Physics-Based Semantic Reasoning for Function Model Decomposition

2018 ◽  
Author(s):  
Xiaoyang Mao ◽  
Chiradeep Sen
Keyword(s):  
Data Representation ◽  
Function Model ◽  
Semantic Reasoning ◽  
Model Decomposition ◽  
Box Models ◽  
Design Alternatives ◽  
Structure Of Flow ◽  
Multiple Alternative ◽  
Step Algorithm ◽  
Function Models

In graph-based function models, the function verb and flow noun types are usually controlled by vocabularies of standard classes. The grammar is also controlled at different levels of formalism and contribute to reasoning. However, the text written in plain English for the names of the functions and flows is not used for formal reasoning to help with modeling or exploring the design space. This paper presents a formalism for semantic and physics-based reasoning on function model graphs, esp. to automatically decompose black box models and to generate design alternatives using those plain-English texts. A previously established formal language, which ensures that function models are consistent with physics laws, is used as a baseline. Semantic reasoning is added to use the unstructured information of the flow phrases to infer possible means of decomposing the model into a topology connecting appropriate subfunctions and to generate multiple alternative decompositions. A data structure of flow nouns, flow attributes, qualitative value scales, and qualitative physics laws is used as the data representation. An eight-step algorithm manipulates this data for reasoning. The paper shows two validation case studies to demonstrate the workings of the language.

Semantic and Qualitative Physics-Based Reasoning on Plain-English Flow Terms for Generating Function Model Alternatives

2020 ◽  
Vol 20 (4) ◽  
Author(s):  
Xiaoyang Mao ◽  
Chiradeep Sen
Keyword(s):  
Data Representation ◽  
Black Box ◽  
Formal Reasoning ◽  
Design Alternatives ◽  
Topological Reasoning ◽  
Multiple Alternative ◽  
Energy Exchanges ◽  
Qualitative Physics ◽  
Step Algorithm ◽  
Function Models

Abstract In graph-based function models, the function verbs and flow nouns are usually chosen from predefined vocabularies. The vocabulary class definitions, combined with function modeling grammars defined at various levels of formalism, enable function-based reasoning. However, the text written in plain English for the names of the functions and flows is presently not exploited for formal reasoning. This paper presents a formalism (representation and reasoning) to support semantic and physics-based reasoning on the information hidden in the plain-English flow terms, especially for automatically decomposing black box function models, and to generate multiple design alternatives. First, semantic reasoning infers the changes of flow types, flow attributes, and the direction of those changes between the input and output flows attached to the black box. Then, a representation of qualitative physics is used to determine the material and energy exchanges between the flows and the function features needed to achieve them. Finally, a topological reasoning is used to infer multiple options of composing those function features into topologies and to thus generate multiple alternative decompositions of the functional black box. The data representation formalizes flow phases, flow attributes, qualitative value scales for the attributes, and qualitative physics laws. An eight-step algorithm manipulates these data for reasoning. This paper shows four validation case studies to demonstrate the workings of this formalism.

A Statistical Adaptable Distribution Function Model for Low Probabilities of Failure

1993 ◽  
Author(s):  
Suryaji R. Bhonsle ◽  
Paul Thompson
Keyword(s):  
Experimental Data ◽  
Distribution Function ◽  
Experimental Results ◽  
Function Model ◽  
Log Normal ◽  
Function Models

Abstract Weibull, log normal, and some other Distribution function models (D.F.M.) have a tendency to deviate from experimental results. This deviation, either exceedingly conservative or nonconservative, is amplified at low probabilities of failure. To remedy such problems a new D.F.M. is derived. It is then used to predict low probabilities of failure. The predictions are consistent with experimental data and are not too conservative or too nonconservative.

scholarly journals Correction to: DNMT3A reads and connects histone H3K36me2 to DNA methylation

2019 ◽  
Vol 11 (3) ◽  
pp. 230-230
Author(s):  
Wenqi Xu ◽  
Jiahui Li ◽  
Bowen Rong ◽  
Bin Zhao ◽  
Mei Wang ◽  
...  
Keyword(s):  
Dna Methylation ◽  
Histone Mark ◽  
The Other ◽  
Therapeutic Implication ◽  
Loss Of Function ◽  
Function Model ◽  
Gain Of Function ◽  
Intergenic Dna ◽  
Preferential Recognition ◽  
Function Models

The author would like to add the below information in this correction. A similar study from Chao Lu group was published online on 5 September 2019 in Nature, entitled “The histone mark H3K36me2 recruits DNMT3A and shapes the intergenic DNA methylation landscape” (Weinberg et al., 2019). Although both the studies reported the preferential recognition of H3K36me2 by DNMT3A PWWP, ours in addition uncovered a stimulation function by such interaction on the activity of DNMT3A. On the disease connections, we used a NSD2 gain-of-function model which led to the discovery of potential therapeutic implication of DNA inhibitors in the related cancers, while the other study only used NSD1 and DNMT3A loss-of-function models.

scholarly journals Time Function Model of Surface Subsidence Based on Inversion Analysis in Deep Soil Strata

2020 ◽  
Vol 2020 ◽  
pp. 1-6
Author(s):  
Jihuan Han ◽  
Chenchen Hu ◽  
Jiuqun Zou
Keyword(s):  
Soil Surface ◽  
Measured Data ◽  
Surface Subsidence ◽  
Time Function ◽  
Model Parameters ◽  
Deep Soil ◽  
Function Model ◽  
Composite Function ◽  
Inversion Analysis ◽  
Function Models

As a common geological disaster, surface subsidence caused by mining underground resources has always been a hot and difficult topic in the civil engineering field. Aimed at the shortcomings of existing time function models in predicting mining subsidence in deep soil strata, a more accurate and reasonable time function model, called the composite function model, was established based on an inverted analysis of measured data. The results showed that the composite function model could describe the whole subsidence process of a deep soil surface and agreed well with the measured data. The model parameters were calculated by specific formulas, which improved the reliability of the subsidence prediction results under different mining conditions. The new model provided important guiding significance for preventing subsidence geological disasters and determining the coal mining time under the buildings, the railways, and the water bodies in deep soil strata.

Statistical and Mathematical Approach for Distribution Function Model for Low Probability of Failures

1991 ◽  
Author(s):  
S. R. Bhonsle ◽  
C. V. VanKarsen ◽  
J. R. Michler
Keyword(s):  
Distribution Function ◽  
Statistical Models ◽  
Mathematical Approach ◽  
Probabilistic Design ◽  
Function Model ◽  
Lower Tail ◽  
Low Probability ◽  
Statistical Distribution Function ◽  
Function Models ◽  
Design Factors

In probabilistic design it is common practice to use statistical models such as normal, lognormal, and Weibull to describe random design factors. However these distribution function models deviate in the lower tail, i.e. percentiles below 1%. The deviation is nonconservative in that since it predicts life longer than observed. A Statistical Distribution Function called Adaptive Distribution Function Model similar to Abelkis model was developed. It is compatible with the collected data, and it produces conservative designs at low tail ends. It is also relatively easy to use.

scholarly journals Peramalan Kebutuhan Daya Listrik Menggunakan Model ARIMA dan Fungsi Transfer (Studi Kasus: PT. PLN (Persero) Area Sumbawa)

2018 ◽  
Vol 4 (2) ◽  
pp. 122-127
Author(s):  
Mikhratunnisa Mikhratunnisa ◽  
Tri Susilawati
Keyword(s):  
Economic Development ◽  
Transfer Function ◽  
Arima Model ◽  
Transfer Function Model ◽  
Customer Demand ◽  
Function Model ◽  
Electrical Capacity ◽  
The Right ◽  
Function Models ◽  
Transfer Function Models

Energy is one of the basic need of human being. One of the vital energy is electricity. The need of electricity in NTB is increase along with the citizen economic development in NTB especially in Sumbawa regency. Therefore, there is a need for the right way in adjusting the amount of electrical capacity to match customer demand. One way that can be done is to forecast/ predict the need for electricity. The forecast can be used by using the ARIMA and Transfer Function models. The results of the study show that using the ARIMA model is estimated to require electricity in 2018 experienced an increase of 18,21% from the previous year, while using the transfer function model is estimated to increase by 18,18% from the previous year.

scholarly journals Transfer Function Models with Time-Varying Coefficients

2012 ◽  
Vol 2012 ◽  
pp. 1-31 ◽  
Author(s):  
Maria Sílvia de A. Moura ◽  
Pedro A. Morettin ◽  
Clélia M. C. Toloi ◽  
Chang Chiann
Keyword(s):  
Transfer Function ◽  
Least Squares Method ◽  
Estimation Procedure ◽  
Time Varying ◽  
Function Model ◽  
Varying Coefficients ◽  
Wavelet Expansions ◽  
Time Varying Coefficients ◽  
Function Models ◽  
Transfer Function Models

We consider a transfer function model with time-varying coefficients. We propose an estimation procedure, based on the least squares method and wavelet expansions of the time-varying coefficients. We discuss some statistical properties of the estimators and assess the validity of the methodology through a simulation study. We also present an application of the proposed procedure to a real pair of series.

scholarly journals Study on Dielectric Function Models for Surface Plasmon Resonance Structure

2014 ◽  
Vol 2014 ◽  
pp. 1-6 ◽  
Author(s):  
Peyman Jahanshahi ◽  
Mostafa Ghomeishi ◽  
Faisal Rafiq Mahamd Adikan
Keyword(s):  
Experimental Data ◽  
Finite Element Method ◽  
Finite Element ◽  
Surface Plasmon Resonance ◽  
Dielectric Properties ◽  
Surface Plasmon ◽  
Plasmon Resonance ◽  
Resonance Structure ◽  
Function Model ◽  
Function Models

The most common permittivity function models are compared and identifying the best model for further studies is desired. For this study, simulations using several different models and an analytical analysis on a practical surface Plasmon structure were done with an accuracy of∼94.4% with respect to experimental data. Finite element method, combined with dielectric properties extracted from the Brendel-Bormann function model, was utilized, the latter being chosen from a comparative study on four available models.

Evolutionary Approach to Function Model Synthesis: Development of Parameterization and Synthesis Rules

2020 ◽  
Author(s):  
Amaninder Singh Gill ◽  
Chiradeep Sen
Keyword(s):  
Evolutionary Algorithm ◽  
A Priori ◽  
Automated Synthesis ◽  
Function Model ◽  
Model Synthesis ◽  
Parameterization Method ◽  
Electromechanical Products ◽  
Feature Based ◽  
Algorithm Framework ◽  
Function Models

Abstract The goal of this paper is to develop the groundwork for automated synthesis of function models. To this end, an evolutionary algorithm based framework has been developed. A parameterization method that can completely describe any given function models has been proposed. The parameterization makes the function models compatible for use within the evolutionary algorithm framework. Validation of the parameterization method is carried out by using an evolutionary algorithm to synthesize the function models for five different electromechanical products. The algorithm converged in each case, indicating that the method is satisfactory and that function models can actually be synthesized using an evolutionary framework. In addition, the adaptation of several a priori rules for use in this framework has been proposed. These rules are categorized as grammar, logical and feature based rules. An updated evolutionary framework that incorporates these rules is also presented.

scholarly journals From bio-mineralization to tumorogenesis — the expanding insight of the physiological and pathological roles of Fam20C

2021 ◽  
Author(s):  
Yanbing Wu ◽  
Haoru Wang ◽  
Chao Liu
Keyword(s):  
Immune Cells ◽  
Poor Prognosis ◽  
Secreted Proteins ◽  
Loss Of Function ◽  
Function Model ◽  
Multiple Cancers ◽  
The Poor ◽  
History Of ◽  
Medical University ◽  
Function Models

Fam20C is a Golgi kinase phosphorylating the majority of the secreted proteins. In this decade, the roles of Fam20C has been largely disclosed in the loss-of function models. How the influence of the over-expressed Fam20C on cells or organs, and whether Fam20C was associated to tumorogensis still remain unknown. In the latest publication in Bioscience Reports, a group from the second affiliated hospital of Harbin Medical University established a correlation between the elevated Fam20C expression and the poor prognosis of multiple cancers. In addition, they also proposed the potential mechanisms how the increased Fam20C expression played a detrimental role in tumor progression by suggesting that the up-regulated Fam20C level impacted the infiltration of immune cells and the capability of cancer metastasism. To give an overview of the expanding knowledge of Fam20C involved in the physiological and pathological events, we first reviewed the history of Fam20C studies in this commentary, then, evaluated the correlation of the elevated Fam20C expression to the prognosis of multiple cancers, and finally, interpreted the perspectives that the Fam20C gain-of-function model was also critical for cancer therapy.

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