Search

With the coverage of sensor-rich smart devices (smartphones, iPads, etc.), combined with the need to collect large amounts of data, mobile crowd sensing (MCS) has gradually attracted the attention of academics in recent years. MCS is a new and promising model for mass perception and computational data collection. The main function is to recruit a large group of participants with mobile devices to perform sensing tasks in a given area. Task assignment is an important research topic in MCS systems, which aims to efficiently assign sensing tasks to recruited workers. Previous studies have focused on greedy or heuristic approaches, whereas the MCS task allocation problem is usually an NP-hard optimisation problem due to various resource and quality constraints, and traditional greedy or heuristic approaches usually suffer from performance loss to some extent. In addition, the platform-centric task allocation model usually considers the interests of the platform and ignores the feelings of other participants, to the detriment of the platform’s development. Therefore, in this paper, deep reinforcement learning methods are used to find more efficient task assignment solutions, and a weighted approach is adopted to optimise multiple objectives. Specifically, we use a double deep Q network (D3QN) based on the dueling architecture to solve the task allocation problem. Since the maximum travel distance of the workers, the reward value, and the random arrival and time sensitivity of the sensing tasks are considered, this is a dynamic task allocation problem under multiple constraints. For dynamic problems, traditional heuristics (eg, pso, genetics) are often difficult to solve from a modeling and practical perspective. Reinforcement learning can obtain sub-optimal or optimal solutions in a limited time by means of sequential decision-making. Finally, we compare the proposed D3QN-based solution with the standard baseline solution, and experiments show that it outperforms the baseline solution in terms of platform profit, task completion rate, etc., the utility and attractiveness of the platform are enhanced.

Efficient enantioselective separation using porous materials requires tailored and diverse pore environments to interact with chiral substrates; yet, current cage materials usually feature uniform pores. Herein, we report two porous assemblies

This paper studied the trajectory tracking control of a failure satellite that has actuator jumping fault. It proposed a design method based on the jumping control input for the model reference tracking controller. By comb-ining the stochastic stability definition with model reference tracking, it gave the mathematical descriptions of the trajectory tracking control of the failure satellite. It used the linear matrix inequality method and the parametric solution method of the nonhomogeneous generalized Sylvester matrix equation to design the robust H∞ state feedback control law and the complete parametric feed-forward tracking compensator respectively, with the disturbance of the failure satellite and its thrust constraint considered simultaneously. The numerical simulation results on the model of the satellite rendezvous system show that the design method proposed in this paper is effective.

A chiral metal-organic framework (CMOF) with open chiral channels and multiple recognition sites is constructed from camphoric acid and a dipyridyl ligand. It can act as an efficient chiral solid adsorbent, capable of separating a variety of racemic alcohols and epoxides with excellent enantioselectivities.

A biomimetic metal–organic framework with cuboid inner cavities and multiple recognition sites was constructed from a phenylalanine-derived ligand. It can enantioselectively separate various racemic alcohols, diols and epoxides with ee up to 99.5%.

A novel hybrid structure of bimetallic dicyanamide decorated BiVO4 is developed via a simple method to accelerate interfacial water oxidation kinetics. Two types of bimetallic dicyanamides, CoNi(dca)2 and CoFe(dca)2, are coated on BiVO4 photoanodes and are found to exhibit far more enhanced PEC performance than Co(dca)2 or Ni(dca)2 as cocatalysts. The successful deposition of metal dicyanamides on BiVO4 photoanodes is confirmed by physical characterizations including X-ray photoelectron spectroscopy (XPS). The optimized Co0.9Ni0.1(dca)2/BiVO4 photoanode exhibits the highest photocurrent density of 2.58 mA/cm2 at 1.23 V vs. RHE under 100 mW/cm2 AM 1.5 G irradiation, which is 2.5 times that of bare BiVO4. The substantial enhancement of PEC performance can be ascribed to the advantageous interfacial charge transfer and improved charge injection efficiencies. This work presents a feasible strategy using different types of bimetallic dicyanamides to design a modified BiVO4-based photoanode system for enhanced water oxidation efficiency.

The birth of the Internet has brought great changes to people's production and life. China's Internet development has gone through more than 10 years, during which it experienced the initial stage of ignorance and rapid development. The subsequent economic bubble brought the Internet economy to a low level. After the baptism of the Internet economic bubble, the China Internet reopened its vitality and gradually moved toward the right track. Tencent and Chinese Internet companies have experienced the ups and downs of the Internet economy and become the leader of Chinese Internet enterprises after more than ten years of development. What on earth did Tencent achieve so much? What is the difference between its competitive strategy and other Internet companies? These are the problems that this paper attempts to study in depth.

To solve surface carrier recombination and sluggish water oxidation kinetics of hematite (α-Fe

Metal-nitrides-based electrocatalysts for efficient oxygen-evolution have been extensively studied as one of the most promising candidates to fulfil the demand for future energy-conversion and storage. Herein, a series of NixCo1−xO- and NixCo1−xN-based nanostructures on nickel foams were reported to show excellent activities for oxygen-evolution reaction. The catalysts were prepared and modulated rationally via a facile-hydrothermal method, followed by high-temperature calcination under air or nitrogen atmosphere. The optimal bimetallic-nitride catalyst Ni0.3Co0.7N shows a small overpotential of 268 mV at 20 mA cm−2, and a Tafel slope of 66 mV dec−1 with good stability. The enhanced OER-performance is ascribed to the synergetic effect of the unique morphology and the intrinsic catalytic property of the nanostructure after nitridation.

Bismuth vanadate (BiVO4) is one of the most promising metal oxide semiconductors for photoelectrochemical (PEC) water oxidation. Much efforts have been dedicated on accelerating the sluggish surface water oxidation kinetics. In this study, plasma enhanced atomic layer deposition and subsequent removal of Al2O3 ultrathin overlayers on bismuth vanadate were implemented to achieve the successful passivation of surface states and significant enhancement of PEC performance. Al2O3 ultrathin overlayers were first coated on BiVO4 surface via plasma enhanced atomic layer deposition with various deposition cycles, which resulted in the decrease of PEC water oxidation activity due to the poor conductivity. The subsequent removal of surface amorphous Al2O3 passivated the surface states of the photoanodes and significantly enhanced the photocurrent densities. The passivated BiVO4 exhibited a photocurrent density of 1.34 mA·cm−2 at 1.23 V vs. RHE, which is 73% higher than that of unmodified BiVO4. This work provides a novel strategy and deep insights on surface modification of semiconductor for photoelectrochemical energy conversion.