A route for large-scale preparation of multifunctional superhydrophobic coating with electrochemically-modified kaolin for efficient corrosion protection of magnesium alloys

Superhydrophobic coating has been widely studied for its great applicational potential, such as for corrosion protection of magnesium alloys while it has been restrained by expensive materials, sophisticated preparation process and infirm rough structures. In this study, the electrochemical method was adopted by using a two-electrode system for rapid hydrophobic modification to obtain superhydrophobic kaolin.By mixing the modified superhydrophobic kaolin with commercial epoxy resin and polydimethylsiloxane glue, a paint can be formed and easily used on various substrates for preparation of superhydrophobic coating via spraying method. The influence factors on wettability of the modified kaolin and the mixing ratio of each component of the coating were explored. Also, the wettability, durability and anticorrosion of the prepared coating were evaluated comprehensively. The coating was able to maintain superhydrophobic after immersed in HCl solution at pH 1, the NaOH solution at pH 14, and 3.5 wt.% NaCl solution for 16, 21, 30 days, respectively. In addition, the coating exhibited 4A grade adhesion, high hydrophobicity after abraded for 200 cycles on a 600-mesh sandpaper with 100 g weight, and 99.86% anticorrosion efficiency after soaked in 3.5 wt.% NaCl solution for 20 days, demonstrating a good robustness and anti-corrosion property. Furthermore, the coating showed good transparency, flexibility and was easy to make in a large scale by the spraying method, which is of great significance to promote the practical application of superhydrophobic coatings and the anticorrosion Mg alloys.

A Two-Layer Encoding Learning Swarm Optimizer Based on Frequent Itemsets for Sparse Large-Scale Multi-Objective Optimization

Traditional large-scale multi-objective optimization algorithms(LSMOEAs)encounter difficulties when dealing with sparse large-scale multi-objective optimization problems(SLM-OPs)where most decision variables are zero.As a result,many algorithms use a two-layer encoding approach to optimize binary variable Mask and real variable Dec separately.Nevertheless,existing optimizers often focus on locating non-zero variable posi-tions to optimize the binary variables Mask.However,approxi-mating the sparse distribution of real Pareto optimal solutions does not necessarily mean that the objective function is optimized.In data mining,it is common to mine frequent itemsets appear-ing together in a dataset to reveal the correlation between data.Inspired by this,we propose a novel two-layer encoding learning swarm optimizer based on frequent itemsets(TELSO)to address these SLMOPs.TELSO mined the frequent terms of multiple particles with better target values to find mask combinations that can obtain better objective values for fast convergence.Experi-mental results on five real-world problems and eight benchmark sets demonstrate that TELSO outperforms existing state-of-the-art sparse large-scale multi-objective evolutionary algorithms(SLMOEAs)in terms of performance and convergence speed.

Enhancing Evolutionary Algorithms With Pattern Mining for Sparse Large-Scale Multi-Objective Optimization Problems

Sparse large-scale multi-objective optimization problems(SLMOPs)are common in science and engineering.However,the large-scale problem represents the high dimensionality of the decision space,requiring algorithms to traverse vast expanse with limited computational resources.Furthermore,in the context of sparse,most variables in Pareto optimal solutions are zero,making it difficult for algorithms to identify non-zero variables efficiently.This paper is dedicated to addressing the challenges posed by SLMOPs.To start,we introduce innovative objective functions customized to mine maximum and minimum candidate sets.This substantial enhancement dramatically improves the efficacy of frequent pattern mining.In this way,selecting candidate sets is no longer based on the quantity of nonzero variables they contain but on a higher proportion of nonzero variables within specific dimensions.Additionally,we unveil a novel approach to association rule mining,which delves into the intricate relationships between non-zero variables.This novel methodology aids in identifying sparse distributions that can potentially expedite reductions in the objective function value.We extensively tested our algorithm across eight benchmark problems and four real-world SLMOPs.The results demonstrate that our approach achieves competitive solutions across various challenges.

On-spot preparation of EDTA solution for the treatment of band keratopathy:a case report

Dear Editor,We report a case of band keratopathy who experienced several ocular surgeries,secondary uveitis and glaucoma which eventually led to severe band keratopathy.We use commonly-used vacuum blood collection tubes with ethylenediaminetetraacetic acid dipotassium salt(EDTA-K2)to prepare chelation solution on-spot to remove the calcium deposits on the corneal surface.It is proved to be relatively safety,efficacy,cost-effective.

Common Artifacts and Remedies in Histological Preparations

The importance of the accuracy of preparing biological specimen as histological sections that can be examined under a microscope lies in reflecting a true image of the tissue that includes all its components, which are used in scientific research or for the purpose of diagnosing various diseases of the body. Despite this, some cellular structures within the tissue may suffer from some alterations that result from the appearance of defects during any stage of preparing these microscopic sections, which alter or interfere with the precise cellular structures and morphology that constitute the tissue and thus give a different image for tissue features and cause confusion in the work histopathologist in the diagnosis. There are several reasons that can cause a misdiagnosis of the sample that occurs during the surgical separation process or after separation during the stages of microscopic preparation techniques from fixation stage, tissue processing, embedding or microtomy, staining until mounting procedures. The constant need to identify these defects and their causes in addition to try to reduce them is one of the biggest challenges evident in pathology laboratories. Therefore, this study aims to review the most common defects that occur in any stage of tissue processing, with an explanation of their causes and appropriate ways to avoid them.

Preparation and Thermal Stability of AlMoON Based Solar Selective Absorption Coating

AlMoON based solar selective absorption coatings were deposited on stainless steel substrate by magnetron sputtering.The coatings included infrared reflection layer Mo,absorption layer AlMoN,absorption layer AlMoON and antireflection layer AlMoO from bottom to top.The surface of the deposited coatings is flat without obvious defects.The absorptivity and emissivity are 0.896 and 0.09,respectively,and the quality factor is 9.96.After heat treatment at 500℃-36 h,the surface roughness of the coating increases,a small number of cracks and other defects appear,and the broken part is still attached to the coating surface.A certain degree of element diffusion occurs in the coatings,resulting in the decline of the optical properties of the coatings.The absorptivity and emissivity are 0.883 and 0.131,respectively,the quality factor is 7.06,and the PC value is 0.0335.The coatings do not fail under this condition and have certain thermal stability.

Progress in preparation of AlN-reinforced magnesium matrix composites:A review

As a ceramic material,AlN has very good thermophysical and mechanical properties.In addition,AlN is an effective refining agent for Mg alloys because it has a lattice constant similar to that of Mg.Therefore,AlN is an ideal reinforcement for magnesium matrix composites(MMCs),and is attracting increasing attention.This review addresses the development of preparation technologies for AlN-reinforced Mg matrix composites.The mainstream preparation techniques include stir casting,melt infiltration,powder metallurgy,and in-situ methods.In addition,the advantages and disadvantages of these techniques are analyzed in depth,and it is pointed out that the next direction for the preparation of high-performance AlN-reinforced MMCs is less aluminization and multiple technologies integration.

Research Progress of Bowel Preparation for Colonoscopy

Colorectal cancer ranks third in the global cancer data in 2020. Colorectal scope is the most effective method to diagnose colorectal diseases such as benign and malignant colorectal tumors. The poor quality of intestinal preparation causes an increased rate of missed diagnosis of colorectal tumors, reduces the rate of cecal intubation for colorectal examination, increases the discomfort, and reduces the compliance of re-examination. Therefore, we should try our best to improve the quality of intestinal preparation. This study reviewed the latest advances related to the preoperative preparation for colonoscopy. Recent research shows that smartphone apps can provide more detailed education and guidance on bowel preparation;Pre-packaged foods are more suitable as a way to eat before colonoscopy. The use of smaller doses, better taste of cathartic agents, and some auxiliary measures, combined with the patient’s situation to provide personalized intestinal preparation measures to improve the quality of intestinal preparation. Starting from the quality of colonoscopy bowel preparation, continuous improvement of patients’ tolerance to bowel preparation, continuous improvement of bowel preparation plan based on individual factors’ needs, and better communication with examined subjects by using existing scientific information technology, may be the hot spot of colonoscopy bowel preparation research in the next few years.

Assessment of Wet Season Precipitation in the Central United States by the Regional Climate Simulation of the WRFG Member in NARCCAP and Its Relationship with Large-Scale Circulation Biases

Assessment of past-climate simulations of regional climate models(RCMs)is important for understanding the reliability of RCMs when used to project future regional climate.Here,we assess the performance and discuss possible causes of biases in a WRF-based RCM with a grid spacing of 50 km,named WRFG,from the North American Regional Climate Change Assessment Program(NARCCAP)in simulating wet season precipitation over the Central United States for a period when observational data are available.The RCM reproduces key features of the precipitation distribution characteristics during late spring to early summer,although it tends to underestimate the magnitude of precipitation.This dry bias is partially due to the model’s lack of skill in simulating nocturnal precipitation related to the lack of eastward propagating convective systems in the simulation.Inaccuracy in reproducing large-scale circulation and environmental conditions is another contributing factor.The too weak simulated pressure gradient between the Rocky Mountains and the Gulf of Mexico results in weaker southerly winds in between,leading to a reduction of warm moist air transport from the Gulf to the Central Great Plains.The simulated low-level horizontal convergence fields are less favorable for upward motion than in the NARR and hence,for the development of moist convection as well.Therefore,a careful examination of an RCM’s deficiencies and the identification of the source of errors are important when using the RCM to project precipitation changes in future climate scenarios.

A semantic vector map-based approach for aircraft positioning in GNSS/GPS denied large-scale environment

Accurate positioning is one of the essential requirements for numerous applications of remote sensing data,especially in the event of a noisy or unreliable satellite signal.Toward this end,we present a novel framework for aircraft geo-localization in a large range that only requires a downward-facing monocular camera,an altimeter,a compass,and an open-source Vector Map(VMAP).The algorithm combines the matching and particle filter methods.Shape vector and correlation between two building contour vectors are defined,and a coarse-to-fine building vector matching(CFBVM)method is proposed in the matching stage,for which the original matching results are described by the Gaussian mixture model(GMM).Subsequently,an improved resampling strategy is designed to reduce computing expenses with a huge number of initial particles,and a credibility indicator is designed to avoid location mistakes in the particle filter stage.An experimental evaluation of the approach based on flight data is provided.On a flight at a height of 0.2 km over a flight distance of 2 km,the aircraft is geo-localized in a reference map of 11,025 km~2using 0.09 km~2aerial images without any prior information.The absolute localization error is less than 10 m.

Online identification and extraction method of regional large-scale adjustable load-aggregation characteristics

This article introduces the concept of load aggregation,which involves a comprehensive analysis of loads to acquire their external characteristics for the purpose of modeling and analyzing power systems.The online identification method is a computer-involved approach for data collection,processing,and system identification,commonly used for adaptive control and prediction.This paper proposes a method for dynamically aggregating large-scale adjustable loads to support high proportions of new energy integration,aiming to study the aggregation characteristics of regional large-scale adjustable loads using online identification techniques and feature extraction methods.The experiment selected 300 central air conditioners as the research subject and analyzed their regulation characteristics,economic efficiency,and comfort.The experimental results show that as the adjustment time of the air conditioner increases from 5 minutes to 35 minutes,the stable adjustment quantity during the adjustment period decreases from 28.46 to 3.57,indicating that air conditioning loads can be controlled over a long period and have better adjustment effects in the short term.Overall,the experimental results of this paper demonstrate that analyzing the aggregation characteristics of regional large-scale adjustable loads using online identification techniques and feature extraction algorithms is effective.

Preparation of single atom catalysts for high sensitive gas sensing

Single atom catalysts(SACs)have garnered significant attention in the field of catalysis over the past decade due to their exceptional atom utilization efficiency and distinct physical and chemical properties.For the semiconductor-based electrical gas sensor,the core is the catalysis process of target gas molecules on the sensitive materials.In this context,the SACs offer great potential for highly sensitive and selective gas sensing,however,only some of the bubbles come to the surface.To facilitate practical applications,we present a comprehensive review of the preparation strategies for SACs,with a focus on overcoming the challenges of aggregation and low loading.Extensive research efforts have been devoted to investigating the gas sensing mechanism,exploring sensitive materials,optimizing device structures,and refining signal post-processing techniques.Finally,the challenges and future perspectives on the SACs based gas sensing are presented.

Characterization, preparation, and reuse of metallic powders for laser powder bed fusion: a review

Laser powder bed fusion(L-PBF) has attracted significant attention in both the industry and academic fields since its inception, providing unprecedented advantages to fabricate complex-shaped metallic components. The printing quality and performance of L-PBF alloys are infuenced by numerous variables consisting of feedstock powders, manufacturing process,and post-treatment. As the starting materials, metallic powders play a critical role in infuencing the fabrication cost, printing consistency, and properties. Given their deterministic roles, the present review aims to retrospect the recent progress on metallic powders for L-PBF including characterization, preparation, and reuse. The powder characterization mainly serves for printing consistency while powder preparation and reuse are introduced to reduce the fabrication costs.Various powder characterization and preparation methods are presented in the beginning by analyzing the measurement principles, advantages, and limitations. Subsequently, the effect of powder reuse on the powder characteristics and mechanical performance of L-PBF parts is analyzed, focusing on steels, nickel-based superalloys, titanium and titanium alloys, and aluminum alloys. The evolution trends of powders and L-PBF parts vary depending on specific alloy systems, which makes the proposal of a unified reuse protocol infeasible. Finally,perspectives are presented to cater to the increased applications of L-PBF technologies for future investigations. The present state-of-the-art work can pave the way for the broad industrial applications of L-PBF by enhancing printing consistency and reducing the total costs from the perspective of powders.

Large-Scale Multi-Objective Optimization Algorithm Based on Weighted Overlapping Grouping of Decision Variables

The large-scale multi-objective optimization algorithm(LSMOA),based on the grouping of decision variables,is an advanced method for handling high-dimensional decision variables.However,in practical problems,the interaction among decision variables is intricate,leading to large group sizes and suboptimal optimization effects;hence a large-scale multi-objective optimization algorithm based on weighted overlapping grouping of decision variables(MOEAWOD)is proposed in this paper.Initially,the decision variables are perturbed and categorized into convergence and diversity variables;subsequently,the convergence variables are subdivided into groups based on the interactions among different decision variables.If the size of a group surpasses the set threshold,that group undergoes a process of weighting and overlapping grouping.Specifically,the interaction strength is evaluated based on the interaction frequency and number of objectives among various decision variables.The decision variable with the highest interaction in the group is identified and disregarded,and the remaining variables are then reclassified into subgroups.Finally,the decision variable with the strongest interaction is added to each subgroup.MOEAWOD minimizes the interactivity between different groups and maximizes the interactivity of decision variables within groups,which contributed to the optimized direction of convergence and diversity exploration with different groups.MOEAWOD was subjected to testing on 18 benchmark large-scale optimization problems,and the experimental results demonstrate the effectiveness of our methods.Compared with the other algorithms,our method is still at an advantage.

A Large-Scale Group Decision Making Model Based on Trust Relationship and Social Network Updating

With the development of big data and social computing,large-scale group decisionmaking(LGDM)is nowmerging with social networks.Using social network analysis(SNA),this study proposes an LGDM consensus model that considers the trust relationship among decisionmakers(DMs).In the process of consensusmeasurement:the social network is constructed according to the social relationship among DMs,and the Louvain method is introduced to classify social networks to form subgroups.In this study,the weights of each decision maker and each subgroup are computed by comprehensive network weights and trust weights.In the process of consensus improvement:A feedback mechanism with four identification and two direction rules is designed to guide the consensus of the improvement process.Based on the trust relationship among DMs,the preferences are modified,and the corresponding social network is updated to accelerate the consensus.Compared with the previous research,the proposedmodel not only allows the subgroups to be reconstructed and updated during the adjustment process,but also improves the accuracy of the adjustment by the feedbackmechanism.Finally,an example analysis is conducted to verify the effectiveness and flexibility of the proposed method.Moreover,compared with previous studies,the superiority of the proposed method in solving the LGDM problem is highlighted.

Insight into demand-driven preparation of single-atomic mediators for lithium–sulfur batteries

Lithium-sulfur(Li-S) batteries have attracted considerable attention as one of the most appealing energy storage systems.Strenuous efforts have been devoted to tackling the tremendous challenges,mainly pertaining to the severe shuttle effect,sluggish redox kinetics and lithium dendritic growth.Single-atomic mediators as promising candidates exhibit impressive performance in addressing these intractable issues.Related research often utilizes a trial-and-error approach,proposing solutions to fabricate single-atomic materials with diversified features.However,comprehensive review articles especially targeting demand-driven preparation are still in a nascent stage.Inspired by these considerations,this review summarizes the design of single-atomic mediators based on the application case-studies in LiS batteries and other metal-sulfur systems.Emerging preparation routes represented by chemical vapor deposition technology are introduced in a demand-oriented classification.Finally,future research directions are proposed to foster the advancement of single-atomic mediators in Li-S realm.

Effects of site preparation methods on soil physical properties and outplanting success of coniferous seedlings in boreal forests

This study assessed the effect of patch scarification and mounding on the physical properties of the root layer and the success of tree planting in various types of forests.This study was conducted on 12 forest sites in taiga forests of the European part of Russia.A total of 54 plots were set up to assess seedling survival;root collar diameter,height,and heigh increment were measured for 240 seedlings to assess growth.In the rooting layer,240 soil samples were taken to determine physical properties.The study showed that soil treatment methods had no effect on bulk density and total porosity in Cladina sites.However,reduced soil moisture was noted,particularly in mounds,resulting in increased aeration.In Myrtillus sites,there were increased bulk density,reduced soil moisture,and total porosity in the mounds.Mounding treatment in Polytrichum sites resulted in reduced soil moisture and increased aeration porosity.In the Myrtillus and Polytrichum sites,patch scarification had no effects on physical properties.In Polytrichum sites,survival rates,heights,and heigh increments of bareroot Norway spruce seedlings in mounds were higher than in patches;however,the same did not apply to diameter.In Cladina and Myrtillus sites,there was no difference in growth for bareroot and containerised seedlings with different soil treatments.Growing conditions and soil types should be considered when applying different soil treatment methods to ensure high survival rates and successful seedling growth.

Protected simultaneous quantum remote state preparation scheme by weak and reversal measurements in noisy environments

We discuss a quantum remote state preparation protocol by which two parties, Alice and Candy, prepare a single-qubit and a two-qubit state, respectively, at the site of the receiver Bob. The single-qubit state is known to Alice while the two-qubit state which is a non-maximally entangled Bell state is known to Candy. The three parties are connected through a single entangled state which acts as a quantum channel. We first describe the protocol in the ideal case when the entangled channel under use is in a pure state. After that, we consider the effect of amplitude damping(AD) noise on the quantum channel and describe the protocol executed through the noisy channel. The decrement of the fidelity is shown to occur with the increment in the noise parameter. This is shown by numerical computation in specific examples of the states to be created. Finally, we show that it is possible to maintain the label of fidelity to some extent and hence to decrease the effect of noise by the application of weak and reversal measurements. We also present a scheme for the generation of the five-qubit entangled resource which we require as a quantum channel. The generation scheme is run on the IBMQ platform.

Large-scale spatial data visualization method based on augmented reality

Background A task assigned to space exploration satellites involves detecting the physical environment within a certain space.However,space detection data are complex and abstract.These data are not conducive for researchers'visual perceptions of the evolution and interaction of events in the space environment.Methods A time-series dynamic data sampling method for large-scale space was proposed for sample detection data in space and time,and the corresponding relationships between data location features and other attribute features were established.A tone-mapping method based on statistical histogram equalization was proposed and applied to the final attribute feature data.The visualization process is optimized for rendering by merging materials,reducing the number of patches,and performing other operations.Results The results of sampling,feature extraction,and uniform visualization of the detection data of complex types,long duration spans,and uneven spatial distributions were obtained.The real-time visualization of large-scale spatial structures using augmented reality devices,particularly low-performance devices,was also investigated.Conclusions The proposed visualization system can reconstruct the three-dimensional structure of a large-scale space,express the structure and changes in the spatial environment using augmented reality,and assist in intuitively discovering spatial environmental events and evolutionary rules.

Numerical and theoretical study of large-scale failure of strata overlying sublevel caving mines with steeply dipping discontinuities

The deformation and fracture evolution mechanisms of the strata overlying mines mined using sublevel caving were studied via numerical simulations.Moreover,an expression for the normal force acting on the side face of a steeply dipping superimposed cantilever beam in the surrounding rock was deduced based on limit equilibrium theory.The results show the following:(1)surface displacement above metal mines with steeply dipping discontinuities shows significant step characteristics,and(2)the behavior of the strata as they fail exhibits superimposition characteristics.Generally,failure first occurs in certain superimposed strata slightly far from the goaf.Subsequently,with the constant downward excavation of the orebody,the superimposed strata become damaged both upwards away from and downwards toward the goaf.This process continues until the deep part of the steeply dipping superimposed strata forms a large-scale deep fracture plane that connects with the goaf.The deep fracture plane generally makes an angle of 12°-20°with the normal to the steeply dipping discontinuities.The effect of the constant outward transfer of strata movement due to the constant outward failure of the superimposed strata in the metal mines with steeply dipping discontinuities causes the scope of the strata movement in these mines to be larger than expected.The strata in the metal mines with steeply dipping discontinuities mainly show flexural toppling failure.However,the steeply dipping structural strata near the goaf mainly exhibit shear slipping failure,in which case the mechanical model used to describe them can be simplified by treating them as steeply dipping superimposed cantilever beams.By taking the steeply dipping superimposed cantilever beam that first experiences failure as the key stratum,the failure scope of the strata(and criteria for the stability of metal mines with steeply dipping discontinuities mined using sublevel caving)can be obtained via iterative computations from the key stratum,moving downward toward and upwards away from the goaf.