Statistical key variable analysis and model-based control for improvement performance in a deep reactive ion etching process

This paper proposes to develop a data-driven via＇s depth estimator of the deep reactive ion etching process based on statistical identification of key variables.Several feature extraction algorithms are presented to reduce the high-dimensional data and effectively undertake the subsequent virtual metrology（VM） model building process.With the available on-line VM model,the model-based controller is hence readily applicable to improve the quality of a via＇s depth.Real operational data taken from a industrial manufacturing process are used to verify the effectiveness of the proposed method.The results demonstrate that the proposed method can decrease the MSE from 2.2×10^（-2） to 9×10^（-4） and has great potential in improving the existing DRIE process.

Correlation and variability analysis of yield and quality related traits in different peanut varieties across various ecological zones of China

Peanut cultivation in China spans various ecological zones, each with unique environmental conditions. Identifying suitable peanut varieties for these regions has been challenging due to significant phenotypic variations observed across environments. This study, based on a comprehensive analysis of 256 peanut varieties, selected nine representative varieties(Huayu23, Yuanza9102, Silihong, Wanhua2, Zhonghua6, Zhonghua16, Zhonghua21,Zhonghua215, Zhonghua24) for cultivation in five distinct ecological zones including Chengdu, Hefei, Nanjing,Shijiazhuang, and Wuhan. The yield and quality related phenotypic traits of these varieties were thoroughly assessed, revealing a complex interplay between genetic and environmental factors. Principal component analysis(PCA) effectively distinguished varieties based on yield and quality traits. Strong correlations were observed between specific traits, such as seed size and quality components. The G × E interaction was evident, as some varieties consistently performed better in certain environments. Varieties with lower coefficient of variation(CV)values exhibited stable trait expression, making them reliable choices for broad cultivation. In contrast, varieties with higher CV values displayed greater sensitivity to environmental fluctuations, potentially due to specific genetic factors. Two high oleic acid varieties, Zhonghua24 and Zhonghua215, demonstrated remarkable stability in oleic acid content across diverse environments, suggesting the presence of genetic mechanisms that buffer against environmental variations. Overall, this study underscores the importance of selecting peanut varieties based on their adaptability and performance in specific ecological zones. These findings provide valuable insights for peanut breeders and farmers, facilitating informed decisions for improved crop production and quality.

A comprehensive approach to a variability analysis between earthquake activity and hydro-environmental factors on the Korean Peninsula

Following the Pohang and Gyeongju earthquakes and their aftershocks,there is no longer any zone that is safe from earthquake-related disasters in the Korean Peninsula.In order to monitor and predict earthquakes,correlation analysis of earthquakes and hydro-environmental factors are insufficient,and the development and application of hydro-environmental factor measurement equipment is still in the early stages.This study developes and verifies a more precise radon measurement device.Four specific earthquake cases(2019–2020)were selected,and the correlation of the analyses of the earthquakes and hydro-environmental factors(radon,electric conductivity(EC),water-level(WL),and water-temperature(WT))was conducted at the three specific groundwater stations.Accordingly,was confirmed that four factors are affected by earthquakes or seismic movement.Furthermore,the variability of the EC showed an identical tendency for a certain period before an earthquake occurred,and,in particular,the variability trends for radon,WL,and EC coincided at the time of the earthquake′s occurrence.

Analysis of Weather Anomalies to Assess the 2021 Flood Events in Yaounde, Cameroon (Central Africa)

Extreme weather anomalies such as rainfall and its subsequent flood events are governed by complex weather systems and interactions between them. It is important to understand the drivers of such events as it helps prepare for and mitigate or respond to the related impacts. In line with the above statements, quarter-hourly data for the year 2021 recorded in the Yaounde meteorological station were synthesized to come out with daily and dekadal (10-day averaged) anomalies of six climate factors (rainfall, temperature, insolation, relative humidity, dew point and wind speed), in order to assess the occurrences and severity of floods to changing weather patterns in Yaounde. In addition, Precipitation Concentration Index (PCI) was computed to evaluate the distribution and analyse the frequency and intensity of precipitation. Coefficient of variation (CV) was used to estimate the seasonal and annual variation of rainfall patterns, while Mann-Kendall (MK) trend test was performed to detect weather anomalies (12-month period variation) in quarter-hourly rainfall data from January 1st to December 31st 2021. The Standard Precipitation Index (SPI) was also used to quantify the rainfall deficiency of the observed time scale. Results reveal that based on the historical data from 1979 to 2018 in the bimodal rainfall forest zone, maximum and minimum temperature averages recorded in Yaounde in 2021 were mostly above historical average values. Precipitations were rare during dry seasons, with range value of 0 - 13.6 mm for the great dry season and 0 - 21.4 mm for the small dry season. Whereas during small and great rainy seasons, rainfalls were regular with intensity varying between 0 and 50 mm, and between 0 and 90.4 mm, respectively. The MK trend test showed that there was a statistical significant increase in rainfall trend for the month of August at a 5% level of significance, while a significant decreasing trend was observed in July and December. There was a strong irregular rainfall distribution during the months of February, July and December 2021, with a weather being mildly wetted during all the dry seasons and extremely wetted in August. Recorded flooding days within the year of study matched with heavy rainy days including during dry seasons.

The spatial multiscale variability of heavy metals based on factorial kriging analysis: A case study in the northeastern Beibu Gulf

Factorial kriging analysis is applied to the research on the spatial multiscale variability of heavy metals in submarine. It is used to analyze the multiscale spatial structures of seven heavy metals, Ni, Cu, Zn, Pb, Cr, As and Cd in the surface sediment from the northeastern of Beibu Gulf, identify and separate spatial variations at different scales of heavy metals, and discuss the provenance of heavy metals and the influencing factors. The results show that the existence of three-scale spatial variations those consist of nugget effect, a spherical structure with range of 30 km（short-range scale） and a spherical structure with range of 140 km（long-range scale） in the linear model of coregionalization fitted. The spatial distribution features of seven heavy metals at short-range scale reflect ＂spot-like＂ or ＂stripe-like＂ local-scale spatial variations; the spatial distribution features of the seven heavy metals at long-range scale represent ＂slice-like＂ regional-scale spatial variations. At local scale, Zn, Cr, Ni,Cu, Pb and Cd are derived primarily from parent materials of Hainan Island, Leizhou Peninsula and Guangxi land, whose spatial distribution characteristics are controlled by granularity of sediments, while As is influenced dominantly by human pollution components from Hainan Island and Leizhou Peninsula. At regional scale, Zn,Cr, Ni and Cu originate primarily from parent rock materials of Leizhou Peninsula and Hainan Island, secondly from Guangxi land; As originated primarily from parent rock materials from Hainan Island, secondly from Leizhou Peninsula and Guangxi land. These metals are transported and migrated with sediments dominated by the anticlockwise circulation of Beibu Gulf year-round, deposited in ＂convergence center＂, forming the whole sedimentary pattern in direction of NWW-NNW at regional scale. The difference in distribution type between As and other metals at regional scale is mainly due to their different geochemical behavior.

Optimizing progress variable definition in flamelet-based dimension reduction in combustion

An automated method to optimize the definition of the progress variables in the flamelet-based dimension reduction is proposed. The performance of these optimized progress variables in coupling the flamelets and flow solver is presented. In the proposed method, the progress variables are defined according to the first two principal components （PCs） from the principal component analysis （PCA） or kernel-density-weighted PCA （KEDPCA） of a set of flamelets. These flamelets can then be mapped to these new progress variables instead of the mixture fraction/conventional progress variables. Thus, a new chemistry look-up table is constructed. A priori validation of these optimized progress variables and the new chemistry table is implemented in a CH4/N2/air lift-off flame. The reconstruction of the lift-off flame shows that the optimized progress variables perform better than the conventional ones, especially in the high temperature area. The coefficient determinations （R2 statistics） show that the KEDPCA performs slightly better than the PCA except for some minor species. The main advantage of the KEDPCA is that it is less sensitive to the database. Meanwhile, the criteria for the optimization are proposed and discussed. The constraint that the progress variables should monotonically evolve from fresh gas to burnt gas is analyzed in detail.

Mathematical theory of signal analysis vs. complex analysis method of harmonic analysis

We present recent work of harmonic and signal analysis based on the complex Hardy space approach.

An Optimal Control Theory Based Analysis of Brushless DC Motor Drive

The application of BLDC motor drives in industries is becoming more popular nowadays. An error will occur in the drive that is originated by some disturbances which are the major problems to reduce the stability of the system. To obtain the minimum performance index, the optimal control signal is formulated, which is the main objective of this paper. Based on quadratic performance index, the optimal control system of BLDC motor drive is a design which spotlights in this paper. The complexity of the mathematical expressions has been reduced by using state space approach to the BLDC system. The burden to the control engineers has reduced based on tedious computation by using thus optimal design. To provide the desired operating performance, this optimal design helps to realize the BLDC system with practical components.

Research on Campus Traffic Space Based on Space Syntax Theory:A Case Study of the Main Campus of North China University of Technology

As urban construction enters the era of stock development,the overall spatial environment of the main campus of North China University of Technology also needs to be updated.In this paper,the traffic network in the campus is extracted to draw the axis map,and the space syntax theory of Depth Map software is used to quantitatively analyze the integration and intelligence degree of the main campus of North China University of Technology.It is found that the overall spatial integration and intelligence degree of the campus are high,but the local space shows poor accessibility and insufficient space carrying capacity.Specific spatial optimization measures are proposed for the corresponding problems.The study compares and analyzes the experience information obtained from actual research with the quantitative index data,integrates the respective advantages of qualitative and quantitative analysis,and hopes to provide a certain theoretical basis for the construction of related campus space.

A neural network-based production process modeling and variable importance analysis approach in corn to sugar factory

Corn to sugar process has long faced the risks of high energy consumption and thin profits.However,it’s hard to upgrade or optimize the process based on mechanism unit operation models due to the high complexity of the related processes.Big data technology provides a promising solution as its ability to turn huge amounts of data into insights for operational decisions.In this paper,a neural network-based production process modeling and variable importance analysis approach is proposed for corn to sugar processes,which contains data preprocessing,dimensionality reduction,multilayer perceptron/convolutional neural network/recurrent neural network based modeling and extended weights connection method.In the established model,dextrose equivalent value is selected as the output,and 654 sites from the DCS system are selected as the inputs.LASSO analysis is first applied to reduce the data dimension to 155,then the inputs are dimensionalized to 50 by means of genetic algorithm optimization.Ultimately,variable importance analysis is carried out by the extended weight connection method,and 20 of the most important sites are selected for each neural network.The results indicate that the multilayer perceptron and recurrent neural network models have a relative error of less than 0.1%,which have a better prediction result than other models,and the 20 most important sites selected have better explicable performance.The major contributions derived from this work are of significant aid in process simulation model with high accuracy and process optimization based on the selected most important sites to maintain high quality and stable production for corn to sugar processes.

Conscious Slower Breathing Predominates Parasympathetic Activity and Provides a Relaxing Effect, in Healthy Japanese Adult Women

Background: The optimal breathing pattern (BP) to effectively regulate autonomic nervous activity is yet to be determined. Objective: We aimed to clarify the effects of four BPs (BP-1, BP-2, BP-3, and BP-4) on autonomic nervous activity and mood changes. Methods: Eleven healthy adult female volunteers performed each BP in a sitting position for 5 min in a resting state. The time required for one breathing for BP-1 (30 breaths/min), BP-2 (20 breaths/min), BP-3 (15 breaths/min), and BP-4 (10 breaths/min) were 2 s, 3 s, 4 s, and 6 s, respectively. The inspiratory/expiratory time of one breathing was 1 s/1 s, 1 s/2 s, 2 s/2 s, and 2 s/4 s. The high-frequency component (HF) and low-frequency component (LF)/HF ratio during and before (control) performing a BP were calculated from heart rate variability data recorded using the wearable biometric information tracer M-BIT. Three mood changes, which are, “pleasure—unpleasure”, “relaxation—tension”, and “sleepiness—arousal”, in the subjects were assessed using the visual analog scale (VAS) before and after performing a BP. Results: Slower breathing induced an increase in HF power and a reduction in LF/HF ratio, indicating increased parasympathetic activity and decreased sympathetic dominance. Furthermore, VAS revealed that slower breathing increased the tendency to feel “pleasure”, “relaxation”, and “sleepiness”. Conclusion: Our results suggest that slower breathing predominates parasympathetic activity in the autonomic nervous system, resulting in a relaxing effect. This result may help lay the foundation for deriving breathing methods that efficiently regulate an individual’s autonomic activity.

Is Social Distancing, and Quarantine Effective in Restricting COVID-19 Outbreak? Statistical Evidences from Wuhan, China

The flow of novel coronavirus(COVID-19)has affected almost every aspect of human life around the globe.Being the emerging ground and early sufferer of the virus,Wuhan city-data remains a case of multifold significance.Further,it is of notable importance to explore the impact of unique and unprecedented public health response of Chinese authorities—the extreme lockdown of the city.In this research,we investigate the statistical nature of the viral transmission concerning social distancing,extreme quarantine,and robust lockdown interventions.We observed highly convincing and statistically significant evidences in favor of quarantine and social distancing approaches.These findings might help countries,now facing,or likely to face the wave of the virus.We analyzed Wuhan-based data of“number of deaths”and“confirmed cases,”extracted from China CDC weekly database,dated from February 13,2020,to March 24,2020.To estimate the underlying group structure,the assembled data is further subdivided into three blocks,each consists of two weeks.Thus,the complete data set is studied in three phases,such as,phase 1(Ph 1)=February 13,2020,to February 26,2020;phase 2(Ph 2)=February 27,2020 to March 11,2020;and phase 3(Ph 3)=March 12,2020 to March 24,2020.We observed the overall median proportion of deaths in those six weeks remained 0.0127.This estimate is highly influenced by Ph1,when the early flaws of weak health response were still prevalent.Over the time,we witnessed a median decline of 92.12%in the death proportions.Moreover,a non-parametric version of the variability analysis of death data,estimated that the average rank of reported proportions in Ph 3 remained 7,which was 20.5 in Ph 2,and stayed 34.5 in the first phase.Similar patterns were observed,when studying the confirmed cases data.We estimated the overall median of the proportion of confirmed cases in Wuhan as 0.0041,which again,is highly inclined towards Ph 1 and Ph 2.We also witnessed minimum average rank proportions for Ph 3,such as 7,which was noticeably lower than Ph 2,21.71,and Ph 1, 32.29. Moreover, the varying degree of clustering indicates that the effectivenessof quarantine based policies is time-dependent. In general, the declinein coronavirus transmission in Wuhan significantly coincides with the lockdown.

Measurement and Evaluation of the Autonomic Nervous Function in Daily Life

We developed a smart-phone based system to measure the activities of autonomic nervous system during everyday life. Using commonly marketed smart phones, by touching your fingertips on the phone’s camera over a short time of about 30 seconds, it will detect changes in the brightness of the blood flow and in turn analyze your heart rate variability. By using this system, about 100,000 cases were measured and from this large amount of data regarding heart rate variability, we evaluated the autonomic nervous function in their daily life. As a result, for the correlation between autonomic nervous system and age, we found that as the increase of age, the total power becomes decreased and the sympathetic nervous system tends to increase between thirties and fifties. For the correlation between autonomic nervous system and BMI (Body Mass Index), it is found that in general, the higher the BMI, the lower the total power and the stronger the sympathetic nervous system. In other words, people who are fat are lower about the total power and stronger about the sympathetic nervous system. In addition, for the correlation between autonomic nervous system and one day life, it is found that total power and sympathetic function tend to increase, while as evening approaches, sympathetic function tends to become suppressed.

Locality Preserving Discriminant Projection for Speaker Verification

In this paper, a manifold subspace learning algorithm based on locality preserving discriminant projection (LPDP) is used for speaker verification. LPDP can overcome the deficiency of the total variability factor analysis and locality preserving projection (LPP). LPDP can effectively use the speaker label information of speech data. Through optimization, LPDP can maintain the inherent manifold local structure of the speech data samples of the same speaker by reducing the distance between them. At the same time, LPDP can enhance the discriminability of the embedding space by expanding the distance between the speech data samples of different speakers. The proposed method is compared with LPP and total variability factor analysis on the NIST SRE 2010 telephone-telephone core condition. The experimental results indicate that the proposed LPDP can overcome the deficiency of LPP and total variability factor analysis and can further improve the system performance.

Geographical classification of Nanfeng mandarin by near infrared spectroscopy coupled with chemometrics methods

Near infrared spectroscopy(NIRS),coupled with principal component analysis and wavelength selection techniques,has been sed to develop a robust and reliable reduced-spectrum classifi-cation model for determining the geographical origins of Nanfeng mandarins.The application of the changeable size moving window principal component analysis(CSMWPCA)provided a notably improved lassification model,with correct classification rates of 92.00%,100.00%,90.00%,100.00%,100.00%,100.00%and 100.00%for Fujian,Guangxi,Hunan,Baishe,Baofeng,Qiawan,Sanxi samples,respectively,as well as,a total dassification rate of 97.52%in the wavelength range from 1007 to 1296 nm.To test and apply the proposed method,the procedure was applied to the analysis of 59 samples in an independent test set.Good identification results(correct rate of 96.61%)were also received.The improvement achieved by the application of CSMWPCA method was particularly remarkable when taking the low complexities of the final model(290 variables)into account.The results of the study showed the great potential of NIRS as a fast,nondestructive and environmentally acceptable method for the rapid and reliable determination for geographical classifcation of Nanfeng mandarins.

COVARIANCE STRUCTURAL ANALYSIS WITH POLYTOMOUS VARIABLES IN SEVERAL POPULATIONS

In this paper, covariance structures with polytomous variables in several populations are analyzed. A computationally efficient multistage procedure is proposed to estimate the thresholds and the covariance structure parameters.Statistical properties of the estimators are derived, and a computer program is implemented to obtain the solution. An artificial example is presented to illustrate the method.

Digital mapping of soil phosphorous sorption parameters (PSPs) using environmental variables and machine learning algorithms

In this study some soil phosphorous sorption parameters(PSPs)by using different machine learning models(Cubist(Cu),random forest(RF),support vector machines(SVM)and Gaussian process regression(GPR))were predicted.The results showed that using the topographic attributes as the sole auxiliary variables was not adequate for predicting the PSPs.However,remote sensing data and its combination with soil properties were reliably used to predict PSPs(R^(2)=0.41 for MBC by RF model,R^(2)=0.49 for PBC by Cu model,R^(2)=0.37 for SPR by Cu model,and R^(2)=0.38 for SBC by RF model).The lowest RMSE values were obtained for MBC by RF model,PBC by SVM model,SPR by Cubist model and SBC by RF model.The results also showed that remote sensing data as the easily available datasets could reliably predict PSPs in the given study area.The outcomes of variable importance analysis revealed that among the soil properties cation exchange capacity(CEC)and clay content,and among the remote sensing indices B5/B7,Midindex,Coloration index,Saturation index,and OSAVI were the most imperative factors for predicting PSPs.Further studies are recommended to use other proximally sensed data to improve PSPs prediction to precise decision-making throughout the landscape.

Real-Variable Theory of Local Variable Hardy Spaces

In this paper, we give a complete real-variable theory of local variable Hardy spaces.First, we present various real-variable characterization in terms of several local maximal functions.Next, the new atomic and the finite atomic decomposition for the local variable Hardy spaces are established. As an application, we also introduce the local variable Campanato space which is showed to be the dual space of the local variable Hardy spaces. Analogous to the homogeneous case, some equivalent definitions of the dual of local variable Hardy spaces are also considered. Finally, we show the boundedness of inhomogeneous Calderon–Zygmund singular integrals and local fractional integrals on local variable Hardy spaces and their duals.

Resonance System Reliability and Sensitivity Analysis Method for Axially FGM Pipes Conveying Fluid with Adaptive Kriging Model

This paper aims to solve the resonance failure probability and develop an effective method to estimate the effects of variables and failure modes on failure probability of axially functionally graded material(FGM)pipe conveying fluid.Correspondingly,the natural frequency of axially FGM pipes conveying fluid is calculated using the differential quadrature method(DQM).A variable sensitivity analysis(VSA)is introduced to measure the effect of each random variable,and a mode sensitivity analysis(MSA)is introduced to acquire the importance ranking of failure modes.Then,an active learning Kriging(ALK)method is established to calculate the resonance failure probability and sensitivity indices,which greatly improves the application of resonance reliability analysis for pipelines in engineering practice.Based on the resonance reliability analysis method,the effects of fluid velocity,volume fraction and fluid density of axially FGM pipe conveying fluid on resonance reliability are analyzed.The results demonstrate that the proposed method has great performance in the anti-resonance analysis of pipes conveying fluid.