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Tudn radio en vivoYou will receive a link and will create a new password via email. Sorry, you do not have permission to ask a question, You must login to ask question. Become VIP Member. Minimum reinforcement in either direction shall be 0. Main reinforcement which is based on the maximum bending moment shall not be less than 0.

The pitch of the main bars shall not exceed the following:. Distribution bars are running at right angles to the main reinforcement and the pitch shall not exceed. The diameter of main bars may be from 8 mm to 14 mm.

The maximum bending moment per meter width of slab. Suppose a slab is supported at the ends and also at intermediate points on beams, the maximum sagging and hogging moments to which the slab is subjected to due to uniformly distributed load, can be computed as follows:. Your responses were successfully submitted. Thank you! Kindly check your email and confirm the same to receive your free ebook. This table is not related to loads.

Team piemonteBS provides a table for span to depth ratio which is based one total unfactored dead and imposed load. I have a project to design a classroom block. Kindly assist on how to go about it. This method is obsolete. I dont think designers practically use Working stress method any more!!! Sign Up Sign Up to The Constructor to ask questions, answer questions, write articles, and connect with other people.Documentation Help Center.

The point x0 can be a scalar, vector, or matrix. Passing Extra Parameters explains how to pass extra parameters to the objective function and nonlinear constraint functions, if necessary.

If your problem has constraints, generally use fmincon. See Optimization Decision Table. Use optimoptions to set these options. See fminunc Hessian. To do so, write an anonymous function fun that calculates the objective. Call fminunc to find a minimum of fun near [1,1]. Write an objective function that returns the gradient as well as the function value. Use the conditionalized form described in Including Gradients and Hessians.

The objective function is Rosenbrock's function.

## Quasi-experimental Research Designs

The code for the objective function with gradient appears at the end of this example. Also, set the algorithm to 'trust-region'. Set the initial point to [-1,2 ]. Then call fminunc. The following code creates the rosenbrockwithgrad function, which includes the gradient as the second output.

Solve the same problem as in Supply Gradient using a problem structure instead of separate arguments. For the required fields in this structure, see problem. Find both the location of the minimum of a nonlinear function and the value of the function at that minimum. The objective function is. Choose fminunc options and outputs to examine the solution process.

Set options to obtain iterative display and use the 'quasi-newton' algorithm. The output structure shows the number of iterations, number of function evaluations, and other information. Function to minimize, specified as a function handle or function name.The problem of finding a maximum clique in a graph is prototypical for many clustering and similarity problems; however, in many real-world scenarios, the classical problem of finding a complete subgraph needs to be relaxed to finding an almost complete subgraph, a so-called quasi-clique.

In this work, we demonstrate how two previously existing definitions of quasi-cliques can be unified and how the resulting, more general quasi-clique finding problem can be solved by extending two state-of-the-art stochastic local search algorithms for the classical maximum clique problem.

Preliminary results for these algorithms applied to both, artificial and real-world problem instances demonstrate the usefulness of the new quasi-clique definition and the effectiveness of our algorithms. Unable to display preview. Download preview PDF.

Skip to main content. This service is more advanced with JavaScript available. Advertisement Hide. International Conference on Learning and Intelligent Optimization. Conference paper. This is a preview of subscription content, log in to check access. Matsuda, H. Abello, J. Pei, J. In: Conference on Knowledge Discovery in Data, pp. Serrano, M. General formalism. Hopcroft, J. Palla, G.

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Everett, M. Du, N. Pullan, W. Battiti, R. Glover, F. Personalised recommendations. Cite paper How to cite?

Risoluzione i esonero febbraio 2011ENW EndNote. Buy options.Evidence-based nursing EBN has been an important training mechanism for improving the quality of clinical care. This study applied the flipped classroom approach to conduct evidence-based nursing EBN teaching.

The aim of this study is to elevate the learning effectiveness of the flipped classroom group to the traditional teaching group in terms of knowledge and self-efficacy in practice. The study recruited nurses, of whom 75 were in the control group and 76 were in the experimental group.

During the EBN course, the control group received training via traditional pedagogy while the experimental group engaged the flipped classroom approach. The learning effectiveness of EBN knowledge and self-efficacy in practice were evaluated across the three time points: pre-course, post-course, and one month after the course. In both group the scores of the EBN knowledge and self-efficacy in practice improved after training. The scores of the experimental group increased significantly than in the control group.

However, the scores declined in both groups one month after the course.

The implementation of the flipped classroom approach and team-based learning effectively enhanced the learners EBN knowledge accumulation and self-efficacy in practice. The research results can be used as an important reference for improving clinical nursing teaching quality. This is an open access article distributed under the terms of the Creative Commons Attribution Licensewhich permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.

Data Availability: The data underlying the results presented in the study are available from supporting information section. The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript. Competing interests: The authors have declared that no competing interests exist. Medical personnel is encouraged to make clinical decisions with the best evidence available to provide patients with appropriate nursing care strategies [ 1 — 3 ].

Evidence-based nursing EBN was devleoped on the basis of offering patient care with scientific methodolgy, which means systematic search of the literature and access to the best literature as the best evidence to support clinical decision making in clinical care [ 4 ]. It consists of seven steps, which were identified as step 0 Cultivate a spirit of inquiry, step 1 Ask an answerable question via the PICOT Problem, Intervention, Comparison, Outcome, Time format, step 2 Acquire the best available evidence according to the questions, step 3 Appraise the evidence for its validity, step 4 Apply the strategy to your patients, step 5 Audit the performance of above procedures and step 6 Disseminate EBP results [ 5 — 7 ].

Evidence-based nursing care has been of considerable concern for clinical continuing education [ 38 ] and become one of the clinical education evaluation indexes in nursing. From the institutional perspective, the lack of earmarked fund [ 15 ], human resource shortage, unorganized supervision system, insufficient databases, and inadequate hardware have been shown to be obstacles for EBN implementation [ 16 ].

Furthermore, nurses form non-English speaking countries had an additional language factors affecting their implementation of EBN in appraising English research articles [ 17 ]. In Taiwan, nurses who lack of confidence in reviewing English literature show low motivation to conduct literature appraisal and presentation.

Therefore, it urges for reformation of the EBN education[ 18 ]. Indeed, the present methods of delivery information to students are no longer applicable.

The flipped classroom has been widely used in various countries and is recognized as an innovative and important teaching strategy in the field of higher education [ 19 ].

There is a call for education institutes to develop the flipped classroom method and improve the classroom atmospheres and learning attitude [ 2021 ]. It moves learning outside the classroom and allows students to preview new material, while leave classroom time for content-related activities and collaborative tasks [ 22 — 25 ].

The in-class activities can be held with several learning strategies, such as role playing, group problem solving, simulation, case studies, and feedback [ 26 ]. The role of educator is converted from leader to coordinator, providing guidance and assistance [ 252729 ]. Consequently, the flipped classroom model can improve teaching and enable learners to apply and integrate the taught contents and even learning to evaluate and create [ 30 ]. Flipped learning enhances learner engagement and academic outcomes [ 2631 ], however, its success varies by discipline and research measurement issues.

There are still few issues on the evaluation of learning effectiveness of flipped classroom. Some of the previous researches lacked statistical data, significant intervention, and comparison group and needed further examination on flipped classroom strategy efficacy [ 18 ]. Also, recent studies found inconsistent intervention outcomes of applying the flipped classroom strategy [ 32 ]. Thus, multi-disciplinary collaborations have been recommended in order for the development of an effectiveness evaluation of the flipped classroom [ 33 ].

As technology advances and affects education, learning is no longer confined to traditional classroom and textbooks. The latest information and resources can be accessed via online learning without the limitations of time and space [ 2834 ].

Applying mobile technology to deliver information can break the time barriers and enable learners to access information in moments [ 35 ].Cliques are one of the basic concepts of graph theory and are used in many other mathematical problems and constructions on graphs. Cliques have also been studied in computer science : the task of finding whether there is a clique of a given size in a graph the clique problem is NP-completebut despite this hardness result, many algorithms for finding cliques have been studied.

Cliques have many other applications in the sciences and particularly in bioinformatics. This is equivalent to the condition that the induced subgraph of G induced by C is a complete graph.

In some cases, the term clique may also refer to the subgraph directly. A maximal clique is a clique that cannot be extended by including one more adjacent vertex, that is, a clique which does not exist exclusively within the vertex set of a larger clique.

Some authors define cliques in a way that requires them to be maximal, and use other terminology for complete subgraphs that are not maximal. A maximum clique of a graph, Gis a clique, such that there is no clique with more vertices. The clique cover number of a graph G is the smallest number of cliques of G whose union covers the set of vertices V of the graph. A maximum clique transversal of a graph is a subset of vertices with the property that each maximum clique of the graph contains at least one vertex in the subset.

The opposite of a clique is an independent setin the sense that every clique corresponds to an independent set in the complement graph. The clique cover problem concerns finding as few cliques as possible that include every vertex in the graph. A related concept is a bicliquea complete bipartite subgraph.

The bipartite dimension of a graph is the minimum number of bicliques needed to cover all the edges of the graph. Closely related concepts to complete subgraphs are subdivisions of complete graphs and complete graph minors. In particular, Kuratowski's theorem and Wagner's theorem characterize planar graphs by forbidden complete and complete bipartite subdivisions and minors, respectively. In computer sciencethe clique problem is the computational problem of finding a maximum clique, or all cliques, in a given graph.

Nevertheless, many algorithms for computing cliques have been developed, either running in exponential time such as the Bron—Kerbosch algorithm or specialized to graph families such as planar graphs or perfect graphs for which the problem can be solved in polynomial time. The same definition was used by Festinger in an article using less technical terms. Both works deal with uncovering cliques in a social network using matrices. For continued efforts to model social cliques graph-theoretically, see e.

Many different problems from bioinformatics have been modeled using cliques. Sugihara uses cliques to model ecological niches in food webs. Power graph analysis is a method for simplifying complex biological networks by finding cliques and related structures in these networks. Cliques have also been used in automatic test pattern generation : a large clique in an incompatibility graph of possible faults provides a lower bound on the size of a test set.

In chemistryRhodes et al. From Wikipedia, the free encyclopedia. For other uses, see Clique disambiguation.

Bonner county police scannerMain article: Clique problem. Alba, Richard D. Cong, J. Day, William H. Doreian, Patrick; Woodard, Katherine L. Festinger, Leon"The analysis of sociograms using matrix algebra", Human Relations2 2 : —, doi : Graham, R. Hamzaoglu, I. Karp, Richard M.Mathematical optimization deals with the problem of finding numerically minimums or maximums or zeros of a function.

In this context, the function is called cost functionor objective functionor energy. Here, we are interested in using scipy. Note that this expression can often be used for more efficient, non black-box, optimization. Mathematical optimization is very … mathematical.

Paratactic and syntacticIf you want performance, it really pays to read the books:. Not all optimization problems are equal.

**6.12 Finding All Bridges(cut edge) in a Graph - Data structures and algorithms**

Knowing your problem enables you to choose the right tool. The scale of an optimization problem is pretty much set by the dimensionality of the problemi. Optimizing convex functions is easy.

Optimizing non-convex functions can be very hard. It can be proven that for a convex function a local minimum is also a global minimum. Then, in some sense, the minimum is unique. Optimizing smooth functions is easier true in the context of black-box optimization, otherwise Linear Programming is an example of methods which deal very efficiently with piece-wise linear functions. Many optimization methods rely on gradients of the objective function.

If the gradient function is not given, they are computed numerically, which induces errors. In such situation, even if the objective function is not noisy, a gradient-based optimization may be a noisy optimization. You can use different solvers using the parameter method. Gradient descent basically consists in taking small steps in the direction of the gradient, that is the direction of the steepest descent. The core problem of gradient-methods on ill-conditioned problems is that the gradient tends not to point in the direction of the minimum.

We can see that very anisotropic ill-conditioned functions are harder to optimize. Take home message: conditioning number and preconditioning. If you know natural scaling for your variables, prescale them so that they behave similarly. This is related to preconditioning. Also, it clearly can be advantageous to take bigger steps. This is done in gradient descent code using a line search. The more a function looks like a quadratic function elliptic iso-curvesthe easier it is to optimize.

As can be seen from the above experiments, one of the problems of the simple gradient descent algorithms, is that it tends to oscillate across a valley, each time following the direction of the gradient, that makes it cross the valley. The conjugate gradient solves this problem by adding a friction term: each step depends on the two last values of the gradient and sharp turns are reduced.

The simple conjugate gradient method can be used by setting the parameter method to CG. Gradient methods need the Jacobian gradient of the function. They can compute it numerically, but will perform better if you can pass them the gradient:. Newton methods use a local quadratic approximation to compute the jump direction.

For this purpose, they rely on the 2 first derivative of the function: the gradient and the Hessian.Energy bands consisting of a large number of closely spaced energy levels exist in crystalline materials. The bands can be thought of as the collection of the individual energy levels of electrons surrounding each atom. The wavefunctions of the individual electrons, however, overlap with those of electrons confined to neighboring atoms.

The Pauli exclusion principle does not allow the electron energy levels to be the same so that one obtains a set of closely spaced energy levels, forming an energy band.

The energy band model is crucial to any detailed treatment of semiconductor devices. It provides the framework needed to understand the concept of an energy bandgap and that of conduction in an almost filled band as described by the empty states.

In this section, we present the free electron model and the Kronig-Penney model. Then we discuss the energy bands of semiconductors and present a simplified band diagram. We also introduce the concept of holes and the effective mass. The free electron model of metals has been used to explain the photo-electric effect see section 1. This model assumes that electrons are free to move within the metal but are confined to the metal by potential barriers as illustrated by Figure 2.

The minimum energy needed to extract an electron from the metal equals q F Mwhere F M is the workfunction. This model is frequently used when analyzing metals. However, this model does not work well for semiconductors since the effect of the periodic potential due to the atoms in the crystal has been ignored.

The analysis of periodic potentials is required to find the energy levels in a semiconductor. This requires the use of periodic wave functions, called Bloch functions which are beyond the scope of this text. The result of this analysis is that the energy levels are grouped in bands, separated by energy band gaps. The behavior of electrons at the bottom of such a band is similar to that of a free electron. However, the electrons are affected by the presence of the periodic potential.

The combined effect of the periodic potential is included by adjusting the value of the electron mass. This mass will be referred to as the effective mass. The effect of a periodic arrangement on the electron energy levels is illustrated by Figure 2. Shown are the energy levels of electrons in a carbon crystal with the atoms arranged in a diamond lattice. These energy levels are plotted as a function of the lattice constant, a. Isolated carbon atoms contain six electrons, which occupy the 1s, 2s and 2p orbital in pairs.

The energy of an electron occupying the 2s and 2p orbital is indicated on the figure. The energy of the 1s orbital is not shown. As the lattice constant is reduced, there is an overlap of the electron wavefunctions occupying adjacent atoms.

This leads to a splitting of the energy levels consistent with the Pauli exclusion principle. The splitting results in an energy band containing 2N states in the 2s band and 6N states in the 2p band, where N is the number of atoms in the crystal.

A further reduction of the lattice constant causes the 2s and 2p energy bands to merge and split again into two bands containing 4N states each. At zero Kelvin, the lower band is completely filled with electrons and labeled as the valence band.

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