Measurements in degassing processes of CO2_{{2}} solution with particular reference to CO2_{{2}}-driven limnic eruptions

CO2-driven limnic eruptions are lethal phenomena that occur in lakes with aqueous CO2 solutions that become supersaturated. The exsolution of massive CO2 dissolved in the water can happen in a very short time, possibly leading to a natural disaster as happened in the Lake Nyos (Cameroon, Africa) in 1986. More than 1700 people died. In recent years, with the utilization of the technology of CO2 sequestration in brines in geological reservoirs, there are possibilities of the CO2-brine leakage. The brine may stay in the near surface water leading to the potential of an eruption. In this experimental study, measurements have been carried out to investigate the degassing processes of CO2 solutions under different depressurizing conditions. Based on the experimental data and using the ImagePro Plus® to process the recorded images, two correlations have been obtained: (1) the relationship between the supersaturation (${\Delta }P$) required for degassing and the initial pressure; (2) the relationship between the time delay (${\Delta }t$) corresponding to bubble formation and the initial pressure. Variations of key quantities (void fraction, number of bubbles, and average diameter of bubbles) over time have been analyzed. In addition, the void fractions measured in two different depressurizing ways have been compared. The experimental data and correlations obtained in this study are useful in establishing transient fluid dynamic models for simulating CO2-driven eruptions

Measurements in degassing processes of CO2_{{2}} solution with particular reference to CO2_{{2}}-driven limnic eruptions

CO2-driven limnic eruptions are lethal phenomena that occur in lakes with aqueous CO2 solutions that become supersaturated. The exsolution of massive CO2 dissolved in the water can happen in a very short time, possibly leading to a natural disaster as happened in the Lake Nyos (Cameroon, Africa) in 1986. More than 1700 people died. In recent years, with the utilization of the technology of CO2 sequestration in brines in geological reservoirs, there are possibilities of the CO2-brine leakage. The brine may stay in the near surface water leading to the potential of an eruption. In this experimental study, measurements have been carried out to investigate the degassing processes of CO2 solutions under different depressurizing conditions. Based on the experimental data and using the ImagePro Plus® to process the recorded images, two correlations have been obtained: (1) the relationship between the supersaturation (${\Delta }P$) required for degassing and the initial pressure; (2) the relationship between the time delay (${\Delta }t$) corresponding to bubble formation and the initial pressure. Variations of key quantities (void fraction, number of bubbles, and average diameter of bubbles) over time have been analyzed. In addition, the void fractions measured in two different depressurizing ways have been compared. The experimental data and correlations obtained in this study are useful in establishing transient fluid dynamic models for simulating CO2-driven eruptions

A novel plantar pressure analysis method to signify gait dynamics in Parkinson's disease

Plantar pressure can signify the gait performance of patients with Parkinson's disease (PD). This study proposed a plantar pressure analysis method with the dynamics feature of the sub-regions plantar pressure signals. Specifically, each side's plantar pressure signals were divided into five sub-regions. Moreover, a dynamics feature extractor (DFE) was designed to extract features of the sub-regions signals. The radial basis function neural network (RBFNN) was used to learn and store gait dynamics. And a classification mechanism based on the output error in RBFNN was proposed. The classification accuracy of the proposed method achieved 100.00% in PD diagnosis and 95.89% in severity assessment on the online dataset, and 96.00% in severity assessment on our dataset. The experimental results suggested that the proposed method had the capability to signify the gait dynamics of PD patients

Serum metabolites reflecting gut microbiome alpha diversity predict type 2 diabetes

Type 2 diabetes (T2D) is associated with reduced gut microbiome diversity, although the cause is unclear. Metabolites generated by gut microbes also appear to be causative factors in T2D. We therefore searched for serum metabolites predictive of gut microbiome diversity in 1018 females from TwinsUK with concurrent metabolomic profiling and microbiome composition. We generated a Microbial Metabolites Diversity (MMD) score of six circulating metabolites that explained over 18% of the variance in microbiome alpha diversity. Moreover, the MMD score was associated with a significantly lower odds of prevalent (OR[95%CI] = 0.22[0.07;0.70], P = .01) and incident T2D (HR[95%CI] = 0.31[0.11,0.90], P = .03). We replicated our results in 1522 individuals from the ARIC study (prevalent T2D: OR[95%CI] = 0.79[0.64,0.96], P = .02, incident T2D: HR[95%CI] = 0.87[0.79,0.95], P = .003). The MMD score mediated 28%[15%,94%] of the total effect of gut microbiome on T2D after adjusting for confounders. Metabolites predicting higher microbiome diversity included 3-phenylpropionate(hydrocinnamate), indolepropionate, cinnamoylglycine and 5-alpha-pregnan-3beta,20 alpha-diol monosulfate(2) of which indolepropionate and phenylpropionate have already been linked to lower incidence of T2D. Metabolites correlating with lower microbial diversity included glutarate and imidazole propionate, of which the latter has been implicated in insulin resistance. Our results suggest that the effect of gut microbiome diversity on T2D is largely mediated by microbial metabolites, which might be modifiable by diet

GIS mapping and gene-environment interaction

Gene-environment interaction and GIS mapping were two major methods for spatial data analysis in public health sector. In the past, researchers often used the gene-environment interaction method to study the relationship between the environmental exposures and genetics factors, and how they affect each other. However, gene-environment interaction method only focused on the environmental factors at personal level. Along with the rapid development of geographic information systems (GIS), spatial data analysis has gained considerable attention, and has played a major role in public health. [4] The Geographic information system (GIS) is widely used in the public health sector, because it can combine the factors such as incidence of the disease, health services, geographic characteristics and environmental factors together when analyzing. The overall objective of this thesis was to present a comprehensive analysis for the spatial distribution of disease rate data and their linkage with location information and environmental risk factors through the application of GIS and spatial statistics. The research aims for this study included the investigation of: (1) whether there is gene-environment interaction (2) whether the data points are equally distributed across the area, and (3) whether there is spatial autocorrelation analysis. The simulation datasets I built included (4) study groups: Environmental exposures case group (the group who have disease gene), Environmental exposures control group (the group who do not have disease gene), People who have the gene for specific disease case group, People who do not have the gene for specific disease control group. Using 60 disease rate data in Boston area with location information, I employed gene-environment interaction, Quadrate methods, K function estimation, L function, Kriging Density, Spatial Autocorrelation Analysis (Moran\u27s I) to analyze disease data. Throughout this article, I demonstrated that the disease risk near Boston area tended to cluster by both gene-environment interaction and GIS analysis. And both environment and gene risk will affect the disease risk. The similar environment and life style may be the reason that caused spatial autocorrelation

Comparing the discourses of #BlackLivesMatter and #StopAsianHate on Twitter: Diversity and emotional and moral sentiments

AbstractAmid the COVID-19 pandemic, two important antiracist movements, namely, Black Lives Matter and Stop Asian Hate, swept across the United States between 2020 and early 2021. Social media platforms such as Twitter have become an increasingly important tool for mobilizing social movements. To gain a comprehensive understanding of social media users’ attention and reactions to racial injustice during this unprecedented time, the current study explores and compares the discursive characteristics of Twitter discussions of these two movements: their volume changes, word diversities, and moral and emotional sentiments. By analyzing the text of approximately 5 million tweets from April 2020 to April 2021 using a dictionary-based word count method, this research showed that compared to #BlackLivesMatter, #StopAsianHate was less diverse, more morally charged, and less positive in emotional sentiment. Additionally, #StopAsianHate contained stronger moral emotions than #BlackLivesMatter. The study connects these distinct characteristics to the two movements’ differences in their objectives, progress and participants’ demographics and provides implications for effective antiracist activism on social media

Patient Mix Optimization in Admission Planning under Multitype Patients and Priority Constraints

Hospital beds are one of the most critical medical resources. Large hospitals in China have caused bed utilization rates to exceed 100% due to long-term extra beds. To alleviate the contradiction between the supply of high-quality medical resources and the demand for hospitalization, in this paper, we address the decision of choosing a case mix for a respiratory medicine department. We aim to generate an optimal admission plan of elective patients with the stochastic length of stay and different resource consumption. We assume that we can classify elective patients according to their registration information before admission. We formulated a general integer programming model considering heterogeneous patients and introducing patient priority constraints. The mathematical model is used to generate a scientific and reasonable admission planning, determining the best admission mix for multitype patients in a period. Compared with model II that does not consider priority constraints, model I proposed in this paper is better in terms of admissions and revenue. The proposed model I can adjust the priority parameters to meet the optimal output under different goals and scenarios. The daily admission planning for each type of patient obtained by model I can be used to assist the patient admission management in large general hospitals