Author: admin

Headlamp wouldn’t turn on, and there is damn little info out on how to open the #$@! things. I figured it out, and to pay my karma for other projects, I documented what worked for me.

So my trusty old Petzl Tikka XP2 stopped working. The top button got loose and became a flap, then even pushing the micro button directly, it wouldn’t turn on.

The first order of business is how to open the damn thing. I began by opening up the headlamp to get access to the batteries. It hinges at the bottom, some minor side force and the headband back of the lamp will pop off cleanly.

Then came the screw problem. Once you remove the back of the headlamp, and then the batteries, you’ll see two tiny screws. Those are not Philips, they are Torx screws. Specifically they are Torx T5 screws. So I had to buy a set of Torx screwdrivers off Amazon. $12 right there, but I will use them for other project.

Unscrew those two screws. Once they are out, the electronics still won’t come out of the shell. You will have to unclip the two plastic clips at the top of the battery area near the switch. Use a small screwdriver, and very little force.

It popped out with some gentle tugging. Once the electronics was out, I examined the case. The little front diffuser door popped off, but I’ll deal with that later. To fix the red button I used superglue to seal up the red rubber button to the case so it was water tight again. Then I clicked on the PCB micro switch button to see if I could figure out what was wrong.

It turned on once but that was it. As a wild guess, I assumed a corroded contactor so I sprayed some electric contact cleaner on it typically used for scratchy potietiometers.

I clicked the button over and over and…nothing. So I kept clicking then it turned on….click, click, click, click..and off. The more I clicked, the more reliable it became. Finally each click turned it on and off. Maybe 100 clicks before the contact cleaner had kicked in.

Finally, it was working! So I reversed the steps, put in the diffuser door (not sure what worked but it got back in place) snapped the electronics back in, screwed in the T5 Torx screws, and bam! Working again.

Source: Das Kochbuch der Sabina Welserin (c. 1553)

(Apparently one of the first cookbooks published by a woman. )

Weltt jr gútt prattwirst machen

So nempt 4 pfúnd schweinis vnnd 4 pfúnd rinderis, das last klainhacken, nempt darnach 2 pfúnd speck darúnder vnnd hackts anainander vnnd vngeferlich 3 seidlen wasser giest daran, thiet aúch saltz, pfeffer daran, wie jrs geren est, oder wan jr geren kreúter darin megt haben/ múgt jr nemen ain wenig ain salua vnnd ain wenig maseron, so habt jr gút brattwirst/.

“If you would make good bratwurst”If you would make good bratwurst

Take four pounds of pork and four pounds of beef and chop it finely. After that mix with it two pounds of bacon and chop it together and pour approximately one quart of water on it. Also add salt and pepper thereto, however you like to eat it, or if you would like to have some good herbs, you could take some sage and some marjoram, then you have good bratwurst.”

10OCT23 – Here are my notes and addendums:

So she basically says 8 parts meat, equal pork and beef, and 2 parts fat. Which is in the traditional 20-30% fat range needed for good sausage. So that checks out. Then she adds a little water which is normal to help the bind of the sausage, so that checks. Salt, pepper, yep, that is all normal. Sage and marjoram, that checks but she doesn’t give the ratio on any of this. Going off of my experience 1.5%-2.5% is appropriate for salt content for modern taste pallets. 1.8-2.0% is more my preference. However, I suspect it was made with much higher salt content. In history, I’ve seen roman recipe as high at 10%! This is quite salty and unpalatable, but it would preserve and would likely be shelf stable and hung all year at room temp. However, it would have to be soaked to be used, or added to a large pot of stew and the salt would diffuse.

But I ran with 2% salt and no curing salt #1 (Prague powder #1/Sodium nitrate) to stay true as possible. This prevented me from smoking it due to food safety. I split it 50/50 pork and venison and added in pork fat to balance out the venison. She doesn’t mention the requirement to kneed the mince to make it tacky for a good bind, but you must do that. Kneed until it is sticky that a small ball of mince in your hand clings to your glove.

Substitute Venison for beef. Use a 25% fat ratio, 2.0% salt, 0.25% pepper, 0.25% Sage, 0.25% marjoram. Optional- You must use 0.25% Cure #1 if you are going to smoke it because of anaerobic conditions induces risk of botulism.

I’ve made 9 types of sausage, and I have to say this is a good sausage! I will recommend it. Simple – just salt, pepper, marjoram, sage. It has passed the test of time, 5 centuries!

A quest for the uncomputable it seems. I’ve lost 4 hours trying to solve this problem.

I am loading a stock ticker, and the object class is xts/zoo. I thought I’d model the percent rank problem like I did for a simple moving average:

VXX$VXX_30day_SMA<-zoo::rollmean(VXX$VXX.Adjusted, k=30, align="right")

But that didn’t work as there is no roll_perc_rank function. So I went to use rollapply with a function like:

VXX$VXX_vol_22day_pct_rank<-zoo::rollapply(VXX$VXX.Volume, width=22, FUN = percent_rank, align='left', fill = NA)

Which uses the dply::percent_rank function, but that didn’t work.

What finally worked was TTR::runPercentRank

https://www.rdocumentation.org/packages/TTR/versions/0.2/topics/runPercentRank

VXX$VXX_vol_220day_pct_rank<-runPercentRank(VXX$VXX.Volume, n = 220, cumulative = FALSE, exact.multiplier = 0.5)

Since the internet doesn’t have a good page to solve this issue, I wanted to post on it. Be sure to load the TTR library first!

There is a strong market for lodging before a faire, be it die hard rennies, families, or handicap attendees.

But what structures works? Balance of cost, speed of construction, labor input, materials, and cooling costs for summer.

For housing for groups, you need 55sq ft per bunk bed. So 200sq ft for 6 people, 800sq ft for 26 people.

Masonry structures are more expensive, but cool, quiet, stormproof, and low maintenance

Earthbag houses are cost effective but more labor.

The ‘Gargoyles’ are added. But they are really called grotesques given that they do not channel rain water. I’ve used oxide paint for the bases to match. There are 4 Grotesques, each is different from the others. The smallest is in the back where there is little light. I tell people they all were the same size when I installed them.

In other good news, the water that was getting into the shed has been fixed. It was mainly splash water hitting the door and running down and trapped inside instead off away from the structure. The next heavy rain should confirm that.

So the wife said I could build a shed. I began in 2016. I’ve added a vent and used a Dubrovnik style chimney in order to let moisture out when it rains. Plus, I’ve restained the door and strengthened the Attic flooring.

What a frilling week. Market turmoil, oil crashes, and Covid arrived to come kick our asses. I chose to not sit idly by as a spectator, and instead forge a weapon (web app) that will help hospitals around the world predict the wave IN THEIR AREA and tell them how many beds they need and if they will be swamped.

It needs to predict when the wave arrives, and how many of each type of beds will be needed. Standard, ICU, and Critical. Plus, as the data for Covid gets better, the user can change the inputs easily on the webpage.

I haven’t named it officially yet, maybe call it COBE Tool – COvid Bed Estimator Tool. I will put in in Shiny at shiny.io hosted.

Early results show what will happen if Covid19 burns without social distancing. 72% of America catches it.

Tools used : R, with packages Epimdr, Desolve, Shiny. The core code for generating the S E I R table is below.

require(deSolve)
times = seq(0, 450, by=1)
#paras = c(mu = 0, N = 1, beta = 1000, sigma = 365/15, gamma = 365/5)
sigma<- 1/5 # this is     The rate at which an exposed person becomes infective. Inverse of 
gamma<-1/15 #  The rate at which an exposed person becomes resistant (recovers)
paras = c(mu = 0, N = 1, beta = .1173, sigma = sigma, gamma = gamma)
start = c(S=(.999) , E=0, I=.001, R = 0.0)
#start = c(S=0.06, E=0, I=0.001, R = 0.939)
out=ode(y=start, times=times, func=seirmod, parms=paras)
#init       <- c(S = 1-1e-6, I = 1e-6, R = 0.0)
#parameters <- c(beta = 1.4247, gamma = 0.14286)
#out <- ode(y = init, times = times, func = sir, parms = parameters)
out <- as.data.frame(out)
head(out)
tail(out)
plot(out$time,out$R)
plot(out$time,out$I)