Bayesian Numerical Methods as a Case Study for Statistical Data Science

2018 ◽  
pp. 99-110
Author(s):  
François-Xavier Briol ◽  
Mark Girolami
Keyword(s):  
Numerical Methods ◽  
Data Science ◽  
Statistical Data

scholarly journals Improving the Downwind Sail Design Process by Means of a Novel FSI Approach

2021 ◽  
Vol 9 (6) ◽  
pp. 624
Author(s):  
Antonino Cirello ◽  
Tommaso Ingrassia ◽  
Antonio Mancuso ◽  
Vincenzo Nigrelli ◽  
Davide Tumino
Keyword(s):  
Numerical Methods ◽  
Low Cost ◽  
The Novel ◽  
The Real ◽  
Weakly Coupled ◽  
Design And Manufacturing ◽  
Sailing Yacht ◽  
Computational Resources ◽  
Effective Use

The process of designing a sail can be a challenging task because of the difficulties in predicting the real aerodynamic performance. This is especially true in the case of downwind sails, where the evaluation of the real shapes and aerodynamic forces can be very complex because of turbulent and detached flows and the high-deformable behavior of structures. Of course, numerical methods are very useful and reliable tools to investigate sail performances, and their use, also as a result of the exponential growth of computational resources at a very low cost, is spreading more and more, even in not highly competitive fields. This paper presents a new methodology to support sail designers in evaluating and optimizing downwind sail performance and manufacturing. A new weakly coupled fluid–structure interaction (FSI) procedure has been developed to study downwind sails. The proposed method is parametric and automated and allows for investigating multiple kinds of sails under different sailing conditions. The study of a gennaker of a small sailing yacht is presented as a case study. Based on the numerical results obtained, an analytical formulation for calculating the sail corner loads has been also proposed. The novel proposed methodology could represent a promising approach to allow for the widespread and effective use of numerical methods in the design and manufacturing of yacht sails.

The influence of spatial design characteristics on low-rise residential neighbourhoods in Basra city

2014 ◽  
Vol 7 (4) ◽  
pp. 559-585
Author(s):  
Hani Alahmed ◽  
Wa’el Alaghbari ◽  
Rahinah Ibrahim ◽  
Azizah Salim
Keyword(s):  
Social Interaction ◽  
Social Interactions ◽  
Significant Influence ◽  
Statistical Data ◽  
Residential Building ◽  
Content Type ◽  
Spatial Design ◽  
Two Factors ◽  
Design Characteristics

Purpose – This paper aims to investigate the ways that could enhance residents’ social interaction in low-rise residential building neighbourhoods of Basra city in Iraq. The lack of social interaction among residents of Basra city prompted the authors to frame a strategy for this case study. Design/methodology/approach – The spatial design characteristics of low-rise residential building neighbourhoods implicated to support the residents in terms of social interactions in comparison to those exhibited by a single home and traditional neighbourhoods. The statistical data demonstrated that by using this strategy, several unique features of secured, collective, responsive and supportive spaces could enhance the residents’ social interaction. Findings – This study found that all collective space factors have a significant influence on social interaction. “Fostering proper proximity and accessibility” factor was ranked first and the most significant factor with an influence on social interaction. Secured spaces (hierarchical spatial structure, physical security supports and construct) have a significant influence on social interaction. The most interesting finding in this study is that all factors of the supportive spaces construct have a significant influence on social interaction. Finally, this study showed that two factors of the responsive spaces construct, increasing variety and increasing legibility, have an insignificant influence on social interaction. Originality/value – The design of low-rise residential building neighbourhoods in Basra city may be used to develop social interaction as the contributing factor for maintaining values of traditional neighbourhood communities. This study highlights certain recommendations for architects, especially urban designers, to reinforce residents’ social interaction in low-rise residential building neighbourhoods in Basra city.

scholarly journals Event Monitoring and Intelligence Gathering Using Twitter Based Real-Time Event Summarization and Pre-Trained Model Techniques

2021 ◽  
Vol 11 (22) ◽  
pp. 10596
Author(s):  
Chung-Hong Lee ◽  
Hsin-Chang Yang ◽  
Yenming J. Chen ◽  
Yung-Lin Chuang
Keyword(s):  
Real Time ◽  
Data Science ◽  
Clustering Algorithm ◽  
Language Models ◽  
Event Monitoring ◽  
Data Set ◽  
Intelligence Gathering ◽  
Twitter Data ◽  
The Government

Recently, an emerging application field through Twitter messages and algorithmic computation to detect real-time world events has become a new paradigm in the field of data science applications. During a high-impact event, people may want to know the latest information about the development of the event because they want to better understand the situation and possible trends of the event for making decisions. However, often in emergencies, the government or enterprises are usually unable to notify people in time for early warning and avoiding risks. A sensible solution is to integrate real-time event monitoring and intelligence gathering functions into their decision support system. Such a system can provide real-time event summaries, which are updated whenever important new events are detected. Therefore, in this work, we combine a developed Twitter-based real-time event detection algorithm with pre-trained language models for summarizing emergent events. We used an online text-stream clustering algorithm and self-adaptive method developed to gather the Twitter data for detection of emerging events. Subsequently we used the Xsum data set with a pre-trained language model, namely T5 model, to train the summarization model. The Rouge metrics were used to compare the summary performance of various models. Subsequently, we started to use the trained model to summarize the incoming Twitter data set for experimentation. In particular, in this work, we provide a real-world case study, namely the COVID-19 pandemic event, to verify the applicability of the proposed method. Finally, we conducted a survey on the example resulting summaries with human judges for quality assessment of generated summaries. From the case study and experimental results, we have demonstrated that our summarization method provides users with a feasible method to quickly understand the updates in the specific event intelligence based on the real-time summary of the event story.

scholarly journals Pengaruh Kualitas Produk dan Harga Terhadap Minat Beli Beras Selancar (Studi Kasus pada Ibu Rumah Tangga di Kota Baru Jalan Kapten Satar RT.10 RW.03 Kecamatan Lahat)

2020 ◽  
Vol 9 (2) ◽  
pp. 1-12
Author(s):  
Darwin Kesuma
Keyword(s):  
Product Quality ◽  
Purchase Intention ◽  
Statistical Data ◽  
Regression Coefficient ◽  
Coefficient Of Determination ◽  
Statistical Data Analysis ◽  
Joint Influence ◽  
The Individual

The Effect of Product Quality and Price on Purchase Intention for Selancar Rice (Case Study on Housewives in Kota Baru Jalan Kapten Satar RT. 10 RW. 03 Lahat). This research was conducted on housewives located at Jalan Kapten Satar RT 10 RW 03 No. 25 Kelurahan Kota Baru, Lahat District. The research objective was to see the effect of the product and price on buying interest in surfing rice. The regression equation Y = 10.588 + 0.453 X1 + 0.339 X2 + e. Based on statistical data analysis, the indicators in this study are valid and reliable. The individual order of each variable with the most influence is the product quality variable with a regression coefficient of 0.453 then the price variable with a regression coefficient of 0.339. Obtained t count variable product quality (X1) of 2.658> 2.011 and variable price (X2) obtained at 2.905> 2.011. This means that t is greater than t table, then H_0 is rejected and H1 is accepted. Obtained an F calculated value of 7.009> 3.20 so that it can be ignored that there is a simultaneous (joint) influence between product quality (X1) and price (X2) on buying interest (Y) of surfing rice. Analysis of the coefficient of determination of 23% means that there is a very weak relationship between the independent variables and the related variables and the rest is 77%. By other factors that are not discussed in this study.

scholarly journals Optimal Staffing for Vessel Traffic Service Operators: A Case Study of Yeosu VTS

Sensors ◽  
2021 ◽  
Vol 21 (23) ◽  
pp. 8004
Author(s):  
Sang-Lok Yoo ◽  
Kwang-Il Kim
Keyword(s):  
Statistical Data ◽  
Optimal Number ◽  
Traffic Volume ◽  
Near Miss ◽  
Trajectory Data ◽  
Staffing Level ◽  
Microscopic Approach ◽  
Marine Accidents ◽  
Traffic Organization

Vessel traffic volume and vessel traffic service (VTS) operator workloads are increasing with the expansion of global maritime trade, contributing to marine accidents by causing difficulties in providing timely services. Therefore, it is essential to have sufficient VTS operators considering the vessel traffic volume and near-miss cases. However, no quantitative method for determining the optimal number of workstations, which is necessary for calculating the VTS operator staffing level, has yet been proposed. This paper proposes a new, microscopic approach for calculating the number of workstations from vessel trajectories and voice recording communication data between VTS operators and navigators. The vessel trajectory data are preprocessed to interpolate different intervals. The proposed method consists of three modules: Information services, navigational assistance services, and traffic organization service. The developed model was applied to the Yeosu VTS in Korea. Another workstation should be added to the current workstation based on the proposed method. The results showed that even without annual statistical data, a reasonable VTS operator staffing level could be calculated. The proposed approach helps prevent vessel accidents by providing timely services even if the vessel traffic is congested if VTS operators are deployed to a sufficient number of workstations.

scholarly journals Teaching Computational Social Science Skills to Psychology Students: An Undergraduate Research Lab Case Study

2020 ◽  
Vol 4 (1) ◽  
pp. 5-14
Author(s):  
Brian A. Eiler ◽  
◽  
Patrick C. Doyle ◽  
Rosemary L. Al-Kire ◽  
Heidi A. Wayment ◽  
...  
Keyword(s):  
Social Science ◽  
Data Science ◽  
Undergraduate Research ◽  
Psychology Students ◽  
Psychological Research ◽  
Open Science ◽  
Computational Social Science ◽  
Research Experience ◽  
Science Practices

This article provides a case study of a student-focused research experience that introduced basic data science skills and their utility for psychological research, providing practical learning experiences for students interested in learning computational social science skills. Skills included programming; acquiring, visualizing, and managing data; performing specialized analyses; and building knowledge about open-science practices.

scholarly journals Closing the research-implementation gap using data science tools: a case study with pollinators of British Columbia

2020 ◽  
Author(s):  
Laura Melissa Guzman ◽  
Tyler Kelly ◽  
Lora Morandin ◽  
Leithen M’Gonigle ◽  
Elizabeth Elle
Keyword(s):  
British Columbia ◽  
Data Science ◽  
Academic Research ◽  
Science Methods ◽  
Research Implementation ◽  
Conservation Practice ◽  
Implementation Gap ◽  
Web App ◽  
Using Data

AbstractA challenge in conservation is the gap between knowledge generated by researchers and the information being used to inform conservation practice. This gap, widely known as the research-implementation gap, can limit the effectiveness of conservation practice. One way to address this is to design conservation tools that are easy for practitioners to use. Here, we implement data science methods to develop a tool to aid in conservation of pollinators in British Columbia. Specifically, in collaboration with Pollinator Partnership Canada, we jointly develop an interactive web app, the goal of which is two-fold: (i) to allow end users to easily find and interact with the data collected by researchers on pollinators in British Columbia (prior to development of this app, data were buried in supplements from individual research publications) and (ii) employ up to date statistical tools in order to analyse phenological coverage of a set of plants. Previously, these tools required high programming competency in order to access. Our app provides an example of one way that we can make the products of academic research more accessible to conservation practitioners. We also provide the source code to allow other developers to develop similar apps suitable for their data.

scholarly journals Regional Entrepreneurship Support Infrastructure: Volgograd Region Case Study

2020 ◽  
Vol 16 ◽  
Keyword(s):  
Complex Analysis ◽  
Statistical Data ◽  
High Tech ◽  
Volgograd Region ◽  
Technological Entrepreneurship ◽  
New Type ◽  
Knowledge Intensive

In the paper, the complex analysis of the regional infrastructure of support of technological entrepreneurship of the Volgograd region, based on the statistical data reflecting the activity of enterprise structures is carried out. The scientific relevance of this research is related to the fact that technological entrepreneurship is a new type of enterprise that meets the requirements of the postindustrial period. The functioning of this type of entrepreneurship is based on a high-tech or knowledge-intensive idea, which contributes to the development of the environment. According to the results of the analysis, the main drawbacks of the regional infrastructure of support of technological entrepreneurship of the Volgograd region, which create barriers in the development of technological entrepreneurship. Based on the identified problems, measures have been developed to overcome them.

scholarly journals High Performance Numerical Computing for High Energy Physics: A New Challenge for Big Data Science

2014 ◽  
Vol 2014 ◽  
pp. 1-13 ◽  
Author(s):  
Florin Pop
Keyword(s):  
Monte Carlo ◽  
Big Data ◽  
Numerical Methods ◽  
High Performance ◽  
Data Science ◽  
Experimental Validation ◽  
High Energy Physics ◽  
High Energy ◽  
Performance Computing ◽  
Energy Physics

Modern physics is based on both theoretical analysis and experimental validation. Complex scenarios like subatomic dimensions, high energy, and lower absolute temperature are frontiers for many theoretical models. Simulation with stable numerical methods represents an excellent instrument for high accuracy analysis, experimental validation, and visualization. High performance computing support offers possibility to make simulations at large scale, in parallel, but the volume of data generated by these experiments creates a new challenge for Big Data Science. This paper presents existing computational methods for high energy physics (HEP) analyzed from two perspectives: numerical methods and high performance computing. The computational methods presented are Monte Carlo methods and simulations of HEP processes, Markovian Monte Carlo, unfolding methods in particle physics, kernel estimation in HEP, and Random Matrix Theory used in analysis of particles spectrum. All of these methods produce data-intensive applications, which introduce new challenges and requirements for ICT systems architecture, programming paradigms, and storage capabilities.

Sign in / Sign up

Export Citation Format

Share Document