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
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
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
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
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
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
Feature Selection(FS)is a key pre-processing step in pattern recognition and data mining tasks,which can effectively avoid the impact of irrelevant and redundant features on the performance of classification models.In...Feature Selection(FS)is a key pre-processing step in pattern recognition and data mining tasks,which can effectively avoid the impact of irrelevant and redundant features on the performance of classification models.In recent years,meta-heuristic algorithms have been widely used in FS problems,so a Hybrid Binary Chaotic Salp Swarm Dung Beetle Optimization(HBCSSDBO)algorithm is proposed in this paper to improve the effect of FS.In this hybrid algorithm,the original continuous optimization algorithm is converted into binary form by the S-type transfer function and applied to the FS problem.By combining the K nearest neighbor(KNN)classifier,the comparative experiments for FS are carried out between the proposed method and four advanced meta-heuristic algorithms on 16 UCI(University of California,Irvine)datasets.Seven evaluation metrics such as average adaptation,average prediction accuracy,and average running time are chosen to judge and compare the algorithms.The selected dataset is also discussed by categorizing it into three dimensions:high,medium,and low dimensions.Experimental results show that the HBCSSDBO feature selection method has the ability to obtain a good subset of features while maintaining high classification accuracy,shows better optimization performance.In addition,the results of statistical tests confirm the significant validity of the method.展开更多
For the first time,proteolysis-targeting chimeras(PROTAC)technology was utilized to achieve the isoform-selective degradation of class I phosphoinositide 3-kinases(PI3Ks)in this study.Through screening and optimizatio...For the first time,proteolysis-targeting chimeras(PROTAC)technology was utilized to achieve the isoform-selective degradation of class I phosphoinositide 3-kinases(PI3Ks)in this study.Through screening and optimization,the PROTAC molecule ZM-PI05 was identified as a selective degrader of p110αin multiple breast cancer cells.More importantly,the degrader can down-regulate p85 regulatory subunit simultaneously,thereby inhibiting the non-enzymatic functions of PI3K that are independent on p110catalytic subunits.Therefore,compared with PI3K inhibitor copanlisib,ZM-PI05 displayed the stronger anti-proliferative activity on breast cancer cells.In brief,a selective and efficient PROTAC molecule was developed to induce the degradation of p110αand concurrent reduction of p85 proteins,providing a tool compound for the biological study of PI3K-αby blocking its enzymatic and non-enzymatic functions.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
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
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
Dementias such as Alzheimer disease(AD)and mild cognitive impairment(MCI)lead to problems with memory,language,and daily activities resulting from damage to neurons in the brain.Given the irreversibility of this neuro...Dementias such as Alzheimer disease(AD)and mild cognitive impairment(MCI)lead to problems with memory,language,and daily activities resulting from damage to neurons in the brain.Given the irreversibility of this neuronal damage,it is crucial to find a biomarker to distinguish individuals with these diseases from healthy people.In this study,we construct a brain function network based on electroencephalography data to study changes in AD and MCI patients.Using a graph-theoretical approach,we examine connectivity features and explore their contributions to dementia recognition at edge,node,and network levels.We find that connectivity is reduced in AD and MCI patients compared with healthy controls.We also find that the edge-level features give the best performance when machine learning models are used to recognize dementia.The results of feature selection identify the top 50 ranked edge-level features constituting an optimal subset,which is mainly connected with the frontal nodes.A threshold analysis reveals that the performance of edge-level features is more sensitive to the threshold for the connection strength than that of node-and network-level features.In addition,edge-level features with a threshold of 0 provide the most effective dementia recognition.The K-nearest neighbors(KNN)machine learning model achieves the highest accuracy of 0.978 with the optimal subset when the threshold is 0.Visualization of edge-level features suggests that there are more long connections linking the frontal region with the occipital and parietal regions in AD and MCI patients compared with healthy controls.Our codes are publicly available at https://github.com/Debbie-85/eeg-connectivity.展开更多
Selective dinitrogen(N_(2))capture from coalbed methane(CBM)is significant in chemical industries,but it remains a challenge because of similar physicochemical properties of N_(2) and CH_(4).Herein,the adsorption of t...Selective dinitrogen(N_(2))capture from coalbed methane(CBM)is significant in chemical industries,but it remains a challenge because of similar physicochemical properties of N_(2) and CH_(4).Herein,the adsorption of them on the 2D porphyrin sheets doped with various 3d transition metal ions(marked as MPor,M=Sc,Ti,V,Cr,Mn,Fe,Co,Ni,Cu,Zn)were comparatively investigated by using density functional theory to screen a suitable adsorbent for CBM separation.Though systematical comparison of adsorption energies of gas molecules and Gibbs free energy change the N_(2) desorption process on all MPor surfaces,FePor is confirmed to be a promising adsorbent because of its undemanding regeneration conditions and modest chemical bonding state with N_(2) molecule.Further mechanism analysis reveals that the charge transferred from lone pair of N_(2) molecule to d_(z2)orbital of Fe ion and back-donated from d_(xz)and d_(yz)orbitals of Fe ion to the unoccupiedπ*orbital of N_(2) molecule.Such hybridization of orbitals improves the selective adsorption of N_(2) from CBM.展开更多
As the density of wireless networks increases globally, the vulnerability of overlapped dense wireless communications to interference by hidden nodes and denial-of-service (DoS) attacks is becoming more apparent. Ther...As the density of wireless networks increases globally, the vulnerability of overlapped dense wireless communications to interference by hidden nodes and denial-of-service (DoS) attacks is becoming more apparent. There exists a gap in research on the detection and response to attacks on Medium Access Control (MAC) mechanisms themselves, which would lead to service outages between nodes. Classifying exploitation and deceptive jamming attacks on control mechanisms is particularly challengingdue to their resemblance to normal heavy communication patterns. Accordingly, this paper proposes a machine learning-based selective attack mitigation model that detects DoS attacks on wireless networks by monitoring packet log data. Based on the type of detected attack, it implements effective corresponding mitigation techniques to restore performance to nodes whose availability has been compromised. Experimental results reveal that the accuracy of the proposed model is 14% higher than that of a baseline anomaly detection model. Further, the appropriate mitigation techniques selected by the proposed system based on the attack type improve the average throughput by more than 440% compared to the case without a response.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
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
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
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.
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.
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.
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.
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This research was funded by the Short-Term Electrical Load Forecasting Based on Feature Selection and optimized LSTM with DBO which is the Fundamental Scientific Research Project of Liaoning Provincial Department of Education(JYTMS20230189)the Application of Hybrid Grey Wolf Algorithm in Job Shop Scheduling Problem of the Research Support Plan for Introducing High-Level Talents to Shenyang Ligong University(No.1010147001131).
文摘Feature Selection(FS)is a key pre-processing step in pattern recognition and data mining tasks,which can effectively avoid the impact of irrelevant and redundant features on the performance of classification models.In recent years,meta-heuristic algorithms have been widely used in FS problems,so a Hybrid Binary Chaotic Salp Swarm Dung Beetle Optimization(HBCSSDBO)algorithm is proposed in this paper to improve the effect of FS.In this hybrid algorithm,the original continuous optimization algorithm is converted into binary form by the S-type transfer function and applied to the FS problem.By combining the K nearest neighbor(KNN)classifier,the comparative experiments for FS are carried out between the proposed method and four advanced meta-heuristic algorithms on 16 UCI(University of California,Irvine)datasets.Seven evaluation metrics such as average adaptation,average prediction accuracy,and average running time are chosen to judge and compare the algorithms.The selected dataset is also discussed by categorizing it into three dimensions:high,medium,and low dimensions.Experimental results show that the HBCSSDBO feature selection method has the ability to obtain a good subset of features while maintaining high classification accuracy,shows better optimization performance.In addition,the results of statistical tests confirm the significant validity of the method.
基金supported by National Key R&D Program of China(Nos.2021YFA1302100,2021YFA1300200,2020YFE0202200)National Natural Science Foundation of China(Nos.82125034,82330115)。
文摘For the first time,proteolysis-targeting chimeras(PROTAC)technology was utilized to achieve the isoform-selective degradation of class I phosphoinositide 3-kinases(PI3Ks)in this study.Through screening and optimization,the PROTAC molecule ZM-PI05 was identified as a selective degrader of p110αin multiple breast cancer cells.More importantly,the degrader can down-regulate p85 regulatory subunit simultaneously,thereby inhibiting the non-enzymatic functions of PI3K that are independent on p110catalytic subunits.Therefore,compared with PI3K inhibitor copanlisib,ZM-PI05 displayed the stronger anti-proliferative activity on breast cancer cells.In brief,a selective and efficient PROTAC molecule was developed to induce the degradation of p110αand concurrent reduction of p85 proteins,providing a tool compound for the biological study of PI3K-αby blocking its enzymatic and non-enzymatic functions.
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.
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supported by the National Natural Science Foundation of China(Grant Nos.62071451,62331025,and U21A20447)the National Key Research and Development Project(Grant No.2021YFC3002204)the CAMS Innovation Fund for Medical Sciences(Grant No.2019-I2M-5-019).
文摘Dementias such as Alzheimer disease(AD)and mild cognitive impairment(MCI)lead to problems with memory,language,and daily activities resulting from damage to neurons in the brain.Given the irreversibility of this neuronal damage,it is crucial to find a biomarker to distinguish individuals with these diseases from healthy people.In this study,we construct a brain function network based on electroencephalography data to study changes in AD and MCI patients.Using a graph-theoretical approach,we examine connectivity features and explore their contributions to dementia recognition at edge,node,and network levels.We find that connectivity is reduced in AD and MCI patients compared with healthy controls.We also find that the edge-level features give the best performance when machine learning models are used to recognize dementia.The results of feature selection identify the top 50 ranked edge-level features constituting an optimal subset,which is mainly connected with the frontal nodes.A threshold analysis reveals that the performance of edge-level features is more sensitive to the threshold for the connection strength than that of node-and network-level features.In addition,edge-level features with a threshold of 0 provide the most effective dementia recognition.The K-nearest neighbors(KNN)machine learning model achieves the highest accuracy of 0.978 with the optimal subset when the threshold is 0.Visualization of edge-level features suggests that there are more long connections linking the frontal region with the occipital and parietal regions in AD and MCI patients compared with healthy controls.Our codes are publicly available at https://github.com/Debbie-85/eeg-connectivity.
基金Supported by the National Natural Science Foundation of China(Nos.52476170 and 51906232)the Natural Science Foundation of Shanxi Province of China(Nos.202303021211156,202203021212140)Project of Science and Technology Innovation Team of Shanxi Province(No.202204051002023)。
文摘Selective dinitrogen(N_(2))capture from coalbed methane(CBM)is significant in chemical industries,but it remains a challenge because of similar physicochemical properties of N_(2) and CH_(4).Herein,the adsorption of them on the 2D porphyrin sheets doped with various 3d transition metal ions(marked as MPor,M=Sc,Ti,V,Cr,Mn,Fe,Co,Ni,Cu,Zn)were comparatively investigated by using density functional theory to screen a suitable adsorbent for CBM separation.Though systematical comparison of adsorption energies of gas molecules and Gibbs free energy change the N_(2) desorption process on all MPor surfaces,FePor is confirmed to be a promising adsorbent because of its undemanding regeneration conditions and modest chemical bonding state with N_(2) molecule.Further mechanism analysis reveals that the charge transferred from lone pair of N_(2) molecule to d_(z2)orbital of Fe ion and back-donated from d_(xz)and d_(yz)orbitals of Fe ion to the unoccupiedπ*orbital of N_(2) molecule.Such hybridization of orbitals improves the selective adsorption of N_(2) from CBM.
基金supported by the Ministry of Trade,Industry and Energy(MOTIE)under Training Industrial Security Specialist for High-Tech Industry(RS-2024-00415520)supervised by the Korea Institute for Advancement of Technology(KIAT)the Ministry of Science and ICT(MSIT)under the ICT Challenge and Advanced Network of HRD(ICAN)Program(No.IITP-2022-RS-2022-00156310)supervised by the Institute of Information&Communication Technology Planning&Evaluation(IITP).
文摘As the density of wireless networks increases globally, the vulnerability of overlapped dense wireless communications to interference by hidden nodes and denial-of-service (DoS) attacks is becoming more apparent. There exists a gap in research on the detection and response to attacks on Medium Access Control (MAC) mechanisms themselves, which would lead to service outages between nodes. Classifying exploitation and deceptive jamming attacks on control mechanisms is particularly challengingdue to their resemblance to normal heavy communication patterns. Accordingly, this paper proposes a machine learning-based selective attack mitigation model that detects DoS attacks on wireless networks by monitoring packet log data. Based on the type of detected attack, it implements effective corresponding mitigation techniques to restore performance to nodes whose availability has been compromised. Experimental results reveal that the accuracy of the proposed model is 14% higher than that of a baseline anomaly detection model. Further, the appropriate mitigation techniques selected by the proposed system based on the attack type improve the average throughput by more than 440% compared to the case without a response.
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.
