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Wind turbine towers are subjected to highly varying internal loads, characterized by large uncertainty. The uncertainty stems from many factors, including what the actual wind fields experienced over time will be, modeling uncertainties given the various operational states of the turbine with and without controller interaction, the influence of aerodynamic damping, and so forth. To monitor the true experienced loading and assess the fatigue, strain sensors can be installed at fatigue-critical locations on the turbine structure. A more cost-effective and practical solution is to predict the strain response of the structure based only on a number of acceleration measurements. In this contribution, an approach is followed where the dynamic strains in an existing onshore wind turbine tower are predicted using a Gaussian process latent force model. By employing this model, both the applied dynamic loading and strain response are estimated based on the acceleration data. The predicted dynamic strains are validated using strain gauges installed near the bottom of the tower. Fatigue is subsequently assessed by comparing the damage equivalent loads calculated with the predicted as opposed to the measured strains. The results confirm the usefulness of the method for continuous tracking of fatigue life consumption in onshore wind turbine towers.
The effect of milorganite, a commercially available organic soil amendment, on soil nutrients, plant growth, and yield has been investigated. However, its effect on soil hydraulic properties remains less understood. Therefore, this study aimed to investigate the effect of milorganite amendment on soil evaporation, moisture retention, hydraulic conductivity, and electrical conductivity of a Krome soil. A column experiment was conducted with two milorganite application rates (15 and 30% v/v) and a non-amended control soil. The results revealed that milorganite reduced evaporation rates and the length of Stage I of the evaporation process compared with the control. Moreover, milorganite increased moisture retention at saturation and permanent wilting point while decreasing soil hydraulic conductivity. In addition, milorganite increased soil electrical conductivity. Overall, milorganite resulted in increased soil moisture retention; however, moisture in the soil may not be readily available for plants due to increased soil salinity.
During the past half-century, exponential families have attained a position at the center of parametric statistical inference. Theoretical advances have been matched, and more than matched, in the world of applications, where logistic regression by itself has become the go-to methodology in medical statistics, computer-based prediction algorithms, and the social sciences. This book is based on a one-semester graduate course for first year Ph.D. and advanced master's students. After presenting the basic structure of univariate and multivariate exponential families, their application to generalized linear models including logistic and Poisson regression is described in detail, emphasizing geometrical ideas, computational practice, and the analogy with ordinary linear regression. Connections are made with a variety of current statistical methodologies: missing data, survival analysis and proportional hazards, false discovery rates, bootstrapping, and empirical Bayes analysis. The book connects exponential family theory with its applications in a way that doesn't require advanced mathematical preparation.
Cyclones are a severe storm system with a defined center, occurring in the tropical regions. Upon landfall, it causes massive damage to both lives and the economy. With the increase in frequency and intensity of tropical cyclones occurring over the years and growing coastal settlements, the study of cyclone landfall remains of paramount importance for disaster control and mitigation. Cyclones experience rapid changes, with various environmental factors modulating the trajectory and intensity. Thus predicting cyclone landfall demands a highly precise technique coupled with knowledge of environmental parameters. With the complexity and nonlinearity of the cyclone track data, determining parameters conducive for the landfall prediction of a cyclone remains crucial for precision and knowledge of the storm system. While numerous methods have been employed for detecting causal interactions among weather systems like Granger Causality and Transfer Entropy, each comes with its limitation and computational overhead. In this work, we investigate the where and when of a cyclone landfall by studying the influencing factors regulating the location and time of a cyclone landfall over the North Indian Ocean with mutual information (MI). We utilize dilated recurrent neural network with gated recurrent unit cells coupled with feature selection via MI criterion for predicting the cyclone landfall location and intensity between 12 and 36 training hours. The model efficacy is validated further on the landfall data of a recent devastating storm—Fani.
There are various matrices to represent parallel mechanisms. It is essential to design a kind of approach to not only denote the parallel structures but also disclose the joint directions. In this paper, a novel methodology called the kinematic joint matrix (KJM) is proposed. It possesses the mapping relations with parallel manipulators with three kinds of kinematic joints. The size of such matrix is smaller when compared with that of topology matrix. A series of two to six degrees-of-freedom parallel architectures is denoted by the KJM. A convenient approach using a special block diagram is introduced to distinguish various kinds of kinematic joint matrices. In addition, detailed comparisons between KJM and topology matrix are investigated. Three regulations are proposed for the latter to be applicable to parallel mechanisms.
The short timescale of the solar flare reconnection process has long proved to be a puzzle. Recent studies suggest the importance of the formation of plasmoids in the reconnecting current sheet, with quantifying the aspect ratio of the width to length of the current sheet in terms of a negative power $ \alpha $ of the Lundquist number, that is, $ {S}^{-\alpha } $, being key to understanding the onset of plasmoids formation. In this paper, we make the first application of theoretical scalings for this aspect ratio to observed flares to evaluate how plasmoid formation may connect with observations. For three different flares that show plasmoids we find a range of $ \alpha $ values of $ \alpha =0.26 $ to $ 0.31 $. The values in this small range implies that plasmoids may be forming before the theoretically predicted critical aspect ratio ($ \alpha =1/3 $) has been reached, potentially presenting a challenge for the theoretical models.
In the twenty-first century, machine learning and deep learning have been successfully used to find hidden information from coarse-grained data in various domains. In Computer Vision, scientists have used neural networks to identify hidden pixel-level information from low-resolution (LR) image data. This approach of estimating high-resolution (HR) information from LR data is called the super-resolution (SR) approach. This approach has been borrowed by climate scientists to downscale coarse-level measurements of climate variables to obtain their local-scale projections. Climate variables are spatial in nature and can be represented as images where each pixel denotes a grid point where the variables can be measured. We can apply the deep learning-based SR techniques on such “images” for statistical downscaling of such variables. This approach of downscaling can be termed as deep downscaling. In this work, we have tried to make HR projection of the Indian summer monsoon rainfall by using a novel deep residual network called ResDeepD. The aim is to downscale the 10 × 10 low LR precipitation data to get the values at 0.250 × 0.250 resolution. The proposed model uses a series of skip connections across residual blocks to give better results as compared to the existing models like super-resolution convolutional neural network, DeepSD, and Nest-UNet that have been used previously for this task. We have also examined the model’s performance for downscaling rainfall during some extreme climatic events like cyclonic storms and deep depression and found that the model performs better than the existing models.
We consider supercritical site percolation on the $d$-dimensional hypercube $Q^d$. We show that typically all components in the percolated hypercube, besides the giant, are of size $O(d)$. This resolves a conjecture of Bollobás, Kohayakawa, and Łuczak from 1994.
This book proves some important new theorems in the theory of canonical inner models for large cardinal hypotheses, a topic of central importance in modern set theory. In particular, the author “completes” the theory of Fine Structure and Iteration Trees (FSIT) by proving a comparison theorem for mouse pairs parallel to the FSIT comparison theorem for pure extender mice, and then using the underlying comparison process to develop a fine structure theory for strategy mice.
Great effort has been taken to make the book accessible to non-experts so that it may also serve as an introduction to the higher reaches of inner model theory. It contains a good deal of background material, some of it unpublished folklore, and includes many references to the literature to guide further reading. An introductory essay serves to place the new results in their broader context.
This is a landmark work in inner model theory that should be in every set theorist’s library.
This book proves some important new theorems in the theory of canonical inner models for large cardinal hypotheses, a topic of central importance in modern set theory. In particular, the author “completes” the theory of Fine Structure and Iteration Trees (FSIT) by proving a comparison theorem for mouse pairs parallel to the FSIT comparison theorem for pure extender mice, and then using the underlying comparison process to develop a fine structure theory for strategy mice.
Great effort has been taken to make the book accessible to non-experts so that it may also serve as an introduction to the higher reaches of inner model theory. It contains a good deal of background material, some of it unpublished folklore, and includes many references to the literature to guide further reading. An introductory essay serves to place the new results in their broader context.
This is a landmark work in inner model theory that should be in every set theorist’s library.
This new book on mathematical logic by Jeremy Avigad gives a thorough introduction to the fundamental results and methods of the subject from the syntactic point of view, emphasizing logic as the study of formal languages and systems and their proper use. Topics include proof theory, model theory, the theory of computability, and axiomatic foundations, with special emphasis given to aspects of mathematical logic that are fundamental to computer science, including deductive systems, constructive logic, the simply typed lambda calculus, and type-theoretic foundations.
Clear and engaging, with plentiful examples and exercises, it is an excellent introduction to the subject for graduate students and advanced undergraduates who are interested in logic in mathematics, computer science, and philosophy, and an invaluable reference for any practicing logician’s bookshelf.
This new book on mathematical logic by Jeremy Avigad gives a thorough introduction to the fundamental results and methods of the subject from the syntactic point of view, emphasizing logic as the study of formal languages and systems and their proper use. Topics include proof theory, model theory, the theory of computability, and axiomatic foundations, with special emphasis given to aspects of mathematical logic that are fundamental to computer science, including deductive systems, constructive logic, the simply typed lambda calculus, and type-theoretic foundations.
Clear and engaging, with plentiful examples and exercises, it is an excellent introduction to the subject for graduate students and advanced undergraduates who are interested in logic in mathematics, computer science, and philosophy, and an invaluable reference for any practicing logician’s bookshelf.
This book proves some important new theorems in the theory of canonical inner models for large cardinal hypotheses, a topic of central importance in modern set theory. In particular, the author “completes” the theory of Fine Structure and Iteration Trees (FSIT) by proving a comparison theorem for mouse pairs parallel to the FSIT comparison theorem for pure extender mice, and then using the underlying comparison process to develop a fine structure theory for strategy mice.
Great effort has been taken to make the book accessible to non-experts so that it may also serve as an introduction to the higher reaches of inner model theory. It contains a good deal of background material, some of it unpublished folklore, and includes many references to the literature to guide further reading. An introductory essay serves to place the new results in their broader context.
This is a landmark work in inner model theory that should be in every set theorist’s library.
This new book on mathematical logic by Jeremy Avigad gives a thorough introduction to the fundamental results and methods of the subject from the syntactic point of view, emphasizing logic as the study of formal languages and systems and their proper use. Topics include proof theory, model theory, the theory of computability, and axiomatic foundations, with special emphasis given to aspects of mathematical logic that are fundamental to computer science, including deductive systems, constructive logic, the simply typed lambda calculus, and type-theoretic foundations.
Clear and engaging, with plentiful examples and exercises, it is an excellent introduction to the subject for graduate students and advanced undergraduates who are interested in logic in mathematics, computer science, and philosophy, and an invaluable reference for any practicing logician’s bookshelf.
This new book on mathematical logic by Jeremy Avigad gives a thorough introduction to the fundamental results and methods of the subject from the syntactic point of view, emphasizing logic as the study of formal languages and systems and their proper use. Topics include proof theory, model theory, the theory of computability, and axiomatic foundations, with special emphasis given to aspects of mathematical logic that are fundamental to computer science, including deductive systems, constructive logic, the simply typed lambda calculus, and type-theoretic foundations.
Clear and engaging, with plentiful examples and exercises, it is an excellent introduction to the subject for graduate students and advanced undergraduates who are interested in logic in mathematics, computer science, and philosophy, and an invaluable reference for any practicing logician’s bookshelf.
This book proves some important new theorems in the theory of canonical inner models for large cardinal hypotheses, a topic of central importance in modern set theory. In particular, the author “completes” the theory of Fine Structure and Iteration Trees (FSIT) by proving a comparison theorem for mouse pairs parallel to the FSIT comparison theorem for pure extender mice, and then using the underlying comparison process to develop a fine structure theory for strategy mice.
Great effort has been taken to make the book accessible to non-experts so that it may also serve as an introduction to the higher reaches of inner model theory. It contains a good deal of background material, some of it unpublished folklore, and includes many references to the literature to guide further reading. An introductory essay serves to place the new results in their broader context.
This is a landmark work in inner model theory that should be in every set theorist’s library.
This new book on mathematical logic by Jeremy Avigad gives a thorough introduction to the fundamental results and methods of the subject from the syntactic point of view, emphasizing logic as the study of formal languages and systems and their proper use. Topics include proof theory, model theory, the theory of computability, and axiomatic foundations, with special emphasis given to aspects of mathematical logic that are fundamental to computer science, including deductive systems, constructive logic, the simply typed lambda calculus, and type-theoretic foundations.
Clear and engaging, with plentiful examples and exercises, it is an excellent introduction to the subject for graduate students and advanced undergraduates who are interested in logic in mathematics, computer science, and philosophy, and an invaluable reference for any practicing logician’s bookshelf.
This book proves some important new theorems in the theory of canonical inner models for large cardinal hypotheses, a topic of central importance in modern set theory. In particular, the author “completes” the theory of Fine Structure and Iteration Trees (FSIT) by proving a comparison theorem for mouse pairs parallel to the FSIT comparison theorem for pure extender mice, and then using the underlying comparison process to develop a fine structure theory for strategy mice.
Great effort has been taken to make the book accessible to non-experts so that it may also serve as an introduction to the higher reaches of inner model theory. It contains a good deal of background material, some of it unpublished folklore, and includes many references to the literature to guide further reading. An introductory essay serves to place the new results in their broader context.
This is a landmark work in inner model theory that should be in every set theorist’s library.