library(tidyverse)
library(cowplot)
library(broom)
library(modelr)
library(viridis)
library(lubridate)
library(hms)
knitr::opts_chunk$set(tidy.opts=list(width.cutoff=60),tidy=TRUE, echo = TRUE, message=FALSE, warning=FALSE, fig.align="center")
source("../../tools/echem_processing_tools.R")
source("../../tools/plotting_tools.R")
theme_set(theme_1())
Here I will attempt to fit the SWV decays and extract a value for \(D_m\) from the control biofilms.
First, let’s import and look at the data:
dm_data <- read_csv("../Processing/01_17_19_swv_control_dm_processed.csv")
ggplot(dm_data, aes(x = rep, y = signal, color = factor(run),
shape = reactor)) + geom_line() + geom_point() + facet_wrap(~electrode) +
scale_color_viridis(discrete = T)
We have two runs of the control biofilm. Each SWV scan was taken at i1 and i2, which yielded similar results. The decays also look pretty similar between runs. The last thing to notice is that we have the soak data points associated with each decay. Recall that the latest strategy for estimating \(I_0\) is to use soak SWV as the upper bound and the first transfer SWV as the lower bound. You can see that the values actually are reasonably close to each other, which is good since it will give us relatively tight bound.
Let’s fit the datasets with the model we have been using, which assumes a no flux boundary at x=0 and extends to infinity in the other direction.
e = exp(1)
dm_data_norm <- dm_data %>% group_by(run, electrode, reactor) %>%
mutate(min_time = min(minutes)) %>% mutate(norm_time = minutes -
min_time + 0.6)
ggplot(dm_data_norm %>% filter(reactor == "transfer" & electrode ==
"i1"), aes(x = norm_time, y = signal, color = factor(run),
shape = reactor)) + geom_point() + geom_smooth(method = "nls",
formula = y ~ b * (x)^-0.5 + a, method.args = list(start = c(b = 0.1,
a = 1e-07)), se = F) + facet_wrap(~run, scales = "free")
Well, to be honest, I think those are pretty bad fits. For this reason, I thought about another model that we could try, which would place another no flux boundary at some spot, \(L\). Therefore, we would go from using a semi-infinite model above to a finite model below. Let’s see how it does:
ggplot(dm_data_norm %>% filter(reactor == "transfer" & electrode ==
"i1"), aes(x = norm_time, y = signal, color = factor(run),
shape = reactor)) + geom_point() + geom_smooth(method = "nls",
formula = y ~ (a * (x)^-0.5) * (2 + e^(b * (x)^-0.5)) + c,
method.args = list(start = c(a = 0.1, b = 0.1, c = 0)), se = F) +
facet_wrap(~run)
# ggplot(dm_data_norm %>% filter(reactor=='transfer'), aes(x
# = norm_time^-0.5, y = signal, color=factor(run), shape =
# reactor)) + geom_point()+ facet_wrap(~electrode)+
# scale_color_viridis(discrete = T)
Well, it’s still not perfect, but that’s definitely better than the original model. I think it captures both the decay part of the plot and the long tail much better. Let’s write out the derivation for this model, so that we can extract a \(D_m\) from the coefficients we pull out.
Starting with 1D Diffusion over time (t) and space (x) the concentration in the system is described by: \[C(x,t) = \frac{M_0}{A \sqrt{4 \pi D t}}e^{\frac{-x^2}{4 D t}}\] With a no flux boundary at L=0 (our original diffusion model): \[C(x,t) = C_{\text{real}}+C_{\text{image}} = \frac{M_0}{A \sqrt{4 \pi D t}}(e^{\frac{-x^2}{4 D t}} + e^{\frac{-(x+2L)^2}{4 D t}})\] evaluate at L=0 \[C(x,t) = C_{\text{real}}+C_{\text{image}} = \frac{M_0}{A \sqrt{4 \pi D t}}(e^{\frac{-x^2}{4 D t}} + e^{\frac{-x^2}{4 D t}})\] You can see that the right side will reduce to 2, when evaluated at x=0, which was exactly our original diffusion model.
Now, if we add a no flux boundry at L=0 and L=L: \[C(x,t) = C_{\text{real}}+C_{\text{image}_1}+C_{\text{image}_2} = \frac{M_0}{A \sqrt{4 \pi D t}}(e^{\frac{-x^2}{4 D t}} + e^{\frac{-x^2}{4 D t}} + e^{\frac{-(x+2L)^2}{4 D t}})\] *Note that when adding multiple image sources we should really add infinite image sources to counteract the two images interacting (see MIT HW), however I don’t think this will make a major difference and I don’t know how to incorporate it right now…so let’s move forward.
Evaluate at x=0 \[C(x,t) = \frac{M_0}{A \sqrt{4 \pi D t}}(e^0 + e^0 + e^{\frac{-(2L)^2}{4 D t}})\] \[C(x,t) = \frac{M_0}{A \sqrt{4 \pi D t}}(2 + e^{\frac{-(2L)^2}{4 D t}})\] Substituting for SWV \[I_{swv} = \frac{I_0 V}{A \sqrt{4 \pi D_m t}}(2 + e^{\frac{-(2L)^2}{4 D t}})\] if we fit the data to a model of the form \[y=a t^{-0.5} (2 + e^{b/t})\] then \[a = \frac{I_0 V}{A \sqrt{4 \pi D_m}}\] and \[b = \frac{-(2L)^2}{4 D_m}\] rearranging for \(D_m\)
\[D_m = \frac{I_0^2 V^2}{A^2 4 \pi a^2}\] or if we replace \(\frac{V}{A} = z\) with \(z = \sqrt{D_{ap} t_s}\) then we get the following. This expression will allow us to calculate a \(D_m\) from the fit coefficient \(a\), \(I_0\), \(D_{ap}\) and \(t_s\), as we were able to do with the original model. \[D_m = \frac{I_0^2 D_{ap} t_s}{4 \pi a^2} \] and \[D_m = \frac{-(2L)^2}{4 b}\]
Ok, with the expression for \(D_m\) from the fit coefficient \(a\) in hand, no we can fit all of the data and compare the two models.
Ok, so now I’ll use nonlinear least squares to fit the datasets with each model. Let’s look at the estimates for a and b:
nls_no_flux <- dm_data_norm %>% filter(reactor == "transfer") %>%
group_by(run, electrode) %>% do(tidy(nls(., formula = signal ~
(a * (norm_time)^-0.5) * (2 + e^(b/norm_time)) + c, start = list(a = 0.1,
b = 0.1, c = 0)))) %>% mutate(model = "no_flux")
nls_og <- dm_data_norm %>% filter(reactor == "transfer") %>%
group_by(run, electrode) %>% do(tidy(nls(., formula = signal ~
(a * (norm_time)^-0.5) + c, start = list(a = 0.1, c = 0)))) %>%
mutate(model = "og")
nls_fits <- rbind(nls_no_flux, nls_og)
ggplot(nls_fits %>% filter(term != "c"), aes(x = model, y = estimate,
color = electrode)) + geom_point() + facet_wrap(~term, scales = "free") +
ylim(0, NA)
Ok, this seems reasonable given that the estimate for the finite (no flux) model will be multiplied by 4 in the \(D_m\) calculation. It looks like there may be ~4x difference in the two model estimates of \(a\).
Now let’s define our functions to calculate \(D_m\) from \(a\).
So, for the new finite model we will use \(D_m = \frac{I_0^2 D_{ap} t_s}{4 \pi a^2}\) to calculate \(D_m\) and for the original model we will use \(D_m = \frac{I_0^2 D_{ap} t_s}{\pi a^2}\), notice they only differ by the 4 in the denominator, because the 2 in the numerator of the slope term has been moved in the finite model.
dm_from_a_noFlux <- function(estimate, i_0, dap, t_s = 0.1) {
dm <- (i_0^2 * dap * t_s)/(4 * pi * estimate^2)
dm
}
dm_from_a_OG <- function(estimate, i_0, dap, t_s = 0.1) {
dm <- (i_0^2 * dap * t_s)/(pi * estimate^2)
dm
}
# dm_a_noFlux <- dm_from_a_noFlux(estimate = 6e-7, i_0 =
# 3e-6, dap = 1e-6)
# dm_a_OG <- dm_from_a_OG(estimate = 2e-6, i_0 = 3e-6, dap =
# 1e-6)
# sqrt(-1 * 4 * dm_a_noFlux) / 2
And now we can take the rep 0s from the soak and transfer conditions to get the soak scan and the first transfer scan, which will be used as the upper and lower bounds of the true \(I_0\). Then we can calculate a \(D_m\) with both sets of parameters and both models for each dataset. Let’s plot the \(D_m\) estimates to see what it looks like!
soak_i0 <- dm_data_norm %>% mutate(i0_soak = signal) %>% filter(rep ==
0 & reactor == "soak") %>% select(electrode, reactor, run,
i0_soak)
transfer_i0 <- dm_data_norm %>% mutate(i0_transfer = signal) %>%
filter(rep == 0 & reactor == "transfer") %>% select(electrode,
reactor, run, i0_transfer)
dm_estimates_soak <- left_join(nls_fits %>% filter(term == "a"),
soak_i0, by = c("electrode", "run"))
dm_estimates <- left_join(dm_estimates_soak, transfer_i0, by = c("electrode",
"run"))
dm_estimates_og <- dm_estimates %>% filter(model == "og") %>%
mutate(dm_soak = dm_from_a_OG(estimate = estimate, i_0 = i0_soak,
dap = 1e-06, t_s = 0.1)) %>% mutate(dm_transfer = dm_from_a_OG(estimate = estimate,
i_0 = i0_transfer, dap = 1e-06, t_s = 0.1))
dm_estimates_noFlux <- dm_estimates %>% filter(model == "no_flux") %>%
mutate(dm_soak = dm_from_a_noFlux(estimate = estimate, i_0 = i0_soak,
dap = 1e-06, t_s = 0.1)) %>% mutate(dm_transfer = dm_from_a_noFlux(estimate = estimate,
i_0 = i0_transfer, dap = 1e-06, t_s = 0.1))
dm_estimates_tidy <- rbind(dm_estimates_og, dm_estimates_noFlux)
ggplot(dm_estimates_tidy, aes(x = model, shape = electrode)) +
geom_point(aes(y = dm_soak), color = "red") + geom_point(aes(y = dm_transfer),
color = "blue") + geom_hline(yintercept = 1e-06, linetype = 2) +
facet_wrap(~run) + scale_y_log10(limits = c(1e-09, 1e-06))
Ok, using this set of parameters the models give reasonable results I suppose. First, notice all the estimates are below the dashed line, which was the input \(D_{ap}\), so that’s reassuring. The other thing to note is that the original (og) model consistently estimates \(D_m\) lower than the finite no flux model. However, these results are dependent on the parameter \(t_s\), which I discuss further below.
Let’s compare our analysis used for the biofilm data to the blank IDA data, where we can be more confident that \(D_{ap}\) and \(D_m\) should be very similar. First let’s read the data and fit with the original model:
blank_data <- read_csv("../../11_28_18_blank_IDA/Processing/11_28_18_swv_decays_Processed.csv") %>%
filter(PHZadded != "10uM")
ggplot(blank_data, aes(x = norm_time, y = signal_from_transfer,
color = PHZadded, shape = electrode)) + geom_point() + geom_smooth(method = "nls",
formula = y ~ b * (x)^-0.5 + a, method.args = list(start = c(b = 0.1,
a = 1e-07)), se = F) + facet_wrap(~electrode) + scale_color_viridis(discrete = T)
The fit looks ok, not great. Let’s look at the no flux model:
ggplot(blank_data, aes(x = norm_time, y = signal_from_transfer,
color = PHZadded, shape = electrode)) + geom_point() + geom_smooth(method = "nls",
formula = y ~ (a * (x)^-0.5) * (2 + e^(b * (x)^-0.5)) + c,
method.args = list(start = c(a = 0.01, b = 0.5, c = 0.1)),
se = F) + scale_color_viridis(discrete = T)
Ok, I would say this fit is significantly better. Let’s now extract those fit coefficients and calculate \(D_m\) in the same way.
blank_no_flux <- blank_data %>% group_by(PHZadded) %>% do(tidy(nls(.,
formula = signal_from_transfer ~ (a * (norm_time)^-0.5) *
(2 + e^(b/norm_time)) + c, start = list(a = 0.1, b = 0.1,
c = 0)))) %>% mutate(model = "no_flux")
blank_og <- blank_data %>% group_by(PHZadded) %>% do(tidy(nls(.,
formula = signal_from_transfer ~ (a * (norm_time)^-0.5) +
c, start = list(a = 0.1, c = 0)))) %>% mutate(model = "og")
blank_fits <- rbind(blank_no_flux, blank_og)
ggplot(blank_fits %>% filter(term != "c"), aes(x = model, y = estimate,
color = PHZadded)) + geom_point() + facet_wrap(~term, scales = "free") +
ylim(0, NA)
Ok, seems reasonable. Notice that these estimates for \(a\) are significantly lower than the estimates for the biofilm above, which is consistent with faster \(D_m\).
Let’s convert to \(D_m\) and look at the results:
blank_i0 <- blank_data %>% group_by(PHZadded) %>% mutate(i0_transfer = max(signal_from_transfer)) %>%
filter(signal_from_transfer == i0_transfer) %>% mutate(i0_soak = i0) %>%
select(PHZadded, i0_transfer, i0_soak)
blank_estimates <- left_join(blank_fits %>% filter(term == "a"),
blank_i0, by = c("PHZadded"))
blank_estimates_og <- blank_estimates %>% filter(model == "og") %>%
mutate(dm_soak = dm_from_a_OG(estimate = estimate, i_0 = i0_soak,
dap = 1e-05, t_s = 0.1)) %>% mutate(dm_transfer = dm_from_a_OG(estimate = estimate,
i_0 = i0_transfer, dap = 1e-05, t_s = 0.1))
blank_estimates_noFlux <- blank_estimates %>% filter(model ==
"no_flux") %>% mutate(dm_soak = dm_from_a_noFlux(estimate = estimate,
i_0 = i0_soak, dap = 1e-05, t_s = 0.1)) %>% mutate(dm_transfer = dm_from_a_noFlux(estimate = estimate,
i_0 = i0_transfer, dap = 1e-05, t_s = 0.1))
blank_estimates_tidy <- rbind(blank_estimates_og, blank_estimates_noFlux)
ggplot(blank_estimates_tidy, aes(x = model, shape = PHZadded)) +
geom_point(aes(y = dm_soak), color = "red") + geom_point(aes(y = dm_transfer),
color = "blue") + geom_hline(yintercept = 1e-05, linetype = 2) +
facet_wrap(~model, scales = "free") + scale_y_log10()
Interesting, so with these parameters, it seems that the no flux model gives a huge overestimate, where the og model actually does provide reasonable (albeit wide) upper and lower bounds around the expected \(D_{ap}\). I find this a little curious though, because the no flux model fit is much better, and if anything the og model fit has a slower decay/\(D_m\) than the actual data. See below for further discussion.
Could it be that one of our parameters is quite off? With this parameter set, the only parameter that we aren’t already attempting to estimate somehow is \(t_s\), scan time. The \(I_0\) is taken into account with the soak and first transfer datapoints, \(D_{ap}\) we are already estimating, and the \(D_m\) estimate should scale with it anyway, so I don’t think that matters. The estimate (aka “a”) is coming from the fit anyway, so that only leaves \(t_s\). Can we think of an informed way to ballpark a scan time?
For the SWV fast parameter set we use: * Init E (V) = 0.1 * Final E (V) = -0.5 * Incr E (V) = 0.001 * Amplitude (V) = 0.025 * Frequency (Hz) = 300
Therefore the scan window is 0.6V or 600mV, with a 1mV increment will require 600 cycles. 1 cycle takes 1/frequency or 1/300 seconds. 1/300 seconds * 600 cycles = 2 seconds for the total scan time. If the electrode is actually only driving a reaction from -0.2V to -0.4V (200mV) that would be 1/3 * 2 seconds = 0.666 seconds. However, the peak is reached in half that amount of time ( 0.333 seconds). That said, each cycle is partially renewing the diffusion layer for each redox species because the amplitude is 25mV.
Only, starting 12.5mV before PYO redox potential there’s net reduction occuring? 12 cycles would be 12 / 300 = 0.04 seconds (but that includes up and down steps), but even after those steps, most of the PYO is re-oxidized by the downward step. So for the one step up at the peak the scan time would be the pulse time \(t_p\), which is 1/2 * 1/300 = 0.001666, if we take that time over 12 cyles we get ~0.02…which empirically provides the expected results, not sure how to proceed.
All this is to say, that I do not know how to theoretically get at the scan time, and as far as I know now anything between 0.3 and 0.001 seconds could probably be justified…See below for how the blank data look when we use an empirically “good” value of \(t_s = 0.008\):
blank_estimates_og <- blank_estimates %>% filter(model == "og") %>%
mutate(dm_soak = dm_from_a_OG(estimate = estimate, i_0 = i0_soak,
dap = 1e-05, t_s = 0.008)) %>% mutate(dm_transfer = dm_from_a_OG(estimate = estimate,
i_0 = i0_transfer, dap = 1e-05, t_s = 0.008))
blank_estimates_noFlux <- blank_estimates %>% filter(model ==
"no_flux") %>% mutate(dm_soak = dm_from_a_noFlux(estimate = estimate,
i_0 = i0_soak, dap = 1e-05, t_s = 0.008)) %>% mutate(dm_transfer = dm_from_a_noFlux(estimate = estimate,
i_0 = i0_transfer, dap = 1e-05, t_s = 0.008))
blank_estimates_tidy <- rbind(blank_estimates_og, blank_estimates_noFlux)
ggplot(blank_estimates_tidy, aes(x = model, shape = PHZadded)) +
geom_point(aes(y = dm_soak), color = "red") + geom_point(aes(y = dm_transfer),
color = "blue") + geom_hline(yintercept = 1e-05, linetype = 2) +
facet_wrap(~model, scales = "free") + scale_y_log10()
Now both models have the expected behavior. The no flux/finite model estimates straddle the given \(D_{ap}\). The soak \(I_0\) estimates in this case are the true \(I_0\) values, while the transfer \(I_0\) estimates are underestimates. Further, the og model now significantly underestimates \(D_m\) as would be expected from the poor fit of the data.
I do not know if we can justify the use of an empirical value for \(t_s\) by saying that we expect at a blank IDA with PYO in solution we assume \(D_m \approx D_{ap}\) with some citations? If we can, then let’s see what the biofilm data would look like with this parameter set.
dm_estimates_og <- dm_estimates %>% filter(model == "og") %>%
mutate(dm_soak = dm_from_a_OG(estimate = estimate, i_0 = i0_soak,
dap = 1e-06, t_s = 0.008)) %>% mutate(dm_transfer = dm_from_a_OG(estimate = estimate,
i_0 = i0_transfer, dap = 1e-06, t_s = 0.008))
dm_estimates_noFlux <- dm_estimates %>% filter(model == "no_flux") %>%
mutate(dm_soak = dm_from_a_noFlux(estimate = estimate, i_0 = i0_soak,
dap = 1e-06, t_s = 0.008)) %>% mutate(dm_transfer = dm_from_a_noFlux(estimate = estimate,
i_0 = i0_transfer, dap = 1e-06, t_s = 0.008))
dm_estimates_tidy <- rbind(dm_estimates_og, dm_estimates_noFlux)
ggplot(dm_estimates_tidy, aes(x = model, shape = electrode)) +
geom_point(aes(y = dm_soak), color = "red") + geom_point(aes(y = dm_transfer),
color = "blue") + geom_hline(yintercept = 1e-06, linetype = 2) +
facet_wrap(~run) + scale_y_log10(limits = c(1e-09, 1e-06))
By using this 0.008 value for \(t_s\) you can see that the estimates for the biofilm \(D_m\) shift downward. If this process is defensible then we may have a case that \(D_m\) could be slower than \(D_{ap}\) in the biofilm. Also note that run 5 had the better fit at the beginning, so I would trust that estimate slightly more than run 4.
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An Automated Matchmaking Platform for Job Hunting & Hiring process
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Implemented Machine Learning Algorithms for better Recommendation
Created Ontology for domain knowledge representation
Developed various modules to enhance the performance & the quality


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 Joseph Goebbel’s Reichs Ministry of Public Enlightenment and Propaganda confiscated thousands of artworks between 1937 and 1938.
650 of them were shown in the exhibition “Degenerate Art”. Several works were sold abroad, some possibly destroyed. It was only in 1997 that a list appeared in London. On it Goebbel's Ministry had labeled about 16,000 of the artworks and what had happened to them. This list has been publicly availably since 2014 but so far couldn’t be searched.
With the help of church historian Hans Prolingheuer the taz now provides a search function for the list.
Download Displayed Pages PDF, View the Entire Inventory PDF, 53 MB


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New Applications of Atmospheric carbon reduction: 2020 Virtual Jumpstart
A Jumpstart to Expand Capacity to Address Important Questions and Identify Outcomes for Reducing Atmospheric Carbon
Join a 3-day workshop to develop novel cross-disciplinary collaborations!
Applications are due August 31st 2020 (5pm Eastern Time). Apply Today!
Overview
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Date and Location
The event will be held online from 12:00- 3:00 PM EST, and will take place on September 28th, October 1st and October 6th
Applicants must commit to participating for the entire duration of the Jumpstart if selected as a meeting participant.
There will also be some required prework and light background reading/viewing.
The Challenge
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Additional paragraph if you want/need it
Who Should Apply
We encourage a wide representation of participation spanning research, government, industry and public policy sectors. All levels of seniority with diverse expertise across the continuum of carbon capture research and complementary fields including, but not limited to: engineering, systems biology, chemistry, materials science, physics, computational sciences, data science, mathematics, and geology to consider how your experience could help shape and transform the future of carbon sustainability research technologies.
Irrespective of expertise, we are most interested in new, innovative ideas and original thinking that arise from new collaborations between people of diverse backgrounds and varying expertise.
Approximately 100 applicants will be selected to participate in the Jumpstart on the basis of the interests, expertise, and other characteristics solicited in the application. Most participants are expected to be academic faculty, from early career to senior investigators. Original thinkers from outside academia (e.g., industry, public sector) or earlier career stages are also encouraged to apply. When selecting participants, consideration will be given to balance across a range of diverse disciplinary experience and expertise. All participants should be willing to engage in frank disclosure and assessment of ideas in a collegial and professional fashion. To facilitate open sharing, all meeting discussions will be considered a private communication and not to be shared outside of the meeting, unless approved by the contributor. All research-related application information will be kept confidential.
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Application Deadline: August 31st 2020 (5pm Eastern Time)
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In a speech to the National College annual conference in Birmingham today (15 June 2012), Sir Michael Wilshaw, HMCI, launched a new review of access and achievement in education.
Addressing the audience of headteachers and school leaders Sir Michael said:
Twenty years ago, Ofsted produced a landmark report entitled Access and Achievement in Urban Education, which described the lack of educational success and the paucity of good-quality provision in deprived communities. Ten years later, David Bell, the then Chief Inspector of Schools, marked the anniversary by producing another report under the same headline. What was so depressing was that his report painted a similarly bleak picture of underperformance in these same communities.
I am asking the educational leaders of this country to take ownership of the situation and show the leadership needed to change the learning landscape. Everyone who agrees that all children deserve a good education needs to work in partnership to introduce the radical solutions needed today to make a real difference for the children of tomorrow.
The original report, published in 1993, gave recommendations for closing the gap on the educational achievements of children from rich and poor backgrounds in the English education system. Based on evidence collected from visits to schools, youth and adult education providers in Bristol, Derby, Kingston-Upon-Hull, Manchester, Slough, Thamesmead and Thurrock the report painted a bleak picture of the quality of education received by the majority of children and young people at the time.
In 2003, David Bell, the then HMCI, published Access and Achievement in Urban Education: 10 years on, which collected data from 1,000 schools serving areas of urban deprivation and having more than 35% of their pupils on free school meals. The 2003 report identified a lack of progress in most of the schools covered by the 1993 study and pointed to some common features such as high pupil turnover and difficulties in teacher recruitment preventing schools from improving sufficiently.
The programme of work to inform Access and Achievement in Urban Education: 20 years on, will include a review panel of leading heads and academic experts who will seek to answer 5 key questions:
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Why are some children and young people more affected by socio-economic and educational disadvantage than others?
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What more can be done by parents and education providers to ensure the best possible start for those that need it most?
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Some schools are doing a great job for pupils from disadvantaged backgrounds. So what can we learn from them and how can we ensure that many more schools do as well as the best?
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Schools and colleges have greater autonomy now over their policies and use of resources. So how do we secure effective coordination and improvement of local education services in areas of the greatest educational need?
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What more needs to happen to ensure that young people from disadvantaged backgrounds achieve success in employment and as citizens?
The expert panel will include representation from schools, local authorities, academia and the third sector.
Notes to editors
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The report will be published in May 2013.
-
The Office for Standards in Education, Children’s Services and Skills (Ofsted) regulates and inspects to achieve excellence in the care of children and young people, and in education and skills for learners of all ages. It regulates and inspects childcare and children’s social care, and inspects the Children and Family Court Advisory Support Service (Cafcass), schools, colleges, initial teacher training, work-based learning and skills training, adult and community learning, and education and training in prisons and other secure establishments. It assesses council children’s services, and inspects services for looked after children, safeguarding and child protection.


Monthly Archives: September 2016
NAAAP Seattle Scholarshipshttp://naaapseattle.org/scholarship/ Hurry up and get your applications in. This $1,000 scholarship is awarded to one undergraduate, graduate, AND high school applicant. Application is due September 30th, 2016!
SE Asian Summit: Oct. 29th, 2016Save the date for the SEAed Coalition SE Asian Summit coming up. Fellowship members will be introducing their projects they've been working on in the community and you'll be hearing from a ton of SEAA community members. It's a free event for the community, so please join!
That's a wrap, Summer!
As everyone goes back to school, we've still been hard at work planning our Hmong New Year festival coming up in November 2016. Here's a brief overview on what we've been up to the last few months.
HAW Inaugural Picnic
On July 30th, our community got together for a gathering up north in Washington. It's great to see the community come together to enjoy some great food, fun games, and each other's company. We even recognized our veterans that fought during the Secret War.
SYC + HAW Car WashSpiral Youth Club and Hmong Association of Washington came together to collaborate on a Car Wash near Renton, WA. We raised a little over $500 with just a few hands on deck. Good job, everyone!
Even Senator Bob Hasegawa showed up to our car wash! L to R: Jimmy, Zer, Senator Hasegawa, Kabao. Photo from: Zer Vue


Famous for its birdlife, Strangford Lough is the U.K.’s largest sea inlet, being about 17 miles long and 4 miles wide. It provides an all weather, all tide shelter on a stretch of the west coast which has no other. It has numerous bays, inlets and islands, and is an important and protected wildlife site. At the northern end of the 5 mile long entrance narrows, lies the town of Strangford, and opposite, on the eastern shore, the village of Portaferry. It is a very popular sailing area and the many clubs in the area provide visting yachtsmen with a warm welcome.
If unfamiliar with the area, use the eastern channel through the narrows and beware of the strong tides and the car ferry shuttling between Stangford and Portaferry.
In April 2008 the new tidal turbines within the narrows at the entrance to Strangford Lough, Northern Ireland, were installed. Turbine blades are not a major navigational hazard fort yachts as they are set at a considerable depth. The only part of the structure that protrudes above the water is a large concrete post. The turbine is identified by the colours and top-mark of an "isolated danger mark".
Pilotage:
When there is wind against tide there are overfalls at the SE approach to the Lough and at the bar.
At the narrows of the lough, by the turbine, the flood starts at HW - 3 1/2 hours Belfast and the ebb starts at HW + 2 1/2 . At Springs the tidal stream can reach 7 knots.
Moorings:
There are a few visitors' moorings at Strangford. The Quoile yacht club has a visitors pontoon as well as moorings, as do several of the yacht and sailing clubs around the lough.
There are plenty of anchorages and it will be possible to find shelter whatever the weather. Castleward Bay, Grey Point and between Jackdaw and Chapel Islands are some useful ones.
Last updated January 2010.


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OneGoal is a philanthropic organization founded at the University of Virginia that works to promote healthy, athletic behavior by collecting used sporting equipment and raising money to send that gear to impoverished areas both nationally and internationally. In the past we've worked largely with villages in South Africa but we are expanding our outreach to work in other areas such as Haiti, Nicaragua, Brazil, Argentina, and South Dakota.


The unified OpenStack command-line client project, python-openstackclient
(OSC), has been around for over a decade. As noted in the
README, “[t]he primary goal is to provide a unified shell command
structure and a common language to describe operations in OpenStack.” In
essence, this means OSC should replace the shell command implementations of the
various project-specific clients, such as python-novaclient
and
python-glanceclient
. It does not aim to replace the client library
implementations (Python binding) of these libraries: that task is handled by
another project, the unified OpenStack client library, openstacksdk
(SDK).
For most high-level “simpler” OpenStack-related activities, OSC has long been
“good enough” as it provided the ability to do CRUD operations on the essential
resources in a cloud including instances, volumes and images. However, as soon
as you started trying to use more advanced or less common features, you’d run
into a swathe of feature gaps. These would generally mean having to revert to
using the legacy project-specific clients like python-novaclient
(e.g. nova foo
) or python-glanceclient
(glance foo
). There were exceptions to this,
most notably neutronclient, but ultimately the
result of this partial switchover was a poor user experience that was more
confusing than it should have been.
Over the course of the past number of cycles (starting with Train), there has been a concerted effort to close these gaps and achieve feature parity between the shells of the various legacy clients and OSC. As of Yoga, we have effectively achieved this for nova and are well on our way to doing so for cinder.
I’ve put together a little FAQ below that should be helpful in helping people understand what has gone into this effort and how you can identify features gaps to to be closed.
How do we determine where these gaps lie?
Unfortunately, pretty manually. OSC only aims to implement support for the six “core” services: nova, neutron, keystone, cinder, glance, and swift. We provide CSV files mapping commands found in the legacy clients with their OSC equivalents. The first step is looking for obvious gaps in this document, by which I mean missing entries for an OSC equivalent to a given legacy client command.
The next step is looking for command that should exist in those documents but
don’t. This is pretty easy: just run e.g. nova help
, capture the output, and
reformat it so that it resembles the CSV file (this would actually be one of
the few good places where scripting could help!). It’s worth noting that many
of the legacy clients change their help output depending on the API version
requested so you should run this a couple of times with different API versions.
For example:
$ OS_COMPUTE_API_VERSION=2.88 nova help
The final and most involved step required is examining individual commands.
There are two things we’re concerned with: missing options and missing
microversion support. Assessing the first can be achieved similarly to
identifying missing commands: just run e.g. nova help boot
, capture the
output and compare the options supported with those supported by the
equivalent OSC command (openstack server create
in this case). The second
is a little trickier and usually involves manual inspection of the
corresponding shell and client library code in the legacy code. You could also
look at the API reference documentation for the particular service but projects
like Cinder have large documentation gaps that means the client is a better
source of truth.
How do I fix them?
Just push a patch. It probably makes sense to push a highly WIP patch first
since OSC has some design conventions, noted in the contributor
guide, which can take some time to get a feel for. There
are also plenty of existing examples to look for: search for the topics
cinder-gaps
and nova-gaps
to get an idea.
What about non-“core” projects?
OSC is built on top of osc-lib
, which is in turn built on top of
cliff
. cliff
is highly extensible and provides a number of hook
points that allow users to build plugins which can extend an existing CLI tool.
Projects like manila
, ironic
and
cyborg
all provide their own OSC plugins. This is also the
mechanism that neutron uses to provide more advanced commands.
Many of these projects never had a legacy client to speak of, so there are
fewer (if any) gaps to worry about.
What about OpenStackSDK?
We’re working on that too and the work has been mostly going on in parallel. There has been debate on whether newly added commands should use SDK or the legacy client under the hood. I tend to favour using the latter first and switching to SDK later since it makes it easier to compare the OSC and legacy client shell implementations during development and review. In addition, an OSC command can sometimes be the first real-world user of the SDK implementation which can highlight bugs. This is particularly an issue for the quirkier, less utilized APIs. It would be a shame to get stuck implementing an OSC command due to an SDK bug. The sole exception to this is where library clients have removed support for an API, such as Cinder dropping support for API v2 in cinderclient 9.0.0. In this case, we’ve no choice but to use SDK.
Where can I get more info? / How can I get involved?
Just ask! You can find us on IRC at #openstack-sdks (OFTC) or via email at openstack-discuss@openstack.org.


| HBASE-2428 |
NPE in ProcessRegionClose because meta is offline kills master and thus the cluster |
Blocker |
. |
stack |
stack |
| HBASE-2482 |
regions in transition do not get reassigned by master when RS crashes |
Blocker |
master |
Todd Lipcon |
Todd Lipcon |
| HBASE-2503 |
PriorityQueue isn't thread safe, KeyValueHeap uses it that way |
Critical |
. |
Jean-Daniel Cryans |
Jean-Daniel Cryans |
| HBASE-2545 |
Unresponsive region server, potential deadlock |
Blocker |
regionserver |
Kris Jirapinyo |
Todd Lipcon |
| HBASE-2530 |
HBASE-2165 removed compactionQueueSize metric |
Major |
. |
Kannan Muthukkaruppan |
Ruifang Ge |
| HBASE-2606 |
NativeException: org.apache.hadoop.hbase.client.NoServerForRegionException: No server address listed in .META. for region item,,1274235159640 |
Major |
. |
Sam Pullara |
|
| HBASE-2610 |
ValueFilter copy pasted javadoc from QualifierFilter |
Major |
. |
Jean-Daniel Cryans |
Jean-Daniel Cryans |
| HBASE-2617 |
Load balancer falls into pathological state if one server under average - slop; endless churn |
Blocker |
. |
stack |
stack |
| HBASE-2635 |
ImmutableBytesWritable ignores offset in several cases |
Blocker |
. |
Todd Lipcon |
Todd Lipcon |
| HBASE-2615 |
M/R on bulk imported tables |
Major |
. |
Azza Abouzeid |
stack |
| HBASE-2712 |
Cached region location that went stale won't recover if asking for first row |
Blocker |
. |
Jean-Daniel Cryans |
Jean-Daniel Cryans |
| HBASE-2740 |
NPE in ReadWriteConsistencyControl |
Blocker |
. |
Dave Latham |
ryan rawson |
| HBASE-2728 |
Support for HADOOP-4829 |
Major |
. |
Jean-Daniel Cryans |
Jean-Daniel Cryans |
| HBASE-2683 |
Make it obvious in the documentation that ZooKeeper needs permanent storage |
Major |
. |
Jean-Daniel Cryans |
Jean-Daniel Cryans |
| HBASE-2616 |
TestHRegion.testWritesWhileGetting flaky on trunk |
Critical |
regionserver |
Todd Lipcon |
ryan rawson |
| HBASE-2455 |
"Cached an already cached block" RTE in RS after split |
Major |
. |
Todd Lipcon |
ryan rawson |


Tyspeed helps you improve your typing speed and accurracy. Type the words that appear on your screen as fast as possible before your time elaps
Tyspeed helps you improve your typing speed and accurracy. Type the words that appear on your screen as fast as possible before your time elaps


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boost::signals2::signal — Safe multicast callback.
// In header: <boost/signals2/signal.hpp> template<typename Signature, // Function type R (T1, T2, ..., TN) typename Combiner = boost::signals2::optional_last_value<R>, typename Group = int, typename GroupCompare = std::less<Group>, typename SlotFunction = boost::function<Signature>, typename ExtendedSlotFunction = boost::function<R (const connection &, T1, T2, ..., TN)>, typename Mutex = boost::signals2::mutex> class signal : public boost::signals2::signal_base { public: // types typedef Signature signature_type; typedef typename Combiner::result_type result_type; typedef Combiner combiner_type; typedef Group group_type; typedef GroupCompare group_compare_type; typedef SlotFunction slot_function_type; typedef typename signals2::slot<Signature, SlotFunction> slot_type; typedef ExtendedSlotFunction extended_slot_function_type; typedef typename signals2::slot<R (const connection &, T1, ..., TN), ExtendedSlotFunction> extended_slot_type; typedef typename SlotFunction::result_type slot_result_type; typedef unspecified slot_call_iterator; typedef T1 argument_type; // Exists iff iff arity == 1 typedef T1 first_argument_type; // Exists iff iff arity == 2 typedef T2 second_argument_type; // Exists iff iff arity == 2 // static constants static const int arity = N; // The number of arguments taken by the signal. // member classes/structs/unions template<unsigned n> class arg { public: // types typedef Tn type; // The type of the signal's (n+1)th argument }; // construct/copy/destruct signal(const combiner_type& = combiner_type(), const group_compare_type& = group_compare_type()); ~signal(); // connection management connection connect(const slot_type&, connect_position = at_back); connection connect(const group_type&, const slot_type&, connect_position = at_back); connection connect_extended(const extended_slot_type&, connect_position = at_back); connection connect_extended(const group_type&, const extended_slot_type&, connect_position = at_back); void disconnect(const group_type&); template<typename S> void disconnect(const S&); void disconnect_all_slots(); bool empty() const; std::size_t num_slots() const; // invocation result_type operator()(arg<0>::type, arg<1>::type, ..., arg<N-1>::type); result_type operator()(arg<0>::type, arg<1>::type, ..., arg<N-1>::type) const; // combiner access combiner_type combiner() const; void set_combiner(const combiner_type&); };
See the tutorial for more information on how to use the signal class.
signal
public
typestypedef typename signals2::slot<R (const connection &, T1, ..., TN), ExtendedSlotFunction> extended_slot_type;
Slots of the extended_slot_type
may be connected to the signal using the
connect_extended methods. The extended_slot_type
has an additional signals2::connection argument in its signature,
which gives slot functions access to their connection to the signal
invoking them.
typedef typename SlotFunction::result_type slot_result_type;
This is the type returned when dereferencing the input iterators passed to the signal's combiner.
typedef unspecified slot_call_iterator;
The input iterator type passed to the combiner when the signal is invoked.
signal
public
construct/copy/destructsignal(const combiner_type& combiner = combiner_type(), const group_compare_type& compare = group_compare_type());
Effects: |
Initializes the signal to contain no slots, copies the given combiner into internal storage, and stores the given group comparison function object to compare groups. |
Postconditions: |
|
~signal();
Effects: |
Disconnects all slots connected to |
signal
connection managementconnection connect(const slot_type& slot, connect_position at = at_back); connection connect(const group_type& group, const slot_type& slot, connect_position at = at_back);
Effects: |
Connects the signal this to the incoming
slot. If the slot is inactive, i.e., any of the slots's tracked
objects have been destroyed, then the
call to connect is a no-op. If the second version of
|
Returns: |
A
|
Throws: |
This routine meets the strong exception guarantee, where any exception thrown will cause the slot to not be connected to the signal. |
Complexity: |
Constant time when connecting a slot without a group name or logarithmic in the number of groups when connecting to a particular group. |
Notes: |
It is unspecified whether connecting a slot while the signal is calling will result in the slot being called immediately. |
connection connect_extended(const extended_slot_type& slot, connect_position at = at_back); connection connect_extended(const group_type& group, const extended_slot_type& slot, connect_position at = at_back);
The connect_extended
methods work the same as the connect
methods, except they take slots of type extended_slot_type
.
This is useful if a slot needs to access the connection between it and the
signal invoking it, for example if it wishes to disconnect or block its own connection.
void disconnect(const group_type& group); template<typename S> void disconnect(const S& slot_func);
Effects: |
If the parameter is (convertible to) a group name, any slots in the given group are disconnected. Otherwise, any slots equal to the given slot function are disconnected. Note, the |
Throws: |
Will not throw unless a user destructor or
equality operator |
Complexity: |
If a group is given, O(lg g) + k where g is the number of groups in the signal and k is the number of slots in the group. Otherwise, linear in the number of slots connected to the signal. |
void disconnect_all_slots();
Effects: |
Disconnects all slots connected to the signal. |
Postconditions: |
|
Throws: |
If disconnecting a slot causes an exception to be thrown, not all slots may be disconnected. |
Complexity: |
Linear in the number of slots known to the signal. |
Notes: |
May be called at any time within the lifetime of the signal, including during calls to the signal's slots. |
bool empty() const;
Returns: |
|
Throws: |
Will not throw. |
Complexity: |
Linear in the number of slots known to the signal. |
Rationale: |
Slots can disconnect at any point in time, including while those same slots are being invoked. It is therefore possible that the implementation must search through a list of disconnected slots to determine if any slots are still connected. |
std::size_t num_slots() const;
Returns: |
The number of slots connected to the signal |
Throws: |
Will not throw. |
Complexity: |
Linear in the number of slots known to the signal. |
Rationale: |
Slots can disconnect at any point in time, including while those same slots are being invoked. It is therefore possible that the implementation must search through a list of disconnected slots to determine how many slots are still connected. |
signal
invocationresult_type operator()(arg<0>::type a1, arg<1>::type a2, ..., arg<N-1>::type aN); result_type operator()(arg<0>::type a1, arg<1>::type a2, ..., arg<N-1>::type aN) const;
Effects: |
Invokes the combiner with a
|
Returns: |
The result returned by the combiner. |
Throws: |
If an exception is thrown by a slot call, or if the combiner does not dereference any slot past some given slot, all slots after that slot in the internal list of connected slots will not be invoked. |
Notes: |
Only the slots associated with iterators that are actually dereferenced will be invoked. Multiple dereferences of the same iterator will not result in multiple slot invocations, because the return value of the slot will be cached. The |


This is a built-in middleware function in Express. It parses incoming requests with urlencoded payloads and is based on body-parser.
Returns middleware that only parses urlencoded bodies and only looks at
requests where the Content-Type
header matches the
type
option. This parser accepts only UTF-8 encoding of the
body and supports automatic inflation of gzip
and
deflate
encodings.
A new body
object containing the parsed data is populated on
the request
object after the middleware
(i.e. req.body
), or an empty object ({}
) if
there was no body to parse, the Content-Type
was not matched,
or an error occurred. This object will contain key-value pairs, where the
value can be a string or array (when extended
is
false
), or any type (when extended
is
true
).
As req.body
’s shape is based on user-controlled input, all
properties and values in this object are untrusted and should be
validated before trusting. For example,
req.body.foo.toString()
may fail in multiple ways, for
example foo
may not be there or may not be a string, and
toString
may not be a function and instead a string or
other user-input.
The following table describes the properties of the optional
options
object.
| Property | Description | Type | Default |
|---|---|---|---|
extended |
This option allows to choose between parsing the URL-encoded data
with the querystring library (when false )
or the qs library (when true ). The
“extended” syntax allows for rich objects and arrays to be encoded
into the URL-encoded format, allowing for a JSON-like experience
with URL-encoded. For more information, please
see the qs library.
|
Boolean | true |
inflate |
Enables or disables handling deflated (compressed) bodies; when disabled, deflated bodies are rejected. | Boolean | true |
limit |
Controls the maximum request body size. If this is a number, then the value specifies the number of bytes; if it is a string, the value is passed to the bytes library for parsing. | Mixed | "100kb" |
parameterLimit |
This option controls the maximum number of parameters that are allowed in the URL-encoded data. If a request contains more parameters than this value, an error will be raised. | Number | 1000 |
type |
This is used to determine what media type the middleware will parse.
This option can be a string, array of strings, or a function. If not
a function, type option is passed directly to the
type-is
library and this can be an extension name (like
urlencoded ), a mime type (like
application/x-www-form-urlencoded ), or a mime type with
a wildcard (like */x-www-form-urlencoded ). If a
function, the type option is called as
fn(req) and the request is parsed if it returns a
truthy value.
|
Mixed | "application/x-www-form-urlencoded" |
verify |
This option, if supplied, is called as
verify(req, res, buf, encoding) , where
buf is a Buffer of the raw request body
and encoding is the encoding of the request. The
parsing can be aborted by throwing an error.
|
Function | undefined |


Update workflow
The lazy way
If you only need to add some post/meeting/workshop to the website, here is a simple way to deploy your new content to the website without much hassle.
- Create your content—The first step is to create your specific content by producing the necessary Markdown files. See Creating content for details on how to do so.
- Push your content—You then need to add your content to the website
content
directory. If you have access to the UMichStatistics repository, then you can do it directly using your favorite ‘git’ workflow. Otherwise, contact the maintainers for them to add your content to the site. - Deploy your content—As mentionned, an update of the files outside the
docs
folder will not affect the public website. The deployment must be done through Hugo and, for now, this must be done manually. Contact the maintainers for it to happen.
The good way
Hopefully, you will want to deploy your changes yourself. To do this, here is the summary of the procedure:
- Have a local copy of the website on your machine.
- Update your local version.
- Deploy your local version to your localhost using Hugo in order to update the
docs
contents. - Push your changes, including both the source and the updated
docs
to the site’s repository.
With the workflow, your changes will instantaneously update the public website. Also, since you first deploy on your local machine, you are able to see the results of your changes before them becoming public. Finally, pushing both the source and the updated docs
prevents conflicts in future updates.
Requirements
To use this process, you will need:
- Access to the organization repository;
- git installed on your machine (see this guide);
- Basic git knowledge (
clone
,pull
,add
,commit
andpush
). Note that most modern IDEs offer git support so you may execute all those steps using your interface; - Hugo installed on your machine (see this guide. The installation on Windows is tedious and the only way I could make it work was
R
> install packageblogdown
>blogdown::install_hugo()
and manually add its path to the system environment variables.)
Update the local version
First, you will need the latest version of the website stored locally on your machine.
If you have not cloned the repository yet, do so:
git clone https://github.com/UMichStatistics/ComputingClub.git <new-folder-name>
The website relies on the Academic theme for Hugo, so you will to clone that as well. Fortunately, it is included as a submodule of the repository, so you only need to update all submodules (there is only one):
git submodule update [--init] [--recursive]
If you already have cloned the repository, make sure your local version is up to date with the current online version to avoid merge conflicts when pushing. In a command prompt, move to the site local folder and pull
the website (or fetch
and merge
).
Create your content
Create your specific content by producing the necessary Markdown files and place them in the correct folders. See Creating content for details on how to do so.
Deploy your local website
Now that your local repository contains updated content, it is time to deploy it to your local docs
folder. To do so, open a command prompt and run hugo at the root of the site’s local repository and type in:
hugo
which will compile the content into the static html
pages. Unless your code does not compile, you should get an output such as (possibly containing warnings)
Building sites … WARN 2019/09/24 17:08:23 In the next Hugo version (0.58.0) we will change how $home.Pages behaves. If you want to list all regular pages, replace .Pages or .Data.Pages with .Site.RegularPages in your home page template.
| EN
+------------------|----+
Pages | 52
Paginator pages | 0
Non-page files | 6
Static files | 8
Processed images | 9
Aliases | 7
Sitemaps | 1
Cleaned | 0
Total in 265 ms
This means that Hugo has successfuly deployed your site to the docs
folder. If you wish to view the website, you can host it locally using Hugo:
hugo server
which should yield something like
Watching for changes in /home/simon/git/ComputingClub/{content,data,static,themes}
Watching for config changes in /home/simon/git/ComputingClub/config.toml, /home/simon/git/ComputingClub/config/_default
Environment: "development"
Serving pages from memory
Running in Fast Render Mode. For full rebuilds on change: hugo server --disableFastRender
Web Server is available at http://localhost:1313/ComputingClub/ (bind address <IP_ADDRESS>)
Press Ctrl+C to stop
The url http://localhost:1313/ComputingClub/
is where you find your local website (use the one in your output as it may differ from mine). Also, as long a you do not close the server (by closing the command prompt or by typing Ctrl+C
), Hugo will listen to any changes, meaning that any file saved in the root repository will trigger deployment. This is useful if you want to create your content and see it deployed instantaneously locally without calling Hugo each time. Note that creating new pages or making large changes may not appear in this “Fast Render Mode”, so you may need to exit and re-host.
Push your changes
Once your are satisfied with your local version, you need to push your changes to the master
branch. In the case your Hugo server is still on, close it and run hugo
one last time to redo the docs
file for the online version, not the local version.


It is possible to both store and retrieve parts of data items in all Berkeley DB access methods. This is done by setting the DB_DBT_PARTIAL flag DBT structure passed to the Berkeley DB method.
The DB_DBT_PARTIAL flag is based on the values of two fields of the DBT structure: dlen and doff. The value of dlen is the number of bytes of the record in which the application is interested. The value of doff is the offset from the beginning of the data item where those bytes start.
For example, if the data item were ABCDEFGHIJKL, a doff value of 3 would indicate that the bytes of interest started at D, and a dlen value of 4 would indicate that the bytes of interest were DEFG.
When retrieving a data item from a database, the dlen bytes starting doff bytes from the beginning of the record are returned, as if they comprised the entire record. If any or all of the specified bytes do not exist in the record, the retrieval is still successful and any existing bytes are returned.
When storing a data item into the database, the dlen bytes starting doff bytes from the beginning of the specified key's data record are replaced by the data specified by the data and size fields. If dlen is smaller than size, the record will grow, and if dlen is larger than size, the record will shrink. If the specified bytes do not exist, the record will be extended using nul bytes as necessary, and the store call will still succeed.
The following are various examples of the put case for the DB_DBT_PARTIAL flag. In all examples, the initial data item is 20 bytes in length: ABCDEFGHIJ0123456789
size = 20 doff = 0 dlen = 20 data = abcdefghijabcdefghij Result: The 20 bytes at offset 0 are replaced by the 20 bytes of data; that is, the entire record is replaced. ABCDEFGHIJ0123456789 -> abcdefghijabcdefghij
size = 10 doff = 20 dlen = 0 data = abcdefghij Result: The 0 bytes at offset 20 are replaced by the 10 bytes of data; that is, the record is extended by 10 bytes. ABCDEFGHIJ0123456789 -> ABCDEFGHIJ0123456789abcdefghij
size = 10 doff = 10 dlen = 5 data = abcdefghij Result: The 5 bytes at offset 10 are replaced by the 10 bytes of data. ABCDEFGHIJ0123456789 -> ABCDEFGHIJabcdefghij56789
size = 10 doff = 10 dlen = 0 data = abcdefghij Result: The 0 bytes at offset 10 are replaced by the 10 bytes of data; that is, 10 bytes are inserted into the record. ABCDEFGHIJ0123456789 -> ABCDEFGHIJabcdefghij0123456789
size = 10 doff = 2 dlen = 15 data = abcdefghij Result: The 15 bytes at offset 2 are replaced by the 10 bytes of data. ABCDEFGHIJ0123456789 -> ABabcdefghij789
size = 10 doff = 0 dlen = 0 data = abcdefghij Result: The 0 bytes at offset 0 are replaced by the 10 bytes of data; that is, the 10 bytes are inserted at the beginning of the record. ABCDEFGHIJ0123456789 -> abcdefghijABCDEFGHIJ0123456789
size = 0 doff = 0 dlen = 10 data = "" Result: The 10 bytes at offset 0 are replaced by the 0 bytes of data; that is, the first 10 bytes of the record are discarded. ABCDEFGHIJ0123456789 -><PHONE_NUMBER>
size = 10 doff = 25 dlen = 0 data = abcdefghij Result: The 0 bytes at offset 25 are replaced by the 10 bytes of data; that is, 10 bytes are inserted into the record past the end of the current data (\0 represents a nul byte). ABCDEFGHIJ0123456789 -> ABCDEFGHIJ0123456789\0\0\0\0\0abcdefghij


Public Member Functions | |
| FPSBoxController (GameObject *, std::string, double, const btVector3 &, btScalar, int, int) | |
| virtual void | update () |
Public Attributes | |
| FPSCamera * | fps_camera |
| OiJE::CharacterController * | controller |
| bool | can_move |
| double | movement_speed_multiplier |
| float | slowDown |
| float | speedUp |
| btVector3 | currVel |
| void(* | onCollision )(btVector3, btVector3, GameObject *) |
Protected Member Functions | |
| void | testCollision () |
| void | updateTransform () |
| void | detectInput () |
Protected Attributes | |
| double | base_movement_speed |
| Transform * | _transform |
| btPairCachingGhostObject * | _ghostObject |
| btConvexShape * | _collisionShape |
| btDiscreteDynamicsWorld * | dynamics_world |
Additional Inherited Members | |
| Private Member Functions inherited from Component | |
| Component (GameObject *) | |
| Private Attributes inherited from Component | |
| int | id |
| bool | active |
| GameObject * | _gameObject |


I recently attempted to make smokeable opium from dried poppy pods I bought cheaply in the village I live. I used about seven perhaps old heads; broke them down and ground them to a semi-fine powder (coffee grinder). I boiled two cups of water and added some lemon juice. After taking the pot from the stove I added the powder and let it steep for 20 minutes while sometimes stirring. I used a T-shirt as a filter as I emptied the 'opium' tea into a bowl. I then added the semi-spent powder into another pot of boiling water, this time only one cup. After filtering, I combined the teas in a pot and let it simmer for about two hours (I've read boiling destroys the goods). What remained was a brown goo that looked amber-yellow when spread out. I scraped most of it onto a small glass plate which I allowed to further dry in the hot sun for a few hours the next day. I then scraped it into a big wad of brown gum which was very bitter.
When I tried smoking it I put some on top of a small bowl of quality grass. It didn't burn well and when it did it wasn't good tasting and somewhat harsh. I took around twelve medium sized tokes and felt a more mellow largely cannabis influenced high. The added 'mellowness' didn't last long so I later ate a couple small lumps hoping for added effect. I may have felt something an hour or two later but they may have been perhaps placebo effects. I also made a cup of tea with a nice lump of tar/gum but only got lite effects.
I wonder did I do something wrong to make it less active or was it because they were perhaps old poppies? Maybe they were the wrong kind but I'm pretty sure after looking through pictures of Papaver somniferum.
Experience Reports are the writings and opinions of the individual authors who submit them.
Some of the activities described are dangerous and/or illegal and none are recommended by Erowid.


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Model documents for PFI schools converting to academy status.
MS Word Document , 79.6KB
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MS Word Document , 92KB
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MS Word Document , 249KB
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MS Word Document , 132KB
MS Word Document , 123KB
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MS Word Document , 163KB
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MS Word Document , 196KB
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Schools that form part of a closed private finance initiative ( PFI ) contract and who want to convert to an academy should use the documents on this page.
Use these documents to create your academy’s:
Read more guidance on the process for becoming an academy .
Published: 5 December 2012
Updated: 30 September 2014


Revised WebApp Workflow
Workflow for a sign-in, sign-up, and sign-out page.
- Create Migration
- Create Corresponding Model
- Create Helper Methods For Authentication/Authorization/Sign-Out Via Sessions
- Make Controller/View Sign-Up
- Make Controller/View Sign-In(Authentication)
- Make Main Controller/View conditional logic for dispaying different partials(Authorization)
- Figure out layout.erb view template conditional logic for viewing Sign-Up/Sign-In buttons or Sign-Out button depending on Authorization.
- Make DELETE route in controller for Sign-Out button


75% of components are ready in Angular2
This module consists of native Angular2 components and directives, no jQuery, Material or Bootstrap javascript is required.
Plus this module plays nice with Material Design CSS
Currently preferable way to install this module is npm
:
npm install --save md2
Each md2
components has api and annotation docs, examples and working demo. Each property
and event
has type annotation and default value if any.


Watch: Union minister Babul Supriyo sings ‘Ae Zindagi Gale Laga Le’ at the closing ceremony of IFFI 29 Nov 2019, 13:38 by Scroll Staff The controversy-prone minister of state was on familiar territory: music. Read Comments


Expressing my personality and showcasing my design and CSS/SASS skills while keeping things simple.
Made using SASS, CSS Grid, Flexbox and JavaScript.
This was an excellent learning experience and made me realise the importance of organisation, and thinking through the smaller details (like the favicon being used).
Working on nikhilvijayan.com highlihted the importance of organising my code in a sensible and modular way to be able to re-use elements, while also maintaining the ability to customise the code.
The objective of the site was to keep things simple on the front-end, which is often not the easiest thing to accomplish. I decided on using containers with different backgrounds to visually differentiate sections. These sections are also separated into separate SASS files for ease (this has already saved me hours as locating bugs is easier) while using modular mixins for grid items, containers and typography.
The site uses CSS Grid and Flexbox for the 'My Work' section, which makes it much easier to make responsive using @media queries and changing the order of the elements.
I'm also a fan of using @mixins in my SASS in order to make the code more declarative and readable. For eg: @include for-phone-only {} is much more readable than '@media (max-width: 599px) {}'
Here is a collection of website components from popular websites written from scratch.


Play on Words
问题描述 :
Detective Liu recently got his eye on someone suspicious and also he got a letter from the guy. Detective Liu has his own method to judge whether a person was evil. He got a list words, which was collected during the years he worked. Those are the words that the evils often used.
Here comes your task. Give a list of the “evil” words and another word; you are going to find the “evilness” of it. The “evilness” of the word is defined by the times that it appears in the list. If A is evil under the word B, then every letter of A should appear in B in order but could leave some of B unmatched.
Here comes your task. Give a list of the “evil” words and another word; you are going to find the “evilness” of it. The “evilness” of the word is defined by the times that it appears in the list. If A is evil under the word B, then every letter of A should appear in B in order but could leave some of B unmatched.
输入:
The first line give M (10 <= M <= 5000), the number of the evil words.
Then M lines follows, which is the evil words each in one line, at most 20 characters, all lowercase.
Next comes N (1 <= N <= 1000) stand for the number of query words.
Then comes the query words with the same constraints.
Then M lines follows, which is the evil words each in one line, at most 20 characters, all lowercase.
Next comes N (1 <= N <= 1000) stand for the number of query words.
Then comes the query words with the same constraints.
输出:
The first line give M (10 <= M <= 5000), the number of the evil words.
Then M lines follows, which is the evil words each in one line, at most 20 characters, all lowercase.
Next comes N (1 <= N <= 1000) stand for the number of query words.
Then comes the query words with the same constraints.
Then M lines follows, which is the evil words each in one line, at most 20 characters, all lowercase.
Next comes N (1 <= N <= 1000) stand for the number of query words.
Then comes the query words with the same constraints.
样例输入:
3 hello summer goodbye 1 good
样例输出:
1
#include <iostream> #include <cstdio> #include <cmath> #include <cstring> #include <algorithm> #include <string> #include <vector> #include <stack> #include <queue> #include <set> #include <map> #include <sstream> using namespace std; typedef long long ll; #define REP(i,s,t) for(int i=(s);i<(t);i++) #define FORST(X,S,T) for(int X=(S); X<=(T); X++) #define RFORST(X,S,T) for(int X=(S); X>=(T); X--) #define FOR(X,XB) for(int X=0; X<(XB); X++) #define RFOR(X,XB) for(int X=(XB)-1; X>=0; X--) #define FOREACH(i,v) for(typeof((v).begin()) i=(v).begin();i!=(v).end();i++) #define FILL(x,v) memset(x,v,sizeof(x)) const int INF = (int)1E9; #define MAXN 1005 int N; char buf[MAXN]; int odeg[26], ideg[26]; bool g[26][26], v[26], h[26]; void dfs(int k){ v[k] = 1; REP(i,0,26) if(g[k][i] && !v[i]) dfs(i); } int main(){ int cs; cin >> cs; while(cs--){ int st; scanf("%d", &N); FILL(odeg,0); FILL(ideg,0); FILL(g,0); FILL(h,0); REP(i,0,N){ scanf("%s", buf); int l = strlen(buf); int c0 = buf[0]-'a', cl = buf[l-1]-'a'; odeg[c0]++; ideg[cl]++; g[c0][cl] = 1; h[c0] = h[cl] = 1; st = c0; } bool ok = 1; int ii=0, oo=0; REP(i,0,26){ if(ideg[i]!=odeg[i]){ if(ideg[i]==odeg[i]+1) ii++; else if(odeg[i]==ideg[i]+1) { oo++; st = i; } else{ ok = 0; break; } } } if(ok && (ii==1 && oo==1 || ii==0 && oo==0)){ FILL(v,0); dfs(st); REP(i,0,26) if(h[i] && !v[i]) { ok = 0; break; } }else ok = 0; if(ok) puts("Ordering is possible."); else puts("The door cannot be opened."); } return 0; }
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情节似乎略中二
情节似乎略中二
情节似乎略中二
情节似乎略中二
情节似乎略中二
情节似乎略中二
1234百位上出现的次数应该是200吧。
太变态了。我用hash居然超时了。这样break掉居然都行啊。。。
楼主你能把scanf( "%d%*" ,&T );中%*的用法详细说明吗
多谢支持！
程序只能输出一个子序列
Good task for the group. Hold it up for every yeara??s winner. This is a excellent oppotunity for a lot more enhancement. Indeed, obtaining far better and much better is constantly the crucial. Just like my pal suggests on the truth about ab muscles, he just keeps obtaining much better.
博主，你这个是wordpress哪个主题啊，不错哦~
贴的有些代码的头文件不见了，这是为啥
这个 。。。。不是动态规划吧？
不需要输出，只要函数返回值正确就行
如果两个序列的最后字符不匹配（即X [M-1]！= Y [N-1]）
L（X [0 .. M-1]，Y [0 .. N-1]）= MAX（L（X [0 .. M-2]，Y [0 .. N-1]），L（X [0 .. M-1]，Y [0 .. N-1]）
这里写错了吧。
a是根先忽略掉，递归子树。剩下前缀bejkcfghid和后缀jkebfghicd，分拆的原则的是每个子树前缀和后缀的节点个数是一样的，根节点出现在前缀的第一个，后缀的最后一个。根节点b出现后缀的第四个位置，则第一部分为四个节点，前缀bejk，后缀jkeb，剩下的c出现在后缀的倒数第2个，就划分为cfghi和 fghic，第3部分就为c、c
map[nodes .index][nodes .index] == 0 就是判断不相邻的。这个是可以证明的。
其实不需要用LCA，用DFS+vector更简单


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- Method in class deepdive.cnm.edu.coins.activities.
SignInActivity
onBackPressed()
- Method in class deepdive.cnm.edu.coins.activities.
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onBindViewHolder(NewsAdapter.ViewHolder, int)
- Method in class deepdive.cnm.edu.coins.adapter.
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- Method in class deepdive.cnm.edu.coins.adapter.
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- Method in class deepdive.cnm.edu.coins.activities.
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- Method in class deepdive.cnm.edu.coins.application.
CoinsApplication
onCreate(Bundle)
- Method in class deepdive.cnm.edu.coins.fragments.
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- Method in class deepdive.cnm.edu.coins.fragments.
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- Method in class deepdive.cnm.edu.coins.activities.
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- Method in class deepdive.cnm.edu.coins.fragments.
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- Method in class deepdive.cnm.edu.coins.fragments.
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onCreateView(LayoutInflater, ViewGroup, Bundle)
- Method in class deepdive.cnm.edu.coins.fragments.
WelcomeFragment
onCreateView(LayoutInflater, ViewGroup, Bundle)
- Method in class deepdive.cnm.edu.coins.fragments.
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onCreateViewHolder(ViewGroup, int)
- Method in class deepdive.cnm.edu.coins.adapter.
NewsAdapter
onItemClick(View, int)
- Method in interface deepdive.cnm.edu.coins.adapter.
NewsAdapter.ItemClickListener
On item click.
onNavigationItemSelected(MenuItem)
- Method in class deepdive.cnm.edu.coins.activities.
MainActivity
onOptionsItemSelected(MenuItem)
- Method in class deepdive.cnm.edu.coins.activities.
MainActivity
onPostExecute(User)
- Method in class deepdive.cnm.edu.coins.activities.
MainActivity.UpdateCoins
onStart()
- Method in class deepdive.cnm.edu.coins.activities.
SignInActivity
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---
layout: post
title: I might have to go back to the dark side
tags:
- Open Source Software
- Work Practices
status: publish
type: post
published: true
meta: {}
---
andLinux
a complete Linux system designed for developing applications and runs seemlessly in Windows 2000 based systems (2000, XP, 2003)
My new Windows Mobile 5 phone will not sync with Evolution and I am not convinced my WiFi card is working as well as it should.
However I currently have problems running Windows:
- I don't want to because I like Linux and OSS
- Windows is less secure
- Installing svk is a pain. It would appear the only option is to install by hand each time there is an upgrade
- I need to run GnuCash for my accounts
- I have to have access to RT, assuming my paper is accepted for OSDC
andLinux might represent a practical solution to all the points above except 1 and 2 providing I put with re-building svk each time it gets updated. I've already run GnuCash under coLinux in the past with great success.
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We need to take arms against this force of destruction to keep our homes anf families safe!
We here at Parkland believe we have the perfect solution to the 2014 Lego League Competion.


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