delay models
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2021 ◽  
Vol 11 (20) ◽  
pp. 9771
Author(s):  
Liya Wang ◽  
Jun Chen ◽  
Xiaofeng Cao ◽  
Jian Chen ◽  
Chu Zhang

With the surge of motor vehicle ownership and land intensification, plenty of large parking lots emerge as the times demand. Although it solves the problem of insufficient parking spaces, it intensifies the interaction between dynamic and static traffic. This paper presented an impact assessment method for the interaction of dynamic and static traffic flow in parking lots. Firstly, the average vehicle delay was selected as the evaluation index. The delay effect caused by the interaction of dynamic and static traffic flow was determined according to the driving path of vehicles. Then, the average vehicle delay models of arrival vehicles, departure vehicles, and road vehicles in the parking lot were established. Finally, for the parameters difficult to determine directly in the delay model, this paper proposed a method to calibrate the model parameters by using the simulation experimental data on the VISSIM platform. The results showed that the errors of the three models are within the controllable range, and the delay model parameters had high reliability and feasibility. The delay models can provide a quantitative basis for the parking lot management department to formulate regulation strategies and realize more refined information guidance and navigation in the parking lot.


2021 ◽  
Vol 13 (12) ◽  
pp. 322
Author(s):  
José Alberto Hernández ◽  
Amin Ebrahimzadeh ◽  
Martin Maier ◽  
David Larrabeiti

2021 ◽  
Vol 9 (2) ◽  
pp. 99
Author(s):  
Junita Indarti ◽  
Affan Solihin ◽  
Arresta V. Suastika ◽  
Dyah P. Wardhani ◽  
Muhammad T. Ramadhani ◽  
...  

Maternal mortality remains a worldwide concern to this day. Three main causes of maternal mortality during 2010–2013 were hemorrhage, hypertension, and infection, which all of them are the direct causes. The high MMR is also due to the presence of 3 delay which is Delay in seeking assistance (type–1), delay in identifying and accessing medical center (type–2) and delay in having prompt treatment (type–3) . Therefore, this study aims to describe maternal mortality cases in tertiary hospital which is Cipto Mangunkusumo Hospital (CMH) so that the root of problems in maternal deaths can be discovered and improvements can be done in the future. This was a descriptive study conducted in the Department of Obstetrics and Gynecology at CMH, Jakarta. Data collection was taken from 2016 – 2018 where subjects were taken from secondary data on maternal mortality. Based on the data that has been collected in CMH total live births in Emergency Department CMH during 2016-2018 which was 4.226 cases. There was 22 maternal death cases (0.52%). Most deaths were occurred in 2017 (50% of all cases). Indirect causes of maternal mortality were the leading cause in this study, including septic shock, hypovolemic shock due to Dengue Shock Syndrome, cardiogenic shock, and acute respiratory failure. Three delay models were three main factors contributing to maternal mortality interrelated and influenced by other factors with delay in looking for assistance and treatment (31,8%) was the upmost factor of maternal mortality. More than half maternal deaths in CMH during 2016 – 2018 caused by indirect causes. Among three delay models, delay in looking for assistance and treatment was the upmost factor of maternal mortality. Keywords: maternal mortality, three-delay model.   Tiga Model Keterlambatan pada Kasus Kematian Ibu di Rumah Sakit Tersier di Indonesia Kematian ibu masih menjadi perhatian dunia hingga saat ini. Tiga penyebab utama angka kematian ibu (AKI) selama 2010-2013 adalah perdarahan, hipertensi, dan infeksi, yang semuanya merupakan penyebab langsung. Tingginya AKI juga disebabkan oleh adanya 3 keterlambatan yaitu keterlambatan dalam mencari pertolongan, keterlambatan dalam mengidentifikasi dan mengakses pusat kesehatan, dan keterlambatan dalam mendapatkan pengobatan yang tepat. Penelitian ini bertujuan untuk mendeskripsikan kasus kematian ibu di rumah sakit tersier yaitu Rumah Sakit Cipto Mangunkusumo (RSCM) sehingga akar permasalahan kematian ibu dapat ditemukan dan dapat dilakukan perbaikan di masa yang akan datang. Penelitian ini merupakan penelitian deskriptif yang dilakukan di Bagian Obstetri dan Ginekologi RSCM, Jakarta. Pengambilan data diambil dari tahun 2016 – 2018, subjek diambil dari data sekunder kematian ibu. Berdasarkan data yang terkumpul di RSCM jumlah kelahiran hidup di Instalasi Gawat Darurat RSCM selama tahun 2016-2018 sebanyak 4.226 kasus. Terdapat 22 kasus kematian ibu (0,52%). Kematian terbanyak terjadi pada tahun 2017 (50% dari seluruh kasus). Penyebab tidak langsung kematian ibu merupakan penyebab utama dalam penelitian ini, antara lain syok septik, syok hipovolemik akibat dengue shock syndrome, syok kardiogenik, dan gagal napas akut. Tiga model keterlambatan merupakan tiga faktor utama penyebab kematian ibu yang saling berkaitan dan dipengaruhi oleh faktor lain dengan keterlambatan mencari pertolongan dan pengobatan (31,8%) merupakan faktor penyebab kematian ibu yang paling tinggi. Lebih dari separuh kematian ibu di RSCM selama tahun 2016 – 2018 disebabkan oleh penyebab tidak langsung. Di antara tiga model keterlambatan, keterlambatan dalam mencari bantuan dan pengobatan merupakan faktor utama kematian ibu. Kata kunci: kematian maternal, model tiga terlambat.


2021 ◽  
Author(s):  
Ehsan Kharazmi ◽  
Min Cai ◽  
Xiaoning Zheng ◽  
Guang Lin ◽  
George Em Karniadakis

ABSTRACTWe analyze a plurality of epidemiological models through the lens of physics-informed neural networks (PINNs) that enable us to identify multiple time-dependent parameters and to discover new data-driven fractional differential operators. In particular, we consider several variations of the classical susceptible-infectious-removed (SIR) model by introducing more compartments and delay in the dynamics described by integer-order, fractional-order, and time-delay models. We report the results for the spread of COVID-19 in New York City, Rhode Island and Michigan states, and Italy, by simultaneously inferring the unknown parameters and the unobserved dynamics. For integer-order and time-delay models, we fit the available data by identifying time-dependent parameters, which are represented by neural networks (NNs). In contrast, for fractional differential models, we fit the data by determining different time-dependent derivative orders for each compartment, which we represent by NNs. We investigate the identifiability of these unknown functions for different datasets, and quantify the uncertainty associated with NNs and with control measures in forecasting the pandemic.


Author(s):  
Sedighe Rastaghi ◽  
Noushin Akbari Shark ◽  
Azadeh Saki

Introduction:The COVID-19 infectious epidemic has become a serious worry all over the world, including Iran. The high outbreak of disease ranked Iran as second in Asia and 11th in the world. Given the growing progress of this epidemic in infecting and killing individuals, it is essential to forecast the delay effect of the number of hospitalized upon the hospitalized mortality rate. Methods: In this study, we used the daily Hospitalization cases of COVID-19 of IRAN for the period of 15-May 2020 to 5-Oct 2020 which were obtained from the online database. Five distribution delay models were compared for estimating and forecasting. Results: Based on measurement errors DDM selected as the best model for forecasting the number of death. According to this model, the long-run effects show that observing the effect of hospitalization counts on death counts takes an average of five days and the long-run hospitalized mortality rate was 12%. Conclusion: The overall hospitalized mortality rate of COVID-19 in Iran is less than the global rate of 15%. The mean of delay effect of daily hospitalization on mortality is approximately 5 days. Our findings showed distributed delay model (DDM) has better performance in the forecasting of the future behavior of the Coronavirus mortality, and providing to government and health care decision- makers the possibility to predict the outcomes of their decision on public health.


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