class: center, middle, inverse, title-slide # Application of Global Sensitivity Analysis in Bayesian Population Physiologically Based Kinetic Modeling ##
###
Nan-Hung Hsieh, PhD
### .small[Postdoctoral Research Associate @ TAMU Interdisciplinary Faculty of Toxicology Program] --- background-image: url(https://proxy.duckduckgo.com/iu/?u=https%3A%2F%2Fweltbild.scene7.com%2Fasset%2Fvgw%2Fcomputational-toxicology-072231575.jpg&f=1) background-size: 150px background-position: 90% 15% # Content
 ## Pharmacokinetic modeling - Estimate external exposure to internal dose ## Probabilistic modeling - Characterize .large[**uncertainty**] and .large[**variability**]
 ## Sensitivity analysis - Quantify the .large[**uncertainty**] from model input to model output ??? --- ## From toxicology to risk assessment ![](https://i.ibb.co/mHw2KYT/Screen-Shot-2019-05-05-at-5-57-19-PM.png ) ??? The goal of the toxicological research is to apply the result in human health risk assessment. --- background-image: url(https://image.ibb.co/jNZi4e/drug_concentration_time.png) background-size: 150px background-position: 80% 98% # Pharmacokinetics (PK) .large[ - The fate of chemical in a living organism - .bolder[ADME] process .center[ .bolder[A]bsorption - How will it get in? .bolder[D]istribution - Which tissue organ it will go? .bolder[M]etabolism - How is it broken down and transformation? .bolder[E]limination - How it leave the body? ] ] .large[ - Kinetics: rates of change - PK is focus on .bolder[TIME (t)] and .bolder[CONCENTRATION (C)] ] ??? Kinetics is a branch of chemistry which describe the change of one or more variables as a function of time. So we focus on the time and concentration. It’ll go up after intake the drug and then go down after reach the maximum concentration. --- # Pharmacokinetics Model To .large[.bolder["predict"]] the **cumulative does** by constructed **compartmental model**. .pull-left[ ![](http://www.mdpi.com/mca/mca-23-00027/article_deploy/html/images/mca-23-00027-g006.png) ] .pull-right[ ![](https://image.ibb.co/nJTM7z/fitting.png) ] `\(D\)`: Intake dose (mass) `\(C\)`: Concentration (mass/vol.) `\(k_a\)`: Absorption rate (1/time) `\(V_{max}\)`: Maximal metabolism rate `\(K_m\)`: Concentration of chemical achieve half `\(V_{max}\)` (mass/vol.) `\(k_{el}\)`: Elimination rate (1/time) ??? But it’s difficult to realize the whole time-concentration relationship due to it will take a lot of time and money in the experiment. Therefore, we need to use toxicokinetic modeling. The basic model is constructed by a single variable with few parameters. We can set up input dose and define the output such as chemical concentration in blood or other tissue organs. Then, we can set up the parameters such as absorption,elimination rate constant. --- ## Physiologically-Based Pharmacokinetic (PBPK) Model Mathematically transcribe .bolder[physiological] and .bolder[physicochemical] descriptions of the phenomena involved in the complex .bolder[ADME] processes. ![:scale 90%](http://pharmguse.net/pkdm/pbpm.jpg) .footnote[Img: http://pharmguse.net/pkdm/prediction.html] ??? However, the real world is not as simple as we think. Sometimes we need to consider the complex mechanism and factors that can help us make a good prediction. So people developed the PBPK that include physiological and physicochemical parameters that correspond to the real-world situation and can be used to describe the complex ADME processes. It can be applied to drug discovery and development and risk assessment. --- background-image: url(https://wol-prod-cdn.literatumonline.com/cms/attachment/87cce77b-8dd4-4928-869f-b230e0cffcc4/psp412134-fig-0003-m.jpg) background-size: 540px background-position: 90% 90% # Why Pharmacokinetic Modeling - **Health risk assessment** - **Pharmaceutical research** - **Drug development** . . . .footnote[https://doi.org/10.1002/psp4.12134] --- class:clear, middle  --- Application --- ## If you have *known* "parameters" ## ------------------------------------> ##  Parameters / Model / Data ## <------------------------------------ ## If you have *known* "data" --- Calibration --- --- background-image: url(https://ars.els-cdn.com/content/image/1-s2.0-S0300483X10002623-gr5.jpg) background-size: 400px background-position: 70% 50% ## Bayesian Population Model .large[**Individuals level**] `\(E\)`: Exposure `\(t\)`: Time `\(\theta\)`: Specific parameters `\(y\)`: condition the data
 .large[**Population level**] `\(\mu\)`: Population means `\(\Sigma^2\)`: Population variances `\(\sigma^2\)`: Residual errors .footnote[ https://doi.org/10.1016/j.tox.2010.06.007. ] --- class: center # Population PBPK Model .large[ Characterizing and quantifying **variability** and **uncertainty** Cross-species comparisons of metabolism ] ![:scale 80%](https://ars.els-cdn.com/content/image/1-s2.0-S0041008X09003238-gr3_lrg.jpg) .footnote[ https://doi.org/10.1016/j.taap.2009.07.032 ] --- background-image: url(https://i.ibb.co/Kq4nStY/Screen-Shot-2019-05-08-at-7-25-22-AM.png) background-size: contain ### Variability & Uncertainty .footnote[ https://cran.r-project.org/web/packages/mc2d/index.html ] --- # Population pharmacokinetics ```r ggplot(Theoph, aes(x = Time, y =conc, color = Subject)) + geom_point() + geom_line() + labs(x = "Time (hr)", y = "Concentration (mg/L)") ``` ![](190508_talk_files/figure-html/unnamed-chunk-2-1.svg) --- background-image: url(https://raw.githubusercontent.com/nanhung/MCSim_under_R/master/doc/fig/mcmc_diagram.png) background-size: 800px background-position: 50% 80% # Markov-chain Monte Carlo (MCMC) simulation Priors, posteriors, likelihood, multilevel models `\(p(\theta|y) \propto p(y|\theta) \times p(\theta)\)` .footnote[ [Source](http://sbml.org/images/1/17/StatMeeting_F_Bois.pdf) ] --- class: middle background-image: url(https://upload.wikimedia.org/wikipedia/commons/5/5a/Mcsimlogo.png) background-size: 100px background-position: 90% 30% # SOFTWARE ## [GNU MCSim](http://www.gnu.org/software/mcsim/) Creator: [Dr. Frédéric Y. Bois](https://en.wikipedia.org/wiki/Fr%C3%A9d%C3%A9ric_Y._Bois) .large[ - Design and run statistical or simulation models (e.g., differential equations) - Do parametric simulations (e.g., sensitivity analysis) - Perform Monte Carlo (stochastic) simulations .highlight[ - Do Bayesian inference for hierarchical models through .bolder[Markov Chain Monte Carlo] simulations ] ] --- background-image: url(https://ars.els-cdn.com/content/image/1-s2.0-S0300483X18301781-gr2_lrg.jpg) background-size: 280px background-position: 90% 90% ## Related publication Luo Y-S, Hsieh N-H, Soldatow VY, Chiu WA, Rusyn I. 2018. [Comparative analysis of metabolism of trichloroethylene and tetrachloroethylene among mouse tissues and strains](https://www.sciencedirect.com/science/article/pii/S0300483X18301781). Toxicology 409(1): 33-43. Luo Y-S, Cichocki JA, Hsieh N-H, Lewis L, Threadgill DW, Chiu WA, Rusyn I. [Using collaborative cross mouse population to fill data gaps in risk assessment a case study of population-based analysis of toxicokinetics and kidney toxicodynamics of tetrachloroethylene](). (Accepted)  ??? In our recent research, we incorporate more animal experiment results. The first study included an additional measurement of GSH conjugate in mice tissues, to build more comprehensive PBPK model to make Bayesian inference. In the second and the third study we build the multicompartment PK model to estimate the metabolism of TCE and PCE in liver, kidney, brain, lung, and serum. We didn't use the PBPK model because the current is PBPK model takes a very long time in model calibration. Therefore, we tried to re-build this simplified model to compare and describe the Trichloroethylene and tetrachloroethylene among mouse tissues and strains. In our third study, it is also the extensive research that used the same model with Collaborative Cross Mouse and applies the study result in risk assessment. --- # Challenge .large[Currently, the **Bayesian Markov chain Monte Carlo (MCMC) algorithm** is a effective way to do population PBPK model calibration. ] .xlarge[.bolder[**BUT**]] ??? But, we have a challenge in our calibration process. -- .large[ This method often have challenges to reach **"convergence"** with acceptable computational times .bolder[(More parameters, More time!)] ] .right[![](https://slides.yihui.name/gif/questions.gif)] --- class: center, middle .xxxlarge[**Our Problem Is ...**]
 .xxxlarge[**How to Improve the .bolder[Computational Efficiency]**] --- background-image: url(https://blitzmetrics.com/wp-content/uploads/2013/09/QuestionData.png) background-size: contain # Possible solutions .center[ ## **From Model?** ## **From Data?** ## **From Parameter?** ## **From Algorithm?** ] --- # Project Funder: **Food and Drug Administration (FDA)** Project Start: **Sep-2016** Name: **Enhancing the reliability, efficiency, and usability of Bayesian population PBPK modeling** ??? Based on these reasons, we started this project. -- - Specific Aim 1: Develop, implement, and evaluate methodologies for parallelizing time-intensive calculations and enhancing a simulated tempering-based MCMC algorithm for Bayesian parameter estimation (.bolder[Revise algorithm in model calibration]) -- .highlight[ - Specific Aim 2: Create, implement, and evaluate a robust Global Sensitivity Analysis algorithm to reduce PBPK model parameter dimensionality (.bolder[Parameter fixing]) ] -- - Specific Aim 3: Design, build, and test a user-friendly, open source computational platform for implementing an integrated approach for population PBPK modeling (.bolder[User friendly interface]) --- background-image: url(https://upload.wikimedia.org/wikipedia/commons/5/5a/Mcsimlogo.png) background-size: 100px background-position: 90% 90% # GNU MCSim  February 19th, 2019 - Release of GNU MCSim version 6.1.0.  >Version 6.1.0 offers an automatic setting of the inverse-temperature scale in simulated tempering MCMC. This greatly facilitates the uses of this powerful algorithm (convergence is reached faster, **only one chain needs** to be run if inverse-temperature zero is in the scale, **Bayes factors** can be calculated for model choice). .pull-right[ ] .xsmall[ - 6.0.1 (05 May 2018) - 6.0.0 (24 February 2018) - 5.6.6 (21 January 2017) - 5.6.5 (27 February 2016) - 5.6.4 (30 January 2016) - 5.6.3 (1 January 2016) - 5.6.2 (24 December 2015) - 5.6.1 (21 December 2015) - 5.6.0 (16 December 2015) - 5.5.0 (17 March 2013) - 5.4.0 (18 January 2011) - 5.3.1 (3 March 2009) - 5.3.0 (12 January 2009) - 5.2 beta (29 January 2008) - 5.1 beta (18 September 2006) - 5.0.0 (4 January 2005) - 4.2.0 (15 October 2001) - 4.1.0 (1 August 1997) - 4.0.0 (24 March 1997) ] --- # Related publication - Hsieh N-H, Reisfeld B, Bois FY and Chiu WA. 2018. [Applying a Global Sensitivity Analysis Workflow to Improve the Computational Efficiencies in Physiologically-Based Pharmacokinetic Modeling](https://www.frontiersin.org/articles/10.3389/fphar.2018.00588/full). Frontiers in Pharmacology 9:588. - Bois FY, Hsieh N-H, Gao W, Reisfeld B, Chiu WA. [Well tempered MCMC simulations for population pharmacokinetic models](). (Submitted) ??? This is our project from FDA that aim to improve the functionality in MCSim and the research of the Bayesian framework. One of the current challenges in the Bayesian method is time-consuming if our model becomes more complex or more comprehensive and we have more data in our multi-individuals and multi-groups testing. It will take need very long time to calibrate model parameter. Therefore, in our 2018 paper, we proposed a workflow to fix the non-influential parameter in our PBPK model based on the global sensitivity analysis. Through this way, we can save about half time in our model calibration. Our recent submitted paper is to show how we developed and used tempering MCMC in PBPK modeling. This algorithm can also have a faster convergence time compared with the original one. --- # Proposed Solution - Parameter Fixing .large[We usually fix the "possible" non-influential model parameters through **"expert judgment"**.] .xlarge[.bolder[BUT]] -- .large[This approach might cause .xlarge[**"bias"**] in parameter estimates and model predictions.] .center[![](http://reliawiki.org/images/3/35/Doe34.png)] ??? Actually, we already applied the concept of parameter fixing to improve the computational time. But, This is why we need an alternative way to fix the unimportant parameter. So we chose sensitivity analysis. --- background-image: url(https://media.wiley.com/product_data/coverImage300/74/04700599/0470059974.jpg) background-size: 140px background-position: 80% 10% class: middle # Sensitivity Analysis
 .xlarge[ > The study of how uncertainty in the output of a model (numerical or otherwise) can be apportioned to different sources of uncertainty in the model input." ] --- # Why we need SA? .xlarge[.bolder[Parameter Prioritization]] - Identifying the most important factors - Reduce the uncertainty in the model response if it is too large (ie not acceptable) .xlarge[.bolder[Parameter Fixing]] - Identifying the least important factors - Simplify the model if it has too many factors .xlarge[.bolder[Parameter Mapping]] - Identify critical regions of the inputs that are responsible for extreme value of the model response --- background-image: url(https://i.ibb.co/dcm90HB/Screen-Shot-2019-04-30-at-8-48-27-PM.png) background-size: contain ??? Here we have observations (assumed error-free for simplicity’s sake) and a model whose parameters are esti- mated from the data. Estimation can take different courses. Usually it is achieved by minimizing, e.g. by least squares, some measure of distance between the model’s prediction and the data. At the end of the estimation step, ‘best’ parameter values as well as their errors are known. At this point we might consider the model ‘true’ and run an uncertainty analysis by propagating the uncertainty in the parameters through the model, all the way to the model output. In this case the estimated parameters become our factors. --- background-image: url(https://i.ibb.co/30S1XtS/Screen-Shot-2019-04-30-at-9-10-08-PM.png) background-size: contain background-position: 50% 10% .footnote[ https://doi.org/10.1002/psp4.6 ] ??? It consequently provides useful insight into which model input contributes most to the variability of the model output --- # Classification of SA Methods .footnote[http://evelynegroen.github.io] .pull-left[ **Local** (One-at-a-time)  **"Local"** SA focus on sensitivity at a particular set of input parameters, usually using gradients or partial derivatives ] ??? Usually, some people have experience in modeling they have the knowledge in local sensitivity analysis. This method is very simple. You move one parameter and fix other parameters then check the change of model outputs. On the other side, some researcher also developed the approach that moves all parameters at a time and checks the change of model output. We call it Global sensitivity analysis or variance-based sensitivity analysis. -- .pull-right[ **Global** (All-at-a-time)  **"Global"** SA calculates the contribution from the variety of all model parameters, including .bolder[Single parameter effects] and .bolder[Multiple parameter interactions] ] -- .large[.center[**"Global" sensitivity analysis is good at** .bolder[parameter fixing]]] ??? Usually, some people have experience in modeling they have the knowledge in local sensitivity analysis. This method is very simple. You move one parameter and fix other parameters then check the change of model outputs. On the other side, some researcher also developed the approach that moves all parameters at a time and checks the change of model output. We call it Global sensitivity analysis or variance-based sensitivity analysis. --- class: center # Sensitivity index .xlarge[First order] `\((S_i)\)`

 .xlarge[Interaction] `\((S_{ij})\)`
 .xlarge[Total order] `\((S_{T})\)`

 ??? This is the quantification of the impact of the model parameter. --- class: center # Sensitivity index .xlarge[First order] `\((S_i)\)` The output variance contributed by the specific parameter `\(x_i\)`, also known as .bolder[main effect] .xlarge[Interaction] `\((S_{ij})\)`
 .xlarge[Total order] `\((S_{T})\)`

 ??? First order just like how your friend's face looks like when he tastes the specific fruit. --- class: center # Sensitivity index .xlarge[First order] `\((S_i)\)` The output variance contributed by the specific parameter `\(x_i\)`, also known as .bolder[main effect] .xlarge[Interaction] `\((S_{ij})\)` The output variance contributed by any pair of input parameter .xlarge[Total order] `\((S_{T})\)` ??? The Interaction just like the mixing flavors from two or more fruits. -- The output variance contributed by the specific parameter and interaction, also known as .bolder[total effect] --
 .left[ .bolder[“Local”] SA usually only addresses first order effects .bolder[“Global”] SA can address total effect that include main effect and interaction ] --- # Variance-based methods .large[**Variance-based**] methods for sensitivity analysis were first employed by chemists in the early 1970s. The method, known as .bolder[FAST] (Fourier Amplitude Sensitivity Test). ### Main effect `$$S_{i}=\frac{V\left[E\left(Y | X_{i}\right)\right]}{V(Y)}$$` `$$S_{i}=Corr(Y, E(Y|X_i))$$`
 ### Total effect Example of parameter 1 for model with three parameters `$$S_{T 1}=S_{1}+S_{12}+S_{13}+S_{123}$$` `$$S_{1}+ S_2 + S_{3} + S_{12}+S_{13} + S_{23} + S_{123} = 1$$` --- ## The basic step to performing sensitivity analysis 1. Define the variable of interest `\((y_{j,t})\)` 2. Identify all model factor `\((x_i)\)` which should be consider in GSA 3. Characterise the uncertainty for each selected input factor `\(p(x_i)\)` 4. Generate a sample of a given size `\((n)\)` from the previously defined probability distribution 5. Execute the model for each combination of factor 6. .bolder[Visualiza the output, and interpret the outputs] 7. Estimate the sensitivity measures --- `$$f(\mathbf{x})=\sin \left(x_{1}\right)+a \sin ^{2}\left(x_{2}\right)+b x_{3}^{4} \sin \left(x_{1}\right)$$` ```r x <- fast99(model = ishigami.fun, factors = 3, n = 400, q = "qunif", q.arg = list(min = -pi, max = pi)) par(mfrow = c(1,3)) plot(x$X[,1], x$y); plot(x$X[,2], x$y); plot(x$X[,3], x$y) ``` ![](190508_talk_files/figure-html/unnamed-chunk-3-1.svg) --- ```r plot(x) ``` ![](190508_talk_files/figure-html/unnamed-chunk-4-1.svg) --- # Challenges (1) **Many** algorithms have been developed, but we don't have knowledge to identify which one is the best, and (2) there is **NO** suitable reference in parameter fixing for PBPK model. ```r ls(pos = "package:sensitivity") ``` ``` ## [1] "ask" "atantemp.fun" "campbell1D.fun" ## [4] "delsa" "dgumbel.trunc" "dnorm.trunc" ## [7] "fast99" "ishigami.fun" "linkletter.fun" ## [10] "morris" "morris.fun" "morrisMultOut" ## [13] "parameterSets" "pcc" "pgumbel.trunc" ## [16] "PLI" "PLIquantile" "plot3d.morris" ## [19] "plotFG" "pnorm.trunc" "PoincareConstant" ## [22] "PoincareOptimal" "qgumbel.trunc" "qnorm.trunc" ## [25] "rgumbel.trunc" "rnorm.trunc" "sb" ## [28] "scatterplot" "sensiFdiv" "sensiHSIC" ## [31] "shapleyPermEx" "shapleyPermRand" "sobol" ## [34] "sobol.fun" "sobol2002" "sobol2007" ## [37] "sobolEff" "sobolGP" "soboljansen" ## [40] "sobolmara" "sobolmartinez" "sobolMultOut" ## [43] "sobolowen" "sobolroalhs" "sobolroauc" ## [46] "sobolSalt" "sobolSmthSpl" "sobolTIIlo" ## [49] "sobolTIIpf" "soboltouati" "src" ## [52] "support" "tell" "template.replace" ``` --- # Materials (1/3) - Model .center[  ] .bolder[Acetaminophen] (APAP) is a widely used pain reliver and fever reducer. The therapeutic index (ratio of toxic to therapeutic doses) is unusually small. Phase I (.bolder[APAP to NAPQI]) is toxicity pathway at high dose. Phase II (.bolder[APAP to conjugates]) is major pathways at therapeutic dose.
 .center[ .highlight[ A typical pharmacological agent with large amounts of both human and animal data. Well-constructed and studied PBPK model. ] ] ??? I'll move on to our materials, the first one is the PBPK model. In this study, we used acetaminophen, which is a widely used medication. It includes two types of metabolic pathways that can have therapeutic and toxicity effects. But the most important thing is that acetaminophen has a large amount of PK data for both human and other animals. And we also had a well-constructed and studied PBPK model. --- background-image: url(https://image.ibb.co/b9F8Ze/uSCphVE.gif) background-size: 500px background-position: 50% 80% # Materials (1/3) - Model The simulation of the disposition of .bolder[acetaminophen (APAP)] and two of its key metabolites, .bolder[APAP-glucuronide (APAP-G)] and .bolder[APAP-sulfate (APAP-S)], in plasma, urine, and several pharmacologically and toxicologically relevant tissues. .footnote[[Zurlinden, T.J. & Reisfeld, B. Eur J Drug Metab Pharmacokinet (2016) 41: 267. ](https://link.springer.com/article/10.1007%2Fs13318-015-0253-x)] ??? The acetaminophen-PBPK model is constructed with several tissue compartments such as Fat muscle, liver, and kidney. It can be used to simulate and predict the concentration of its two metabolites in plasma and urine. Also, it can be used to describe the different dosing method include oral and intravenous. --- background-image: url(https://image.ibb.co/e7eccz/JjqgWwg.gif) background-size: 300px background-position: 95% 95% # Parameters calibrated in previous study .large[ .large[.bolder[2]] Acetaminophen absorption (Time constant) .highlight[ .large[.bolder[2]] Phase I metabolism: Cytochrome P450 (M-M constant) .large[.bolder[4]] Phase II metabolism: sulfation (M-M constant) .large[.bolder[4]] Phase II metabolism: glucuronidation (M-M constant) .large[.bolder[4]] Active hepatic transporters (M-M constant) ] .large[.bolder[2]] Cofactor synthesis (fraction) .large[.bolder[3]] Clearance (rate constant) ] .footnote[ M-M: Michaelis–Menten ] ??? In the previous study, they only choose 21 parameters to do the model calibration. These parameters can be simply classified into four types, which includes You can see most parameters are Michaelis–Menten constant that determined the metabolism of acetaminophen. --- # Parameters fixed in previous study `$$V_T\frac{dC^j_T}{dt}=Q_T(C^j_A-\frac{C^j_T}{P_{T:blood}})$$`
 .pull-left[ .large[**Physiological parameters**] 1 cardiac output 6 blood flow rate `\((Q)\)` 8 tissue volume `\((V)\)` ] .pull-right[ .large[**Physicochemical parameters**] 22 partition coefficient for APAP, APAP-glucuronide, and APAP-sulfate `\((P)\)` ] ??? On the other side, we found 37 parameters that were be fixed in the previous study that include cardiac output, blood flow rate, and tissue volume in physiological parameters. In physicochemical parameters, we also tested the sensitivity of partition coefficients acetaminophen and its conjugates. We further used these parameters and gave the uncertainty to do the calibration. --- # Materials (2/3) - Population PK Data .large[ Eight studies (n = 71) with single oral dose and three different given doses. ] .center[  ] ??? Also, experiment data is very important in our model calibration. We used the PK data from eight studies. This data included 71 subjects. All these subjects are administrated with single oral dose. The given dose levels were from 325 mg in group A to 80 mg/kg in group H. --- # Materials (3/3) - Sensitivity Analysis .large[ **Local:** - Morris screening **Global:** - Extended Fourier Amplitude Sensitivity Testing (eFAST) - Jansen's algorithm - Owen's algorithm ] ??? Finally, we used four sensitivity analysis algorithms as candidates and compared the final result with each other. --- background-image<EMAIL_ADDRESS>background-size: 400px background-position: 50% 60% # Morris screening - Perform parameter sampling in Latin Hypercube following One-Step-At-A-Time - Can compute the importance `\((\mu^*)\)` and interaction `\((\sigma)\)` of the effects ??? The first one is the Morris method. Morris can be classified into the local sensitivity analysis. But it is an unusual local approach. Unlike the traditional local method, it can also estimate the interaction in sensitivity index. On the right-hand side is an example of Morris screening. --

 .highlight[ **1. It is semi-quantitative – the factors are ranked on an interval scale** **2. It is numerically efficient** **3. Not very good for factor fixing** ] --- ```r set.seed(1111) x <- morris(model = ishigami.fun, factors = c("Factor A", "Factor B","Factor C"), r = 30, binf = -pi, bsup = pi, design = list(type = "oat", levels = 5, grid.jump = 3)) par(mfrow = c(1,3)) plot(x$X[,1], x$y); plot(x$X[,2], x$y); plot(x$X[,3], x$y) ``` ![](190508_talk_files/figure-html/unnamed-chunk-6-1.svg) --- ```r par(mar=c(4,4,1,1)) plot(x, xlim = c(0,10), ylim = c(0,10)) abline(0,1) # non-linear and/or non-monotonic abline(0,0.5, lty = 2) # monotonic abline(0,0.1, lty = 3) # almost linear legend("topleft", legend = c("non-linear and/or non-monotonic", "monotonic", "linear"), lty = c(1:3)) ``` ![](190508_talk_files/figure-html/unnamed-chunk-7-1.svg) --- # Variance-based sensitivity analysis ### eFAST Defines a .bolder[search curve] in the input space. ### Jansen Generates .bolder[TWO] independent random sampling parameter matrices. ### Owen Combines .bolder[THREE] independent random sampling parameter matrices.
 .highlight[ 1. **Full quantitative** 2. **The computational cost is more expensive** 3. **Good for factor fixing** ] ??? The main difference in our global methods is they used the different ways to do parameter sampling. The eFAST method is using the generated search curve and samples the parameters on this curve. Jansen and Owen are used Monte Carlo-based method to sample the parameters in the parameter space. Compare to Morris; the global methods have these characteristics. --- class: center # Workflow .large[Reproduce result from original paper (21 parameters)]  .large[Full model calibration (58 parameters)]  ??? Our workflow started by reproducing the result from the previous study. -- .highlight[ .large[Sensitivity analysis using different algorithms] **Compare the .bolder[time-cost] for sensitivity index to being stable** **Compare the .bolder[consistency] across different algorithms** ] --  .highlight[ .large[Bayesian model calibration by SA-judged influential parameters] **Compare the model .bolder[performance] under the setting "cut-off"** **Exam the .bolder[bias] for expert and SA-judged parameters** ] --- # Computational time & convergence .center[  ] Time-spend in SA (min): Morris (2.4) < **eFAST (19.8)** `\(\approx\)` Jansen (19.8) < Owen (59.4) Variation of SA index: Morris (2.3%) < **eFAST (5.3%)** < Jansen (8.0%) < Owen (15.9%) --- # Consistency .pull-left[ ![](https://image.ibb.co/jKtsnz/fig2_1.jpg) ] .pull-right[ ![](https://image.ibb.co/mt11fK/fig2_2.jpg) ] .center[ .xlarge[ .bolder[eFAST] `\(\approx\)` Jansen `\(\approx\)` Owen > Morris ] ] .footnote[Grey: first-order; Red: interaction] --- # Parameter fixing (cut-off) .center[ .pull-left[ Original model parameters (**OMP**)  .bolder[Setting cut-off]  Original influential parameters (**OIP**) ] .pull-right[ Full model parameters (**FMP**)  .bolder[Setting cut-off]  Full influential parameters (**FIP**) ]  .bolder[Model calibration and validation] ] --- .center[  ] .footnote[OIP: original influential parameters; OMP: original model parameters; FIP: full influential parameter; FMP: full model parameters] --- # Parameter fixing (cut-off) .pull-left[ ### The non-influential original parameters .bolder[1] Absorption parameters .bolder[2] Phase I metabolism parameters .bolder[4] Phase II metabolism parameters ### The influential additional parameters .bolder[1] physiological parameter - Cardiac output .bolder[1] chemical-specific transport parameter - Blood:plasma ratio of APAP .bolder[4] partition coefficients ] .pull-right[

 ![](https://image.ibb.co/k8KUnU/fig3.jpg) ] ??? Here is the summary of the number of parameters that were be classified to the non-influential parameter in original parameter setting. These parameters are from absorption and metabolism. In addition, we found 6 parameters that were fixed in the previous study. --- # Accuracy and precision .center[  ] .footnote[OIP: original influential parameters; OMP: original model parameters; FIP: full influential parameter; FMP: full model parameters] ??? We compared the observed human experiment data, and the predicted values then summarized the residual. We can see the full influential parameter with the cut-off at .01 that only used 1/3 of full model parameters can provide the similar result with full parameter sets. Also, using the cut-off at .05 that only used 10 parameters can generate the similar result with original parameter sets. --- # Accuracy and precision .center[ ![](https://image.ibb.co/gUu1s9/fig5.jpg) ] --- # Time cost on model calibration - Summary of the time-cost for - All model parameters (this study, 58), - GSA-judged parameters (this study, 20), - Expert-judged parameters ([Zurlinden and Reisfeld, 2016](https://link.springer.com/article/10.1007%2Fs13318-015-0253-x), 21)

  Parameter group 	  Time-cost in calibration (hr)

  All model parameters 	  104.6 (0.96)

  GSA-judged parameters 	  42.1 (0.29)

  Expert-judged parameters 	  40.8 (0.18)

 - All time-cost value are shown with mean and standard deviation (n = 10). --- # Posterior and data likelihood .center[  ] --- # Take away - .bolder[Bayesian statistics] is a powerful tool. It can be used to understand and characterize the **"uncertainty"** and **"variability"** from individuals to population through data and model. - .bolder[Global sensitivity analysis] can be an essential tool to reduce the dimensionality of model parameter and improve the **"computational efficiency"** in Bayesian PBPK model calibration.
 If you want to do model calibration for PK or PBPK model, you can, .highlight[ - Use the .bolder[eFAST] to detect the "influential" model parameter and making sure to check "convergence". - Distinguish “influential” and “non-influential” parameters with .bolder[cut-off] from sensitivity index - Conduct model calibration for only the "influential" parameters. ] --- ## Following Work Reproducible research - Software development (R package) ![:scale 95%](https://i.ibb.co/5cJtxbz/Screen-Shot-2019-05-05-at-4-23-29-PM.png) --- ## Following Work Application of GSA in trichloroethylene (TCE)-PBPK model ![](https://i.ibb.co/d65xHxF/Screen-Shot-2019-05-06-at-1-12-08-PM.png) --- class: inverse, center, middle # Thanks!
 question?


@Model.SoftwareName – release @Model.SoftwareVersion
@foreach (IGrouping
featureGroup in Model.Groups) {
@featureGroup.Key (@featureGroup.Count())
@foreach (TrelloReleaseNotes.Core.Trello.ReleaseNotesCard card in featureGroup) {
<EMAIL_ADDRESS><EMAIL_ADDRESS>}
}


CREATE CONVERSION defines a new conversion between character set encodings. Also, conversions that are marked DEFAULT can be used for automatic encoding conversion between client and server. For this purpose, two conversions, from encoding A to B and from encoding B to A, must be defined.
To be able to create a conversion, you must have EXECUTE privilege on the function and CREATE privilege on the destination schema.
The DEFAULT clause indicates that this conversion is the default for this particular source to destination encoding. There should be only one default encoding in a schema for the encoding pair.
The name of the conversion. The conversion name can be schema-qualified. If it is not, the conversion is defined in the current schema. The conversion name must be unique within a schema.
The source encoding name.
The destination encoding name.
The function used to perform the conversion. The function name can be schema-qualified. If it is not, the function will be looked up in the path.
The function must have the following signature:
conv_proc( integer, -- source encoding ID integer, -- destination encoding ID cstring, -- source string (null terminated C string) internal, -- destination (fill with a null terminated C string) integer -- source string length ) RETURNS void;
Use DROP CONVERSION to remove user-defined conversions.
The privileges required to create a conversion might be changed in a future release.
To create a conversion from encoding UTF8 to
LATIN1 using myfunc
:
CREATE CONVERSION myconv FOR 'UTF8' TO 'LATIN1' FROM myfunc;


PNG writer class template. This is the low-level writing interface--use image class or generator class to actually write images. More...
#include <writer.hpp>
Public Member Functions | |
| writer (ostream &stream) | |
| Constructs a writer prepared to write PNG image into a stream. | |
| ~writer () | |
| void | write_png () const |
| void | write_info () const |
| Write info about PNG image. | |
| void | write_row (byte *bytes) |
| Writes a row of image data at a time. | |
| void | write_end_info () const |
| Reads ending info about PNG image. |
PNG writer class template. This is the low-level writing interface--use image class or generator class to actually write images.
The ostream
template parameter specifies the type of output stream to work with. The ostream
class should implement the minimum of the following interface:
class my_ostream { public: void write(char const*, size_t); void flush(); bool good(); };
With the semantics similar to the std::ostream
. Naturally, std::ostream
fits this requirement and can be used with the writer class as is.
| png::writer< ostream >::writer | ( | ostream & | stream | ) | [inline, explicit] |
Constructs a writer prepared to write PNG image into a stream.
References png::io_base::m_png.
| png::writer< ostream >::~writer | ( | ) | [inline] |
| void png::writer< ostream >::write_png | ( | ) | const [inline] |
| void png::writer< ostream >::write_info | ( | ) | const [inline] |
Write info about PNG image.
References png::io_base::m_error, png::io_base::m_info, png::io_base::m_png, and png::info::write().
Referenced by png::generator< pixel, pixel_generator, image_info_ref_holder, true >::write().
| void png::writer< ostream >::write_row | ( | byte * | bytes | ) | [inline] |
Writes a row of image data at a time.
References png::io_base::m_error, and png::io_base::m_png.
Referenced by png::generator< pixel, pixel_generator, image_info_ref_holder, true >::write().
| void png::writer< ostream >::write_end_info | ( | ) | const [inline] |
Reads ending info about PNG image.
References png::io_base::m_end_info, png::io_base::m_error, png::io_base::m_png, and png::end_info::write().
Referenced by png::generator< pixel, pixel_generator, image_info_ref_holder, true >::write().


High Expectations
How hard is this class?
Ivy League Schools
Big Research Universities
- * 4-year Liberal Arts Colleges such as Old Westbury *
2-year Community Colleges
High School
Why is this class hard?
Your skills must be demonstrable
Simply attending/participating isn't enough to pass.
To pass you must demonstrate certain math skills.
Skimming or skipping parts of class can have a big negative impact.
You must be willing to grow
Learning will challenge you in new ways.
Your old study strategies may or may not work.
You will need to learn new study strategies to succeed.
How can I succeed?
Be okay with past failures
Remember that failure is part of success!
Reflect on past failures and ask yourself what barriers led to your failure.
Find new strategies for success
Success in the first few weeks is central to improving moving forward.
Osmosis or passively watching is not learning!
Research new study strategies that will work for you.
Why learn math if we have calculators?
Humans need to be able to understand computer output
Why does this work?
What is the process?
What logic would give this answer?
Reasons and rationale are equally important as final answers.
How can we explain the math steps?
How do we know calculators are correct?
How could we check calculator results on our own?
- Use calculators only as a check, not as a substitute for learning.
Copying an app does not constitute learning.
When will I use math?
Direct uses in careers include...
science research
working in a science industry
other math fields like finance etc.
teaching
Indirect uses include cognitive skills such as ...
breaking things down into parts
analyzing information
problem solving
abstract reasoning
perseverence and precision
How does communication help math?
Talking and discussing helps with understanding the why and how.
Your perspective may be different than other students.
You might recognize one part, but need to ask about another part.
You can learn from seeing how other students do math.
Get to know the Professor
- Communicate often and frequently.
- The Professor should know you and your needs.
Time Commitment
This class is like a part time job.
Devote at least 8 hours / week outside of class time.
Including class time you can easily spend 10-12 hours / week.
Question: Would one hour every single day be enough?
Advice From Students
"I wish I had ...
- ... stopped working full time to study more."
- ... gotten help for personal problems."
- ... made time for Prof's Student Hours."
- ... gotten past my ego and found tutoring that I needed."
More advice from students
- "Finishing HW early actually helped me to understand the material better."
- "I didn't expect that making a summary sheet would help me, but it really did!"
- "Working with peers helped me to talk out the concepts, even though I was initially nervous."
- "Going to Student Hours, the Prof helped me clear up in five minutes something I was confused about for months!"
- "I had no idea how much more I could understand simply by reading and preparing before class starts. I wish I had done that earlier."
How To Navigate This Class
BEFORE CLASS - check syllabus, read the book, watch videos, try problems
DURING CLASS - participate, ask questions, discuss, practice problems
AFTER CLASS - review, practice more, get help
Warning on Outside Materials
Warning on Outside Materials
Math is vast!
Outside materials:
... explain things differently.
... explain things we are not doing in this class.
... can be helpful!
... can be confusing!
Use outside materials as supplement not replacement
for our course materials.
Grading Standards
Aim high. Leave room for error!
Your Professors Can Help You
- Students sometimes struggle with pursuing a degree their parents or someone else picked.
- If you choose a career path based on what you think you "should" do, rather than your passions, you may struggle more.
- Talk to your Professors, we can help you navigate and find the best path for you.


This value can be used to control the unique ID of an extension, app, or theme when it is loaded during development.
Note: You don't usually need to use this value. Instead, write your code so that the key value doesn't matter by using relative paths and extension.getURL.
To get a suitable key value, first
install your extension from a .crx
file
(you may need to
upload your extension
or package it manually).
Then, in your
user
data directory, look in the file
Default/Extensions/<extensionId>/<versionString>/manifest.json
.
You will see the key value filled in there.


| Application Overview | Application Cross Reference |
A number of optional classes are provided as part of the Portfolio Management Application Layer. These classes can be installed as is or modified as needed, depending on your requirements. Many of the Headstart Applications use data defined for these classes. Supplemental classes include:
Object | DataRecord | | | |-- AnalystEstimate | | | |-- EconomicData | | | |-- FundamentalData | | | |-- FundamentalDataA | | | |-- FundamentalDataM | | | |-- FundamentalDataQ | Entity | |-- Analyst | |-- Classification | |-- RangeClassification | |-- MCapGroup | |-- PBGroup | |-- PEGroup
| Copyright © 1998 Innovative Systems Techniques, Inc. | Created March 11, 1998 |


You'll have to define macros to log messages in code. Such a macro needs to do the following:
To see if the logger is enabled, you'll use a filter. Note that you can reuse the same filter for multiple loggers, in case you want to.
To define a macro to log messages in code, I've provided these few macros to help you:
BOOST_LOG_USE_LOG_IF_FILTER(logger, filter_is_enabled)
BOOST_LOG_USE_LOG_IF_LEVEL(logger, filter, level)
BOOST_LOG_USE_SIMPLE_LOG_IF_FILTER(logger, filter_is_enabled)
The parameters are:
logger
- a pointer to a logger you've declaredfilter_is_enabled
- how to determine if the filter is enabled.filter()->is_enabled()
BOOST_LOG_USE_LOG_IF_LEVEL(l, holder, the_level) = BOOST_LOG_USE_LOG_IF_FILTER(l, holder->is_enabled(the_level))
level
- in case you have a filter based on levelHow you define your macros is up to you.
typedef logger_format_write< > logger_type; typedef level::no_ts filter_type; BOOST_DECLARE_LOG_FILTER(g_log_filter, filter_type ) BOOST_DECLARE_LOG(g_l, logger_type) #define L_ BOOST_LOG_USE_LOG_IF_FILTER(g_l(), g_log_filter()->is_enabled() )
Using it in code:
L_ << "this is a cool message " << i++; L_ << "this is another cool message " << i++;
L_(level)
macro, which will care about the level or,L..._
macro for each level you want (for instance, LDBG_, LAPP_, LERR_).Here's how you'd do each of the above:
typedef logger_format_write< > logger_type; typedef level::holder filter_type; BOOST_DECLARE_LOG_FILTER(g_log_level, filter_type ) BOOST_DECLARE_LOG(g_l, logger_type) // Example 1 - have one macro depending on one parameter #define L_(lvl) BOOST_LOG_USE_LOG_IF_LEVEL(g_l(), g_log_level(), lvl ) // Example 2 #define LDBG_ BOOST_LOG_USE_LOG_IF_LEVEL(g_l(), g_log_level(), debug ) #define LERR_ BOOST_LOG_USE_LOG_IF_LEVEL(g_l(), g_log_level(), error ) #define LAPP_ BOOST_LOG_USE_LOG_IF_LEVEL(g_l(), g_log_level(), info )
Using it in code:
// Example 1 L_(debug) << "this is a debug message " << i++; L_(info) << "this is an info " << i++; L_(error) << "this is an error " << i++; // Example 2 LDBG_ << "this is a debug message " << i++; LAPP_ << "this is an info " << i++; LERR_ << "this is an error " << i++;
file_filter
class, which filters based on file name (a file can be turned on or off):
struct file_filter { bool is_enabled(const char * file_name) const ; // ... };
In this case, the macro used to do logging would look like:
typedef logger_format_write< > logger_type; typedef file_filter filter_type; BOOST_DECLARE_LOG_FILTER(g_log_filter, filter_type ) BOOST_DECLARE_LOG(g_l, logger_type) #define L_ BOOST_LOG_USE_LOG_IF_FILTER(g_l(), g_log_filter()->is_enabled(__FILE__) )
Using it in code:
L_ << "this is a message " << i++; L_ << "hello world";
module_filter
class, which filters based on module name (logging for a module can be turne on or off) :
struct module_filter { bool is_enabled(const char * module_name) const ; // ... };
In this case, the macro used to do logging would look like:
typedef logger_format_write< > logger_type; typedef module_filter filter_type; BOOST_DECLARE_LOG_FILTER(g_log_filter, filter_type ) BOOST_DECLARE_LOG(g_l, logger_type) #define L_(mod) BOOST_LOG_USE_LOG_IF_FILTER(g_l(), g_log_filter()->is_enabled(mod) )
Using it in code:
L_("chart") << "activex loaded"; L_("connect") << "connecting to server";


Image Classification API
Analytics Zoo provides a collection of pre-trained models for Image Classification. These models can be used for out-of-the-box inference if you are interested in categories already in the corresponding datasets. According to the business scenarios, users can embed the models locally, distributedly in Spark such as Apache Storm and Apache Flink.
Image Classification examples
Analytics Zoo provides several typical kind of pre-trained Image Classfication models : Alexnet, Inception-V1, VGG, Resnet, Densenet, Mobilenet, Squeezenet models. To use these models, please check below examples.
Scala
It's very easy to apply the model for inference with below code piece.
val imc = ImageClassifier.loadModel[Float](params.model)
val data = ImageSet.read(params.image, sc, params.nPartition)
val output = imc.predictImageSet(data)
User can also define his own configuration to do the inference with below code piece.
val imc = ImageClassifier.loadModel[Float](params.model)
val data = ImageSet.read(params.image, sc, params.nPartition)
val preprocessing = ImageResize(256, 256)-> ImageCenterCrop(224, 224) ->
ImageChannelNormalize(123, 117, 104) ->
ImageMatToTensor[Float]() ->
ImageSetToSample[Float]()
val config = ImageConfigure[Float](preprocessing)
val output = imc.predictImageSet(data, config)
Python
It's very easy to apply the model for inference with below code piece.
imc = ImageClassifier.load_model(model_path)
image_set = ImageSet.read(img_path, sc)
output = imc.predict_image_set(image_set)
User can also define his own configuration to do the inference with below code piece.
imc = ImageClassifier.load_model(model_path)
image_set = ImageSet.read(img_path, sc)
preprocessing = ChainedPreprocessing(
[ImageResize(256, 256), ImageCenterCrop(224, 224),
ImageChannelNormalize(123.0, 117.0, 104.0), ImageMatToTensor(),
ImageSetToSample()])
config = ImageConfigure(preprocessing)
output = imc.predict_image_set(image_set)
For preprocessors for Image Classification models, please check Image Classification Config
Download link
- Alexnet
- Alexnet Quantize
- Inception-V1
- Inception-V1 Quantize
- Inception-V3
- Inception-V3 Quantize
- VGG-16
- VGG-16 Quantize
- VGG-19
- VGG-19 Quantize
- Resnet-50
- Resnet-50 Quantize
- Resnet-50 Int8
- Densenet-161
- Densenet-161 Quantize
- Mobilenet
- Mobilenet-V2
- Mobilenet-V2 Quantize
- Squeezenet
- Squeezenet Quantize


Elon Musk makes Rick and Morty cameo and now his Twitter alter ego makes sense
It finally makes sense why Elon Musk changed his name to ‘Elon Tusk’ on Twitter earlier this year and the answer lies in his recent Rick and Morty cameo.
The 48-year-old co-founder and CEO of Tesla appeared on season four, episode three of the hit cartoon, entitled One Crew Over the Crewcoo’s Morty.
He played, you guessed it, Elon Tusk – basically exactly like Elon Musk, but with tusks.
Warning: Spoilers ahead!
During the episode, Rick and Morty surprise Elon at his company headquarters, aptly titled Tuskla.
Morty hurriedly told Elon: ‘Listen, we need your help!’
Then Mr Tusk replied: ‘Rick, Morty – why me? Did something happen to Elon Musk in your own reality?’
Advertisement Advertisement
He went on to explain Elon Tusk and Elon Musk ‘aren’t opposites’, one just has tusks.
The rest of the episode sees the team complete a series of random tasks, like Elon taking out a turkey from the oven and putting it in the fridge, as well as playing Minecraft.
Probably easier to explain if you just watch the episode…
Back in February, the billionaire mysteriously changed his Twitter name to Elon Tusk, complete with an elephant emoji and now we know why.
At the time, fans speculated the alter ego (which, of course, used the initials ET) had something to do with aliens.
And they were kinda right.
People also had a hunch the name change could have had something to do with Fleetwood Mac’s twelfth studio album, Tusk.
Alas, that guess was way off.
Advertisement Advertisement
Season four of Rick and Morty premiered on E4 on 20 November.
Got a showbiz story?
If you've got a celebrity story, video or pictures get in touch with the Metro.co.uk entertainment team by emailing us<EMAIL_ADDRESS>calling 020 3615 2145 or by visiting our Submit Stuff page - we'd love to hear from you.


WHITE SQUALL A film review by James Berardinelli Copyright 1996 James Berardinelli
RATING (0 TO 10): 8.0 Alternative Scale: ***1/2 out of ****
United States, 1996 Running Length: 2:08 MPAA Classification: PG-13 (Profanity, mature themes, violence) Theatrical Aspect Ratio: 2.35:1
Cast: Jeff Bridges, Caroline Goodall, John Savage, Scott Wolf, Jeremy Sisto, Balthazar Getty, Ryan Phillipe, Jason Marsden Director: Ridley Scott Producers: Mimi Polk Gitlin and Rocky Lang Screenplay: Todd Robinson based on the memoirs of Chuck Gieg Cinematography: Hugh Johnson Music: Jeff Rona with contributions by Hans Zimmer U.S. Distributor: Hollywood Pictures
WHITE SQUALL combines male bonding drama, high seas adventure, and coming-of-age stories. For his first film since the disastrously dull 1492: CONQUEST OF PARADISE, director Ridley Scott (BLADE RUNNER, ALIEN, THELMA AND LOUISE) has again turned to the ocean. This time, however, instead of sinking the film with a needlessly meandering plot, he energizes the screen with a movie that gives the audience an involving narrative and characters to care about.
Based on the true-life memoirs of Chuck Gieg, WHITE SQUALL introduces us to 13 high school students who have elected to spend the 1960-61 academic year on the American schoolship Albatross (a windjammer based out of Mystic, Connecticut). The Captain is Christopher Sheldon (Jeff Bridges), a man who preaches two lessons: the importance of unity among the crew and that "the ship beneath [them] is not a toy and sailing's not a game." His wife, Alice (Caroline Goodall), is the ship's doctor and the boys' science teacher.
The young crew includes Chuck (Tom Cruse look-alike Scott Wolf), an all-American student who wants to break away from his parents' vision of what his future should be; Tod (Balthazar Getty), a tough-as-nails kid who hides a secret; Frank (Jeremy Sisto), who can't escape from under his rich father's thumbnail; and Gill (Ryan Phillipe), a young man with a desperate fear of heights. The trip on the Albatross is their rite of passage. By the end of WHITE SQUALL, those who have survived are no longer boys, but men.
Given the title, it's no secret that the Albatross runs into real trouble. In nautical language, a "white squall" is a sudden, violent, wind-and-lightning storm that can take the sturdiest ship and snap it in two. However, this meteorological catastrophe doesn't make an appearance until the film's last forty-five minutes. Until then, WHITE SQUALL devotes itself to developing its young characters, their growing admiration for their skipper, and the relationships amongst them. While some of this drama relies on familiar formulas, a capable director like Scott can maintain the audience's attention through a long setup.
As is typical of a Scott film, the visuals are marvelous. The scenes of the Albatross as it rises and falls with the swells of the sea are majestic, and the fury of the squall is as terrifying an experience as you're likely to find in an adventure film these days. With the aid of cinematographer Hugh Johnson, Scott composes each of these grand sequences carefully, in order to convey the maximum effect.
Jeff Bridges, who is beginning to look more like his father with every passing picture, is in top form, providing a compelling father- figure/mentor. The role isn't exactly unique, but Bridges brings enough personality to Chris Sheldon to create an interesting character. John Savage, who hasn't done a film in a while, is delightful as McCrea. The young actors playing the boys vary from passable to good, with those having key roles doing the better jobs.
For nearly two hours, WHITE SQUALL is a completely involving motion picture, keeping its comic, dramatic, and tragic elements in perfect balance. Unfortunately, the need to end with a DEAD POETS' SOCIETY-like catharsis brings the conclusion down. Hopelessly corny and needlessly- manipulative, the last scene is bad melodrama. Mercifully, it lasts only a few minutes, and the overriding image left with the viewer as the end credits roll is not of this silliness but of the Albatross riding the seas in the face of a hurricane-force wind. WHITE SQUALL is a success because the good elements are so well-orchestrated that they dwarf the few obvious flaws. This film offers just about everything, including a twenty-minute white-knuckle sequence and a chance to shed a few tears. In short, it's first-rate entertainment.
- James Berardinelli e-mail<EMAIL_ADDRESS><EMAIL_ADDRESS>web: http://www2.cybernex.net/~berardin
The review above was posted to the
rec.arts.movies.reviews newsgroup (de.rec.film.kritiken for German reviews).
The Internet Movie Database accepts no responsibility for the contents of the
review and has no editorial control. Unless stated otherwise, the copyright
belongs to the author.
Please direct comments/criticisms of the review to relevant newsgroups.
Broken URLs inthe reviews are the responsibility of the author.
The formatting of the review is likely to differ from the original due
to ASCII to HTML conversion.
Related links: index of all rec.arts.movies.reviews reviews


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| Interface Summary | |
|---|---|
| FlatRecordField | |
| FlatRecordFieldFactory | |
| FlatRecordFieldReader | |
| FlatRecordFieldWriter | |
| FlatRecordType | The FlatRecordType object writes a header line |
| FlatRecordTypeFactory | |
| FlatRecordTypeSelection | Class for flat file record type objects. |
| FlatRecordTypeSelectionFactory | Class for flat file record type objects. |
| RecordCombinationFactory |
|
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{{globalize/USA|date=December 2010}} {{refimprove|date=April 2015}}
Organic beef is beef grown according to organic food principles.
Contents |
According to the USDA<ref>National Organic Program (NOP) Standards</ref> rules passed on October 22, 2002, certified organic beef must come from a fully verifiable production system that collects information on the history of every animal in the program, including its breed history, veterinary care, and feed. Further, to be certified as organic, all cattle should meet the following criteria:
With the arrival of the Organic label, many people assumed that the terms “organic” and “natural” were interchangeable, failing to understand the strict regulations required to raise certified organic beef. The USDA defines “natural” beef as all meats raised for human consumption without additives and minimally processed. Natural Beef producers may choose not to use antibiotics or growth-promoting hormones, but there is no third-party verification system required by the USDA. Beef from feedlots can be labeled natural, according to the USDA’s definition.
The U.S. Department of Agriculture allows any fresh meat to be described as "natural" if it includes no artificial flavoring, coloring, preservative or any other artificial ingredients. Minimally processed products, such as ground meat, also count as "natural." {{citation needed|date=November 2013}} To be marketed as "natural," the product can not contain any additives, such as monosodium glutamate or salt.{{fact|date=April 2015}}
As organic cattle approach market weight, there are two feeding methods that producers most commonly use to deliver beef products to their customers: Grass-Fed and Grain-Fed. In the grass-fed program, the cattle continue to eat certified organic grass right up to the time of slaughtering. The USDA is currently developing guidelines to define the term Grass-Fed, and it is expected to call for an all grass diet of at least 95%. Strictly grass-fed cattle tend to be leaner than grain-fed. Grain finishing produces cattle with a higher percentage of fat. All grains must be certified organic to ensure the integrity of the program.
<references/>
{{Beef}}
{{DEFAULTSORT:Organic Beef}}


I like to build beautiful and interactive websites.
Born in Southern California, raised on the island of Oahu, went to college in San Francisco, living in Oregon. I like to roam...
I am always hungry to grow and learn new things, so I took the risk to make the career switch from a project coordinator at a conference planning company to web developer. While working as a project coordinator and collaborating with our web developer, I gained experience creating web based products, and serving the clients that used them. I effectively communicated with clients to help them translate loose ideas into content our web developer could visualize. It was incredibly fulfilling, working through bugs and developing unique innovative planning systems my previous employer continues to use. Wanting to expand my technical skills beyond the planning phase, I started at Epicodus in their Ruby, JavaScript and Rails track.
Having dedicated six months of full time coding, I am confident in my ability to develop Ruby on Rails products as well as work with JavaScript on the front-end. Additionally, the most important skill I have gained from this program is the ability to take something completely foreign and in a short time become proficient in it.


TQ3FileIdleMethod |
typedef Q3_CALLBACK_API_C (TQ3Status, TQ3FileIdleMethod) (
TQ3FileObject _Nonnull theFile,
const void * _Nonnull idlerData
);
Idle callback for reading/writing.
- theFile
- The file being process.
- idlerData
- Application-specific data.
TQ3XDataDeleteMethod |
typedef Q3_CALLBACK_API_C (void, TQ3XDataDeleteMethod) (
void * _Nonnull data
);
Delete method.
- data
- The data to delete.
TQ3XFFormatCanReadMethod |
typedef Q3_CALLBACK_API_C (TQ3Boolean, TQ3XFFormatCanReadMethod) (
TQ3StorageObject _Nonnull storage,
TQ3ObjectType* _Nonnull theFileFormatFound
);
This method is called for every subclass of kQ3FileFormatTypeReader from Q3File_OpenRead.
Once found a FileFormat that returns something other than kQ3ObjectTypeInvalid in the theFileFormatFound
parameter the search stops.
- storage
- Storage Object - positioned at zero - that contains the model to read.
- theFileFormatFound
- returns the signature of the FileFormat able to read this file or kQ3ObjectTypeInvalid.
TQ3XFFormatCloseMethod |
typedef Q3_CALLBACK_API_C (TQ3Status, TQ3XFFormatCloseMethod) (
TQ3FileFormatObject _Nonnull format,
TQ3Boolean abort
);
Close method.
- format
- The format to close.
- abort
- Closing due to an abort.
TQ3XFFormatFloat32ReadArrayMethod |
typedef Q3_CALLBACK_API_C (TQ3Status, TQ3XFFormatFloat32ReadArrayMethod) (
TQ3FileFormatObject _Nonnull format,
TQ3Uns32 numFloats,
TQ3Float32* _Nonnull data
);
Read an array of 32-bit floats from a file.
- format
- The file format.
- numFloats
- The number of floats to read.
- data
- Receives the data being read.
TQ3XFFormatFloat32ReadMethod |
typedef Q3_CALLBACK_API_C (TQ3Status, TQ3XFFormatFloat32ReadMethod) (
TQ3FileFormatObject _Nonnull format,
TQ3Float32* _Nonnull data
);
Read a 32-bit float from a file.
- format
- The file format.
- data
- Receives the data being read.
TQ3XFFormatFloat32WriteMethod |
typedef Q3_CALLBACK_API_C (TQ3Status, TQ3XFFormatFloat32WriteMethod) (
TQ3FileFormatObject _Nonnull format,
const TQ3Float32 * _Nonnull data
);
Write a 32-bit float to a file.
- format
- The file format.
- data
- The data to write.
TQ3XFFormatFloat64ReadMethod |
typedef Q3_CALLBACK_API_C (TQ3Status, TQ3XFFormatFloat64ReadMethod) (
TQ3FileFormatObject _Nonnull format,
TQ3Float64* _Nonnull data
);
Read a 64-bit float from a file.
- format
- The file format.
- data
- Receives the data being read.
TQ3XFFormatFloat64WriteMethod |
typedef Q3_CALLBACK_API_C (TQ3Status, TQ3XFFormatFloat64WriteMethod) (
TQ3FileFormatObject _Nonnull format,
const TQ3Float64 * _Nonnull data
);
Write a 64-bit float to a file.
- format
- The file format.
- data
- The data to write.
TQ3XFFormatGetFormatTypeMethod |
typedef Q3_CALLBACK_API_C (TQ3FileMode, TQ3XFFormatGetFormatTypeMethod) (
TQ3FileObject _Nonnull theFile
);
Get the format type for a file.
- theFile
- The file to query.
TQ3XFFormatGetNextTypeMethod |
typedef Q3_CALLBACK_API_C (TQ3ObjectType, TQ3XFFormatGetNextTypeMethod) (
TQ3FileObject _Nonnull theFile
);
Get the next type from a file.
- theFile
- The file to process.
TQ3XFFormatInt16ReadArrayMethod |
typedef Q3_CALLBACK_API_C (TQ3Status, TQ3XFFormatInt16ReadArrayMethod) (
TQ3FileFormatObject _Nonnull format,
TQ3Uns32 numNums,
TQ3Int16* _Nonnull data
);
Read an array of 16-bit integers from a file.
- format
- The file format.
- numNums
- The number of numbers to read.
- data
- Receives the data being read.
TQ3XFFormatInt16ReadMethod |
typedef Q3_CALLBACK_API_C (TQ3Status, TQ3XFFormatInt16ReadMethod) (
TQ3FileFormatObject _Nonnull format,
TQ3Int16* _Nonnull data
);
Read a 16-bit integer from a file.
- format
- The file format.
- data
- Receives the data being read.
TQ3XFFormatInt16WriteMethod |
typedef Q3_CALLBACK_API_C (TQ3Status, TQ3XFFormatInt16WriteMethod) (
TQ3FileFormatObject _Nonnull format,
const TQ3Int16 * _Nonnull data
);
Write a 16-bit integer to a file.
- format
- The file format.
- data
- The data to write.
TQ3XFFormatInt32ReadArrayMethod |
typedef Q3_CALLBACK_API_C (TQ3Status, TQ3XFFormatInt32ReadArrayMethod) (
TQ3FileFormatObject _Nonnull format,
TQ3Uns32 numNums,
TQ3Int32* _Nonnull data
);
Read an array of 32-bit integers from a file.
- format
- The file format.
- numNums
- The number of numbers to read.
- data
- Receives the data being read.
TQ3XFFormatInt32ReadMethod |
typedef Q3_CALLBACK_API_C (TQ3Status, TQ3XFFormatInt32ReadMethod) (
TQ3FileFormatObject _Nonnull format,
TQ3Int32* _Nonnull data
);
Read a 32-bit integer from a file.
- format
- The file format.
- data
- Receives the data being read.
TQ3XFFormatInt32WriteMethod |
typedef Q3_CALLBACK_API_C (TQ3Status, TQ3XFFormatInt32WriteMethod) (
TQ3FileFormatObject _Nonnull format,
const TQ3Int32 * _Nonnull data
);
Write a 32-bit integer to a file.
- format
- The file format.
- data
- The data to write.
TQ3XFFormatInt64ReadMethod |
typedef Q3_CALLBACK_API_C (TQ3Status, TQ3XFFormatInt64ReadMethod) (
TQ3FileFormatObject _Nonnull format,
TQ3Int64* _Nonnull data
);
Read a 64-bit integer from a file.
- format
- The file format.
- data
- Receives the data being read.
TQ3XFFormatInt64WriteMethod |
typedef Q3_CALLBACK_API_C (TQ3Status, TQ3XFFormatInt64WriteMethod) (
TQ3FileFormatObject _Nonnull format,
const TQ3Int64 * _Nonnull data
);
Write a 64-bit integer to a file.
- format
- The file format.
- data
- The data to write.
TQ3XFFormatInt8ReadArrayMethod |
typedef Q3_CALLBACK_API_C (TQ3Status, TQ3XFFormatInt8ReadArrayMethod) (
TQ3FileFormatObject _Nonnull format,
TQ3Uns32 numNums,
TQ3Int8* _Nonnull data
);
Read an array of 8-bit integers from a file.
- format
- The file format.
- numNums
- The number of numbers to read.
- data
- Receives the data being read.
TQ3XFFormatInt8ReadMethod |
typedef Q3_CALLBACK_API_C (TQ3Status, TQ3XFFormatInt8ReadMethod) (
TQ3FileFormatObject _Nonnull format,
TQ3Int8* _Nonnull data
);
Read an 8-bit integer from a file.
- format
- The file format.
- data
- Receives the data being read.
TQ3XFFormatInt8WriteMethod |
typedef Q3_CALLBACK_API_C (TQ3Status, TQ3XFFormatInt8WriteMethod) (
TQ3FileFormatObject _Nonnull format,
const TQ3Int8 * _Nonnull data
);
Write a 8-bit integer to a file.
- format
- The file format.
- data
- The data to write.
TQ3XFFormatIsEndOfContainerMethod |
typedef Q3_CALLBACK_API_C (TQ3Boolean, TQ3XFFormatIsEndOfContainerMethod) (
TQ3FileObject _Nonnull theFile
);
Test whether there could be any more objects in the current container. Optional, used only in binary 3DMF.
- theFile
- The file being read.
TQ3XFFormatRawReadMethod |
typedef Q3_CALLBACK_API_C (TQ3Status, TQ3XFFormatRawReadMethod) (
TQ3FileFormatObject _Nonnull format,
unsigned char* _Nonnull data,
TQ3Uns32 length
);
Read a block of data from a file.
- format
- The file format.
- data
- Receives the data being read.
- length
- Receives the number of bytes read.
TQ3XFFormatRawWriteMethod |
typedef Q3_CALLBACK_API_C (TQ3Status, TQ3XFFormatRawWriteMethod) (
TQ3FileFormatObject _Nonnull format,
const unsigned char* _Nonnull data,
TQ3Uns32 length
);
Write a block of data to a file.
- format
- The file format.
- data
- The data to write.
- length
- The number of bytes to write.
TQ3XFFormatReadHeaderMethod |
typedef Q3_CALLBACK_API_C (TQ3Status, TQ3XFFormatReadHeaderMethod) (
TQ3FileObject _Nonnull theFile
);
Read the header from a file.
- theFile
- The file to process.
TQ3XFFormatReadObjectMethod |
typedef Q3_CALLBACK_API_C (TQ3Object _Nullable, TQ3XFFormatReadObjectMethod) (
TQ3FileObject _Nonnull theFile
);
Read an object from a file.
- theFile
- The file to process.
TQ3XFFormatSkipObjectMethod |
typedef Q3_CALLBACK_API_C (TQ3Status, TQ3XFFormatSkipObjectMethod) (
TQ3FileObject _Nonnull theFile
);
Skip an object in a file.
- theFile
- The file to process.
TQ3XFFormatStringReadMethod |
typedef Q3_CALLBACK_API_C (TQ3Status, TQ3XFFormatStringReadMethod) (
TQ3FileFormatObject _Nonnull format,
char* _Nonnull data,
TQ3Uns32 * _Nonnull length
);
Read a string from a file.
- format
- The file format.
- data
- Receives the data being read.
- length
- Receives the number of bytes read.
TQ3XFFormatStringWriteMethod |
typedef Q3_CALLBACK_API_C (TQ3Status, TQ3XFFormatStringWriteMethod) (
TQ3FileFormatObject _Nonnull format,
const char* _Nonnull data
);
Write a C-string to a file.
- format
- The file format.
- data
- The data to write.
TQ3XFileFormatSubmitObjectMethod |
typedef Q3_CALLBACK_API_C (TQ3Status, TQ3XFileFormatSubmitObjectMethod) (
TQ3ViewObject _Nonnull theView,
void * _Nonnull fileFormatPrivate,
TQ3Object _Nonnull theObject,
TQ3ObjectType objectType,
const void * _Nonnull objectData
);
Submit an object to a file.
- theView
- The view being submitted to.
- fileFormatPrivate
- File-format specific data.
- theObject
- The object to submit.
- objectType
- The type of theObject.
- objectData
- The data for theObject.
TQ3XObjectAttachMethod |
typedef Q3_CALLBACK_API_C (TQ3Status, TQ3XObjectAttachMethod) (
TQ3Object _Nonnull childObject,
TQ3Object _Nonnull parentObject
);
Attach method.
- childObject
- The child object.
- parentObject
- The parent object.
TQ3XObjectReadDataMethod |
kQ3XMethodTypeObjectReadData
for a
custom element or attribute.
typedef Q3_CALLBACK_API_C (TQ3Status, TQ3XObjectReadDataMethod) (
TQ3Object _Nonnull parentObject,
TQ3FileObject _Nonnull theFile
);
The method normally reads the element's data using functions such as
Q3Float32_Read
and Q3File_ReadObject
(for child objects),
then adds the element data to its parent set.
- parentObject
- Set object to receive the new element or attribute.
- theFile
- A file object.
TQ3XObjectReadDefaultMethod |
typedef Q3_CALLBACK_API_C (TQ3Object _Nullable, TQ3XObjectReadDefaultMethod) (
TQ3FileObject _Nonnull theFile
);
Read method for default geometries (geometries represented with no data in 3DMF files).
This method is not available in QD3D.
- theFile
- A file object.
TQ3XObjectReadMethod |
typedef Q3_CALLBACK_API_C (TQ3Object _Nullable, TQ3XObjectReadMethod) (
TQ3FileObject _Nonnull theFile
);
Read method.
- theFile
- The file to read.
TQ3XObjectTraverseDataMethod |
typedef Q3_CALLBACK_API_C (TQ3Status, TQ3XObjectTraverseDataMethod) (
TQ3Object _Nonnull object,
void * _Nonnull data,
TQ3ViewObject _Nonnull view
);
This is the custom object method of type kQ3XMethodTypeObjectTraverseData
.
The QuickDraw 3D documention mentions this method, but does not explain how it
differs from TQ3XObjectTraverseMethod
. Quesa does not use it.
TQ3XObjectTraverseMethod |
kQ3XMethodTypeObjectTraverse
) for a
custom element or attribute.
typedef Q3_CALLBACK_API_C (TQ3Status, TQ3XObjectTraverseMethod) (
TQ3Object _Nonnull object,
void * _Nonnull data,
TQ3ViewObject _Nonnull view
);
Writing of 3DMF data is done in two phases: traversal, which figures out which
objects are to be written and how much space each will use, and the actual
writing. Two phases are necessary because the start of a container includes
the total length of the container, but this cannot be known without asking
each member of the container.
Typically, this method will call Q3XView_SubmitWriteData
to
specify the immediate object data, and then call Q3Object_Submit
for each child object.
- object
- The object to traverse.
- data
- The object's instance data. In the case of a custom element, this is the internal form of the element data.
- view
- A view object. result Success or failure of the operation.
TQ3XObjectWriteMethod |
kQ3XMethodTypeObjectWrite
for a
custom element or attribute.
typedef Q3_CALLBACK_API_C (TQ3Status, TQ3XObjectWriteMethod) (
const void * _Nonnull data,
TQ3FileObject _Nonnull theFile
);
Writing of 3DMF data is done in two phases: traversal, which figures out which
objects are to be written and how much space each will use, and the actual
writing. Two phases are necessary because the start of a container includes
the total length of the container, but this cannot be known without asking
each member of the container.
This method will write the object's instance data using functions such as
Q3Float32_Write
. An class with no instance data other than
child objects does not need to provide this method.
- data
- Instance data to write. This will be the data passed to
Q3XView_SubmitWriteData
by the traversal method.- theFile
- A file object.
© 1999-2023 Quesa Developers (Last Updated 1/27/2023)


Class Address
Inheritance
System.Object
Address
Implements
Dazzling.Studio.SqlRepository.IDazzlingEntity
Inherited Members
System.Object.Equals(System.Object)
System.Object.Equals(System.Object, System.Object)
System.Object.ReferenceEquals(System.Object, System.Object)
System.Object.GetHashCode()
System.Object.GetType()
System.Object.MemberwiseClone()
Assembly: Uvendia.Domain.dll
Syntax
public class Address : Webshop, IDazzlingEntity
Constructors
Address()
Declaration
Properties
AddresseeName
Declaration
public string AddresseeName { get; set; }
Property Value
| Type |
Description |
| System.String |
|
AddressLine1
Declaration
[Required(ErrorMessage = "Address line 1 is required")]
[StringLength(500, ErrorMessage = "Address line 1 is too long")]
public string AddressLine1 { get; set; }
Property Value
| Type |
Description |
| System.String |
|
AddressLine2
Declaration
[StringLength(500, ErrorMessage = "Address line 2 is too long")]
public string AddressLine2 { get; set; }
Property Value
| Type |
Description |
| System.String |
|
AddressType
Declaration
public AddressType AddressType { get; }
Property Value
AddressTypeId
Declaration
public short AddressTypeId { get; set; }
Property Value
| Type |
Description |
| System.Int16 |
|
BirthDate
Declaration
[DisplayFormat(ApplyFormatInEditMode = true, DataFormatString = "{0:dd MMMM yyyy}")]
public DateTime? BirthDate { get; set; }
Property Value
| Type |
Description |
| System.Nullable<System.DateTime> |
|
City
Declaration
[Required(ErrorMessage = "City is required")]
[StringLength(255, ErrorMessage = "City is too long")]
public string City { get; set; }
Property Value
| Type |
Description |
| System.String |
|
Country
Declaration
public Country Country { get; }
Property Value
| Type |
Description |
| ISO3166.Country |
|
CountryCode
Declaration
[Required(ErrorMessage = "Country is required")]
public string CountryCode { get; set; }
Property Value
| Type |
Description |
| System.String |
|
Email
Declaration
[EmailAddress(ErrorMessage = "E-mailadres is invalid")]
public string Email { get; set; }
Property Value
| Type |
Description |
| System.String |
|
FirstName
Declaration
[StringLength(512, ErrorMessage = "FirstName is too long")]
public string FirstName { get; set; }
Property Value
| Type |
Description |
| System.String |
|
Housenumber
Declaration
public int? Housenumber { get; set; }
Property Value
| Type |
Description |
| System.Nullable<System.Int32> |
|
HousenumberExtension
Declaration
[StringLength(25, ErrorMessage = "House number extension is too long")]
public string HousenumberExtension { get; set; }
Property Value
| Type |
Description |
| System.String |
|
LastName
Declaration
[StringLength(512, ErrorMessage = "FirstName is too long")]
public string LastName { get; set; }
Property Value
| Type |
Description |
| System.String |
|
Phone
Declaration
[Required(ErrorMessage = "Phone number is required")]
[StringLength(50, ErrorMessage = "Phone is too long")]
public string Phone { get; set; }
Property Value
| Type |
Description |
| System.String |
|
Zipcode
Declaration
[StringLength(50, ErrorMessage = "Zip code is too long")]
public string Zipcode { get; set; }
Property Value
| Type |
Description |
| System.String |
|
Methods
ToString()
Declaration
public override string ToString()
Returns
| Type |
Description |
| System.String |
|
Overrides
System.Object.ToString()
Implements
Dazzling.Studio.SqlRepository.IDazzlingEntity


Hardware Architecture and Design Process
While hardware design has long utilized a waterfall-style engineering methodology, the fast-paced, ever-changing, cost-competitive nature of the world of IoT has taught us that some of the same Agile principles that have revolutionized software development can and often must be successfully applied (with some modification) to the IoT hardware design and development process.
Of course software and hardware development are quite different. As a result, applying Agile development to hardware requires a few basic parameters. First, everyone needs to be on the same team to see plans, progress and calculate the physical effects of one part on the rest. The high-costs associated with hardware development means it is especially important to have transparency. Altering a product once the architectural decisions are completed does not mean erasing a few bits of code and rewriting them. Instead, you may have to order new parts, apply heat to weld or add more microchips. All these add expenses and end up affecting both the project timeline and cost. Applying Agile development processes will inevitably lower the price for hardware devices and result in more profit for your company and/or lower prices for your customers.
While most would argue the refinement of Agile for hardware development is still a work in progress, IOTA2Z has found that IoT project success is optimized though the use of a modified variant of Agile, including features such as:
- A focus on iterative development and rapid prototyping, while embracing changing requirements during the process.
- Using longer sprint periods to accommodate the timeframes typically associated with even granular hardware development tasks.
- Focusing on more modular hardware designs where some sprints might focus on a particular module, while other sprints might focus on integrating multiple modules.
- Realizing that delivering a customer-presentable product at the end of each sprint is often not practical or cost prohibitive, and therefore replacing such sprint deliverables with virtual simulations of functionality which still can provide strong visibility into project progress and requirements alignment.
- IOTA2Z will work with you to develop an optimal and fully-transparent hardware design process that will best optimize your project success.


| sunday | monday | tuesday | wenesday | thursday | friday | saturday |
| 28 twenty |
29 twenty-one |
30 twenty-two |
31 twenty-three |
1 twenty-four |
2 twenty-five |
3 twenty-six |
| 4 twenty-seven |
5 twenty-eight |
6 twenty-nine |
7 lidong |
8 october |
9 two |
10 three |
| 11 four |
12 five |
13 six |
14 seven |
15 eight |
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| 18 eleven |
19 twelve |
20 thirteen |
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22 xiayuanjian |
23 sixteen |
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27 twenty |
28 twenty-one |
29 twenty-two |
30 twenty-three |
1 twenty-four |


--- layout: post title: Build a Simple REST Application with AngularJS Pt 3 LoopBack AngularJS SDK FTW date: 2014-10-30 14:23:05.000000000 -07:00 type: post published: true status: publish categories: - AngularJS - JavaScript - Node - Programming tags: [] meta: _edit_last: '8' _nina_cross_promo: a:1:{s:8:"template";s:17:"custom_1399328552";} _thumbnail_id: '942' _yoast_wpseo_primary_category: '' _wpcom_is_markdown: '1' author: login: ohmadmin email<EMAIL_ADDRESS>display_name: Lukas Ruebbelke first_name: Lukas last_name: Ruebbelke ---
Welcome back to another installment of the of building a simple REST application with AngularJS! In the previous lesson, we learned how to consume a REST API in AngularJS using $http. In this lesson, we are going to see an alternate approach using an automatically generated service from the LoopBack AngularJS SDK that is compatible with $resource.
If you haven't had a chance to check out Part 1 and Part 2 of the series, be sure to check them out first as this post builds heavily on top of them.
The project has two parts: the REST API and the website that consumes it. The REST API repository remains unchanged and we will be working from the angular-sdk branch of the website repository.
[button link="https://github.com/simpulton/simple-rest-api" size="large" window="yes"]Simple Rest API[/button]
[button link="https://github.com/simpulton/simple-rest-website/tree/angular-sdk" size="large" window="yes"]Simple Rest Website[/button]
The LoopBack AngularJS SDK gives us the ability to generate an AngularJS service based off of our Node.js model using the cli tools that come with the SDK. So just to clarify why I like this, we are able to generate an entire REST API using StrongLoop and THEN! generate an AngularJS service to consume the generated REST API. I do not think code generators are the answer for everything and one should understand what is happening under the hood when the use them but for rote work... I admit...
The commands we are going to use are operating on the assumption that the simple-rest-api and simple-rest-website directories are siblings to each other. If this is not the case, you may need to update the commands appropriately.
The first thing we need to do is to navigate to the folder in the simple-rest-api project where we want to generate our service. We will jump into the client folder since it is empty and a good of choice as any.
[raw]
cd simple-rest-api
cd client # or wherever you want the generated file to go...client is conveniently empty
[/raw]
From here, we are going to use the lb-ng command to generate our service. We are telling it to generate our service based on server.js and call it lb-services.js.
[raw]
lb-ng ../server/server.js lb-services.js # path_to_application_js path_to_file_to_write
[/raw]
And now we are going to move the generated service into the app directory of our simple-rest-website.
[raw]
mv lb-services.js ../../simple-rest-website/public/app/
[/raw]
Phase one of Operation LoopBack SDK complete!
We need to flip a few switches and turn a few knobs to integrate our service with our AngularJS application.
We need to include the angular-resource.min.js and lb-services.js source files into our application.
{% highlight html %}
{% endhighlight %}
Then we inject the lbServices submodule into our main application module.
[js]
angular.module('SimpleRESTWebsite', ['lbServices'])
[/js]
Because we have separated our API and our web application into two distinct parts, we need to update the urlBase variable in lb-services.js to point to http://localhost:1337/api instead of /api.
[js]
var urlBase = "http://localhost:1337/api";
[/js]
Phase two of Operation LoopBack SDK complete!
We need to update the MainCtrl to use the Item service instead of the ItemsService.
[js]
angular.module('SimpleRESTWebsite', ['lbServices'])
.controller('MainCtrl', function (Item) {
//...
});
[/js]
And then update the getItems method to use Item.find.
[js]
function getItems() {
Item.find(
function (result) {
main.items = result;
});
}
[/js]
We will then update createItems to use Item.create.
[js]
function createItem(item) {
Item.create(item,
function (result) {
initCreateForm();
getItems();
});
}
[/js]
We will modify updateItem to use Item.upsert. Upsert!? Whaaaa... after a quick Google search I found the explanation below. Thanks Wiki! Although I am going to let you look up portmanteau on your own.
Some database implementations adopted the term "Upsert" (a portmanteau of update and insert) to a database statement, or combination of statements, that inserts a record to a table in a database if the record does not exist or, if the record already exists, updates the existing record. It is also used to abbreviate the "MERGE" equivalent pseudo-code.
[js]
function updateItem(item) {
Item.upsert(item,
function (result) {
cancelEditing();
getItems();
});
}
[/js]
We are in the homestretch now... deleteItem uses Item.deleteById.
[js]
function deleteItem(itemId) {
Item.deleteById({id: itemId},
function (result) {
cancelEditing();
getItems();
});
}
[/js]
Operation LoopBack SDK complete!
I found working with the Loopback AngularJS SDK to be really pleasant and low friction. The AngularJS SDK is installed with the StrongLoop package and so if you are using it to generate your REST API it is REALLY easy to use it to generate your AngularJS services as well.
The general steps we executed are as follows
I also found the generated service to be very well documented and easy to read so I recommend reading through that as well.
LoopBack AngularJS SDK Documentation
[button link="https://github.com/simpulton/simple-rest-api" size="large" window="yes"]Simple Rest API[/button]
[button link="https://github.com/simpulton/simple-rest-website/tree/angular-sdk" size="large" window="yes"]Simple Rest Website[/button]


---
layout: post
title: Your Desert Island Lens?
tags:
- 50mm
- Articles
- General Photography
- lenses
status: publish
type: post
published: true
meta:
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aktt_notify_twitter: "yes"
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ghostpool_post_template: Default
ghostpool_link: Full Size Image/Video
ghostpool_post_image: http://photo.rwboyer.com/wp-content/uploads/2011/08/P1010893-ico.jpg
ghostpool_post_faded_image: http://photo.rwboyer.com/wp-content/uploads/2011/08/P1010893-ico.jpg
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---
Here is the question - if you were stuck with only one lens of one focal length which one would that be. I am only talking about focal length here in terms of angle of view - not the real focal length. For arguments sake lets discuss this in 35mm equivalent terms since everybody is familiar with that and most software and cameras will do the EXIF conversion for you. I guess the question of both your mind's eye and what you think would be best in some combination of what focal lengths you actually use the most as I mention here as an interesting study for you to do some introspection.
For me - it has got to be a 50mm both in terms of how I feel and the reality that seems to creep into my actual use based on hard numbers, now that I can easily get there. How about you? Feel free to include other very specific conditions that are must haves for you in comments - I am always interested in what people think are essential qualities - like maximum aperture, no-or-low distortion, wonderful bokeh, whatever. Try not to pick lenses that don't exist in reality if you comment...
[poll id="27"]
RB


| timeBasedSeq {xts} | R Documentation |
A function to create a vector of time-based objects suitable for indexing an xts object, given a string conforming to the ISO 8601 time and date standard for range-based specification. The resultant series can be of any class supported by xts, including POSIXct, Date, chron, timeDate, yearmon, and yearqtr.
timeBasedRange
creates a vector of length 1 or 2
as seconds since the epoch (1970-01-01) for use
internally.
timeBasedSeq(x, retclass = NULL, length.out = NULL) timeBasedRange(x, ...)
x |
a string representing the time-date range desired |
retclass |
the return class desired |
length.out |
passed to |
... |
unused |
Designed to provide uniform creation of valid time-based objects for use within xts, the interface conforms (mostly) to the ISO recommended format for specifying ranges.
In general, the format is a string specifying a time and/or date from, to, and optionally by delineated by either ‘"/"’ or ‘"::"’.
The first argument need not be quoted, as it is converted internally if need be.
The general form is from/to/by or from::to::by, where to and by are optional if the length.out arg is specified.
The from
and to
elements of the string
must be left-specified with respect to the standard
CCYYMMDD HHMMSS form. All dates-times
specified will be set to either the earliest point (from)
or the latest (to), given the level of specificity.
For example ‘1999’ in the from field would set the start to the beginning of 1999. The opposite occurs in the to field.
The level of detail in the request is interpretted as the level of detail in the result. The maximum detail of either from or to is the basis of the sequence, unless the optional by element is specified, which will be covered later.
To request a yearly series, it is only necessary to
use ‘"1999/2008"’. Alternately, one could
request a monthly series (returned by default as
class yearmon
) with ‘"199901/2008"’ or
‘"1999-01/2008"’, or even ‘"1999/2008-01"’.
As the level of granularity increases, so does the
resultant sequence granularity - as does its length.
Using the optional third by field (the third
delimited element to the string), will override the
granularity intepretation and return the requested
periodicity. The acceptable arguments include
Y
for years, m
for months, d
for
days, H
for hours, M
for minutes and S
for
seconds.
A sequence or range of time-based objects.
If retclass
is NULL
, the result is a named
list of from, to, by and length.out.
Jeffrey A. Ryan
International Organization for Standardization: ISO 8601 https://www.iso.org
timeBasedSeq('1999/2008') timeBasedSeq('199901/2008') timeBasedSeq('199901/2008/d') timeBasedSeq('20080101 0830',length=100) # 100 minutes timeBasedSeq('20080101 083000',length=100) # 100 seconds


|
QuantLib: a free/open-source library for quantitative finance
Fully annotated sources - version 1.30
|
statistics class with convergence table More...
#include <ql/math/statistics/convergencestatistics.hpp>
Public Types | |
| typedef T::value_type | value_type |
| typedef std::vector< std::pair< Size, value_type > > | table_type |
Public Member Functions | |
| ConvergenceStatistics (const T &stats, const U &rule=U()) | |
| ConvergenceStatistics (const U &rule=U()) | |
| void | add (const value_type &value, Real weight=1.0) |
| template<class DataIterator > | |
| void | addSequence (DataIterator begin, DataIterator end) |
| template<class DataIterator , class WeightIterator > | |
| void | addSequence (DataIterator begin, DataIterator end, WeightIterator wbegin) |
| void | reset () |
| const std::vector< std::pair< Size, value_type > > & | convergenceTable () const |
Private Attributes | |
| table_type | table_ |
| U | samplingRule_ |
| Size | nextSampleSize_ |
statistics class with convergence table
This class decorates another statistics class adding a convergence table calculation. The table tracks the convergence of the mean.
It is possible to specify the number of samples at which the mean should be stored by mean of the second template parameter; the default is to store \( 2^{n-1} \) samples at the \( n \)-th step. Any passed class must implement the following interface:
as well as a copy constructor.
Definition at line 58 of file convergencestatistics.hpp.
| typedef T::value_type value_type |
Definition at line 60 of file convergencestatistics.hpp.
| typedef std::vector<std::pair<Size,value_type> > table_type |
Definition at line 61 of file convergencestatistics.hpp.
| ConvergenceStatistics | ( | const T & | stats, |
| const U & | rule = U() |
||
| ) |
| ConvergenceStatistics | ( | const U & | rule = U() | ) |
| void add | ( | const value_type & | value, |
| Real | weight = 1.0 |
||
| ) |
| void addSequence | ( | DataIterator | begin, |
| DataIterator | end | ||
| ) |
Definition at line 67 of file convergencestatistics.hpp.
| void addSequence | ( | DataIterator | begin, |
| DataIterator | end, | ||
| WeightIterator | wbegin | ||
| ) |
Definition at line 72 of file convergencestatistics.hpp.
| void reset |
Definition at line 116 of file convergencestatistics.hpp.
| const ConvergenceStatistics< T, U >::table_type & convergenceTable |
Definition at line 124 of file convergencestatistics.hpp.
|
private |
Definition at line 81 of file convergencestatistics.hpp.
|
private |
Definition at line 82 of file convergencestatistics.hpp.
|
private |
Definition at line 83 of file convergencestatistics.hpp.


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Let's Play!
When it is your turn, click on the button and roll the dice to see how many spaces you can move your pawn


---
bg: "local.jpg"
layout: default
crawlertitle: "Jonathan Ma"
title: "Welcome"
summary: " to Jonathan's homepage"
---
Jonathan Ma
I am a forth year graduate student pursuing a Ph.D. degree in physics at the University of California, Berkeley. I have been doing research on noval materials and I utilize the technique of Angle Resolved Photoemission Spectroscopy to interogate the electronic structure of condensed matter systems. Most of my time is spent on data analysis, mostly images of some sort.
Born in Sydney, Australia and raised in Hong Kong, I am currently residing in Berkeley, California.
Since I do a lot of data analysis and programming (and of course, writing) for my studies, I do run some data analysis or programming side projects. They are fun and sometimes useful!
Latest Post


I can remember a time in high school where I felt Steriotype threat and I believe impacted the way I pursued something. In high school, I enjoyed playing baseball and so naturally, I joined the baseball team. Now, I am Korean. Back in the 90's, there weren't many korean baseball players in Major League Baseball. I believe the only one was Chan Ho Park of the Los Angeles Dodgers. He literally started the year I joined the high school team. The other piece of releveant information is that I had recently moved into a neighborhood that was predominantly white. I went from living in Queens, NY which many say is the melting pot of New York, to living in a city in Westchester County in New York that consisted mainly of whites. There was literally only one black kid in the school and maybe a handful of other asians.
So here is this new asian kid that shows up to tryouts and wants to try out to be a pitcher. Obviously, the pressure was on. I remember throwing just a few pitches and after seeing the coaches face, I told him nevermind, I'm OK with playing right field. Arguably an area with the least amount of balls coming your way. I still played on the team, but somehow remained "invisible". I felt that had the coach pushed me to try more pitches, I could have been a pitcher for the team.
Today, being part of DBC, I don't have that particular stereotype threat. Just the fact that their is a large asian community in web and software development takes that threat out of the picture. However, I am now in my mid 30's. The average age in the industry is somwhere in the mid 20's. This makes me think thoughts of being inadequate to learn the needed skills to keep up. Has my brain aged too much to not be able to learn as fast. Can an old dog learn new tricks? When I was doing the required reading this week in Sandi Metz's Practical Object-Oriented Design in Ruby, I constantly got frustrated with myself because some of the concepts didn't instantly become part of my knowledge pool. This brought on thoughts of how my old brain isn't fast enough anymore to learn this. I hope that this is not the case and that I will learn these concepts better if presented to me in a way I can grasp it better in the coming weeks.


Robert Karl Perez Reimi
42 South Richmond Street, Dublin 2.
Tel:<PHONE_NUMBER>
21th April 2014
Griffith College Dublin
Griffith Institute of Language
Dear Richard McHugh,
I am a student of your school English program currently coursing the B2 level, I arrived from Venezuela the fifth of February 2014. Since that day I stayed for a month in the host family house which was assigned to me. During my stay I experienced some problems that I would like to list below for you consideration:
The lady living in the house smokes a lot every day and the smoke concentrated in my room and my clothes smelling were very bad, even after washing.
She became angry two times I arrived after 6pm without calling her. Those days was the second and the fourth day when I was learning about the city and I did not have my cell phone yet. If I arrived after 6pm there was no dinner. She said to me in a disrespectful way ‘I only work until 6pm’. At the end, I missed some dinners which I already paid for because of that rule.
The heating was turned on only few times a day and sometimes never. She had a room with a fireplace when she stay at nights watching TV, I think she did not mind about whats happening outside.
She complained about Internet bill increase and blame me about that. But, the truth is that the Internet in that house was not working properly and I had to use the Internet connection given by my phone many times during my stay.
A letter for me was open by her once and she never apologize.
On the other hand, when I asked her to sign me a reference for a landlord, a guest was in the house and she started to tell him bad things about me. She was thinking that I was not understanding her, but I was. She said to the guest that she can not sign the reference because I have not paid for my accommodation yet. I complained in front of her and the guest and she changed her saying to ‘the college have not paid me to yet’. I said I have a contract with the college and I already paid for my accommodation.
I lost money during my stay in the house, that episode put me in a lot of stress, there was some calls and the police was involved in the situation and the money could not be recovered at the end. This is the worst part because is a serious situation, many students came her bringing their savings to learn English and accomplish a dream, losing money can be an unrecoverable situation for many students.
This is my experience but currently I am not the only one with problems related to host families, I have heard from friends about cases of racism and I am pretty sure that definitely this is not the image which Irish people want to show to us.
I encourage you to choose carefully your host family services and choose families that really want to and are willing to host foreign students, to be clear with terms and conditions not only with students but also with families, ask students to let feedback about families and families about students as well. Also, encourage students to keep their valuables and money safe to avoid that kind of problems in the future.
Also I want to thank you because you supported me in some of the situtations described above.
Finally, I let you below the details about the host family I referenced above:
Yours Sincerelly, Robert Karl Pérez Reimi


Change Faction: Entities Page
This tab page of the Change Faction dialog allows you to edit the unplaced entities that initially belong to a player faction.
This box allows you to select the entity category shown in the Faction Entities and Available Entities lists.
- Choose Unit (Alt+U) to show units and unit classes.
- Choose Terrain (Alt+T) to show terrains and terrain classes.
- Choose Upgrade (Alt+G) to show upgrades and upgrade classes.
This list shows all unplaced entities of the selected Category that belong to this faction. Each entity is represented by the unique internal identifier associated with its class.
- Asterisks (*) indicate entities with edited individual data that differs from their default class data.
- Left-click on an entry to select it.
- Double-click on an entry to display its Change Entity Instance dialog, as with the Change Entity button.
- Choose Add Entity (Alt+A) to add an instance of the selected Available Entity to the faction entity list. Duplicate identifiers are possible and will cause Hexkit Game to create multiple instances of the same entity class.
- Choose Change Entity… (Alt+E) to display the Change Entity Instance dialog for the selected entry, allowing you to edit the individual data of the entity.
- Choose Remove Entity (Alt+R) to remove the selected entry.
This list shows the identifiers of all entity classes in the selected Category currently defined on the Entities Page.
- Left-click on an entry to select it.
- Double-click on an entry to add it to the Faction Entities list, as with the Add Entity button.


Alappuzha (Loudspeaker pronunciation (help·info)) (Malayalam: ആലപ്പുഴ) is one of the 14 districts in the state of Kerala in India. It was formed as Alleppey District on August 17, 1957. The name of the district was officially changed to Alappuzha in 1990. The district is a widely known tourist destination, and is well known for its coir factories. Most of Kerala's coir industries are situated in and around Alappuzha. The district is also known for its communist traditions. It is home to the Punnapra Vayalar strikes against the British and also the revolt against feudal raj. Alappuzha is strongly connected by waterways to various other parts of Kerala, including the famous tourist destination, Kumarakom.


{{ content }}
{% if page.excerpt %}{% endif %} {% if page.tags %}{% endif %} {% if page.author %} {% assign author = site.data.authors[page.author] %}{% else %}{% assign author = site.owner %} {% endif %} {% include open-graph.html %} {% if author.google.plus %}{% endif %}
{% include header.html %} {% include navigation-sliding.html %} {% if page.image.feature %}28 Highland Rd, Glen Cove, NY 11542
516.676.0015
Please tread softly as our site is currently being developed.
The times below are accurate so feel free to stop by.


GDS is deprecating the Performance Platform effective from 15 March 2021. The historical data will be made available via the National Archives in late March 2021. Future Performance data hosted by services will be available via data.gov.uk.


{% extends "base.html" %} {% block content %}
Demonstrations of web utilities by Unidata's Python team
These tools are all free and licensed under the MIT license. They can be used for commerical, educational, or any other use. If you need custom tools, contact us to see how we can fulfill your needs!
All of our tools are tested against known data to be sure you always get the right answer.
Not only can these web apps run on your desktop, but they are mobile friendly as well. Run on your phone/tablet in the field!