Decoupling of temporal/spatial broadening effects in Doppler wind LiDAR by 2D spectral analysis

Pulse echo accumulation is commonly employed in coherent Doppler wind LiDAR(light detection and ranging)under the assumption of steady wind.Here,the measured spectral data are analyzed in the time dimension and frequency dimension to cope with the temporal wind shear and achieve the optimal accumulation time.A hardware-efficient algorithm combining the interpolation and cross-correlation is used to enhance the wind retrieval accuracy by reducing the frequency sampling interval and then reduce the spectral width calculation error.Moreover,the temporal broadening effect and spatial broadening effect are decoupled according to the strategy we developed.

Whole-volume histogram analysis of spectral-computed tomography iodine maps characterizes HER2 expression in gastric cancer

BACKGROUND Although surgery remains the primary treatment for gastric cancer(GC),the identification of effective alternative treatments for individuals for whom surgery is unsuitable holds significance.HER2 overexpression occurs in approximately 15%-20%of advanced GC cases,directly affecting treatment-related decisions.Spectral-computed tomography(sCT)enables the quantification of material compositions,and sCT iodine concentration parameters have been demonstrated to be useful for the diagnosis of GC and prediction of its invasion depth,angioge-nesis,and response to systemic chemotherapy.No existing report describes the prediction of GC HER2 status through histogram analysis based on sCT iodine maps(IMs).AIM To investigate whether whole-volume histogram analysis of sCT IMs enables the prediction of the GC HER2 status.METHODS This study was performed with data from 101 patients with pathologically confirmed GC who underwent preoperative sCT examinations.Nineteen parameters were extracted via sCT IM histogram analysis:The minimum,maximum,mean,standard deviation,variance,coefficient of variation,skewness,kurtosis,entropy,percentiles(1st,5th,10th,25th,50th,75th,90th,95th,and 99th),and lesion volume.Spearman correlations of the parameters with the HER2 status and clinicopathological parameters were assessed.Receiver operating characteristic curves were used to evaluate the parameters’diagnostic performance.RESULTS Values for the histogram parameters of the maximum,mean,standard deviation,variance,entropy,and percentiles were significantly lower in the HER2+group than in the HER2–group(all P<0.05).The GC differentiation and Lauren classification correlated significantly with the HER2 status of tumor tissue(P=0.001 and 0.023,respectively).The 99th percentile had the largest area under the curve for GC HER2 status identification(0.740),with 76.2%,sensitivity,65.0%specificity,and 67.3%accuracy.All sCT IM histogram parameters correlated positively with the GC HER2 status(r=0.237-0.337,P=0.001-0.017).CONCLUSION Whole-lesion histogram parameters derived from sCT IM analysis,and especially the 99th percentile,can serve as imaging biomarkers of HER2 overexpression in GC.

Measurement Analysis and Evaluation of Twenty-Five Years of Mathematics Education Research in China:Visual Econometric Analysis Based on CiteSpace Knowledge Mapping(1999-2024)

Since the new century,China’s mathematics curriculum reform in basic education has continued to move forward in attempts and explorations,presenting many new changes,trends,movements,and developments.Sorting out,analyzing,and summarizing the achievements,experiences,problems,and challenges in this journey are conducive to providing insights for the reform and development of the Chinese basic education mathematics curriculum in the new era.This paper analyses the research on mathematics education in China(1999-2024)using the visual measurement of CiteSpace knowledge mapping,hoping to provide directions for the future of mathematics education in China.

Hyperspectral remote sensing identification of marine oil spills and emulsions using feature bands and double-branch dual-attention mechanism network

The accurate identification of marine oil spills and their emulsions is of great significance for emergency response to oil spill pollution.The selection of characteristic bands with strong separability helps to realize the rapid calculation of data on aircraft or in orbit,which will improve the timeliness of oil spill emergency monitoring.At the same time,the combination of spectral and spatial features can improve the accuracy of oil spill monitoring.Two ground-based experiments were designed to collect measured airborne hyperspectral data of crude oil and its emulsions,for which the multiscale superpixel level group clustering framework(MSGCF)was used to select spectral feature bands with strong separability.In addition,the double-branch dual-attention(DBDA)model was applied to identify crude oil and its emulsions.Compared with the recognition results based on original hyperspectral images,using the feature bands determined by MSGCF improved the recognition accuracy,and greatly shortened the running time.Moreover,the characteristic bands for quantifying the volume concentration of water-in-oil emulsions were determined,and a quantitative inversion model was constructed and applied to the AVIRIS image of the deepwater horizon oil spill event in 2010.This study verified the effectiveness of feature bands in identifying oil spill pollution types and quantifying concentration,laying foundation for rapid identification and quantification of marine oil spills and their emulsions on aircraft or in orbit.

Development of Spectral Features for Monitoring Rice Bacterial Leaf Blight Disease Using Broad-Band Remote Sensing Systems

As an important rice disease, rice bacterial leaf blight (RBLB, caused by the bacterium Xanthomonas oryzae pv.oryzae), has become widespread in east China in recent years. Significant losses in rice yield occurred as a result ofthe disease’s epidemic, making it imperative to monitor RBLB at a large scale. With the development of remotesensing technology, the broad-band sensors equipped with red-edge channels over multiple spatial resolutionsoffer numerous available data for large-scale monitoring of rice diseases. However, RBLB is characterized by rapiddispersal under suitable conditions, making it difficult to track the disease at a regional scale with a single sensorin practice. Therefore, it is necessary to identify or construct features that are effective across different sensors formonitoring RBLB. To achieve this goal, the spectral response of RBLB was first analyzed based on the canopyhyperspectral data. Using the relative spectral response (RSR) functions of four representative satellite or UAVsensors (i.e., Sentinel-2, GF-6, Planet, and Rededge-M) and the hyperspectral data, the corresponding broad-bandspectral data was simulated. According to a thorough band combination and sensitivity analysis, two novel spectralindices for monitoring RBLB that can be effective across multiple sensors (i.e., RBBRI and RBBDI) weredeveloped. An optimal feature set that includes the two novel indices and a classical vegetation index was formed.The capability of such a feature set in monitoring RBLB was assessed via FLDA and SVM algorithms. The resultdemonstrated that both constructed novel indices exhibited high sensitivity to the disease across multiple sensors.Meanwhile, the feature set yielded an overall accuracy above 90% for all sensors, which indicates its cross-sensorgenerality in monitoring RBLB. The outcome of this research permits disease monitoring with different remotesensing data over a large scale.

Teaching Reform and Practice of “Mathematical Analysis” under the New Engineering Education Paradigm

With the advent and promotion of the new engineering education paradigm,university mathematics courses face new challenges and opportunities.As a fundamental course for engineering majors,the reform of“Mathematical Analysis”is particularly crucial.This paper explores the necessity,specific practices,and outcomes of teaching reform in“Mathematical Analysis”within the context of the new engineering education.By reforming teaching content,methods,and assessment approaches,this study aims to enhance students’mathematical literacy and comprehensive abilities to meet the demands of the new engineering education development.

Low-dimensional multi-spectral space for color reproduction based on nonnegative constrained principal component analysis

In order to overcome the shortcomings that the reconstructed spectral reflectance may be negative when using the classic principal component analysis （PCA）to reduce the dimensions of the multi-spectral data, a nonnegative constrained principal component analysis method is proposed to construct a low-dimensional multi-spectral space and accomplish the conversion between the new constructed space and the multispectral space. First, the reason behind the negative data is analyzed and a nonnegative constraint is imposed on the classic PCA. Then a set of nonnegative linear independence weight vectors of principal components is obtained, by which a lowdimensional space is constructed. Finally, a nonlinear optimization technique is used to determine the projection vectors of the high-dimensional multi-spectral data in the constructed space. Experimental results show that the proposed method can keep the reconstructed spectral data in [ 0, 1 ]. The precision of the space created by the proposed method is equivalent to or even higher than that by the PCA.

Ice-Induced Fatigue Analysis by Spectral Approach for Offshore Jacket Platforms with Ice-Breaking Cones

The spectral methods and ice-induced fatigue analysis are discussed based on Miner＇s linear cumulative fatigue hypothesis and S-N curve data. According to the long-term data of full-scale tests on the platforms in the Bohai Sea, the ice force spectrum of conical structures and the fatigue environmental model are established. Moreover, the finite element model of JZ20-2MSW platform, an example of ice-induced fatigue analysis, is built with ANSYS software. The mode analysis and dynamic analysis in frequency domain under all kinds of ice fatigue work conditions are carded on, and the fatigue life of the structure is estimated in detail. The methods in this paper can be helpful in ice-induced fatigue analysis of ice-resistant platforms.

CALCULATING RESPONSE SPECTRAL MOMENTS BY COMPLEX MODAL ANALYSIS

Response spectral moments are useful for system reliability analysis.Usually,spectral mo- ments are calculated by the frequency domain method.Based on the time domain modal analysis of random vibrations,the authors present a new method for calculating response spectral moments through response correlation functions.The method can be applied to both classical and non-classical damping cases and to three kinds of random excitations,i.e.,white noise,band-limited white noise, and filtered white noise.

Study on the application of spectral fatigue analysis

Fatigue failure has long been an important issue for ships and offshore structures. Among the numerous methods for predicting fatigue life, the spectral method is accepted as the most reliable. Although the theory behind spectral analysis is straight-forward, the analysis itself is complicated and time-consuming because it is closely related to critical technical details such as the application of fatigue loading (wave pressures and the inertial forces due to cargoes), the extraction of the stress, and the calculation of stress RAO. Here, four key technical details-loading application, displacement boundary condition, the calculation of stress RAO, and the extraction of the fatigue stress-are discussed thoroughly. For each aspect, a resolution is presented based on the finite element pre-and post-processing software MSC/PATRAN or FE solver MSC/NASTRAN. The resolutions are effective and efficient, which can help engineers perform spectral fatigue analysis accurately and faster.

Application of Wavelet Algorithm to Spectral Analysis of Oceanic Waves and Offshore Structure Responses

Fourier transform （FF） is a commonly used method in spectral analysis of ocean wave and offshore structure responses, but it is not suitable for records of short length. In this paper another method, wavelet transform （WT）, is applied to ＇analyze the data of short length. The Morlet wavelet is employed to calculate the spectra density functions for wave records and simulated Floating Production Storage and Offloading （FPSO） vessels＇ responses. Computed wave data include simulated wave data based on JONSWAP spectrum and the recorded data of Storm 149 from North Alwyn. Wavelet method is validated by comparing the statistical characteristics by WF method and those by fast Fourier transform （FFT） method with those of target spectra. The spectral density fnnctions＇ shapes calculated by WT are less malformed and have less error of statistical characteristics compared with those by FT especially when the record lengths decrease.

Unsupervised seismic facies analysis using sparse representation spectral clustering

Traditional unsupervised seismic facies analysis techniques need to assume that seismic data obey mixed Gaussian distribution.However,fi eld seismic data may not meet this condition,thereby leading to wrong classifi cation in the application of this technology.This paper introduces a spectral clustering technique for unsupervised seismic facies analysis.This algorithm is based on on the idea of a graph to cluster the data.Its kem is that seismic data are regarded as points in space,points can be connected with the edge and construct to graphs.When the graphs are divided,the weights of the edges between the different subgraphs are as low as possible,whereas the weights of the inner edges of the subgraph should be as high as possible.That has high computational complexity and entails large memory consumption for spectral clustering algorithm.To solve the problem this paper introduces the idea of sparse representation into spectral clustering.Through the selection of a small number of local sparse representation points,the spectral clustering matrix of all sample points is approximately represented to reduce the cost of spectral clustering operation.Verifi cation of physical model and fi eld data shows that the proposed approach can obtain more accurate seismic facies classification results without considering the data meet any hypothesis.The computing efficiency of this new method is better than that of the conventional spectral clustering method,thereby meeting the application needs of fi eld seismic data.

Functional Spectral Analysis of Paleoclimatic Evolution in Lanzhou Area over the Last 15 ka

In this paper, we make use of the functional spectral analysis to infer the periodicity of paleoclimate in the Hongzuisi section since about 15 ka. Through combined analysis of organic carbon isotope and CaCO\-3 content, the law of paleoclimatic evolution of the Hongzuisi section is obtained. There were climatic changes from 10 ka to about 0.1 ka over the last 15 ka. Among these cycles, the cycle of several ka is most remarkable. The result indicates that functional spectral analysis is helpful for paleoclimatic study, which can provide useful information about paleoclimatic reconstruction and future forecast.

Spectral analysis of bowel sounds in intestinal obstruction using an electronic stethoscope

AIM: To determine the value of bowel sounds analysis using an electronic stethoscope to support a clinical diagnosis of intestinal obstruction. METHODS: Subjects were patients who presented with a diagnosis of possible intestinal obstruction based on symptoms, signs, and radiological findings. A 3MTH Littmann Model 4100 electronic stethoscope was used in this study. With the patients lying supine, six 8-second recordings of bowel sounds were taken from each patient from the lower abdomen. The recordings were analysed for sound duration, soundto-sound interval, dominant frequency, and peak frequency. Clinical and radiological data were reviewed and the patients were classified as having either acute, subacute, or no bowel obstruction. Comparison of bowel sound characteristics was made between these subgroups of patients. In the presence of an obstruction, the site of obstruction was identified and bowel calibre was also measured to correlate with bowel sounds. RESULTS: A total of 71 patients were studied during the period July 2009 to January 2011. Forty patientshad acute bowel obstruction (27 small bowel obstruction and 13 large bowel obstruction), 11 had subacute bowel obstruction (eight in the small bowel and three in large bowel) and 20 had no bowel obstruction (diagnoses of other conditions were made). Twenty-five patients received surgical intervention (35.2%) during the same admission for acute abdominal conditions. A total of 426 recordings were made and 420 recordings were used for analysis. There was no significant difference in sound-to-sound interval, dominant frequency, and peak frequency among patients with acute bowel obstruction, subacute bowel obstruction, and no bowel obstruction. In acute large bowel obstruction, the sound duration was significantly longer (median 0.81 s vs 0.55 s, P = 0.021) and the dominant frequency was significantly higher (median 440 Hz vs 288 Hz, P = 0.003) when compared to acute small bowel obstruction. No significant difference was seen between acute large bowel obstruction and large bowel pseudoobstruction. For patients with small bowel obstruction, the sound-to-sound interval was significantly longer in those who subsequently underwent surgery compared with those treated non-operatively (median 1.29 s vs 0.63 s, P < 0.001). There was no correlation between bowel calibre and bowel sound characteristics in both acute small bowel obstruction and acute large bowel obstruction. CONCLUSION: Auscultation of bowel sounds is nonspecific for diagnosing bowel obstruction. Differences in sound characteristics between large bowel and small bowel obstruction may help determine the likely site of obstruction.

Analysis of rice paper's morphological features based on multispectral imaging technology

Computer forensics and identification for traditional Chinese painting arts have caught the attention of various fields. Rice paper＇s feature extraction and analysis methods are of high significance for the rice paper is an important carrier of traditional Chinese painting arts. In this paper, rice paper＇s morphological feature analysis is done using multi spectral imaging technology. The multispectral imaging system is utilized to acquire rice paper＇s spectral images in different wave- length channels, and then those spectral images are measured using texture parameter statistics to acquire sensitive bands for rice paper＇s feature. The mathematical morphology and grayscale statistical principle are utilized to establish a rice paper＇s morphological feature analytical model which is used to acquire rice paper＇ s one-dimensional vector. For the purpose of eval- uating these feature vectors＇ accuracy, they are entered into the support vector machine（SVM） classifier for detection and classification. The results show that the rice paper＇s feature is out loud in the spectral band 550 nm, and the average classifi- cation accuracy of feature vectors output from the analytical model is 96 %. The results indicate that the rice paper＇s feature analytical model can extract most of rice paper＇s features with accuracy and efficiency.

TIME-SERIES MODELI NG AND FAULT FORECAST STUDY ON SPECTRAL ANALYSIS OF LUBRICATING OIL

The application of ti me-series modeling and forecasting method to the spectral analysis for lubricat ing oil of mechanical equipment is discussed. The AR model is used to perform a time-series modeling and forecasting analysis for the spectral analysis data co llected from aero-engines. In the oil condition monitoring field of mechanical equipment, the use of the method of time-series analysis has rarely been report ed. As indicated in the satisfactory example, a practical method for condition m onitoring and fault forecasting of mechanical equipment has been achieved.

The Time Series Spectral Analysis of Satellite Altimetry and Coastal Tide Gauges and Tide Modeling in the Coast of Caspian Sea

This study endeavors to deal with the least square spectral analysis on the time series, to find present significant frequencies, to analyze 40 tide components using harmonic methods and to show relationship between discovered frequencies and 40 components of tide. For the purpose of collecting data of altimetry satellites of Topex/Poseidon (T/P), Jason 1, Jason 2 and coastal tide gauges of Bandar Anzali, Noshahr, and Nekah were utilized. In this time series formed by cross over points of altimetry satellite and then using least square spectral analysis on time series derived from altimetry satellite and coastal tide gauges the significant components were found and annual, biannual, and monthly components were discovered. Then, analysis of 40 tide components was conducted using harmonic method to find the amplitude and phase. It represented that solar annual (Sa) plays the most significant role on Caspian Sea corresponded to the least square spectral analysis of the time series. The results shows that the annual (Sa) and semi-annual Solar (Ssa) constituents on all of the ports listed have the highest amplitude in comparison with the other constituents which are respectively 16 cm, 18 cm and 15 cm for annual constituent and 2.8 cm, 5.4 cm and 3.7 cm for semi-annual constituent.

Influence of signal-to-noise ratio on accuracy of spectral analysis by near infrared spectroscopy

As one of the important indicators of spectrometer,signal-to-noise ratio(SNR)reflects the ability of spectrometer to detect weak signals.To investigate the influence of SNR on the prediction accuracy of spectral analysis,we first introduce the major factors affecting the spectral SNR.Taking green tea as an example,the influence of spectral SNR on the prediction accuracy of the origin identification model is analyzed by experiments.At the same time,the relationship between the spectral SNR and prediction accuracy of spectral analysis model is fitted.Based on this,the common methods for improving the spectral SNR are discussed.The results show that the accuracy of the prediction set model first decreases slowly,then decreases linearly,and finally tends to be flat as the spectral SNR decreases.Through calculation,in order to achieve the prediction accuracy of prediction model reaching 90%and 85%,the spectral SNR is required to be higher than 23.42 dB and 21.16 dB,respectively.The overall results provide certain parameters support for the development of new online analytical spectroscopic instruments,especially for the technical indicators of SNR.

Greek Texts and the Rigorization of Analysis:An Inquiry into J.L.Lagrange's Work on the History of Mathematics

This paper offers a study of J.L.Lagrange's research on history of mathematics,aiming to clarify Lagrange's intention in carrying out historical work.To this end,I first document how Lagrange worked with and exerted his influence on other scholars in the translation and diffusion of ancient Greek texts.Second,investigating Lagrange's style in doing and writing history of mathematics,this paper takes a new perspective and elucidates his motivation in these activities.In particular,it focuses on Lagrange's presentation of the history of calculus while he was teaching analysis at the Ecole Polytechnique(1795-1799)so as to clarify the function of history in Lagrange's mathematical works.My thesis is that Lagrange's intention in examining the different methods employed by his predecessors was to find inspiration and useful contents in his search for the proper approach to mathematical problems.I thus argue in this paper that history served as a guide or methodology for Lagrange's mathematics.Meanwhile,through an analysis of his historical writing,this paper points to four epistemological values according to which Lagrange judged various historical methods of differential calculus:generality,simplicity,clarity,and rigor.Lagrange's move to rigorize analysis was connected to his interest in and research of Greek texts;he was attempting to introduce the rigor of the ancient Greeks'demonstration in his works of analysis.

Spectral Analysis of the Light Flash Produced by a Natural Dolomite Plate Under Strong Shock

In order to obtain the elemental compositions of the projectile and target materials during 2A12 aluminum projectile shot on a natural dolomite plate, three kinds of experiments have been conducted using a spectral acquirement system established on a two-stage light gas gun for impact velocities ranging from 2.20 km/s to 4.20 km/s, at the same projectile incidence angle of 30°. Experimental results show that the elemental compositions of the projectile and target materials in the strong shock experiments have a good agreement with the original elemental compositions of the projectile and target. In addition, the relations between spectral radiant intensity and elemental compositions of the projectile and target materials have been obtained for different impact velocities, in which the spectral radiant intensity of the main elements in the material increases with increasing impact velocity, and more elements appear with increasing impact velocity since more energy would result from a higher velocity impact.