Public Articles

Sizing optimization Energy Systems of PV-Turbine-Generator of in dormitory buildings (National Jurnal Accreditaion)

250 kata, Abstract content goes here

Chapter 14. Network

In this chapter, we briefly review a broad range of mathematical topics relevant to the rest of the book. Readers intereasted in this book are likely to have a working knowledge of ___.

Tarea TEMA IV

Ejercicios unidad IV Termodinámica.1.-Un termómetro te dice que tienes fiebre de 39.4 ° C. ¿Cuanto es esto en grados Fahrenheit?Datos:T(°C)=29.4°Fórmula:T°F=9/5[T°C]+32Solución:\(=\frac{9}{5}\)\(\left[T\left(39.4\right)\right]+32\)=\(102.92°F\)2.-La Torre Eiffel está construida en hierro forjado de aproximadamente 300 m de altura. Estime cuánto cambia su altura entre enero (temperatura promedio de 2 ° C) y julio (temperatura promedio de 25 ° C). Ignora los ángulos de las vigas de hierro y trata la torre como una viga vertical.

Report

After all these updates to the model I discussed above, I used it to constraint the Titan's haze particles. I will now discuss the results and the procedure that I followed briefly.

The constraints for the size of the aerosol particles mostly come from the forward scattering measurements. The solar aureola (SA) camera direclty measures the forward scattering part of the phase functions in the DISR model for the SA channels. The rest of the phase function obtained from the parameterization model except for the adjustments required by the observations, such as backscattering peak. Therefore, constraints from the forward scattering angles should be more accurate as this portion is not affected from any change in the parameterization model. I still used the backscattering portion of the DISR phase functions when comparing to model outputs, because the best fitting phase functions in the DISR model are obtained by comparing the measured radiances to the radiative transfer model outputs. Also, especially in the red channel the difference in the both version of the model is not significant. However, this time I allowed more flexibility in the acceptable error margin comparing to the forward angles. Consider adding comparison of old and new plot.

I did not use the DISR phase functions given in \citep{Tomasko_2008} for the red channel, because of the possibly too strong backward peak artificially added to phase functions for longer wavelengths, at and below 80km altitude. Here in figure 3 some phase functions with or without enhanced backward peak are shown calculated by the T-Matrix model. Although in this figure monomer numbers of the aggregates, N, are smaller than best fitting aggregate models to the observations, the backward peak is not a strong function of N, but rather depend strongly on size parameter, α, and the refractive index. The backward peak is enhanced as the size parameter and real part of the refractive index, n_{r}, increases and as the imaginary part decreases. Although the constraints from single scattering albedo suggest that there is little absorption in Red channel, the size parameter is small in the Red channel, most likely less than 0.35. As shown in figure 3 b. a strong backward peak is not observed in aggregates with 1000 monomers.

Moreover, \citet{de_Bergh_2012} found that DISR phase functions produce too strong intentisities at the some NIR wavelengths comparing to VIMS observations, and removing the backward peak reduces the positive bias from 40% to 10% as shown in fig 11 ( here in fig 5). Thus, unlike \citet{Tomasko_2008} they adopted a single aerosol phase function throughout the atmosphere without a backward peak, to be able to match the VIMS observations. More recently \citet{Doose_2016} modified the original aerosol model in the \citep{Tomasko_2008} by analzying DISR imagery including some measurements which were not used before. Similar to \citet{de_Bergh_2012}, they found that backward peak in the phase functions below 80 km is too strong. As a result they adopted a single phase function for each wavelenght thourughout the atmosphere which is a combination of 85% of the upper-level which does not include backward peak and 15% of the lower level phase function. The new phase functions fit the observations better than the original two-layer phase functions, and has a moderate backward peak which better fits the expectations.

I, therefore, updated the

Since the

The **figure 1** shows the best fitting models to the SA observations. These models

Discrete Mathematics

Remember to try to solve the problems in the textbook before looking at the solutions :)Don't forget slader.com for solutions if you really really need them.If a theorem doesn't have a name, consider naming it! uwu# NoteUtil Parameters # Blocks are '^^^' # Comments are >>> # Separator is '~' # Heading character is '#' # The 2 headings are 'Units' and 'Sub-units' # Number of Extensions: 6 # Extensions: (&&, &&, Additional Information), (%%, %%, Example) (\[, \], LaTeX) (Simple Definition, , ), (![](, ), Image), (![, ], Image Explanation) # Categories are: # ! Important definition

Materials Selection for High Voltage Transmission Lines

A. BackgroundTwo types of aluminum are used for AAC and ACSS, Al 1350-H19 and Al 1350-O. The difference between these materials is the temper. In the case of H19All Aluminum Conductor (AAC). Is used for short spans.This cable uses Al 1350-H19.

EJERCICIOS IV TERMODINAMICA

La **termodinámica** es la disciplina que dentro de la ciencia madre, la **Física**, se ocupa del **estudio de las relaciones que se establecen entre el calor y el resto de las formas de ****energía**. Entre otras cuestiones la termodinámica se ocupa de analizar los efectos que producen los cambios de magnitudes tales como: la temperatura, la densidad, la presión, la masa, el volumen, en los sistemas y a un nivel macroscópico.

a)la temperatura ambiente comunmente es de 60°f cuanto es esto en grados °c

T(°c)\(=\frac{5}{9}\)[T(°f)-32]=\(\frac{5}{9}\left[68-32\right]=\frac{5}{9}\left(36\right)=20\)

b) la temperatura del filamento de una bombilla es cerca de 1900°c cuanto es en °f

T(°F)=\(\frac{9}{5}\)\(T+32=\)\(\frac{9}{5}\left(1900\right)\)=\(\frac{9}{5}\left(38\cdot5\cdot10\right)=\)\(3420°f\)

LA TORRE EIFFEL ESTA HECHA DE HIERRO FORJADO Y MIDE APROXIMADAMENTE 300 M DE ALTO. ESTIME CUANTO CAMBIA SU ALTURA ENTRE JULIO (TEMPERATURA PROMEDIO DE 25 ° C) Y ENERO (TEMPERATURA PROMEDIO DE 2 ° C). CONSIDERE LA TORRE COMO UNA VIGA VERTICAL.

Ti=2°c

Tf=25°c

\(\alpha=12x10^6\)

para esto multiplicamos lo que es la altura de la torre con la propiedad de el hierro

\(=\left(12x10^{-6}\right)\)\(\left(300m\right)\)\(\left(23\right)\)°f

=\(36x10^{-4}\)(23)

=\(828x10^{-4}\)=0.0828m

Una persona activa consume al rededor de 2500 kcal por dia cuanto es joules y cuanto es en kilowats hora

1kcal= 4.18 kj

entonces 2500kcal son igual a 10465 kj

ahora recordemos que 1w=\(\frac{1j}{1s}\) para el calculo por dia \(\frac{10465}{3600}=\)2.9 kwh

un motor ejerse un trabajo de 2600 con un Ql de 7800j calular el porcentaje de calor

W=2600

Ql=7800J

\(e=\frac{W}{W+Ql}=\frac{2600}{2600+7800}=0.25=25\%\)

El sistema de enfriamiento de un auto es de 18L de agua. cuanto calor absorbe si su temperatura aumenta de 15° a 95°

para esto convertimos los 10 litros en kilos con ayuda de la medida de decimetros

\(\beta=\frac{1000\ Kg}{1m^3}\)

Ti=15°c

Tf=95°c

se hace la resta en las temperaturas que es 95°-15°=80°c

aplicamos la formula del calor

\(Q=mC\ ΔT\)

=(18kg)(4186 j/kgc)(80°c)=

\(6x10^6J\)= \(6000\ KJ\)

\(1027\frac{kg}{m3}\)

\(Q=mC\ ΔT=M=\beta V\)

\(=1027\left(5X10^{-4}m^3\right)=m=0.5135kg\)

Ahora agregamos el calor especifico del agua de mar que es =\(3850\)

\(Q\left(0.5130\ \right)\left(3850\right)\left(25\right)\) =\(49424j\)

para poder convertir los kcal a barras tenemos que:

1kcal=4184 j

1 barra =300 kcal

aplicamos la regla de tres

Lattice Theory

LATTICE THEORY Ordered Sets ^ PARTIAL ORDER ~ Let P be a set. A ___ on P is a binary relation \(\leq\) on P that, for all \(x, y, z \in P\), satisfies the three following conditions: (i) REFLEXIVITY - \(x \leq x\) (ii) ANTISYMMETRY - \(x \leq y\) and \(y \leq x\) implies \(x = y\) (iii) TRANSITIVITY - \(x \leq y\) and \(y \leq z\) implies \(x \leq z\) ^ RELATION ~ A ___ R on a set P is a subset \(R \subset P x P\) (cartesian product). If \(a, b \in R \to a R b\). (PARTIALLY) ORDERED SET (POSET) ~ A set P equipped with an order relation \(\leq\) is said to be an ___. (DISCRETE/(QUASI/PRE)) ORDER ~ On any set, = is an order called the ___. A relation \(\leq\) on a set P which is reflexive and transitive, but not necessarily antisymmetric is called a ___. &&An order relation \(\leq\) on P gives rise to a relation < of STRICT INEQUALITY: x < y in P if and only if \(x \leq y\) and \(x \neq y\).&& NON-COMPARABILITY ~ This is a binary relation \(\parallel\) used to denote ___. We write x \(\parallel\) y if x \(\nleq\) y and y \(\nleq\) x. INDUCED ORDER ~ This is when an order relation on a subset is inherited from its superset. Let P be an ordered set and let Q be a subset of P. Then Q inherits an order relation from P; given x, y ∈ Q, \(x \leq y\) in Q if and only if \(x \leq y\) in P. TOTALLY ORDERED SET ~ Let P be an ordered set. Then P is a ___ if, for all \(x, y \in P\), either \(x \leq y\) or \(y \leq x\). (This is another way of saying any two elements of P are comparable). &&Alternative names for a chain are LINEARLY ORDERED SET and CHAIN.&& &&On the other hand, the ordered set P is an antichain if \(x \leq y\) in P only if \(x = y\).&& &&Clearly, any subset of a chain/antichain is a chain/antichain.&& &&Let P be the n-element set {0, 1, ..., n - 1}. We write N to denote the chain obtained by giving P the order in which 0 < 1 < ... < n - 1 and \(\) for P regarded as an antichain. Any set S may be converted into an antichain \(\) by giving S the discrete order. ORDER-ISOMORPHIC ~ We say that two ordered sets P and Q are ___ and write \(P \cong Q\) if there exists a map \(\psi\) from P onto Q such that \(x \leq y\) in P if and only if \(\psi (x) \leq \psi (y)\) in Q. &&\(\psi\) is called an ORDER-ISOMORPHISM&& COVERED (COVERING RELATION) ~ Let P be an ordered set and let \(x, y \in P\). We say x is ___ by y, and write \(x \prec y y \succ x\) if \(x < y\) and \(x \leq z < y\) implies z = x. The latter condition is demanding that there be no element z of P with x < z < y. ORDER-ISOMORPHIC COVER-RELATION LEMMA ~ Let \(P\) and \(Q\) be finite ordered sets and let \(\varphi: P \to Q\) be a bijective map. Then the following are equivalent: 1. \(\varphi\) is an order-isomorphism. 2. \(x < y\) in \(P\) if and only if \(\varphi(x) < \varphi(y)\) in \(Q\). 3. \(x \prec y\) in \(P\) if and only if \(\varphi(x) \prec \varphi(y)\) in \(Q\). ORDER-ISOMORPHIC DIAGRAM PROPOSITION ~ Two finite ordered sets P and Q are order-isomorphic if and only if they can be drawn with identical diagrams. THE DUALITY PRINCIPLE ~ Given a statement \(\Phi\) about ordered sets which is true in all ordered sets, the dual statement \(\Phi^\delta\) is also true in all ordered sets. BOTTOM AND TOP OF AN ORDERED SET ~ Let P be an ordered set. We say P has a ___ if there exists \(\bot \in P\) with the property that \(\bot R x\) for all \(x \in P\). Dually, P has a ___ element if there exists \(\top \in P\) such that \(x R \top\) for all \(x \in P\). LIFTED ~ Given an ordered set P (with or without \(\bot\), form \(P_\bot\) (called P-___) as follows: Take an element \(0 \not \in P\) and define \(\leq\) on \(P_\bot := P \cup \{0\}\) by \(x \leq y\) if and only if \(x = 0\) or \(x \leq y\) in P. FLAT ~ MAXIMAL AND MINIMAL ELEMENT ~ Let P be an ordered set and let \(Q \subseteq P\). Then \(a \in Q\) is a ___ of Q if \(a R x\) and \(x \in Q\) imply \(a = x\). On the other hand, a ___ of \(Q \subseteq P\) is defined dually, by reversing the order. &&We denote the set of maximal elements of Q by max Q. If Q (with order inherited from P) has a top element, \(\top_Q\), then Max Q = {_Q}; in this case \(\top_Q\) is called the MAXIMUM element of Q and we write \(\top_Q\) = max Q. Same thing but for the dual, you get minimal element and the minimum element \(\bot_Q\) = Min Q&& DISJOINT UNION OF ORDERED SETS ~ Suppose P and Q are disjoint ordered sets. The ___ (P U) LINEAR SUM OF ORDERED SETS ~ Let P and Q be disjoint ordered sets. The ___ \(P \oplus Q\) is defined by taking the following order relation on \(P \cup Q : x R y \) if and only if \({ll}x, y \in P x R y \in P \\ x, y \in Q x R y \in Q \\ x \in P y \in Q\) PRODUCT OF ORDERED SETS ~ Lattices and Complete Lattices Formal Concept Analysis Modular, Distributive, and Boolean lattices Representation: The Finite Case Congruences Complete Lattices and Galois Connections CPOs and Fixpoint Theorems Domains and Information Systems Maximality Principles Representation: The General Case PROBLEMS

MPhil project: single cell analysis of mice lung

and 1 collaborator

Bulk RNA-seq (i.e. measuring gene expression in multi-cell samples, often including mixtures of cell types) is an extremely common experiment. Often bulk RNA-seq samples are collected for two samples and their gene expression values are compared (i.e. differential gene expression analysis). For instance, these two samples could be a healthy mouse lung and a diseased mouse lung or it could be two diseased lungs where one of the two samples are exposed to a potential treatment. There are a number of reasons why a particular gene might be over-expressed in one sample compared to another. One reason could be that the tissue ratio is different between the two samples-- for instance, the diseased tissue might have more white blood cells than a control tissue. Another reason could be that some specific cell types (or all the cells) are over-expressing that gene. The focus of this MPhil project will be on these first two explanations, but it is also important to remember that this up-regulation may be because of an uncontrolled confounding factor (potentially things like the time of day the sample was collected, the age of the patient, the stress level of the patient) or another observable physiological difference that distinguishes the two samples (like how quickly the cells divide). In particular, you will attempt to answer the question: how much of the gene expression changes could be explained by differences in the cell type composition between the RNA-seq samples?

Now it is possible to perform RNA-seq on single cells. With bulk RNA-seq you would find a single gene expression value for each gene (a single column in a table), but with single cell RNA-seq you would find the gene expression for each gene within each cell that is sampled (a table with a column for each cell). This means that you can graph the *distribution* of gene expression values in a population of cells for each cell type. However, some of this variability arrises from technical noise (from the experimental procedure) rather than biological noise (true variation between cells). The most notorious issue is zero-inflation: for each individual cell there will be many genes that will have a recorded gene expression of 'zero', but this may be technical, rather than biological.

If you have an atlas of gene expression from single cells or single tissues, then it may be possible to determine the composition of cell types or tissues in a bulk RNA-seq experiment. I sent you the CIBERSORT article which describes one way to do this in detail. There are a few issues with CIBERSORT that we found when we tried to apply it to our (plant) data: (i) it gives you an estimated tissue ratio, but it doesn't give you a sense of how confident it is in these results (ii) it performs poorly when the tissue ratios are more extreme (like 90% coming from one tissue or cell type and only a few % coming from other tissue/cell types) and (iii) it doesn't take into account difference in the age of the sample. We developed a strategy for overcoming these issues (which we call TissueTimer). The master student who worked on this project is currently writing up a paper about this work (I'll give it to you to read once its in better shape). However, he used a 'tissue atlas' rather than a 'cell atlas' (samples taken from whole tissues rather than single cell data. An ideal outcome of the MPhil would be to adapt this method to be able to utilise single cell RNA-seq data. The main challenge is to deal with is zero-inflation, but also there might need to be other changes to the method to account for the fact that we have data from 100s of cells per sample, instead of data from 3 RNA-seq replicates. Before we make these changes, we need to do a thorough analysis of the single cell RNA-seq atlas that we will be using, and this will inform how we modify TissueTimer.

We might eventually get access to new mouse lung data from collaborators. In the meantime, there are lots of lung RNA-seqs available, both single cell and bulk, which we can use.

Single cell RNA-seq datasets:

Bulk RNA-seq datasets (same strain as first article):

also: GSE49114

many more can be found through google scholar

Overall aim: We will develop a tool to enable us to predict the cell type ratio of whole-lung RNA-seq, based on single cell gene expression data. We will use this tool to explore how the cellular composition of lungs changes during disease progression.

- How consistent are the single cell lung RNA-seq datasets across the three papers?
- What genes are most resilient to batch effect? (i.e. what genes have expression levels that are most consistent across the experiments?) . Are there certain cell types that seem more resilient to batch effects than others?
- How consistent are immune system cells across tissues? Can we predict the tissue that an immune system cell came from, based on its gene expression values? (If immune systems cells from other tissues are similar to immune system cells in lung, then we can use those cells to identify good single cell markers in the next phase of the project)
- Can we distinguish between lung cell type based on the gene expression of single cells? What genes are most informative?
- Can we infer the cell cycle phase of the cells in the lung? How does the distribution of cell cycle phase vary across cell types? (https://rdrr.io/bioc/scran/man/cyclone.html)
- Do any pairs of genes tend to have highly correlated gene expression values within a cell type? Are these the same or different across different cell types?

- What are the gene markers that are selected by CIBERSORT: do they seem reasonable, given your previous analysis?
- If you only provide CIBERSORT gene expression values for genes that you think would be decent markers from your previous analysis, does it perform better?
- How accurately does it perform? (make simulated bulk RNA-seq from your single cell RNA-seq data)
- Does it perform accurately on data from a different 'batch'?
- What cell type composition is predicted for RNA-seq datasets from public databases? Do you see a difference in predicted tissue ratio?

- Go through the math: what do we need to modify to take into account zero-inflation?
- Are there any assumptions we can remove now that we have data from 100s of cells instead of 3 replicates?
- Can we incorporate age, using the aging lung atlas? Can we think about how to incorporate cell cycle phase, as well?
- Repeat all the CIBERSORT analysis with TIssueTimer: How different are the results from CIBERSORT and TissueTimer? What are the benefits of using one over the other?

Learn how to perform the following operations in R and knowing what they mean (I can help with this):

- PCA (visualising how similar cells are to one another)
- t-SNE (visualising how similar cells are to one another and clustering-- i.e. finding groups of cells that are similar to one another)
- Kmeans clustering and hierarchical clustering (clustering-- i.e. finding groups of cells that are similar to one another)
- Drawing results as a heatmap or scatterplot
- Supervised learning techniques: randomForest, SVM (if you have labelled data-- such as tissue type-- these are strategies to build models to help classify the cells by their labels. You can then look at the model and see which genes were most useful for predicting the label.)
- Single cell versions of differential gene expression analysis: https://hms-dbmi.github.io/scde/ or read https://bmcbioinformatics.biomedcentral.com/articles/10.1186/s12859-019-2599-6

Obat Kuat Pria Murah Lengkap Wa082221214145

OBAT KUAT PRIA MERUPAKAN SOLUSI AMPUH UNTUK MENCEGAH PARA LELAKI SUPAYA TIDAK GAGAL MENYENANGKAN SE ORANG WANITA DI DALAM KEHIDUPAN $EK$UAL.

How To Write Mathematical Equations, Expressions, and Symbols with LaTeX: A cheatsheet.

and 3 collaborators

WHAT IS LATEX? LaTeX is a programming language that can be used for writing and typesetting documents. It is especially useful to write mathematical notation such as equations and formulae. HOW TO USE LATEX TO WRITE MATHEMATICAL NOTATION There are three ways to enter “math mode” and present a mathematical expression in LaTeX: 1. _inline_ (in the middle of a text line) 2. as an _equation_, on a separate dedicated line 3. as a full-sized inline expression (_displaystyle_) _inline_ Inline expressions occur in the middle of a sentence. To produce an inline expression, place the math expression between dollar signs ($). For example, typing $E=mc^2$ yields E = mc². _equation_ Equations are mathematical expressions that are given their own line and are centered on the page. These are usually used for important equations that deserve to be showcased on their own line or for large equations that cannot fit inline. To produce an inline expression, place the mathematical expression between the symbols \[! and \verb!\]. Typing \[x=}{2a}\] yields \[x=}{2a}\] _displaystyle_ To get full-sized inline mathematical expressions use \displaystyle. Typing I want this $\displaystyle ^{\infty} {n}$, not this $^{\infty} {n}$. yields: I want this $\displaystyle ^{\infty}{n}$, not this $^{\infty}{n}.$ SYMBOLS (IN _MATH_ MODE) The basics As discussed above math mode in LaTeX happens inside the dollar signs ($...$), inside the square brackets \[...\] and inside equation and displaystyle environments. Here’s a cheatsheet showing what is possible in a math environment: -------------------------- ----------------- --------------- _description_ _command_ _output_ addition + + subtraction - − plus or minus \pm ± multiplication (times) \times × multiplication (dot) \cdot ⋅ division symbol \div ÷ division (slash) / / simple text text infinity \infty ∞ dots 1,2,3,\ldots 1, 2, 3, … dots 1+2+3+\cdots 1 + 2 + 3 + ⋯ fraction {b} ${b}$ square root $$ nth root \sqrt[n]{x} $\sqrt[n]{x}$ exponentiation a^b ab subscript a_b ab absolute value |x| |x| natural log \ln(x) ln(x) logarithms b logab exponential function e^x=\exp(x) ex = exp(x) deg \deg(f) deg(f) degree \degree $\degree$ arcmin ^\prime ′ arcsec ^{\prime\prime} ′′ circle plus \oplus ⊕ circle times \otimes ⊗ equal = = not equal \ne ≠ less than < < less than or equal to \le ≤ greater than or equal to \ge ≥ approximately equal to \approx ≈ -------------------------- ----------------- ---------------

Anteproyecto - Diseño de una metodología para evaluar los procesos psicológicos relacionados con la implementación de la metodología 5Ss

and 5 collaborators

RESUMEN La metodología 5Ss es considerada una de las metodologías japonesas más conocidas y extendidas en el ámbito organizacional, necesarias para introducir a las organizaciones en la cultura de la mejora continua. Diferentes investigaciones revelan que son necesarias ciertas condiciones para que un programa de 5Ss pueda desarrollarse con éxito, encontrándose diferencias en los resultados que se consiguen entre empresas orientales (concretamente en Japón) y occidentales. Algunos investigadores reflejan que el éxito de las 5Ss en empresas orientales es debido a la influencia positiva que ejercen los aspectos culturales y religiosos de sus creencias en la formación de las personas desde la infancia. En este sentido, la metodología de las 5Ss resulta de interés particular dado que, si bien en nuestro contexto se usa como medio para fomentar el orden, la limpieza y la estandarización en el ambiente de trabajo, al momento de implementarlas no se tienen en cuenta las características de las personas frente a estos aspectos para así generar estrategias de formación que sean pertinentes y que contribuyan al éxito de la metodología. Con el presente proyecto, se busca diseñar una metodología que permita evaluar los procesos psicológicos que subyacen al orden, la limpieza y el seguimiento de los estándares como elementos de la metodología 5Ss, a fin de obtener un perfil descriptivo de las personas en estos aspectos.

Hvor hurtig løber en læge? - Natarbejde og hviletid for forvagter i FAM

and 1 collaborator

Engelsk resume <600 tegnNoter til intro:What is the problem to be solved?Are there any existing solutions?Which is the best?What is its main limitation?What do you hope to achieve?Rune Berg - Temporal analysis?

Datos_Carllinni Colombo

and 3 collaborators

Oscilaciones 1-g1

and 4 collaborators

Resultados y DiscusionesActividad 1. Estudio del movimiento oscilatorio simple y determinación de la constante elástica de un resorte.

Algorithms & Data Structures

Comments: //Blocks: +++++Separator: ~Heading Character: #Number of Headings: 1Heading Names: TopicNumber of Categories: 2Category Prefixes and Names: (!, Important), (^, Algorithm)Number of Extensions: 6Extensions: (&&, &&, Additional Information), (Simple Definition, , ), (\[, \], LaTeX), (![](, ), Image), (![, ], Image Explanation), (```java, ```, Code)

Regional and local scale refuges combine to buffer aquatic biodiversity in the face of exotic invasion

ABSTRACT
Multi-scalar refuges have an important yet under-appreciated role in
synergistically maintaining native community diversity in face of exotic
invasion. The objective of our study was to consider the role of
multi-scale refuges, created by spatial environmental heterogeneity, in
maintaining aquatic native species biodiversity by reducing the
abundance of an invasive fish, the Round Goby. Using spatial surveys for
fish and macroinvertebrate communities as well as environmental data, we
detected both a regional-scale refuge, as generated by conductivity
gradients from the convergence of different water types from different
rivers, and also local-scale refuges, as generated by the presence of
wetlands. Refuges at both scales provided native macroinvertebrate and
fish communities some protection from the impacts of exotic Round Goby
invasion within the Upper St. Lawrence River. We provide a rare study
that addresses the effects of multi-scale refuges on native biodiversity
in a freshwater ecosystem. Our findings point to the significance of
preserving wetlands for their dampening of exotic invasion impacts on
native communities in freshwater ecosystems. Our study more generally
underscores the importance of maintaining environmental heterogeneity at
multiple spatial scales, especially in large, continuous ecosystems.

Datos_g7

and 5 collaborators

Adquisición digital de datos y princinpio de cuadrados mínimosResumenEn el siguiente trabajo práctico se realizó un experimento con el fin de obtener un valor experimental de la gravedad a partir de las ecuaciones del oscilador armónico vistas en la teórica. Luego de realizado el experimento, se obtuvo un valor de g que no coincide con tabulado.IntroduccionEl objetivo del trabajo práctico fue mediante las ecuaciones del oscilador armónico presentadas en la teórica de la materia obtener el valor de la gravedad y comparar este con el valor teórico (979,68520 cm/s2) para saber si estas son exactas.Procedimiento:Para medir el período de las oscilaciones del péndulo a diferentes longitudes se utilizó el instrumento presente en la figura 1.

Ondas en una cuerda

and 3 collaborators

ResumenEn la primera parte, se arma el dispositivo de la figura 1 de la guía "Ondas en una cuerda" y se determina la frecuencia caracteristica de los distintos modos a una tensión fija y se observa que la velocidad se mantiene constante. Luego se utilizan distintas masas para variar la tension de la cuerda y observar que en un mismo modo, la relación de las mismas con la velocidad al cuadrado es lineal tal como es de esperar por la ecuación (3) de la guia "Ondas en una cuerda". En la tercera parte se verifica que la suposicion de que el hilo tiene una longitud fija no es certera, ya que se estira y tiene una constante elastica asociada.

Data Visualization

Today is the first day of my data visualization education plan that will take me through to the beginning of May, 30 weeks from now. My goals are to learn skills, techniques, tools, methods, but also to gather a bunch of certificates and a portfolio of my work. This is really important. It's also important to 'blog' about what I'm doing: to write about my experiments and courses and progress. I don't intend to do this publicly during this period, but I may later. Like my athletic goals, I have certain long-term goals that are more of an overall direction, and certain short-term goals that are either achieved or not. I have to make sure to constrain the scope of what I'm trying to do during this period. I'm not becoming a data scientist, I'm not even becoming an expert in data visualization. What I'm doing is completing a number of courses in data visualization, recording the results, and communicating that I've done it. Pretty simple. I'm not trying to get a new job out of this, but a new job is definitely my long-term goal.

My goals for this week:

- Stay on top of my regular work!
- Act intentionally with my time, don't flit about
- Record my progress
- Plan ahead
- Complete another chapter of Codecademy (finish 2 and complete 3)
- Do readings for Alberto Cairo (from here on AC) course
- Manage my stress and anxiety
- Communicate what I'm doing to someone

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Creating teaching and learning materials for non-dominant languages: a comparative analysis (Intro, research methods)

Abstract content goes here

Getting Started with Authorea🚶

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Hello, and welcome to Authorea!👋 We're happy to have you join us on this journey towards making writing and publishing smoother, data-driven, interactive, open, and simply awesome. This document is a short guide on how to get started with Authorea, specifically how to take advantage of some of our powerful tools. Of course, feedback and questions are not only welcome, but encouraged--just hit the comment icon to the right of this text 💬 (You can also highlight specific parts of the text to leave a comment on). (Ha. That's your first lesson!).The BasicsAuthorea is a document editing and publishing system built primarily for researchers. It allows you to collaborate on documents and publish them easily. Each Authorea document can include data, interactive figures, and code. But first, let's get started! 1. Sign up.If you're not already signed up, do so at authorea.com/signup. Tip: if you are part of an organization, sign up with your organizational email. 2. First stepsDuring the signup process you will be asked a few questions: your location, your title, etc. You will be also prompted to join a group. Groups are awesome! They allow you to become part of a shared document workspace. Tip: during signup, join a group or create a new one for your team. Overall, we suggest you fill out your profile information to get the best possible Authorea experience and to see if any of your friends are already on the platform. If you don't do it initially during sign up, don't worry; you can always edit your user information in your settings later on.Once you've landed on your profile page (see below). There are a few things you should immediately do:Add a profile picture. You've got a great face, show it to the world :) For reference, please see Pete, our chief dog officer (CDO), below. Add personal and group information. If you haven't added any personal information, like a bio, a group affiliation, or your location, do it! You might find some people at your organization already part of Authorea, plus it is a great way to build your online footprint, which is always good for getting jobs.Invite your colleagues. Click here to invite contacts from your Gmail. You'll get extra private documents in your account and you'll make Pete very happy!

A Better Way to Track Changes for Writing Research Documents

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Today, we're happy to announce the release of a new and improved ability to track changes on Authorea. Based on git, the most sophisticated version control system available today, researchers using Authorea can now see word by word edits from co-authors or a per-day summary of changes made. Because the version control system is built into the document creation process and not simply added on, like track changes used in Word or Google Docs, the full life cycle of a paper will be available. Such functionality will make collaborating and editing even easier amongst researchers on Authorea, accelerating the time it takes to report your findings and saving your inbox from the deluge of emails with different versions of the same paper that is typical of collaboration in other editors, like Word.