Multi-factor and multi-level predictive models of building natural period

In this paper, a new method is developed to find the ductility ratio in blast walls, resulted by hydrocarbon explosions. In this method, only the explosion energy and distance from the centre of explosion are required to find the damage by using simple predictive models in terms of empirical-type formulas. The explosion model herein is a TNO multiphysic method. This provides the maximum overpressure and pulse duration in terms of the explosion length and distance from explosion centre. Thereafter, the obtained results are combined with the SDOF model of the blast wall to determine the ductility ratio and the damage. By using advanced optimisation techniques, two types of predictive models are found. In the first model, the formula is found in terms of 2 parameters of explosion length and distance from explosion centre. However, the 2nd model has 3 parameters of explosion length, distance, and also the natural period of the blast wall. These predictive models are then used to find explosion damages and ductility ratio. The results are compared with FEM analysis and pressure-impulse (P-I) method. It is shown that both types of models fit well with the outputs of the simulation. Moreover, results of both models are close to FEM analysis. The comparison tables provided in this paper show that, in the asymptotic region of P-I diagrams, results are not accurate. Therefore, this new method is superior to classical pressure-impulse (P-I) diagrams in the literature. Advantage of the new method is the easy damage assessment by using simple empirical-type formulas. Therefore, the researchers can use the method in this paper, for damage assessment in other types of blast resistive structures.

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LMLFM: Longitudinal Multi-Level Factorization Machine

Proceedings of the AAAI Conference on Artificial Intelligence ◽

10.1609/aaai.v34i04.5916 ◽

2020 ◽

Vol 34 (04) ◽

pp. 4811-4818

Author(s):

Junjie Liang ◽

Dongkuan Xu ◽

Yiwei Sun ◽

Vasant Honavar

Keyword(s):

Longitudinal Data ◽

Random Effects ◽

Predictive Models ◽

Fixed Effects ◽

Predictive Accuracy ◽

State Of The Art ◽

Art Methods ◽

Factorization Machine ◽

Multi Level ◽

Over Time

We consider the problem of learning predictive models from longitudinal data, consisting of irregularly repeated, sparse observations from a set of individuals over time. Such data often exhibit longitudinal correlation (LC) (correlations among observations for each individual over time), cluster correlation (CC) (correlations among individuals that have similar characteristics), or both. These correlations are often accounted for using mixed effects models that include fixed effects and random effects, where the fixed effects capture the regression parameters that are shared by all individuals, whereas random effects capture those parameters that vary across individuals. However, the current state-of-the-art methods are unable to select the most predictive fixed effects and random effects from a large number of variables, while accounting for complex correlation structure in the data and non-linear interactions among the variables. We propose Longitudinal Multi-Level Factorization Machine (LMLFM), to the best of our knowledge, the first model to address these challenges in learning predictive models from longitudinal data. We establish the convergence properties, and analyze the computational complexity, of LMLFM. We present results of experiments with both simulated and real-world longitudinal data which show that LMLFM outperforms the state-of-the-art methods in terms of predictive accuracy, variable selection ability, and scalability to data with large number of variables. The code and supplemental material is available at https://github.com/junjieliang672/LMLFM.

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Decision tree as a tool for implementing a scenario approach for multi-level predictive models

IOP Conference Series Earth and Environmental Science ◽

10.1088/1755-1315/839/3/032050 ◽

2021 ◽

Vol 839 (3) ◽

pp. 032050

Author(s):

A M Kumratova ◽

E V Popova ◽

V V Aleshchenko ◽

A A Bykov ◽

A K Bashieva

Keyword(s):

Decision Tree ◽

Predictive Models ◽

Multi Level ◽

Scenario Approach

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Predictive Models for Cochlear Implantation in Elderly Candidates

Yearbook of Otolaryngology-Head and Neck Surgery ◽

10.1016/s1041-892x(07)70049-5 ◽

2007 ◽

Vol 2007 ◽

pp. 52

Author(s):

M.M. Paparella

Keyword(s):

Predictive Models ◽

Cochlear Implantation

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Multi-level Issues in Organizational Behavior and Processes

10.1016/s1475-9144(2005)3 ◽

2005 ◽

Keyword(s):

Organizational Behavior ◽

Multi Level

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Verlaufsanalyse wöchentlicher Selbst- und Fremdeinschätzungen in der Langzeittherapie suchtkranker Jugendlicher

Zeitschrift für Kinder- und Jugendpsychiatrie und Psychotherapie ◽

10.1024/1422-4917/a000594 ◽

2019 ◽

Vol 47 (2) ◽

pp. 126-137

Author(s):

Ferdinand Keller ◽

Tatjana Stadnitski ◽

Jakob Nützel ◽

Renate Schepker

Keyword(s):

Allen Vier ◽

Multi Level

Zusammenfassung. Fragestellung: Über Veränderungen in der emotionalen Befindlichkeit von Jugendlichen während einer Suchttherapie ist wenig bekannt. Methode: Die Jugendlichen füllten wöchentlich einen entsprechenden Fragebogen aus, analog ihre Bezugsbetreuer eine parallelisierte Kurzfassung. Von 42 Jugendlichen liegen insgesamt 853 Bogen und von den Bezugsbetreuern 708 Bogen vor. Die Fragebogen wurden zunächst faktorenanalytisch hinsichtlich ihrer Dimensionalität ausgewertet, anschließend wurden gruppenbezogene Verlaufsanalysen (Multi-Level-Modelle) und Abhängigkeitsanalysen auf Einzelfallebene (Zeitreihenanalysen) durchgeführt. Ergebnisse: Im Jugendlichenfragebogen ergaben sich vier Faktoren: negative Befindlichkeit, Wertschätzung von Therapie/Betreuung, Motivation und Suchtdynamik. Die Übereinstimmung zwischen den Jugendlichen- und der (einfaktoriellen) Betreuereinschätzung fiel insgesamt niedrig bis mäßig aus, brachte aber auf Einzelfallebene differenziertere Ergebnisse. Im Verlauf nahmen die Werte auf allen vier Jugendlichenskalen ab. Einzig der Verlauf der Wertschätzung in der Eingewöhnungsphase war prädiktiv für den späteren Abbruch der Maßnahme: Bei den Abbrechern nahm die Wertschätzung ab, während sie bei den Beendern initial stieg. Schlussfolgerungen: Der bedeutsamste Faktor in Bezug auf die Therapiebeendigung suchtkranker Jugendlicher scheint die Wertschätzung von Therapie/Betreuung zu sein, während die Motivation jugendtypische Schwankungen aufweist. Der Suchtdynamik kam eine deutlich weniger bedeutende Rolle zu als allgemein angenommen. Programme in der Langzeittherapie sollten die Wertschätzung von Therapie/Betreuung künftig mehr fokussieren als die Suchtdynamik.

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