NET Core 7的問題，透過圖書和論文來找解法和答案更準確安心。 我們找到下列問答集和資訊懶人包

Visual C# 2022基礎必修課

為了解決NET Core 7的問題，作者蔡文龍,張志成,何嘉益,張力元,歐志信 這樣論述：

　　體貼初學者的學習流程：語法解說->範例說明->問題分析->程式設計 　　**專家與教師共同執筆** 　　由微軟MVP、微軟認證專家及科技大學教授程式設計教師共同編著，針對目前初學者學習程式設計所應具備的基本素養，編寫的入門教材，內容由淺入深，帶領初學者靈活運用C#開發應用程式。 　　**程式設計技能的養成** 　　內容兼具理論與實務，書中範例程式有別於市面程式設計入門書。特別由語法解說、範例說明、問題分析、程式設計進行循序漸進的範例實作與解說，訓練初學者邏輯思考與解決問題的能力。 　　**內容多元與豐富範例** 　　內容由資料型別、變數、運算式、流程控制、陣列、

函式、物件導向程式設計、功能表、對話方塊、檔案存取、多媒體操作，同時配合 .NET Core視窗程式控制項設計進階的視窗應用程式，所提供範例貼近日常生活，讓初學者能學以致用。 　　**Entity Framework Core、電腦視覺與專題製作** 　　提供LINQ與Entity Framework Core資料庫程式設計，以及拉霸、記憶大考驗遊戲、產品管理系統三個專題，綜合運用前面各章節所學，達到學以致用的目標。同時介紹AI電腦視覺開發，讓初學者一探人工智慧應用程式開發的奧妙。  

NET Core 7進入發燒排行的影片

B2B會展參展商展位人員的個人特徵、互動行為及交流表現

為了解決NET Core 7的問題，作者盧盟方 這樣論述：

企業對企業(B2B)會展被視為行銷人員與現有和潛在顧客進行聯繫的成本效益措施(Bello&Barksdale,1988;Lilien,1983)。 此外，它們被視為企業資訊收集、產品曝光、關係建立和組織學習的寶貴渠道(Hansen,1996;Kerin&Cron,1987; Li,2006)。B2B會展作為行銷媒體的組成部分，為產業買賣雙方提供了一個進行交易、關係和資訊交流的良好平台(Siemieniao&Gębarowski,2017)。現有的展覽文獻缺乏對B2B會展背景下展位人員-展覽參觀者二元互動的前因和後果的系統性和理論性調查。從社會交換理論的角度來看，這項研究的主要目的是研究被忽視

但關鍵的主題，希望提供有趣的理論討論和管理建議。具體而言，本研究試圖發展和測試與展覽相關的模型，該模型的構建旨在找到有關展位人員與參觀者互動的三個問題的初步答案。一、個人特質是否會影響展位人員的互動行為？二、展位人員的顧客導向行為是否可以調解個人特質與互動行為之間的聯繫？ 三、展位人員的互動行為是否會影響參觀者對展位人員的資訊績效和關係績效的評估？本研究模型包含三個主要組成部分。一，展位人員的個人特徵包括以下因素：尋求挑戰(challenge seeking)、工作樂趣(task enjoyment)和外向性格(extraversion personality)。二是展位人員的互動行為，包括資

訊蒐集(information gathering)、B2B會展的關係行銷視角(Relationship Marketing Perspective)和關係綁定(relationship bonding)。三是兩個變數代表展位參觀者在對展位人員的資訊績效(informational performationce)和關係績效(relational performance)的評估。此外，本研究還探討了顧客導向(customer orientation)對個人特徵和互動行為之間Relationship Marketing Perspective的中介效果。 最後，本研究表明：一、內在動機、外向性格和

顧客導向是展位人員互動行為的重要先決條件。二、二元數據表明互動行為與展位參觀者的信息和關係表現密切相關。三、展位人員的顧客導向積極地調解了內在動機和互動行為因素之間的關係。四、展位人員的顧客導向積極地調解了外向性與互動行為因素之間的關係。

Blazor in Action

為了解決NET Core 7的問題，作者Sainty, Chris 這樣論述：

An example-driven guide to building reusable UI components and web frontends--all with Blazor, C#, and .NET.In Blazor in Action, you will learn about: Blazor + WebAssembly Picking the right hosting model Building reusable UI components Building forms with validation Integrating with JavaScript l

ibraries Securing your application Testing your applications Blazor in Action is a practical guide to building stunning UIs and client-side applications using C# and .NET. You’ll use the Blazor frontend framework to create a fun and exciting web application for plotting hiking routes. As you build u

p your new application, you’ll master the key features of Blazor, such as routing, forms and validation, and dynamic and reusable components. By the time you’re done, you’ll be ready to develop beautiful sites and apps that seamlessly execute your C# code natively in the browser. The book is written

to the most recent stable build of Blazor and seamlessly integrates fresh features from .NET 6. Purchase of the print book includes a free eBook in PDF, Kindle, and ePub formats from Manning Publications. About the technology Create rich web frontends without relying on JavaScript. Microsoft’s Bl

azor framework uses WebAssembly to extend the ultra-popular ASP.NET platform. In Blazor, you can build interactive web components that run natively in the browser without plug-ins or transpilers. And because it’s C# end-to-end, it’s easy to share code between the server and your web UI. About the b

ookBlazor in Action teaches you to create full-stack ASP.NET applications end-to-end in C#. You’ll start by learning to build Blazor web components, working through core topics like routing and forms. As you go, you’ll implement a hiking route web application that includes reusable code, integration

with JavaScript libraries, and role-based security. To make sure your app is production ready, this practical book also covers state management, data persistence, and testing. What’s inside Dynamic and reusable UI components Sharing client and server code Role-based security using Auth0 Persistin

g state using local browser storage About the reader For web developers with C# and .NET experience. About the authorChris Sainty has been a part of the Blazor community from the beginning. He’s an active blogger, open source developer, international speaker, and a Microsoft MVP. Table of Contents

1 Starting your Blazor journey 2 Your first Blazor app 3 Working with Blazor’s component model 4 Routing 5 Forms and validation--Part 1: Fundamentals 6 Forms and validation--Part 2: Beyond the basics 7 Creating more reusable components 8 Integrating with JavaScript libraries 9 Securing Blazor applic

ations 10 Managing state 11 Testing your Blazor application

應用於HER2乳癌標靶治療 之 DISH影像分析深度學習方法

為了解決NET Core 7的問題，作者Muhammad Adil Khalil 這樣論述：

The amount of overexpression of the human epidermal growth factor receptor 2 (HER2) is a predictive biomarker for metastatic breast cancer that may be used to evaluate therapeutic treatments. In HER2 immunohistochemical scores 2+ equivocal cases, accurate HER2 testing is critical for determining th

e most suitable precision treatment. HER2 in situ hybridization has generally been carried out using fluorescence in situ hybridization techniques (FISH). In recent years, brightfield dual in situ hybridization (DISH) has emerged as an efficient and viable alternative approach to replace fluorescen

t in situ hybridization (FISH) in various labs, including ours. The presented deep learning based framework allows effective and precise automated detection of ERBB2 to CEN17 signals ratio and the mean HER2 copies for each nucleus in DISH images for clinical usage, consequently avoid inter-oberserv

er variability and shortening the time required for routine manual assessment. To the best of author's knowledge, this is the first study to explore the use of deep learning technology to automatically detect HER2 overexpression in HER2 DISH images acquired from clinical breast cancer samples. On tw

o datasets, we assess the efficiency of the proposed models. The results illustrate that the proposed method 1 accomplishes better performance than the baseline techniques with an accuracy of 97.11±2.39, precision of 96.92±1.45, recall of 92.49±2.04, F1-score of 94.65±3.59, and Jaccard Index of 88.

43±10.27 on dataset 1 and an accuracy of 97.80±1.05, precision of 97.48±1.07, recall of 91.80±3.84, F1-score of 94.56±3.04, and Jaccard Index of 88.39±5.16 for dataset 2. Additionally, using Fisher's LSD, the proposed method 1 outperforms the baseline approaches by a significant margin (P