Succinonitrile(SN)-based polymer plastic crystal electrolytes(PPCEs)are regarded as promising candidates for lithium metal batteries but suffer from serious side reactions with Li metal.Herein,we propose a multi-dimen...Succinonitrile(SN)-based polymer plastic crystal electrolytes(PPCEs)are regarded as promising candidates for lithium metal batteries but suffer from serious side reactions with Li metal.Herein,we propose a multi-dimensional optimization strategy to alleviate the side reactions between SN and Li metal,and develop a highly stable poly-vinylethylene carbonate-based PPCE(PPCE-VEC).Moreover,we identify the intrinsic factors of multi-dimensional polymer structures on the electrolyte stability by three typical classes of polyesters.The PPCE-VEC constructed by in situ polymerization exhibits much better stability than poly-vinylene carbonate-based PPCE(PPCE-VCA)and poly-trifluoroethyl acrylate-based PPCE(PPCE-TFA),which is verified by its fewer SN-decomposition species in X-ray photoelectron spectroscopy(XPS)and outstanding full cell performance.The PPCE-VEC-enabled LiNi_(0.6)Co_(0.2)Mn_(0.2)O_(2)full cell achieve 73.7%capacity retention after 1400 cycles,which outperforms PPCE-VCA-and PPCE-TFA-enabled full cells(61.9%and 46.9%).Spectral analysis and theoretical calculation reveal that the high solvation ability of the carbonyl site,flexible polymer chain,and homogeneous electrolyte phase of PPCE-VEC are favorable to maximizing competition coordination with Li^(+)to weaken the Li^(+)–SN binding and shape an anion-rich solvation structure.This optimized polymer-involved Li^(+)solvation enhances SN stability and facilitates the formation of B/F enriched solid-electrolyte interphase(SEI),thus significantly improving PPCE stability.展开更多
The distance-based outlier is a widely used definition of outlier. A point is distinguished as an outlier on the basis of the distances to its nearest neighbors. In this paper, to solve the problem of outlier computin...The distance-based outlier is a widely used definition of outlier. A point is distinguished as an outlier on the basis of the distances to its nearest neighbors. In this paper, to solve the problem of outlier computing in distributed environments, DBOZ, a distributed algorithm for distance-based outlier detection using Z-curve hierarchical tree （ZH-tree） is proposed. First, we propose a new index, ZH-tree, to effectively manage the data in a distributed environment. ZH-tree has two desirable advantages, including clustering property to help search the neighbors of a point, and hierarchical structure to support space pruning. We also design a bottom-up approach to build ZH-tree in parallel, whose time complexity is linear to the number of dimensions and the size of dataset. Second, DBOZ is proposed to compute outliers in distributed environments. It consists of two stages. 1） To avoid calculating the exact nearest neighbors of all the points, we design a greedy method and a new ZH-tree based k-nearest neighbor searching algorithm （ZHkNN for short） to obtain a threshold LW. 2） We propose a filter-and-refine approach, which first filters out the unpromising points using LW, and then outputs the final outliers through refining the remaining points. At last, the efficiency and the effectiveness of ZH-tree and DBOZ are testified through a series of experiments.展开更多
A composite anti-disturbance predictive control strategy employing a Multi-dimensional Taylor Network(MTN)is presented for unmanned systems subject to time-delay and multi-source disturbances.First,the multi-source di...A composite anti-disturbance predictive control strategy employing a Multi-dimensional Taylor Network(MTN)is presented for unmanned systems subject to time-delay and multi-source disturbances.First,the multi-source disturbances are addressed according to their specific characteristics as follows:(A)an MTN data-driven model,which is used for uncertainty description,is designed accompanied with the mechanism model to represent the unmanned systems;(B)an adaptive MTN filter is used to remove the influence of the internal disturbance;(C)an MTN disturbance observer is constructed to estimate and compensate for the influence of the external disturbance;(D)the Extended Kalman Filter(EKF)algorithm is utilized as the learning mechanism for MTNs.Second,to address the time-delay effect,a recursiveτstep-ahead MTN predictive model is designed utilizing recursive technology,aiming to mitigate the impact of time-delay,and the EKF algorithm is employed as its learning mechanism.Then,the MTN predictive control law is designed based on the quadratic performance index.By implementing the proposed composite controller to unmanned systems,simultaneous feedforward compensation and feedback suppression to the multi-source disturbances are conducted.Finally,the convergence of the MTN and the stability of the closed-loop system are established utilizing the Lyapunov theorem.Two exemplary applications of unmanned systems involving unmanned vehicle and rigid spacecraft are presented to validate the effectiveness of the proposed approach.展开更多
The ancient tacit knowledge behind the logic system permeated the culture and promoted numerous impactful inventions throughout the history. Traditional Chinese medicine with its effectiveness should also have stemmed...The ancient tacit knowledge behind the logic system permeated the culture and promoted numerous impactful inventions throughout the history. Traditional Chinese medicine with its effectiveness should also have stemmed out from such logic system. This article aims to rearticulate the underlying lucid multi-dimensional logic system, which faded in obscurity only because of time-out loss of the mid-right concept. Retracing this past tacit but important concept could uncover a multi-dimensional system over a point relating to all matters while capturing the central core of the matter. The seemingly unmanageable multidimensional logic was strengthened by verification processes which affirmed its further extensions, and made up the language of the people, the concepts of yin-yang(阴阳), and the development of extensions of Ba Gua(八卦) derivatives, which furthered the interpretation of the space-time properties and Chinese medicine.展开更多
Stress accumulation is a key factor leading to sodium storage performance deterioration for NiSe_(2)-based anodes.Therefore,inhibiting the concentrated local stress during the sodiataion/desodiation process is crucial...Stress accumulation is a key factor leading to sodium storage performance deterioration for NiSe_(2)-based anodes.Therefore,inhibiting the concentrated local stress during the sodiataion/desodiation process is crucial for acquiring stable NiSe2-based materials for sodium-ion batteries(SIBs),Herein,a stress dissipation strategy driven by architecture engineering is proposed,which can achieve ultrafast and ultralong sodium storage properties.Different from the conventional sphere-like or rod-like architecture,the three-dimensional(3D)flower-like NiSe_(2)@C composite is delicately designed and assembled with onedimensional nanorods and carbon framework.More importantly,the fundamental mechanism of improved structure stability is unveiled by simulations and experimental results simultaneously.It demonstrates that this designed multidimensional flower-like architecture with dispersed nanorods can balance the structural mismatch,avoid concentrated local strain,and relax the internal stress,mainly induced by the unavoidable volume variation during the repeated conversion processes.Moreover,it can provide more Na^(+)-storage sites and multi-directional migration pathways,leading to a fast Na^(+)-migration channel with boosted reaction kinetic.As expected,it delivers superior rate performance(441 mA h g^(-1)at 5.0 A g^(-1))and long cycling stability(563 mA h g^(-1)at 1.0 A g^(-1)over 1000 cycles)for SIBs.This work provides useful insights for designing high-performance conversion-based anode materials for SIBs.展开更多
This paper explores whole-process engineering consulting,including its application models in public buildings and elderly-friendly projects,such as service integration and whole lifecycle management.It also addresses ...This paper explores whole-process engineering consulting,including its application models in public buildings and elderly-friendly projects,such as service integration and whole lifecycle management.It also addresses the construction of multi-dimensional collaborative theoretical models,public space streamline organization,and other aspects,emphasizing the importance of multi-dimensional collaboration.Additionally,it highlights the role of talent cultivation and digital transformation in enhancing project efficiency.展开更多
The multi-dimensional interactive teaching model significantly enhances the effectiveness of college English instruction by emphasizing dynamic engagement between teachers and students,as well as among students themse...The multi-dimensional interactive teaching model significantly enhances the effectiveness of college English instruction by emphasizing dynamic engagement between teachers and students,as well as among students themselves.This paper explores practical strategies for implementing this model,focusing on four key aspects:deepening teachers’understanding of the model through continuous learning,innovating interactive methods such as questioning techniques and practical activities,leveraging modern technology to integrate resources and track learning progress,and establishing a communication platform that centers on student participation.By adopting these approaches,the model fosters a student-centered classroom environment,improves comprehensive English application skills,and optimizes overall teaching quality.展开更多
During the critical transformation period of landscape architecture major after the adjustment of disciplinary structure and the changes in market demand,private colleges and universities,as important places for culti...During the critical transformation period of landscape architecture major after the adjustment of disciplinary structure and the changes in market demand,private colleges and universities,as important places for cultivating local talents,have pain points such as uneven quality of teachers and students and weak innovation and practice.The practice system with“multi-dimensional Integration”integrates four dimensions:interdisciplinary integration,spatial and temporal intersection,historical inheritance,and behavioral activity,deepens the disciplinary connotation,and integrates the three elements of nature,humanity,and technology,aiming to provide a new path for private colleges and universities to cultivate application-oriented and compound talents with innovative capabilities.In terms of optimizing talent cultivation and adapting to industry changes,this system provides thinking and reference for landscape architecture major,helping the major reshape its core competitiveness and promoting educational innovation and industry development.展开更多
This paper proposes a reliability evaluation model for a multi-dimensional network system,which has potential to be applied to the internet of things or other practical networks.A multi-dimensional network system with...This paper proposes a reliability evaluation model for a multi-dimensional network system,which has potential to be applied to the internet of things or other practical networks.A multi-dimensional network system with one source element and multiple sink elements is considered first.Each element can con-nect with other elements within a stochastic connection ranges.The system is regarded as successful as long as the source ele-ment remains connected with all sink elements.An importance measure is proposed to evaluate the performance of non-source elements.Furthermore,to calculate the system reliability and the element importance measure,a multi-valued decision diagram based approach is structured and its complexity is analyzed.Finally,a numerical example about the signal transfer station system is illustrated to analyze the system reliability and the ele-ment importance measure.展开更多
Constructing multi-dimensional hydrogen bond(H-bond)regulated single-molecule systems with multiemission remains a challenge.Herein,we report the design of a new excited-state intramolecular proton transfer(ESIPT)feat...Constructing multi-dimensional hydrogen bond(H-bond)regulated single-molecule systems with multiemission remains a challenge.Herein,we report the design of a new excited-state intramolecular proton transfer(ESIPT)featured chromophore(HBT-DPI)that shows flexible emission tunability via the multidimensional regulation of intra-and intermolecular H-bonds.The feature of switchable intramolecular Hbonds is induced via incorporating several hydrogen bond acceptors and donors into one single HBT-DPI molecule,allowing the“turn on/off”of ESIPT process by forming isomers with distinct intramolecular Hbonds configurations.In response to different external H-bonding environments,the obtained four types of crystal/cocrystals vary in the contents of isomers and the molecular packing modes,which are mainly guided by the intermolecular H-bonds,exhibiting non-emissive features or emissions ranging from green to orange.Utilizing the feature of intermolecular H-bond guided molecular packing,we demonstrate the utility of this fluorescent material for visualizing hydrophobic/hydrophilic areas on large-scale heterogeneous surfaces of modified poly(1,1-difluoroethylene)(PVDF)membranes and quantitatively estimating the surface hydrophobicity,providing a new approach for hydrophobicity/hydrophilicity monitoring and measurement.Overall,this study represents a new design strategy for constructing multi-dimensional hydrogen bond regulated ESIPT-based fluorescent materials that enable multiple emissions and unique applications.展开更多
To break the limitations of the multi-dimensional(M-D)vibration isolation(VI)platforms with the Stewart-Gough design,such as strongly coupling motions,excessive friction in connections,heavy weight,and limited workspa...To break the limitations of the multi-dimensional(M-D)vibration isolation(VI)platforms with the Stewart-Gough design,such as strongly coupling motions,excessive friction in connections,heavy weight,and limited workspace,this study processes a novel platform integrated by a stiffness-adjustable origami spring sub-structure and a parallel mechanism.The origami-based stiffness-adjustable spring realizes low-frequency VI,and the parallel mechanism symmetry design realizes motions decoupling.In the origami-based sub-leg,the parallel-stack-assembly(PSA)design mechanism with two Miura origami configurations is proposed to generate a symmetrical negative stiffness property.Paired with a linear positive stiffness spring,the origami-based sub-leg has wide-amplitude-high-static-low-dynamic stiffness(WA-HSLDS)characteristics in one direction.Then,with construction of the parallel mechanism connected with origami-based sub-legs,an M-D VI platform is achieved,whose motions in the vertical direction and yaw direction are decoupled with the motions in the other directions.Based on the dynamic model and incremental harmonic balance(IHB)with the arc-length continuation method,appropriate structural parameters in the parallel mechanism part are figured out,and the accurate transmissibility in different directions is defined,which gives the parametric influencing investigations for realization of low-frequency VI performances.Finally,experiments are conducted to validate the accuracy and feasibility of the theoretical methods,and to demonstrate the performance of M-D low-frequency isolation with load-carrying capacity of the proposed VI platform.The integration of the origami into the parallel mechanism results in a compact,efficient,and flexible platform with nonlinear adjustability,offering new possibilities for lightweight M-D VI,and developing the practical applications in high-precision platforms in ocean and aerospace environments.展开更多
Parkinson’s disease(PD)is a debilitating neurological disorder affecting over 10 million people worldwide.PD classification models using voice signals as input are common in the literature.It is believed that using d...Parkinson’s disease(PD)is a debilitating neurological disorder affecting over 10 million people worldwide.PD classification models using voice signals as input are common in the literature.It is believed that using deep learning algorithms further enhances performance;nevertheless,it is challenging due to the nature of small-scale and imbalanced PD datasets.This paper proposed a convolutional neural network-based deep support vector machine(CNN-DSVM)to automate the feature extraction process using CNN and extend the conventional SVM to a DSVM for better classification performance in small-scale PD datasets.A customized kernel function reduces the impact of biased classification towards the majority class(healthy candidates in our consideration).An improved generative adversarial network(IGAN)was designed to generate additional training data to enhance the model’s performance.For performance evaluation,the proposed algorithm achieves a sensitivity of 97.6%and a specificity of 97.3%.The performance comparison is evaluated from five perspectives,including comparisons with different data generation algorithms,feature extraction techniques,kernel functions,and existing works.Results reveal the effectiveness of the IGAN algorithm,which improves the sensitivity and specificity by 4.05%–4.72%and 4.96%–5.86%,respectively;and the effectiveness of the CNN-DSVM algorithm,which improves the sensitivity by 1.24%–57.4%and specificity by 1.04%–163%and reduces biased detection towards the majority class.The ablation experiments confirm the effectiveness of individual components.Two future research directions have also been suggested.展开更多
Small datasets are often challenging due to their limited sample size.This research introduces a novel solution to these problems:average linkage virtual sample generation(ALVSG).ALVSG leverages the underlying data st...Small datasets are often challenging due to their limited sample size.This research introduces a novel solution to these problems:average linkage virtual sample generation(ALVSG).ALVSG leverages the underlying data structure to create virtual samples,which can be used to augment the original dataset.The ALVSG process consists of two steps.First,an average-linkage clustering technique is applied to the dataset to create a dendrogram.The dendrogram represents the hierarchical structure of the dataset,with each merging operation regarded as a linkage.Next,the linkages are combined into an average-based dataset,which serves as a new representation of the dataset.The second step in the ALVSG process involves generating virtual samples using the average-based dataset.The research project generates a set of 100 virtual samples by uniformly distributing them within the provided boundary.These virtual samples are then added to the original dataset,creating a more extensive dataset with improved generalization performance.The efficacy of the ALVSG approach is validated through resampling experiments and t-tests conducted on two small real-world datasets.The experiments are conducted on three forecasting models:the support vector machine for regression(SVR),the deep learning model(DL),and XGBoost.The results show that the ALVSG approach outperforms the baseline methods in terms of mean square error(MSE),root mean square error(RMSE),and mean absolute error(MAE).展开更多
Accurate purchase prediction in e-commerce critically depends on the quality of behavioral features.This paper proposes a layered and interpretable feature engineering framework that organizes user signals into three ...Accurate purchase prediction in e-commerce critically depends on the quality of behavioral features.This paper proposes a layered and interpretable feature engineering framework that organizes user signals into three layers:Basic,Conversion&Stability(efficiency and volatility across actions),and Advanced Interactions&Activity(crossbehavior synergies and intensity).Using real Taobao(Alibaba’s primary e-commerce platform)logs(57,976 records for 10,203 users;25 November–03 December 2017),we conducted a hierarchical,layer-wise evaluation that holds data splits and hyperparameters fixed while varying only the feature set to quantify each layer’s marginal contribution.Across logistic regression(LR),decision tree,random forest,XGBoost,and CatBoost models with stratified 5-fold cross-validation,the performance improvedmonotonically fromBasic to Conversion&Stability to Advanced features.With LR,F1 increased from 0.613(Basic)to 0.962(Advanced);boosted models achieved high discrimination(0.995 AUC Score)and an F1 score up to 0.983.Calibration and precision–recall analyses indicated strong ranking quality and acknowledged potential dataset and period biases given the short(9-day)window.By making feature contributions measurable and reproducible,the framework complements model-centric advances and offers a transparent blueprint for production-grade behavioralmodeling.The code and processed artifacts are publicly available,and future work will extend the validation to longer,seasonal datasets and hybrid approaches that combine automated feature learning with domain-driven design.展开更多
The study constructed a multi-dimensional feedback mode integrating teacher feedback, peer feedback and network feedback, and applied it in the teaching of College English Writing. After 16 weeks of teaching, the stud...The study constructed a multi-dimensional feedback mode integrating teacher feedback, peer feedback and network feedback, and applied it in the teaching of College English Writing. After 16 weeks of teaching, the students in the multi-dimen-sional feedback class had significantly better overall writing scores than those in the teacher-feedback class. In terms of individual scores, multi-dimensional feedback played a better role in improving vocabulary and grammar than the class using teacher feed-back. However, there were no significant differences in the responses of writing tasks, coherence and cohesion. The study showed that most students were satisfied with the mode, believing that it was helpful to relieve writing anxiety, stimulate writing interest and improve their writing level.展开更多
Maize （Zea mays） is the most widely grown grain crop in the world, playing important roles in agriculture and industry. However, the functions of maize genes remain largely unknown. High-quality genome- wide transcr...Maize （Zea mays） is the most widely grown grain crop in the world, playing important roles in agriculture and industry. However, the functions of maize genes remain largely unknown. High-quality genome- wide transcriptome datasets provide important biological knowledge which has been widely and suc- cessfully used in plants not only by measuring gene expression levels but also by enabling co-expression analysis for predicting gene functions and modules related to agronomic traits. Recently, thousands of maize transcriptomic data are available across different inbred lines, development stages, tissues, and treatments, or even across different tissue sections and cell lines. Here, we integrated 701 transcriptomic and 108 epigenomic data and studied the different conditional networks with multi-dimensional omics levels. We constructed a searchable, integrative, one-stop online platform, the maize conditional co- expression network （MCENet） platform. MCENet provides 10 global/conditional co-expression net- works, 5 network accessional analysis toolkits （i.e., Network Search, Network Remodel, Module Finder, Network Comparison, and Dynamic Expression View） and multiple network functional support toolkits （e.g., motif and module enrichment analysis）. We hope that our database might help plant research communities to identify maize functional genes or modules that regulate important agronomic traits.展开更多
Recently,multilevel structural carbon aerogels are deemed as attractive candidates for microwave absorbing materials.Nevertheless,excessive stack and agglomeration for low-dimension carbon nanomaterials inducing imped...Recently,multilevel structural carbon aerogels are deemed as attractive candidates for microwave absorbing materials.Nevertheless,excessive stack and agglomeration for low-dimension carbon nanomaterials inducing impedance mismatch are significant challenges.Herein,the delicate“3D helix-2D sheet-1D fiber-0D dot”hierarchical aerogels have been successfully synthesized,for the first time,by sequential processes of hydrothermal self-assembly and in-situ chemical vapor deposition method.Particularly,the graphene sheets are uniformly intercalated by 3D helical carbon nanocoils,which give a feasible solution to the mentioned problem and endows the as-obtained aerogel with abundant porous structures and better dielectric properties.Moreover,by adjusting the content of 0D core-shell structured particles and the parameters for growth of the 1D carbon nanofibers,tunable electromagnetic properties and excellent impedance matching are achieved,which plays a vital role in the microwave absorption performance.As expected,the optimized aerogels harvest excellent performance,including broad effective bandwidth and strong reflection loss at low filling ratio and thin thickness.This work gives valuable guidance and inspiration for the design of hierarchical materials comprised of dimensional gradient structures,which holds great application potential for electromagnetic wave attenuation.展开更多
Fragility analysis for highway bridges has become increasingly important in the risk assessment of highway transportation networks exposed to seismic hazards. This study introduces a methodology to calculate fragility...Fragility analysis for highway bridges has become increasingly important in the risk assessment of highway transportation networks exposed to seismic hazards. This study introduces a methodology to calculate fragility that considers multi-dimensional performance limit state parameters and makes a first attempt to develop fragility curves for a multi-span continuous (MSC) concrete girder bridge considering two performance limit state parameters: column ductility and transverse deformation in the abutments. The main purpose of this paper is to show that the performance limit states, which are compared with the seismic response parameters in the calculation of fragility, should be properly modeled as randomly interdependent variables instead of deterministic quantities. The sensitivity of fragility curves is also investigated when the dependency between the limit states is different. The results indicate that the proposed method can be used to describe the vulnerable behavior of bridges which are sensitive to multiple response parameters and that the fragility information generated by this method will be more reliable and likely to be implemented into transportation network loss estimation.展开更多
Because the hydraulic directional valve usually works in a bad working environment and is disturbed by multi-factor noise,the traditional single sensor monitoring technology is difficult to use for an accurate diagnos...Because the hydraulic directional valve usually works in a bad working environment and is disturbed by multi-factor noise,the traditional single sensor monitoring technology is difficult to use for an accurate diagnosis of it.Therefore,a fault diagnosis method based on multi-sensor information fusion is proposed in this paper to reduce the inaccuracy and uncertainty of traditional single sensor information diagnosis technology and to realize accurate monitoring for the location or diagnosis of early faults in such valves in noisy environments.Firstly,the statistical features of signals collected by the multi-sensor are extracted and the depth features are obtained by a convolutional neural network(CNN)to form a complete and stable multi-dimensional feature set.Secondly,to obtain a weighted multi-dimensional feature set,the multi-dimensional feature sets of similar sensors are combined,and the entropy weight method is used to weight these features to reduce the interference of insensitive features.Finally,the attention mechanism is introduced to improve the dual-channel CNN,which is used to adaptively fuse the weighted multi-dimensional feature sets of heterogeneous sensors,to flexibly select heterogeneous sensor information so as to achieve an accurate diagnosis.Experimental results show that the weighted multi-dimensional feature set obtained by the proposed method has a high fault-representation ability and low information redundancy.It can diagnose simultaneously internal wear faults of the hydraulic directional valve and electromagnetic faults of actuators that are difficult to diagnose by traditional methods.This proposed method can achieve high fault-diagnosis accuracy under severe working conditions.展开更多
基金supported by the National Natural Science Foundation of China(22072048)the Guangdong Provincial Department of Science and Technology(2021A1515010128 and 2022A0505050013).
文摘Succinonitrile(SN)-based polymer plastic crystal electrolytes(PPCEs)are regarded as promising candidates for lithium metal batteries but suffer from serious side reactions with Li metal.Herein,we propose a multi-dimensional optimization strategy to alleviate the side reactions between SN and Li metal,and develop a highly stable poly-vinylethylene carbonate-based PPCE(PPCE-VEC).Moreover,we identify the intrinsic factors of multi-dimensional polymer structures on the electrolyte stability by three typical classes of polyesters.The PPCE-VEC constructed by in situ polymerization exhibits much better stability than poly-vinylene carbonate-based PPCE(PPCE-VCA)and poly-trifluoroethyl acrylate-based PPCE(PPCE-TFA),which is verified by its fewer SN-decomposition species in X-ray photoelectron spectroscopy(XPS)and outstanding full cell performance.The PPCE-VEC-enabled LiNi_(0.6)Co_(0.2)Mn_(0.2)O_(2)full cell achieve 73.7%capacity retention after 1400 cycles,which outperforms PPCE-VCA-and PPCE-TFA-enabled full cells(61.9%and 46.9%).Spectral analysis and theoretical calculation reveal that the high solvation ability of the carbonyl site,flexible polymer chain,and homogeneous electrolyte phase of PPCE-VEC are favorable to maximizing competition coordination with Li^(+)to weaken the Li^(+)–SN binding and shape an anion-rich solvation structure.This optimized polymer-involved Li^(+)solvation enhances SN stability and facilitates the formation of B/F enriched solid-electrolyte interphase(SEI),thus significantly improving PPCE stability.
基金This work was supported by the National Basic Research 973 Program of China under Grant No. 2012CB316201, the National Natural Science Foundation of China under Grant Nos. 61033007 and 61472070, and the Fundamental Research Funds for the Central Universities of China under Grant No. N120816001.
文摘The distance-based outlier is a widely used definition of outlier. A point is distinguished as an outlier on the basis of the distances to its nearest neighbors. In this paper, to solve the problem of outlier computing in distributed environments, DBOZ, a distributed algorithm for distance-based outlier detection using Z-curve hierarchical tree （ZH-tree） is proposed. First, we propose a new index, ZH-tree, to effectively manage the data in a distributed environment. ZH-tree has two desirable advantages, including clustering property to help search the neighbors of a point, and hierarchical structure to support space pruning. We also design a bottom-up approach to build ZH-tree in parallel, whose time complexity is linear to the number of dimensions and the size of dataset. Second, DBOZ is proposed to compute outliers in distributed environments. It consists of two stages. 1） To avoid calculating the exact nearest neighbors of all the points, we design a greedy method and a new ZH-tree based k-nearest neighbor searching algorithm （ZHkNN for short） to obtain a threshold LW. 2） We propose a filter-and-refine approach, which first filters out the unpromising points using LW, and then outputs the final outliers through refining the remaining points. At last, the efficiency and the effectiveness of ZH-tree and DBOZ are testified through a series of experiments.
基金co-supported by the National Key R&D Program of China(No.2023YFB4704400)the Zhejiang Provincial Natural Science Foundation of China(No.LQ24F030012)the National Natural Science Foundation of China General Project(No.62373033)。
文摘A composite anti-disturbance predictive control strategy employing a Multi-dimensional Taylor Network(MTN)is presented for unmanned systems subject to time-delay and multi-source disturbances.First,the multi-source disturbances are addressed according to their specific characteristics as follows:(A)an MTN data-driven model,which is used for uncertainty description,is designed accompanied with the mechanism model to represent the unmanned systems;(B)an adaptive MTN filter is used to remove the influence of the internal disturbance;(C)an MTN disturbance observer is constructed to estimate and compensate for the influence of the external disturbance;(D)the Extended Kalman Filter(EKF)algorithm is utilized as the learning mechanism for MTNs.Second,to address the time-delay effect,a recursiveτstep-ahead MTN predictive model is designed utilizing recursive technology,aiming to mitigate the impact of time-delay,and the EKF algorithm is employed as its learning mechanism.Then,the MTN predictive control law is designed based on the quadratic performance index.By implementing the proposed composite controller to unmanned systems,simultaneous feedforward compensation and feedback suppression to the multi-source disturbances are conducted.Finally,the convergence of the MTN and the stability of the closed-loop system are established utilizing the Lyapunov theorem.Two exemplary applications of unmanned systems involving unmanned vehicle and rigid spacecraft are presented to validate the effectiveness of the proposed approach.
文摘The ancient tacit knowledge behind the logic system permeated the culture and promoted numerous impactful inventions throughout the history. Traditional Chinese medicine with its effectiveness should also have stemmed out from such logic system. This article aims to rearticulate the underlying lucid multi-dimensional logic system, which faded in obscurity only because of time-out loss of the mid-right concept. Retracing this past tacit but important concept could uncover a multi-dimensional system over a point relating to all matters while capturing the central core of the matter. The seemingly unmanageable multidimensional logic was strengthened by verification processes which affirmed its further extensions, and made up the language of the people, the concepts of yin-yang(阴阳), and the development of extensions of Ba Gua(八卦) derivatives, which furthered the interpretation of the space-time properties and Chinese medicine.
基金the financial support from the Guangxi Natural Science Foundation(grant no.2021GXNSFDA075012,2023GXNSFGA026002)National Natural Science Foundation of China(52104298,22075073,52362027,52462029)Fundamental Research Funds for the Central Universities(531107051077).
文摘Stress accumulation is a key factor leading to sodium storage performance deterioration for NiSe_(2)-based anodes.Therefore,inhibiting the concentrated local stress during the sodiataion/desodiation process is crucial for acquiring stable NiSe2-based materials for sodium-ion batteries(SIBs),Herein,a stress dissipation strategy driven by architecture engineering is proposed,which can achieve ultrafast and ultralong sodium storage properties.Different from the conventional sphere-like or rod-like architecture,the three-dimensional(3D)flower-like NiSe_(2)@C composite is delicately designed and assembled with onedimensional nanorods and carbon framework.More importantly,the fundamental mechanism of improved structure stability is unveiled by simulations and experimental results simultaneously.It demonstrates that this designed multidimensional flower-like architecture with dispersed nanorods can balance the structural mismatch,avoid concentrated local strain,and relax the internal stress,mainly induced by the unavoidable volume variation during the repeated conversion processes.Moreover,it can provide more Na^(+)-storage sites and multi-directional migration pathways,leading to a fast Na^(+)-migration channel with boosted reaction kinetic.As expected,it delivers superior rate performance(441 mA h g^(-1)at 5.0 A g^(-1))and long cycling stability(563 mA h g^(-1)at 1.0 A g^(-1)over 1000 cycles)for SIBs.This work provides useful insights for designing high-performance conversion-based anode materials for SIBs.
文摘This paper explores whole-process engineering consulting,including its application models in public buildings and elderly-friendly projects,such as service integration and whole lifecycle management.It also addresses the construction of multi-dimensional collaborative theoretical models,public space streamline organization,and other aspects,emphasizing the importance of multi-dimensional collaboration.Additionally,it highlights the role of talent cultivation and digital transformation in enhancing project efficiency.
文摘The multi-dimensional interactive teaching model significantly enhances the effectiveness of college English instruction by emphasizing dynamic engagement between teachers and students,as well as among students themselves.This paper explores practical strategies for implementing this model,focusing on four key aspects:deepening teachers’understanding of the model through continuous learning,innovating interactive methods such as questioning techniques and practical activities,leveraging modern technology to integrate resources and track learning progress,and establishing a communication platform that centers on student participation.By adopting these approaches,the model fosters a student-centered classroom environment,improves comprehensive English application skills,and optimizes overall teaching quality.
基金Sponsored by the Quality Engineering Project of Education Department of Anhui Province(2022jyxm671)Research Team Project of Anhui Xinhua University(kytd202202)+1 种基金Key Project of Scientific Research(Natural Science)of Higher Education Institutions in Anhui Province(2022AH051861)Teaching Reform Research and Practice Quality Engineering Project of Anhui Xinhua University(2024jy035).
文摘During the critical transformation period of landscape architecture major after the adjustment of disciplinary structure and the changes in market demand,private colleges and universities,as important places for cultivating local talents,have pain points such as uneven quality of teachers and students and weak innovation and practice.The practice system with“multi-dimensional Integration”integrates four dimensions:interdisciplinary integration,spatial and temporal intersection,historical inheritance,and behavioral activity,deepens the disciplinary connotation,and integrates the three elements of nature,humanity,and technology,aiming to provide a new path for private colleges and universities to cultivate application-oriented and compound talents with innovative capabilities.In terms of optimizing talent cultivation and adapting to industry changes,this system provides thinking and reference for landscape architecture major,helping the major reshape its core competitiveness and promoting educational innovation and industry development.
基金supported by the National Natural Science Foundation of China(72101025,72271049),the Interdisciplinary Research Project for Young Teachers of USTB(Fundamental Research Funds for the Central Universities,FRF-IDRY-24-024)the Hebei Natural Science Foundation(F2023501011)+1 种基金the Fundamental Research Funds for the Central Universities(FRF-TP-20-073A1)the R&D Program of Beijing Municipal Education Commission(KM202411232015).
文摘This paper proposes a reliability evaluation model for a multi-dimensional network system,which has potential to be applied to the internet of things or other practical networks.A multi-dimensional network system with one source element and multiple sink elements is considered first.Each element can con-nect with other elements within a stochastic connection ranges.The system is regarded as successful as long as the source ele-ment remains connected with all sink elements.An importance measure is proposed to evaluate the performance of non-source elements.Furthermore,to calculate the system reliability and the element importance measure,a multi-valued decision diagram based approach is structured and its complexity is analyzed.Finally,a numerical example about the signal transfer station system is illustrated to analyze the system reliability and the ele-ment importance measure.
基金supported by the National Key R&D Program of China(No.2021YFC2103600)the National Natural Science Foundation of China(Nos.21878156,21978131,22275085,and 22278224)+2 种基金the Natural Science Foundation of Jiangsu Province(Nos.BK20200089 and BK20200691)the Project of Priority Academic Program Development of Jiangsu Higher Education Institutions(PAPD)the State Key Laboratory of Materials-Oriented Chemical Engineering(No.KL21-08).
文摘Constructing multi-dimensional hydrogen bond(H-bond)regulated single-molecule systems with multiemission remains a challenge.Herein,we report the design of a new excited-state intramolecular proton transfer(ESIPT)featured chromophore(HBT-DPI)that shows flexible emission tunability via the multidimensional regulation of intra-and intermolecular H-bonds.The feature of switchable intramolecular Hbonds is induced via incorporating several hydrogen bond acceptors and donors into one single HBT-DPI molecule,allowing the“turn on/off”of ESIPT process by forming isomers with distinct intramolecular Hbonds configurations.In response to different external H-bonding environments,the obtained four types of crystal/cocrystals vary in the contents of isomers and the molecular packing modes,which are mainly guided by the intermolecular H-bonds,exhibiting non-emissive features or emissions ranging from green to orange.Utilizing the feature of intermolecular H-bond guided molecular packing,we demonstrate the utility of this fluorescent material for visualizing hydrophobic/hydrophilic areas on large-scale heterogeneous surfaces of modified poly(1,1-difluoroethylene)(PVDF)membranes and quantitatively estimating the surface hydrophobicity,providing a new approach for hydrophobicity/hydrophilicity monitoring and measurement.Overall,this study represents a new design strategy for constructing multi-dimensional hydrogen bond regulated ESIPT-based fluorescent materials that enable multiple emissions and unique applications.
基金Project supported by the National Natural Science Foundation of China(Nos.U2441202,12372043,and 12372022)the Fundamental Research Funds for Central Universities。
文摘To break the limitations of the multi-dimensional(M-D)vibration isolation(VI)platforms with the Stewart-Gough design,such as strongly coupling motions,excessive friction in connections,heavy weight,and limited workspace,this study processes a novel platform integrated by a stiffness-adjustable origami spring sub-structure and a parallel mechanism.The origami-based stiffness-adjustable spring realizes low-frequency VI,and the parallel mechanism symmetry design realizes motions decoupling.In the origami-based sub-leg,the parallel-stack-assembly(PSA)design mechanism with two Miura origami configurations is proposed to generate a symmetrical negative stiffness property.Paired with a linear positive stiffness spring,the origami-based sub-leg has wide-amplitude-high-static-low-dynamic stiffness(WA-HSLDS)characteristics in one direction.Then,with construction of the parallel mechanism connected with origami-based sub-legs,an M-D VI platform is achieved,whose motions in the vertical direction and yaw direction are decoupled with the motions in the other directions.Based on the dynamic model and incremental harmonic balance(IHB)with the arc-length continuation method,appropriate structural parameters in the parallel mechanism part are figured out,and the accurate transmissibility in different directions is defined,which gives the parametric influencing investigations for realization of low-frequency VI performances.Finally,experiments are conducted to validate the accuracy and feasibility of the theoretical methods,and to demonstrate the performance of M-D low-frequency isolation with load-carrying capacity of the proposed VI platform.The integration of the origami into the parallel mechanism results in a compact,efficient,and flexible platform with nonlinear adjustability,offering new possibilities for lightweight M-D VI,and developing the practical applications in high-precision platforms in ocean and aerospace environments.
基金The work described in this paper was fully supported by a grant from Hong Kong Metropolitan University(RIF/2021/05).
文摘Parkinson’s disease(PD)is a debilitating neurological disorder affecting over 10 million people worldwide.PD classification models using voice signals as input are common in the literature.It is believed that using deep learning algorithms further enhances performance;nevertheless,it is challenging due to the nature of small-scale and imbalanced PD datasets.This paper proposed a convolutional neural network-based deep support vector machine(CNN-DSVM)to automate the feature extraction process using CNN and extend the conventional SVM to a DSVM for better classification performance in small-scale PD datasets.A customized kernel function reduces the impact of biased classification towards the majority class(healthy candidates in our consideration).An improved generative adversarial network(IGAN)was designed to generate additional training data to enhance the model’s performance.For performance evaluation,the proposed algorithm achieves a sensitivity of 97.6%and a specificity of 97.3%.The performance comparison is evaluated from five perspectives,including comparisons with different data generation algorithms,feature extraction techniques,kernel functions,and existing works.Results reveal the effectiveness of the IGAN algorithm,which improves the sensitivity and specificity by 4.05%–4.72%and 4.96%–5.86%,respectively;and the effectiveness of the CNN-DSVM algorithm,which improves the sensitivity by 1.24%–57.4%and specificity by 1.04%–163%and reduces biased detection towards the majority class.The ablation experiments confirm the effectiveness of individual components.Two future research directions have also been suggested.
基金funding support from the National Science and Technology Council(NSTC),under Grant No.114-2410-H-011-026-MY3.
文摘Small datasets are often challenging due to their limited sample size.This research introduces a novel solution to these problems:average linkage virtual sample generation(ALVSG).ALVSG leverages the underlying data structure to create virtual samples,which can be used to augment the original dataset.The ALVSG process consists of two steps.First,an average-linkage clustering technique is applied to the dataset to create a dendrogram.The dendrogram represents the hierarchical structure of the dataset,with each merging operation regarded as a linkage.Next,the linkages are combined into an average-based dataset,which serves as a new representation of the dataset.The second step in the ALVSG process involves generating virtual samples using the average-based dataset.The research project generates a set of 100 virtual samples by uniformly distributing them within the provided boundary.These virtual samples are then added to the original dataset,creating a more extensive dataset with improved generalization performance.The efficacy of the ALVSG approach is validated through resampling experiments and t-tests conducted on two small real-world datasets.The experiments are conducted on three forecasting models:the support vector machine for regression(SVR),the deep learning model(DL),and XGBoost.The results show that the ALVSG approach outperforms the baseline methods in terms of mean square error(MSE),root mean square error(RMSE),and mean absolute error(MAE).
基金supported by the research fund of Hanyang University(HY-202500000001616).
文摘Accurate purchase prediction in e-commerce critically depends on the quality of behavioral features.This paper proposes a layered and interpretable feature engineering framework that organizes user signals into three layers:Basic,Conversion&Stability(efficiency and volatility across actions),and Advanced Interactions&Activity(crossbehavior synergies and intensity).Using real Taobao(Alibaba’s primary e-commerce platform)logs(57,976 records for 10,203 users;25 November–03 December 2017),we conducted a hierarchical,layer-wise evaluation that holds data splits and hyperparameters fixed while varying only the feature set to quantify each layer’s marginal contribution.Across logistic regression(LR),decision tree,random forest,XGBoost,and CatBoost models with stratified 5-fold cross-validation,the performance improvedmonotonically fromBasic to Conversion&Stability to Advanced features.With LR,F1 increased from 0.613(Basic)to 0.962(Advanced);boosted models achieved high discrimination(0.995 AUC Score)and an F1 score up to 0.983.Calibration and precision–recall analyses indicated strong ranking quality and acknowledged potential dataset and period biases given the short(9-day)window.By making feature contributions measurable and reproducible,the framework complements model-centric advances and offers a transparent blueprint for production-grade behavioralmodeling.The code and processed artifacts are publicly available,and future work will extend the validation to longer,seasonal datasets and hybrid approaches that combine automated feature learning with domain-driven design.
文摘The study constructed a multi-dimensional feedback mode integrating teacher feedback, peer feedback and network feedback, and applied it in the teaching of College English Writing. After 16 weeks of teaching, the students in the multi-dimen-sional feedback class had significantly better overall writing scores than those in the teacher-feedback class. In terms of individual scores, multi-dimensional feedback played a better role in improving vocabulary and grammar than the class using teacher feed-back. However, there were no significant differences in the responses of writing tasks, coherence and cohesion. The study showed that most students were satisfied with the mode, believing that it was helpful to relieve writing anxiety, stimulate writing interest and improve their writing level.
基金supported by the National Natural Science Foundation of China (Nos. 31771467, 31571360 and 31371291)
文摘Maize （Zea mays） is the most widely grown grain crop in the world, playing important roles in agriculture and industry. However, the functions of maize genes remain largely unknown. High-quality genome- wide transcriptome datasets provide important biological knowledge which has been widely and suc- cessfully used in plants not only by measuring gene expression levels but also by enabling co-expression analysis for predicting gene functions and modules related to agronomic traits. Recently, thousands of maize transcriptomic data are available across different inbred lines, development stages, tissues, and treatments, or even across different tissue sections and cell lines. Here, we integrated 701 transcriptomic and 108 epigenomic data and studied the different conditional networks with multi-dimensional omics levels. We constructed a searchable, integrative, one-stop online platform, the maize conditional co- expression network （MCENet） platform. MCENet provides 10 global/conditional co-expression net- works, 5 network accessional analysis toolkits （i.e., Network Search, Network Remodel, Module Finder, Network Comparison, and Dynamic Expression View） and multiple network functional support toolkits （e.g., motif and module enrichment analysis）. We hope that our database might help plant research communities to identify maize functional genes or modules that regulate important agronomic traits.
基金This work was financially supported by the National Natural Science Foundation of China(Nos.51972039,51803018,and 51661145025)LiaoNing Revitalization Talents Program(No.XLYC1902122).
文摘Recently,multilevel structural carbon aerogels are deemed as attractive candidates for microwave absorbing materials.Nevertheless,excessive stack and agglomeration for low-dimension carbon nanomaterials inducing impedance mismatch are significant challenges.Herein,the delicate“3D helix-2D sheet-1D fiber-0D dot”hierarchical aerogels have been successfully synthesized,for the first time,by sequential processes of hydrothermal self-assembly and in-situ chemical vapor deposition method.Particularly,the graphene sheets are uniformly intercalated by 3D helical carbon nanocoils,which give a feasible solution to the mentioned problem and endows the as-obtained aerogel with abundant porous structures and better dielectric properties.Moreover,by adjusting the content of 0D core-shell structured particles and the parameters for growth of the 1D carbon nanofibers,tunable electromagnetic properties and excellent impedance matching are achieved,which plays a vital role in the microwave absorption performance.As expected,the optimized aerogels harvest excellent performance,including broad effective bandwidth and strong reflection loss at low filling ratio and thin thickness.This work gives valuable guidance and inspiration for the design of hierarchical materials comprised of dimensional gradient structures,which holds great application potential for electromagnetic wave attenuation.
基金National Natural Science Foundation of China Under Award Number 50878184National High Technology Research and Development Program (863 Program) of China Under Grant No. 2006AA04Z437Graduate Starting Seed Fund of Northwestern Polytechnical University Under the Grant No. Z2012059
文摘Fragility analysis for highway bridges has become increasingly important in the risk assessment of highway transportation networks exposed to seismic hazards. This study introduces a methodology to calculate fragility that considers multi-dimensional performance limit state parameters and makes a first attempt to develop fragility curves for a multi-span continuous (MSC) concrete girder bridge considering two performance limit state parameters: column ductility and transverse deformation in the abutments. The main purpose of this paper is to show that the performance limit states, which are compared with the seismic response parameters in the calculation of fragility, should be properly modeled as randomly interdependent variables instead of deterministic quantities. The sensitivity of fragility curves is also investigated when the dependency between the limit states is different. The results indicate that the proposed method can be used to describe the vulnerable behavior of bridges which are sensitive to multiple response parameters and that the fragility information generated by this method will be more reliable and likely to be implemented into transportation network loss estimation.
基金supported by the National Natural Science Foundation of China(Nos.51805376 and U1709208)the Zhejiang Provincial Natural Science Foundation of China(Nos.LY20E050028 and LD21E050001)。
文摘Because the hydraulic directional valve usually works in a bad working environment and is disturbed by multi-factor noise,the traditional single sensor monitoring technology is difficult to use for an accurate diagnosis of it.Therefore,a fault diagnosis method based on multi-sensor information fusion is proposed in this paper to reduce the inaccuracy and uncertainty of traditional single sensor information diagnosis technology and to realize accurate monitoring for the location or diagnosis of early faults in such valves in noisy environments.Firstly,the statistical features of signals collected by the multi-sensor are extracted and the depth features are obtained by a convolutional neural network(CNN)to form a complete and stable multi-dimensional feature set.Secondly,to obtain a weighted multi-dimensional feature set,the multi-dimensional feature sets of similar sensors are combined,and the entropy weight method is used to weight these features to reduce the interference of insensitive features.Finally,the attention mechanism is introduced to improve the dual-channel CNN,which is used to adaptively fuse the weighted multi-dimensional feature sets of heterogeneous sensors,to flexibly select heterogeneous sensor information so as to achieve an accurate diagnosis.Experimental results show that the weighted multi-dimensional feature set obtained by the proposed method has a high fault-representation ability and low information redundancy.It can diagnose simultaneously internal wear faults of the hydraulic directional valve and electromagnetic faults of actuators that are difficult to diagnose by traditional methods.This proposed method can achieve high fault-diagnosis accuracy under severe working conditions.
