exists not only outside but also inside the time series because it reflects a succession of events in the real world. It inspires us to seek the relationship between internal subsequences. However, the challenges are the hardship of discovering causality from subsequences and utilizing the causal natural structures to improve NNs. To address these challenges, we propose a novel framework called Mining Causal Natural Structure (MCNS), which is automatic and domain-agnostic and helps to find the causal natural structures inside time series via the internal causality scheme. We evaluate the MCNS framework and impregnation NN with MCNS on time series classification tasks. Experimental results illustrate that our impregnation, by refining attention, shape selection classification, and pruning datasets, drives NN, even the data itself preferable accuracy and interpretability. Besides, MCNS provides an in-depth, solid summary of the time series and datasets.Comment: 9 pages, 6 figure

Printing surface charge as a new paradigm to program droplet transport

Directed, long-range and self-propelled transport of droplets on solid surfaces, especially on water repellent surfaces, is crucial for many applications from water harvesting to bio-analytical devices. One appealing strategy to achieve the preferential transport is to passively control the surface wetting gradients, topological or chemical, to break the asymmetric contact line and overcome the resistance force. Despite extensive progress, the directional droplet transport is limited to small transport velocity and short transport distance due to the fundamental trade-off: rapid transport of droplet demands a large wetting gradient, whereas long-range transport necessitates a relatively small wetting gradient. Here, we report a radically new strategy that resolves the bottleneck through the creation of an unexplored gradient in surface charge density (SCD). By leveraging on a facile droplet printing on superamphiphobic surfaces as well as the fundamental understanding of the mechanisms underpinning the creation of the preferential SCD, we demonstrate the self-propulsion of droplets with a record-high velocity over an ultra-long distance without the need for additional energy input. Such a Leidenfrost-like droplet transport, manifested at ambient condition, is also genetic, which can occur on a variety of substrates such as flexible and vertically placed surfaces. Moreover, distinct from conventional physical and chemical gradients, the new dimension of gradient in SCD can be programmed in a rewritable fashion. We envision that our work enriches and extends our capability in the manipulation of droplet transport and would find numerous potential applications otherwise impossible.Comment: 11 pages, 4 figure