国内从 Docker Hub 拉取镜像有时会遇到困难,此时可以配置镜像加速器。
Dockerized 实践 https://github.com/y0ngb1n/dockerized
国内从 Docker Hub 拉取镜像有时会遇到困难,此时可以配置镜像加速器。
Dockerized 实践 https://github.com/y0ngb1n/dockerized
#!/bin/bash | |
for d in $(find /sys/kernel/iommu_groups/ -type l | sort -n -k5 -t/); do | |
n=${d#*/iommu_groups/*}; n=${n%%/*} | |
printf 'IOMMU Group %s ' "$n" | |
lspci -nns "${d##*/}" | |
done; |
You are Manus, an AI agent created by the Manus team. | |
You excel at the following tasks: | |
1. Information gathering, fact-checking, and documentation | |
2. Data processing, analysis, and visualization | |
3. Writing multi-chapter articles and in-depth research reports | |
4. Creating websites, applications, and tools | |
5. Using programming to solve various problems beyond development | |
6. Various tasks that can be accomplished using computers and the internet |
In addition to a significant decrease in hepatic lipid accumulation in the IOE group, which inhibited energy intake by propionate enrichment, hepatic lipids were also significantly reduced in the mice in the IOP group, which was largely enriched with butyrate. Compared with the IOE group, IOP had a stronger regulatory effect on hepatic metabolism and triglyceride metabolism and higher levels of TCA cycle in the host. In addition, butyrate has the ability to promote browning of white adipose tissue (WAT) to brown adipose tissue (BAT).^[@ref39],[@ref40]^ WAT stores energy, whereas BAT uses energy for heating and consequently host energy expenditure increases.^[@ref41],[@ref42]^ However, adipose tissue weight does not change after WAT browning.^[@ref43]^ Therefore, the weight of adipose tissue of mice in the IOP group dominated by butyrate was greater than that of the mice in the IOE group dominated by propionate. | |
In conclusion ([Figure [7](#fig7){ref-type="fig"}](#fig7){ref-type="fig"}C), the improvement of ob |
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.# Meine Sammlung Sensoren Viessmann Vitotronic 200 V200KW2 für esphome optolink | |
# Auskommentierte Sensoren haben bei mir keine sinnvollen Werte geliefert | |
# "type: RAW" funktioniert aktuell nicht | |
# My collection of Viessmann Vitotronic 200 V200KW2 sensors for esphome optolink | |
# Commented out sensors did not provide me with any meaningful values | |
# "type: RAW" is currently not working | |
substitutions: | |
name: Vitoconnect |
In the last years I've been asked multiple times about the comparison between raylib and SDL libraries. Unfortunately, my experience with SDL was quite limited so I couldn't provide a good comparison. In the last two years I've learned about SDL and used it to teach at University so I feel that now I can provide a good comparison between both.
Hope it helps future users to better understand this two libraries internals and functionality.
set -euo pipefail | |
# Reduce backup IO. | |
sed -i -E 's/^\s*#?\s*(ionice:).*/\1 7/' /etc/vzdump.conf | |
sed -i -E 's/^\s*#?\s*(bwlimit:).*/\1 32768/' /etc/vzdump.conf | |
tee /etc/cron.hourly/xfs_fsr <<EOF | |
#!/bin/sh | |
xfs_fsr -t 600 |
import { Duration, Effect, Either, Scope, Cause } from 'effect'; | |
// This function allows you call to a scoped effect, return the response | |
// then use the ctx.waitUntil api to ensure the worker continues | |
// to run until the scope is done closing. It handles, success, | |
// failures and defects. | |
const runAwaitingScopeClose = | |
<Input, S extends Response, E>(effect: (input: Input, env: Env, ctx: ExecutionContext) => Effect.Effect<S, E, Scope.Scope>) => | |
(input: Input, env: Env, ctx: ExecutionContext): Promise<Response> => { |