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Women of the West

為了解決Whats that mean的問題，作者West, Hannah 這樣論述：

Once Upon a Mail Order Bride: Mail Order Brides: Book One New York - 1869 Carrow and Emmy Cole are left with an angry and mean tempered great uncle after their parents are murdered. Their inheritance and money is being gambled away by their uncle and they have one of two choice to make. Wait until t

hey are twenty-one or marry to get their money. After a face to face assault by their uncle, Emmy has the idea that they should run away to become mail order brides. Carrow, the wild child of the twins, agrees and off the go to the Washington Territory to marry their men. Washington Territory - 1869

For Carrow that man is Clayton Stone, a well off man with a good paying job at the railroad, who has kept a few things to himself in his letters. But when a tall, darkly handsome man comes your way and offers you adventure and your parents last gift on a silver platter what would you do? Marry him

and let him sooth your sorrow...and your body? Welcome to the West. Once Upon a Mail Order Lady: Mail Order Brides: Book Two It's Emmy's turn at her own tale and she is happy in Seattle with her new husband. But crimes are being committed against women and there is a secret as to why. Emmy spots her

Uncle and runs to tell her husband, but she fall victim to the twisted man committing the crimes. After fighting for her life and taken a beating that causes her to lose her memory, Emmy is rescued by Black Hawk, a Native American, the brother of a young Native girl who Emmy had made friends with a

t market. She stays with the tribe while she heals and she is falling in love with Black Hawk, but must first have answers to her past before she can move forward. Eric is driven mad after his wife was attacked and kidnapped, as time goes by he has no idea if she is alive or dead and why it was her.

But the puzzle falls together in his lap when Carrow's and Emmy's uncle is taken in for almost beating a saloon girl to death. The plan is cracked open and they try to find out what has happened to Emmy, but between a letter sent to a lawyer and a gun shot out, no one knows whats going to happen an

d who is going to die. All over the money possessed by one family. Once Upon a Mail Order Orphan: Mail Order Brides: Book Three Isabeau has to leave her home at the orphanage because she is to old and there are to many mouths to feed, so she seeks to become a mail order bride to a man out west. All

she wants is a man who will treat her kindly and give her a family. When she picks a letter that had been left for years with no answer she decided it was the one for her. So she and one of her friends, another girl from the Home, named Amy travel to meet Isabeau's new husband. In for a big surprise

things won't go as planned but then again the best surprise is love is it not?

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Applications of Search Engine Query Data in Infodemiology Study: A Case Study of the Coronavirus Disease 2019 (COVID-19) Pandemic

為了解決Whats that mean的問題，作者ATINA HUSNAYAIN 這樣論述：

背景：在新型冠状病毒肺炎（新冠肺炎）疫情爆發之際，使用者於網路搜尋關鍵字之趨勢與新興流行病之疫情具高度關聯，因此運用訊息流行病學研究中利用搜尋引擎查詢資料之應用是相當重要的。目標：研究目的主要於發展新冠肺炎預測方法，並分析網際網路相關查詢數據之可能用途（研究1－3），並評估線上搜尋模型於包括（研究4）位置（集群和非集群區域）、（研究5）時期（疫情爆發階段）和（研究6）模型種類這些不同情況下之預測表現。方法：本研究使用來自 Google Trends 和 NAVER 搜尋引擎查詢數據以及新冠肺炎相關資料、Google 和 Apple 移動數據，並選擇台灣、菲律賓、美國和南韓為研究地點。在初步研

究（研究1－3）中，研究使用時間序列、相關分析和地圖視覺化等方法。而在主要研究（4）中，採用正規化線性回歸模型、廣義線性模型和空間狀態模型做為預測模型之開發，並利用G 統計量來定義群聚樣式，以預測每日新增之新冠肺炎病例數和死亡人數（研究 5 和 6）。針對預測模型之開發，在第一例確診病例被報導後，將新冠肺炎資料分成疫情爆發後前3、6、12 和 18 個月之四個不同疫情時期之資料集。於資料分區時，取資料集前80%資料作為訓練集，其餘資料作為測試集。並使用均方根誤差、峰值日誤差和峰值幅度誤差作為模型預測能力之評估指標。結果：研究結果顯示搜尋數據可用於定義（研究1）進行健康風險溝通之時機與地點，（研

究2）可供民眾搜尋基本健康資訊類型，以及（研究3）民眾在新冠肺炎大規模流行初期對健康風險之看法。此外，本研究發現新冠肺炎病例數和網路搜尋關鍵字間呈中度至高度之相關性，研究結果亦顯示使用者搜尋之關鍵字可用於預測新冠肺炎之確診案例數。在評估不同（研究4）位置（集群和非集群區域）時，線上搜尋模型表現時顯示在美國不同州別和疫情時期之間，線上搜尋模型之預測能力存在差異。這代表在使用查詢數據時，必須在每個州別建立不同研究框架。基於發現搜尋數據與疫情具高度相關之情況下，使用者查詢數據可用於預測新冠肺炎。另外，在不同（研究5）時期（疫情爆發階段）下，本研究亦分析線上搜尋模型之預測能力，結果顯示搜尋數據對於預測

每日新增新冠肺炎之病例數和死亡人數為有效變數。尤其於疫情爆發前六個月，研究結果發現搜尋數據在此時期具更高影響力。此外最後一項研究（研究6）顯示，使用時間序列之預測方法可提高線上搜尋模型之預測表現。結論：本研究發現搜尋數據可作為預測每日新增新冠肺炎病例和死亡人數之解釋變數，建議研究人員可利用這些搜尋數據之框架以建立預測模型。本研究亦顯示線上搜尋模型在高度相關之區域、疫情爆發前六個月以及包含趨勢成分之模型中能獲更佳預測表現。