ESD Ideas: A Global Warming Scaling Law

In this study, we highlight a component of global warming variability, a scaling law that is based purely on fundamental physical properties of the climate system. We suggest that three similarity parameters define the system response to external forcing, and an argument of physical similarity with observed climate responses in the past can be made when all three parameters are identical for the current and historical climates. We determined that the scaling law of global warming is the (𝜆 + 1 + m) – power of time, where 𝜆 is prescribed by external forcing and m is defined by climate system internal dynamics. When the climate system develops in the direction of intensified positive feedbacks, the power m changes from m = −1 (negative feedbacks dominate) to m ≥ 1 (positive feedbacks dominate). We also establish that a “hothouse” climate with dominant positive feedbacks will be preceded by a climate having a property of incomplete similarity in feedbacks similarity parameters. It implies that the same future scenario may be produced by climate feedbacks of different magnitudes as long as their positive-to-negative ratio is the same.

WVMDA: Predicting miRNA–Disease Association Based on Weighted Voting

An increasing number of experiments had verified that miRNA expression is related to human diseases. The miRNA expression profile may be an indicator of clinical diagnosis and provides a new direction for the prevention and treatment of complex diseases. In this work, we present a weighted voting-based model for predicting miRNA–disease association (WVMDA). To reasonably build a network of similarity, we established credibility similarity based on the reliability of known associations and used it to improve the original incomplete similarity. To eliminate noise interference as much as possible while maintaining more reliable similarity information, we developed a filter. More importantly, to ensure the fairness and efficiency of weighted voting, we focus on the design of weighting. Finally, cross-validation experiments and case studies are undertaken to verify the efficacy of the proposed model. The results showed that WVMDA could efficiently identify miRNAs associated with the disease.

Incomplete similarity of the ice-climate system

Reconstruction and explanation of past climate evolution using proxy records is the essence of paleoclimatology. In this study, we use dimensional analysis and concepts of similarity to recognize theoretical limits of such forensic inquiries. Specifically, we demonstrate that incomplete similarity in the dynamical ice-climate system implies the absence of physical similarity in conglomerate similarity parameters. It means that major events of the past such as, for example, the middle-Pleistocene transition could have been produced by different physical processes, and, therefore, the task of disambiguation of the historical paleo-records may be fundamentally difficult, if not impossible. It also means that any future scenario may not have a unique cause and, in this sense, the orbital time-scale future may be to some extent insensitive to specific physical circumstances.

Error Analysis Method of Geometrically Incomplete Similarity of End-Plate Connection Based on Linear Regression

Due to the limitations of processing errors, test conditions and other factors, geometric similarity errors in scale tests of steel structure joints are difficult to avoid, but the research on this error is little known. Based on the similarity theory and the basic idea of the component method, this paper deduces the similar macro conditions of beam–column end-plate connections and derives the main influencing factors of geometric similarity of these types of structures. Aiming at the factor of the thickness of the end-plate, the formation mechanism of the geometrically incomplete similarity error of this type of node was studied. Through the establishment of accurate finite element models for parameterized analysis, the influence of end plate thickness on incomplete similarity error is analyzed. Based on this model and linear regression analysis methods, the prediction formulas of geometric incomplete similarity errors of beam–column end-plate connections have been established, which can significantly reduce similar errors due to end plate thickness. This article aims to propose a method for simulating the distribution of incompletely similar errors and provide a reference for the research of similar problems.

Incomplete Similarity Error Analysis Method of Steel Frame Structure Based on Correlation

In most scale model tests of steel frame structures, researchers have usually paid more attention to the outcome of one single experiment rather than to the similitude error between the scale model and the prototype. As a result, only the experimental data of the scale model are obtained rather than the relationship between the scale model and the prototype, which greatly limits scale model tests in the investigation and is also a significant waste of resources. In addition, the effect of the geometric incomplete similarity in steel joints and its contribution to similitude error of the entire frame has rarely been evaluated in the past. This paper is based on similarity theory, investigating the effect of three incomplete geometric similarity factors on the similitude error between a scale model and a prototype, which are the geometric similarity of steel components, the stiffness of steel joints and the stiffness of the column base. Global sensitivity is found by calculation. Furthermore, this paper presents the correlation of the three factors mentioned based on structural sensitivity analysis by use of BP (Back Propagation) algorithm. A method to investigate the similitude error between the scale model and prototype is proposed that can correct similitude errors in the experimental results.

Design and Experimental Verification of a 1/20 Scale Model of Quayside Container Crane Using Distortion Theory

The scale model is an effective method to research the performance of quayside container crane (QCC) under the seismic condition, but the model distortion usually exists in the similar design process which leads to the incomplete similarity between the scale model and prototype. In this investigation, the distortion theory and the prediction coefficient correction method are used to upgrade the quality of 1/20 QCC scale model and, then, the seismic response of the QCC prototype is obtained from the shake table scale model test. In the first step, the similarity ratio of the 1/20 QCC scale model is calculated by the similitude law and the size of scale model is obtained from the similarity constants. In the second step, the bending stiffness is selected and determined as the distortion term and, then, the relationship between the distortion coefficient and the prediction coefficient is obtained by the finite element prediction coefficient method. Furthermore, the three different scale models are manufactured and tested in the shake table experiment under different seismic conditions. It is found that the experimental test results are consistent with the numerical simulation results of the QCC prototype. It can be concluded that the QCC scale model can be used to predict the performance of the prototype under the different seismic conditions after corrected by distortion theory, and the distortion theory is an effective method to solve the incomplete similarity between the scale model and prototype.

Spectral Perturbation Meets Incomplete Multi-view Data

Beyond existing multi-view clustering, this paper studies a more realistic clustering scenario, referred to as incomplete multi-view clustering, where a number of data instances are missing in certain views. To tackle this problem, we explore spectral perturbation theory. In this work, we show a strong link between perturbation risk bounds and incomplete multi-view clustering. That is, as the similarity matrix fed into spectral clustering is a quantity bounded in magnitude O(1), we transfer the missing problem from data to similarity and tailor a matrix completion method for incomplete similarity matrix. Moreover, we show that the minimization of perturbation risk bounds among different views maximizes the final fusion result across all views. This provides a solid fusion criteria for multi-view data. We motivate and propose a Perturbation-oriented Incomplete multi-view Clustering (PIC) method. Experimental results demonstrate the effectiveness of the proposed method.