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Junjia Y, Alias AH, Haron NA, Abu Bakar N. Identification and analysis of hoisting safety risk factors for IBS construction based on the AcciMap and cases study. Heliyon 2024; 10:e23587. [PMID: 38192814 PMCID: PMC10772131 DOI: 10.1016/j.heliyon.2023.e23587] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/15/2023] [Revised: 11/25/2023] [Accepted: 12/07/2023] [Indexed: 01/10/2024] Open
Abstract
Hoisting is an essential aspect of Industrial Building System (IBS) construction. Although research on hoisting safety in China has made strides to focus on "worker," "data," "task," "site," and "accident," there still needs to be more approaches based on multi-dimensional social system thinking. Therefore, the paper aims to fill this gap. We investigated 105 hoisting accidents in China and found that hoisting accidents occurred most frequently in China's southeast coastal region; truck-mounted cranes and tower cranes were the most common types of machinery involved in accidents; hoisting load off, capsizing of crane machinery, and workers falling from height are the three most common accident types; the average impact of a single hoisting accident is approximately RMB 2.43 million direct economic loss, 1.543 deaths and 0.829 injured. This study used three algorithms (Rindge regression, Lasson regression, and partial least squares regression) to explore the impact of deaths and injuries on direct economic losses. By combining Rasmussen's risk framework with the characteristics of hoisting construction, six risk domains and thirty-six safety risk factors were identified. Finally, we used AcciMap technology to construct a qualitative IBS hoisting management model, which exhaustively presents the systematic levels and propagation paths of the influencing factors by the PDCA method. The research helps academics explore strategies to improve the safety of hoisting construction in IBS. Moreover, the study outcomes can inform the policy-making process towards promoting healthy and sustainable construction development.
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Affiliation(s)
- Yin Junjia
- Department of Civil Engineering, Faculty of Engineering, Universiti Putra Malaysia, Serdang, 43400, Selangor, Malaysia
| | - Aidi Hizami Alias
- Department of Civil Engineering, Faculty of Engineering, Universiti Putra Malaysia, Serdang, 43400, Selangor, Malaysia
| | - Nuzul Azam Haron
- Department of Civil Engineering, Faculty of Engineering, Universiti Putra Malaysia, Serdang, 43400, Selangor, Malaysia
| | - Nabilah Abu Bakar
- Department of Civil Engineering, Faculty of Engineering, Universiti Putra Malaysia, Serdang, 43400, Selangor, Malaysia
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Outlet Liquid Material Concentration Prediction of an Evaporation Process Based on Knowledge and Data Information. Processes (Basel) 2022. [DOI: 10.3390/pr10122525] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/29/2022] Open
Abstract
The outlet liquid material concentration is a key production indicator to evaluate the evaporation quality and an important basis to adjust the evaporation operation parameters. However, the online concentration analyzer has strict installation conditions and high prices, and it is difficult to obtain the liquid material concentration in time. Usually, the field works perform imprecise operations according to the time delay information. In addition, the process data contain errors, which affects the accuracy and timeliness of process optimization and control. Therefore, a hybrid prediction model of concentration based on data reconciliation is presented in this paper. First, to obtain the high-quality process data, the data reconciliation method is applied for preprocessing. Moreover, the process mechanistic model is constructed by utilizing the process knowledge and the balance principle. Taking into account the volatility and nonlinearity characteristics, a data-driven model based on autoregressive integrated moving average integrated generalized autoregressive conditional heteroscedasticity is established, and then the support vector regression model is built for prediction residual optimization. Furthermore, the prediction results of the mechanistic model and the data-driven model are balanced comprehensively. Finally, an evaporation process is selected for simulation verification. The results demonstrate that the proposed hybrid prediction model has improved the prediction condition and performance.
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Beyaztas U, Shang HL. A robust partial least squares approach for function-on-function regression. BRAZ J PROBAB STAT 2022. [DOI: 10.1214/21-bjps523] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/19/2022]
Affiliation(s)
- Ufuk Beyaztas
- Department of Statistics, Marmara University, 34722, Kadikoy-Istanbul, Turkey
| | - Han Lin Shang
- Department of Actuarial Studies and Business Analytics, Level 7, 4 Eastern Road, Macquarie University, Sydney, New South Wales 2109, Australia
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Study on Resistant Hierarchical Fuzzy Neural Networks. ELECTRONICS 2022. [DOI: 10.3390/electronics11040598] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/16/2022]
Abstract
Novel resistant hierarchical fuzzy neural networks are proposed in this study and their deep learning problems are investigated. These fuzzy neural networks can be used to model complex controlled plants and can also be used as fuzzy controllers. In general, real-world data are usually contaminated by outliers. These outliers may have undesirable or unpredictable influences on the final learning machines. The correlations between the target and each of the predictors are utilized to partition input variables into groups so that each group becomes the input variables of a fuzzy system in each level of the hierarchical fuzzy neural network. In order to enhance the resistance of the learning machines, we use the least trimmed squared error as the cost function. To test the resistance of learning machines to adverse effects of outliers, we add at the output node some noise from three different types of distributions, namely, normal, Laplace, and uniform distributions. Real-world datasets are used to compare the performances of the proposed resistant hierarchical fuzzy neural networks, resistant densely connected artificial neural networks, and densely connected artificial neural networks without noise.
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Boukhiar S, Mourid T. Resolvent estimators for functional autoregressive processes with random coefficients. J MULTIVARIATE ANAL 2021. [DOI: 10.1016/j.jmva.2021.104884] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/28/2022]
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7
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Sparse semiparametric regression when predictors are mixture of functional and high-dimensional variables. TEST-SPAIN 2021. [DOI: 10.1007/s11749-020-00728-w] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/23/2022]
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Kawano S. Sparse principal component regression via singular value decomposition approach. ADV DATA ANAL CLASSI 2021. [DOI: 10.1007/s11634-020-00435-2] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/25/2022]
Abstract
AbstractPrincipal component regression (PCR) is a two-stage procedure: the first stage performs principal component analysis (PCA) and the second stage builds a regression model whose explanatory variables are the principal components obtained in the first stage. Since PCA is performed using only explanatory variables, the principal components have no information about the response variable. To address this problem, we present a one-stage procedure for PCR based on a singular value decomposition approach. Our approach is based upon two loss functions, which are a regression loss and a PCA loss from the singular value decomposition, with sparse regularization. The proposed method enables us to obtain principal component loadings that include information about both explanatory variables and a response variable. An estimation algorithm is developed by using the alternating direction method of multipliers. We conduct numerical studies to show the effectiveness of the proposed method.
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Palma M, Tavakoli S, Brettschneider J, Nichols TE. Quantifying uncertainty in brain-predicted age using scalar-on-image quantile regression. Neuroimage 2020; 219:116938. [PMID: 32502669 PMCID: PMC7443707 DOI: 10.1016/j.neuroimage.2020.116938] [Citation(s) in RCA: 9] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/05/2019] [Revised: 05/07/2020] [Accepted: 05/08/2020] [Indexed: 12/12/2022] Open
Abstract
Prediction of subject age from brain anatomical MRI has the potential to provide a sensitive summary of brain changes, indicative of different neurodegenerative diseases. However, existing studies typically neglect the uncertainty of these predictions. In this work we take into account this uncertainty by applying methods of functional data analysis. We propose a penalised functional quantile regression model of age on brain structure with cognitively normal (CN) subjects in the Alzheimer's Disease Neuroimaging Initiative (ADNI), and use it to predict brain age in Mild Cognitive Impairment (MCI) and Alzheimer's Disease (AD) subjects. Unlike the machine learning approaches available in the literature of brain age prediction, which provide only point predictions, the outcome of our model is a prediction interval for each subject.
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Affiliation(s)
- Marco Palma
- Department of Statistics, University of Warwick, Coventry, CV4 7AL, United Kingdom.
| | - Shahin Tavakoli
- Department of Statistics, University of Warwick, Coventry, CV4 7AL, United Kingdom
| | - Julia Brettschneider
- Department of Statistics, University of Warwick, Coventry, CV4 7AL, United Kingdom; The Alan Turing Institute, London, NW1 2DB, United Kingdom
| | - Thomas E Nichols
- Department of Statistics, University of Warwick, Coventry, CV4 7AL, United Kingdom; Oxford Big Data Institute, Li Ka Shing Centre for Health Information and Discovery, Nuffield Department of Population Health, University of Oxford, Oxford, OX3 7LF, United Kingdom; Wellcome Centre for Integrative Neuroimaging, FMRIB, Nuffield Department of Clinical Neurosciences, University of Oxford, Oxford, OX3 9DU, United Kingdom
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Kalogridis I, Van Aelst S. Robust functional regression based on principal components. J MULTIVARIATE ANAL 2019. [DOI: 10.1016/j.jmva.2019.04.003] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.6] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/27/2022]
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Dynamical multiple regression in function spaces, under kernel regressors, with ARH(1) errors. TEST-SPAIN 2019. [DOI: 10.1007/s11749-018-0614-2] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/28/2022]
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Histogram-based models on non-thin section chest CT predict invasiveness of primary lung adenocarcinoma subsolid nodules. Sci Rep 2019; 9:6009. [PMID: 30979926 PMCID: PMC6461662 DOI: 10.1038/s41598-019-42340-5] [Citation(s) in RCA: 6] [Impact Index Per Article: 1.2] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/05/2018] [Accepted: 03/27/2019] [Indexed: 02/07/2023] Open
Abstract
109 pathologically proven subsolid nodules (SSN) were segmented by 2 readers on non-thin section chest CT with a lung nodule analysis software followed by extraction of CT attenuation histogram and geometric features. Functional data analysis of histograms provided data driven features (FPC1,2,3) used in further model building. Nodules were classified as pre-invasive (P1, atypical adenomatous hyperplasia and adenocarcinoma in situ), minimally invasive (P2) and invasive adenocarcinomas (P3). P1 and P2 were grouped together (T1) versus P3 (T2). Various combinations of features were compared in predictive models for binary nodule classification (T1/T2), using multiple logistic regression and non-linear classifiers. Area under ROC curve (AUC) was used as diagnostic performance criteria. Inter-reader variability was assessed using Cohen’s Kappa and intra-class coefficient (ICC). Three models predicting invasiveness of SSN were selected based on AUC. First model included 87.5 percentile of CT lesion attenuation (Q.875), interquartile range (IQR), volume and maximum/minimum diameter ratio (AUC:0.89, 95%CI:[0.75 1]). Second model included FPC1, volume and diameter ratio (AUC:0.91, 95%CI:[0.77 1]). Third model included FPC1, FPC2 and volume (AUC:0.89, 95%CI:[0.73 1]). Inter-reader variability was excellent (Kappa:0.95, ICC:0.98). Parsimonious models using histogram and geometric features differentiated invasive from minimally invasive/pre-invasive SSN with good predictive performance in non-thin section CT.
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Febrero-Bande M, Galeano P, González-Manteiga W. Estimation, imputation and prediction for the functional linear model with scalar response with responses missing at random. Comput Stat Data Anal 2019. [DOI: 10.1016/j.csda.2018.07.006] [Citation(s) in RCA: 8] [Impact Index Per Article: 1.6] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/29/2022]
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High-dimensional functional time series forecasting: An application to age-specific mortality rates. J MULTIVARIATE ANAL 2019. [DOI: 10.1016/j.jmva.2018.10.003] [Citation(s) in RCA: 20] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/20/2022]
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18
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Shang HL. Estimation of a functional single index model with dependent errors and unknown error density. COMMUN STAT-SIMUL C 2018. [DOI: 10.1080/03610918.2018.1535068] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.2] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/27/2022]
Affiliation(s)
- Han Lin Shang
- Research School of Finance, Actuarial Studies and Statistics, Australian National University, Canberra, Australia
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Kraus D, Stefanucci M. Classification of functional fragments by regularized linear classifiers with domain selection. Biometrika 2018. [DOI: 10.1093/biomet/asy060] [Citation(s) in RCA: 10] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/13/2022] Open
Affiliation(s)
- David Kraus
- Department of Mathematics and Statistics, Masaryk University, Kotlářská 2, Brno, Czech Republic
| | - Marco Stefanucci
- Department of Statistical Sciences, Sapienza University of Rome, Piazzale Aldo Moro 5, Roma, Italy
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Prediction of soil organic carbon in a coal mining area by Vis-NIR spectroscopy. PLoS One 2018; 13:e0196198. [PMID: 29677214 PMCID: PMC5909913 DOI: 10.1371/journal.pone.0196198] [Citation(s) in RCA: 12] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/30/2017] [Accepted: 04/09/2018] [Indexed: 11/28/2022] Open
Abstract
Coal mining has led to increasingly serious land subsidence, and the reclamation of the subsided land has become a hot topic of concern for governments and scholars. Soil quality of reclaimed land is the key indicator to the evaluation of the reclamation effect; hence, rapid monitoring and evaluation of reclaimed land is of great significance. Visible-near infrared (Vis-NIR) spectroscopy has been shown to be a rapid, timely and efficient tool for the prediction of soil organic carbon (SOC). In this study, 104 soil samples were collected from the Baodian mining area of Shandong province. Vis-NIR reflectance spectra and soil organic carbon content were then measured under laboratory conditions. The spectral data were first denoised using the Savitzky-Golay (SG) convolution smoothing method or the multiple scattering correction (MSC) method, after which the spectral reflectance (R) was subjected to reciprocal, reciprocal logarithm and differential transformations to improve spectral sensitivity. Finally, regression models for estimating the SOC content by the spectral data were constructed using partial least squares regression (PLSR). The results showed that: (1) The SOC content in the mining area was generally low (at the below-average level) and exhibited great variability. (2) The spectral reflectance increased with the decrease of soil organic carbon content. In addition, the sensitivity of the spectrum to the change in SOC content, especially that in the near-infrared band of the original reflectance, decreased when the SOC content was low. (3) The modeling results performed best when the spectral reflectance was preprocessed by Savitzky-Golay (SG) smoothing coupled with multiple scattering correction (MSC) and first-order differential transformation (modeling R2 = 0.86, RMSE = 2.00 g/kg, verification R2 = 0.78, RMSE = 1.81 g/kg, and RPD = 2.69). In addition, the first-order differential of R combined with SG, MSC with R, SG together with MSC and R also produced better modeling results than other pretreatment combinations. Vis-NIR modeling with specific spectral preprocessing methods could predict SOC content effectively.
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Reiss PT, Goldsmith J, Shang HL, Ogden RT. Methods for scalar-on-function regression. Int Stat Rev 2017; 85:228-249. [PMID: 28919663 PMCID: PMC5598560 DOI: 10.1111/insr.12163] [Citation(s) in RCA: 72] [Impact Index Per Article: 10.3] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/08/2015] [Accepted: 12/28/2015] [Indexed: 01/16/2023]
Abstract
Recent years have seen an explosion of activity in the field of functional data analysis (FDA), in which curves, spectra, images, etc. are considered as basic functional data units. A central problem in FDA is how to fit regression models with scalar responses and functional data points as predictors. We review some of the main approaches to this problem, categorizing the basic model types as linear, nonlinear and nonparametric. We discuss publicly available software packages, and illustrate some of the procedures by application to a functional magnetic resonance imaging dataset.
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Affiliation(s)
- Philip T. Reiss
- Department of Child and Adolescent Psychiatry and Department of Population Health, New York University School of Medicine
- Department of Statistics, University of Haifa
| | - Jeff Goldsmith
- Department of Biostatistics, Columbia University Mailman School of Public Health
| | - Han Lin Shang
- Research School of Finance, Actuarial Studies and Statistics, Australian National University
| | - R. Todd Ogden
- Department of Biostatistics, Columbia University Mailman School of Public Health
- New York State Psychiatric Institute
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