User needs for extreme wind information in the forest, insurance and offshore energy sectors

2020 ◽  
Vol 2 (1) ◽  
Keyword(s):  
User Needs ◽  
Extreme Wind ◽  
Forest Insurance

EURODEMO: End-user needs

2009 ◽  
Vol 17 (3) ◽  
pp. 333-334 ◽  
Author(s):  
David Edwards
Keyword(s):  
User Needs ◽  
End User

Pemahaman Komponen dan Fungsi Perangkat Lunak Komputer Dalam Pembelajaran Daring

2020 ◽  
Author(s):  
wulan sipahutar
Keyword(s):  
Online Communication ◽  
Computer Application ◽  
User Needs ◽  
Application Software ◽  
System Operation ◽  
Microsoft Excel ◽  
Financial Reports ◽  
Network Operations ◽  
Video And Audio ◽  
The Brain

Software Component is a computer application that is useful for carrying out a desired job. This application is needed as a liaison between the brain (brainware) with hardware (hardware). Software also means a computer program that serves to perform special tasks, such as making documents, manipulating photos, making financial reports, or designing a house. Software can be divided into two, namely: System Operation. Software that functions to connect between users and computers, for example Windows 7, Windows 8, Linux, and Windows 10. Application. Software that can be applied to meet user needs. System network adapter drivers, network operations, and network protocols. As for examples of software in online communication, as follows. skype, hangout, web conference, whatsapp, Microsoft excel, video and audio, earlerning

ANALISA ALAT PERMAINAN EDUKATIF ANAK

2020 ◽  
Vol 1 (2) ◽  
pp. 91-96
Author(s):  
Wiyogo
Keyword(s):  
Cognitive Development ◽  
Engineering Education ◽  
Motor Skills ◽  
Mechanical Engineering ◽  
Research Data ◽  
Educational Game ◽  
User Needs ◽  
Initial Questionnaire ◽  
Design And Manufacturing ◽  
Index Value

Abstrak: Alat permainan edukatif memiliki manfaat penting dalam perkembangan kognitif anak, keterampilan motorik, dan kebugaran anak. Desain dan pembuatan yang dilakukan di Laboratorium Pendidikan Teknik Mesin Universitas Palangka Raya perlu dianalisis untuk mengetahui sejauh mana alat ini berguna. Hal pertama yang harus dilakukan adalah mempelajari literatur dan kemudian mengidentifikasi variabel penelitian yang bertujuan mendapatkan gambaran tentang apa yang dibutuhkan pengguna. Dari hasil kuesioner awal, tiga variabel diperoleh, yaitu desain, permainan mudah, dan keamanan. Data penelitian lebih lanjut yang telah diperoleh dengan nilai indeks dari produk yang diperoleh adalah 94,83%.   Abstract: Educational game tools have important benefits in a child's cognitive development, motor skills, and children's fitness. The design and manufacturing done at the Mechanical Engineering Education Laboratory of Palangka Raya University needs to be analyzed to find out how far the tool is useful. The first thing to do is to study the literature and then identify research variables aimed at getting a picture of what the user needs. From the results of the initial questionnaire, three variables were obtained, namely design, easy game and safety. Further research data that have been obtained with an index value of the products obtained are 94.83%.

Forecasting of Tornadoes and Downbursts

2020 ◽  
Author(s):  
Djordje Romanic
Keyword(s):  
Chaos Theory ◽  
Prediction Models ◽  
Weather Prediction ◽  
Small Scale ◽  
Weather Forecasts ◽  
Wind Speeds ◽  
The Past ◽  
Extreme Wind ◽  
Extreme Wind Speeds ◽  
Numerical Weather Prediction Models

Tornadoes and downbursts cause extreme wind speeds that often present a threat to human safety, structures, and the environment. While the accuracy of weather forecasts has increased manifold over the past several decades, the current numerical weather prediction models are still not capable of explicitly resolving tornadoes and small-scale downbursts in their operational applications. This chapter describes some of the physical (e.g., tornadogenesis and downburst formation), mathematical (e.g., chaos theory), and computational (e.g., grid resolution) challenges that meteorologists currently face in tornado and downburst forecasting.

scholarly journals Antecedent lake conditions shape resistance and resilience of a shallow lake ecosystem following extreme wind storms

2021 ◽  
Author(s):  
Michael W. Thayne ◽  
Benjamin M. Kraemer ◽  
Jorrit P. Mesman ◽  
Bastiaan W. Ibelings ◽  
Rita Adrian
Keyword(s):  
Shallow Lake ◽  
Lake Ecosystem ◽  
Extreme Wind ◽  
Wind Storms

scholarly journals The Budapest Amyloid Predictor and Its Applications

Biomolecules ◽  
2021 ◽  
Vol 11 (4) ◽  
pp. 500
Author(s):  
László Keresztes ◽  
Evelin Szögi ◽  
Bálint Varga ◽  
Viktor Farkas ◽  
András Perczel ◽  
...  
Keyword(s):  
Neural Network ◽  
Neural Networks ◽  
Artificial Neural Networks ◽  
Support Vector ◽  
User Needs ◽  
Computational Technique ◽  
Linear Support Vector Machine ◽  
Β Sheets ◽  
Artificial Neural ◽  
Amorphous Aggregation

The amyloid state of proteins is widely studied with relevance to neurology, biochemistry, and biotechnology. In contrast with nearly amorphous aggregation, the amyloid state has a well-defined structure, consisting of parallel and antiparallel β-sheets in a periodically repeated formation. The understanding of the amyloid state is growing with the development of novel molecular imaging tools, like cryogenic electron microscopy. Sequence-based amyloid predictors were developed, mainly using artificial neural networks (ANNs) as the underlying computational technique. From a good neural-network-based predictor, it is a very difficult task to identify the attributes of the input amino acid sequence, which imply the decision of the network. Here, we present a linear Support Vector Machine (SVM)-based predictor for hexapeptides with correctness higher than 84%, i.e., it is at least as good as the best published ANN-based tools. Unlike artificial neural networks, the decisions of the linear SVMs are much easier to analyze and, from a good predictor, we can infer rich biochemical knowledge. In the Budapest Amyloid Predictor webserver the user needs to input a hexapeptide, and the server outputs a prediction for the input plus the 6 × 19 = 114 distance-1 neighbors of the input hexapeptide.

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