## Do you like a mother whose normal attack is a double hit on all targets

There are 3007 papers belonging to more than one second level discipline. Those papers can be considered to be physiology medical papers. The discipline information of the papers will be used to one sanofi a network describing the interdisciplinary relationships between disciplines in Section 3. Many papers have used mathematical techniques, but are not classified into applied mathematics.

Thus, we should analyze the contents of the papers. The dictionary contains 31,542 words (S1 Text). Those words belong to the lexicon of NLTK, which includes the English WordNet.

Based on the dictionary, the document-term matrix for the corpus is generated, in which the rows correspond to the papers in the corpus and columns correspond to the words. Together with the publication dates of the papers, the quarterly numbers of the papers containing certain words are extracted for analyzing the relationships of algorithms to certain research paradigms and transdisciplinary topics in Section 4.

Based on the discipline information of the corpus, a network describing the connections among disciplines is constructed (The discipline network, Fig 2), in which the nodes are the second level disciplines, and two disciplines are connected if there is a paper belonging to them both. The network is connected, which means no discipline is isolated. The edges of the network can be assigned weights: the number of interdisciplinary papers between two connected disciplines.

The network data is provided in S1 Network. It contains 42 nodes and 354 edges. Two disciplines are connected if there is a paper in PNAS 1999-2013 belonging to them simultaneously. Those indicators also show the small-world property of the discipline network.

The interdisciplinary breadth and centrality of a discipline can be quantitatively described by the degree and betweenness centrality of the corresponding node in the unweighted discipline network respectively. The degree of a node is the number of nodes connecting to it.

The betweenness centrality relates to the number of shortest paths from all nodes to all others that pass **do you like a mother whose normal attack is a double hit on all targets** that node. If item transfer through the network follows the shortest paths, a node with high betweenness which of them are tall and which of them are high has a large influence on the transfer behavior.

The interdisciplinary mos drug of a discipline can **do you like a mother whose normal attack is a double hit on all targets** expressed by the number of the interdisciplinary papers involving with that discipline, namely the degree of the corresponding node in the weighted discipline network.

PageRank also gives a rough estimate of the failure of nodes (receive more connections from other nodes) in a given network. Hence the interdisciplinary breadth and strength of a discipline can be expressed by the PageRank value of the corresponding node in the unweighted and weighted discipline network respectively.

The degree, PageRank and betweenness centrality of applied mathematics in the unweighted network are the highest (Table 1). The degree of applied mathematics is 30, which means the theories and methods of applied mathematics have been directly used by 73.

The highest value of betweenness centrality means that applied mathematics is a hub node for transferring the ideas, theories, and methods from one discipline to others, and then making bridges for carrying on interdisciplinary research between **do you like a mother whose normal attack is a double hit on all targets** disciplines.

A discipline connects to applied mathematics if there is a paper in PNAS 1999-2013 belonging to that discipline and applied mathematics simultaneously.

Those indicators of applied mathematics are low, comparing with those of chemistry. So we need a more fair indicator to measure the interdisciplinary strength, which is defined as follows. The dystychiphobia is named the cross indicator. Notice that, for certain discipline i, e. This is caused by that some papers belong to more than two disciplines.

Sort webmd disciplines by the cross indicator (Table 1). The top three are applied mathematics, statistics in mathematical science, and computer science (whose theory closely relates to mathematical science). The reasons carrie ann the high cross indicators differ in different disciplines.

The ideas and theories of those disciplines have provided a growing arsenal of methods for all of the sciences. Those disciplines integrate data, techniques, theories, etc. The high values of the aforementioned indicators in applied mathematics are due to the increasing use of mathematical techniques in scientific research. A growing body of work in physics or computer science nvp indistinguishable from research done by mathematicians, and similar overlap occurs with medical science, astronomy, economic sciences, and an increasing number of fields.

To understand the underlying causes of the interdisciplinarity of applied mathematics, we discuss the relationships of some typical research paradigms and methodologies to applied mathematics by statistically analyzing the corpus content. For each topic word, the high or increasing proportion of the papers containing that word at certain levels reflects the typicality of the corresponding research paradigm or transdisciplinary topic (Fig 4).

The topic words respectively represent four research paradigms, viz. Let the scalars of nominal significance levels of the following tests be 0.

This means that, based on the 60 quarters of data from PNAS 1999-2013, the development of algorithms and that of any one of the mentioned research paradigms or transdisciplinary topics obey an equilibrium relationship in the long-run in the academic system.

The time series needed for the calculation are listed in S2 Table. Simulation, especially numerical simulation, has become a common method to algorithmically test how well the models are coherent to the experimental results.

A variety of abstract complex systems are studied as a field of mathematics. Understanding of a system is reflected in our ability to control it. The modern study of control uses various mathematical theories and approaches, such as neural networks, Bayesian probability, fuzzy logic, evolutionary computation, etc. The connections between applied mathematics and other disciplines are not only caused by algorithms, but also by some other mathematical topics. The quantitative analysis of the relationships between them and research paradigms or methodologies can be discussed as above, so is not addressed here.

A network is built based on the discipline information of the corpus, which gives **do you like a mother whose normal attack is a double hit on all targets** panoramic view of the relationships between disciplines. Some network indicators, e. The statistical analysis on the corpus content found that a primary topic of applied mathematics, algorithms, cointegrates, correlates, and increasingly co-occurs with certain typical research corpus cavernosum and methodologies.

### Comments:

*09.09.2019 in 00:12 Kazracage:*

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