A Coevolutionary Genetic Algorithm-Based Vehicle Assignment and Routing Algorithm for Day-Care Facilities

2020 ◽  
Vol 2020 (0) ◽  
pp. 204
Author(s):  
Tatsuhiko SAKAGUCHI ◽  
Taichi HAYASAKA ◽  
Kei OOKUGO ◽  
Koichi INAGAKI ◽  
Yuki IIJIMA
2019 ◽  
Vol 84 (764) ◽  
pp. 2065-2075
Author(s):  
Mahito NAKAZONO ◽  
Sachiko MISHIMA ◽  
Sachiko YAMAMOTO ◽  
Syohken KOH

2012 ◽  
Vol 23 (4) ◽  
pp. 765-775 ◽  
Author(s):  
Quan LIU ◽  
Xiao-Yan WANG ◽  
Qi-Ming FU ◽  
Yong-Gang ZHANG ◽  
Xiao-Fang ZHANG

2021 ◽  
Author(s):  
Robert Walter Körner ◽  
Lutz Thorsten Weber

Abstract Background In Germany, widespread full closures of schools and day care facilities were part of lockdown measures to control the spread of coronavirus disease 2019 (COVID-19). In the state of North Rhine-Westphalia closures took place on March 16, 2020 and were gradually eased from end of April 2020 until beginning of June 2020. Objective This study aims to evaluate the prevalence of COVID-19 among children and adolescents during the reopening period of schools and day care facilities in Cologne, North Rhine-Westphalia, Germany. It further depicts medical history and results of physical examinations of pediatric patients undergoing a test for severe acute respiratory distress syndrome coronavirus 2 (SARS-CoV-2). Methods Testing for SARS-CoV-2 was carried out by a naso- and / or oropharyngeal swab by local pediatricians at the time of presentation. Samples were analyzed by real-time reverse transcription polymerase chain reaction (RT-PCR). Medical history and physical examination results were retrospectively analyzed. Results 525 children and adolescents presented mainly with mild upper respiratory tract infections. Three patients were diagnosed with COVID-19. Their medical history and examination results did not stand out from the other patients. Conclusion A precautious stepwise opening of schools and day care facilities was not associated with the occurrence of a relevant prevalence of COVID-19 among children and adolescents. However, a low general prevalence of COVID-19 at the end of the observation period has to be taken into account. Systematic testing might enable adjusted regulations in favor of full closures, especially in the light of increasing evidence for pediatric patients constituting a low-risk group for COVID-19.


2018 ◽  
Vol 23 (12) ◽  
pp. 1232-1247
Author(s):  
Anne Lohmann ◽  
Heidrun Wulfekühler ◽  
Silvia Wiedebusch ◽  
Gregor Hensen

PEDIATRICS ◽  
1994 ◽  
Vol 94 (6) ◽  
pp. 1039-1041
Author(s):  
Susan E. E. Good ◽  
R. Gibson Parrish ◽  
Roy T. Ing

In the US, there is no mechanism readily available to identify children who die while attending day care. The National Center for Health Statistics (NCHS) uses the International Classification of Diseases 9th Edition (ICD-9) to categorize US mortality data; however, the International Classification of Diseases does not contain a category for "day care" as the place of death. Thus, researchers using NCHS mortality data are unable to count day-care deaths on a national basis. To identify children who died while in day care, it is necessary to review the records of those who investigated the deaths. In most counties and states, medical examiners or coroners (ME/C) are responsible for investigating unusual and sudden deaths. Their records often contain detailed information on the place and circumstances of death. The Medical Examiners and Coroners Information Sharing Programs (MECISP) at the Centers for Disease Control and Prevention (CDC) works with state and local medical examiners and coroners to improve the availability of data on deaths that they investigate. Computerized data are gathered on all deaths investigated by participating ME/C offices; data are obtained from six state and seven county jurisdictions. These jurisdictions have a combined population of 37 million people and record 56 000 deaths per year. Of these jurisdictions, one state and three county offices have text within their computerized data that describes the circumstances, place, and cause of each death. The objective of our study was to demonstrate the usefulness of ME/C death-investigation records as a means of identifying and characterizing the types of deaths that occur among children while attending day care facilities.


Author(s):  
Ingeborg Lunde Vestad ◽  
Petter Dyndahl

Processes of musical canonization occur at different levels of culture and society. People have a strong propensity to categorize, differentiate, and evaluate the music that is important to them, and music is ascribed value in action by people in real-life settings. Based in these premises, the article discusses two questions: First, how does the idea of a canon of children’s music influence the daily musical activities and repertoires used in children’s day care facilities and family homes? Second, in what ways is music legitimized in the everyday lives of children? Our data is collected by observation and interviews conducted in two pedagogical day care facilities and nine family homes. Children, day care staff and parents participated in the study. We find that a discussion of canonization in children’s music along the following four paths of legitimation is meaningful: the “good, old stuff,” the need for renewal, the inclusion of other types of music other than that aimed at a child audience, and the need for a wide array of genres and sentiments. Finally, we argue that although the legitimation and canonization in children’s music obviously involve considerations of musical aspects, separating these canonization processes from the prevailing socio-cultural ideas of childhood and children’s best interest is impossible.  


2017 ◽  
Vol 4 (3) ◽  
pp. 1-16 ◽  
Author(s):  
Amol V. Dhumane ◽  
Rajesh S. Prasad ◽  
Jayashree R. Prasad

In Internet of things and its relevant technologies the routing of data plays one of the major roles. In this paper, a routing algorithm is presented for the networks consisting of smart objects, so that the Internet of Things and its enabling technologies can provide high reliability while the transmitting the data. The proposed technique executes in two stages. In first stage, the sensor nodes are clustered and an optimal cluster head is selected by using k-means clustering algorithm. The clustering is performed based on energy of sensor nodes. Then the energy cost of the cluster head and the trust level of the sensor nodes are determined. At second stage, an optimal path will be selected by using the Genetic Algorithm (GA). The genetic algorithm is based on the energy cost at cluster head, trust level at sensor nodes and path length. The resultant optimal path provides high reliability, better speed and more lifetimes.


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