scie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

Hidden in Plain Sight: A Lights-Out Mystery at the Aquarium! (Middle Grade Mysteries, Books for Kids Ages 8-12, Books about Scie

為了解決scie的問題，作者Jerome, Kate B. 這樣論述：

Kate B. Jerome is an award-winning author who has written more than 100 science books for children, including a NYT bestseller. A lifelong science literacy proponent with a special interest in ocean conservation, she is a Director Emeritus of the South Carolina Aquarium.

scie進入發燒排行的影片

生物炭和植物生長促進根圈細菌對污染土壤中鎘移動性與植物吸收之影響

為了解決scie的問題，作者洪振愷 這樣論述：

近年來隨著工業發展產生不少環境污染問題，其中土壤重金屬污染問題增加，也提高對人體危害風險發生之可能。添加生物炭與植物生長促進根圈細菌（plant-growth-promoting rhizobacteria簡稱PGPR）作為重要的土壤改良劑，利用PGPR之特性與生物炭相互作用增強土壤修復過程，對重金屬污染土壤復育應用具有極大潛力。本研究探討聯合施用生物炭與耐重金屬PGPR對受鎘污染之農田土壤中植物有效性鎘含量與吸收之影響。主要成果分述如下:1.利用熱裂解技術將菱角殼、稻殼和雜木等農業廢棄物轉化成良好生物炭，生物炭之pH值（1:5，生物炭/水）均為鹼性，約為8.35~10.75，其中以菱殼炭之

pH值與EC值較高，約為0.98~5.02 mS/cm，生物炭EC值因質材不同而有所不同。生物炭之孔隙性、比表面積與元素組成不盡相同，以雜木炭的比表面積（173 m^2/g）為最高。2.收集台南地區4處鎘污染土壤的9個植物根圈土壤樣品，篩選出53株耐受20 mg/L鎘之菌株，其中41株菌株具有固氮能力；34株菌株具有溶磷能力；其中13株菌株同時具有固氮、溶磷與IAA產生能力。3.挑選出5株具有4項以上植物促進功能的菌株並完成菌種鑑定；耐鎘菌TA794-9之溶磷能力最佳，培養50小時（菌數達10^8 CFU/mL），即可產生132.9 mg/L的水溶性磷酸鹽；各菌株之IAA產生量介於4.0~29

.9 mg/L，以耐鎘菌TA751-8分泌IAA的能力為最佳。4.在不同稀釋倍率下，TA751-4與TA751-6菌株胞外分泌物有較佳之促進萵苣胚根與胚莖生長作用。5.芥菜盆栽試驗中，施用生物炭對於土壤pH值、EC值與有機質含量有顯著提升作用，單獨接種TA751-6對植體鮮重、乾重與降低植物吸收鎘有顯著效應;施用雜木炭與稻殼炭對植體鮮重、乾重與降低植物吸收鎘有顯著效應;耐鎘菌TA751-6配合生物炭之共同施用時，對提升植物生長與降低植體吸收重金屬之功效較為顯著。

Pandemic Detection and Analysis Through Smart Computing Technologies

為了解決scie的問題，作者 這樣論述：

Ram Shringar Raw, PhD, is working in the Department of Computer Science and Engineering of Netaji Subhas University of Technology, East Campus, Delhi, India. He formerly worked as an Associate Professor in the Department of Computer Science, Indira Gandhi National Tribal University (A Central Univer

sity, MP). He has more than 18 years of teaching, administrative, and research experience. Currently, he is associated with a wide range of journals and conferences as chief editor, editor, chair, and member. He has guided many MTech and PhD students for their dissertation and thesis. Currently, he

is guiding two MTech and four PhD students. His current research interest includes mobile ad hoc networks, vehicular ad hoc networks, and IoT cloud-based networks. Dr. Raw has published two books and more than 100 research papers in international journals and conferences, including those from IEEE,

Elsevier. Springer, Wiley & Sons, Taylor & Francis, Inderscience, Hindawi, IERI Letters, American Institute of Physics, etc.Vishal Jain, PhD, is Associate Professor in the Department of Computer Science and Engineering, School of Engineering and Technology, Sharda University, Greater Noida, U. P., I

ndia. Before that, he has worked for several years as Associate Professor at Bharati Vidyapeeth’s Institute of Computer Applications and Management (BVICAM), New Delhi. He has more than 14 years of experience in the academics. He has more than 400 research citation indices with Google Scholar (h-ind

ex score 10 and i-10 index 11). He has authored more than 85 research papers in conferences and journals, including the Web of Science and Scopus. He has authored and edited more than 10 books with various reputed publishers, including Springer, Apple Academic Press, CRC, Taylor and Francis Group, S

crivener, Wiley, Emerald, and IGI-Global. His research areas include information retrieval, semantic web, ontology engineering, data mining, ad hoc networks, and sensor networks. He received a Young Active Member Award for the year 2012-13 from the Computer Society of India and a Best Faculty Award

for the year 2017 and Best Researcher Award for the year 2019 from BVICAM, New Delhi. He holds PhD (CSE), MTech (CSE), MBA (HR), MCA, MCP and CCNA.Sanjoy Das, PhD, is Associate Professor and Head, Department of Computer Science, Indira Gandhi National Tribal University (a Central Government Universi

ty), Amarkantak, M.P. (Manipur Campus), India. Before joining IGNTU he worked as an Associate Professor at the School of Computing Science and Engineering, Galgotias University, India, as well as Assistant Professor. He was also Assistant Professor at G. B. Pant Engineering College, Uttarakhand, and

Assam University, Silchar, India. His current research interests include mobile ad hoc networks and vehicular ad hoc networks, distributed systems, and data mining. He has published numerous papers in international journals and conferences, including those associated with IEEE and Springer. He did

his BE, MTech, and PhD degrees in Computer Science.Meenakshi Sharma, PhD, is Dean and Professor in the School of Computer Science & Engineering of the University Center of Research & Development (UCRD) at Galgotias University, Greater Noida, India. She has over 16 years of experience in teaching and

research. She is a Senior Member of IEEE and is a highly qualified professional with an MTech in Computer Science & Engineering and a PhD in Computer Science (both from Kurukshetra University). Her research interests are machine learning, image processing, big data analytics, data compression, and

digital and data warehousing. She has published over 60 research papers in IEEE Transaction, SCIE, SCI and Scopus in machine learning, deep learning, and AI, in collaboration with international authors. She has published four international grant patents and seven Indian patents. She was awarded a Be

st Research and Teacher Award in 2017 and 2018. Dr. Sharma has capably guided three PhD candidates and 40+ students in undergraduate/postgraduate programs, with five currently under guidance. She is valuable member of various engineering societies, including ISTE, ACM, InSc, ISDS Society, Japan, IEA

E, and many others.

消防人員於黑暗複雜建築環境之搜索教育訓練研究

為了解決scie的問題，作者林世明 這樣論述：

依據內政部消防署統計年報，1997至2020年間共計84名消防人員因公死亡(殉職)，5人因公全殘、17人因公半殘、2,903人因公受傷，其中最嚴重的傷亡都是執行黑暗複雜環境的火災勤務所致，消防人員在黑暗又複雜的火場環境中搜索可能會失去空間方向，嚴重時將會在火場上危及自身生命安全，充分突顯加強黑暗複雜環境的搜救能力訓練刻不容緩。本研究於新北市政府消防局汐止保長坑訓練中心（New Taipei City Government Fire Department,NTFD）的黑暗複雜空間(濃煙搜索訓練立體鐵籠)進行，由58位消防人員參與實驗，全身穿戴消防衣、帽、鞋(PPE)及空氣呼吸器(SCBA)完成

重裝體能訓練，接續執行全尺寸黑暗複雜空間(Dark and Complex Environments,DCEs)的尋路時間實驗，針對性別、年齡、消防分隊特性（都市、郊區或山區）、服務年資、救助隊訓練、慣用手、尋路的起始方向、火場搜索經驗、懼高症、怕黑、害怕陌生人、容易緊張及幽閉恐懼症等13個因子間尋路時間的差異，透過t檢定獲得敘述性統計資料，並進行多元線性回歸分析檢定顯著有意義因子，最後綜合問卷資料與統計分析資料獲得結果。經實驗結果得知，「年齡」可能是「次要顯著」及「幽閉恐懼症」為「顯著」的有意義因子，針對研究結論提出，協助透過行為、藥物理療降低對幽閉空間恐懼程度並評估調整執行之勤務性質建議，

及透過訓練可以改善「年齡」次要顯著影響，提出建立標準化黑暗複雜環境(DCEs)尋路行為安全訓練模式、成立制度化專業訓練教官團、建置機關專屬黑暗複雜環境(DCEs)訓練設施等短、中、長程建議，提供各消防機關參考，期有效縮短消防人員處於黑暗及複雜的火場環境尋路時間，提升緊急應變與確保自身安全能力。