The advantages of genome selection(GS) in animal and plant breeding are self-evident.Traditional parametric models have disadvantage in better fit the increasingly large sequencing data and capture complex effects acc...The advantages of genome selection(GS) in animal and plant breeding are self-evident.Traditional parametric models have disadvantage in better fit the increasingly large sequencing data and capture complex effects accurately.Machine learning models have demonstrated remarkable potential in addressing these challenges.In this study,we introduced the concept of mixed kernel functions to explore the performance of support vector machine regression(SVR) in GS.Six single kernel functions(SVR_L,SVR_C,SVR_G,SVR_P,SVR_S,SVR_L) and four mixed kernel functions(SVR_GS,SVR_GP,SVR_LS,SVR_LP) were used to predict genome breeding values.The prediction accuracy,mean squared error(MSE) and mean absolute error(MAE) were used as evaluation indicators to compare with two traditional parametric models(GBLUP,BayesB) and two popular machine learning models(RF,KcRR).The results indicate that in most cases,the performance of the mixed kernel function model significantly outperforms that of GBLUP,BayesB and single kernel function.For instance,for T1 in the pig dataset,the predictive accuracy of SVR_GS is improved by 10% compared to GBLUP,and by approximately 4.4 and 18.6% compared to SVR_G and SVR_S respectively.For E1 in the wheat dataset,SVR_GS achieves 13.3% higher prediction accuracy than GBLUP.Among single kernel functions,the Laplacian and Gaussian kernel functions yield similar results,with the Gaussian kernel function performing better.The mixed kernel function notably reduces the MSE and MAE when compared to all single kernel functions.Furthermore,regarding runtime,SVR_GS and SVR_GP mixed kernel functions run approximately three times faster than GBLUP in the pig dataset,with only a slight increase in runtime compared to the single kernel function model.In summary,the mixed kernel function model of SVR demonstrates speed and accuracy competitiveness,and the model such as SVR_GS has important application potential for GS.展开更多
The recovery of gold from waste electronic and electric equipment(WEEE) has gained great attention with the increased number of WEEE,because it can largely alleviate the pressure on the environment and resources.Coval...The recovery of gold from waste electronic and electric equipment(WEEE) has gained great attention with the increased number of WEEE,because it can largely alleviate the pressure on the environment and resources.Covalent organic frameworks(COFs) are ideal adsorbents for gold recovery owing to their large surface area,good stability,easily functionalized ability,periodic structures,and definitive nanopores.Herein,a cyano-functionalized COF(COF-CN) with high crystallinity was large-scale prepared under mild conditions for the recovery of gold.The introduction of cyano groups enable COF-CN to exhibit excellent gold recovery performance,which possesses fast adsorption kinetics,high cycling stability,and adsorption capacity up to 663.67 mg/g.Excitingly,COF-CN showed extremely high selectivity for gold ions,even in the presence of various competing cations and anions.The COF-CN maintained excellent selectivity and removal efficiency in gold recovery experiments from WEEE.The facile synthesis of COF-CN and its outstanding selectivity in actual samples make it an attractive opportunity for practical gold recovery.展开更多
Emerging and powerful genome editing tools,particularly CRISPR/Cas9,are facilitating functional genomics research and accelerating crop improvement(Jiang et al.2021;Cao et al.2023;Chen C et al.2023;Liu et al.2023a).Ho...Emerging and powerful genome editing tools,particularly CRISPR/Cas9,are facilitating functional genomics research and accelerating crop improvement(Jiang et al.2021;Cao et al.2023;Chen C et al.2023;Liu et al.2023a).However,the detection and screening of transgenic lines remain major bottlenecks,being time-consuming,labor-intensive,and inefficient during transformation and subsequent mutation identification.A simple and efficient visual marker system plays a critical role in addressing these challenges.Recent studies demonstrated that the GmW1 and RUBY reporter systems were used to obtain visual transgenic soybean(Glycine max) plants(Chen L et al.2023;Chen et al.2024).展开更多
The radial basis function （RBF）, a kind of neural networks algorithm, is adopted to select clusterheads. It has many advantages such as simple parallel distributed computation, distributed storage, and fast learning...The radial basis function （RBF）, a kind of neural networks algorithm, is adopted to select clusterheads. It has many advantages such as simple parallel distributed computation, distributed storage, and fast learning. Four factors related to a node becoming a cluster-head are drawn by analysis, which are energy （ energy available in each node）, number （the number of neighboring nodes）, centrality （ a value to classify the nodes based on the proximity how central the node is to the cluster）, and location （the distance between the base station and the node）. The factors are as input variables of neural networks and the output variable is suitability that is the degree of a node becoming a cluster head. A group of cluster-heads are selected according to the size of network. Then the base station broadcasts a message containing the list of cluster-heads＇ IDs to all nodes. After that, each cluster-head announces its new status to all its neighbors and sets up a new cluster. If a node around it receives the message, it registers itself to be a member of the cluster. After identifying all the members, the cluster-head manages them and carries out data aggregation in each cluster. Thus data flowing in the network decreases and energy consumption of nodes decreases accordingly. Experimental results show that, compared with other algorithms, the proposed algorithm can significantly increase the lifetime of the sensor network.展开更多
Dipper throated optimization(DTO)algorithm is a novel with a very efficient metaheuristic inspired by the dipper throated bird.DTO has its unique hunting technique by performing rapid bowing movements.To show the effi...Dipper throated optimization(DTO)algorithm is a novel with a very efficient metaheuristic inspired by the dipper throated bird.DTO has its unique hunting technique by performing rapid bowing movements.To show the efficiency of the proposed algorithm,DTO is tested and compared to the algorithms of Particle Swarm Optimization(PSO),Whale Optimization Algorithm(WOA),Grey Wolf Optimizer(GWO),and Genetic Algorithm(GA)based on the seven unimodal benchmark functions.Then,ANOVA and Wilcoxon rank-sum tests are performed to confirm the effectiveness of the DTO compared to other optimization techniques.Additionally,to demonstrate the proposed algorithm’s suitability for solving complex realworld issues,DTO is used to solve the feature selection problem.The strategy of using DTOs as feature selection is evaluated using commonly used data sets from the University of California at Irvine(UCI)repository.The findings indicate that the DTO outperforms all other algorithms in addressing feature selection issues,demonstrating the proposed algorithm’s capabilities to solve complex real-world situations.展开更多
Covariance functions have been proposed as an alternative to model longitudinal data in animal breeding because of their various merits in comparison to the classical analytical methods.In practical estimation,differe...Covariance functions have been proposed as an alternative to model longitudinal data in animal breeding because of their various merits in comparison to the classical analytical methods.In practical estimation,different models and polynomial orders fitted can influence the estimates of covariance functions and thus genetic parameters.The objective of this study was to select model for estimation of covariance functions for body weights of Angora goats at 7 time points.Covariance functions were estimated by fitting 6 random regression models with birth year,birth month,sex,age of dam,birth type,and relative birth date as fixed effects.Random effects involved were direct and maternal additive genetic,and animal and maternal permanent environmental effects with different orders of fit.Selection of model and orders of fit were carried out by likelihood ratio test and 4 types of information criteria.The results showed that model with 6 orders of polynomial fit for direct additive genetic and animal permanent environmental effects and 4 and 5 orders for maternal genetic and permanent environmental effects,respectively,were preferable for estimation of covariance functions.Models with and without maternal effects influenced the estimates of covariance functions greatly.Maternal permanent environmental effect does not explain the variation of all permanent environments,well suggesting different sources of permanent environmental effects also has large influence on covariance function estimates.展开更多
Neuro-fuzzy(NF)networks are adaptive fuzzy inference systems(FIS)and have been applied to feature selection by some researchers.However,their rule number will grow exponentially as the data dimension increases.On the ...Neuro-fuzzy(NF)networks are adaptive fuzzy inference systems(FIS)and have been applied to feature selection by some researchers.However,their rule number will grow exponentially as the data dimension increases.On the other hand,feature selection algorithms with artificial neural networks(ANN)usually require normalization of input data,which will probably change some characteristics of original data that are important for classification.To overcome the problems mentioned above,this paper combines the fuzzification layer of the neuro-fuzzy system with the multi-layer perceptron(MLP)to form a new artificial neural network.Furthermore,fuzzification strategy and feature measurement based on membership space are proposed for feature selection. Finally,experiments with both natural and artificial data are carried out to compare with other methods,and the results approve the validity of the algorithm.展开更多
The transition metal-catalyzed C–H activation have been considered as increasingly useful approach for installing new functional groups onto organic small molecules due to their high step-and atom-economy,the abundan...The transition metal-catalyzed C–H activation have been considered as increasingly useful approach for installing new functional groups onto organic small molecules due to their high step-and atom-economy,the abundance of hydrocarbon compounds,and the potential for late-stage functionalization of complex organic molecules.The ortho-and meta-C-H activation and functionalization of aromatic compounds have been widely explored in recent years,however the distal para-C-H activation and functionalization has remained a significant challenge because of the difficulty in forming energetically favorable metallacyclic transition states.The utilization of appropriate directing groups or templates as well as the meticulous design of catalysts and ligands has proven to be effective in transition-metal-catalyzed remote para-C-H bonds activation and functionalization of aromatic compounds.This review aims to summarize the strategies for controlling para-selective C–H functionalization using the directing group,template engineering,and catalyst/ligand design under transition metals catalysis in recent years.展开更多
From the perspective of ecological construction of roads, the reduction and purifying effects of greening plants on noise, raising dust and automobile exhaust, selection principles of arbors, shrubs, ground cover plan...From the perspective of ecological construction of roads, the reduction and purifying effects of greening plants on noise, raising dust and automobile exhaust, selection principles of arbors, shrubs, ground cover plants and herbaceous fl owers, and the methods of collocating arbors shrubs and grass in the construction of ecological roads were discussed in this study.展开更多
The selection and scaling of ground motion records is considered a primary and essential task in performing structural analysis and design.Conventional methods involve using ground motion models and a conditional spec...The selection and scaling of ground motion records is considered a primary and essential task in performing structural analysis and design.Conventional methods involve using ground motion models and a conditional spectrum to select ground motion records based on the target spectrum.This research demonstrates the influence of adopting different weighted factors for various period ranges during matching selected ground motions with the target hazard spectrum.The event data from the Next Generation Attenuation West 2(NGA-West 2)database is used as the basis for ground motion selection,and hazard de-aggregation is conducted to estimate the event parameters of interest,which are then used to construct the target intensity measure(IM).The target IMs are then used to select ground motion records with different weighted vector-valued objective functions.The weights are altered to account for the relative importance of IM in accordance with the structural analysis application of steel moment resisting frame(SMRF)buildings.Instead of an ordinary objective function for the matching spectrum,a novel model is introduced and compared with the conventional cost function.The results indicate that when applying the new cost function for ground motion selection,it places higher demands on structures compared to the conventional cost function.Moreover,submitting more weights to the first-mode period of structures increases engineering demand parameters.Findings demonstrate that weight factors allocated to different period ranges can successfully account for period elongation and higher mode effects.展开更多
Price prediction plays a crucial role in portfolio selection (PS). However, most price prediction strategies only make a single prediction and do not have efficient mechanisms to make a comprehensive price prediction....Price prediction plays a crucial role in portfolio selection (PS). However, most price prediction strategies only make a single prediction and do not have efficient mechanisms to make a comprehensive price prediction. Here, we propose a comprehensive price prediction (CPP) system based on inverse multiquadrics (IMQ) radial basis function. First, the novel radial basis function (RBF) system based on IMQ function rather than traditional Gaussian (GA) function is proposed and centers on multiple price prediction strategies, aiming at improving the efficiency and robustness of price prediction. Under the novel RBF system, we then create a portfolio update strategy based on kernel and trace operator. To assess the system performance, extensive experiments are performed based on 4 data sets from different real-world financial markets. Interestingly, the experimental results reveal that the novel RBF system effectively realizes the integration of different strategies and CPP system outperforms other systems in investing performance and risk control, even considering a certain degree of transaction costs. Besides, CPP can calculate quickly, making it applicable for large-scale and time-limited financial market.展开更多
Functionally graded material(FGM)can tailor properties of components such as wear resistance,corrosion resistance,and functionality to enhance the overall performance.The selective laser melting(SLM)additive manufactu...Functionally graded material(FGM)can tailor properties of components such as wear resistance,corrosion resistance,and functionality to enhance the overall performance.The selective laser melting(SLM)additive manufacturing highlights the capability in manufacturing FGMs with a high geometrical complexity and manufacture flexibility.In this work,the 316L/CuSn10/18Ni300/CoCr four-type materials FGMs were fabricated using SLM.The microstructure and properties of the FGMs were investigated to reveal the effects of SLM processing parameters on the defects.A large number of microcracks were found at the 316L/CuSn10 interface,which initiated from the fusion boundary of 316L region and extended along the building direction.The elastic modulus and nano-hardness in the 18Ni300/CoCr fusion zone decreased significantly,less than those in the 18Ni300 region or the CoCr region.The iron and copper elements were well diffused in the 316L/CuSn10 fusion zone,while elements in the CuSn10/18Ni300 and the 18Ni300/CoCr fusion zones showed significantly gradient transitions.Compared with other regions,the width of the CuSn10/18Ni300 interface and the CuSn10 region expand significantly.The mechanisms of materials fusion and crack generation at the 316L/CuSn10 interface were discussed.In addition,FGM structures without macro-crack were built by only altering the deposition subsequence of 316L and CuSn10,which provides a guide for the additive manufacturing of FGM structures.展开更多
Based on density functional theory(DFT)and basic structure models,the chemical reactions on the surface of vanadium-titanium based selective catalytic reduction(SCR)denitrification catalysts were summarized.Reasonable...Based on density functional theory(DFT)and basic structure models,the chemical reactions on the surface of vanadium-titanium based selective catalytic reduction(SCR)denitrification catalysts were summarized.Reasonable structural models(non-periodic and periodic structural models)are the basis of density functional calculations.A periodic structure model was more appropriate to represent the catalyst surface,and its theoretical calculation results were more comparable with the experimental results than a nonperiodic model.It is generally believed that the SCR mechanism where NH3 and NO react to produce N2 and H2 O follows an Eley-Rideal type mechanism.NH2 NO was found to be an important intermediate in the SCR reaction,with multiple production routes.Simultaneously,the effects of H2 O,SO2 and metal on SCR catalysts were also summarized.展开更多
The industrial silica fume pretreated by nitric acid at 80 °C was re-used in this work. Then, the obtained silica nanoparticles were surface functionalized by silane coupling agents, such as(3-Mercaptopropyl) tri...The industrial silica fume pretreated by nitric acid at 80 °C was re-used in this work. Then, the obtained silica nanoparticles were surface functionalized by silane coupling agents, such as(3-Mercaptopropyl) triethoxysilane(MPTES) and(3-Amincpropyl) trithoxysilane(APTES). Some further modifications were studied by chloroaceetyl choride and 1,8-Diaminoaphalene for amino modified silica. The surface functionalized silica nanoparticles were characterized by Fourier transform infrared(FI-IR) and X-ray photoelectron spectroscopy(XPS). The prepared adsorbent of surface functionalized silica nanoparticles with differential function groups were investigated in the selective adsorption about Pb2+, Cu2+, Hg2+, Cd2+ and Zn2+ions in aqueous solutions. The results show that the(3-Mercaptopropyl) triethoxysilane functionalized silica nanoparticles(SiO2-MPTES) play an important role in the selective adsorption of Cu2+ and Hg2+, the(3-Amincpropyl) trithoxysilane(APTES) functionalized silica nanoparticles(SiO2-APTES) exhibited maximum removal efficiency towards Pb2+ and Hg2+, the 1,8-Diaminoaphalene functionalized silica nanoparticles was excellent for removal of Hg2+ at room temperature, respectively.展开更多
Feature selection has been widely used in data mining and machine learning.Its objective is to select a minimal subset of features according to some reasonable criteria so as to solve the original task more quickly.In...Feature selection has been widely used in data mining and machine learning.Its objective is to select a minimal subset of features according to some reasonable criteria so as to solve the original task more quickly.In this article,a feature selection algorithm with local search strategy based on the forest optimization algorithm,namely FSLSFOA,is proposed.The novel local search strategy in local seeding process guarantees the quality of the feature subset in the forest.Next,the fitness function is improved,which not only considers the classification accuracy,but also considers the size of the feature subset.To avoid falling into local optimum,a novel global seeding method is attempted,which selects trees on the bottom of candidate set and gives the algorithm more diversities.Finally,FSLSFOA is compared with four feature selection methods to verify its effectiveness.Most of the results are superior to these comparative methods.展开更多
Porous ionic liquids have demonstrated excellent performance in the field of separation,attributed to their high specific surface area and efficient mass transfer.Herein,task-specific protic porous ionic liquids(PPILs...Porous ionic liquids have demonstrated excellent performance in the field of separation,attributed to their high specific surface area and efficient mass transfer.Herein,task-specific protic porous ionic liquids(PPILs)were prepared by employing a novel one-step coupling neutralization reaction strategy for extractive desulfurization.The single-extraction efficiency of PPILs reached 75.0%for dibenzothiophene.Moreover,adding aromatic hydrocarbon interferents resulted in a slight decrease in the extraction efficiency of PPILs(from 45.2%to 37.3%,37.9%,and 33.5%),indicating the excellent extraction selectivity of PPILs.The experimental measurements and density functional theory calculations reveal that the surface channels of porous structures can selectively capture dibenzothiophene by the stronger electrophilicity(Eint(HS surface channel/DBT)=-39.8 kcal mol^(-1)),and the multiple extraction sites of ion pairs can effectively enrich and transport dibenzothiophene from the oil phase into PPILs throughπ...π,C-H...πand hydrogen bonds interactions.Furthermore,this straightforward synthetic strategy can be employed in preparing porous liquids,offering new possibilities for synthesizing PPILs with tailored functionalities.展开更多
We consider the model selection problem of the dependency between the?terminal event and the non-terminal event under semi-competing risks data. When the relationship between the two events is unspecified, the inferen...We consider the model selection problem of the dependency between the?terminal event and the non-terminal event under semi-competing risks data. When the relationship between the two events is unspecified, the inference on the non-terminal event is not identifiable. We cannot make inference on the non-terminal event without extra assumptions. Thus, an association model for?semi-competing risks data is necessary, and it is important to select an appropriate dependence model for a data set. We construct the likelihood function for semi-competing risks data to select an appropriate dependence model. From?simulation studies, it shows the performance of the proposed approach is well. Finally, we apply our method to a bone marrow transplant data set.展开更多
基金supported by the China Agriculture Research System of MOF and MARAthe National Natural Science Foundation of China (31872337 and 31501919)the Agricultural Science and Technology Innovation Project,China (ASTIP-IAS02)。
文摘The advantages of genome selection(GS) in animal and plant breeding are self-evident.Traditional parametric models have disadvantage in better fit the increasingly large sequencing data and capture complex effects accurately.Machine learning models have demonstrated remarkable potential in addressing these challenges.In this study,we introduced the concept of mixed kernel functions to explore the performance of support vector machine regression(SVR) in GS.Six single kernel functions(SVR_L,SVR_C,SVR_G,SVR_P,SVR_S,SVR_L) and four mixed kernel functions(SVR_GS,SVR_GP,SVR_LS,SVR_LP) were used to predict genome breeding values.The prediction accuracy,mean squared error(MSE) and mean absolute error(MAE) were used as evaluation indicators to compare with two traditional parametric models(GBLUP,BayesB) and two popular machine learning models(RF,KcRR).The results indicate that in most cases,the performance of the mixed kernel function model significantly outperforms that of GBLUP,BayesB and single kernel function.For instance,for T1 in the pig dataset,the predictive accuracy of SVR_GS is improved by 10% compared to GBLUP,and by approximately 4.4 and 18.6% compared to SVR_G and SVR_S respectively.For E1 in the wheat dataset,SVR_GS achieves 13.3% higher prediction accuracy than GBLUP.Among single kernel functions,the Laplacian and Gaussian kernel functions yield similar results,with the Gaussian kernel function performing better.The mixed kernel function notably reduces the MSE and MAE when compared to all single kernel functions.Furthermore,regarding runtime,SVR_GS and SVR_GP mixed kernel functions run approximately three times faster than GBLUP in the pig dataset,with only a slight increase in runtime compared to the single kernel function model.In summary,the mixed kernel function model of SVR demonstrates speed and accuracy competitiveness,and the model such as SVR_GS has important application potential for GS.
基金financially supported by the National Natural Science Foundation of China (No.51972302)。
文摘The recovery of gold from waste electronic and electric equipment(WEEE) has gained great attention with the increased number of WEEE,because it can largely alleviate the pressure on the environment and resources.Covalent organic frameworks(COFs) are ideal adsorbents for gold recovery owing to their large surface area,good stability,easily functionalized ability,periodic structures,and definitive nanopores.Herein,a cyano-functionalized COF(COF-CN) with high crystallinity was large-scale prepared under mild conditions for the recovery of gold.The introduction of cyano groups enable COF-CN to exhibit excellent gold recovery performance,which possesses fast adsorption kinetics,high cycling stability,and adsorption capacity up to 663.67 mg/g.Excitingly,COF-CN showed extremely high selectivity for gold ions,even in the presence of various competing cations and anions.The COF-CN maintained excellent selectivity and removal efficiency in gold recovery experiments from WEEE.The facile synthesis of COF-CN and its outstanding selectivity in actual samples make it an attractive opportunity for practical gold recovery.
基金supported by the Jilin Science and Technology Development Program,China (20240602032RC)the Jilin Agricultural Science and Technology Innovation Project,China (CXGC2024ZD001)+1 种基金the Jilin Agricultural Science and Technology Innovation Project,China (CXGC2024ZY012)the Jilin Province Development and Reform Commission-Project for Improving the Independent Innovation Capacity of Major Grain Crops,China (2024C002)。
文摘Emerging and powerful genome editing tools,particularly CRISPR/Cas9,are facilitating functional genomics research and accelerating crop improvement(Jiang et al.2021;Cao et al.2023;Chen C et al.2023;Liu et al.2023a).However,the detection and screening of transgenic lines remain major bottlenecks,being time-consuming,labor-intensive,and inefficient during transformation and subsequent mutation identification.A simple and efficient visual marker system plays a critical role in addressing these challenges.Recent studies demonstrated that the GmW1 and RUBY reporter systems were used to obtain visual transgenic soybean(Glycine max) plants(Chen L et al.2023;Chen et al.2024).
基金The National Natural Science Foundation of China(No.60472053),the Natural Science Foundation of Jiangsu Province(No.BK2003055),the Specialized Research Fund for the Doctoral Pro-gram of Higher Education (No.20030286017).
文摘The radial basis function （RBF）, a kind of neural networks algorithm, is adopted to select clusterheads. It has many advantages such as simple parallel distributed computation, distributed storage, and fast learning. Four factors related to a node becoming a cluster-head are drawn by analysis, which are energy （ energy available in each node）, number （the number of neighboring nodes）, centrality （ a value to classify the nodes based on the proximity how central the node is to the cluster）, and location （the distance between the base station and the node）. The factors are as input variables of neural networks and the output variable is suitability that is the degree of a node becoming a cluster head. A group of cluster-heads are selected according to the size of network. Then the base station broadcasts a message containing the list of cluster-heads＇ IDs to all nodes. After that, each cluster-head announces its new status to all its neighbors and sets up a new cluster. If a node around it receives the message, it registers itself to be a member of the cluster. After identifying all the members, the cluster-head manages them and carries out data aggregation in each cluster. Thus data flowing in the network decreases and energy consumption of nodes decreases accordingly. Experimental results show that, compared with other algorithms, the proposed algorithm can significantly increase the lifetime of the sensor network.
文摘Dipper throated optimization(DTO)algorithm is a novel with a very efficient metaheuristic inspired by the dipper throated bird.DTO has its unique hunting technique by performing rapid bowing movements.To show the efficiency of the proposed algorithm,DTO is tested and compared to the algorithms of Particle Swarm Optimization(PSO),Whale Optimization Algorithm(WOA),Grey Wolf Optimizer(GWO),and Genetic Algorithm(GA)based on the seven unimodal benchmark functions.Then,ANOVA and Wilcoxon rank-sum tests are performed to confirm the effectiveness of the DTO compared to other optimization techniques.Additionally,to demonstrate the proposed algorithm’s suitability for solving complex realworld issues,DTO is used to solve the feature selection problem.The strategy of using DTOs as feature selection is evaluated using commonly used data sets from the University of California at Irvine(UCI)repository.The findings indicate that the DTO outperforms all other algorithms in addressing feature selection issues,demonstrating the proposed algorithm’s capabilities to solve complex real-world situations.
基金funded by the Young Academic Leaders Supporting Project in Institutions of Higher Education of Shanxi Province,China
文摘Covariance functions have been proposed as an alternative to model longitudinal data in animal breeding because of their various merits in comparison to the classical analytical methods.In practical estimation,different models and polynomial orders fitted can influence the estimates of covariance functions and thus genetic parameters.The objective of this study was to select model for estimation of covariance functions for body weights of Angora goats at 7 time points.Covariance functions were estimated by fitting 6 random regression models with birth year,birth month,sex,age of dam,birth type,and relative birth date as fixed effects.Random effects involved were direct and maternal additive genetic,and animal and maternal permanent environmental effects with different orders of fit.Selection of model and orders of fit were carried out by likelihood ratio test and 4 types of information criteria.The results showed that model with 6 orders of polynomial fit for direct additive genetic and animal permanent environmental effects and 4 and 5 orders for maternal genetic and permanent environmental effects,respectively,were preferable for estimation of covariance functions.Models with and without maternal effects influenced the estimates of covariance functions greatly.Maternal permanent environmental effect does not explain the variation of all permanent environments,well suggesting different sources of permanent environmental effects also has large influence on covariance function estimates.
基金Supported by National Natural Science Foundation of P.R.China(60135020)the Project of National Defense Basic Research of P.R.China(A1420061266) the Foundation for University Key Teacher by the Ministry of Education
文摘Neuro-fuzzy(NF)networks are adaptive fuzzy inference systems(FIS)and have been applied to feature selection by some researchers.However,their rule number will grow exponentially as the data dimension increases.On the other hand,feature selection algorithms with artificial neural networks(ANN)usually require normalization of input data,which will probably change some characteristics of original data that are important for classification.To overcome the problems mentioned above,this paper combines the fuzzification layer of the neuro-fuzzy system with the multi-layer perceptron(MLP)to form a new artificial neural network.Furthermore,fuzzification strategy and feature measurement based on membership space are proposed for feature selection. Finally,experiments with both natural and artificial data are carried out to compare with other methods,and the results approve the validity of the algorithm.
基金support from the National Natural Science Foundation of China(No.21901206)Postdoctoral Science Foundation of China(No.2022M712589)+2 种基金General Key R&D Projects in Shaanxi Province(No.2023-YBGY-321)Natural Science Foundation of Chongqing(No.CSTB2022NSCQ-MSX0826)National&Local Joint Engineering Research Center for mineral Salt Deep Utilization,Huaiyin Institute of Technology(No.SF202407)for financial support。
文摘The transition metal-catalyzed C–H activation have been considered as increasingly useful approach for installing new functional groups onto organic small molecules due to their high step-and atom-economy,the abundance of hydrocarbon compounds,and the potential for late-stage functionalization of complex organic molecules.The ortho-and meta-C-H activation and functionalization of aromatic compounds have been widely explored in recent years,however the distal para-C-H activation and functionalization has remained a significant challenge because of the difficulty in forming energetically favorable metallacyclic transition states.The utilization of appropriate directing groups or templates as well as the meticulous design of catalysts and ligands has proven to be effective in transition-metal-catalyzed remote para-C-H bonds activation and functionalization of aromatic compounds.This review aims to summarize the strategies for controlling para-selective C–H functionalization using the directing group,template engineering,and catalyst/ligand design under transition metals catalysis in recent years.
基金Sponsored by Scientific Research Project of Public Welfare Industry of the Ministry of Land and Resources,China(201311006-4)
文摘From the perspective of ecological construction of roads, the reduction and purifying effects of greening plants on noise, raising dust and automobile exhaust, selection principles of arbors, shrubs, ground cover plants and herbaceous fl owers, and the methods of collocating arbors shrubs and grass in the construction of ecological roads were discussed in this study.
基金financial support from Teesside University to support the Ph.D. program of the first author.
文摘The selection and scaling of ground motion records is considered a primary and essential task in performing structural analysis and design.Conventional methods involve using ground motion models and a conditional spectrum to select ground motion records based on the target spectrum.This research demonstrates the influence of adopting different weighted factors for various period ranges during matching selected ground motions with the target hazard spectrum.The event data from the Next Generation Attenuation West 2(NGA-West 2)database is used as the basis for ground motion selection,and hazard de-aggregation is conducted to estimate the event parameters of interest,which are then used to construct the target intensity measure(IM).The target IMs are then used to select ground motion records with different weighted vector-valued objective functions.The weights are altered to account for the relative importance of IM in accordance with the structural analysis application of steel moment resisting frame(SMRF)buildings.Instead of an ordinary objective function for the matching spectrum,a novel model is introduced and compared with the conventional cost function.The results indicate that when applying the new cost function for ground motion selection,it places higher demands on structures compared to the conventional cost function.Moreover,submitting more weights to the first-mode period of structures increases engineering demand parameters.Findings demonstrate that weight factors allocated to different period ranges can successfully account for period elongation and higher mode effects.
文摘Price prediction plays a crucial role in portfolio selection (PS). However, most price prediction strategies only make a single prediction and do not have efficient mechanisms to make a comprehensive price prediction. Here, we propose a comprehensive price prediction (CPP) system based on inverse multiquadrics (IMQ) radial basis function. First, the novel radial basis function (RBF) system based on IMQ function rather than traditional Gaussian (GA) function is proposed and centers on multiple price prediction strategies, aiming at improving the efficiency and robustness of price prediction. Under the novel RBF system, we then create a portfolio update strategy based on kernel and trace operator. To assess the system performance, extensive experiments are performed based on 4 data sets from different real-world financial markets. Interestingly, the experimental results reveal that the novel RBF system effectively realizes the integration of different strategies and CPP system outperforms other systems in investing performance and risk control, even considering a certain degree of transaction costs. Besides, CPP can calculate quickly, making it applicable for large-scale and time-limited financial market.
基金Project(2020B090922002)supported by Guangdong Provincial Key Field Research and Development Program,ChinaProjects(51875215,52005189)supported by the National Natural Science Foundation of ChinaProject(2019B1515120094)supported by Guangdong Provincial Basic and Applied Basic Research Fund,China。
文摘Functionally graded material(FGM)can tailor properties of components such as wear resistance,corrosion resistance,and functionality to enhance the overall performance.The selective laser melting(SLM)additive manufacturing highlights the capability in manufacturing FGMs with a high geometrical complexity and manufacture flexibility.In this work,the 316L/CuSn10/18Ni300/CoCr four-type materials FGMs were fabricated using SLM.The microstructure and properties of the FGMs were investigated to reveal the effects of SLM processing parameters on the defects.A large number of microcracks were found at the 316L/CuSn10 interface,which initiated from the fusion boundary of 316L region and extended along the building direction.The elastic modulus and nano-hardness in the 18Ni300/CoCr fusion zone decreased significantly,less than those in the 18Ni300 region or the CoCr region.The iron and copper elements were well diffused in the 316L/CuSn10 fusion zone,while elements in the CuSn10/18Ni300 and the 18Ni300/CoCr fusion zones showed significantly gradient transitions.Compared with other regions,the width of the CuSn10/18Ni300 interface and the CuSn10 region expand significantly.The mechanisms of materials fusion and crack generation at the 316L/CuSn10 interface were discussed.In addition,FGM structures without macro-crack were built by only altering the deposition subsequence of 316L and CuSn10,which provides a guide for the additive manufacturing of FGM structures.
基金supported by the National Key Research&Development(R&D)Program of China(No.2017YFC0210500)the National Natural Science Foundation of China(No.51938014)
文摘Based on density functional theory(DFT)and basic structure models,the chemical reactions on the surface of vanadium-titanium based selective catalytic reduction(SCR)denitrification catalysts were summarized.Reasonable structural models(non-periodic and periodic structural models)are the basis of density functional calculations.A periodic structure model was more appropriate to represent the catalyst surface,and its theoretical calculation results were more comparable with the experimental results than a nonperiodic model.It is generally believed that the SCR mechanism where NH3 and NO react to produce N2 and H2 O follows an Eley-Rideal type mechanism.NH2 NO was found to be an important intermediate in the SCR reaction,with multiple production routes.Simultaneously,the effects of H2 O,SO2 and metal on SCR catalysts were also summarized.
基金Project(2012CB722803)supported by the Key Project of National Basic Research and Development Program of ChinaProject(U1202271)supported by the National Natural Science Foundation of ChinaProject(IRT1250)supported by the Program for Innovative Research Team in University of Ministry of Education of China
文摘The industrial silica fume pretreated by nitric acid at 80 °C was re-used in this work. Then, the obtained silica nanoparticles were surface functionalized by silane coupling agents, such as(3-Mercaptopropyl) triethoxysilane(MPTES) and(3-Amincpropyl) trithoxysilane(APTES). Some further modifications were studied by chloroaceetyl choride and 1,8-Diaminoaphalene for amino modified silica. The surface functionalized silica nanoparticles were characterized by Fourier transform infrared(FI-IR) and X-ray photoelectron spectroscopy(XPS). The prepared adsorbent of surface functionalized silica nanoparticles with differential function groups were investigated in the selective adsorption about Pb2+, Cu2+, Hg2+, Cd2+ and Zn2+ions in aqueous solutions. The results show that the(3-Mercaptopropyl) triethoxysilane functionalized silica nanoparticles(SiO2-MPTES) play an important role in the selective adsorption of Cu2+ and Hg2+, the(3-Amincpropyl) trithoxysilane(APTES) functionalized silica nanoparticles(SiO2-APTES) exhibited maximum removal efficiency towards Pb2+ and Hg2+, the 1,8-Diaminoaphalene functionalized silica nanoparticles was excellent for removal of Hg2+ at room temperature, respectively.
基金National Science Foundation of China(Nos.U1736105,61572259,41942017)The authors extend their appreciation to the Deanship of Scientific Research at King Saud University for funding this work through research group no.RGP-VPP-264.
文摘Feature selection has been widely used in data mining and machine learning.Its objective is to select a minimal subset of features according to some reasonable criteria so as to solve the original task more quickly.In this article,a feature selection algorithm with local search strategy based on the forest optimization algorithm,namely FSLSFOA,is proposed.The novel local search strategy in local seeding process guarantees the quality of the feature subset in the forest.Next,the fitness function is improved,which not only considers the classification accuracy,but also considers the size of the feature subset.To avoid falling into local optimum,a novel global seeding method is attempted,which selects trees on the bottom of candidate set and gives the algorithm more diversities.Finally,FSLSFOA is compared with four feature selection methods to verify its effectiveness.Most of the results are superior to these comparative methods.
基金financially supported by the National Natural Science Foundation of China (Nos.22078135,21808092,21978119,22202088)。
文摘Porous ionic liquids have demonstrated excellent performance in the field of separation,attributed to their high specific surface area and efficient mass transfer.Herein,task-specific protic porous ionic liquids(PPILs)were prepared by employing a novel one-step coupling neutralization reaction strategy for extractive desulfurization.The single-extraction efficiency of PPILs reached 75.0%for dibenzothiophene.Moreover,adding aromatic hydrocarbon interferents resulted in a slight decrease in the extraction efficiency of PPILs(from 45.2%to 37.3%,37.9%,and 33.5%),indicating the excellent extraction selectivity of PPILs.The experimental measurements and density functional theory calculations reveal that the surface channels of porous structures can selectively capture dibenzothiophene by the stronger electrophilicity(Eint(HS surface channel/DBT)=-39.8 kcal mol^(-1)),and the multiple extraction sites of ion pairs can effectively enrich and transport dibenzothiophene from the oil phase into PPILs throughπ...π,C-H...πand hydrogen bonds interactions.Furthermore,this straightforward synthetic strategy can be employed in preparing porous liquids,offering new possibilities for synthesizing PPILs with tailored functionalities.
文摘We consider the model selection problem of the dependency between the?terminal event and the non-terminal event under semi-competing risks data. When the relationship between the two events is unspecified, the inference on the non-terminal event is not identifiable. We cannot make inference on the non-terminal event without extra assumptions. Thus, an association model for?semi-competing risks data is necessary, and it is important to select an appropriate dependence model for a data set. We construct the likelihood function for semi-competing risks data to select an appropriate dependence model. From?simulation studies, it shows the performance of the proposed approach is well. Finally, we apply our method to a bone marrow transplant data set.
