Combining Global Geopotential Models, Digital Elevation Models, and GNSS/Leveling for Precise Local Geoid Determination in Some Mexico Urban Areas: Case Study

This work shows improvements of geoid undulation values obtained from a high-resolution Global Geopotential Model (GGM), applied to local urban areas. The methodology employed made use of a Residual Terrain Model (RTM) to account for the topographic masses effect on the geoid. This effect was computed applying the spherical tesseroids approach for mass discretization. The required numerical integration was performed by 2-D integration with 1DFFT technique that combines DFT along parallels with direct numerical integration along meridians. In order to eliminate the GGM commission error, independent geoid undulations values obtained from a set of GNSS/leveling stations are employed. A corrector surface from the associated geoid undulation differences at the stations was generated through a polynomial regression model. The corrector surface, in addition to the GGM commission error, also absorbs the GNSS/leveling errors as well as datum inconsistencies and systematic errors of the data. The procedure was applied to five Mexican urban areas that have a geodetic network of GNSS/leveling points, which range from 166 to 811. Two GGM were evaluated: EGM2008 and XGM2019e_2159. EGM2008 was the model that showed relatively better agreement with the GNSS/leveling stations having differences with RMSE values in the range of 8–60 cm and standard deviations of 5–8 cm in four of the networks and 17 cm in one of them. The computed topographic masses contribution to the geoid were relatively small, having standard deviations on the range 1–24 mm. With respect to corrector surface estimations, they turned out to be fairly smooth yielding similar residuals values for two geoid models. This was also the case for the most recent Mexican gravity geoid GGM10. For the three geoid models, the second order polynomial regression model performed slightly better than the first order with differences up to 1 cm. These two models produced geoid correction residuals with a standard deviation in one test area of 14 cm while for the others it was of about 4–7 cm. However, the kriging method that was applied for comparison purposes produced slightly smaller values: 8 cm for one area and 4–6 cm for the others.

Assessment of the Rice Panicle Initiation by Using NDVI-Based Vegetation Indexes

The assessment of rice panicle initiation is crucial for the management of nitrogen fertilizer application that affects yield and quality of grain. The occurrence of panicle initiation could be determined via either green ring, internode-elongation, or a 1–2 mm panicle, and was observed through manual dissection. The quadratic polynomial regression model was used to construct the model of the trend of normalized difference vegetation index-based vegetation indexes (NDVI-based VIs) between pre-tillering and panicle differentiation stages. The slope of the quadratic polynomial regression model tended to be alleviated in the period in which the panicle initiation stage should occur. The results indicated that the trend of the NDVI-based VIs was correlated with panicle initiation. NDVI-based VIs could be a useful indicator to remotely assess panicle initiation.

Optimal exit choice during highway tunnel evacuations based on the fire locations

In the case of a fire, the choice of exit in the highway tunnel is strictly limited by fire location, which seriously affects the evacuation time. A spontaneous or disorderly exit choice might result in a decreased evacuation efficiency and utilization rate of exits. In this paper, we propose a strategy to obtain the optimal exit choice based on fire location during highway tunnel evacuations. In our strategy, first, the vehicle distributions and locations of evacuating occupants are determined in the traffic simulation program VISSIM. The evacuation simulation software BuildingEXODUS is employed to obtain the corresponding parameters of the evacuation process and analyze the impacts of different fire locations on the evacuation time. During the analysis, the optimal productivity statistics (OPS) is selected as the evaluation index. Then, the feature points of the crowding occupants are captured by the fuzzy c-means (FCM) cluster algorithm. Next, based on the feature points, the relationship between the location of the fire and boundary of the optimal exit choice under the optimal OPS is obtained through the polynomial regression model. It is found that the R-squared(R2) and sum of squares for error (SSE) of the polynomial regression model, reflecting the accuracy estimation, are 98.02% and 2.79×10−4, respectively. Moreover, different fire locations impact the evacuation time of tunnel entrance and evacuation passageway. This paper shows that the location of the fire and boundary of optimal exit choice have a negative linear correlation. Taking the fire 110 m away from the evacuation passageway as an example, the OPS of our strategy can be decreased by 35.6% when compared with no strategies. Our proposed strategy could be applied to determine the location of variable evacuation signs to help evacuating occupants make optimal exit choices.

Polynomial Regression Model Analysis of Abdominal Wall Tension and Intra-abdominal Pressure in Critically ill Patients

BackgroundAbdominal wall tension (AWT) plays an important role in the pathogenesis of abdominal compliance (AC). This study uses a polynomial regression model to analyze the correlation between intra-vesical pressure(IVP) and AWT in critically ill patients and provides new ideas for the diagnosis and treatment of critically ill patients with intra-abdominal hypertension（IAH）.MethodsA retrospective analysis was conducted in critically ill patients who met the inclusion criteria and were admitted to the Department of intensive care unit of Daping Hospital of Army Medical University from March 14, 2019, to May 23, 2020. According to the IVP on the first day of ICU admission and death within 28 days, the patients were divided into the IAH group (IVP ≥12 mmHg), the non-IAH group, the survival group and the nonsurvival group. The demographic and clinical data, prognostic indicators, AWT and IVP on days 1-7 after entering the ICU, IAH risk factors, and 28-day death risk factors were collected.ResultsA total of 100 patients were enrolled, with an average age of 45.59±11.4 years. There were 55 males (55%), 30 patients from departments of internal medicine (30%), 43 patients from surgery departments (43%), and 27 trauma patients (27%). In the IAH group, there were 50 patients (29 males, 58%), with an average age of 45.28±12.27 years; there were 50 patients (26 males, 52%) in the non-IAH group, with an average age of 45.90±10.58 years. The IVP on the 1st day and the average IVP within 7 days of the IAH group was 18.99（17.52，20.77）mmHg and 19.43（16.87，22.25）mmHg, respectively, which was higher than that of the non-IAH group [ 6.14（3.48，8.70）mmHg, 6.66（2.74，9.08）mmHg], p＜0.001. The AWT on the 1st day and the average AWT within 7 days of the IAH group was 2.89±0.32 N/mm and 2.82±0.46 N/mm, respectively, which was higher than that of the non-IAH group [(2.45±0.29)N/mm,(2.43±0.39)N/mm],p＜0.001.The polynomial regression models showed that the average AWT and IVP on the 1st day and within 7 days were AWTday1 = -2.450×10-3IVP2+9.695×10-2 IVP+2.046，r=0.667（p＜0.0001），and AWTmean = -2.293×10-3IVP2+9.273×10-2 IVP+2.081, respectively. The logistic regression analysis showed that AWTday1 of 2.73-2.97 N/mm increased the patient's 28-day mortality risk (OR: 6.834; 95%: 1.105-42.266, p=0.010).ConclusionsThere is a nonlinear correlation between AWT and IVP in critically ill patients, and a high AWT may indicate poor prognosis.

A Clinical Study of AWT Measurement and Polynomial Regression Model Analysis in Critically Ill patients: A Retrospective Study

Background：In this study, a new measurement device was used to measure the AWT in critically ill patients and a polynomial regression model was applied to analyze the correlation between intra-abdominal hypertension (IAH) and AWT in critically ill patients.Methods: A retrospective analysis was conducted in critically ill patients who were admitted to the Department of Critical Care Medicine of Daping Hospital of Army Medical University from March 13, 2019, to May 23, 2020. According to the intravesical pressure (IVP) on the first day of ICU admission and death within 28 days, the patients were divided into the IAH group (IVP ≥12 mmHg), the non-IAH group, the survival group and the nonsurvival group. The demographic and clinical data, prognostic indicators, AWT and IVP on days 1-7 after entering the ICU, IAH risk factors, and 28-day death risk factors were collected.Results: The AWT on the 1st and mean 7th day of the IAH group was (2.89±0.32)N/mm and (2.82±0.46) N/mm, respectively, which was higher than that of the non-IAH group [ (2.45±0.29) N/mm, (2.43±0.39) N/mm], p＜0.001. The average IVP on the 1st and mean 7th day of all patients were 12.78 (6.14, 18.99) and 11.49 (6.66, 19.43) mmHg, and the AWT on the 1st and mean 7th days were (2.75±0.38) and (2.75±0.47) N/mm, respectively, with significant differences (p< 0.0001). The polynomial regression models showed that the average AWT and IVP on the 1st and mean 7th were AWTday1=-2.450×10-3, IVP2+9.695×10-2 IVP+2.046，r=0.667（p＜0.0001），and AWTmean=-2.293×10-3, IVP2+9.273×10-2 IVP+2.081, respectively. The logistic regression analysis showed that AWTday1 2.73-2.97N/mm increased the patient's 28-day mortality risk (OR: 6.834; 95%: 1.105-42.266, p=0.010).Conclusion: There is a nonlinear correlation between AWT and IVP in critically ill patients, and a high AWT may indicate poor prognosis.Trial registration：ChiCTR，ChiCTR1900020562. Registered 8 January 2019，http://www.chictr.org.cn/showproj.aspx?proj=34441

COVID-19 Sex-Age Mortality Modeling - A Use Case of Risk-Based Vaccine Prioritization

This research builds upon the previous publications claiming that the male sex population and both sex individuals of advanced age are more susceptible to COVID-19’s risks. Relations between sex and age gradients are explored analytically based upon the proposed log-polynomial regression model of COVID-19 mortality. This model enables predicting mortality risk at any arbitrary age, as well as the derivation of several useful secondary metrics:•Sex differential: a ratio of male-to-female death risks for a given age group.•Age parity: age at which both sexes have an equal vulnerability.•Age lag: the number of years to subtract from a male’s age to match a female’s death risk.•Male equal risk age: male’s age at which male’s odds of dying from COVID-19 will equate female’s given the cutoff age. These metrics allow solving such practical problems as, e.g., prioritizing vaccine based on COVID-19 mortality risk associated with sex and age. Modeling techniques, refined in the paper, are by no means unique to COVID-19 and would apply to analyses of other diseases.

A Clinical Study of AWT Measurement and Polynomial Regression Model Analysis in Critically Ill Patients

Objective: In this study, a new measurement device was used to measure the AWT in critically ill patients and a polynomial regression model was applied to analyze the correlation between intra-abdominal hypertension (IAH) and AWT in critically ill patients.Methods: A retrospective analysis was conducted in critically ill patients who were admitted to the Department of Critical Care Medicine of Daping Hospital of Army Medical University from August 30, 2018, to June 30, 2020. According to the intravesical pressure (IVP) on the first day of ICU admission and death within 28 days, the patients were divided into the IAH group (IVP ≥12 mmHg), the non-IAH group, the survival group and the nonsurvival group. The demographic and clinical data, prognostic indicators, AWT and IVP on days 1-7 after entering the ICU, IAH risk factors, and 28-day death risk factors were collected.Results: The AWT on the 1st and mean 7th day of the IAH group was (2.89±0.32)N/mm and (2.82±0.46) N/mm, respectively, which was higher than that of the non-IAH group [ (2.45±0.29) N/mm, (2.43±0.39) N/mm], p＜0.001. The average IVP on the 1st and mean 7th day of all patients were 12.78 (6.14, 18.99) and 11.49 (6.66, 19.43) mmHg, and the AWT on the 1st and mean 7th days were (2.75±0.38) and (2.75±0.47) N/mm, respectively, with significant differences (p< 0.0001). The polynomial regression models showed that the average AWT and IVP on the 1st and mean 7th were AWTday1=-2.450×10-3, IVP2+9.695×10-2 IVP+2.046，r=0.667（p＜0.0001），and AWTmean=-2.293×10-3, IVP2+9.273×10-2 IVP+2.081, respectively. The logistic regression analysis showed that AWTday1 2.73-2.97N/mm increased the patient's 28-day mortality risk (OR: 6.834; 95%: 1.105-42.266, p=0.010).Conclusion: There is a nonlinear correlation between AWT and IVP in critically ill patients, and a high AWT may indicate poor prognosis.

Forecasting COVID-19 cases in Algeria using logistic growth and polynomial regression models

The novel Coronavirus respiratory disease 2019 (COVID-19) is still expanding through the world since it started in Wuhan (China) on December 2019 reporting a number of more than 84.4 millions cases and 1.8 millions deaths on January 3rd 2021.In this work and to forecast the COVID-19 cases in Algeria, we used two models: the logistic growth model and the polynomial regression model using data of COVID-19 cases reported by the Algerian ministry of health from February 25th to December 2nd, 2020. Results showed that the polynomial regression model fitted better the data of COVID-19 in Algeria the Logistic model. The first model estimated the number of cases on January, 19th 2021 at 387673 cases. This model could help the Algerian authorities in the fighting against this disease.

Growth of D614G Virus : Morgan – Morgan – Finney ( MMF ) Regression

In our earlier work [ 1 , ] a correlation was established between Infection fatality rate ( IFR ) vs age of the patient by logistic or sigmoid regression model . The regression model shows a more probable IFR with a sigmoid rise after 46 yrs and becoming most significant after 70 yrs and above. In the present study the frequency of infected patient by ‘ G ‘ type of mutants of SARS-COV-2 with time from Jan, 2020 to June, 2020 for 6.5 months duration of experimental study. The best fit analysis is compared to Morgan – Morgan – Finney ( MMF ) model ( 3,4 ) statistics and Polynomial regression model. Both the above model correlates a significant result with Pearson coefficient ( r ), Probability ( p-value ). The RBD of D614G( 10 ) interaction with Angiotensin Converting Enzyme-II ( ACE2) of host cell can be also correlated to another new spike mutant like N501Y ( 11) through Pearson coefficient ( r ) with p-value of significance.