Numerical Simulation and Machine Learning Prediction of the Direct Chill Casting Process of Large-Scale Aluminum Ingots.

The large-scale ingot of the 7xxx-series aluminum alloys fabricated by direct chill (DC) casting often suffers from foundry defects such as cracks and cold shut due to the formidable challenges in the precise controlling of casting parameters. In this manuscript, by using the integrated computational method combining numerical simulations with machine learning, we systematically estimated the evolution of multi-physical fields and grain structures during the solidification processes. The numerical simulation results quantified the influences of key casting parameters including pouring temperature, casting speed, primary cooling intensity, and secondary cooling water flow rate on the shape of the mushy zone, heat transport, residual stress, and grain structure of DC casting ingots. Then, based on the data of numerical simulations, we established a novel model for the relationship between casting parameters and solidification characteristics through machine learning. By comparing it with experimental measurements, the model showed reasonable accuracy in predicting the sump profile, microstructure evolution, and solidification kinetics under the complicated influences of casting parameters. The integrated computational method and predicting model could be used to efficiently and accurately determine the DC casting parameters to decrease the casting defects.

Mapping Total Exceedance PM2.5 Exposure Risk by Coupling Social Media Data and Population Modeling Data.

The PM2.5 exposure risk assessment is the foundation to reduce its adverse effects. Population survey-related data have been deficient in high spatiotemporal detailed descriptions. Social media data can quantify the PM2.5 exposure risk at high spatiotemporal resolutions. However, due to the no-sample characteristics of social media data, PM2.5 exposure risk for older adults is absent. We proposed combining social media data and population survey-derived data to map the total PM2.5 exposure risk. Hourly exceedance PM2.5 exposure risk indicators based on population modeling (HEPEpmd) and social media data (HEPEsm) were developed. Daily accumulative HEPEsm and HEPEpsd ranged from 0 to 0.009 and 0 to 0.026, respectively. Three peaks of HEPEsm and HEPEpsd were observed at 13:00, 18:00, and 22:00. The peak value of HEPEsm increased with time, which exhibited a reverse trend to HEPEpsd. The spatial center of HEPEsm moved from the northwest of the study area to the center. The spatial center of HEPEpsd moved from the northwest of the study area to the southwest of the study area. The expansion area of HEPEsm was nearly 1.5 times larger than that of HEPEpsd. The expansion areas of HEPEpsd aggregated in the old downtown, in which the contribution of HEPEpsd was greater than 90%. Thus, this study introduced various source data to build an easier and reliable method to map total exceedance PM2.5 exposure risk. Consequently, exposure risk results provided foundations to develop PM2.5 pollution mitigation strategies as well as scientific supports for sustainability and eco-health achievement.

Using nighttime light data to identify the structure of polycentric cities and evaluate urban centers.

Studying the structure of polycentric cities promotes a better understanding of the process of urban development and contributes to urban planning and management. However, existing studies cannot compare the level differences between urban centers and quantify the overall urban center development level within polycentric cities. Therefore, we combined nighttime light (NTL) data and the natural city (NC) to conduct this study from multiple perspectives. First, NCs were determined from Luojia-1A NTL data with high spatial resolution. Second, urban centers were selected from NCs. Third, urban center level (UCL) was proposed to compare the level differences of urban centers. Fourth, urban center development index (UCDI) was proposed to quantify the overall urban center development level within a polycentric city. A quantitative verification method was used to confirm that the detection accuracy of urban center identification is high. Furthermore, the applicability of the proposed method in different NTL datasets was verified by the identification of urban centers and the calculation of UCDIs. Unlike traditional methods, the shape and scope of the urban center identified using the proposed method are not restricted by administrative boundaries. Moreover, the level differences of urban centers and the overall urban center development level within a polycentric city, can be expressed by quantitative indicators, which helps in comparing the differences between urban centers.

Explicit the urban waterlogging spatial variation and its driving factors: The stepwise cluster analysis model and hierarchical partitioning analysis approach.

Urban waterlogging is a hydrological cycle problem that seriously affects people's life and property. Characterizing waterlogging variation and explicit its driving factors are conducive to prevent the damage of such disasters. Conventional methods, because of the high spatial heterogeneity and the non-stationary complex mechanism of urban waterlogging, are not able to fully capture the urban waterlogging spatial variation and identify the waterlogging susceptibility areas. A more robust method is recommended to quantify the variation trend of urban waterlogging. Previous studies have simulated the waterlogging variation in relatively small areas. However, the relationship between variables is often ignored, which cannot comprehensively reveal the dominant drivers affecting urban waterlogging. Therefore, a novel approach is proposed that combined stepwise cluster analysis model (SCAM) and hierarchical partitioning analysis (HPA) within a general framework and verifies the applicability through logistic regression, artificial neural network, and support vector machine. According to the dominant driving factors, different simulation scenarios are established to analyze waterlogging density variation. Results found that the SCAM provides accurate and detailed simulated results both in urban centers where waterlogging frequently occurs and urban fringe with few waterlogging events, which shows an excellent performance with a high classification accuracy and generalization capability. HPA detected that the impervious surface abundance (28.07%), vegetation abundance (20.80%), and cumulate precipitation (16.25%) are the dominant drivers of waterlogging. This result suggests that priority should be given to controlling these three factors to mitigate the risk of waterlogging. It is interesting to note that under different urbanization and rainfall scenarios, the urban waterlogging susceptibility has a considerable variation. The watershed spatial location and watershed characteristics are relevant aspects to be considered in identifying and assessing waterlogging susceptibility, which provides original insights that urban waterlogging mitigation strategies should be developed according to different local conditions and future scenarios.

High Neutrophil/Lymphocyte Ratio as an Independent Risk Factor for the First Occurrence of Stroke in Peritoneal Dialysis Patients.

INTRODUCTION: Though neutrophil/lymphocyte ratio (NLR) level appears to be related with stroke events in general population, its relationship with stroke in peritoneal dialysis (PD) patients is still uncertain. This study aims to investigate the association between NLR and the first occurrence of stroke in PD patients. METHODS: In this retrospective cohort study, 1507 PD patients were enrolled from four centers in China and stratified into tertiles of NLR levels. The incidence of the first occurrence of stroke was analyzed by Kaplan-Meier cumulative incidence curve among different NLR tertiles, competing risk analysis was used to calculate the incidence of the first occurrence of stroke in the presence of competing risk of other events, multivariable COX regression analysis was performed to estimate the hazard ratios (HRs) for the first occurrence of stroke, as well as forest plot was utilized to describe the relationship between NLR and the first occurrence of stroke in different subgroups. RESULTS: During follow-up, 84 new-onset stroke events were recorded. Kaplan-Meier cumulative incidence curves showed significant differences in the incidence of the first occurrence of stroke among three groups (log-rank test: P < .001). In competing risk analysis, the cumulative incidence curves for tertiles of NLR levels were highly significant for the first occurrence of stroke (P < .001), but they were not statistically different for the occurrence of other events. Compared to the lowest tertile of NLR level, the highest tertile was associated with increased risk of the first occurrence of stroke in the adjusted Cox model (HR = 2.39, 95% CI: 1.37 to 4.15; P < .05). As for forest plot, there was no interaction in all subgroups. CONCLUSION: High NLR was an independent risk factor for the first occurrence of stroke in PD patients.

Neutrophil to Lymphocyte Ratio Predicts Adverse Cardiovascular Outcome in Peritoneal Dialysis Patients Younger than 60 Years Old.

BACKGROUND: Neutrophil to lymphocyte ratio (NLR) is a new inflammatory marker; the relationship between NLR and adverse cardiovascular (CV) prognosis has been gradually emphasized in the general population. However, their association in peritoneal dialysis (PD) patients remains unclear. METHODS: From January 1, 2010, to May 31, 2017, a total of 1652 patients were recruited. NLR was categorized in triplicates: NLR ≤ 2.74, 2.74 < NLR ≤ 3.96, and NLR > 3.96. Kaplan-Meier cumulative incidence curve and multivariable COX regression analysis were used to determine the relationship between NLR and the incidence of adverse CV outcome, while a competitive risk model was applied to assess the effects of other outcomes on adverse CV prognosis. Besides, forest plot was investigated to analyze the adverse CV prognosis in different subgroups. RESULTS: During follow-up, 213 new-onset CV events and 153 CV disease (CVD) deaths were recorded. Multivariable COX regression models showed that the highest tertile of NLR level was associated with increased risk of CV events (HR = 1.39, 95%CI = 1.01-1.93, P = 0.046) and CVD mortality (HR = 1.81, 95%CI = 1.22-2.69, P = 0.003), while compared to the lowest tertile. Competitive risk models showed that the differences in CV event (P < 0.001) and CVD mortality (P = 0.004) among different NLR groups were still significant while excluding the effects of other outcomes. In subgroups, with each 1 increased in the NLR level, adjusted HR of new-onset CV event was 2.02 (95%CI = 1.26 - 3.23, P = 0.003) and CVD mortality was 2.98 (95%CI = 1.58 - 5.62, P = 0.001) in the younger group (age < 60 years). CONCLUSIONS: NLR is an independent risk factor for adverse CV prognosis in PD patients younger than 60 years old.

Explicit Spatializing Heat-Exposure Risk and Local Associated Factors by coupling social media data and automatic meteorological station data.

Extremely high temperatures, a major cause for weather-related public health issues, are projected to intensify and become more frequent. To mitigate the adverse effects, a low-cost and effective risk assessment method should be developed. Therefore, we applied automatic meteorological station data and population mobility data to develop a high spatiotemporal resolution temperature risk assessment method. The population mobility analysis results showed the working/residential complex pattern in Tianhe District, with hotspots of spatial clustering located in the north, southwest, and southeast of the study area. Taking the population mobility patterns into consideration, high-temperature risk assessment results with a resolution of 100 m were obtained. The total mortality cases in 2014 and 2015 were used to validate this result. The validation showed that the total mortality in the high-temperature risk areas accounted for over 36% of that in Tianhe District. Thus, the method introduced in this study is capable of reflecting weather-related risk. Furthermore, the high-temperature risk assessment results showed that most of the risky areas were located in the southwest of the study area. Two peak times of the risk areas were determined, being before dawn and in the evening. Compared with the risk areas during weekdays, those at weekends expanded. In addition, we used the geographically weighted regression model to investigate the potential influencing factors. Individual factor contributed more than 22.4% to the spatial distribution of heat exposure. Catering services, transportation services, and living services were higher than others, with mean R2 values of 0.28, 0.23, and 0.25, respectively. More than 47.9% of spatial distribution of heat exposure was attributed to joint function of influencing factors, with global R2 ranged from 0.23 to 0.34. Our research introduces a spatial-specific method to quantitatively assess high-temperature risk. Moreover, the mechanisms behind the spatial distribution of the high-temperature risk were discussed. The theoretical and management implications can help urban designers and energy governors to develop useful strategies to mitigate weather-related public health risks.

The relationship between neutrophil-to-lymphocyte ratio and the first occurrence of pneumonia in peritoneal dialysis patients.

BACKGROUND: Although neutrophil-to-lymphocyte ratio (NLR) is closely associated with pneumonia in the general population, its relationship is unclear in peritoneal dialysis (PD) patients. METHODS: This is a cohort study consisting of 739 PD patients and dividing into two groups. Kaplan-Meier curves were applied to observe the incidence of the first occurrence of pneumonia, competitive risk analysis was conducted to compare whether there was a significant difference in each NLR group in the presence of other competing events, multivariable COX regression analysis was used to evaluate the hazard ratios (HRs), as well as forest plot was used to analyze the relationship between NLR and the first occurrence of pneumonia in different subgroups. RESULTS: Of all the patients, 116 cases of first-time pneumonia were recorded. The first-time pneumonia incidence rate was 71.67/1000 patient-years in high NLR group, which was markedly higher than that of 45.81/1000 patient-years in low NLR group. Kaplan-Meier curves indicated significant differences in the incidence of the first occurrence of pneumonia between two groups (log-rank test p = 0.015). The competitive risk model suggested a significant difference in the cumulative incidence of first pneumonia between the two groups (p = 0.032). Compared to low NLR group, adjusted Cox model showed that high NLR group was associated with increased risk of pneumonia incidence (HR, 1.51; 95% CI 1.04-2.21; p = 0.031). Forest plot showed no interaction was found in subgroups. CONCLUSIONS: The risk of pneumonia was significantly increasing in PD patients with high NLR, which may have a certain guiding significance for the clinic.

Cabin1 involves in renal tubular epithelial cells mitochondrial dysfunction through SIRT1/p53 pathway.

Background: Angiotensin II (AngII) induced Calcineurin binding protein 1 (Cabin1) protein expression significantly increased during Renal tubular epithelial cells (RTEC) injury. However, the detailed function of Cabin1 protein in RTEC was not characterized well. In this study, we aimed to explore the downstream target of Cabin1 in vitro model.Methods: Rat kidney epithelial cells were cultured and stimulated with AngII. Electron microscopy was performed to observe mitochondrial morphology change. Immunofluorescence staining was detected to observe the distribution of cytoskeleton and Cabin1. Mitochondrial morphology change and protein expression were detected by electrical microscopy and western blot.Results: AngII induced the disruption of cytoskeleton at 24 and 48 h. Western blot analysis showed AngII significantly induced the overexpression of Cabin1. AngII induced a great deal of small, long and irregular mitochondria in RTEC, aspect ratio which reflects the length-to-width ratio of mitochondria remarkably increased at 12 and 24 h. Knocking down Cabin1 aggravated mitochondrial morphological abnormality in AngII treated RTEC. In comparison with control, Cabin1, p53 and cyto C level were significantly increased in AngII treated cells, while SIRT1 level was obviously decreased. Knocked down Cabin1 plus AngII stimulated, SIRT1 was further decreased, while p53 and cyto C were significantly increased.Conclusions: Cabin1 involves in RTEC mitochondrial dysfunction through SIRT1/p53 pathway. Cabin1 may be used as a new marker for the mechanisms of RTEC injury.

Complex mechanisms linking land surface temperature to greenspace spatial patterns: Evidence from four southeastern Chinese cities.

Many studies have explored the complex mechanisms of urban heat islands by examining the relationship between land surface temperature (LST) and greenspace spatial patterns. Few, however, have explored the relative contributions of greenspace spatial composition and configuration to LST using comparisons between cities. In this study, the authors sought to identify the relative contributions of greenspace spatial composition and configuration to LST and the stability mechanisms linking LST to greenspace at multiple locations. We looked at four highly-urbanized Chinese cities in a comparative study. Landsat 5/8 images for summer and winter were used to estimate LST and greenspace data were extracted from 0.5-m resolution imagery. The complex relationship between LST and greenspace spatial patterns was quantified and compared using a novel method that combines stepwise regression with hierarchical partitioning analysis concerning statistical size variations. The results indicated that greenspace spatial composition and configuration both consistently affect LST. However, the magnitude and significance of these relationships were very different. The combined contributions of the greenspace landscape metrics played a more critical role in determining LST than their independent contributions, especially in summer. However, the relative importance of spatial composition and spatial configuration was largely dependent on specific variables such as season or selected statistical grid size. The urban heat island (UHI) effect can be reduced not only by increasing the amount of greenspace, but also by optimizing greenspace spatial configuration; the latter is more effective than the former. Although scale dependence continued to be evident in our study, we were not able to confirm a universal "best" scale for analysis. This study extended our understanding of the complex mechanisms of UHI in the region with respect to seasonal and scale factors, and has provided valuable information to support UHI adaptation strategy development by urban planners.

Moxibustion Activates Macrophage Autophagy and Protects Experimental Mice against Bacterial Infection.

Moxibustion is one of main therapies in traditional Chinese medicine and uses heat stimulation on the body surface from the burning of moxa to release pain or treat diseases. Emerging studies have shown that moxibustion can generate therapeutic effects by activating a series of signaling pathways and neuroendocrine-immune activities. Here we show moxibustion promoted profound macrophage autophagy in experimental Kunming mice, with reduced Akt phosphorylation and activated eIF2α phosphorylation. Consequently, moxibustion promoted bacterial clearance by macrophages and protected mice from mortality due to bacterial infection. These results indicate that moxibustion generates a protective response by activating autophagy against bacterial infections.