scholarly journals Statistical Prediction and Analysis on Parameters of Vibrating Sinking Pipe Gravel Pile Machines

2021 ◽  
Vol 1744 (2) ◽  
pp. 022131
Author(s):  
Yuan Tian ◽  
Chao Tu
Keyword(s):  
Statistical Prediction ◽  
Gravel Pile

An evaluation of DSM-5 Section III Personality Disorder Criterion A (Impairment) in accounting for psychopathology

2018 ◽  
Author(s):  
Chelsea Sleep ◽  
Donald Lynam ◽  
Thomas A. Widiger ◽  
Michael L Crowe ◽  
Josh Miller
Keyword(s):  
Personality Disorders ◽  
Discriminant Validity ◽  
Factor Model ◽  
Incremental Validity ◽  
Self Report ◽  
Statistical Prediction ◽  
Dsm 5 ◽  
Criterion A ◽  
Personality Dysfunction ◽  
Axis I

An alternative diagnostic model of personality disorders (AMPD) was introduced in DSM-5 that diagnoses PDs based on the presence of personality impairment (Criterion A) and pathological personality traits (Criterion B). Research examining Criterion A has been limited to date, due to the lack of a specific measure to assess it; this changed, however, with the recent publication of a self-report assessment of personality dysfunction as defined by Criterion A (Levels of Personality Functioning Scale – Self-report; LPFS-SR; Morey, 2017). The aim of the current study was to test several key propositions regarding the role of Criterion A in the AMPD including the underlying factor structure of the LPFS-SR, the discriminant validity of the hypothesized factors, whether Criterion A distinguishes personality psychopathology from Axis I symptoms, the overlap between Criterion A and B, and the incremental predictive utility of Criterion A and B in the statistical prediction of traditional PD symptom counts. Neither a single factor model nor an a priori four-factor model of dysfunction fit the data well. The LPFS-SR dimensions were highly interrelated and manifested little evidence of discriminant validity. In addition, the impairment dimensions manifested robust correlations with measures of both Axis I and II constructs, challenging the notion that personality dysfunction is unique to PDs. Finally, multivariate regression analyses suggested that the traits account for substantially more unique variance in DSM-5 Section II PDs than does personality impairment. These results provide important information as to the functioning of the two main components of the DSM-5 AMPD and raise questions about whether the model may need revision moving forward.Keywords: dysfunction, impairment, personality disorders, Section III, incremental validity Public Significance: The alternative model of personality disorders included in Section III of the 5th addition of the Diagnostic and Statistical Manual of Mental Disorders (DSM-5) includes two primary components: personality dysfunction and maladaptive traits. The current results raise questions about how a new, DSM-5 aligned measure of personality dysfunction operates with regard its factor structure, discriminant validity, ability to differentiate between personality and non-personality based forms of psychopathology, and incremental validity in the statistical prediction of traditional DSM personality disorders.

A comprehensive study on statistical prediction and reduction of tire/road noise

2021 ◽  
pp. 107754632110131
Author(s):  
Somaye Mohammadi ◽  
Abdolreza Ohadi ◽  
Mostafa Irannejad-Parizi
Keyword(s):  
Neural Network ◽  
Road Transport ◽  
Relevance Vector Machine ◽  
Statistical Investigation ◽  
Statistical Prediction ◽  
Support Vector ◽  
Prediction Ability ◽  
Vector Method ◽  
Tire Noise ◽  
Rolling Noise

Promoting safe tires with low external rolling noise increases the environmental efficiency of road transport. Although tire builders have been striving to reduce emitted noise, the issue’s sophisticated nature has made it difficult. This article aims to make the problem straightforward, relying on recent significant improvements in statistical science. In this regard, the prediction ability of new methods in this field, including support vector machine, relevance vector machine, and convolutional neural network, along with the new architecture of the neural network is compared. Tire noise is measured under the coast-by condition. Two training strategies are proposed: extracting features from a tread pattern image and directly importing an image to the model. The relevance vector method, which is trained using the first strategy, has provided the most accurate results with an error of 0.62 dB(A) in predicting the total noise level. This precise model is used instead of experimentation to analyze the sensitivity of tire noise to its parameters using a small central composite design. The parametric study reveals striking tips for reducing noise, especially in terms of interactions between parameters that have not previously been shown. Finally, a novel two-stage approach for reducing noise by tread pattern optimization is proposed, inspired by two regression models derived from statistical investigation and variance analysis. Changes in tread pattern specifications of two case studies and their randomization have resulted in a reduction of 3.2 dB(A) for a high-noise tire and 0.4 dB(A) decrement for a quieter tire.

scholarly journals Intelligent methods for root cause analysis behind the center line deviation of the steel strip

2020 ◽  
Vol 10 (1) ◽  
pp. 386-393
Author(s):  
Henna Tiensuu ◽  
Satu Tamminen ◽  
Olli Haapala ◽  
Juha Röning
Keyword(s):  
Hot Rolling ◽  
Steel Strip ◽  
Decision Support Tool ◽  
Rolling Process ◽  
Statistical Prediction ◽  
Center Line ◽  
Quality Model ◽  
Critical Feature ◽  
Support Tool ◽  
Shape Errors

AbstractThis article presents a statistical prediction model-based intelligent decision support tool for center line deviation monitoring. Data mining methods enable the data driven manufacturing. They also help to understand the manufacturing process and to test different hypotheses. In this study, the original assumption was that the shape of the strip during the hot rolling has a strong effect on the behaviour of the steel strip in Rolling, Annealing and Pickling line (RAP). Our goal is to provide information that enables to react well in advance to strips with challenging shape. In this article, we show that the most critical shape errors arising in hot rolling process will be transferred to critical errors in RAP-line process as well. In addition, our results reveal that the most critical feature characterizes the deviation better than the currently used criterion for rework. The developed model enables the user to understand better the quality of the products, how the process works, and how the quality model predicts and performs.

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