Choose an application

• Reference Manager
• EndNote
• RefWorks (Direct export to RefWorks)

Proceedings of the 10th Annual International Seminar on Trends in Science and Science Education (AISTSSE) 2023. This is the 10th meeting since it was first held in 2014 and the third virtual meeting due to the COVID-19 pandemic. This year's seminar focuses on the significant contribution of research to the development of technology in supporting Society 5.0. The theme of this exciting forum is "Future Mathematics, Natural Sciences, and Education Trends to Achieve Sustainable Development Goals for Addressing the Society 5.0 Challenge", which covers six main topics, including: 1. Fundamental and applied research in mathematics, including basic mathematics, applied mathematics, and statistics. 2. Fundamental and applied research in physics, including theoretical physics and computation, geophysics, materials science, and technology. 3. Fundamental and applied research in biology, including fundamental biology, applied biology, and biotechnology. 4. Fundamental and applied research in chemistry, including instrumentation, basic chemistry, and applied chemistry. 5. Science education. 6. Computer science. For this year's conference, we have invited six plenary speakers with solid expertise from different countries and regions: 1. Prof. Dr. Syawal Gultom, M.Pd - Universitas Negeri Medan, Indonesia, Prof. 2. Teresa J. Kennedy, Ph.D. (STEM Education) - University of Texas at Tyler, USA., Prof. Dr. 3. Yatimah Alias (Chemistry) Universiti Malaya, Malaysia; Sirilak Chumkiew, Ph.D. (Biology) 4. Assoc. Prof. TS Dr. Ahmad Kadri Junoh, Suranaree University of Technology, Thailand 5. (Mathematics) - Universiti Malaysia Perlis, Prof. Dr. Usmeldi, M.Pd. (Physics Education) - Universitas Negeri Padang We have invited speakers with expertise in different fields to initiate the presentations in parallel sessions: 1. Prof. Dr. Suyono, M.Pd. (Chemical Education), Universitas Negeri Surabaya 2. Prof. Dr. Mangasi Alion Marpaung, M.Si. (Physics), Universitas Negeri Jakarta, Endah 3. Retnowati, Ph.D. (Mathematics Education), Universitas Negeri Yogyakarta, 4. Hendra Susanto, Ph.D. (Biology), Universitas Negeri Malang Conference date, placeOctober 10th, 2023, Medan-North Sumatera-IndonesiaConference OrganizerFaculty of Mathematics and Natural Science, Universitas Negeri Medan Peer Review Process Instructions for Authors

SCIENCE / Chemistry / General.

Choose an application

• Reference Manager
• EndNote
• RefWorks (Direct export to RefWorks)

Damit Lehramtsstudierende Lehr-Lernprozesse so gestalten, dass bei Lernenden Erkenntnisprozesse initiiert und unterstützt werden, müssen sie sich vertieft mit den Fachinhalten auseinandersetzen. Die Identifizierung von lernbedeutsamen Zusammenhängen und die Übertragung in Lernschritte sind für Studierende nachweislich schwierig. Daher werden in der vorliegenden Arbeit Verstehens -prozesse von Lehramtsstudierenden bei der Erschließung eines komplexen fachlichen Sachverhaltes und dessen fachdidaktische Verwertung untersucht. Dazu wurde eine Fallstudie mit N ,= ,14 Versuchspersonen durchgeführt, für die ein leitfragenstütztes Lehr-Lernkonzept entwickelt wurde, um eine Vertretungsstunde für einen Leistungskurs Chemie zur Auswertung der Analyse eines Farbstoffgemisches zu erarbeiten. Zur Erschließung der inhaltlichen Zusammenhänge wurden englische Lehrbuchtexte (nachweislich weniger lückenhaft in ihren Argumentationslinien als deutsche) eingesetzt. Ausgewertet wurden die Antworten zu den Leitfragen. Die Auswertung erfolgte mit einem auf Propositionen aufbauenden Kategoriensystem. Die Studierenden haben nur teilweise die relevanten Textpassagen eigenständig erkannt. Entscheidend waren die fachlichen Voraussetzungen. Eine Zunahme im fachlichen Verständnis wirkte sich positiv auf die fachdidaktische Verwertung des erworbenen Wissens aus. Folglich sind für die Lehre leitfragenstützte Lehr-Lernkonzepte, auch unter Einbindung englischer Lehrbuchtexte, für eine vertiefte Wissensverarbeitung zu empfehlen.

Literature --- History and criticism --- Hochschuldidaktik --- Science / Chemistry --- Education --- Literary Criticism / American

Choose an application

• Reference Manager
• EndNote
• RefWorks (Direct export to RefWorks)

The book discusses various inequalities and sharp bounds for the usual order statistics as well as some functions of them. In particular, deterministic bounds, bounds for the case of IID samples from general, symmetric and life distributions, IID samples from shape restricted family of distributions, and samples from finite populations are all discussed in detail. An elaborate numerical evaluation and comparison of various bounds are also presented in order to illustrate their inherent differences as well as their precision. Furthermore, their applications to inference, reliability theory and characterizations are also highlighted. The book provides an in-depth exposure to various mathematical inequalities and bounds established historically as well as in recent years and their applications to order statistics and some important functions of them. It thus presents an up-to-date discussion of all results in this important area of mathematical and statistical research. The results described here are general in nature and therefore could be useful in other areas of Probability and Statistics as well.

Statistics. --- Statistical Theory and Methods. --- Statistics in Engineering, Physics, Computer Science, Chemistry and Earth Sciences.

Choose an application

• Reference Manager
• EndNote
• RefWorks (Direct export to RefWorks)

This monograph explores a set of statistical and machine learning tools that can be effectively utilized for applied data analysis in the context of electricity load forecasting. Drawing on their substantial research and experience with forecasting electricity demand in industrial settings, the authors guide readers through several modern forecasting methods and tools from both industrial and applied perspectives – generalized additive models (GAMs), probabilistic GAMs, functional time series and wavelets, random forests, aggregation of experts, and mixed effects models. A collection of case studies based on sizable high-resolution datasets, together with relevant R packages, then illustrate the implementation of these techniques. Five real datasets at three different levels of aggregation (nation-wide, region-wide, or individual) from four different countries (UK, France, Ireland, and the USA) are utilized to study five problems: short-term point-wise forecasting, selection of relevant variables for prediction, construction of prediction bands, peak demand prediction, and use of individual consumer data. This text is intended for practitioners, researchers, and post-graduate students working on electricity load forecasting; it may also be of interest to applied academics or scientists wanting to learn about cutting-edge forecasting tools for application in other areas. Readers are assumed to be familiar with standard statistical concepts such as random variables, probability density functions, and expected values, and to possess some minimal modeling experience.

Statistics. --- Machine learning. --- Statistics in Engineering, Physics, Computer Science, Chemistry and Earth Sciences. --- Statistical Learning. --- Machine Learning.

Choose an application

• Reference Manager
• EndNote
• RefWorks (Direct export to RefWorks)

This book reports on cutting-edge theories and methods for analyzing complex systems, such as transportation and communication networks and discusses multi-disciplinary approaches to dependability problems encountered when dealing with complex systems in practice. The book presents the most relevant findings discussed at the 23rd International Multidisciplinary Conference on Reliability and Statistics in Transportation and Communication (RelStat 2023), which took place as a hybrid event on October 19 – 21, 2023, in/from Riga, Latvia. It spans a broad spectrum of advanced theories and methods, giving a special emphasis to the digitalization of transport systems, as well as smart, artificial intelligence, and digital twins applications.

Transportation --- Computational intelligence. --- Transportation engineering. --- Traffic engineering. --- Statistics. --- Computational Intelligence. --- Transportation Technology and Traffic Engineering. --- Statistics in Engineering, Physics, Computer Science, Chemistry and Earth Sciences.