「mass transfer」の検索結果

Expert-level mass transfer covering molecular diffusion, convective mass transfer, interphase transfer, mass transfer coefficients, and design of mass transfer equipment.

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。

Use when re-assigning record OwnerId across many records — territory realignment, employee departure, region split, integration cleanup. Triggers: 'mass transfer accounts', 'reassign opportunities to new owner', 'transfer all records on user deactivation', 'OwnerId migration', 'mass transfer records tool', 'bulk owner change', 'transfer before deactivating user', 'account team lost after owner change', 'assignment rule overrode my OwnerId column', 'defer sharing calculations', 'roll back an owner transfer'. NOT for auto-assigning new records — use admin/assignment-rules. NOT for territory assignment data — use data/territory-data-alignment.

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。

Hunt fintech-specific GraphQL vulnerabilities: money-movement mutations (transfers, redemptions, withdrawals, card top-ups), ledger/balance/portfolio query IDOR, decimal-precision and rounding abuse, idempotency-key bypass enabling double-spend, KYC/PII field-level authorization gaps, and admin-override mutations reachable via mass assignment. Distinct from hunt-graphql, which owns generic GraphQL discovery and IDOR/mutation methodology — this skill owns the delta introduced when a GraphQL layer sits in front of a ledger, wallet, payments, banking, brokerage, or lending backend, where a resolver bug moves real money instead of just leaking data. Use when hunting a fintech, banking, payments, wallet, neobank, brokerage, or lending target that exposes a GraphQL API, or when a schema/response includes balance, transfer, ledger, redeem, quote, KYC, or account-linking fields.

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。

Assign cell types from marker expression in IMC/MIBI data using clustering (PhenoGraph/FlowSOM/Leiden/Pixie), marker-based probabilistic classifiers (Astir), or image-context CNNs (CellSighter), covering the double-positive segmentation artifact, lineage-vs-state markers, the two spillover types, and why a "cell type" in imaging is conditioned on a segmentation guess. Use when phenotyping segmented IMC cells, choosing clustering vs classification, diagnosing implausible double-positive populations, separating lineage from functional markers, or transferring labels across a cohort.

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。

Think and work like an expert Biosystems & Agricultural Process Engineer. Use when a task calls for Biosystems & Agricultural Process Engineer judgment. Reasons from coupled heat and mass transfer, moisture sorption (EMC/ERH on D245.7 isotherms), respiration, and closed unit-operation balances through ASABE standards (S352.2 oven moisture, S448 Page thin-layer kinetics, D272.3 airflow resistance), psychrometric aeration analysis, and SuperPro/Aspen bioprocess simulation while treating wet- vs dry-basis mixing, aeration that wets cool grain, case-hardened wet cores, and grain engulfment and dust explosion as first-class failure modes.

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。

Parallel geführte Sprachfassungen eines Dokuments (Paper DE/EN, README + README_de, SKILL.md + SKILL.en.md, Website-Texte) synchron halten: fehlende Fassung nachziehen, Abschnitts-Parallelität prüfen, Divergenzen beheben — mit klarer Leitsprache-Regel und kontrolliertem Rücktransfer, wenn die Nebenfassung etwas besser löst. Nutze diesen Skill bei „sind DE und EN synchron?", „zieh die englische/deutsche Version nach", „Übersetzung ist veraltet", bei zweisprachigen Papers/READMEs/Skills, oder als periodischen Check über einen Dokumentbestand. Enthält auch das Expansions-Audit: bewerten, ob ein Projekt/Dokument WEITERE Sprachen verdient (i18n-Eignung nach Zielgruppe, technische Vorbereitung, kein blindes Massenübersetzen).

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。

Assign cell types from marker expression in IMC/MIBI data using clustering (PhenoGraph/FlowSOM/Leiden/Pixie), marker-based probabilistic classifiers (Astir), or image-context CNNs (CellSighter), covering the double-positive segmentation artifact, lineage-vs-state markers, the two spillover types, and why a "cell type" in imaging is conditioned on a segmentation guess. Use when phenotyping segmented IMC cells, choosing clustering vs classification, diagnosing implausible double-positive populations, separating lineage from functional markers, or transferring labels across a cohort.

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。

Assign cell types from marker expression in IMC/MIBI data using clustering (PhenoGraph/FlowSOM/Leiden/Pixie), marker-based probabilistic classifiers (Astir), or image-context CNNs (CellSighter), covering the double-positive segmentation artifact, lineage-vs-state markers, the two spillover types, and why a "cell type" in imaging is conditioned on a segmentation guess. Use when phenotyping segmented IMC cells, choosing clustering vs classification, diagnosing implausible double-positive populations, separating lineage from functional markers, or transferring labels across a cohort.

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。