Knowledge-Driven Possibilistic Clustering with Automatic Cluster Elimination

Traditional Fuzzy C-Means(FCM)and Possibilistic C-Means(PCM)clustering algorithms are data-driven,and their objective function minimization process is based on the available numeric data.Recently,knowledge hints have been introduced to formknowledge-driven clustering algorithms,which reveal a data structure that considers not only the relationships between data but also the compatibility with knowledge hints.However,these algorithms cannot produce the optimal number of clusters by the clustering algorithm itself;they require the assistance of evaluation indices.Moreover,knowledge hints are usually used as part of the data structure(directly replacing some clustering centers),which severely limits the flexibility of the algorithm and can lead to knowledgemisguidance.To solve this problem,this study designs a newknowledge-driven clustering algorithmcalled the PCM clusteringwith High-density Points(HP-PCM),in which domain knowledge is represented in the form of so-called high-density points.First,a newdatadensitycalculation function is proposed.The Density Knowledge Points Extraction(DKPE)method is established to filter out high-density points from the dataset to form knowledge hints.Then,these hints are incorporated into the PCM objective function so that the clustering algorithm is guided by high-density points to discover the natural data structure.Finally,the initial number of clusters is set to be greater than the true one based on the number of knowledge hints.Then,the HP-PCM algorithm automatically determines the final number of clusters during the clustering process by considering the cluster elimination mechanism.Through experimental studies,including some comparative analyses,the results highlight the effectiveness of the proposed algorithm,such as the increased success rate in clustering,the ability to determine the optimal cluster number,and the faster convergence speed.

Recent advances in protein conformation sampling by combining machine learning with molecular simulation

The rapid advancement and broad application of machine learning(ML)have driven a groundbreaking revolution in computational biology.One of the most cutting-edge and important applications of ML is its integration with molecular simulations to improve the sampling efficiency of the vast conformational space of large biomolecules.This review focuses on recent studies that utilize ML-based techniques in the exploration of protein conformational landscape.We first highlight the recent development of ML-aided enhanced sampling methods,including heuristic algorithms and neural networks that are designed to refine the selection of reaction coordinates for the construction of bias potential,or facilitate the exploration of the unsampled region of the energy landscape.Further,we review the development of autoencoder based methods that combine molecular simulations and deep learning to expand the search for protein conformations.Lastly,we discuss the cutting-edge methodologies for the one-shot generation of protein conformations with precise Boltzmann weights.Collectively,this review demonstrates the promising potential of machine learning in revolutionizing our insight into the complex conformational ensembles of proteins.

Upper and Lower Bounds of the α-Universal Triple I Method for Unified Interval Implications

Theα-universal triple I(α-UTI)method is a recognized scheme in the field of fuzzy reasoning,whichwas proposed by our research group previously.The robustness of fuzzy reasoning determines the quality of reasoning algorithms to a large extent,which is quantified by calculating the disparity between the output of fuzzy reasoning with interference and the output without interference.Therefore,in this study,the interval robustness(embodied as the interval stability)of theα-UTI method is explored in the interval-valued fuzzy environment.To begin with,the stability of theα-UTI method is explored for the case of an individual rule,and the upper and lower bounds of its results are estimated,using four kinds of unified interval implications(including the R-interval implication,the S-interval implication,the QL-interval implication and the interval t-norm implication).Through analysis,it is found that theα-UTI method exhibits good interval stability for an individual rule.Moreover,the stability of theα-UTI method is revealed in the case of multiple rules,and the upper and lower bounds of its outcomes are estimated.The results show that theα-UTI method is stable for multiple rules when four kinds of unified interval implications are used,respectively.Lastly,theα-UTI reasoning chain method is presented,which contains a chain structure with multiple layers.The corresponding solutions and their interval perturbations are investigated.It is found that theα-UTI reasoning chain method is stable in the case of chain reasoning.Two application examples in affective computing are given to verify the stability of theα-UTImethod.In summary,through theoretical proof and example verification,it is found that theα-UTImethod has good interval robustness with four kinds of unified interval implications aiming at the situations of an individual rule,multi-rule and reasoning chain.

基于科教融合的综合化学实验教学设计:银纳米材料的制备及催化性能

开展了研究型综合化学实验教学,将基础教学与科研训练有机结合,以和谐共进的方式加强人才培养效果,有助于推进教育教学改革进程。本实验利用简单的无机化学反应制备银纳米粒子,通过添加不同溴/银比例可调控银纳米胶体溶液颜色,将表面等离子体共振特性引入实验教学,提高了实验的趣味性。实验利用经典的对硝基苯酚催化反应模型验证银纳米粒子的化学活性,结果表明银纳米粒子催化效果良好,可近似为一级反应动力学过程。利用实验室常见的玻璃仪器搭建简易污染物降解反应器,使用自制载银滤纸实现了10次以上的循环降解,降解率可达90%。

Coordinated Rotor-Side Control Strategy for Doubly-FedWind Turbine under Symmetrical and Asymmetrical Grid Faults

In order to solve the problems of rotor overvoltage,overcurrent and DC side voltage rise caused by grid voltage drops,a coordinated control strategy based on symmetrical and asymmetrical low voltage ride through of rotor side converter of the doubly-fed generator is proposed.When the power grid voltage drops symmetrically,the generator approximate equation under steady-state conditions is no longer applicable.Considering the dynamic process of stator current excitation,according to the change of stator flux and the depth of voltage drop,the system can dynamically provide reactive power support for parallel nodes and suppress the rise of DC side voltage and rotor over-current.When the grid voltage drops asymmetrically,the positive and negative sequence components are separated in the rotating coordinate system.The doubly fed generator model is established to suppress the rotor positive sequence current and negative sequence current respectively.At the same time,the output voltage limit of the converter is discussed,and the reference value is adjusted within the allowable output voltage range.In order to adapt to the occurrence of different types of power grid faults and complex operating conditions,a fast switching module of fault type detection and rotor control mode is designed to detect the type of power grid faults and voltage drop depth in real time and switch the rotor side control mode dynamically.Finally,the simulation model of the doubly fed wind turbine is constructed in Matlab/Simulink.The simulation results verify that the proposed control strategy can improve the low-voltage ride through performance of the system when dealing with the symmetrical and asymmetric voltage drop of the power grid and identify the power grid fault type and provide the correct control strategy.

Differently implicational α-universal triple I restriction method of (1, 2, 2) type

From the viewpoints of both fuzzy system and fuzzy reasoning, a new fuzzy reasoning method which contains the α- triple I restriction method as its particular case is proposed. The previous α-triple I restriction principles are improved, and then the optimal restriction solutions of this new method are achieved, especially for seven familiar implications. As its special case, the corresponding results of α-triple I restriction method are obtained and improved. Lastly, it is found by examples that this new method is more reasonable than the α-triple I restriction method.

Structural and dynamical mechanisms of a naturally occurring variant of the human prion protein in preventing prion conversion

Prion diseases are associated with the misfolding of the normal helical cellular form of prion protein (PrPC) into the β-sheet-rich scrapie form (PrPSc) and the subsequent aggregation of PrPSc into amyloid fibrils. Recent studies demonstrated that a naturally occurring variant V127 of human PrPC is intrinsically resistant to prion conversion and aggregation, and can completely prevent prion diseases. However, the underlying molecular mechanism remains elusive. Herein we perform multiple microsecond molecular dynamics simulations on both wildtype (WT) and V127 variant of human PrPC to understand at atomic level the protective effect of V127 variant. Our simulations show that G127V mutation not only increases the rigidity of the S2–H2 loop between strand-2 (S2) and helix-2 (H2), but also allosterically enhances the stability of the H2 C-terminal region. Interestingly, previous studies reported that animals with rigid S2–H2 loop usually do not develop prion diseases, and the increase in H2 C-terminal stability can prevent misfolding and oligomerization of prion protein. The allosteric paths from G/V127 to H2 C-terminal region are identified using dynamical network analyses. Moreover, community network analyses illustrate that G127V mutation enhances the global correlations and intra-molecular interactions of PrP, thus stabilizing the overall PrPC structure and inhibiting its conversion into PrPSc. This study provides mechanistic understanding of human V127 variant in preventing prion conversion which may be helpful for the rational design of potent anti-prion compounds.

<i>β</i>-cyclodextrin Covalently Functionalized Single-Walled Carbon Nanotubes: Synthesis, Characterization and a Sensitive Biosensor Platform

In this study, we presented the preparation of β-cyclodextrin (β-CD) covalently functionalized single-walled carbon nanotubes (SWCNTs) and its application in modifying the solid glass carbon electrode (GCE). Cyclic voltammetry (CV) method was employed to evaluate the performance of the modified GCE. Solubility experiment indicated the conjugation of SWCNTs and β-CD, SWCNTs-β-CD with 8 wt% β-CD content could be well dispersed in water. High-resolution transmission electron microscopy (HRTEM) demonstrated that the aggregated SWCNTs bundle were effectively exfoliated to small bundle, even individual tube. The β-CD component was grafted on the side walls as well as tips of SWCNTs, and the grafted β-CD component was not uniformly coated on the surface of SWCNTs. The CV measurements indicated the performance of the GCE modified by SWCNTs-β-CD was better than that of the GCE modified by the hybrid of SWCNTs/β-CD, where ascorbic acid (AA) and uric acid (UA) were selected as a prelimiltary substrate to evaluate it. The enhanced performance of the modified GCE should be ascribed to the integration of the excellent electrocatalytic property of SWCNTs with the inclusion ability of β-CD to analyte molecule.

Defect-designed Mo-doped BiVO_(4)photoanode for efficient photoelectrochemical degradation of phenol

The monoclinic scheelite BiVO_(4)has impressive properties such as a narrow energy band gap,exceptional stability,and extended absorption in visible light,making it a suitable photoanode.Nevertheless,the BiVO_(4)material encounters challenges such as the high recombination rate of photogenerated electronhole pairs and poor photoelectron conductivity,which limits photocatalytic activity.To address this problem,we developed Mo-doped BiVO_(4)films on FTO substrates for photoelectrocatalytic degradation of phenol.When exposed to visible light,the Mo-BiVO_(4)film attained a 70%degradation of phenol in 120 min with a 1.2 V vs.Ag/AgCl bias—a 3.7 times improvement from pristine BiVO_(4).Mo-doping facilitates better migration and separation of electron-hole pairs and increases the concentration of photogenerated carriers,leading to an upward shift of the valence band potential direction,and an improvement in oxidation capacity.Furthermore,density-functional theory(DFT)calculations were used to explain how Modoping with BiVO_(4)improves the adsorption energy to phenol degradation intermediates,emphasizing its effectiveness in promoting phenol degradation.Therefore,with the inclusion of DFT calculations,this work provides a more comprehensive understanding of the mechanism underlying the enhancement of photocatalytic activity by Mo-doped BiVO_(4),which is crucial information for the further development of effective and efficient photoanodes.

State-of-the-Art Development of Complex Systems and Their Simulation Methods

The research on complex systems is different from that on general systems because the former must consider self-organization,emergence,uncertainty,predetermination,and evolution.As an important method to transform the world,a simulation is one of the most important skills to discover complex systems.In this study,we provide a survey on complex systems and their simulation methods.Initially,the development history of complex system research is summarized from two main lines.Then,the eight common characteristics of the most complex systems are presented.Furthermore,the simulation methods of complex systems are introduced in detail from four aspects,namely,meta-synthesis methods,complex networks,intelligent technologies,and other methods.From the overall point of view,intelligent technologies are the driving force,and complex networks are the advanced structure.Meta-synthesis methods are the integration strategy,and other methods are the supplements.In addition,we show three complex system simulation examples:digital reactor simulation,simulation of a logistics system in the industrial site,and crowd evacuation simulation.The examples show that a simulation is a useful means and an important method in complex system research.Finally,the future development prospects for complex systems and their simulation methods are suggested.

Electro-catalytic activity of CeO_(x) modified graphite felt for carbamazepine degradation via E-peroxone process

E-peroxone(EP)was one of the most attractive AOPs for removing refractory organic compounds from water,but the high energy consumption for in situ generating H_(2)O_(2) and its low reaction efficiency for activating O_(3) under acidic conditions made the obstacles for its practical application.In this study,cerium oxide was loaded on the surface of graphite felt(GF)by the hydrothermal method to construct the efficient electrode(CeO_(x)/GF)for mineralizing carbamazepine(CBZ)via EP process.CeO_(x)/GF was an efficient cathode,which led to 69.4%TOC removal in CeO_(x)/GF-EP process with current intensity of 10 mA in 60 min.Moreover,CeO_(x)/GF had the flexible application in the pH range from 5.0 to 9.0,TOC removal had no obvious decline with decrease of pH.Comparative characterizations showed that CeO_(x)could enhance surface hydrophilicity and reduce the charge-transfer resistance of GF.About 5.4 mg/L H_(2)O_(2) generated in CeO_(x)/GF-EP process,which was 2.1 times as that in GF-EP process.The greater ozone utility was also found in CeO_(x)/GF-EP process.More O_(3) was activated into hydroxyl radicals,which accounted for the mineralization of CBZ.An interfacial electron transfer process was revealed,which involved the function of oxygen vacancies and Ce^(3+)/Ce^(4+)redox cycle.CeO_(x)/GF had the good recycling property in fifth times'use.

Statistical inference for multivariate longitudinal data with irregular auto-correlated error process

Multivariate longitudinal data arise frequently in a variety of applications,where multiple outcomes are measured repeatedly from the same subject.In this paper,we first propose a two-stage weighted least square estimation procedure for the regression coefficients when the random error follows an irregular autoregressive(AR)process,and establish asymptotic normality properties for the resulting estimators.We then apply the smoothly clipped absolute deviation(SCAD)variable selection approach to determine the order of the AR error process.We further propose a test statistic to check whether multiple responses are correlated at the same observation time,and derive the asymptotic distribution of the proposed test statistic.Several simulated examples and real data analysis are presented to illustrate the finite-sample performance of the proposed method.