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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 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.

Atom search optimization algorithm has good searching ability and has been successfully applied to calculate hydrogeological parameters and groundwater dispersion coefficient. Since the atom search optimization algorithm is only based on the atom force motion model in molecular dynamics, it has some shortcomings such as slow search speed and low precision during the later stage of iteration. A modified atom search optimization based on the immunologic mechanism and reinforcement learning is proposed to overcome the abovementioned shortcomings in this paper. The proposed algorithm introduces a vaccine operator to better utilize the dominant position in the current atom population so that the speed, accuracy, and domain search ability of the atom search optimization algorithm can be strengthened. The reinforcement learning operator is applied to dynamically adjust the vaccination probability to balance the global exploration ability and local exploitation ability. The test results of 21 benchmark functions confirm that the performance of the proposed algorithm is superior to seven contrast algorithms in search accuracy, convergence speed, and robustness. The proposed algorithm is used to optimize the permutation flow shop scheduling problem. The experimental results indicate that the proposed algorithm can achieve better optimization results than the seven comparative algorithms, so the proposed algorithm has good practical application value.

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.

This paper presents a study on wave propagation in rotating functionally graded (FG) microbeams reinforced by graphene nanoplatelets (GPLs). The graphene nanoplatelets (GPLs) are considered to distribute in the diameter direction of the micro-beam in a gradient pattern, which leads to the functionally graded structure. By using the Halpin-Tsai micromechanics model and the rule of mixture, the effective material properties of the microbeam are determined. According to the Euler-Bernoulli beam theory and nonlocal elasticity theory, the rotating microbeams are modeled. A comprehensive parametric study is conducted to examine the effects of rotating speed, GPL distribution pattern, GPL length-to-thickness ratio, GPL length-to-width ratio, and nonlocal scale on the wavenumber, phase speed and group speed of the microbeam. The research findings can play an important role on the design of rotating graphene nanoplatelet (GPL) reinforced microbeams for better structural performance.

Plates are commonly used in many engineering disciplines, including aerospace. With the continuous improvement in the capacity of high value-added airplanes, large transport aircrafts, and fighter planes that have high strength, high toughness, and corrosion resistance have gradually become the development direction of airplane plate structure production and research. The strength and stability of metal plate structures can be improved by adding reinforced materials. This paper studies graphene platelets (GPLs) reinforced with a free vibration porous composite plate. The porous plate is constructed with a multi-layer model in a metal matrix containing uniform or non-uniformly distributed open-cell internal pores. Considering the random and directional arrangement of graphene platelets in the matrix, the elastic modulus of graphene composites was estimated using the Halpin–Tsai micromechanical model, and the vibration frequencies of graphene composite were calculated using the differential quadrature method. The effects of the total number of layers, GPL distribution pattern, porosity coefficient, GPL weight fraction, and boundary conditions on the free vibration frequency of GPLs reinforced porous composite plates are studied, and the accuracy of the conclusions are verified by the finite element software.

A chiral structure with a negative Poisson’s ratio containing a hollow circle with varying diameters was designed, and the finite element method was used to investigate the variation in the Poisson’s ratio when the hollow circle diameter was varied (d = 0, 1, 2, 3, and 4 mm). The simulation results showed that the Poisson’s ratio was sensitive to the hollow circle diameter, and the minimum Poisson’s ratio was −0.43. Three specimens with different hollow circle diameters (d′ = 0, 1, and 3 mm) were 3D-printed from thermoplastic polyurethane, and the Poisson’s ratio and equivalent elastic modulus were measured. In the elastic range, the Poisson’s ratio increased and the equivalent elastic modulus decreased as the hollow circle diameter increased. The simulation and experimental results showed good agreement. The proposed structure is expected to be applicable to protective sports gear owing to its high energy absorption and the fact that its properties can be modified as required by adjusting the geometric parameters of the unit cell.

The risk of knee injuries in freestyle skiing athletes that perform aerials is high. The internal stresses in the knee joints of these athletes cannot easily be directly measured. In order to ascertain the mechanical response of knee joints during the landing phase, and to explore the mechanism of damage to the cartilage and ligaments, a finite element model of the knee joint was established. Three successful landing conditions (neutral, backward, or forward landing) from a triple kicker were analyzed. The results demonstrate that the risk of cruciate ligament damage during a neutral landing was lowest. A forward landing carried medium risk, while backward landing was of highest risk. Backward and forward landing carried risk of injury to the anterior cruciate ligament (ACL) and posterior cruciate ligament (PCL), respectively. The magnitude of stress on the meniscus and cartilage varied for all three landing scenarios. Stress was largest during neutral landing and least in backward landing, while forward landing resulted in a medium level of stress. The results also provide the basis for training that is scientifically robust so as to reduce the risk of injury and assist in the development of a professional knee joint protector.

Novel hybrid tetra-nitro-phthalocyanine Copper(II) (TNCuPc)/TiO2 films were successfully synthesized using a simple hydrothermal and solvothermal method. The successful deposition of TNCuPc on the TiO2 nanorods was confirmed by X-ray diffraction (XRD) and X-ray photoelectron spectroscopy (XPS). The precursor concentration for hydrothermal deposition of TiO2 was optimized for light absorption and photoelectrochemical (PEC) water splitting. The hierarchical nanostructure exhibited increased light absorption ability and enhanced PEC properties. Moreover, the concentration of the precursor solution for TNCuPc deposition was also optimized for the performance of the TNCuPc/TiO2 hybrid nanostructure. The optimal sample achieved the highest photocurrent of 1.1 mA cm−2 and the highest IPCE of 51% at 610 nm.

In this study, a ternary nanostructured α-Fe2O3/Au/TiO2 film with integrating a crystalline α-Fe2O3 core, metallic Au nanoparticles (NPs), and an amorphous TiO2 overlayer is fabricated and examined as a photoanode for photoelectrocatalytic water splitting. Under simulated solar illumination, the as-prepared photoanode exhibits a four-fold increase (1.05 mA cm−2) in photocurrent density at 1.23 V versus reversible hydrogen electrode (RHE) relative to bare α-Fe2O3. Based on systematic investigations, it is proposed that Au NPs extract photoholes from the bulk of α-Fe2O3 core and then shuttle them to the outer TiO2 overlayer, and meanwhile, TiO2 overlayer efficiently captures and stores the photoholes and facilitates the hole injection into electrolyte. Thus, the remarkably improved photoelectrocatalytic water splitting performance of α-Fe2O3/Au/TiO2 photoanode is attributed to the significantly suppressed bulk and surface charge recombination due to the relayed pumping of the photogenerated charge carriers through the photoanode/electrolyte interfaces reconstructed by Au NPs and TiO2 overlayer.

A flower pollination algorithm is proposed based on the hormone modulation mechanism (HMM-FPA) to solve the no-wait flow shop scheduling problem (NWFSP). This algorithm minimizes the maximum accomplished time. Random keys are encoded based on an ascending sequence of components to make the flower pollination algorithm (FPA) suitable for the no-wait flow shop scheduling problem. The hormone modulation factor is introduced to strengthen information sharing among the flowers and improve FPA cross-pollination to enhance the algorithm global search performance. A variable neighborhood search strategy based on dynamic self-adaptive variable work piece blocks is constructed to improve the local search quality. Three common benchmark instances are applied to test the proposed algorithm. The result verifies that this algorithm is effective.

Solar hydrogen conversion via photoelectrochemical water splitting is an important technology for energy and environment sustainability. Since the pioneering work of Fujishima and Honda in 1972, tremendous research on semiconductor-based photoelectrochemical water splitting has yielded better understanding of the processes as well as encouraging development of high efficiency photoelectrodes for solar hydrogen generation. Given the narrow band gap enabling excellent optical absorption, increased charge carrier density and accelerated surface oxidation reaction kinetics become the key points for improved photoelectrochemical performances for water splitting over α-Fe2O3 photoanodes. In this talk, some recent progresses in surface modified α-Fe2O3 for photoelectrochemical solar water splitting in our group will be introduced. By engineering the surface structures of α-Fe2O3 nanorods with AgxFe2-xO3, TiO2 and HfO2 overlayers, the surface charge recombination was greatly inhibited and the surface water oxidation kinetics were efficiently accelerated, resulting in remarkable enhancement in PEC water splitting performances.

Hematite (α-Fe2O3) has been extensively suggested as a superior photoanode for the photoelectrochemical (PEC) water splitting, owing to its natural abundance, high chemical stability, and theoretical solar-to-hydrogen (STH) efficiency (16.8%). Nevertheless, the recalcitrant electron−hole recombination resulting in poor charge separation and injection efficiency limits its PEC performance. Herein, a simple hydrothermal/atomic layer deposition (ALD) process was used to fabricate two types of HfO2 functionalized hematite (i.e., HfO2 overlayer and nanoparticles modified α-Fe2O3, HfFe-L and HfFe-P for short) photoanodes. It was revealed that HfO2 overlayer and nanoparticles could successfully passiviate the surface trap states of hematite, resulted in enhanced PEC performances for both HfFe-L and HfFe-P. More surprisingly, the photocurrent density of the HfFe-P reached as high as 1.21 mA cm-2 at 1.23 vs. RHE, with ~4.8 and ~3.3 fold enhancement as compared to that of the bare hematite and even HfFe-L, respectively. Such great PEC performance enhancement in HfFe-P was revealed to be attributed to the multifunction of HfO2 nanoparticles distributed on the surface of hematite nanorod arrays, which not only passivated surface defects to suppress the surface charge recombination by eliminating the surface trapping states, but can also facilitated the hole extraction from bulk of hematite to hematite/electrolyte interface.

The deep volcanic reservoir in Zhungeer Basin is buried in over 4000m depth, which is characterized by complex lithology (breccia, andesite, basalt, etc.), high elastic modulus and massive natural fractures. During hydraulic fracturing, hydraulic fracture will propagate and natural fractures will be triggered by the increasing net pressure. However, the extension of fractures, especially natural fractures, would aggravate the leak-off effect of fracturing fluid, and consequently decrease the fracturing success rate. 4 out of 12 fracturing wells in the field have failed to add enough proppants due to fluid loss. In order to increase the success rate and efficiency of hydraulic fracturing for deep volcanic reservoir, based on theoretical and experimental method, the mechanism of fracturing fluid leak-off is deeply studied. We propose a dualistic proppant scheme and employ the fluid loss reducer to control the fluid leak-off in macro-fractures and micro-fractures respectively. The proposed technique remarkably improved the success rate in deep volcanic rock fracturing. It bears important theoretical value and practical significance to improve the hydraulic fracturing design for deep volcanic reservoir.

Chicken swarm optimization is a new intelligent bionic algorithm, simulating the chicken swarm searching for food in nature. Basic algorithm is likely to fall into a local optimum and has a slow convergence rate. Aiming at these deficiencies, an improved chicken swarm optimization algorithm based on elite opposition-based learning is proposed. In cock swarm, random search based on adaptive t distribution is adopted to replace that based on Gaussian distribution so as to balance the global exploitation ability and local development ability of the algorithm. In hen swarm, elite opposition-based learning is introduced to promote the population diversity. Dimension-by-dimension greedy search mode is used to do local search for individual of optimal chicken swarm in order to improve optimization precision. According to the test results of 18 standard test functions and 2 engineering structure optimization problems, this algorithm has better effect on optimization precision and speed compared with basic chicken algorithm and other intelligent optimization algorithms.

Deep volcanic reservoir in Xinjiang Zhungeer basin is buried over 4000m, the elastic modulus of reservoir rocks is high, hence it is difficult to initiate hydraulic fracture during fracturing operation; fracturing fluid filtration is serious and hard to be controlled due to the massive natural fractures in reservoir. Because of the serious heterogeneity along both horizontal and vertical direction, the optimum targets of design fracturing for different well layers are not the same. 4 out of 12 fracturing wells have not achieved the target amount of proppant. Ultimately, fracturing success rate is low and the production increase after fracturing shows a big difference among different wells. In order to improve the success rate and efficiency of deep volcanic reservoir fracturing, based on the analysis of the difficulties in previous fracturing operation, combined with the geological characteristics of reservoir, we optimized the key engineering parameters of deep volcanic rock fracturing, including perforation parameters, filtration parameters, fracture parameters and operation parameters. Results of fracturing design optimization bear an important guiding significance for improving the fracturing success rate and efficiency of deep volcanic reservoir in Zhungeer basin, Xinjiang province.

Anisotropic nanopatterns have potentials in constructing novel plasmonic structures which have various applications in such as super-resolution microscopy, medicine, and sensors. However, it remains challenging to build big anisotropic nanopatterns that are suitable for big noble metal nanoparticles. Herein, we report a simple and reliable strategy for constructing DNA origami-based big anisotropic nanopatterns with controlled size and shape, nanoscale resolution, and fully addressability. Two kinds of basic DNA origami nanoblocks - cross-shaped and rectangular DNA origami units were used. We have demonstrated that by encoding nanoblocks' edges, anisotropic higher-order nanopatterns, such as dimer, trimer, tetramer and mini "windmill" like pentamer nanopatterns could be constructed. To show the potential use as template to direct the assembly of anisotropic nanoparticles arrays, a proof of concept work was conducted by anchoring streptavidin nanoparticles on the "windmill" template to form a chiral array. Significantly, these nanopatterns have the sizes of hundreds of nanometers, which are in principle also suitable for big noble metal nanoparticles arrays.

To solve chaotic time series prediction problem, a novel Prediction approach for chaotic time series based on Immune Optimization Theory (PIOT) is proposed. In PIOT, the concepts and formal definitions of antigen, antibody and affinity being used for time series prediction are given, and the mathematical models of immune optimization operators being used for establishing time series prediction model are exhibited. Chaotic time series is analyzed and corresponding sample space is reconstructed by phase space reconstruction method; then, the prediction model of chaotic time series is constructed by immune optimization theory; finally, using this prediction model to forecast chaotic time series. To demonstrate the effectiveness of PIOT, the three typical chaotic nonlinear time series are generated by nonlinear dynamics systems that are Lorenz, Mackey-Glass and Henon, respectively, and are used for simulating prediction. The simulation results show that PIOT is a feasible and effective prediction method, and meanwhile provides a novel prediction approach for chaotic time series.

ZnFe2O4 modified α-Fe2O3 nanorod array films were successfully synthesized by aqueous solution growth followed by a spin coating process and studied for photoelectrochemical (PEC) water splitting under solar light. Through the spin coating process, ZnFe2O4 was found mainly to exist at the top surface of the α-Fe2O3 nanorod arrays. After being modified by ZnFe2O4, the α-Fe2O3 nanorod film was identified as a suitable surface treatment, and showed an increased photocurrent density when used as the photoanode. It was demonstrated that ZnFe2O4 increased the charge carrier density and acted as the oxidation cocatalyst. These results suggested that surface treatment by ZnFe2O4 should be a practical strategy to improve the efficiency of α-Fe2O3 or other metal oxide photoanodes for PEC solar fuel conversion.