Azure AI Document Intelligence SDK for .NET. Extract text, tables, and structured data from documents using prebuilt and custom models.
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。
3,127 件 ・ 関連度順
概要と使いどころ
Azure AI Document Intelligence SDK for .NET. Extract text, tables, and structured data from documents using prebuilt and custom models.
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。
Extract text, tables, and structured data from documents using prebuilt and custom models.
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。
Analyze text and images for harmful content with customizable blocklists.
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。
Build container-based Foundry Agents with Azure AI Projects SDK (ImageBasedHostedAgentDefinition). Use when creating hosted agents with custom container images in Azure AI Foundry.
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。
AI agent development workflow for building autonomous agents, multi-agent systems, and agent orchestration with CrewAI, LangGraph, and custom agents.
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。
Build local BLAST databases and run searches using NCBI BLAST+ command-line tools. Use when running >50 queries, building custom databases with -parse_seqids and -taxid, downloading prebuilt NCBI databases via update_blastdb.pl, choosing -task variants (megablast/dc-megablast/blastn/blastn-short), tuning soft/hard masking, scaling threads, or extracting hits with blastdbcmd. Encodes BLAST v5 vs v4 database format, taxonomy filtering, makeblastdb pitfalls.
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。
Build publication-quality figures with matplotlib using the object-oriented Figure/Axes API, constrained_layout, rcParams customization, TrueType (Type-42) font embedding for journal submission, and CVD-safe palettes. Covers seaborn integration, common chart types, axis formatting, and the small gotchas that distinguish reproducible matplotlib from notebook scratch. Use when producing publication figures in Python — RNA-seq scatter, single-cell embeddings, generic biological plotting.
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。
Designs pooled sgRNA libraries for CRISPR knockout, interference (CRISPRi), activation (CRISPRa), Cas12a multiplex, base-editor, and prime-editor screens. Covers on-target scoring (Rule Set 2, Azimuth, DeepSpCas9, CRISPRon), off-target scoring (CFD, MIT), TSS-relative positioning for CRISPRi/a (Horlbeck, Dolcetto, Calabrese), PAM-variant chemistries, control-guide composition, oligo cloning architecture, and library QC. Use when choosing a genome-wide library (GeCKOv2 vs Avana vs Brunello vs TKOv3 vs Inzolia), designing a focused or paralog-focused custom library, picking CRISPRi vs CRISPRa TSS windows, deciding control-guide proportions, or diagnosing library skew and dropout in a freshly cloned pool.
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。
Build sequence logos from aligned DNA, RNA, or protein motifs using ggseqlogo (R), Logomaker (Python), or WebLogo with explicit bits vs probability encoding, background-frequency correction, custom alphabets, and multi-logo stacking. Use when visualizing motif PWMs (TF binding, splice sites, CRISPR spacers), aligned-position composition, or comparing two motif sets.
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。
Predict RBP binding from RNA sequence using deep learning models (RBPNet sequence-to-signal, RNAProt RNN, GraphProt2 GCN with structure, DeepCLIP, DeepRiPe multi-modal CNN) for variant-effect prediction, in silico binding-site discovery, model interpretation, and transfer learning from CLIP and RBNS datasets. Use when computational prediction of RBP binding from sequence is needed, evaluating variant effects on binding without further wet-lab experiments, comparing model performance, or training a custom model on ENCODE eCLIP data.
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 a carrier asks which KPIs to track, how to build a fleet dashboard, what targets to set, cadence for reviewing metrics (daily / weekly / monthly / quarterly), and how to operationalize data into actions. Covers safety, operations, financial, driver, and customer KPIs.
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 a dispatcher, operations manager, or owner asks how to plan loads to minimize empty (deadhead) miles, sequence freight efficiently, balance regional capacity, design dedicated loops, or build a load-pairing strategy across customers. Covers loaded-ratio math, lane balance, region-pair pricing, and software-supported 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。
Use this skill when the user asks about US-Mexico cross-border CMV operations — long-haul cross-border program, B-1 visas for Mexican drivers, FMCSA OP-1(MX) authority, US-Mexico border commercial zones, USMCA / NAFTA implications, customs brokerage (C-TPAT, FAST), and how a US carrier handles cross-border loads. Reference 49 CFR 365.501-507.
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 a manager, dispatcher, or safety director needs to have a coaching conversation with a driver — about a safety event (harsh brake, hard turn, distraction), an HOS violation, a customer complaint, a missed delivery, repeated minor incidents, or to deliver positive recognition. Covers conversation structure, language, documentation, and escalation.
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 this skill when the user asks about CMV operation types — Dedicated, OTR (Over-the-Road), Regional, Local, and the differences in driver lifestyle, pay, equipment, home time, hours, and customer mix. Reference industry segmentation conventions.
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 a carrier (asset-based, brokerage, or 3PL) asks how to respond to a shipper RFP / RFQ — bid packaging, lane analysis, rate strategy, SOPs/SOWs, qualifications response, insurance compliance, EDI requirements, and how to win competitive bids without leaving margin on the table.
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 this skill when the user asks about trucking dispatch software — McLeod, TMW (Trimble), Tailwind, Truckbase, AscendTMS — vs load boards — DAT, Truckstop, 123Loadboard, Convoy, Uber Freight. Covers the workflow (find load, book, dispatch, run, settle), how dispatch software integrates with ELDs + accounting + customer EDI, and what small fleets vs larger fleets typically use.
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 this skill when the user asks about cybersecurity for a trucking carrier — protecting ELD data, customer information, financial systems, EDI integrations, fleet telematics, ransomware risk, vendor / shipper data sharing, incident response, and FMCSA cyber-recommendations. Reference NIST + CISA cybersecurity frameworks.
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 this skill when the user asks about US-Canada cross-border CMV operations — CVOR (Commercial Vehicle Operator's Registration), NSC (National Safety Code), Ontario / Quebec / BC specific rules, US carrier obtaining Canadian operating authority, FAST card for drivers, customs (CBSA), Health Canada hazmat, and how Transport Canada equivalents differ from US FMCSA. Reference USMCA + Transport Canada NSC 14.
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 this skill when the user asks how to manage a trucking carrier's public reputation — handling a major accident in the news, social media + Glassdoor + Indeed reviews, customer disputes that escalate, employee disputes, regulator perception, and how to communicate during a crisis. Reference industry PR + crisis communication practices.
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 a carrier asks about disaster recovery (DR) and business continuity planning (BCP) — cyber incident, natural disaster, terminal loss, key-person departure, financial liquidity event, major customer loss, insurance crisis, or fuel supply disruption. Covers risk assessment, continuity strategy, communication plans, and recovery time objectives.
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 this skill when the user asks how to market a trucking company — sales channels, prospecting, website + brand, industry directories (D&B Hoovers, ZoomInfo), referral programs, content marketing, and ROI of different acquisition channels. Reference industry sales practices.
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 this skill when the user asks about buying or selling a trucking company — valuation drivers (revenue, fleet age, CSA scores, customer concentration), asset purchase vs stock purchase, FMCSA authority transfer, due diligence checklist, common deal structures (cash, earnout, seller-financed), and what changes operationally after acquisition. Reference 49 CFR 365.401 (authority transfer).
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 this skill to explain to customers/sales prospects why FMCSA CSA scores are not available in real-time. Covers the SAFER refresh cycle and what 'real-time' actually means for CSA data.
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。