scholarly journals A Probabilistic Domain-knowledge Framework for Nosocomial Infection Risk Estimation of Communicable Viral Diseases in Healthcare Personnel: A Case Study for COVID-19

2021 ◽  
Author(s):  
Phat Huynh
Keyword(s):  
Health Surveys ◽  
Domain Knowledge ◽  
Risk Estimation ◽  
Infection Risk ◽  
Response Surfaces ◽  
Healthcare Personnel ◽  
Viral Diseases ◽  
Multiple Sources ◽  
Knowledge Framework

Data collected from multiple sources (e.g., COVID-19 transmission databases, health surveys/questionnaires, U.S. Fig. 3. Response surfaces of<br>

Polytechnic to Business Project (P2B) a Case of Innovation and Knowledge Transference

2019 ◽  
Vol 6 (1) ◽  
pp. 40-49
Author(s):  
Teresa Paiva
Keyword(s):  
Knowledge Transfer ◽  
Best Practices ◽  
Education Institution ◽  
Theoretical Background ◽  
Transfer Model ◽  
Case Study Method ◽  
Multiple Sources ◽  
The Subject ◽  
Manage Innovation

Background: The theoretical background of this article is on the model developed of knowledge transfer between universities and the industry in order to access the best practices and adapt to the study case in question regarding the model of promoting and manage innovation within the universities that best contribute with solution and projects to the business field. Objective: The development of a knowledge transfer model is the main goal of this article, supported in the best practices known and, also, to reflect in the main measurement definitions to evaluate the High Education Institution performance in this area. Methods: The method for this article development is the case study method because it allows the fully understanding of the dynamics present within a single setting, and the subject examined to comprehend what is being done and what the dynamics mean. The case study does not have a data collection method, as it is a research that may rely on multiple sources of evidence and data which should be converged. Results: Since it’s a case study this article present a fully description of the model proposed and implemented for the knowledge transfer process of the institution. Conclusion: Still in a discussion phase, this article presents as conclusions some questions and difficulties that could be pointed out, as well as some good perspectives of performed activity developed.

scholarly journals Accessible Routes Integrating Data from Multiple Sources

2020 ◽  
Vol 10 (1) ◽  
pp. 7
Author(s):  
Miguel R. Luaces ◽  
Jesús A. Fisteus ◽  
Luis Sánchez-Fernández ◽  
Mario Munoz-Organero ◽  
Jesús Balado ◽  
...  
Keyword(s):  
Information System ◽  
Data Model ◽  
Large Scale ◽  
Heterogeneous Data ◽  
Multiple Sources ◽  
Heterogeneous Data Sources ◽  
Different Types ◽  
Software Sensors ◽  
The City

Providing citizens with the ability to move around in an accessible way is a requirement for all cities today. However, modeling city infrastructures so that accessible routes can be computed is a challenge because it involves collecting information from multiple, large-scale and heterogeneous data sources. In this paper, we propose and validate the architecture of an information system that creates an accessibility data model for cities by ingesting data from different types of sources and provides an application that can be used by people with different abilities to compute accessible routes. The article describes the processes that allow building a network of pedestrian infrastructures from the OpenStreetMap information (i.e., sidewalks and pedestrian crossings), improving the network with information extracted obtained from mobile-sensed LiDAR data (i.e., ramps, steps, and pedestrian crossings), detecting obstacles using volunteered information collected from the hardware sensors of the mobile devices of the citizens (i.e., ramps and steps), and detecting accessibility problems with software sensors in social networks (i.e., Twitter). The information system is validated through its application in a case study in the city of Vigo (Spain).

Narrative Scientific Data Visualization in an Immersive Environment

2021 ◽  
Author(s):  
Richen Liu ◽  
Hailong Wang ◽  
Chuyu Zhang ◽  
Xiaojian Chen ◽  
Lijun Wang ◽  
...  
Keyword(s):  
Data Visualization ◽  
Domain Knowledge ◽  
User Study ◽  
Scientific Visualization ◽  
Scientific Data ◽  
Immersive Environments ◽  
Related Information ◽  
New Strategy ◽  
Scientific Data Visualization

Abstract Motivation Narrative visualization for scientific data explorations can help users better understand the domain knowledge, because narrative visualizations often present a sequence of facts and observations linked together by a unifying theme or argument. Narrative visualization in immersive environments can provide users with an intuitive experience to interactively explore the scientific data, because immersive environments provide a brand new strategy for interactive scientific data visualization and exploration. However, it is challenging to develop narrative scientific visualization in immersive environments. In this paper, we propose an immersive narrative visualization tool to create and customize scientific data explorations for ordinary users with little knowledge about programming on scientific visualization, They are allowed to define POIs (point of interests) conveniently by the handler of an immersive device. Results Automatic exploration animations with narrative annotations can be generated by the gradual transitions between consecutive POI pairs. Besides, interactive slicing can be also controlled by device handler. Evaluations including user study and case study are designed and conducted to show the usability and effectiveness of the proposed tool. Availability Related information can be accessed at: https://dabigtou.github.io/richenliu/

scholarly journals Estimating individual risks of COVID-19-associated hospitalization and death using publicly available data

PLoS ONE ◽  
2020 ◽  
Vol 15 (12) ◽  
pp. e0243026
Author(s):  
Rajiv Bhatia ◽  
Jeffrey Klausner
Keyword(s):  
Risk Estimation ◽  
Secondary Infection ◽  
Case Fatality ◽  
Infection Risk ◽  
Individual Risk ◽  
Incidence Data ◽  
Household Transmission ◽  
Contact Rates ◽  
Time Period ◽  
Public Data

We describe a method to estimate individual risks of hospitalization and death attributable to non-household and household transmission of SARS-CoV-2 using available public data on confirmed-case incidence data along with estimates of the clinical fraction, timing of transmission, isolation adherence, secondary infection risks, contact rates, and case-hospitalization and case-fatality ratios. Using the method, we estimate that risks for a 90-day period at the median daily summertime U.S. county confirmed COVID-19 case incidence of 10.8 per 100,000 and pre-pandemic contact rates range from 0.4 to 8.9 per 100,000 for the four deciles of age between 20 and 60 years. The corresponding 90-day period risk of hospitalization ranges from 13.7 to 69.2 per 100,000. Assuming a non-household secondary infection risk of 4% and pre-pandemic contact rates, the share of transmissions attributable to household settings ranges from 73% to 78%. These estimates are sensitive to the parameter assumptions; nevertheless, they are comparable to the COVID-19 hospitalization and fatality rates observed over the time period. We conclude that individual risk of hospitalization and death from SARS-CoV-2 infection is calculable from publicly available data sources. Access to publicly reported infection incidence data by setting and other exposure characteristics along with setting specific estimates of secondary infection risk would allow for more precise individual risk estimation.

scholarly journals Leveraging Business Data Analytics and Machine Learning Techniques for Competitive Advantage: Case Study Evidence from Small Businesses

2021 ◽  
Vol 2 (1) ◽  
pp. 73-87
Author(s):  
Rathimala Kannan ◽  
Intan Soraya Rosdi ◽  
Kannan Ramakrishna ◽  
Haziq Riza Abdul Rasid ◽  
Mohamed Haryz Izzudin Mohamed Rafy ◽  
...  
Keyword(s):  
Machine Learning ◽  
Competitive Advantage ◽  
Small Businesses ◽  
Data Analytics ◽  
Research Question ◽  
Machine Learning Techniques ◽  
Multiple Sources ◽  
Learning Techniques ◽  
Business Data

Data analytics is the essential component in deriving insights from data obtained from multiple sources. It represents the technology, methods and techniques used to obtain insights from massive datasets. As data increases, companies are looking for ways to gain relevant business insights underneath layers of data and information, to help them better understand new business ventures, opportunities, business trends and complex challenges. However, to date, while the extensive benefits of business data analytics to large organizations are widely published, micro, small, and medium sized organisations have not fully grasped the potential benefits to be gained from data analytics using machine learning techniques. This study is guided by the research question of how data analytics using machine learning techniques can benefit small businesses. Using the case study method, this paper outlines how small businesses in two different industries i.e. healthcare and retail can leverage data analytics and machine learning techniques to gain competitive advantage from the data. Details on the respective benefits gained by the small business owners featured in the two case studies provide important answers to the research question.

scholarly journals PENDEKATAN PDSA UNTUK PERBAIKAN PROSES PADA INDIKATOR SASARAN KESELAMATAN PASIEN DI RUMAH SAKIT

2018 ◽  
Vol 6 (2) ◽  
pp. 111
Author(s):  
Elly Numa Zahroti
Keyword(s):  
Patient Safety ◽  
Hand Hygiene ◽  
Process Improvement ◽  
Participatory Approach ◽  
Infection Risk ◽  
The Poor ◽  
One Step ◽  
Observational Design

Background: Patient safety is an indicator of hospital service quality. A hospital in Surabaya identified six indicators of patient safety goals. There are two indicators which can not achieve the standard, namely effective communication and infection risk reduction.Aims: This study aims to identify the process improvement that can be done to increase indicator performance by using PDSA cycle.Method: A descriptive observational design was used in this study with a case study and participatory approach. There were 5 subjects selected by purposive sampling. Interview and observation were used to collect data that then were analyzed descriptively. The validity of data was done by triangulation of method, source, and theory.Results: The PDSA results indicated that the cause of the poor indicators performance of both patient safety goals is the poor compliance of the health staffs in carrying out read-back procedure and hand hygiene as written in SOP. It was caused by the lack of knowledge and motivation of the health staffs in implementing the SOP.Conclusion: In conclusion, process improvement can be done by socializing read-back SOP and hand hygiene as well as supervision conducted periodically by managers. Plan stage is one step which should be improved. Commitment in implementing the improvement planning is necessary. In addition, further research on factors that influence compliance should be conducted.Keywords: patient safety, PDSA method, process improvement, quality of hospital

Artificial Intelligence for Extended Software Robots, Applications, Algorithms, and Simulators

2020 ◽  
pp. 71-92
Author(s):  
Gayathri Rajendran ◽  
Uma Vijayasundaram
Keyword(s):  
Artificial Intelligence ◽  
Path Planning ◽  
Domain Knowledge ◽  
Robot Navigation ◽  
Machine Learning Algorithms ◽  
Computational Time ◽  
Advanced Technique ◽  
Navigation Data ◽  
Planning Algorithms

Robotics has become a rapidly emerging branch of science, addressing the needs of humankind by way of advanced technique, like artificial intelligence (AI). This chapter gives detailed explanation about the background knowledge required in implementing the software robots. This chapter has an in-depth explanation about different types of software robots with respect to different applications. This chapter would also highlight some of the important contributions made in this field. Path planning algorithms are required for performing robot navigation efficiently. This chapter discusses several robot path planning algorithms which help in utilizing the domain knowledge, avoiding the possible obstacles, and successfully accomplishing the tasks in lesser computational time. This chapter would also provide a case study on robot navigation data and explain the significant of machine learning algorithms in decision making. This chapter would also discuss some of the potential simulators used in implementing software robots.

scholarly journals Millet Could Be both a Weed and Serve as a Virus Reservoir in Crop Fields

Plants ◽  
2020 ◽  
Vol 9 (8) ◽  
pp. 954
Author(s):  
György Pasztor ◽  
Zsuzsanna Galbacs N. ◽  
Tamas Kossuth ◽  
Emese Demian ◽  
Erzsebet Nadasy ◽  
...  
Keyword(s):  
Mosaic Virus ◽  
Seed Dormancy ◽  
High Throughput ◽  
Small Rna ◽  
Diagnostic Method ◽  
High Throughput Sequencing ◽  
Infection Risk ◽  
Viral Diseases ◽  
Crop Fields ◽  
Special Control

Millet is a dangerous weed in crop fields. A lack of seed dormancy helps it to spread easily and be present in maize, wheat, and other crop fields. Our previous report revealed the possibility that millet can also play a role as a virus reservoir. In that study, we focused on visual symptoms and detected the presence of several viruses in millet using serological methods, which can only detect the presence of the investigated pathogen. In this current work, we used small RNA high-throughput sequencing as an unbiased virus diagnostic method to uncover presenting viruses in randomly sampled millet grown as a volunteer weed in two maize fields, showing stunting, chlorosis, and striped leaves. Our results confirmed the widespread presence of wheat streak mosaic virus at both locations. Moreover, barley yellow striate mosaic virus and barley virus G, neither of which had been previously described in Hungary, were also identified. As these viruses can cause severe diseases in wheat and other cereals, their presence in a weed implies a potential infection risk. Our study indicates that the presence of millet in fields requires special control to prevent the emergence of new viral diseases in crop fields.

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