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A large number of prediction strategies are specific to a dynamic multiobjective optimization problem (DMOP) with only one type of the Pareto set (PS) change. However, a continuous DMOP with more than one type of the unknown PS change has been seldom investigated. We present a multimodel prediction approach (MMP) realized in the framework of evolutionary algorithms (EAs) to tackle the problem. In this paper, we first detect the type of the PS change, followed by the selection of an appropriate prediction model to provide an initial population for the subsequent evolution. To observe the influence of MMP on EAs, optimal solutions obtained by three classical dynamic multiobjective EAs with and without MMP are investigated. Furthermore, to investigate the performance of MMP, three state-of-the-art prediction strategies are compared on a large number of dynamic test instances under the same particle swarm optimizer. The experimental results demonstrate that the proposed approach outperforms its counterparts under comparison on most optimization problems.

Feature selection has been an important research area in data mining, which chooses a subset of relevant features for use in the model building. This paper aims to provide an overview of feature selection methods for big data mining. First, it discusses the current challenges and difficulties faced when mining valuable information from big data. A comprehensive review of existing feature selection methods in big data is then presented. Herein, we approach the review from two aspects: methods specific to a particular kind of big data with certain characteristics and applications of methods in classification analysis, which are significantly different to the existing review work. This paper also highlights the current issues of feature selection in big data and suggests the future research directions.

Dynamic multi-objective optimization problems (DMOPs) not only involve multiple conflicting objectives, but these objectives may also vary with time, raising a challenge for researchers to solve them. This paper presents a cooperative co-evolutionary strategy based on environment sensitivities for solving DMOPs. In this strategy, a new method that groups decision variables is first proposed, in which all the decision variables are partitioned into two subcomponents according to their interrelation with environment. Adopting two populations to cooperatively optimize the two subcomponents, two prediction methods, i.e., differential prediction and Cauchy mutation, are then employed respectively to speed up their responses on the change of the environment. Furthermore, two improved dynamic multi-objective optimization algorithms, i.e., DNSGAII-CO and DMOPSO-CO, are proposed by incorporating the above strategy into NSGA-II and multi-objective particle swarm optimization, respectively. The proposed algorithms are compared with three state-of-the-art algorithms by applying to seven benchmark DMOPs. Experimental results reveal that the proposed algorithms significantly outperform the compared algorithms in terms of convergence and distribution on most DMOPs.

Nonaqueous organic electrolyte was utilized to etch GaN via photo-electrochemical etching technique. From the point of electrochemical window, the organic electrolyte has wider electrochemical window than conventional aqueous etchants. SEM images show that the pore density is up to 1.2 × 107 per square centimeter. The difference of XPS spectra about survey, Ga 3d and N 1s between the non-etched and freshly etched GaN surface can be explained the formation of Ga(BF4)3. The proposed dissolution mechanism can be broadly applied for explaining the etching process of nonaqueous etchants under the assist of electric field and strong light.

The outbreak of COVID-19 caused by SARS-CoV-2 in Wuhan and other cities of China is a growing global concern. Delay in diagnosis and limited hospital resources lead to a rapid spread of COVID-19. In this study, we investigate the effect of delay in diagnosis on the disease transmission with a new formulated dynamic model. Sensitivity analyses and numerical simulations reveal that, improving the proportion of timely diagnosis and shortening the waiting time for diagnosis can not eliminate COVID-19 but can effectively decrease the basic reproduction number, significantly reduce the transmission risk, and effectively prevent the endemic of COVID-19, e.g., shorten the peak time and reduce the peak value of new confirmed cases and new infection, decrease the cumulative number of confirmed cases and total infection. More rigorous prevention measures and better treatment of patients are needed to control its further spread, e.g., increasing available hospital beds, shortening the period from symptom onset to isolation of patients, quarantining and isolating the suspected cases as well as all confirmed patients.

Porous gallium nitride (GaN) luminescent materials were fabricated by photoelectrochemical etching using three different 1-ethyl-3-methylimidazolium ([EMIM])-based ionic liquids as the etchants. The as-etched porous GaN presents honeycomb shape, deep gully with some holes in the surroundings, and overlapping hole structure, which are significantly different from the planar GaN. Moreover, photoluminescence (PL) spectra indicates that the porous GaN has more excellent PL performance than the planar GaN. In general, reducing the etching time to 1 min, the PL intensity of porous GaN etched by [EMIM][OTF] is 9.27 times than that of the planar one. These results suggest that porous GaN etched by ionic liquid can be an outstanding substrate in the field of the luminescent material.

Bismuth-modified gallium nitride (Bi/GaN) electrode was fabricated using an in situ plating technique and simultaneously investigated for the detection of Cd2 + by square wave anodic stripping voltammetry (SWASV). Under the optimized conditions, the Bi/GaN electrode showed good linear amperometric responses for Cd2 + in the concentration range from 1 to 150 μg/L. The sensitivity was 0.54418 μA L μg− 1 (i.e., 59.8598 A M− 1) and the limit of detection (LOD) was 0.3 μg/L (i.e., 2.72 nM). Furthermore, the as-prepared Bi/GaN sensor has high repeatability, and retains 95% of its initial stripping peak current after 50 cycles. More importantly, the applicability of the sensor in real samples such as tap water, milk, and fetal bovine serum was researched. The recovery ranges from 92.3 to 106.7%, indicating that the as-fabricated Bi/GaN sensor has potential to monitor Cd2 + in practical environments.

Three-dimensional (3D) metal-semiconductor nanostructures as surface-enhanced Raman scattering (SERS) substrates were designed by in situ electrodeposition of gold nanoparticles (AuNPs) or in situ photodeposition of silver nanoparticles (AgNPs) on gallium nitride (GaN) nanoflowers (NFs) supports fabricated by metal-assisted photochemical etching of single crystalline GaN. 3D AuNPs/GaN NFs and AgNPs/GaN NFs substrates exhibit excellent enhancement effect for Rhodamine 6G (R6G) due to more “hot spots” in the same probing volume compared to 2D GaN based substrates. The enhancement factors of the AuNPs/GaN NFs and AgNPs/GaN NFs substrates are up to 2.1 × 10 7 and 5.9 × 10 7 , respectively, and the corresponding detection limits of R6G are 10 −8 and 10 −10 M, respectively. Moreover, further study reveals both substrates have good reproducibility and long-term stability. The performance of the prepared substrates for biological application was demonstrated by the detection of bovine serum albumin (BSA). A series of characteristic bands of amides suggest BSA can be well adsorbed on the surface of the AuNPs/GaN NFs and AgNPs/GaN NFs substrates, which demonstrates our substrates have good biocompatibility and are promising candidates for SERS biosensors.

In this study, we report for the preparation of nickel phthalocyanine (NiPc) free-standing films on ionic liquid (IL) surface by a physical vapor deposition method. Different from on the solid substrate, the as-obtained film is α phase and with a 2D network structure. In addition, the good transferability of the film make it can be easily transferred onto any substrate for further device applications. The device based on these films shows good photoelectrical property, high stability and high photosensitivity.

Gallium nitride (GaN) nanowires (NWs) were fabricated by metal-assisted photochemical etching (MaPEtch). Gold nanoparticles (AuNPs) as metal catalyst were electrodeposited on the GaN substrate. SEM and HRTEM images show the surface of GaN NWs is smooth and clean without any impurity. SAED and FFT patterns demonstrate GaN NWs have single crystal structure, and the crystallographic orientation of GaN NWs is (0002) face. On the basis of the assumption of localized galvanic cells, combined with the energy levels and electrochemical potentials of reactants in this etching system, the generation, transfer and consumption of electron-hole pairs reveal the whole MaPEtch reaction process. Such easily fabricated GaN NWs have great potential for the assembly of GaN-based single-nanowire nanodevices.

The structural relaxation times of metallic glasses (MGs) are generally believed to decrease with increasing flow rate, also known as shear thinning. In this work, an extraordinary loading rate-independent creep relaxation time, in contrast to the decreasing Maxwell relaxation time with increasing loading rate, is observed for a La-based MG with pronounced Johari–Goldstein relaxation under nanoindentation. The Johari–Goldstein relaxation in the flow of MGs is suggested to be a form of local Newtonian flow and may be not crucial in shear thinning, whereas the main α relaxation is proposed to predominate in the shear thinning of MGs, based on the disparity between the creep relaxation time and the Maxwell relaxation time. These results improve the current understandings on the structural relaxation and its role in the shear thinning and plastic deformation of MGs.

Bismuth nanodendrites (BiNDs) were electrodeposited on planar gallium nitride (GaN) electrode via a differential pulse voltammetric technique to fabricate the non-enzymatic hydrogen peroxide (H2O2) sensor. SEM images revealed that the as-obtained BiNDs had numerous dendrite sub-branches, whose diameters ranged from 136 to 152 nm. The BiNDs/GaN electrode showed linear amperometric responses for H2O2 in the concentration range from 10 µM to 1 mM with the sensitivity of 60.0 μA mM−1 cm−2. Another linear range was from 1 to 10 mM with the sensitivity of 23.3 μA mM−1 cm−2. The limit of detection (LOD) was 5 µM with the signal-to-noise ratio of 3. The applicability of the sensor was investigated to the H2O2 detection in real samples such as fetal bovine serum and milk, and the sensor exhibited excellent anti-interference capacity. The achieved results indicate that the as-prepared BiNDs/GaN sensor with good reproducibility and long-term stability was promising for detecting H2O2 in practical environments.

Gallium nitride (GaN) surface was etched by 0.3 M ethylenediamine tetraacetic acid disodium (EDTA-2Na) via photoelectrochemical etching technique. SEM images reveal the etched GaN surface becomes rough and irregular. The pore density is up to 1.9 × 10 9 per square centimeter after simple acid post-treatment. The difference of XPS spectra of Ga 3d, N 1s and O 1s between the non-etched and freshly etched GaN surfaces can be attributed to the formation of Ga–EDTA complex at the etching interface between GaN and EDTA-2Na. The proposed complexation dissolution mechanism can be broadly applicable to almost all neutral etchants under the prerequisite of strong light and electric field. From the point of view of environment, safety and energy, EDTA-2Na has obvious advantages over conventionally corrosive etchants. Moreover, as the further and deeper study of such nearly neutral etchants, GaN etching technology has better application prospect in photoelectric micro-device fabrication.

Porous gallium nitride (PGaN) layers were fabricated by metal-assisted photochemical etching (MaPCE) using electrodeposited platinum nanoparticles (PtNPs) or gold nanoparticles (AuNPs) as catalysts. After identification of a suitable negative potential and appropriate cyclic voltammetry (CV) conditions, high-density PtNPs or AuNPs were deposited onto a planar GaN substrate. Based on the concrete numerical values of energy levels, the generation, transfer and consumption of electrons and holes, and the assumption that localized galvanic cells are formed, an etching mechanism was proposed which may provide theoretical guidance for future work on etching of GaN and other semiconductor materials. Keywords: Gallium nitride, Metal-assisted photochemical etching, Electrodeposition, Noble metal nanoparticles

Here we report the electrodeposition of gold nanoparticles (AuNPs) onto porous GaN electrode obtained by photoelectrochemical etching planar GaN to fabricate a non-enzymatic hydrogen peroxide (H2O2) sensor. SEM images revealed porous GaN has uniformly high-porosity structure and the diameter of AuNPs is 5–16 nm. The AuNPs/porous GaN electrode exhibited good electrocatalytic activity toward the reduction of H2O2 and performed as amperometric sensor for the detection of H2O2. The AuNPs/porous GaN electrode showed linear amperometric responses for H2O2 in the concentration range from 10 to 100 μM with the sensitivity of 281.5 μA mM−1. The limit of detection (LOD) is 2 μM with a signal-to-noise ratio of 3. In addition, the AuNPs/porous GaN electrode exhibited good repeatability, reproducibility, selectivity and long-term stability for H2O2 detection, in the meanwhile, AuNPs showed excellent adhesive capacity to the porous GaN electrode, which was tested by continually sonicating the AuNPs/porous GaN electrode for 3 h. Above results demonstrated this simply prepared H2O2 sensor has good practicability and is promising to measure the analyte in practical applications.

Here we report an environment-friendly approach to fabricate porous GaN photoelectrode using ionic liquid as the etchant. SEM images revealed photo-assisted electrochemical etching (PECE) has better etching effect than electrochemical etching (ECE). Furthermore, Raman spectra demonstrated porous GaN obtained by PECE has better lattice integrity than that obtained by ECE. The photocurrent of porous GaN prepared by PECE is six times and two times of planar and porous GaN fabricated by ECE, respectively. Above results indicated porous GaN obtained by PECE can be a promising photoelectrode for optoelectronic applications.

This paper is devoted to exploring a diffusive predator-prey system with prey refuge and predator cannibalism. We investigate dynamics of this system, including dissipation and persistence, local and global stability of constant steady states, Turing instability, and nonexistence and existence of nonconstant steady state solutions. The influence of prey refuge and predator cannibalism on predator and prey biomass density is also considered by using a systematic sensitivity analysis. Our studies suggest that appropriate predator cannibalism has a positive effect on predator biomass density, and then high predator cannibalism may stabilize the predator-prey ecosystem and prevent the paradox of enrichment.

We demonstrated a simple method for the fabrication of gold nanoparticles/porous GaN composite by using photo-assisted electrochemical etching and electrochemical deposition methods based on planar GaN film for photocatalytic degradation of methyl orange. Scanning electron microscopy (SEM) images revealed porous GaN has uniformly high-porosity structure and the diameter of gold nanoparticles (AuNPs) is 4–20 nm. Moreover, the pore diameter of porous GaN can be easily adjusted by changing etching time. Uniformly small AuNPs can be obtained by optimized deposition cycles of cyclic voltammetry (CV). The photocatalysis activity under simulated sunlight irradiation was enhanced via electrodepositing AuNPs onto porous GaN substrate. Above results indicated such porous GaN could be an excellent candidate to study the photocatalytic performance of porous wide band-gap semiconductor-based composite materials.

By virtue of instability analysis, an index of shear banding susceptibility of metallic glasses (MGs): the critical strain localization factor to initiate an embryonic shear band: χ = γ b / γ m ( γ b shear strain in the embryonic shear band and γ m in the matrix) is derived in analogue to the emergence of geometrically necessary dislocations (GND) in crystals. Physical properties, like Poisson ratio υ , Young's modulus E and the surface energy density ζ , are explicitly incorporated into the formulae of χ with which the origin of the plasticity of MGs are unraveled.

Free-standing films of metal octaethylporphyrins (MOEPs) were prepared for the first time by a physical vapor deposition on surface of an ionic liquid (IL). Different from those on solid surfaces, the as-obtained films were very compact and with plannar structure. The monitoring of time-dependent process indicated that the high surface energy of IL and the strong π…π interaction between MOEP molecules played key roles in forming such films. Furthermore, the as-obtained film showed good transferability, which made it possible to be easily transferred to any substrates for further device application. More importantly, the prototype photodetectors based on free-standing films of MOEP showed ultra flexibility, mechanical stability, and durability.

Tungsten oxide thin films were electrochemically deposited onto gallium nitride for the first time and directly used for the fabrication of acetone sensor. This sensor could operate at room temperature and had high sensitivity, fast response, and long-term stability. Moreover, the detection limit of the sensor could reach 0.1 ppm, implying its potential application in non-invasive detection of diabetes.

Here we demonstrate porous GaN electrode can be applied for trace Ag(I) detection. Compared to traditional planar electrodes, porous GaN electrode can detect lower concentration of Ag(I) as it possesses more deposition sites (crystal defects) and larger surface area. Under the optimum conditions, porous GaN electrode shows a linear voltammetric response in the Ag(I) concentration range from 1 to 100 ppb with the detection limit of 0.5 ppb. Such an unmodified, high-porosity and chemically stable electrode is promising to operate in real samples.

This paper presents a cooperative co-evolutionary dynamic multi-objective optimization algorithm, i.e., DNSGAII-CO for solving DMOPs based on environmental variable grouping. In this algorithm, a new method of grouping decision variables is first presented, in which all the decision variables are divided into two subcomponents according to whether they are interrelated with or without environment parameters. Then, when cooperatively optimizing the two subcomponents by using two populations, two prediction methods, i.e., differential prediction and Cauchy mutation, are employed to initialize them, respectively. The proposed algorithm is applied to a benchmark DMOPs, and compared with two state-of-the-art algorithms. The experimental results demonstrate that the proposed algorithm outperforms the compared algorithms in terms of convergence and distribution.

Interval cost feature selection problems (ICFS) are popular in real-world. However, since the optimized objectives not only are multiple but also contain interval coefficients, there have been few solving methods. This paper first transforms the ICFS into a multi-objective one with exact coefficients by the linear interval programming. Second, by combining a multi-objective particle swarm algorithm (which has a good performance in exploration) with a powerful problem-specific local search (which is good at exploitation), we propose a memetic multi-objective feature selection algorithm (MMFS-PSO). Finally, experimental results confirmed the advantages of our method.

In the real word, many multi-objective optimization problems are subject to dynamic changing conditions, which may occur in objectives, constraints and parameters. This paper provides a prediction strategy, called multi-direction prediction strategy (MDP), to enhance the performance of multi-objective evolutionary optimization algorithms in dealing with dynamic environments. Besides, the proposed prediction method makes use of multiple directions determined by several representative individuals. Our experimental results indicate that MDP can well tackle dynamic multi-objective problems.

Multi-label feature selection is a multi-objective optimization problem in nature, which has two conflicting objectives, i.e., the classification performance and the number of features. However, most of existing approaches treat the task as a single objective problem. In order to meet different requirements of decision-makers in real-world applications, this paper presents an effective multi-objective differential evolution for multi-label feature selection. The proposed algorithm applies the ideas of efficient non-dominated sort, the crowding distance and the Pareto dominance relationship to differential evolution to find a Pareto solution set. The proposed algorithm was applied to several multi-label classification problems, and experimental results show it can obtain better performance than two conventional methods. abstract environment.