Multi-source Data-driven Identification of Urban Functional Areas:A Case of Shenyang,China

Urban functional area(UFA)is a core scientific issue affecting urban sustainability.The current knowledge gap is mainly reflected in the lack of multi-scale quantitative interpretation methods from the perspective of human-land interaction.In this paper,based on multi-source big data include 250 m×250 m resolution cell phone data,1.81×105 Points of Interest(POI)data and administrative boundary data,we built a UFA identification method and demonstrated empirically in Shenyang City,China.We argue that the method we built can effectively identify multi-scale multi-type UFAs based on human activity and further reveal the spatial correlation between urban facilities and human activity.The empirical study suggests that the employment functional zones in Shenyang City are more concentrated in central cities than other single functional zones.There are more mix functional areas in the central city areas,while the planned industrial new cities need to develop comprehensive functions in Shenyang.UFAs have scale effects and human-land interaction patterns.We suggest that city decision makers should apply multi-sources big data to measure urban functional service in a more refined manner from a supply-demand perspective.

Iterative Identification of Robot Dynamic Parameters Based on Logistic Function

The dynamic parameter identification of the robot is the basis for the design of the controller based on the dynamic model.Currently,the primary method for solving angular velocity and angular acceleration is to filter and smooth the position sequence and then form a differential signal.However,if the noise and the original signal overlap in the frequency domain,filtering the noise will also filter out the valuable information in the frequency band.This paper proposes an excitation trajectory based on Logistic function,which fully uses the information in the original signal and can accurately solve the angular velocity and angular acceleration without filtering and smoothing the position sequence.The joint angle of the excitation trajectory is mapped to the joint angular velocity and angular acceleration one by one so that the joint angular velocity and joint angular acceleration can be obtained directly according to the position.The genetic algorithm is used to optimize the excitation trajectory parameters to minimize the observation matrix’s condition number and further improve the identification accuracy.By using the strategy of iterative identification,the dynamic parameters identified in each iteration are substituted into the robot controller according to the previous position sequence until the tracking trajectory approaches the desired trajectory,and the actual joint angular velocity and angular acceleration converge to the expected value.The simulation results show that using the step-by-step strategy,the joint angular velocity and joint angular acceleration of the tracking trajectory quickly converge to the expected value,and the identification error of inertia parameters is less than 0.01 in three iterations.With the increase of the number of iterations,the identification error of inertial parameters can be further reduced.

FUZZY IDENTIFICATION METHOD BASED ON A NEW OBJECTIVE FUNCTION

A method of fuzzy identification based on a new objective function is proposed. The method could deal with the issue that input variables of a system have an effect on the input space while output variables of the system do not exert an influence on the input space in the proposed objective functions of fuzzy clustering. The method could simultaneously solve the problems about structure identification and parameter estimation; thus it makes the fuzzy model become optimal. Simulation example demonstrates that the method could identify non linear systems and obviously improve modeling accuracy.

Adaptive proportional integral differential control based on radial basis function neural network identification of a two-degree-of-freedom closed-chain robot

A closed-chain robot has several advantages over an open-chain robot, such as high mechanical rigidity, high payload, high precision. Accurate trajectory control of a robot is essential in practical-use. This paper presents an adaptive proportional integral differential （PID） control algorithm based on radial basis function （RBF） neural network for trajectory tracking of a two-degree-of-freedom （2-DOF） closed-chain robot. In this scheme, an RBF neural network is used to approximate the unknown nonlinear dynamics of the robot, at the same time, the PID parameters can be adjusted online and the high precision can be obtained. Simulation results show that the control algorithm accurately tracks a 2-DOF closed-chain robot trajectories. The results also indicate that the system robustness and tracking performance are superior to the classic PID method.

Identification of Three Interactions to Determine the Conformation Change and to Maintain the Function of Kir2.1 Channel Protein

We find that a conserved mutation residue Glu to residue Asp （E303D）, which both have the same polar and charged properties, makes Kit2.1 protein lose its function. To understand the mechanism, we identify three interactions which control the conformation change and maintain the function of the Kit2.1 protein by combining homology modeling and molecular dynamics with targeted molecular dynamics. We find that the E303D mutation weakens these interactions and results in the loss of the related function. Our data indicate that not only the amino residues but also the interactions determine the function of proteins.

Expression and functional identification of the hypothetical adhesin P32 from Mycoplasma genitalium

Mycoplasma genitalium is the main causative agent for non-gonococcal and non-chlamydial urethritis. P32 is the putative surface-exposed membrane protein of M. genitalium and it has substaintial identity in amino acid sequence with adhesin protein P30 from M. pneumoniae. Since M. pneumoniae mutants lacking P30 protein is defective in cytadherence, P32 protein has been proposed to be an essential adhesin implicated in the adherence of M. genitaliurn to host cells. The prokaryotic expression vector pET-30 （ ＋ ）/p32 was constructed in the present study, and the recombinant protein was expressed in E. coli and purified under denaturing condition. As demonstrated by the immuno- blotting analysis, the recombinant protein could react with rabbit antisera against M. genitalium, and adherence inhibition assays were performed with antisera against this recombinant protein. It was demonstrated that P32 protein apperared to be an adhesion protein of M. genitalium, thus providing the experimental basis for better understanding of the pathogenesis of M. genitalium infection and for the development of the related vaccines against the infection.

Protein profile of human hepatocarcinoma cell line SMMC-7721:Identification and functional analysis

AIM： TO investigate the protein profile of human hepatocarcinoma cell line SMMC-7721, to analyze the specific functions of abundant expressed proteins in the processes of hepatocarcinoma genesis, growth and metastasis, to identify the hepatocarcinoma-specific biomarkers for the early prediction in diagnosis, and to explore the new drug targets for liver cancer therapy. METHODS： Total proteins from human hepatocarcinoma cell line SMMC-7721 were separated by two-dimensional electrophoresis （2DE）. The silver-stained gel was analyzed by 2DE software Image Master 2D Elite. Interesting protein spots were identified by peptide mass fingerprinting based on matrix-assisted laser desorption/ ionization time-of-flight mass spectrometry （MALDI-TOF-MS） and database searching. RESULTS： We obtained protein profile of human hepatocarcinoma cell line SMMC-7721. Among the twenty-one successfully identified proteins, mitofilin, endoplasmic reticulum protein ERp29, ubiquinol-cytochrome C reductase complex core protein I, peroxisomal enoyl CoA hydratase, peroxiredoxin-4 and probable 3-oxoacid CoA transferase 1 precursor were the six novel proteins identified in human hepatocarcinoma cells or tissues. Specific functions of the identified heat-shock proteins were analyzed in detail, and the results suggested that these proteins might promote tumorigenesis via inhibiting cell death induced by several cancer-related stresses or via inhibiting apoptosis at multiple points in the apoptotic signal pathway. Other identified chaperones and cancer-related proteins were also analyzed.CONCLUSION： Based on the protein profile of SMMC-7721 cells, functional analysis suggests that the identified chaperones and cancer-related proteins have their own pathways to contribute to the tumorigenesis, tumor growth and metastasis of liver cancer. Furthermore, proteomic analysis is indicated to be feasible in the cancer study.

PARAMETER IDENTIFICATION OF LINEAR SYSTEMS VIA GENERAL HYBRID ORTHOGONAL FUNCTIONS

In this paper, the key nature of general hybrid urthogonal functions(GHOF)is given.With it,a more concise system model fur identification is ob-tained.Usins this model,the modified recursive algorithni of parameter estimation issiniple, rapid and coiivenient fur practical use,and the store space of computer willbe reduced considerably.

A SYSTEM IDENTIFICATION METHOD USING VECTOR SPACE BASE FUNCTION

This paper presents a new system identification approach using vector space base functions, and proposes two network structures based on Gamma sequence and Laguerre sequence. After analyzing and comparing these structures in detail, some simulation results to demonstrate the conclusions are given.

The Function Identification of the Candidate Disease-Resistance Gene in Cabbage

In order to understand the function of TuR2, a candidate disease-resistance gene was isolated from cabbage, we transformed it into mustard （Brassicajuncea L. Linshicaoyaozi） which was susceptible to TuMV through Agrobacterium tumefacine-mediated method. Transgenic plants were detected by Southern blotting and Northern blotting. Our results confirmed that the TuR2 gene had been integrated into the mustard genome, and it showed different expression levels among primary transplants （T0）. The primary transplants （T0） and the first progenies of transgenic plants （T1） were inoculated with TuMV in a greenhouse. The transgenic plants had high TuMV-resistance, whereas the serious virus disease symptom was observed in CK （no transformation plants）. The TuR2 gene in the first progenies of transgenic plants （T1） showed dominant monogenic inheritance. Compared with CK, the progenies containing TuR2 gene had stronger resistance to TuMV. The TuR2 gene which was isolated from cabbage had the function of TuMV-resistance.

Improved Particle Swarm Optimization for Parameter Identification of Permanent Magnet Synchronous Motor

In the process of identifying parameters for a permanent magnet synchronous motor,the particle swarm optimization method is prone to being stuck in local optima in the later stages of iteration,resulting in low parameter accuracy.This work proposes a fuzzy particle swarm optimization approach based on the transformation function and the filled function.This approach addresses the topic of particle swarmoptimization in parameter identification from two perspectives.Firstly,the algorithm uses a transformation function to change the form of the fitness function without changing the position of the extreme point of the fitness function,making the extreme point of the fitness function more prominent and improving the algorithm’s search ability while reducing the algorithm’s computational burden.Secondly,on the basis of themulti-loop fuzzy control systembased onmultiplemembership functions,it is merged with the filled function to improve the algorithm’s capacity to skip out of the local optimal solution.This approach can be used to identify the parameters of permanent magnet synchronous motors by sampling only the stator current,voltage,and speed data.The simulation results show that the method can effectively identify the electrical parameters of a permanent magnet synchronous motor,and it has superior global convergence performance and robustness.

PARAMETER IDENTIFICATION PROBLEM OF THE FRACTAL INTERPOLATION FUNCTIONS

Parameter identification problem is one of essential problem in order to model effectively experimental data by fractal interpolation function.In this paper,we first present an example to explain a relationship between iteration procedure and fractal function.Then we discuss conditions that vertical scaling factors must obey in one typical case.

Identification of reservoir types in deep carbonates based on mixedkernel machine learning using geophysical logging data

Identification of reservoir types in deep carbonates has always been a great challenge due to complex logging responses caused by the heterogeneous scale and distribution of storage spaces.Traditional cross-plot analysis and empirical formula methods for identifying reservoir types using geophysical logging data have high uncertainty and low efficiency,which cannot accurately reflect the nonlinear relationship between reservoir types and logging data.Recently,the kernel Fisher discriminant analysis(KFD),a kernel-based machine learning technique,attracts attention in many fields because of its strong nonlinear processing ability.However,the overall performance of KFD model may be limited as a single kernel function cannot simultaneously extrapolate and interpolate well,especially for highly complex data cases.To address this issue,in this study,a mixed kernel Fisher discriminant analysis(MKFD)model was established and applied to identify reservoir types of the deep Sinian carbonates in central Sichuan Basin,China.The MKFD model was trained and tested with 453 datasets from 7 coring wells,utilizing GR,CAL,DEN,AC,CNL and RT logs as input variables.The particle swarm optimization(PSO)was adopted for hyper-parameter optimization of MKFD model.To evaluate the model performance,prediction results of MKFD were compared with those of basic-kernel based KFD,RF and SVM models.Subsequently,the built MKFD model was applied in a blind well test,and a variable importance analysis was conducted.The comparison and blind test results demonstrated that MKFD outperformed traditional KFD,RF and SVM in the identification of reservoir types,which provided higher accuracy and stronger generalization.The MKFD can therefore be a reliable method for identifying reservoir types of deep carbonates.

Contamination Identification of Lentinula Edodes Logs Based on Improved YOLOv5s

In order to improve the accuracy and efficiency of Lentinula edodes logs contamination identification,an improved YOLOv5s contamination identification model for Lentinula edodes logs(YOLOv5s-CGGS)is proposed in this paper.Firstly,a CA(coordinate attention)mechanism is introduced in the feature extraction network of YOLOv5s to improve the identifiability of Lentinula edodes logs contamination and the accuracy of target localiza-tion.Then,the CIoU(Complete-IOU)loss function is replaced by an SIoU(SCYLLA-IoU)loss function to improve the model’s convergence speed and inference accuracy.Finally,the GSConv and GhostConv modules are used to improve and optimize the feature fusion network to improve identification efficiency.The method in this paper achieves values of 97.83%,97.20%,and 98.20%in precision,recall,and mAP@0.5,which are 2.33%,3.0%,and 1.5%better than YOLOv5s,respectively.mAP@0.5 is better than YOLOv4,Ghost-YOLOv4,and Mobilenetv3-YOLOv4(improved by 4.61%,5.16%,and 6.04%,respectively),and the FPS increased by two to three times.

Source Quantitative Identification by Reference-Based Cubic Blind Deconvolution Algorithm

The semi-blind deconvolution algorithm improves the separation accuracy by introducing reference information.However,the separation performance depends largely on the construction of reference signals.To improve the robustness of the semi-blind deconvolution algorithm to the reference signals and the convergence speed,the reference-based cubic blind deconvolution algorithm is proposed in this paper.The proposed algorithm can be combined with the contribution evaluation to provide trustworthy guidance for suppressing satellite micro-vibration.The normalized reference-based cubic contrast function is proposed and the validity of the new contrast function is theoretically proved.By deriving the optimal step size of gradient iteration under the new contrast function,we propose an efficient adaptive step optimization method.Furthermore,the contribution evaluation method based on vector projection is presented to implement the source contribution evaluation.Numerical simulation analysis is carried out to validate the availability and superiority of this method.Further tests given by the simulated satellite experiment and satellite ground experiment also confirm the effectiveness.The signals of control moment gyroscope and flywheel were extracted,respectively,and the contribution evaluation of vibration sources to the sensitive load area was realized.This research proposes a more accurate and robust algorithm for the source separation and provides an effective tool for the quantitative identification of the mechanical vibration sources.

Time of Liver Function Abnormal Identification on Prediction of the Risk of Anti-tuberculosis-induced Liver Injury

Background and Aims:Anti-tuberculosis(anti-TB)druginduced liver injury(AT-DILI)is the most common side effect in patients who received anti-TB therapy.AT-DILI management includes monitoring liver function until symptoms arise in patients without high-risk factors for liver damage.The present study aimed to investigate the effect of liver function test(LFT)abnormal identification on the risk of DILI,including liver failure and anti-TB drug resistance in patients without high-risk factors.Methods:A total of 399 patients without high-risk factors for liver damage at baseline and who experienced LFT abnormal during the 6 months of first-line anti-TB treatment were enrolled.The Roussel Uclaf Causal Relationship Assessment Method(RUCAM,2016)was applied in suspected DILI.The correlations between the time of LFT abnormal identification and DILI,liver failure,and anti-TB drug resistance were analyzed by smooth curve fitting and multivariable logistic regression models.Results:Among all study patients,131 met the criteria for DILI with a mean RUCAM causality score of 8.86±0.63.26/131 and 105/131 were in the probable grading and highly probable grading,respectively.The time of abnormal LFT identification was an independent predictor of DILI,liver failure,and anti-TB drug resistance in the crude model and after adjusting for other risk patient factors.The time of abnormal LFT identification was positively correlated with DILI,liver failure,and anti-TB drug resistance.The late identification group(>8 weeks)had the highest risk of DILI,followed by liver failure compared with the other two groups.Conclusions:The time to identification of LFT was positively correlated with DILI,liver failure,and anti-TB drug resistance.The risk of DILI and liver failure was significantly increased in the late identification group with abnormal LFT identified after 8 weeks compared with 4 and 8 weeks.Early monitoring of LFT is recommended for patients without the high-risk factor of DILI after anti-TB treatment is initiated.

Identification, biogenesis, function, and mechanism of action of circular RNAs in plants

CircularRNAs(circRNAs)are a class of single-stranded,closedRNAmolecules with unique functions that are ubiquitously expressed in all eukaryotes.The biogenesis of circRNAs is regulated by specific cis-acting elements and trans-acting factors in humans and animals.circRNAs mainly exert their biological functions by acting as microRNA sponges,forming R-loops,interacting with RNA-binding proteins,or being translated into polypeptides or proteins in human and animal cells.Genome-wide identification of circRNAs has been performedin multiple plant species,and the results suggest that circRNAs are abundant and ubiquitously expressed in plants.There is emerging compelling evidence to suggest that circRNAs play essential roles during plant growthanddevelopment as well as inthe responses to bioticandabiotic stress.However,compared with recent advances in human and animal systems,the roles of most circRNAs in plants are unclear at present.Here we review the identification,biogenesis,function,and mechanism of action of plant circRNAs,which will provide a fundamental understanding of the characteristics and complexity of circRNAs in plants.

IMPROVEMENT ACCURACY OF SYSTEM IDENTIFICATION BASED ON MORLET WAVELET

A time frequency de-noising method is presented in the frequency response function （FRF） preprocessing based on the continuous wavelet transform. Morlet wavelet is employed to construct a filter bank to reduce the noise. The filter bank is a finite impulse response （FIR） linear phase filter thus maintaining phase consistency. A modified Morlet base function is proposed to meet the time frequency resolution by using transient excitation. Numerical simulation is conducted using a Group for Aeronautical Research and Technology in Europe （GARTEUR） aircraft model excited by the transient input. The white noise is added to the simulated data. Results show that the accuracy of the system identification is improved. The estimated error of the mode damping is decreased by 30% compared with that obtained from the noise-corrupted signal.

Analysis and side peaks identification of Chinese DTTB signal ambiguity functions for passive radar

Passive radar is one of the current research focuses. The implementation of the Chinese standard digital television terrestrial broadcasting （DTTB） creates a new opportunity for passive radar. DTTB system contains single-carrier and multicarrier application modes. In this paper, ambiguity functions of the D＇I-I＇B signals in the single-carrier and multicarrier application modes are analyzed. Ambiguity function of the DTTB signal contains one main peak and many side peaks. The relative positions and amplitudes of the side peaks are derived and the reasons for the occurrence of the side peaks are obtained. The side peaks identification （SPI） algorithm is proposed for avoiding the false alarms caused by the side peaks. Experimental results show that the SPI algorithm can indentify all the side peaks without the power loss. This research provides the foundation for designing the DTTB based passive radar.

SVM with Quadratic Polynomial Kernel Function Based Nonlinear Model One-step-ahead Predictive Control

A support vector machine (SVM) with quadratic polynomial kernel function based nonlinear model one-step-ahead predictive controller is presented. The SVM based predictive model is established with black-box identification method. By solving a cubic equation in the feature space, an explicit predictive control law is obtained through the predictive control mechanism. The effect of controller is demonstrated on a recognized benchmark problem and on the control of continuous-stirred tank reactor (CSTR). Simulation results show that SVM with quadratic polynomial kernel function based predictive controller can be well applied to nonlinear systems, with good performance in following reference trajectory as well as in disturbance-rejection.