Coastal Migration Index for Coastal Flooding Events Increased by Sea Level Rise due to Climate Change: Mexico and Cuba Case Studies

This paper presents a coastal migration index (CMI) useful for decision-making in the current scenario of sea-level rise (SLR) due to climate change. The CMI includes coastal human population density, degree of urbanization, and coastal-flooding penetration. Quantitative and qualitative statistical techniques and the geographic information system ArcGIS View 9.0 were used. Further, a panel of fifteen international experts in coastal management issues was consulted to establish and validate the CMI. Results led to three index components based on 22 indicators. CMI was applied in the state of Tamaulipas, Mexico and in Santiago de Cuba province, Cuba. According to CMI estimates, the risk levels associated with SLR for human settlements analyzed in Mexico and Cuba were 5.3% and 11.0%, respectively. The most severely affected communities will require resettlement. Meanwhile, the CMI determined that 15.8% of the Mexican territory studied will be able to withstand the effects of SLR through the management of engineering works that will protect human settlements. The CMI determined that 79.0%, in the case of Tamaulipas, as well as 89.0% of the Cuban territory, will not require new policies or guidelines to promote conservation and protection of coastal natural resources. Lastly, the method used allowed for creation of a CMI stoplight map useful to coastal decision-makers to adopt sound management actions.

Effect of the Sharrard procedure on hip instability in children with Down syndrome: a retrospective study

Purpose The aim of this study was to retrospectively analyze the effect of the Sharrard procedure on hip instability in children with Down syndrome (DS), as measured by the migration index. Methods In total, 17 children (21 hips) were included from six hospitals in the Netherlands between 2003 and 2019. The primary outcome, hip instability, was assessed with the Reimers’ migration index on preoperative and postoperative plain anteroposterior pelvic radiographs. The mean age at surgery was 8.1 years, the majority of children were male (64.7%) and the mean follow-up time was 7.3 years. Results The mean preoperative migration index was 46% (sd 23.5) and the mean postoperative migration index was 37% (sd 28.4). The mean Delta migration index (the difference in pre-operative migration index and most recent post-operative migration index) showed an improvement of 9.3% (sd 22.7). An improvement in migration index was observed in 52%, no change in 29% and deterioration in 19% of hips. No (re)dislocations occurred in 91% of the hips. No major complications were observed during the follow-up period. Conclusion Early intervention is warranted in children with DS showing hip instability or hip migration, in order to succeed with less complex procedures. The Sharrard procedure should be considered in children with DS showing hip instability or hip migration, since it aims to rebalance the muscles of the hip joint, is less complex than bony procedures of the femur and acetabulum, surgery time is often shorter, there are fewer major complications and the rehabilitation period is shorter. Level of Evidence IV - retrospective case series

Analyzing the Contribution of Road Traffic to Changes of Air Pollutants Using Random Forest: Insights from COVID-19 lockdown in Wuhan

During the COVID-19 lockdown in Wuhan, transportation, industrial production and other human activities declined significantly, as did the NO2 concentration. In order to assess the relative contributions of different factors to reductions of air pollutants, sensitivity experiments were implemented by random forest (RF) model, with the comparison of contributions of meteorology, road traffic, and emission sources between different periods. Besides, an emulator was operated to suggest an appropriate limit for control of transportation. The RF models showed different mechanisms for air pollutants. Within-city Migration index (WMI) was more important in the normal, pre-lockdown and post-pandemic model while Out-Migration Index (OMI) was emphasized in the lockdown model. In the COVID-19 lockdown period, 73.3% of the reduction can be attributed to the decreased road traffic, showing massive impact of road traffic on the air quality. In the post-pandemic period, meteorology controlled about 42.2% of the decrease and emissions from industry and household controlled 40.0% while road traffic only contributed to 17.8%. It was suggested that priority of restriction should be given to road traffic within the city. A limit of less than 40% on the control of the road traffic can get a better effect, especially for cities with severe traffic pollution.

Estimating Short- and Long-Term Associations Between Air Quality Index and COVID-19 Transmission: Evidence From 257 Chinese Cities

Objectives: To evaluate the long- and short-term effects of air pollution on COVID-19 transmission simultaneously, especially in high air pollution level countries.Methods: Quasi-Poisson regression was applied to estimate the association between exposure to air pollution and daily new confirmed cases of COVID-19, with mutual adjustment for long- and short-term air quality index (AQI). The independent effects were also estimated and compared. We further assessed the modification effect of within-city migration (WM) index to the associations.Results: We found a significant 1.61% (95%CI: 0.51%, 2.72%) and 0.35% (95%CI: 0.24%, 0.46%) increase in daily confirmed cases per 1 unit increase in long- and short-term AQI. Higher estimates were observed for long-term impact. The stratifying result showed that the association was significant when the within-city migration index was low. A 1.25% (95%CI: 0.0.04%, 2.47%) and 0.41% (95%CI: 0.30%, 0.52%) increase for long- and short-term effect respectively in low within-city migration index was observed.Conclusions: There existed positive associations between long- and short-term AQI and COVID-19 transmission, and within-city migration index modified the association. Our findings will be of strategic significance for long-run COVID-19 control.

Energy Level and Chemical Class of Disperse Dyes—Plausible Characteristics of Level Dyeing Performance

The level dyeing index (LDI) is a measure of dye performance influenced by the dye migration process, ensuring the maximum uniformity of dye redistribution onto the fabric surface. The current study evaluates the level dyeing performance on polyester according to the energy level (low and high) and chemical classes (azo and anthraquinone based) of the three disperse dyes studied. The best levelness was obtained using C.I. Disperse Red 73 (an azo-based, low-energy level disperse dye), which exhibited the highest migration index (MI%) value. LDI results were obtained from the ratio of the exhaustion at the critical dyeing temperature (ECDT% and the final exhaustion Ef%), and the migration index (MI%). Each dye's LDI can be used to determine the compatibility of disperse dyes for combination dyeing.

Untangle contributions of meteorology condition and human mobility to tropospheric NO2 in Chinese mainland during COVID-19 pandemic in early 2020

In early 2020, unprecedented lockdowns and travel bans were implemented in Chinese mainland to stop the spread of novel coronavirus (COVID-19), which have led to large reduction of anthropogenic emissions. This provided a unique opportunity to isolate the effects from emission and meteorology on tropospheric nitrogen dioxide (NO2). Comparing the atmospheric NO2 in 2020 to 2017, we found the changes of emission have led to a -49.3 ± 23.5% reduction, which was ∼12% more than satellite observed reduction of -37.8 ± 16.3%. The discrepancy was mainly due to the changes of meteorology which have contributed to an 8.1 ± 14.2% increase of NO2. We also revealed that the emission induced reduction of NO2 has significantly negative correlations to human mobility, particularly that inside the city. The intra-city migration index derived from Baidu Location-Based-Service can explain 40.4%±17.7% variance of the emission induced reduction of NO2 in 29 megacities which each has a population of over 8 million in Chinese mainland.

Regional infectious risk prediction of COVID-19 based on geo-spatial data

After the first confirmed case of the novel coronavirus disease (COVID-19) was found, it is of considerable significance to divide the risk levels of various provinces or provincial municipalities in Mainland China and predict the spatial distribution characteristics of infectious diseases. In this paper, we predict the epidemic risk of each province based on geographical proximity information, spatial inverse distance information, economic distance and Baidu migration index. A simulation study revealed that the information based on geographical economy matrix and migration index could well predict the spatial spread of the epidemic. The results reveal that the accuracy rate of the prediction is over 87.10% with a rank difference of 3.1. The results based on prior information will guide government agencies and medical and health institutions to implement responses to major public health emergencies when facing the epidemic situation.

A machine learning based forecast model for the COVID-19 pandemic and investigation of the impact of government intervention on COVID-19 transmission in China

The coronavirus disease 2019 (COVID-19) pandemic has killed over 0.3 million people, disrupted people’s normal lives, and severely restricted economic activities globally. In this work, a model for the next-day COVID-19 prediction in China was built based on the ensemble back-propagation neural network machine learning technique, Baidu migration index, internal travel flow index, and confirmed cases from the previous days. The 10-fold cross-validation results showed that the model performs well in estimating the next-day confirmed cases with a correlation coefficient of 0.97. To investigate the impacts of government interventions on the spread of this new coronavirus infection, the Baidu migration index and internal travel flow index multiplied by a factor of two were input into the trained machine learning model, and the results showed that the confirmed cases in the analyzed cities would increase dramatically. The correlation between the daily new confirmed cases and some meteorological factors were also analyzed, and the results revealed that these factors are not dominant in influencing the spread of this disease. Overall, the results of this work suggest that besides early diagnosis and medical treatment, a city lockdown policy is one of the most effective methods in suppressing the rapid spread of COVID-19.

Lateral Migration in a Wandering Reach of the Middle Yellow River in Response to Different Boundary Conditions

The Xiaobeiganliu reach is a typical wandering reach of the Middle Yellow River that has rapid and significant channel lateral migration, which may threaten the safety of riparian land and flood control structures. To investigate the characteristics and mechanism of lateral migration in the Xiaobeiganliu reach, the temporal and spatial variations in bankfull width and thalweg migration distance were identified during the continuous deposition period, quantitatively analyzing the effect of different boundary conditions on the lateral migration index. The reach-scale bankfull width decreased by 32% from 1986 to 2001 because hyperconcentrated floods often occurred in this reach. The thalweg migration distance varied dramatically at cross-sections, with the maximum annual thalweg migration distance reaching 4290 m. The lateral migration index of the Xiaobeiganliu reach responded well to the upstream and downstream boundary conditions. The previous 3-year average water discharge and 4-year average sediment concentration at the upstream station were two key fluvial factors influencing lateral migration, with the relation being established between the lateral migration index and the two parameters. The Tongguan (TG) elevation was the key downstream boundary condition affecting thalweg migration, and a power function was proposed between the lateral migration index and the variations in annual TG elevation.