N box 2021的問題，透過圖書和論文來找解法和答案更準確安心。 我們找到下列各種有用的問答集和懶人包

財經時間序列預測：使用R的計量與機器學習方法

為了解決N box 2021的問題，作者何宗武 這樣論述：

　　本書為進階的教材，需要經濟計量方法和矩陣代數的基礎。時間序列預測是統計學裡非常實用的工具，不論是分析投資組合的數據、全球總經和金融市場，以及預測景氣循環變動等等，可以用過去的數據資料，預測未來趨勢，是可以符合實際決策需要的實用能力。   　　書中並介紹機器學習方法，機器學習不是指特定估計方法，學習指的是如何在資料結構中運算，以追蹤最小預測誤差的方法獲得最佳預測(tuning)。我們應用機器學習演算法訓練歷史資料，執行特徵萃取(features extraction)，再測試預測表現。依此建立一個可預測未來的模型，作為決策之用。   　　使用R語言進行時間序列預測是本書的一大特點，R語言

是統計學中普及且容易上手的分析工具，書中針對一個個資料分析步驟進行深度解說，教給讀者進行預測與評估的最實用方法。

N box 2021進入發燒排行的影片

An Integrated Approach For Uncovering Novel DNA Methylation Biomarkers For Non-small Cell Lung Carcinoma

為了解決N box 2021的問題，作者VAIBHAV KUMAR SUNKARIA 這樣論述：

Introduction - Lung cancer is one of primal and ubiquitous cause of cancer related fatalities in the world. Leading cause of these fatalities is non-small cell lung cancer (NSCLC) with a proportion of 85%. The major subtypes of NSCLC are Lung Adenocarcinoma (LUAD) and Lung Small Cell Carcinoma (LUS

C). Early-stage surgical detection and removal of tumor offers a favorable prognosis and better survival rates. However, a major portion of 75% subjects have stage III/IV at the time of diagnosis and despite advanced major developments in oncology survival rates remain poor. Carcinogens produce wide

spread DNA methylation changes within cells. These changes are characterized by globally hyper or hypo methylated regions around CpG islands, many of these changes occur early in tumorigenesis and are highly prevalent across a tumor type.Structure - This research work took advantage of publicly avai

lable methylation profiling resources and relevant comorbidities for lung cancer patients extracted from meta-analysis of scientific review and journal available at PubMed and CNKI search which were combined systematically to explore effective DNA methylation markers for NSCLC. We also tried to iden

tify common CpG loci between Caucasian, Black and Asian racial groups for identifying ubiquitous candidate genes thoroughly. Statistical analysis and GO ontology were also conducted to explore associated novel biomarkers. These novel findings could facilitate design of accurate diagnostic panel for

practical clinical relevance.Methodology - DNA methylation profiles were extracted from TCGA for 418 LUAD and 370 LUSC tissue samples from patients compared with 32 and 42 non-malignant ones respectively. Standard pipeline was conducted to discover significant differentially methylated sites as prim

ary biomarkers. Secondary biomarkers were extracted by incorporating genes associated with comorbidities from meta-analysis of research articles. Concordant candidates were utilized for NSCLC relevant biomarker candidates. Gene ontology annotations were used to calculate gene-pair distance matrix fo

r all candidate biomarkers. Clustering algorithms were utilized to categorize candidate genes into different functional groups using the gene distance matrix. There were 35 CpG loci identified by comparing TCGA training cohort with GEO testing cohort from these functional groups, and 4 gene-based pa

nel was devised after finding highly discriminatory diagnostic panel through combinatorial validation of each functional cluster.Results – To evaluate the gene panel for NSCLC, the methylation levels of SCT(Secritin), FOXD3(Forkhead Box D3), TRIM58(Tripartite Motif Containing 58) and TAC1(Tachikinin

1) were tested. Individually each gene showed significant methylation difference between LUAD and LUSC training cohort. Combined 4-gene panel AUC, sensitivity/specificity were evaluated with 0.9596, 90.43%/100% in LUAD; 0.949, 86.95%/98.21% in LUSC TCGA training cohort; 0.94, 85.92%/97.37 in GEO 66

836; 0.91,89.17%/100% in GEO 83842 smokers; 0.948, 91.67%/100% in GEO83842 non-smokers independent testing cohort. Our study validates SCT, FOXD3, TRIM58 and TAC1 based gene panel has great potential in early recognition of NSCLC undetermined lung nodules. The findings can yield universally accurate

and robust markers facilitating early diagnosis and rapid severity examination.

野生熱帶叢林驚喜拼圖1000片 Wild Tropics 1000 Piece Surprise Puzzle

為了解決N box 2021的問題，作者 這樣論述：

 　　美國Galison出版社推出全新拼圖「野生熱帶叢林驚喜拼圖1000片」，獨特的風格插畫出自菲律賓藝術家 Raxenne Maniquiz之手，她透過明亮且富有生命力的筆觸，生動描繪家鄉中美麗豐富的熱帶叢林姿態。這套拼圖的驚喜之處在於：有些元素在外盒上有出現，但完整的景色在拼圖過程中才會一一出現，拼的時候可以慢慢發掘每個元素的小細節，拼圖完成時真的會帶來無限驚喜！商品含霧面精裝外盒，適合送禮、重複使用與收藏，附拼圖參考圖。   　　☆外盒：21 x 28 x 5公分，完成尺寸：50 x 68 公分 　　☆拼圖粉屑少 　　☆內附拼圖參考圖 　　☆拼圖灰板含90%再生紙，包裝含70%再生紙

，由FSC認證的材料製成，使用無毒油墨印刷。 　　☆藝術家Raxenne Maniquiz：是位來自馬尼拉的平面設計師及插畫家，作品中常出現花卉植物的元素。她在2021年獲得享譽國際的國際青年設計大獎。         Wild Tropics 1000 Piece Surprise Puzzle from Galison features a jungle-themed surprise image. Some elements are presented on the box and the insert, but the complete scene emerges as the puz

zle is assembled for a truly one-of-a-kind puzzling experience. Galison puzzles are packaged in matte-finish sturdy boxes, perfect for gifting, reuse, and storage. An insert of the full puzzle image is also included. • 1000 Pieces, Ribbon Cut • Box: 8.5 x 10 x 2”, 210 x 286 x 51mm; Puzzle: 20 x 27”

, 508 x 686 mm • Includes Puzzle Insert • Minimal Puzzle Dust • Puzzle greyboard contains 90% recycled paper. Packaging contains 70% recycled paper and is made responsibly from FSC-certified material. Printed with nontoxic inks. - Artist: Raxenne Maniquiz

基於深度學習進行電池性質預測

為了解決N box 2021的問題，作者許家維 這樣論述：

鋰離子電池作為常見的儲能設備，廣泛應用於終端設備上且藉由電池管理系統進行監控確保電池老化程度仍可應付工作所需。然而電池在使用初期並無明顯老化特性的反應，因此對於使用過的電池無法很好評估預期壽命以至於材料的浪費或設備的異常(Early failure)。本研究利用時序資料連續性進行資料擴增更同時對神經網路潛空間進行正則化，並透過包含篩選器與預測器的神經網路架構在僅有少量循環的量測數據下，預測電池產品壽命、剩餘使用壽命、充電所需時間、放電時的電壓電量變化曲線等。其中，僅測量一個充放電完整循環的數據，就能提供僅有57週期方均根誤差的產品壽命預測。本研究亦同時引入注意力機制於此框架中達成僅使用若干個

循環的測量資料便可預測整個電池的產品週期放電電量、放電功耗等特性。