scholarly journals Simple Technology is an Improved Solution for a Post-Pandemic Informative System: A Reference Model

2021 ◽  
Vol 16 (16) ◽  
pp. 35
Author(s):  
Mohamed Housni ◽  
Mohammed Talbi ◽  
Abdelwahed Namir
Keyword(s):  
Data Analytics ◽  
Historical Data ◽  
Reference Model ◽  
Educational Technologies ◽  
Traditional Learning ◽  
System A ◽  
Models Of Development ◽  
Learning Capabilities ◽  
Using Data ◽  
Simple Technology

COVID-19 pandemic accelerated the digitalization and the implementation of technological tools to distribute knowledge and content to certify the instructional process's steadiness despite the restrictions posed in many nations worldwide. However, multi-models of development and integrations based on multitudes of theoretical and conceptual frameworks made it difficult for deciders during the year - especially in developing countries - to follow a clear path based on their contextual needs. Based on a literature review and historical Data, Learning Ana-lytics research, and its empirical results, this article proposes a data-analytics model for growth. Training/educational technologies help stakeholders use data as intelligence sources to implement technologies that will improve traditional learning procedures without constraining practices. As a result, the paper also suggests, according to the pragmatic results, a 4-year plan applicable in multidi-mensional contexts to enhance the organization's learning capabilities as a whole unit to face future trials. The return on experience in the last year of the pandemic contracts the methodological basis of results. Furthermore, this manuscript's aim is defined by the urgent necessity of post-pandemic solutions to positively safe-guard the future of keeping the wheel of knowledge running for the learners and warrant upcoming transition into using data as a source of developing new learn-ing technologies.

scholarly journals Data Analytics for Predicting COVID-19 Cases in Top Affected Countries: Observations and Recommendations

2020 ◽  
Vol 17 (19) ◽  
pp. 7080 ◽  
Author(s):  
Abdelrahman E. E. Eltoukhy ◽  
Ibrahim Abdelfadeel Shaban ◽  
Felix T. S. Chan ◽  
Mohammad A. M. Abdel-Aal
Keyword(s):  
Data Analytics ◽  
Historical Data ◽  
External Factors ◽  
Number Of Patients ◽  
Future Prediction ◽  
The Government ◽  
Using Data ◽  
Novel Coronavirus ◽  
Improved Accuracy ◽  
Nonlinear Autoregressive

The outbreak of the 2019 novel coronavirus disease (COVID-19) has adversely affected many countries in the world. The unexpected large number of COVID-19 cases has disrupted the healthcare system in many countries and resulted in a shortage of bed spaces in the hospitals. Consequently, predicting the number of COVID-19 cases is imperative for governments to take appropriate actions. The number of COVID-19 cases can be accurately predicted by considering historical data of reported cases alongside some external factors that affect the spread of the virus. In the literature, most of the existing prediction methods focus only on the historical data and overlook most of the external factors. Hence, the number of COVID-19 cases is inaccurately predicted. Therefore, the main objective of this study is to simultaneously consider historical data and the external factors. This can be accomplished by adopting data analytics, which include developing a nonlinear autoregressive exogenous input (NARX) neural network-based algorithm. The viability and superiority of the developed algorithm are demonstrated by conducting experiments using data collected for top five affected countries in each continent. The results show an improved accuracy when compared with existing methods. Moreover, the experiments are extended to make future prediction for the number of patients afflicted with COVID-19 during the period from August 2020 until September 2020. By using such predictions, both the government and people in the affected countries can take appropriate measures to resume pre-epidemic activities.

Energy Prediction Using Data Analytics in Smart Grid

2018 ◽  
Vol 06 (06) ◽  
pp. 110-115
Author(s):  
Panchami Anil ◽  
Anas P V ◽  
Naseef Kuruvakkottil ◽  
Anusha K V ◽  
Balagopal N
Keyword(s):  
Smart Grid ◽  
Data Analytics ◽  
Energy Prediction ◽  
Using Data

Quality Management Using Data Analytics: An Application to Pharmaceutical Regulation

2015 ◽  
Author(s):  
Vishal Ahuja ◽  
John R. Birge ◽  
Chad Syverson ◽  
Elbert S. Huang ◽  
Min-Woong Sohn
Keyword(s):  
Quality Management ◽  
Data Analytics ◽  
Pharmaceutical Regulation ◽  
Using Data

Organizing and Managing Data Analytics Teams for Procurement

2020 ◽  
Author(s):  
Benjamin Shao ◽  
Robert D. St. Louis
Keyword(s):  
Best Practices ◽  
Business Strategy ◽  
Data Analytics ◽  
Team Members ◽  
Procurement Process ◽  
Using Data ◽  
And Training ◽  
Different Levels ◽  
The Way

Many companies are forming data analytics teams to put data to work. To enhance procurement practices, chief procurement officers (CPOs) must work effectively with data analytics teams, from hiring and training to managing and utilizing team members. This chapter presents the findings of a study on how CPOs use data analytics teams to support the procurement process. Surveys and interviews indicate companies are exhibiting different levels of maturity in using data analytics, but both the goal of CPOs (i.e., improving performance to support the business strategy) and the way to interact with data analytics teams for achieving that goal are common across companies. However, as data become more reliably available and technologies become more intelligently embedded, the best practices of organizing and managing data analytics teams for procurement will need to be constantly updated.

scholarly journals New dilemmas, old problems: advances in data analysis and its geoethical implications in groundwater management

2021 ◽  
Vol 3 (6) ◽  
Author(s):  
César de Oliveira Ferreira Silva ◽  
Mariana Matulovic ◽  
Rodrigo Lilla Manzione
Keyword(s):  
Decision Making ◽  
Data Analytics ◽  
Data Science ◽  
Final Decision ◽  
Aquifer Recharge ◽  
Groundwater Governance ◽  
Ethical Attitude ◽  
New Information ◽  
Modeling Experiment ◽  
Using Data

Abstract Groundwater governance uses modeling to support decision making. Therefore, data science techniques are essential. Specific difficulties arise because variables must be used that cannot be directly measured, such as aquifer recharge and groundwater flow. However, such techniques involve dealing with (often not very explicitly stated) ethical questions. To support groundwater governance, these ethical questions cannot be solved straightforward. In this study, we propose an approach called “open-minded roadmap” to guide data analytics and modeling for groundwater governance decision making. To frame the ethical questions, we use the concept of geoethical thinking, a method to combine geoscience-expertise and societal responsibility of the geoscientist. We present a case study in groundwater monitoring modeling experiment using data analytics methods in southeast Brazil. A model based on fuzzy logic (with high expert intervention) and three data-driven models (with low expert intervention) are tested and evaluated for aquifer recharge in watersheds. The roadmap approach consists of three issues: (a) data acquisition, (b) modeling and (c) the open-minded (geo)ethical attitude. The level of expert intervention in the modeling stage and model validation are discussed. A search for gaps in the model use is made, anticipating issues through the development of application scenarios, to reach a final decision. When the model is validated in one watershed and then extrapolated to neighboring watersheds, we found large asymmetries in the recharge estimatives. Hence, we can show that more information (data, expertise etc.) is needed to improve the models’ predictability-skill. In the resulting iterative approach, new questions will arise (as new information comes available), and therefore, steady recourse to the open-minded roadmap is recommended. Graphic abstract

scholarly journals Speech, Language and Hearing services in Primary Health Care in Brazil: an analysis of provision and an estimate of shortage, 2005-2015

Revista CEFAC ◽  
2018 ◽  
Vol 20 (3) ◽  
pp. 353-362 ◽  
Author(s):  
Larissa Hellen Teixeira Viégas ◽  
Tatiane Costa Meira ◽  
Brenda Sousa Santos ◽  
Yukari Figueroa Mise ◽  
Vladimir Andrei Rodrigues Arce ◽  
...  
Keyword(s):  
Health Care ◽  
Primary Health Care ◽  
Ecological Study ◽  
Descriptive Analysis ◽  
National Registry ◽  
Health Care Information ◽  
System A ◽  
Primary Health ◽  
Care Information ◽  
Using Data

ABSTRACT Objective: to investigate the evolution and estimate the shortage of Speech, Language and Hearing professionals in Primary Health Care between 2005 and 2015. Methods: a mixed ecological study using data from the National Registry of Health Facilities and the Primary Health Care Information System. A descriptive analysis regarding the evolution of the number of professionals working in Primary Health Care over this period, in Brazilian states and regions, was conducted. The ratio of professionals per 100,000 inhabitants for the years 2005, 2010 and 2015, and the shortages in 2015, were estimated. Results: in 2005, there were 1,717 professionals working in Primary Health Care, that is, one per 100,000 inhabitants. In 2015, there were 4,124, increasing to 2.1/100,000. In 2015, the shortage in supply was 55.1%, varying widely across the states. Conclusion: the shortage in supply is equivalent to an absence of Speech, Language and Hearing service coverage within Primary Health Care for more than half of the Brazilian population. It is worth noting that a conservative parameter was adopted to conduct this estimate. The results suggest a process of consolidation for the inclusion of Speech, Language and Hearing professionals within Primary Health Care, however, still characterized by insufficient and unequal supply across the nation.

Development of a Performance Rating Standard for Residential Fuel Cell Systems

2006 ◽  
Author(s):  
Michael W. Ellis ◽  
Mark W. Davis ◽  
A. Hunter Fanney ◽  
Brian P. Dougherty ◽  
Ian Doebber
Keyword(s):  
Fuel Cell ◽  
Performance Standards ◽  
Cell System ◽  
Fuel Cell System ◽  
Experimental Procedure ◽  
Cell Systems ◽  
Load Following ◽  
System A ◽  
Experimental Apparatus ◽  
Using Data

Fuel cell systems for residential applications are an emerging technology for which specific consumer-oriented performance standards are not well defined. This paper presents a proposed experimental procedure and rating methodology for evaluating residential fuel cell systems. In the proposed procedure, residential applications are classified as grid independent load following; grid connected constant power; grid connected thermal load following; and grid connected water heating. An experimental apparatus and procedures for steady state and simulated use tests are described for each type of system. A rating methodology is presented that uses data from these experiments in conjunction with standard residential load profiles to quantify the net effect of a fuel cell system on residential utility use. The experiments and rating procedure are illustrated using data obtained from a currently available grid connected thermally load following system.

Sign in / Sign up

Export Citation Format

Share Document