A sludge volume index (SVI) model based on the multivariate local quadratic polynomial regression method

In this study, a multivariate local quadratic polynomial regression(MLQPR) method is proposed to design a model for the sludge volume index(SVI). In MLQPR, a quadratic polynomial regression function is established to describe the relationship between SVI and the relative variables, and the important terms of the quadratic polynomial regression function are determined by the significant test of the corresponding coefficients. Moreover, a local estimation method is introduced to adjust the weights of the quadratic polynomial regression function to improve the model accuracy. Finally, the proposed method is applied to predict the SVI values in a real wastewater treatment process(WWTP). The experimental results demonstrate that the proposed MLQPR method has faster testing speed and more accurate results than some existing methods.

Local Polynomial Regression Estimator of the Finite Population Total under Stratified Random Sampling: A Model-Based Approach

In this paper, auxiliary information is used to determine an estimator of finite population total using nonparametric regression under stratified random sampling. To achieve this, a model-based approach is adopted by making use of the local polynomial regression estimation to predict the nonsampled values of the survey variable y. The performance of the proposed estimator is investigated against some design-based and model-based regression estimators. The simulation experiments show that the resulting estimator exhibits good properties. Generally, good confidence intervals are seen for the nonparametric regression estimators, and use of the proposed estimator leads to relatively smaller values of RE compared to other estimators.

Detecting Change Points in Polynomial Regression Models with an Application to Cable Data Sets

In this paper, the Schwarz Information Criterion (SIC) is used to detect the change points in polynomial regression models. Switching quadratic regression models with same amount of model deviation and switching polynomial regression models with different amount of model deviation for different segments of regression are considered. The number of separate regimes and their corresponding regression orders are assume to be known. The method is then applied to cable data sets and the change points are successfully detected.

A performance-based hybrid deep learning model for predicting TBM advance rate using Attention-ResNet-LSTM

The technology of tunnel boring machine(TBM)has been widely applied for underground construction worldwide;however,how to ensure the TBM tunneling process safe and efficient remains a major concern.Advance rate is a key parameter of TBM operation and reflects the TBM-ground interaction,for which a reliable prediction helps optimize the TBM performance.Here,we develop a hybrid neural network model,called Attention-ResNet-LSTM,for accurate prediction of the TBM advance rate.A database including geological properties and TBM operational parameters from the Yangtze River Natural Gas Pipeline Project is used to train and test this deep learning model.The evolutionary polynomial regression method is adopted to aid the selection of input parameters.The results of numerical exper-iments show that our Attention-ResNet-LSTM model outperforms other commonly-used intelligent models with a lower root mean square error and a lower mean absolute percentage error.Further,parametric analyses are conducted to explore the effects of the sequence length of historical data and the model architecture on the prediction accuracy.A correlation analysis between the input and output parameters is also implemented to provide guidance for adjusting relevant TBM operational parameters.The performance of our hybrid intelligent model is demonstrated in a case study of TBM tunneling through a complex ground with variable strata.Finally,data collected from the Baimang River Tunnel Project in Shenzhen of China are used to further test the generalization of our model.The results indicate that,compared to the conventional ResNet-LSTM model,our model has a better predictive capability for scenarios with unknown datasets due to its self-adaptive characteristic.

From partial to complete:Wing-and tail-feather moult sequence and intensity depend on species,life-cycle stage,and moult completeness in passerines

Passerines moult during various life-cycle stages.Some of these moults involve the retention of a variable quantity of wing and tail feathers.This prompts the question whether these partial moults are just arrested complete moults or follow different processes.To address it,I investigated whether three relevant features remain constant across partial and complete moults:1) moult sequence(order of activation) within feather tracts(e.g.,consecutive outward moult of primaries) and among tracts(e.g.,starting with marginal coverts,followed by greater coverts second,tertials,etc.);2) dynamics of moult intensity(amount of feathers growing along the moult progress);and 3) protection of wing quills by overlapping fully grown feathers.To study the effect of moult completeness on these three features,I classified moults of 435 individuals from 61 species in 3 groups:i) complete and partial,ii) without and iii) with retention of feathers within tracts.To study the effect of life-cycle stage,I used postbreeding,postjuvenile,and prebreeding moults.I calculated phylogenetically corrected means to establish feather-moult sequence within tracts.I applied linear regression to analyse moult sequence among tracts,and polynomial regression to study the dynamics of moult intensity as moult progresses.Sequence and intensity dynamics of partial moults tended resemble those of the complete moult as moult completeness increased.Sequence within and among feather tracts tended to shift as moult intensity within tracts and number of tracts increased.Activation of primaries advanced in relation to the other feather tracts as number of moulted primaries increased.Tertial quills were protected by the innermost greater covert regardless of moult completeness.These findings suggest that moult is a self-organised process that adjusts to the degree of completeness of plumage renewal.However,protection of quills and differences among species and between postjuvenile-and prebreeding-moult sequences also suggest an active control linked to feather function,including protection and signalling.

Moult intensity constraints along the complete moult sequence of the House Sparrow(Passer domesticus)

Sequence and intensity are two essential components of bird moult.While the moult sequences of remex tracts are highly homogenous across passerines,other tracts apparently show a high variability.Moreover,order of moult activation among tracts are insufficiently known.Likewise,dynamics of moult intensity as moult progresses remains poorly known.Here,we provide detailed quantitative description of moult sequence and intensity in the House Sparrow(Passer domesticus).To understand their role,we tested two hypotheses on the:1) protection function of moult sequence,and 2) aerodynamic and physiological constraints on moult intensity.We scored percentage growth of 313 captured sparrows using the mass of the feathers of each tract(also length for remiges)to monitor moult intensity throughout the complete moult progress,which is defined as the fraction of new and growing feathers in a moulting bird relative to the total plumage.Moult sequence was highly variable both within wing coverts and among feather tracts,with moult sequence differing among all birds to some degree.We only found support for the protection function between greater coverts and both tertials and secondaries.Remex-moult intensity conformed to theoretical predictions,therefore lending support to the aerodynamic-constraint hypothesis.Furthermore,remex-moult speed plateaued during the central stages of moult progress.However,overall plumage-moult speed did not fit predictions of the physiological-constraint hypothesis,showing that the remex moult is only constrained by aerodynamics.Our results indicate that aerodynamic loss is not simply the inevitable effect of moult,but that moult is finely regulated to reduce aerodynamic loss.We propose that the moult of the House Sparrow is controlled through sequence and intensity adjustments in order to:1) avoid body and wing growth peaks;2) fulfil the protection function between some key feather tracts;3) reduce detrimental effects on flight ability;4) keep remex sequence fixed;and 5) relax remex replacement to last the whole moult duration.

Analyses of unified congestion measures for interrupted traffic flow on urban roads

To study the congestion of interrupted flow on urban roads, a comprehensive evaluation method is proposed. First, based on the results of correlation analysis between different parameters of interrupted flow, the traffic parameters of interrupted traffic flow are divided into two categories： the basic parameters and the operation parameters. Polynomial regression is used to formulize the nonlinear relationships between the basic parameters and the operation parameters. Then, the congestion model incorporating both operational and volume characteristics of traffic flow is proposed. The inputs of the model are the basic parameters, while the output is a dimensionless index value between 0 and 1. Finally, the proposed methods are compared with existing evaluation measures of congestion. Results show that the proposed indices can capture the variation of both the basic parameters and the operation parameters, which is more balanced compared with the existing evaluation measures.

State-of-the-art review of some artificial intelligence applications in pile foundations

Geotechnical engineering deals with materials（e.g. soil and rock） that, by their very nature, exhibit varied and uncertain behavior due to the imprecise physical processes associated with the formation of these materials. Modeling the behavior of such materials in geotechnical engineering applications is complex and sometimes beyond the ability of most traditional forms of physically-based engineering methods. Artificial intelligence（AI） is becoming more popular and particularly amenable to modeling the complex behavior of most geotechnical engineering applications because it has demonstrated superior predictive ability compared to traditional methods. This paper provides state-of-the-art review of some selected AI techniques and their applications in pile foundations, and presents the salient features associated with the modeling development of these AI techniques. The paper also discusses the strength and limitations of the selected AI techniques compared to other available modeling approaches.

Gas-solid Erosion on Bionic Configuration Surface

A three levels orthogonal table-L9（34） was used,namely,impact angle,rotating speed,erodent size,and surface configuration were considered.The three bionic surface configurations are pit,groove,and ring.The experimental results indicate the experiment factors affecting erosive rate are,in their sequence of contribution,erodent size,impact angle,configuration,and rotating speed;the erosive rate increased with increase in rotating speed,erodent size;the erosion resistance of the sample with ring structure is higher than that of the other two samples.Based on this result,regression orthogonal experiment was carried out to select the optimal erosion resistance condition with respect to the ring bionic surface configuration.Regression equations between erosive rate and experimental factors of ring surface configurations were obtained.

COLOR CORRECTION FOR HIGH-DEFINITION ELECTRONIC ENDOSCOPE

The subtle color distinction is the important function of electronic endoscope imaging diagnosis.However,after image acquisition,transmission and display,color distortions of intracorporeal organs or tissues occur inevitably,which are adverse to analyze image features accurately or to diagnose early pathological changes.A real-time color correction algorithm based on fourneighborhood and polynomial regression in YUV color space is proposed.Based on polynomial regression the color correction matrix is calculated in YUV color space according to the dierences between standard values of color checker and measured values of that imaged by the endoscope.As the correction is only executed on U and V components in YUV color space,the defect that the color of corrected images in RGB color space will change along with luminance can be avoided,and then the stability of image color is improved.Owing to four-neighborhood processing,the signal-to-noise ratio of corrected images is enhanced and the processing speed of correction algorithm is accelerated.The average color dierence is reduced from 0.3944 to 0.2850 by application of the proposed algorithm in high-denition electronic endoscope.A total of 17 frames per second can be achieved at the resolution of 1280
800,and the color characteristics of the image after processing match that of human visual system.

Application of orthogonal design in the experiment of plasma arc powder surfacing technology

In this paper, the influence of plasma arc powder surfacing technical parameters on the property of layer is defined using the orthogonal design. By the orthogonal polynomial regression, when plasma arc powder surfacing is used on the surface of the X65 steel plate with the Fe-07 alloy powder, the optimum technical parameters are the following: I=180～190 A , G=41.5 g/min , v=102 mm/min , T_0=350 ℃ , Q_l=280 L/h , Q_s=400 L/h . Further, analysis of the cracking test data showed that the cracking preheat temperature is 350 ℃ .

Discovering optimal weights in weighted‑scoring stock‑picking models: a mixture design approach

Certain literature that constructs a multifactor stock selection model adopted a weighted-scoring approach despite its three shortcomings.First,it cannot effectively identify the connection between the weights of stock-picking concepts and portfolio performances.Second,it cannot provide stock-picking concepts’optimal combination of weights.Third,it cannot meet various investor preferences.Thus,this study employs a mixture experimental design to determine the weights of stock-picking concepts,collect portfolio performance data,and construct performance prediction models based on the weights of stock-picking concepts.Furthermore,these performance prediction models and optimization techniques are employed to discover stock-picking concepts’optimal combination of weights that meet investor preferences.The samples consist of stocks listed on the Taiwan stock market.The modeling and testing periods were 1997–2008 and 2009–2015,respectively.Empirical evidence showed(1)that our methodology is robust in predicting performance accurately,(2)that it can identify significant interactions between stock-picking concepts’weights,and(3)that which their optimal combination should be.This combination of weights can form stock portfolios with the best performances that can meet investor preferences.Thus,our methodology can fill the three drawbacks of the classical weighted-scoring approach.

An Assessment of the Potential Use of Forest Residues for the Production of Bio-Oils in the Urban-Rural Interface of Louisiana

Louisiana is endowed with forest resources. Forest wastes generated after thinning, land clearing, and logging operations, such as wood debris, tree trimmings, barks, sawdust, wood chips, and black liquor, among others, can serve as potential fuels for energy production in Louisiana. This paper aims to evaluate the potential annual volumes of forest wastes established on detailed and existing data on the forest structure in the rural-urban interface of Louisiana. It also demonstrates the state’s prospects of utilizing forest wastes to produce bio-oils. The data specific to the study was deduced from secondary data sources to obtain the annual average total residue production in Louisiana and estimate the number of logging residues available for procurement for bioenergy production. The total biomass production per year was modeled versus years by polynomial regression curve fitting using Microsoft Excel. Results of the model show that the cumulative annual total biomass production for 2025 and 2030 in Louisiana is projected to be 80000000 Bone Dry Ton (BDT) and 16000000 (BDT) respectively. The findings of the study depict that Louisiana has a massive biomass supply from forest wastes for bioenergy production. Thus, the potential for Louisiana to become an influential player in the production of bio-based products from forest residues is evident. The author recommends that future research can use Geographic Information Systems (GIS) to create maps displaying the potential locations and utilization centers of forest wastes for bioenergy production in the state.

Analysis of Precipitation Trends and Prediction in Selected Cities in the Southeast Louisiana

The impacts of climate change are being felt in Louisiana, in the form of changing weather patterns that have resulted in changes in floods, hurricanes, tornadoes frequencies of occurrence, and magnitudes, among others resulting in, flooding. The variabilities in rainfall in a drainage basin affect water availability and sustainability. This study analyzed the precipitation data of Southeastern Louisiana, United States, for the period 1990 to 2020. Data used in the study was from, Donaldsonville, Galliano, Lafourche, Gonzales, Ascension, Morgan, New Orleans, Audubon, Plaquemine, and Ponchatoula, Tangipahoa, weather stations. These stations were selected because the differences between each of their highest and lowest average annual rainfall data were greater than 20 inches. To investigate climate patterns and trends for the given weather stations in Southeastern Louisiana, precipitation data were analyzed on annual time scales using data collected from the World Bank Group Climate Change Knowledge Portal for Development Practitioners and Policy Makers and the Applied Climate Information System (ACIS) of the National Weather Service Prediction Center. The data were further aggregated using annual average blocks of 4 years, and linear and polynomial regression was performed to establish trends. The highest and lowest average annual rainfall data for Donaldsonville, Galliano, Lafourche, Gonzales, Ascension, Morgan, New Orleans, Audubon, Plaquemine, and Ponchatoula, Tangipahoa, weather stations were, 75 and 48, 71 and 44, 73.5 and 52.7, 75 and 46.4, 72 and 41.3, 94 and 55.3, Ponchatoula, and 78.6 and 44, respectively. Plaquemine recorded the highest average annual average rainfall while New Orleans, Audubon station recorded the lowest. The projection of the precipitation in 2030 has been carried out to inform scientists and stakeholders about the approximate quantity of rainfall expected and enable them to make their expected impacts on agriculture, economy, etc. The precipitation for 2030 was predicted by extrapolating models for the weather stations. The data used for the modeling was selected based on the data entries most representative. Hence, the coefficient of correlation and the number of data entries were both considered. Extrapolating results for 2030 precipitation in Donaldsonville, Galliano, Gonzales, Morgan, New Orleans, Audubon, and Plaquemine were found to be within the ranges, (85.6 - 86.7), (75.55 - 76.60), (89.7 - 90.67), (99.9 - 100.5), (71.68 - 72.66), and (107.7 - 108.8) inches, respectively. Hence, the average annual precipitations in areas covered by these stations except for Plaquemine station are expected to significantly increase. A restively low increase in average precipitation is expected for Plaquemine station. The increase could impact agriculture negatively or positively depending on the crop’s soil moisture tolerance.

Application of LSTM and CONV1D LSTM Network in Stock Forecasting Model

Predicting the direction of the stock market has always been a huge challenge.Also,the way of forecasting the stock market reduces the risk in the financial market,thus ensuring that brokers can make normal returns.Despite the complexities of the stock market,the challenge has been increasingly addressed by experts in a variety of disciplines,including economics,statistics,and computer science.The introduction of machine learning,in-depth understanding of the prospects of the financial market,thus doing many experiments to predict the future so that the stock price trend has different degrees of success.In this paper,we propose a method to predict stocks from different industries and markets,as well as trend prediction using traditional machine learning algorithms such as linear regression,polynomial regression and learning techniques in time series prediction using two forms of special types of recursive neural networks:long and short time memory(LSTM)and spoken short-term memory.

Impact characterization on thin structures using machine learning approaches

Machine learning algorithms are trained and compared to identify and to characterise the impact on typical aerospace panels of different geometry.Experimental activities are conducted to build a proper impacts’dataset.Polynomial regression algorithm and artificial neural network are applied and optimised to panels without stringer to test their capability to identify the impacts.Subsequently,the algorithms are applied to panels reinforced with stringers that represent a significant increase of complexity in terms of dynamic features of the system to test:the focus is not only on the impact position’s detection but also on the event’s severity.After the identification of the best algorithm,the corresponding machine learning model is deployed on an ARM processor minicomputer,implementing an impact detection system,able to be installed on board an aerial vehicle,making it a smart aircraft equipped with an artificial intelligence decision-making system.

A Note on the Nonparametric Least-squares Test for Checking a Polynomial Relationship

Recently, Gijbels and Rousson[6] suggested a new approach, called nonparametric least-squares test, to check polynomial regression relationships. Although this test procedure is not only simple but also powerful in most cases, there are several other parameters to be chosen in addition to the kernel and bandwidth. As shown in their paper, choice of these parameters is crucial but sometimes intractable. We propose in this paper a new statistic which is based on sample variance of the locally estimated pth derivative of the regression function at each design point. The resulting test is still simple but includes no extra parameters to be determined besides the kernel and bandwidth that are necessary for nonparametric smoothing techniques. Comparison by simulations demonstrates that our test performs as well as or even better than Gijbels and Rousson’s approach. Furthermore, a real-life data set is analyzed by our method and the results obtained are satisfactory.

EFFICIENT ESTIMATION OF SEEMINGLY UNRELATED ADDITIVE NONPARAMETRIC REGRESSION MODELS

This paper is concerned with the estimating problem of seemingly unrelated(SU)nonparametric additive regression models.A polynomial spline based two-stage efficient approach is proposed to estimate the nonparametric components,which takes both of the additive structure and correlation between equations into account.The asymptotic normality of the derived estimators are established.The authors also show they own some advantages,including they are asymptotically more efficient than those based on only the individual regression equation and have an oracle property,which is the asymptotic distribution of each additive component is the same as it would be if the other components were known with certainty.Some simulation studies are conducted to illustrate the finite sample performance of the proposed procedure.Applying the proposed procedure to a real data set is also made.

Comprehensive evaluation of machine learning algorithms applied to TBM performance prediction

To date,the accurate prediction of tunnel boring machine(TBM)performance remains a considerable challenge owing to the complex interactions between the TBM and ground.Using evolutionary polynomial regression(EPR)and random forest(RF),this study devel-ops two novel prediction models for TBM performance.Both models can predict the TBM penetration rate and field penetration index as outputs with four input parameters:the uniaxial compressive strength,intact rock brittleness index,distance between planes of weakness,and angle between the tunnel axis and planes of weakness(a).First,the performances of both EPR-and RF-based models are examined by comparison with the conventional numerical regression method(i.e.,multivariate linear regression).Subsequently,the performances of the RF-and EPR-based models are further investigated and compared,including the model robustness for unknown datasets,interior relationships between input and output parameters,and variable importance.The results indicate that the RF-based model has greater prediction accuracy,particularly in identifying outliers,whereas the EPR-based model is more convenient to use by field engineers owing to its explicit expression.Both EPR-and RF-based models can accurately identify the relationships between the input and output param-eters.This ensures their excellent generalization ability and high prediction accuracy on unknown datasets.

A standard formulation for the installation of suction caissons in sand

Suction assisted installation of caisson foundations in sand relies on the developed seepage around the caisson wall.Seepage is known to produce soil loosening inside the caisson cavity and an overall reduction in soil resistance to caisson penetration.On the other hand,suction must be controlled so that no excessive piping is induced within the sand volume trapped inside the caisson cavity.When it extends over the full embedded length of the caisson wall,piping may lead to the formation of piping channels,which may compromise the established seal between caisson and soil and ultimately cause the installation process to stop.A safe installation process requires a proper design procedure to ensure that a safe suction can be predicted prior to installation.The present paper provides a framework where analytical expressions are obtained for the required suction magnitude,and for the critical suction that causes piping to initiate at the caisson tip.These analytical expressions are derived for a normalized caisson geometry,based on compiled results obtained from finite element analysis of seepage around a caisson wall,at various installation depths.The developed analytical formulation applies independently of caisson dimensions such as diameter,height and wall thickness.Critical suction for piping condition is also obtained under analytical form as a function of normalized penetration depth.The developed formulation can also be easily incorporated into design procedures or used in design codes without a need for a preliminary seepage analysis to be undertaken.The proposed suction predictions for the whole process of caisson installation in sand are validated against field trials reported in the literature.