Developing Statistical Modellings to Investigate the Internal Drivers for the Trend of Output Values in the Manufacturing Industry:Evidence from Chinese Enterprises

The manufacturing industry is an important pillar of the national economy.It is of vital importance to develop statistical modellings in order to quantify the relationship between potential internal drivers and the trend of output values in the manufacturing industry.However,only a few statistical modellings have been established to investigate such associations.This study developed the correlation coefficient model and generalized linear model(GLM)to measure the single and interactive effects of the internal drivers on the changes of the output values.For the GLM,different predictive variables were developed to fit into the dataset,and the performance of the models were compared using fitness parameters.Furthermore,an industry survey dataset for 1,180 manufacturing enterprises in 2020 was used to validate the models.The use of the GLM combining land area,number of employees,scientific research input,and labor productivity may have a great potential to bolster capacity in monitoring and predicting the trend of output values in the manufacture industry.

The risk of mpox importation and subsequent outbreak potential in Chinese mainland: a retrospective statistical modelling study

Background The 2022-2023 mpox(monkeypox)outbreak has spread rapidly across multiple countries in the non-endemic region,mainly among men who have sex with men(MSM).In this study,we aimed to evaluate mpox's importation risk,border screening effectiveness and the risk of local outbreak in Chinese mainland.Methods We estimated the risk of mpox importation in Chinese mainland from April 14 to September 11,2022 using the number of reported mpox cases during this multi-country outbreak from Global.health and the international air-travel data from Official Aviation Guide.We constructed a probabilistic model to simulate the effectiveness of a border screening scenario during the mpox outbreak and a hypothetical scenario with less stringent quarantine requirement.And we further evaluated the mpox outbreak potential given that undetected mpox infections were introduced into men who have sex with men,considering different transmissibility,population immunity and population activity.Results We found that the reduced international air-travel volume and stringent border entry policy decreased about 94% and 69% mpox importations respectively.Under the quarantine policy,15-19% of imported infections would remain undetected.Once a case of mpox is introduced into active MSM population with almost no population immunity,the risk of triggering local transmission is estimated at 42%,and would rise to>95% with over six cases.Conclusions Our study demonstrates that the reduced international air-travel volume and stringent border entry policy during the COvID-19 pandemic reduced mpox importations prominently.However,the risk could be sub-stantially higher with the recovery of air-travel volume to pre-pandemic level.Mpox could emerge as a public health threat for Chinese mainland given its large MSM community.

Challenges in predictive modelling of chronic kidney disease:A narrative review

The exponential rise in the burden of chronic kidney disease(CKD)worldwide has put enormous pressure on the economy.Predictive modeling of CKD can ease this burden by predicting the future disease occurrence ahead of its onset.There are various regression methods for predictive modeling based on the distribution of the outcome variable.However,the accuracy of the predictive model depends on how well the model is developed by taking into account the goodness of fit,choice of covariates,handling of covariates measured on a continuous scale,handling of categorical covariates,and number of outcome events per predictor parameter or sample size.Optimal performance of a predictive model on an independent cohort is desired.However,there are several challenges in the predictive modeling of CKD.Disease-specific methodological challenges hinder the development of a predictive model that is cost-effective and universally applicable to predict CKD onset.In this review,we discuss the advantages and challenges of various regression models available for predictive modeling and highlight those best for future CKD prediction.

Evaluation of Serum Anti-Müllerian Hormone (AMH) Values for 28,016 Bulgarian Women: Prognostic Statistical Model of Age Specific AMH Declining

The present study aims to establish a relationship between serum AMH levels and age in a large group of women living in Bulgaria, as well as to establish reference age-specific AMH levels in women that would serve as an initial estimate of ovarian age. A total of 28,016 women on the territory of the Republic of Bulgaria were tested for serum AMH levels with a median age of 37.0 years (interquartile range 32.0 to 41.0). For women aged 20 - 29 years, the Bulgarian population has relatively high median levels of AMH, similar to women of Asian origin. For women aged 30 - 34 years, our results are comparable to those of women living in Western Europe. For women aged 35 - 39 years, our results are comparable to those of women living in the territory of India and Kenya. For women aged 40 - 44 years, our results were lower than those for women from the Western European and Chinese populations, close to the Indian and higher than Korean and Kenya populations, respectively. Our results for women of Bulgarian origin are also comparable to US Latina women at age 30, 35 and 40 ages. On the base on constructed a statistical model to predicting the decline in AMH levels at different ages, we found non-linear structure of AMH decline for the low AMH 3.5) the dependence of the decline of AMH on age was confirmed as linear. In conclusion, we evaluated the serum level of AMH in Bulgarian women and established age-specific AMH percentile reference values based on a large representative sample. We have developed a prognostic statistical model that can facilitate the application of AMH in clinical practice and the prediction of reproductive capacity and population health.

Comparative Analysis of Statistical Thickness Models for the Determination of the External Specific Surface and the Surface of the Micropores of Materials: The Case of a Clay Concrete Stabilized Using Sugar Cane Molasses

In this work, four empirical models of statistical thickness, namely the models of Harkins and Jura, Hasley, Carbon Black and Jaroniec, were compared in order to determine the textural properties (external surface and surface of micropores) of a clay concrete without molasses and clay concretes stabilized with 8%, 12% and 16% molasses. The results obtained show that Hasley’s model can be used to obtain the external surfaces. However, it does not allow the surface of the micropores to be obtained, and is not suitable for the case of simple clay concrete (without molasses) and for clay concretes stabilized with molasses. The Carbon Black, Jaroniec and Harkins and Jura models can be used for clay concrete and stabilized clay concrete. However, the Carbon Black model is the most relevant for clay concrete and the Harkins and Jura model is for molasses-stabilized clay concrete. These last two models augur well for future research.

The impact of genotyping strategies and statistical models on accuracy of genomic prediction for survival in pigs

Background:Survival from birth to slaughter is an important economic trait in commercial pig productions.Increasing survival can improve both economic efficiency and animal welfare.The aim of this study is to explore the impact of genotyping strategies and statistical models on the accuracy of genomic prediction for survival in pigs during the total growing period from birth to slaughter.Results:We simulated pig populations with different direct and maternal heritabilities and used a linear mixed model,a logit model,and a probit model to predict genomic breeding values of pig survival based on data of individual survival records with binary outcomes(0,1).The results show that in the case of only alive animals having genotype data,unbiased genomic predictions can be achieved when using variances estimated from pedigreebased model.Models using genomic information achieved up to 59.2%higher accuracy of estimated breeding value compared to pedigree-based model,dependent on genotyping scenarios.The scenario of genotyping all individuals,both dead and alive individuals,obtained the highest accuracy.When an equal number of individuals(80%)were genotyped,random sample of individuals with genotypes achieved higher accuracy than only alive individuals with genotypes.The linear model,logit model and probit model achieved similar accuracy.Conclusions:Our conclusion is that genomic prediction of pig survival is feasible in the situation that only alive pigs have genotypes,but genomic information of dead individuals can increase accuracy of genomic prediction by 2.06%to 6.04%.

Women Entrepreneurship Index Prediction Model with Automated Statistical Analysis

Recently,gender equality and women’s entrepreneurship have gained considerable attention in global economic development.Prior to the design of any policy interventions to increase women’s entrepreneurship,it is significant to comprehend the factors motivating women to become entrepreneurs.The non-understanding of the factors can result in the endurance of low living stan-dards and the design of expensive and ineffectual policies.But female involve-ment in entrepreneurship becomes higher in developing economies compared to developed economies.Women Entrepreneurship Index(WEI)plays a vital role in determining the factors that enable theflourishment of high potential female entrepreneurs which enhances economic welfare and contributes to the economic and social fabric of society.Therefore,it is needed to design an automated and accurate WEI prediction model to improve women’s entrepreneurship.In this view,this article develops an automated statistical analysis enabled WEI predic-tive(ASA-WEIP)model.The proposed ASA-WEIP technique aims to effectually determine the WEI.The proposed ASA-WEIP technique encompasses a series of sub-processes such as pre-processing,WEI prediction,and parameter optimiza-tion.For the prediction of WEI,the ASA-WEIP technique makes use of the Deep Belief Network(DBN)model,and the parameter optimization process takes place using Squirrel Search Algorithm(SSA).The performance validation of the ASA-WEIP technique was executed using the benchmark dataset from the Kaggle repo-sitory.The experimental outcomes stated the better outcomes of the ASA-WEIP technique over the other existing techniques.

Statistical Model of Path Loss for Railway 5G Marshalling Yard Scenario

The railway mobile communication system is undergoing a smooth transition from the Global System for Mobile Communications-Railway(GSM-R)to the Railway 5G.In this paper,an empirical path loss model based on a large amount of measured data is established to predict the path loss in the Railway 5G marshalling yard scenario.According to the different characteristics of base station directional antennas,the antenna gain is verified.Then we propose the position of the breakpoint in the antenna propagation area,and based on the breakpoint segmentation,a large-scale statistical model for marshalling yards is established.

Damage statistical mechanics model of top coal in steep top caving coal

Damage statistical mechanics model of horizontal section height in the top caving was constructed in the paper. The influence factors including supporting pressure, dip angle and characteristic of coal on horizontal section height were analyzed as well. By terms of the practice project analysis, the horizontal section height increases with the increase of dip angle β and thickness of coal seam M. Dip angle of coal seam β has tremendous impact on horizontal section height, while thickness of coal seam M has slight impact. When thickness of coal seam is below 10m, horizontal section height increases sharply. While thickness exceeds 15m, it is not major factor influencing on horizontal section height any long.

Soil erosion susceptibility mapping of Hangu Region,Kohat Plateau of Pakistan using GIS and RS-based models

Soil erosion is a crucial geo-environmental hazard worldwide that affects water quality and agriculture,decreases reservoir storage capacity due to sedimentation,and increases the danger of flooding and landslides.Thus,this study uses geospatial modeling to produce soil erosion susceptibility maps(SESM)for the Hangu region,Khyber Pakhtunkhwa(KPK),Pakistan.The Hangu region,located in the Kohat Plateau of KPK,Pakistan,is particularly susceptible to soil erosion due to its unique geomorphological and climatic characteristics.Moreover,the Hangu region is characterized by a combination of steep slopes,variable rainfall patterns,diverse land use,and distinct soil types,all of which contribute to the complexity and severity of soil erosion processes.These factors necessitate a detailed and region-specific study to develop effective soil conservation strategies.In this research,we detected and mapped 1013 soil erosion points and prepared 12 predisposing factors(elevation,aspect,slope,Normalized Differentiate Vegetation Index(NDVI),drainage network,curvature,Land Use Land Cover(LULC),rainfall,lithology,contour,soil texture,and road network)of soil erosion using GIS platform.Additionally,GIS-based statistical models like the weight of evidence(WOE)and frequency ratio(FR)were applied to produce the SESM for the study area.The SESM was reclassified into four classes,i.e.,low,medium,high,and very high zone.The results of WOE for SESM show that 16.39%,33.02%,29.27%,and 21.30%of areas are covered by low,medium,high,and very high zones,respectively.In contrast,the FR results revealed that 16.50%,24.33%,35.55%,and 23.59%of the areas are occupied by low,medium,high,and very high classes.Furthermore,the reliability of applied models was evaluated using the Area Under Curve(AUC)technique.The validation results utilizing the area under curve showed that the success rate curve(SRC)and predicted rate curve(PRC)for WOE are 82%and 86%,respectively,while SRC and PRC for FR are 85%and 96%,respectively.The validation results revealed that the FR model performance is better and more reliable than the WOE.

The Lambert-G Family:Properties,Inference,and Applications

This study proposes a new flexible family of distributions called the Lambert-G family.The Lambert family is very flexible and exhibits desirable properties.Its three-parameter special sub-models provide all significantmonotonic and non-monotonic failure rates.A special sub-model of the Lambert family called the Lambert-Lomax(LL)distribution is investigated.General expressions for the LL statistical properties are established.Characterizations of the LL distribution are addressed mathematically based on its hazard function.The estimation of the LL parameters is discussed using six estimation methods.The performance of this estimation method is explored through simulation experiments.The usefulness and flexibility of the LL distribution are demonstrated empirically using two real-life data sets.The LL model better fits the exponentiated Lomax,inverse power Lomax,Lomax-Rayleigh,power Lomax,and Lomax distributions.

Development of a novel critical nitrogen concentration-cumulative transpiration curve for optimizing nitrogen management under varying irrigation conditions in winter wheat

Accurate nitrogen(N)nutrition diagnosis is essential for improving N use efficiency in crop production.The widely used critical N(Nc)dilution curve traditionally depends solely on agronomic variables,neglecting crop water status.With three-year field experiments with winter wheat,encompassing two irrigation levels(rainfed and irrigation at jointing and anthesis)and three N levels(0,180,and 270 kg ha1),this study aims to establish a novel approach for determining the Nc dilution curve based on crop cumulative transpiration(T),providing a comprehensive analysis of the interaction between N and water availability.The Nc curves derived from both crop dry matter(DM)and T demonstrated N concentration dilution under different conditions with different parameters.The equation Nc=6.43T0.24 established a consistent relationship across varying irrigation regimes.Independent test results indicated that the nitrogen nutrition index(NNI),calculated from this curve,effectively identifies and quantifies the two sources of N deficiency:insufficient N supply in the soil and insufficient soil water concentration leading to decreased N availability for root absorption.Additionally,the NNI calculated from the Nc-DM and Nc-T curves exhibited a strong negative correlation with accumulated N deficit(Nand)and a positive correlation with relative grain yield(RGY).The NNI derived from the Nc-T curve outperformed the NNI derived from the Nc-DM curve concerning its relationship with Nand and RGY,as indicated by larger R2 values and smaller AIC.The novel Nc curve based on T serves as an effective diagnostic tool for assessing winter wheat N status,predicting grain yield,and optimizing N fertilizer management across varying irrigation conditions.These findings would provide new insights and methods to improve the simulations of water-N interaction relationship in crop growth models.

A thermo-mechanical damage constitutive model for deep rock considering brittleness-ductility transition characteristics

This paper developed a statistical damage constitutive model for deep rock by considering the effects of external load and thermal treatment temperature based on the distortion energy.The model parameters were determined through the extremum features of stress−strain curve.Subsequently,the model predictions were compared with experimental results of marble samples.It is found that when the treatment temperature rises,the coupling damage evolution curve shows an S-shape and the slope of ascending branch gradually decreases during the coupling damage evolution process.At a constant temperature,confining pressure can suppress the expansion of micro-fractures.As the confining pressure increases the rock exhibits ductility characteristics,and the shape of coupling damage curve changes from an S-shape into a quasi-parabolic shape.This model can well characterize the influence of high temperature on the mechanical properties of deep rock and its brittleness-ductility transition characteristics under confining pressure.Also,it is suitable for sandstone and granite,especially in predicting the pre-peak stage and peak stress of stress−strain curve under the coupling action of confining pressure and high temperature.The relevant results can provide a reference for further research on the constitutive relationship of rock-like materials and their engineering applications.

Statistical Superposition of Crosstalk Effects in Cable Bundles

This paper deals with the analytical derivation of phasor-domain statistical properties of crosstalk in random wire cables due to the superposition of several sources of electromagnetic interference.In this study,statistical characterization of crosstalk in cable bundles,which is available in literature for the case of one source of interference,is extended to the case of several sources operating simultaneously.The superposition of crosstalk effects is analysed in statistical terms,also taking into account the correlation between crosstalk contributions.A further random contribution,which is included in the proposed statistical model,is given by the phase relationship between the sources of interference.Analytical approximate expressions for the crosstalk mean value,variance,and probability density function are derived as functions of the cable bundle features and sources.

An Application of Machine Learning to Thalassemia Diagnosis

Mediterranean anemia is a genetic disease that currently relies heavily on expert clinical experience to determine whether patients are affected. This method is overly reliant on expert experience and is not precise enough. This paper proposes two modeling methods to predict whether patients have Mediterranean anemia. The first method involves using Principal Component Analysis (PCA) to reduce the dimensionality of the data, followed by logistic regression modeling (PCA-LR) on the reduced dataset. The second method involves building a Partial Least Squares Regression (PLS) model. Experimental results show that the prediction accuracy of the PCA-LR model is 87.5% (degree = 2, λ=4), and the prediction accuracy of the PLS model is 92.5% (ncomp = 4), indicating good predictive performance of the models.

New perspective in statistical modeling of wall-bounded turbulence

Despite dedicated effort for many decades,statistical description of highly technologically important wall turbulence remains a great challenge.Current models are unfortunately incomplete,or empirical,or qualitative.After a review of the existing theories of wall turbulence,we present a new framework,called the structure ensemble dynamics （SED）,which aims at integrating the turbulence dynamics into a quantitative description of the mean flow.The SED theory naturally evolves from a statistical physics understanding of non-equilibrium open systems,such as fluid turbulence, for which mean quantities are intimately coupled with the fluctuation dynamics.Starting from the ensemble-averaged Navier-Stokes（EANS） equations,the theory postulates the existence of a finite number of statistical states yielding a multi-layer picture for wall turbulence.Then,it uses order functions（ratios of terms in the mean momentum as well as energy equations） to characterize the states and transitions between states.Application of the SED analysis to an incompressible channel flow and a compressible turbulent boundary layer shows that the order functions successfully reveal the multi-layer structure for wall-bounded turbulence, which arises as a quantitative extension of the traditional view in terms of sub-layer,buffer layer,log layer and wake. Furthermore,an idea of using a set of hyperbolic functions for modeling transitions between layers is proposed for a quantitative model of order functions across the entire flow domain.We conclude that the SED provides a theoretical framework for expressing the yet-unknown effects of fluctuation structures on the mean quantities,and offers new methods to analyze experimental and simulation data.Combined with asymptotic analysis,it also offers a way to evaluate convergence of simulations.The SED approach successfully describes the dynamics at both momentum and energy levels, in contrast with all prevalent approaches describing the mean velocity profile only.Moreover,the SED theoretical framework is general,independent of the flow system to study, while the actual functional form of the order functions may vary from flow to flow.We assert that as the knowledge of order functions is accumulated and as more flows are analyzed, new principles（such as hierarchy,symmetry,group invariance,etc.） governing the role of turbulent structures in the mean flow properties will be clarified and a viable theory of turbulence might emerge.

Human induced dryland degradation in Ordos Plateau,China,revealed by multilevel statistical modeling of normalized difference vegetation index and rainfall time-series

Land degradation causes serious environmental problems in many regions of the world, and although it can be effectively assessed and monitored using a time series of rainfall and a normalized difference vegetation index （NDVI） from remotely-sensed imagery, dividing human-induced land degradation from vegetation dynamics due to climate change is not a trivial task. This paper presented a multilevel statistical modeling of the NDVI-rainfall relationship to detect human-induced land degradation at local and landscape scales in the Ordos Plateau of Inner Mongolia, China, and recognized that anthropogenic activities result in either positive （land restoration and re-vegetation） or negative （degradation） trends. Linear regressions were used to assess the accuracy of the multi- level statistical model. The results show that： （1） land restoration was the dominant process in the Ordos Plateau between 1998 and 2012; （2） the effect of the statistical removal of precipitation revealed areas of human-induced land degradation and improvement, the latter reflecting successful restoration projects and changes in land man- agement in many parts of the Ordos; （3） compared to a simple linear regression, multilevel statistical modeling could be used to analyze the relationship between the NDVI and rainfall and improve the accuracy of detecting the effect of human activities. Additional factors should be included when analyzing the NDVI-rainfall relationship and detecting human-induced loss of vegetation cover in drylands to improve the accuracy of the approach and elimi- nate some observed non-significant residual trends.

Adaptive Maneuvering Frequency Method of Current Statistical Model

Current statistical model(CSM) has a good performance in maneuvering target tracking. However, the fixed maneuvering frequency will deteriorate the tracking results, such as a serious dynamic delay, a slowly converging speedy and a limited precision when using Kalman filter(KF) algorithm. In this study, a new current statistical model and a new Kalman filter are proposed to improve the performance of maneuvering target tracking. The new model which employs innovation dominated subjection function to adaptively adjust maneuvering frequency has a better performance in step maneuvering target tracking, while a fluctuant phenomenon appears. As far as this problem is concerned, a new adaptive fading Kalman filter is proposed as well. In the new Kalman filter, the prediction values are amended in time by setting judgment and amendment rules,so that tracking precision and fluctuant phenomenon of the new current statistical model are improved. The results of simulation indicate the effectiveness of the new algorithm and the practical guiding significance.

From statistical analysis to process optimization during cladding using a Nd:YAG laser

Application of statistical methods to optimize the process parameters was achieved by employing full factorial design of experiments,which was accomplished by cladding using stepwise ramped laser power.The correlations between clad geometry and dilution(clad characteristics)and the main process parameters laser power(P_(l)),cladding speed(v_(c)),the powder feed rate(m)were obtained through application of variance analysis technique(ANOVA).The obtained correlations between the main processing parameters and the clad characteristics are discussed and a statistical model was developed.The desirability investigations using the developed statistical model were performed by considering the clad geometry,aspect ratio,dilution and hardness.Optimal parameters for cladding Stellite 6 on AISI 420 steel substrate and for cladding Nucalloy 488V on S355 J2 steel substrate were obtained.The optimal processing parameters can be applied to clad other materials with similar chemical compositions.

A research on statistical retrieval algorithms and spectral characteristics of the total absorption coefficients in the Yellow Sea and the East China Sea

This paper suggests a group of statistical algorithms for calculating the total absorption coefficients based on in situ data of apparent optical property and inherent optical property collected with strict quality assurance according to NASA ocean bio-optic protocols in the Yellow Sea and the East China Sea in spring 2003. The band-ratios ofRrs412/Rrs555, Rrs49o/Rrs555 are used in the algorithms to derive the total absorption coefficients （at） at 412, 440, 488, 510, 532 and 555nm bands, respectively. The average relative errors between inversed and measured values are less than 25.8%, with the correlative coefficients （R2） being 0.75-0.85. Error sensitivity analysis shows that the maximum retrieval error is less than 24.0% at ＋5% error in Rrs＇s. So the statistical algorithms of this paper are practicable. In this paper, the relations between the total absorption coefficients at 412, 488, 510, 532, 555 nm and that of 440nm are also studied. The results show that the relations between the total absorption coefficients of 400-600 nm and that of 440 nm are correlated well and all of their correlative coefficients R2 are greater than 0.99. Furthermore, a regression analysis is also done for the slope of the linear relations and wavelengths, and the R2 is also 0.99. Thus it is possible to retrieve other bands＇ total absorption coefficients with only one band absorption value, which significantly reduce the number of unknown parameters in studying other ocean color related problems.