67 件 ・ 関連度順
概要と使いどころ
Profile and audit mobile app performance -- cold/warm/hot startup time, memory leaks, battery drain, network efficiency, frame rate jank, and binary size. Triggers: diagnosing slow launches, investigating ANRs, reducing app size, or benchmarking rendering performance.
日本語の概要は準備中です。原文の説明を表示しています。
Mobile game development principles. Touch input, battery, performance, app stores.
日本語の概要は準備中です。原文の説明を表示しています。
Embedded/IoT Engineer — Arduino/ESP32/Raspberry Pi, sensor integration, MQTT/BLE/LoRa, firmware development, battery optimization
日本語の概要は準備中です。原文の説明を表示しています。
Firmware development for the WeAct Studio RP2350A Core Board (RP2350A — dual Cortex-M33 @ 150 MHz, 520 KB SRAM, 4 MB or 16 MB QSPI flash) in both of its revisions, RP2350A_V10 and RP2350A_V20 — the 40-pin Pico-2-shaped header, 3-or-4-channel 12-bit ADC, PWM, PIO, USB-C CDC, the V1.0 LDO vs V2.0 buck-boost power wiring, and the PlatformIO + arduino-pico (earlephilhower) setup around them. Use when working on this board, a WeAct RP2350 core board of unknown revision, or any Raspberry Pi Pico 2 / RP2350A clone: project setup, platformio.ini, board = rpipico2 vs weact_rp2350b, telling V1.0 and V2.0 apart, pin mapping, LED and KEY pins, powering it from a battery, 4 MB vs 16 MB flash, erratum RP2350-E9 pull-down trouble, BOOT/RESET UF2 flashing, or debugging why something on the board does not work.
日本語の概要は準備中です。原文の説明を表示しています。
Firmware development for the Raspberry Pi Pico (original, RP2040 — dual Cortex-M0+ @ 133 MHz, 264 KB SRAM, 2 MB QSPI flash) — its 26 exposed GPIO, 4-channel 12-bit ADC, 16-channel PWM, 2× UART/I2C/SPI, PIO state machines, USB 1.1 CDC, VSYS/VBUS/SMPS power wiring, and the PlatformIO + arduino-pico (earlephilhower) setup around them. Use when working on this board, any Pico H or Pico clone, or a bare RP2040: project setup, platformio.ini, the earlephilhower-vs-Mbed core choice, pin mapping, USB CDC serial, analogRead/analogWrite behaviour, Wire/SPI defaults, battery power, EEPROM emulation, BOOTSEL/UF2 flashing, or debugging why something on the board does not work.
日本語の概要は準備中です。原文の説明を表示しています。
Firmware development for the Guition JC4880P443C_I_W (JC4880P443, JC4880P443C, often mistyped JC4880P433) — a 4.3" 480x800 IPS touch display board on the JC-ESP32P4-M3 module (ESP32-P4NRW32, 32 MB PSRAM, 16 MB flash, ESP32-C6 Wi-Fi co-processor on SDIO) — its ST7701S MIPI-DSI panel, GT911 touch, backlight, ES8311 codec with speaker amp and mic, microSD, RS485, battery charger, 2x13 header, and the PlatformIO + ESP-IDF (pioarduino) setup around them. Use when working on this board: project setup, platformio.ini and sdkconfig.defaults, the P4 silicon revision and an "Illegal instruction" boot, a black DSI screen, ST7701 init sequence and timings, GT911 init errors, backlight PWM, choosing a free GPIO, Wi-Fi through esp_hosted and esp_wifi_remote, SD card, audio, flashing over USB-C, or debugging why something on the board does not work.
日本語の概要は準備中です。原文の説明を表示しています。
Estimates bivariate genetic correlation (rg) between traits from GWAS summary statistics or individual-level genotypes using cross-trait LDSC, HDL, LAVA, rho-HESS, GREML-bivariate, Popcorn, and HDL-L. Use when quantifying shared genetic architecture between two traits, screening MR validity before causal inference, distinguishing global from locus-level rg, estimating trans-ancestry rg, separating partial from full causation via LCV gcp, or producing a STROBE-MR-compliant cross-trait sensitivity battery. Cross-trait LDSC intercept absorbs sample overlap and is NOT a bias; HDL is biased under sample overlap above ~5%. High rg between exposure and outcome motivates CHP-aware MR sensitivity (CAUSE, LHC-MR).
日本語の概要は準備中です。原文の説明を表示しています。
Detect and adjust for horizontal pleiotropy in two-sample Mendelian randomization by distinguishing uncorrelated (UHP) from correlated (CHP) pleiotropy and choosing among Egger, MR-PRESSO, MR-RAPS, CAUSE, LHC-MR, LCV, MR-Clust, MR-Mix, and contamination-mixture methods. Use when validating an MR causal claim, running the STROBE-MR sensitivity battery, suspecting a shared heritable confounder, working under weak-instrument or polygenic-exposure regimes, or reconciling discordant estimates across robust methods.
日本語の概要は準備中です。原文の説明を表示しています。
Assesses the patient’s ability to troubleshoot insulin pump malfunctions such as occlusion, battery failure, or error messages. Trigger phrases include “Review pump alarm responses” and “Ensure patient knows steps for pump error”.
日本語の概要は準備中です。原文の説明を表示しています。
Expert-level energy storage covering pumped hydro, battery storage, compressed air, thermal storage, hydrogen, flywheel, and the role of storage in energy transition.
日本語の概要は準備中です。原文の説明を表示しています。
Expert-level electrochemistry knowledge. Use when working with electrochemical cells, electrode potentials, Nernst equation, electrolysis, batteries, fuel cells, corrosion, electroplating, or electroanalytical methods. Also use when the user mentions 'galvanic cell', 'electrolytic cell', 'electrode potential', 'Nernst equation', 'overpotential', 'Butler-Volmer', 'battery', 'fuel cell', 'corrosion', 'electroplating', 'cyclic voltammetry', or 'impedance spectroscopy'.
日本語の概要は準備中です。原文の説明を表示しています。
Mobile game development principles. Touch input, battery, performance, app stores.
日本語の概要は準備中です。原文の説明を表示しています。
End-to-end post-GWAS causal inference pipeline orchestrating heritability partitioning, genetic correlation, Mendelian randomization with CHP-aware sensitivity (CAUSE / LHC-MR), colocalization, fine-mapping with SuSiE / FOCUS, mediation, TWAS triangulation, cis-pQTL drug-target MR, effector-gene prioritization (L2G / PoPS / cS2G), and GenomicSEM common-factor GWAS. Use when triangulating causal inference across multiple complementary methods, prioritizing tissues via stratified LDSC, nominating or de-risking drug targets, mapping a lead SNP to a candidate effector gene, modeling shared genetic architecture across correlated traits, or producing a STROBE-MR-compliant publication-grade evidence battery from GWAS summary statistics.
日本語の概要は準備中です。原文の説明を表示しています。
Estimate bivariate genetic correlation (rg) between traits from GWAS summary statistics or individual-level genotypes using cross-trait LDSC, HDL, LAVA, rho-HESS, GREML-bivariate, Popcorn, and HDL-L. Use when quantifying shared genetic architecture between two traits, screening MR validity before causal inference, distinguishing global from locus-level rg, estimating trans-ancestry rg, separating partial from full causation via LCV gcp, or producing a STROBE-MR-compliant cross-trait sensitivity battery. Cross-trait LDSC intercept absorbs sample overlap and is NOT a bias; HDL is biased under sample overlap above ~5%. High rg between exposure and outcome motivates CHP-aware MR sensitivity (CAUSE, LHC-MR).
日本語の概要は準備中です。原文の説明を表示しています。
Detect and adjust for horizontal pleiotropy in two-sample Mendelian randomization by distinguishing uncorrelated (UHP) from correlated (CHP) pleiotropy and choosing among Egger, MR-PRESSO, MR-RAPS, CAUSE, LHC-MR, LCV, MR-Clust, MR-Mix, and contamination-mixture methods. Use when validating an MR causal claim, running the STROBE-MR sensitivity battery, suspecting a shared heritable confounder, working under weak-instrument or polygenic-exposure regimes, or reconciling discordant estimates across robust methods.
日本語の概要は準備中です。原文の説明を表示しています。
Estimate bivariate genetic correlation (rg) between traits from GWAS summary statistics or individual-level genotypes using cross-trait LDSC, HDL, LAVA, rho-HESS, GREML-bivariate, Popcorn, and HDL-L. Use when quantifying shared genetic architecture between two traits, screening MR validity before causal inference, distinguishing global from locus-level rg, estimating trans-ancestry rg, separating partial from full causation via LCV gcp, or producing a STROBE-MR-compliant cross-trait sensitivity battery. Cross-trait LDSC intercept absorbs sample overlap and is NOT a bias; HDL is biased under sample overlap above ~5%. High rg between exposure and outcome motivates CHP-aware MR sensitivity (CAUSE, LHC-MR).
日本語の概要は準備中です。原文の説明を表示しています。
Detect and adjust for horizontal pleiotropy in two-sample Mendelian randomization by distinguishing uncorrelated (UHP) from correlated (CHP) pleiotropy and choosing among Egger, MR-PRESSO, MR-RAPS, CAUSE, LHC-MR, LCV, MR-Clust, MR-Mix, and contamination-mixture methods. Use when validating an MR causal claim, running the STROBE-MR sensitivity battery, suspecting a shared heritable confounder, working under weak-instrument or polygenic-exposure regimes, or reconciling discordant estimates across robust methods.
日本語の概要は準備中です。原文の説明を表示しています。
针对包含激励信号的10Hz采样电池数据，利用FFT（含去直流和加窗处理）计算特定频率的电化学阻抗并绘制奈奎斯特图。
日本語の概要は準備中です。原文の説明を表示しています。
Query Find My locations and battery status for family devices via iCloud.
日本語の概要は準備中です。原文の説明を表示しています。