scholarly journals Next weeks of SARS-CoV-2: Projection model to predict time evolution scenarios of accumulated cases in Spain

2020 ◽  
Author(s):  
Antonio Monleon-Getino ◽  
Jaume Canela-Soler
Keyword(s):  
Parametric Model ◽  
Short Report ◽  
Case Scenario ◽  
Projection Model ◽  
Worst Case ◽  
First Case ◽  
Projective Curves ◽  
New Type ◽  
General Goal ◽  
Entire Planet

AbstractBackground and objectivesSARS-CoV-2 is a new type of coronavirus that can affect people and causes respiratory disease, COVID-19. It is affecting the entire planet and we focus in Spain, where the first case was detected at the end of January 2020 and in recent weeks it has increased in many cases. We need predictive models in order to be efficient and take actions. The general goal of this work is present a new model of SARS-CoV-2 to predict different scenarios of accumulated cases in Spain.Material and methodsIn this short report is used a model proposed previously, based on a parametric model Weibull and in a the library BDSbiost3 developed in R to infer and predict different scenarios of the evolution of SARS-CoV-2 for the accumulated cases in Spain after the spread that affects Spain detected at the end of January of this year.ResultsIn the analyses presented, projective curves have been generated for the evolution of accumulated cases in which they reach about 4,000 cases or about 15,000 cases, for which the lines of the day in which the value for 90 will be reached can be seen vertically 90, 95 and 99% of the asymptote (maximum number of cases, from that day they will begin to descend or remain the same), that is why the vertical lines would indicate the brake of the disease. For the worst-case scenario, it takes 118, 126 or 142 days to reach the maximum number of cases (n = 15,000) to reach 90, 95 and 99% of the asymptote (maximum number of cases), respectively. This means translated in a time scale that in the worst case the virus will not stop its progress, in Spain, until summer 2020, hopefully before.Comments and conclusionsThis model could be used to plan the resources and see if the policies or means dedicated to the virus are slowing the progress of the virus or it is necessary to implement others that are more effective, and can also validate a method for future outbreaks of diseases such as these.

scholarly journals Updated COVID-19 Outbreak Forecast for San Diego County, April 2020

Outbreak ◽  
2020 ◽  
Author(s):  
Emily Eshraghian ◽  
Nathan Jacobs ◽  
Jeffrey Morgan
Keyword(s):  
San Diego ◽  
Weighted Average ◽  
San Diego County ◽  
Case Scenario ◽  
Worst Case ◽  
Social Distancing ◽  
Hospital Capacity ◽  
First Case ◽  
Scenario Model ◽  
Incident Cases

Here we extend and update our earlier projections of COVID-19 hospitalizations in San Diego County (1), and report a more optimistic outlook through the end of April 2020. San Diego confirmed its first case of COVID-19 on March 7, 2020. Several mitigation efforts were enacted on various dates, including a state-mandated stay-at-home order and enforcement of social distancing in public areas. Though mitigation strategies are helping lower the burden of disease, incident cases continue to increase exponentially. Our updated model includes data up to April 7 and does not forecast beyond April 30. Our approach uses a “wisdom of crowds” strategy (see link to methods for details) where a range of outbreak models from worst case scenario (Model A) to best case scenario (Model C) were presented to experts and non-experts (n=8) who were asked to vote on a most plausible model for expected COVID-19 spread. Final vote tallies were used to create a weighted average (Model M) as the official model projection. Our model predicts that San Diego County will not hit hospital capacity for standard hospital beds (panel a) nor for intensive care unit (ICU) beds (panel b) within April 2020. If current conditions continue, we predict the expected “surge” in hospitalizations to occur without surpassing hospital capacity, and that hospitalizations will decrease thereafter until the outbreak has been contained. However, it is important to note that factors such as changes in social distancing policies, even if occurring when existing or incident cases are low, may still result in new outbreaks and future spikes in hospitalizations. Furthermore, no models have been extensively validated for COVID-19. We encourage all residents of San Diego to continue rigorously following social distancing practices to improve the likelihood of best case scenarios and limit the scope of possible worst case scenarios.

scholarly journals Trend Analysis and Forecasting of COVID-19 outbreak in India

2020 ◽  
Author(s):  
Rajan Gupta ◽  
Saibal K Pal
Keyword(s):  
Arima Model ◽  
Regional Analysis ◽  
Control Measures ◽  
Government Officials ◽  
Case Scenario ◽  
Indian States ◽  
Worst Case ◽  
Medical Practitioners ◽  
Infected Case ◽  
First Case

AbstractCOVID-19 is spreading really fast around the world. The current study describes the situation of the outbreak of this disease in India and predicts the number of cases expected to rise in India. The study also discusses the regional analysis of Indian states and presents the preparedness level of India in combating this outbreak. The study uses exploratory data analysis to report the current situation and uses time-series forecasting methods to predict the future trends. The data has been considered from the repository of John Hopkins University and covers up the time period from 30th January 2020 when the first case occurred in India till the end of 24th March 2020 when the Prime Minister of India declared a complete lockdown in the country for 21 days starting 25th March 2020. The major findings show that number of infected cases in India is rising quickly with the average infected cases per day rising from 10 to 73 from the first case to the 300th case. The current mortality rate for India stands around 1.9. Kerala and Maharashtra are the top two infected states in India with more than 100 infected cases reported in each state, respectively. A total of 25 states have reported at least one infected case, however only 8 of them have reported deaths due to COVID-19. The ARIMA model prediction shows that the infected cases in India may reach up to 700 thousands in next 30 days in worst case scenario while most optimistic scenario may restrict the numbers up to 1000-1200. Also, the average forecast by ARIMA model in next 30 days is around 7000 patients from the current numbers of 536. Based on the forecasting model by Holt’s linear trends, an expected 3 million people may get infected if control measures are not taken in the near future. This study will be useful for the key stakeholders like Government Officials and Medical Practitioners in assessing the trends for India and preparing a combat plan with stringent measures. Also, this study will be helpful for data scientists, statisticians, mathematicians and analytics professionals in predicting outbreak numbers with better accuracy.

Reference Command Shaping Using Specified-Negative-Amplitude Input Shapers for Vibration Reduction

2004 ◽  
Vol 126 (1) ◽  
pp. 210-214 ◽  
Author(s):  
William Singhose ◽  
Erika Ooten Biediger ◽  
Ye-Hwa Chen ◽  
Bart Mills
Keyword(s):  
Rise Time ◽  
Vibration Reduction ◽  
Input Shaping ◽  
Case Scenario ◽  
Worst Case ◽  
Performance Gap ◽  
Command Shaping ◽  
Mode Excitation ◽  
New Type ◽  
Command Signals

Residual vibrations can be greatly reduced by using specially-shaped reference command signals. Input shaping is one such technique that reduces vibration by convolving a sequence of impulses with any desired reference command. Several types of useful impulse sequences have been developed. Most of these have contained only positively valued impulses. However, rise time can be improved by using some negative impulses in the sequence. Unfortunately, the use of negative impulses can excite unmodeled high modes. A new type of impulse sequence containing negative impulses is proposed. These sequences are designed to fill the performance gap between all-positive impulse sequences and the negative sequences previously developed. A proof governing the worst case scenario provides an upper bound on high-mode excitation. The resulting class of impulse sequences allows the designer to make a precise trade off between rise time and vibration reduction.

scholarly journals Randomized Incremental Construction of Delaunay Triangulations of Nice Point Sets

2020 ◽  
Author(s):  
Jean-Daniel Boissonnat ◽  
Olivier Devillers ◽  
Kunal Dutta ◽  
Marc Glisse
Keyword(s):  
Delaunay Triangulation ◽  
Case Scenario ◽  
Worst Case ◽  
Geometric Data Structures ◽  
Delaunay Triangulations ◽  
Randomized Incremental Construction ◽  
First Case ◽  
Worst Case Complexity ◽  
Time Optimal ◽  
Incremental Construction

Abstract Randomized incremental construction (RIC) is one of the most important paradigms for building geometric data structures. Clarkson and Shor developed a general theory that led to numerous algorithms which are both simple and efficient in theory and in practice. Randomized incremental constructions are usually space-optimal and time-optimal in the worst case, as exemplified by the construction of convex hulls, Delaunay triangulations, and arrangements of line segments. However, the worst-case scenario occurs rarely in practice and we would like to understand how RIC behaves when the input is nice in the sense that the associated output is significantly smaller than in the worst case. For example, it is known that the Delaunay triangulation of nicely distributed points in $${\mathbb {E}}^d$$ E d or on polyhedral surfaces in $${\mathbb {E}}^3$$ E 3 has linear complexity, as opposed to a worst-case complexity of $$\Theta (n^{\lfloor d/2\rfloor })$$ Θ ( n ⌊ d / 2 ⌋ ) in the first case and quadratic in the second. The standard analysis does not provide accurate bounds on the complexity of such cases and we aim at establishing such bounds in this paper. More precisely, we will show that, in the two cases above and variants of them, the complexity of the usual RIC is $$O(n\log n)$$ O ( n log n ) , which is optimal. In other words, without any modification, RIC nicely adapts to good cases of practical value. At the heart of our proof is a bound on the complexity of the Delaunay triangulation of random subsets of $${\varepsilon }$$ ε -nets. Along the way, we prove a probabilistic lemma for sampling without replacement, which may be of independent interest.

Mixed Fleet Flying Between Two Commercial Aircraft Types: An Empirical Evaluation of the Role of Negative Transfer

2005 ◽  
Vol 49 (1) ◽  
pp. 45-48 ◽  
Author(s):  
Beth Lyall ◽  
Christopher D. Wickens
Keyword(s):  
Negative Transfer ◽  
Empirical Evaluation ◽  
Commercial Aircraft ◽  
Case Scenario ◽  
Worst Case ◽  
Airline Pilots ◽  
Design Changes ◽  
Commercial Airline ◽  
New Type

We examined the potential vulnerabilities of pilots flying a mixed fleet of two different aircraft types. A “worst case” scenario was evaluated in which a pilot, flying one type exclusively, would need to fly the different type, after 6 months without any recurrency training on the latter. These circumstances invite negative transfer of habits in the “old” aircraft, to performance in the “new” aircraft”. Documents of both aircraft were evaluated to establish those aspects of design and procedures differences that invite such negative transfer; a list of 36 such “vulnerabilities” were identified. Then 40 active commercial airline pilots from a US carrier participated in an evaluation of such negative transfer between two different types within the fleet. The sample was divided into 2 groups each of which normally flew one of the types and not the other. After training on the “new” type, each pilot returned to either 3 or 6 months of flying exclusively with their “old” type, and then returned for simulator evaluations on the “new” type that were targeted to reveal the 36 vulnerabilities. Even with power-sensitive statistical analyses, only slight evidence for negative transfer was found. Those areas where such transfer did emerge were targeted for recommendations of either procedural harmonization or minor design changes.

scholarly journals Trend Analysis and Forecasting of COVID-19 outbreak in India

2020 ◽  
Author(s):  
Rajan Gupta ◽  
Saibal K Pal
Keyword(s):  
Arima Model ◽  
Regional Analysis ◽  
Control Measures ◽  
Government Officials ◽  
Case Scenario ◽  
Indian States ◽  
Worst Case ◽  
Medical Practitioners ◽  
Infected Case ◽  
First Case

COVID-19 is spreading really fast around the world. The current study describes the situation of the outbreak of this disease in India and predicts the number of cases expected to rise in India. The study also discusses the regional analysis of Indian states and presents the preparedness level of India in combating this outbreak. The study uses exploratory data analysis to report the current situation and uses time-series forecasting methods to predict the future trends. The data has been considered from the repository of John Hopkins University and covers up the time period from 30th January 2020 when the first case occurred in India till the end of 24th March 2020 when the Prime Minister of India declared a complete lockdown in the country for 21 days starting 25th March 2020. The major findings show that number of infected cases in India is rising quickly with the average infected cases per day rising from 10 to 73 from the first case to the 300th case. The current mortality rate for India stands around 1.9. Kerala and Maharashtra are the top two infected states in India with more than 100 infected cases reported in each state, respectively. A total of 25 states have reported at least one infected case, however only 8 of them have reported deaths due to COVID-19. The ARIMA model prediction shows that the infected cases in India may reach up to 700 thousands in next 30 days in worst case scenario while most optimistic scenario may restrict the numbers up to 1000-1200. Also, the average forecast by ARIMA model in next 30 days is around 7000 patients from the current numbers of 536. Based on the forecasting model by Holt’s linear trends, an expected 3 million people may get infected if control measures are not taken in the near future. This study will be useful for the key stakeholders like Government Officials and Medical Practitioners in assessing the trends for India and preparing a combat plan with stringent measures. Also, this study will be helpful for data scientists, statisticians, mathematicians and analytics professionals in predicting outbreak numbers with better accuracy.

Reducing Probabilistic Weather Forecasts to the Worst Case Scenario: Anchoring Effects

2008 ◽  
Author(s):  
Sonia Savelli ◽  
Susan Joslyn ◽  
Limor Nadav-Greenberg ◽  
Queena Chen
Keyword(s):  
Case Scenario ◽  
Worst Case ◽  
Weather Forecasts ◽  
Worst Case Scenario ◽  
Anchoring Effects

EXPEDITION TO MALI, 2020

2020 ◽  
pp. 12-28
Author(s):  
D. V. Vaniukova ◽  
◽  
P. A. Kutsenkov ◽  
Keyword(s):  
Mass Murder ◽  
The State ◽  
Case Scenario ◽  
Worst Case ◽  
Traditional Art ◽  
Peter The Great ◽  
State Museum ◽  
Bobo Dioulasso ◽  
Oriental Studies ◽  
The Village

The research expedition of the Institute of Oriental studies of the Russian Academy of Sciences has been working in Mali since 2015. Since 2017, it has been attended by employees of the State Museum of the East. The task of the expedition is to study the transformation of traditional Dogon culture in the context of globalization, as well as to collect ethnographic information (life, customs, features of the traditional social and political structure); to collect oral historical legends; to study the history, existence, and transformation of artistic tradition in the villages of the Dogon Country in modern conditions; collecting items of Ethnography and art to add to the collection of the African collection of the. Peter the Great Museum (Kunstkamera, Saint Petersburg) and the State Museum of Oriental Arts (Moscow). The plan of the expedition in January 2020 included additional items, namely, the study of the functioning of the antique market in Mali (the “path” of things from villages to cities, which is important for attributing works of traditional art). The geography of our research was significantly expanded to the regions of Sikasso and Koulikoro in Mali, as well as to the city of Bobo-Dioulasso and its surroundings in Burkina Faso, which is related to the study of migrations to the Bandiagara Highlands. In addition, the plan of the expedition included organization of a photo exhibition in the Museum of the village of Endé and some educational projects. Unfortunately, after the mass murder in March 2019 in the village of Ogossogou-Pel, where more than one hundred and seventy people were killed, events in the Dogon Country began to develop in the worst-case scenario: The incessant provocations after that revived the old feud between the Pel (Fulbe) pastoralists and the Dogon farmers. So far, this hostility and mutual distrust has not yet developed into a full-scale ethnic conflict, but, unfortunately, such a development now seems quite likely.

Risk-Based Robust Bidding Strategies for EVs’ Aggregators in Day-ahead Markets with Uncertainty

2020 ◽  
Author(s):  
Ahmed Abdelmoaty ◽  
Wessam Mesbah ◽  
Mohammad A. M. Abdel-Aal ◽  
Ali T. Alawami
Keyword(s):  
Robust Optimization ◽  
Electricity Market ◽  
Real Life ◽  
Optimization Approach ◽  
Case Scenario ◽  
Worst Case ◽  
Bidding Strategies ◽  
Wind Generators ◽  
Proposed Model ◽  
Optimal Bidding

In the recent electricity market framework, the profit of the generation companies depends on the decision of the operator on the schedule of its units, the energy price, and the optimal bidding strategies. Due to the expanded integration of uncertain renewable generators which is highly intermittent such as wind plants, the coordination with other facilities to mitigate the risks of imbalances is mandatory. Accordingly, coordination of wind generators with the evolutionary Electric Vehicles (EVs) is expected to boost the performance of the grid. In this paper, we propose a robust optimization approach for the coordination between the wind-thermal generators and the EVs in a virtual<br>power plant (VPP) environment. The objective of maximizing the profit of the VPP Operator (VPPO) is studied. The optimal bidding strategy of the VPPO in the day-ahead market under uncertainties of wind power, energy<br>prices, imbalance prices, and demand is obtained for the worst case scenario. A case study is conducted to assess the e?effectiveness of the proposed model in terms of the VPPO's profit. A comparison between the proposed model and the scenario-based optimization was introduced. Our results confirmed that, although the conservative behavior of the worst-case robust optimization model, it helps the decision maker from the fluctuations of the uncertain parameters involved in the production and bidding processes. In addition, robust optimization is a more tractable problem and does not suffer from<br>the high computation burden associated with scenario-based stochastic programming. This makes it more practical for real-life scenarios.<br>

scholarly journals A Thermally Conductive Pt/AAO Catalyst for Hydrogen Passive Autocatalytic Recombination

Catalysts ◽  
2021 ◽  
Vol 11 (4) ◽  
pp. 491
Author(s):  
Alina E. Kozhukhova ◽  
Stephanus P. du Preez ◽  
Aleksander A. Malakhov ◽  
Dmitri G. Bessarabov
Keyword(s):  
Electron Microscopy ◽  
Particle Size ◽  
Combustion Temperature ◽  
Volume Fraction ◽  
Morphological Characteristics ◽  
Al Alloy ◽  
Impregnation Method ◽  
Case Scenario ◽  
Worst Case ◽  
Thermal Distribution

In this study, a Pt/anodized aluminum oxide (AAO) catalyst was prepared by the anodization of an Al alloy (Al6082, 97.5% Al), followed by the incorporation of Pt via an incipient wet impregnation method. Then, the Pt/AAO catalyst was evaluated for autocatalytic hydrogen recombination. The Pt/AAO catalyst’s morphological characteristics were determined by scanning electron microscopy (SEM) and transmission electron microscopy (TEM). The average Pt particle size was determined to be 3.0 ± 0.6 nm. This Pt/AAO catalyst was tested for the combustion of lean hydrogen (0.5–4 vol% H2 in the air) in a recombiner section testing station. The thermal distribution throughout the catalytic surface was investigated at 3 vol% hydrogen (H2) using an infrared camera. The Al/AAO system had a high thermal conductivity, which prevents the formation of hotspots (areas where localized surface temperature is higher than an average temperature across the entire catalyst surface). In turn, the Pt stability was enhanced during catalytic hydrogen combustion (CHC). A temperature gradient over 70 mm of the Pt/AAO catalyst was 23 °C and 42 °C for catalysts with uniform and nonuniform (worst-case scenario) Pt distributions. The commercial computational fluid dynamics (CFD) code STAR-CCM+ was used to compare the experimentally observed and numerically simulated thermal distribution of the Pt/AAO catalyst. The effect of the initial H2 volume fraction on the combustion temperature and conversion of H2 was investigated. The activation energy for CHC on the Pt/AAO catalyst was 19.2 kJ/mol. Prolonged CHC was performed to assess the durability (reactive metal stability and catalytic activity) of the Pt/AAO catalyst. A stable combustion temperature of 162.8 ± 8.0 °C was maintained over 530 h of CHC. To confirm that Pt aggregation was avoided, the Pt particle size and distribution were determined by TEM before and after prolonged CHC.

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