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Objective: This study aimed to assess the viability of a multivariate regression model utilizing ultrasound findings and serum markers for predicting thyroid cancer metastasis. Methods: A retrospective analysis of 98 thyroid patients admitted from January 2022 to October 2022 was conducted to categorize them into a metastasis group (n=20) and a non-metastasis group (n=78) based on postoperative pathological results. Both groups underwent ultrasound examination and serum marker testing. Correlative analysis was performed to explore the association between various indicators and thyroid cancer metastasis. A multivariate regression model was developed, and receiver operating characteristic (ROC) curves were used to assess the predictive value of ultrasound findings, serum markers, and their combination for thyroid cancer metastasis. Results: Statistically significant differences were found in the levels of ultrasound findings and serum markers between the two groups. Nodule boundaries, presence or absence of halos, margins, lobulation, capsular invasion, surface smoothness, nodule aspect ratio, uric acid, total cholesterol, triglyceride, and LDL cholesterol levels were predictors of metastasis in thyroid cancer. The AUC value of 0.950 for the prediction of thyroid cancer metastasis by ultrasound signs combined with serologic indicators was significantly higher than 0.728 and 0.711 predicted by ultrasound signs or serologic indicators alone. Conclusion: The multivariate regression model incorporating ultrasound findings and serum markers enhances the predictive accuracy for thyroid cancer metastasis, offering essential guidance for early prediction and intervention in a clinical setting. [ABSTRACT FROM AUTHOR]

Rubberized concrete has garnered widespread attention for its environmental friendliness. To improve the compressive strength of rubberized concrete, this study utilized ultrafine powder materials to densify the rubberized concrete. The mechanical and abrasion resistance properties were studied to investigate the effects of different rubber particle sizes and content on high-density rubberized concrete's compressive and abrasion strength. Additionally, the reinforcement mechanism was elucidated through microstructural analysis. The study also developed theoretical prediction models for abrasion strength based on compressive strength and rubber content using multiple linear regression and B-spline-based nonlinear regression models. Results indicate that incorporating rubber powder and rubber particles significantly improves the abrasion resistance of high-density rubberized concrete. In addition, a 10 % rubber content in high-density rubberized concrete was identified as the optimal formulation, considering the compressive strength degradation and abrasion strength improvement. Finally, the fitted models accurately predicted the relative abrasion strength of high-density rubber powder concrete and rubber particle concrete. [ABSTRACT FROM AUTHOR]

Gas flaring (GF) has the negative impact on the environment, climate, and human health. So, regular monitoring of flares and quantification of their volume is necessary. Iran has many natural oil/gas processing plants and petrochemical companies which are concentrated in the southern region. Pars Special Economic Energy Zone (PSEEZ) is an industry part with different kinds of active flares, thus a significant potential source of environmental impacts due to gas flaring. Remotely sensed data are used in gas‐flaring detection, volume estimation, and pollution emission. In this study, we applied day/nighttime radiation and air pollutant data to estimate gas flaring volumes. We developed artificial neural network models (ANN) for finding the relationship between the field measurement of GF volume as the dependent variable and shortwave infrared and thermal infrared bands of Landsat 8, M10 band of Visible Infrared Imaging Radiometer Suite, and air pollutant (NO2, CO, O3, and SO2) of TROPOMI as independent variables. Results showed that R2 values were 0.73 for the ANN model from 2018 to 2019. The sensitivity analysis demonstrated that the thermal infrared bands of B10 and B11 of Landsat 8 had the most important role in the estimation of gas flaring volume. In contrast, the SWIR bands of Landsat 8 and all TROPOMI products were insignificant. The findings of this research help to shed light on the use of remotely sensed data in estimating the volume of gas flaring at the regional/global scale by integration of the ANN model. Plain Language Summary: Gas flaring (GF) is an essential process that used to dispose the unwanted gases in oil/gas processing plants and petrochemical companies. Obviously, burning gases has a huge and negative impact on the environment, climate, and human health. So, the best way to manage these effects are monitoring, detecting, characterizing, and estimating the volume of gas flaring. Remote sensing has enough potential to provide the useful data in all parts of the research on GF in industrial areas. In this study, we estimated the volume of gas flaring in one of the gas industries of Iran, the Pars Special Economic Energy Zone (PSEEZ) by using remotely sensed data (B6, B7, B10, B11/landsat8‐ M10/VIIRS‐ NO2, CO, O3, SO2/Sentinel‐5P) and ground data based on two kinds of models, artificial neural network (ANN), and multivariate regression. Since there was a non‐linear relationship between the variables and the volume of gas flaring, the ANN model showed the most acceptable results. Among all the variables, bands 10 and 11 of Landsat 8 played a very important role in estimating the gas flaring volume. Key Points: Air pollutants and remote sensing radiation used for estimation of gas flaring volumeThermal infrared bands of Landsat 8 showed the most important role in GF volume estimationNO2 showed more significant role in GF volume estimation compared to others pollutants [ABSTRACT FROM AUTHOR]

ObjectiveThis study aimed to assess the viability of a multivariate regression model utilizing ultrasound findings and serum markers for predicting thyroid cancer metastasis.MethodsA retrospective analysis of 98 thyroid patients admitted from January 2022 to October 2022 was conducted to categorize them into a metastasis group (n=20) and a non-metastasis group (n=78) based on postoperative pathological results. Both groups underwent ultrasound examination and serum marker testing. Correlative analysis was performed to explore the association between various indicators and thyroid cancer metastasis. A multivariate regression model was developed, and receiver operating characteristic (ROC) curves were used to assess the predictive value of ultrasound findings, serum markers, and their combination for thyroid cancer metastasis.ResultsStatistically significant differences were found in the levels of ultrasound findings and serum markers between the two groups. Nodule boundaries, presence or absence of halos, margins, lobulation, capsular invasion, surface smoothness, nodule aspect ratio, uric acid, total cholesterol, triglyceride, and LDL cholesterol levels were predictors of metastasis in thyroid cancer. The AUC value of 0.950 for the prediction of thyroid cancer metastasis by ultrasound signs combined with serologic indicators was significantly higher than 0.728 and 0.711 predicted by ultrasound signs or serologic indicators alone.ConclusionThe multivariate regression model incorporating ultrasound findings and serum markers enhances the predictive accuracy for thyroid cancer metastasis, offering essential guidance for early prediction and intervention in a clinical setting.

In this paper, we study the estimation problem about the regression coefficients of a multivariate regression model with measurement errors under some uncertain restrictions. Specifically, we propose the unrestricted estimator (UE) and three restricted estimators (REs), and prove that they are all consistent for the true coefficients. We derive the asymptotic distributions of the proposed estimators under the sequence of local alternative restrictions. We also propose shrinkage estimators (SEs) to address the problem of the uncertainty of the restrictions. In addition, we establish the asymptotic distributional risk (ADR) of the proposed estimators and compare the risk performance of these estimators. It is established that the REs perform better than the UE only near the restriction, while they perform poorly as one moves farther away from the restriction. We also prove that SEs dominate the UE. These theoretical results are confirmed by simulations. [ABSTRACT FROM AUTHOR]

Background: Enhancement in maximal oxygen consumption (VO2max) induced by hypoxic training is important for both athletes and non-athletes. However, the lack of comparison of multiple paradigms and the exploration of related modulating factors leads to the inability to recommend the optimal regimen in different situations. This study aimed to investigate the efficacy of seven common hypoxic training paradigms on VO2max and associated moderators. Methods: Electronic (i.e., five databases) and manual searches were performed, and 42 studies involving 1246 healthy adults were included. Pairwise meta-analyses were conducted to compare different hypoxic training paradigms and hypoxic training and control conditions. The Bayesian network meta-analysis model was applied to calculate the standardised mean differences (SMDs) of pre–post VO2max alteration among hypoxic training paradigms in overall, athlete, and non-athlete populations, while meta-regression analyses were employed to explore the relationships between covariates and SMDs. Results: All seven hypoxic training paradigms were effective to varying degrees, with SMDs ranging from 1.45 to 7.10. Intermittent hypoxia interval training (IHIT) had the highest probability of being the most efficient hypoxic training paradigm in the overall population and athlete subgroup (42%, 44%), whereas intermittent hypoxic training (IHT) was the most promising hypoxic training paradigm among non-athletes (66%). Meta-regression analysis revealed that saturation hours (coefficient, 0.004; P = 0.038; 95% CI [0.0002, 0.0085]) accounted for variations of VO2max improvement induced by IHT. Conclusion: Efficient hypoxic training paradigms for VO2max gains differed between athletes and non-athletes, with IHIT ranking best for athletes and IHT for non-athletes. The practicability of saturation hours is confirmed with respect to dose–response issues in the future hypoxic training and associated scientific research. Registration: This study was registered in the PROSPERO international prospective register of systematic reviews (CRD42022333548).

There are serious concerns about the nature and transmission ofthe COVID-19 pandemic. The COVID-19 has created a national threat and health crisis that is dispersal rapidly in India as well. In the initial phase, the pandemic of COVID-19 has covered highly globalized and developed states and regions of India, i.e., Kerala, Maharashtra (Mumbai), Delhi, etc. But over a period of time, the tentacles ofthe COVID-19 have spread to aillndian states and acquired regional patterns with different magnitude and cause-effect relation. Thus, to overcome the crisis situation, there is a need to understand the special features and causes of COVID- 1 9. Therefore, the main objective of this paper is to analyze the growth pattern and nature of COVID-19 in Indian states and also identify the main factors which have influenced the growth pattern of COVID- 19. For this purpose, a set of indicators has been chosen for analyzing multivariate linear regression model. To examine how the spread o f COVID-19 differs from one state to others, a cluster analysis has been made at the state level, using appropriate indicators. The study is based on secondary data that have been taken from different sources. This study indicates that the distribution of the COVID- 19 confirmed cases and deaths were uneven across India. The selected demographic and socio-economic components, including total population, urban population, poverty, income, are the key factors in regulating overall casualties of COVID- 19 in India. [ABSTRACT FROM AUTHOR]

Enhancement in maximal oxygen consumption (VO 2max) induced by hypoxic training is important for both athletes and non-athletes. However, the lack of comparison of multiple paradigms and the exploration of related modulating factors leads to the inability to recommend the optimal regimen in different situations. This study aimed to investigate the efficacy of seven common hypoxic training paradigms on VO 2max and associated moderators. Electronic (i.e., five databases) and manual searches were performed, and 42 studies involving 1246 healthy adults were included. Pairwise meta-analyses were conducted to compare different hypoxic training paradigms and hypoxic training and control conditions. The Bayesian network meta-analysis model was applied to calculate the standardised mean differences (SMDs) of pre–post VO 2max alteration among hypoxic training paradigms in overall, athlete, and non-athlete populations, while meta-regression analyses were employed to explore the relationships between covariates and SMDs. All seven hypoxic training paradigms were effective to varying degrees, with SMDs ranging from 1.45 to 7.10. Intermittent hypoxia interval training (IHIT) had the highest probability of being the most efficient hypoxic training paradigm in the overall population and athlete subgroup (42%, 44%), whereas intermittent hypoxic training (IHT) was the most promising hypoxic training paradigm among non-athletes (66%). Meta-regression analysis revealed that saturation hours (coefficient, 0.004; P = 0.038; 95% CI [0.0002, 0.0085]) accounted for variations of VO 2max improvement induced by IHT. Efficient hypoxic training paradigms for VO 2max gains differed between athletes and non-athletes, with IHIT ranking best for athletes and IHT for non-athletes. The practicability of saturation hours is confirmed with respect to dose–response issues in the future hypoxic training and associated scientific research. This study was registered in the PROSPERO international prospective register of systematic reviews (CRD42022333548). [ABSTRACT FROM AUTHOR]

Housing pattern and life style comprise a part of the society,s culture. In the current times and in line with the changing of society and culture, we are witnessing the transformation of accommodation style and pattern. Therefore, the aim of this research is analyzing the relationship between changes of life style and the pattern of Islamic housing in Tabriz metropolis. The method of the research is descriptive, survey and inferential and is based on referring to Islamic sources of Quran verses and narratives. In addition, questionnaires have been used to achieve the research purposes. The statistical population of the research consists of all the residents of Tabriz in the whole area of this metropolis and the subjects were selected using simple accidental method of sampling. For confirming or rejecting the hypotheses, SPSS software was used for analyzing the data obtained from the questionnaires. By analyzing the relationship between variables and ranking of the indices and components of the research it became possible by application of multiple regression model and single-sample t test. In addition, using network analysis model and Super Decision software, the ideas of 30 experts were extracted by distributing questionnaires and the elements and components of each factor were ranked. The results of the research indicated that the total multiplier correlation coefficient for independent and dependent variables were 0.906 R and the coefficient of determination was 0.822 and the adjusted coefficient of determination was 0.818. This fact indicates a high correlation between the variable of lifestyle and Islamic housing pattern. From39 sub-indicators, 12 of them which show changes in lifestyle values​​, belong respectively to the index of respect with coefficient (0.367), cultural values ​​(0.243), behavioral patterns (0.215), economic values ​​(0.132). These values have gained high privileges. In addition, social index, religious identity index and spatial and environmental values ​​have lower ranks. Diminishing spiritual values against material ones ​​ are significant in the sense that the changes in lifestyle values ​​have pushed the model of Islamic housing toward modernization.

In this paper, we study a constrained estimation problem in a multivariate measurement error regression model. In particular, we derive the joint asymptotic normality of the unrestricted estimator (UE) and the restricted estimators (REs) of the matrix of the regression coefficients. The derived result holds under the hypothesized restriction as well as under the sequence of alternative restrictions. In addition, we establish Asymptotic Distributional Risk for the UE and the REs and compare their relative performance. It is established that near the restriction, the restricted estimators (REs) perform better than the UE. But the REs perform worse than the UE when one moves far away from the restriction. Further, we explore by simulation the performance of the shrinkage estimators (SEs). The numerical findings corroborate the established theoretical results about the relative risk dominance between the REs and the UE. The findings also show that near the restriction, the REs dominate SEs but as one moves far away from the restriction, REs perform poorly while SEs dominate always the UE. [ABSTRACT FROM AUTHOR]

In this article, a multivariate extension of the unit-sinh-normal (USHN) distribution is presented. The new distribution, which is obtained from the conditionally specified distributions methodology, is absolutely continuous, and its marginal distributions are univariate USHN. The properties of the multivariate USHN distribution are studied in detail, and statistical inference is carried out from a classical approach using the maximum likelihood method. The new multivariate USHN distribution is suitable for modeling bounded data, especially in the (0 , 1) p region. In addition, the proposed distribution is extended to the case of the regression model and, for the latter, the Fisher information matrix is derived. The numerical results of a small simulation study and two applications with real data sets allow us to conclude that the proposed distribution, as well as its extension to regression models, are potentially useful to analyze the data of proportions, rates, or indices when modeling them jointly considering different degrees of correlation that may exist in the study variables is of interest. [ABSTRACT FROM AUTHOR]

Objectives: This study aims to examine and explain the differences at city level in cumulative COVID-19 cases and time from first to last infection during the first 6 weeks of the epidemic in China. Methods: A quantitative study is conducted in China based on the multisource spatial data of 315 Chinese cities. Firstly, the spatial discrepancy of COVID-19 transmission was examined based on spatial autocorrelation analysis and hot pot analysis. Next, a comprehensive indicator framework was established by including a wide range of factors such as human mobility, geographical features, public health measures, and residents’ awareness. Finally, multivariate regression models using these variables were constructed to identify the determinants of COVID-19 transmission. Results: Significant spatial discrepancy of transmission was proved, and 10 determinants were identified. Conclusions: The transmission consequence (measured by the number of cumulative cases) was mostly correlated with the migration scale from Wuhan, followed by socioeconomic factors. Transmission duration (measured as the time from the first to last case within the city) was mostly determined by total migration scale and lockdown speed, which suggests that timely implementation of public health measures facilitated fast control of transmission. Residents’ attention to COVID-19 was proved to be not only helpful for reducing confirmed cases, but also in favor of rapid transmission control. Altitude produced slight but significant effect on transmission duration. These conclusions are expected to provide decision support for the local governments of China and other jurisdictions.

Several authors refer to product portfolio management as an essential process because it may be used as a corporate management tool. However, the product portfolio management methods which are often adopted have limitations that prevent its use in practice, mainly due to the high dimensionality of selecting an optimal portfolio. Moreover, the large amount of available data is a relevant issue for practical applications. Thus, the contribution of this article is to propose a method for the product life cycle to monitor time-series behaviour patterns. The goal is to identify changes that may indicate that the product portfolio needs to be revised. The proposed method uses a multivariate regression model to relate financial variables associated with the products portfolio, the performance of products against competition, and even macroeconomic data. The objective is, through profile monitoring, to identify the specific time for the product portfolio review decision-making. We adopted three tools to develop a method -- principal component analysis, multivariate regression model, and profile monitoring with Hotelling T² Control chart. A Monte Carlo simulation validated the approach. The results showed false alarm rate and average time to signal to be similar to previous studies. Finally, the application of the model is illustrated in a real case, using data provided by a company's portfolio of agricultural equipment. [ABSTRACT FROM AUTHOR]

To assist water managers in south‐central Oklahoma prepare for future drought, reliable place‐based drought forecasts are produced. Past‐, present‐, and future‐forecasted climate indices (Multivariate ENSO Index, Pacific Decadal Oscillation index, and Atlantic Multidecadal Oscillation index) and past and present Palmer Drought Severity Index (PDSI) are employed as predictor variables to forecast PDSI using a multivariate regression technique. PDSI is forecasted 18 months in advance with sufficient skill to provide water managers early warning of drought. Using a training data set obtained from the period January 1901 to November 2021, a second‐order model equation that contains, without any restriction, all the predictors and their interaction terms is built to predict drought intensity. Significant predictors are selected through stepwise regression, with cross‐validation producing the simplest restricted model that describes the data well. PDSI values are predicted using 1000 fitted restricted models produced from bootstrapping, then averaged monthly. The technique found the best‐fitting model and estimated the model coefficients that minimized the sum of squared deviations between the fitted model and the predictor variables. The adjusted R‐squared value of the restricted model is large enough to explain an adequately accurate model, and relatively low values of error measures point to good predictive ability of the model. Although the model slightly overestimates the PDSI forecast maxima and minima, it necessarily captures the timing of the periods of severe to exceptional drought. Plain Language Summary: Natural resource managers in south‐central Oklahoma experienced severe‐to‐exceptional drought conditions during much of 2011–2014. Water managers from the Chickasaw Nation expressed the desire to obtain earlier advisories of impending drought. As a result, the author team developed an experimental predictive drought model using several statistical techniques. Results indicate that the model can adequately predict the timing and intensity of a common drought indicator (i.e., the Palmer Drought Severity Index) for south‐central Oklahoma out to 18 months in advance. This lead time is useful for water managers to plan for the future risk of water quantity and quality problems. Key Points: The 2011–2014 drought in the Southern Great Plains stressed water management and tourismThe authors developed an experimental predictive drought model for south‐central OklahomaThe model predicted monthly Palmer Drought Severity Index well out to 18 months [ABSTRACT FROM AUTHOR]

This study uses metakaolin, sodium hydroxide, sodium metasilicate, and water content as the reaction variables in the application of the design of experiment (DOE) method. At the same time, the various component factors and their interactions were analyzed to understand how these factors affect the mechanical properties of a metakaolin-based geopolymer incorporated with SiC sludge (SCSGPs). The results of the statistical analysis showed that the compressive strength of SCSGPs was affected by the Na/Si molar ratio (NSR) (p-level = 0.000 <0.05), the Na/Al molar ratio (NAR) (p-level= 0.000 <0.05), and the interaction between the dissolution rate of Si (DRA). Within the design scope of this study, the maximum compressive strength of SCSGPs and the coefficients of the NSR, the NAR, and the DRA of SCSGPs was evaluated. The multiple regression analysis and the tested coefficient of r2 were also studied. The multiple regression analysis models provide an effective reference for the application of SCSGPs. [ABSTRACT FROM AUTHOR]

Background: Preoperative radiographic templating for total hip arthroplasty (THA) has been shown to be inaccurate, although essential for streamlining operating room efficiency. Although demographic data have shown to predict total knee arthroplasty component sizes, the unique contour and design among femoral stem implants have limited a similar application for hip arthroplasty. The purpose of this study was to determine whether demographic data may predict cementless THA size independent of the stem design.Methods: A consecutive series of 1,653 index cementless metaphyseal-fitting THAs were reviewed between 2007 and 2019. This included 12 unique femoral component designs, 6 acetabular component designs, 60 femur size-design combinations, and 23 acetabular size-design combinations. Implanted component sizes and patient demographic data were collected, including gender, height, weight, laterality, age, race, and ethnicity. Multivariate linear regressions were formulated to predict implanted femur and acetabular component sizes from the demographic data.Results: There was a significant linear correlation between gender, implant model, age, height, and weight for femur (R2 = 0.778; P < .001) and acetabular (R2 = 0.491; P < .001) sizes. Calculated femur and acetabular component sizes averaged within 0.97 and 0.95 sizes of those implants, respectively. Femur and acetabular sizes were predicted within 1 size 79.1% and 78.2% and within 2 sizes 94.3% and 94.6% of the time, respectively.Conclusions: Multivariate regression models were created based on specific demographics data to predict femur and acetabular component sizes. The model allows for simplified preoperative planning and potential cost savings implementation. A free phone application named EasyTJA was constructed for ease of implementation. [ABSTRACT FROM AUTHOR]

Abstract Background Different classification of hospitals (COH) have an important impact on medical expenditures in China. The objective of this study is to examine the impact of COH on medical expenditures with the hope of providing insights into appropriate care and resource allocation. Methods From the perspective of COH framework, using the Urban Employee Basic Medical Insurance (UEBMI) data of Chengdu City from 2011 to 2015, with sample size of 488,623 hospitalized patients, our study empirically analyzed the effect of COH on medical expenditure by multivariate regression modeling. Results The average medical expenditure was 5468.86 Yuan (CNY), the average expenditure of drug, diagnostic testing, medical consumables, nursing care, bed, surgery and blood expenditures were 1980.06 Yuan (CNY), 1536.27 Yuan (CNY), 500.01 Yuan (CNY), 166.23 Yuan (CNY), 221.98 Yuan (CNY), 983.18 Yuan (CNY) and 1733.21 Yuan (CNY) respectively. Patients included in the analysis were mainly elderly, with an average age of 86.65 years old. Female and male gender were split evenly. The influence of COH on total medical expenditures was significantly negative (p

The advent of the Internet of Things (IoT) allows the Cyber-Physical System (CPS) components to communicate with other devices, and to interact with safety-critical systems, posing new research challenges in security, privacy, and reliability. Efficient power measurement in smart IoT devices has become one of the key research topics. In this paper, we design and develop a CPS to detect IoT security threats via behavioral power profiling of a heterogeneous wireless sensor device using a Raspberry Pi and a smartphone. Experimentation and verification have been conducted on a group of smart IoT devices with different test scenarios, including the device in an idle and active state with distributed denial-of-service (DDoS) and a man-in-the-middle (MitM) attack. We propose to use the device power consumption rate to predict and detect a security threat using statistical signal processing and multivariate regression model. The proposed system can detect a potential security threat with an average accuracy of 80% and a device high of 89%.

This paper empirically examines the difference in productivity existing between the public and private sector of the mining industry in India. The literature on the effect of firm ownership on productive efficiency stands highly divided; hence, in this context, our study adds to the literature by attempting to study the effect of firm ownership on total factor productivity (TFP) in the four sectors of the Indian mining industry from 2000 to 2016. Here, we have sought to compare the productivity difference between the public and private mining firms in the four sectors, namely metallic, non-metallic, coal and petroleum. This paper uses the Levinson and Petrin (LP) method for estimating the TFP of each firm. The results indicate the superiority of private firms in three sectors—metallic, non-metallic, and coal—whereas the petroleum sector reports quite the opposite result. The results raise questions about the stability of the public sector firms over time. Overall, the paper suggests measures like provision of incentives, improvement in infrastructural facilities, upgradation of manpower and so on to improve the productivity performance of public sector firms. [ABSTRACT FROM AUTHOR]

Multivariate regression models are commonly used in industrial process measurements. Locally weighted partial least squares (LWPLS) is a just-in-time learning (JITL) method for nonlinear multivariate regression models. However, it considers only the sample importance. In this article, a novel JITL multivariate regression model termed double LWPLS (D-LWPLS) is proposed to solve complex nonlinear problems and promote the model accuracy. Both the sample importance and variable importance are taken into consideration for the weighted model. Variable contribution is modified according to the variable importance. Sample similarity measurement is a significant basis for the model sample selection and sample weight calculation. The traditional sample similarity measurement methods take only the input information of samples as the index. In the D-LWPLS, a weighted similarity measurement is employed. The information on both input and output is considered by weighting the input variables according to the correlation between them. The effectiveness and the flexibility of the D-LWPLS model are demonstrated by the prediction results of a numerical example, a low-dimensional data set of an industrial process, and a high-dimensional data set of UV-Vis spectra of a polymetallic ion solution, which represent three kinds of nonlinear problems of classical, time-varying, and blind background fluctuation. [ABSTRACT FROM AUTHOR]

Graphical abstract Highlights • Evaluate the WF prod and WF con of crops in net water import and export regions. • Analyze the statistical significances of 7 influential factors through LRM and NLRM. • GDP and Pop were major positive factors for crops' WFs in water import regions. • Y unit and D were major negative factors for crops' WFs in water import regions. • Y unit was a major negative factor for crops' WFs in net water export region. Abstract Agriculture is a key sector and a major consumer of water resources; therefore, a clear understanding of the agricultural water demand for crop production and consumption is imperative for addressing water scarcity problems, particularly in water export regions. This study provides new insights into the influential factors driving the changes in the agricultural production water footprint (WF prod) and consumption water footprint (WF con) in the net water import and net water export regions. The WF prod and WF con of wheat are evaluated in Beijing city (the net water import region) and Heilongjiang province (the net water export region) over the period 1996–2015. The statistical significances of the influential factors, i.e., climate change, gross domestic product, population, dietary demand and technology update are determined using a multivariate linear regression model (LRM) and nonlinear regression model (NLRM). The results indicate that the gross domestic product and population were the dominant positive influential factors, whereas technology update and dietary demand were the dominant negative influential factors affecting the changes in the WF prod and WF con in the net water import region. In the net water export region, technology update was the dominant negative influential factor affecting the changes in the WF prod and WF con. Climate change did not contribute significantly to the changes in the WF prod and WF con of wheat; however, it was an important factor (especially precipitation for the green WF with an average relative importance of more than 22% and the blue WF with an average relative importance of more than 15%) affecting the changes in the WF prod and WF con of the crop. An in-depth analysis of the influential factors that contribute to the changes in the WFs is fundamentally important for decision-makers to develop countermeasures and strategic planning implementations to mitigate water resource pressure in China. [ABSTRACT FROM AUTHOR]

An experimental study that contributes to the understanding of the relationships between petrographic features and engineering properties of igneous rocks is conducted. To this end, a wide range of igneous rocks were tested for their engineering properties including abrasivity (Cerchar abrasivity index), mechanical (point load strength index Is(50)), basic physical (dry density and porosity) and dynamic (P-wave velocity) characteristics. Moreover, a semi-automatic method has been developed to analyze petrographic data that relies on digital image acquisition from representative parts of representative thin sections of samples, semi-automatic image segmentation and image analysis. The method quantifies 18 petrographic features including size descriptors (area, perimeter, equivalent circular diameter, minimum Feret's diameter, maximum Feret's diameter), shape descriptors (elongation, orientation, eccentricity, compactness, rectangularity, solidity, convexity), rock fabric coefficients (index of interlocking, index of grain size homogeneity, texture coefficient) and mineralogical indices (saturation index, feldspathic index, colouration index). The Pearson's correlation coefficient and multivariate regression analysis are employed to analyze the relationships between extracted petrographic features and engineering properties. In general, fine-grained and basic igneous rocks compared to the acidic and coarse-grained ones possess higher engineering quality and lower abrasiveness potential. The results imply that mineralogical composition tends to be more important than rock fabric characteristics in determining the engineering properties of igneous rocks. Furthermore, among rock fabric characteristics, size descriptors have significant influence on the engineering properties. Overall, it was found that mineralogical composition and rock fabric characteristics provide a suitable complement to reliably predict engineering properties of igneous rocks. [ABSTRACT FROM AUTHOR]

Regression analysis with compositional response, observations carrying relative information, is an appropriate tool for statistical modelling in many scientific areas (e.g. medicine, geochemistry, geology, economics). Even though this technique has been recently intensively studied, there are still some practical aspects that deserve to be further analysed. Here we discuss the issue related to the coordinate representation of compositional data. It is shown that linear relation between particular orthonormal coordinates and centred log-ratio coordinates can be utilized to simplify the computation concerning regression parameters estimation and hypothesis testing. To enhance interpretation of regression parameters, the orthogonal coordinates and their relation with orthonormal and centred log-ratio coordinates are presented. Further we discuss the quality of prediction in different coordinate system. It is shown that the mean squared error (MSE) for orthonormal coordinates is less or equal to the MSE for log-transformed data. Finally, an illustrative real-world example from geology is presented.

Xing Zhang, Tatsuo Oyama National Graduate Institute for Policy Studies, Tokyo, Japan Abstract: Japan's health care system is considered one of the best health care systems in the world. Hospitals are one of the most important health care resources in Japan. As such, we investigate Japanese hospitals from various viewpoints, including their roles, ownership, regional distribution, and characteristics with respect to the number of beds, staff, doctors, and financial performance. Applying a multivariate analysis and regression model techniques, we show the functional differences between urban populated prefectures and remote ones; the equality gap among all prefectures with respect to the distribution of the number of beds, staff, and doctors; and managerial differences between private and public hospitals. We also review and evaluate the local public hospital reform executed in 2007 from various financial aspects related to the expenditure and revenue structure by comparing public and private hospitals. We show that the 2007 reform contributed to improving the financial situation of local public hospitals. Strategic differences between public and private hospitals with respect to their management and strategy to improve their financial situation are also quantitatively analyzed in detail. Finally, the remaining problems and the future strategy to further improve the Japanese health care system are described. Keywords: health care system, health care resource, public hospital, multivariate regression model, financial performance

The study examines the levels of poverty, income inequality and determinants of income of five major ethnic communities (i.e., Chakma, Garo, Khasi, Marma and Tripura) of Bangladesh using a survey data of 250 households. Results revealed that agriculture constitutes 72.6% of the total income followed by salaried employment at 13.4% and off-farm income at 6.1%. The amount of cultivated land, the share of land rented-in and the number of working family members significantly increase agricultural income whereas education, the number of working family members and experience significantly increase non-agricultural income. Income is significantly higher for the Garo and Khasi communities and lower for the Tripura community. Income inequality varies across communities with high level of inequality amongst the Khasi and Garo communities (Gini coefficient 0.39 and 0.35) while the Chama, Marma and Tripura communities have a low and similar level of inequality (Gini coefficient 0.23-0.26). Poverty level is high for the Garo community and low for the Chakma and Marma communities. However, the depth of poverty is highest for the Tripura community. Policy implications include investment in education and land and tenurial reform measuresto consolidate farm size as well as smooth functioning of the land rental market in order to increase agricultural and/or total household income of the ethnic communities. [ABSTRACT FROM AUTHOR]

To address the existing and limited original data and lower prediction robustness, a new multivariate regression forecasting model combined with gliding data barycenter was proposed. In this new forecasting method, the original data was interpolated and the corresponding data barycenter was optimized. Then, the important impact factors of ration consumption were analysed and chosen for the multivariate regression model. In simulation experiments, the training data of 35 years (1981-2015) were used, and the results have shown that the proposed method can greatly improve prediction accuracy and robustness. [ABSTRACT FROM AUTHOR]

Color detection from a light emitting diode (LED) array using a smartphone camera is very difficult in a visual multiple-input multiple-output (visual-MIMO) system. In this paper, we propose a method to determine the LED color using a smartphone camera by applying regression analysis. We employ a multivariate regression model to identify the LED color. After taking a picture of an LED array, we select the LED array region, and detect the LED using an image processing algorithm. We then apply the k-means clustering algorithm to determine the number of potential colors for feature extraction of each LED. Finally, we apply the multivariate regression model to predict the color of the transmitted LEDs. In this paper, we show our results for three types of environmental light condition: room environmental light, low environmental light (560 lux), and strong environmental light (2450 lux). We compare the results of our proposed algorithm from the analysis of training and test R-Square (%) values, percentage of closeness of transmitted and predicted colors, and we also mention about the number of distorted test data points from the analysis of distortion bar graph in CIE1931 color space. [ABSTRACT FROM AUTHOR]

Abstract: In low‐resource settings, there is a need to develop models that can address contributions of household and outdoor sources to population exposures. The aim of the study was to model indoor PM2.5 using household characteristics, activities, and outdoor sources. Households belonging to participants in the Mother and Child in the Environment (MACE) birth cohort, in Durban, South Africa, were randomly selected. A structured walk‐through identified variables likely to generate PM2.5. MiniVol samplers were used to monitor PM2.5 for a period of 24 hours, followed by a post‐activity questionnaire. Factor analysis was used as a variable reduction tool. Levels of PM2.5 in the south were higher than in the north of the city (P < .05); crowding and dwelling type, household emissions (incense, candles, cooking), and household smoking practices were factors associated with an increase in PM2.5 levels (P < .05), while room magnitude and natural ventilation factors were associated with a decrease in the PM2.5 levels (P < .05). A reasonably robust PM2.5 predictive model was obtained with model R2 of 50%. Recognizing the challenges in characterizing exposure in environmental epidemiological studies, particularly in resource‐constrained settings, modeling provides an opportunity to reasonably estimate indoor pollutant levels in unmeasured homes. [ABSTRACT FROM AUTHOR]

Land prices are among the most important parameters of urbanization and have been an important subject of urban geography studies for many years. The relationship between urban geography and land prices was examined in the first established models, which had linear and static structures. In these models, which have a radial form, cities are considered to be commercial centers. However, since the 20th century, it has been accepted that cities have structures without obvious order, consisting of many subsystems related to political, social, and economic life and space. This irregular structure that repeats itself independent of scale has a fractal geometry. Developments in the field of geographic information systems in the last 30 years have provided great convenience in analyzing the structure of cities with fractal dimensions. The geometric shapes of buildings, streets, and blocks that create the physical city form at the same time constitute the urban geometry. This study, which aims to investigate the spatial relationship between urban geometry and land prices, examines the relationship between the fractal dimension values of buildings, streets, blocks, and land prices and whether the factors of population and distance to the center have an impact on this relationship by using geostatistical methods. In this context, the fractal dimension values of urban geometry components were calculated separately in the study area, consisting of 65 neighborhoods. A two-step cluster analysis was used to determine how these obtained fractal values are dispersed geographically within the study area. By measuring the success of clustering through the independent samples t-test, it was decided which data would be used in the regression model in which the relationship between urban geometry and land prices would be established. By using exploratory factor analysis, intercorrelated data to be used in the regression model were eliminated. According to the results of the multivariate regression model, it was revealed that there was a directly proportional relationship between the fractal dimension values of building-block geometry and land prices, and an inversely proportional relationship between the fractal dimension values of street geometry and land prices.

The increasingly mechanical requirements of offshore structures have established the relevance of fracture mechanics-based quality control in welded joints. For this purpose, crack tip opening displacement (CTOD) at a given distance from the crack tip has been considered one of the most suited parameters for modeling and control of crack growth, and it is broadly used at the industrial level. We have modeled, through multivariate analysis techniques, the relationships among CTOD values and other material properties (such as hardness, chemical composition, toughness, and microstructural morphology) in high-thickness offshore steel welded joints. In order to create this model, hundreds of tests were done on 72 real samples, which were welded with a wide range of real industrial parameters. The obtained results were processed and evaluated with different multivariate techniques, and we established the significance of all the chosen explanatory variables and the good predictive capability of the CTOD tests within the limits of the experimental variation. By establishing the use of this model, significant savings can be achieved in the manufacturing of wind generators, as CTOD tests are more expensive and complex than the proposed alternatives. Additionally, this model allows for some technical conclusions.