31 research outputs found

    Utilization Rate of Outsourcing in Selected Municipalities

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    Cílem bakalářské práce je míra využití outsourcingu v obcích Olomouckého kraje. Zvolený problém jsem vyřešila pomocí metodiky dotazníkového šetření a analýzy získaných dat. Podařilo se získat dostatečné množství vzorků a tím potvrdit, že obce outsourcing využívají. Hlavním přínosem této práce je zjištění, že obce outsourcing využívají ve velké míře. Většina obcí, které odpověděly na dotazník, své činnosti zajišťují externím dodavatelem. Z práce je taky zřejmé, že nejčastější outsourcované činnosti jsou informačních technologie, komunální odpad a právní služby. Obce k zavedení outsourcingu vedou různé důvody, nejčastějším důvodem je přístup k technologiím a lidským zdrojům externím dodavatele. U každého smluvního vztahu mohou vzniknout rizika. Dalším zjištěním je, že nejčastějším rizikem je kvalita poskytované služby a kvalifikace pracovníků dodavatelské firmy.The aim of this thesis is ulitization rate of outsourcing in the selected municipalities of the Olomouc region. The chosen problem I solved using the methodology of the questionnaire survey and the analysis of the obtained data. It was managed to get a sufficient number of samples and thus to confirm that the municipalities use outsourcing. The main contribution of this thesis is findig out that the level of outsourcing use in selected municipalities is very often. The major part of municipalities, which took part in questionnaire survey, make use of external suppliers for their activities. According to questionnaire survey results the most common outsourced activities are information technology, municipal waste and legal services. The municipalities use outsourcing because of a variety of reasons, the most common reason is access to the technology and human resources of external contractor. For each contractual relationship risks can arise. Another finding is that the most common risk is the quality of the provided services and the qualifications of the contractor's staff.153 - Katedra veřejné ekonomikyvýborn

    Visualization of the proposed algorithm.

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    <p>Search for alternative paths to edge (1, 6) (dotted arrow), i.e., <i>v</i><sub><i>s</i></sub> = 1 and <i>v</i><sub><i>t</i></sub> = 6. Solutions <math><mrow><msubsup><mi>L</mi><msub><mi>v</mi><mi>s</mi></msub><mi>n</mi></msubsup><mrow><mo>(</mo><msub><mi>w</mi><mi>i</mi></msub><mo>;</mo><mo>⇝</mo><msub><mi>v</mi><mi>j</mi></msub><mo>)</mo></mrow></mrow></math> to subproblems are managed in a two dimensional solution array indexed by path weight <i>w</i><sub><i>i</i></sub> and vertex number <i>v</i><sub><i>j</i></sub>. Solutions are calculated iteratively over <i>w</i><sub><i>i</i></sub> (rows) and <i>v</i><sub><i>j</i></sub> (columns). (A) Solution matrix after first iterative step (subproblem <math><mrow><msubsup><mi>L</mi><mrow><msub><mi>v</mi><mi>s</mi></msub></mrow><mn>1</mn></msubsup><mrow><mo>(</mo><mn>1</mn><mo>;</mo><mo>⇝</mo><msub><mi>v</mi><mn>2</mn></msub><mo>)</mo></mrow></mrow></math>): There are two edges leading to vertex 2 of which only edge (1, 2) yields a valid solution by pointing to an earlier solved subproblem (green box), whereas edge (5, 2) has a weight of 7 leading to a negative difference in weights <i>i</i> − <i>w</i><sub>(5,2)</sub> (red box) for which no earlier solution exists; (B) Solution matrix after third iterative step <math><mrow><msubsup><mi>L</mi><mrow><msub><mi>v</mi><mi>s</mi></msub></mrow><mn>3</mn></msubsup><mrow><mo>(</mo><mn>1</mn><mo>;</mo><mo>⇝</mo><msub><mi>v</mi><mn>3</mn></msub><mo>)</mo></mrow></mrow></math>: Here, no valid solution exists (none of the arrows leading to vertex 2 are part of a path with summed weight 1); (C) Solution array after iteration over all vertices <i>v</i><sub><i>j</i></sub> for <i>w</i><sub><i>i</i></sub> = 1 (all vertices have been checked for a path of weight 1, originating from the start vertex 1); (D) Solution to subproblem <math><mrow><msubsup><mi>L</mi><mrow><msub><mi>v</mi><mi>s</mi></msub></mrow><mi>n</mi></msubsup><mrow><mo>(</mo><mn>6</mn><mo>;</mo><mo>⇝</mo><msub><mi>v</mi><mn>6</mn></msub><mo>)</mo></mrow></mrow></math>: edge (4, 6) together with the solution <math><mrow><msub><mi>L</mi><msub><mi>v</mi><mi>s</mi></msub></msub><mrow><mo>(</mo><mn>5</mn><mo>;</mo><mo>⇝</mo><msub><mi>v</mi><mn>4</mn></msub><mo>)</mo></mrow></mrow></math> form a valid path, whereas edge (5, 6) is not part of a valid solution as <math><mrow><msub><mi>L</mi><msub><mi>v</mi><mi>s</mi></msub></msub><mrow><mo>(</mo><mn>3</mn><mo>;</mo><mo>⇝</mo><msub><mi>v</mi><mn>5</mn></msub><mo>)</mo></mrow></mrow></math> is empty; (E) The algorithm terminates after iteration over all vertices <i>v</i><sub><i>j</i></sub> and path weights up to <i>w</i><sub>(<i>v</i><sub><i>s</i></sub>, <i>v</i><sub><i>t</i></sub>)</sub> + <i>θ</i>, where <i>θ</i> is a user defined threshold of 1. Backtracking is conducted for all entries in the reconstruction interval <i>w</i><sub>(<i>v</i><sub><i>s</i></sub>, <i>v</i><sub><i>t</i></sub>)</sub> ± <i>θ</i> (entries marked blue); (F) Reconstructed alternative path by backtracking of subproblems.</p

    Directed interactions in the turtle brain during visual stimulation with random light pulses (modified from [19], creative common attribution license CC BY).

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    <p>(A) Raw traces recorded in the tectum (blue) and from the retina (green) overlaid on the light pulses (yellow). (B) Turtle brain explant with eyes attached. Transfer entropy was found from the retina of the right eye to the left tectum, as well as from the light source (yellow) to the retina and to the tectum (***** denotes <i>p</i> < 10<sup>(−5)</sup>). P-values for the opposite directions were not significant (<i>n</i>.<i>s</i>.). Note, that the interaction between light source and optic tectum shows a interaction delay roughly equal to the summed interaction delay between light source and retina and retina and optic tectum (deviation ≤ 5%).</p

    Notation.

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    <p>Notation.</p

    Schematic example of a subproblem of the proposed algorithm.

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    <p>(A) Example subproblem <math><msubsup><mi>L</mi><mrow><msub><mi>w</mi><mi>i</mi></msub><mo>,</mo><msub><mi>v</mi><mi>j</mi></msub></mrow><mi>n</mi></msubsup></math>: At the <i>n</i><sup><i>th</i></sup> algorithmic step, we search for all paths of weight <i>w</i><sub><i>i</i></sub> leading to node <i>v</i><sub><i>j</i></sub>; (B) Finding a solution for the current subproblem by investigating solutions to prior subproblems: We investigate all predecessors <i>v</i><sub><i>p</i></sub> of the current node <i>v</i><sub><i>j</i></sub>; if there exists a solution to <math><msubsup><mi>L</mi><mrow><msub><mi>w</mi><mi>p</mi></msub><mo>,</mo><msub><mi>v</mi><mi>p</mi></msub></mrow><mi>M</mi></msubsup></math>, i.e., there is a solution to the prior subproblem <math><mrow><msub><mi>v</mi><mi>s</mi></msub><mo>⇝</mo><msub><mi>w</mi><mi>p</mi></msub><msub><mi>v</mi><mi>p</mi></msub></mrow></math> of finding a path of weight <i>w</i><sub><i>p</i></sub> leading from <i>v</i><sub><i>s</i></sub> to <i>v</i><sub><i>p</i></sub>, and <i>w</i><sub><i>p</i></sub> + <i>w</i><sub>(<i>v</i><sub><i>p</i></sub>, <i>v</i><sub><i>j</i></sub>)</sub> = <i>w</i><sub><i>i</i></sub>, we find a solution to the current subproblem <math><msubsup><mi>L</mi><mrow><msub><mi>w</mi><mi>i</mi></msub><mo>,</mo><msub><mi>v</mi><mi>j</mi></msub></mrow><mi>n</mi></msubsup></math>.</p

    Results empirical data sets.

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    <p>(A) Running time of the complete algorithm by number of nodes plus number of edges ∣<b>V</b>∣ + ∣<b>E</b>∣; (B) Mean percentage of tagged, potentially spurious edges by chosen threshold <i>θ</i> after application of the algorithm, error bars indicate 1 standard deviation (SD); the value for <i>θ</i> obtained from bootstrapping in two example data sets is marked in red; (C) Mooney Stimulus [<a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0140530#pone.0140530.ref042" target="_blank">42</a>]; (D) Cortical sources after beamforming of MEG data (l.,left; r., right: l. orbitofrontal cortex (OFC); r. middle frontal gyrus (MiFG); l. inferior frontal gyrus (IFG left); r. inferior frontal gyrus (IFG right); l. anterior inferotemporal cortex (aTL left); l. cingulate gyrus (cing); r. premotor cortex (premotor); r. superior temporal gyrus (STG); r. anterior inferotemporal cortex (aTL right); l. fusiform gyrus (FFA); l. angular/supramarginal gyrus (SMG); r. superior parietal lobule/precuneus (SPL); l. caudal ITG/LOC (cITG); r. primary visual cortex (V1)), see also [<a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0140530#pone.0140530.ref040" target="_blank">40</a>]; (E) Example of removal of tagged edges: MEG data of a face detection task in two subjects. First column shows transfer entropy values prior to detection of potentially spurious edges (<b>Pre</b>). The second column shows color-coded tagged edges (red: Potential cascade effects, blue: potential common drive effects; <i>θ</i> = 3<i>ms</i>). The third column shows the network of directed interactions after removal of all tagged edges (<b>Post</b>).</p

    Results running time.

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    <p>Running times [log(s)] for dynamic programming (A, B) and backtracking (C, D) by number of vertices ∣<b>V</b>∣ and maximum path weight <i>w</i><sub><i>crit</i></sub>. Running times are shown for different graph types (SW: small-world, SF: scale-free, RN: random networks with density <i>ρ</i>). Red markers indicate cases of intractability (execution was aborted after a pre-defined limit of reconstructed alternative paths was reached).</p

    Graph representation of neural data.

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    <p>(A) Recorded signals from various sources in the brain; (B) Pairwise estimation of transfer entropy (TE) and reconstruction of interaction delays <i>u</i> between any two sources; (C) Adjacency matrix: representation of estimated delay times between all source combinations, every entry represents an information transfer from the <i>i</i>th row to the <i>j</i>th column; (D) Adjacency matrix after test for statistical significance; (E) Visualization of the graph represented by the connectivity matrix: every source is represented by a vertex, every significant information transfer is represented by an edge. (The blue circle indicates the respective representation of an exemplary interaction between source 1 and source 3 throughout all steps of graph reconstruction.)</p

    Overview of the proposed algorithm.

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    <p>The algorithm expects a weighted and directed graph <b>G</b> = {<b>V</b>,<b>E</b>} and a threshold <i>θ</i> as input. In a preprocessing step, the algorithm creates graph <b>G</b>′ from input <b>G</b>, as an input for the dynamic programming algorithm, by removing edge (<i>v</i><sub><i>a</i></sub>, <i>v</i><sub><i>b</i></sub>) and by relabeling and reordering nodes. Then, in the next step, alternative paths for (<i>v</i><sub><i>a</i></sub>, <i>v</i><sub><i>b</i></sub>), are searched through dynamic programming (see also main text). If at least one alternative path is found, paths are reconstructed using a depth first search (DFS, [<a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0140530#pone.0140530.ref027" target="_blank">27</a>]) to ensure that alternative paths do not contain loops. If an alternative path contains no loops, the currently investigated edge (<i>v</i><sub><i>a</i></sub>, <i>v</i><sub><i>b</i></sub>) is tagged as potentially spurious. If no alternative edge is found, (<i>v</i><sub><i>a</i></sub>, <i>v</i><sub><i>b</i></sub>) is considered non-spurious. The algorithm then enters the next iteration, in which the next edge (<i>v</i><sub><i>a</i></sub>, <i>v</i><sub><i>b</i></sub>) ∈ <b>E</b> is investigated for alternative paths.</p

    The logistic function produces a sigmoid curve, where y represents the probability of rejection () and x the number of invited reviewers

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    <p><b>Copyright information:</b></p><p>Taken from "A retrospective analysis of submissions, acceptance rate, open peer review operations, and prepublication bias of the multidisciplinary open access journal Head & Face Medicine"</p><p>http://www.head-face-med.com/content/3/1/27</p><p>Head & Face Medicine 2007;3():27-27.</p><p>Published online 11 Jun 2007</p><p>PMCID:PMC1913501.</p><p></p> Inviting a minimum of 2 reviewer corresponds to a probability of rejection = 0.44. Inviting 15 reviewers increases to 0.85
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