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Bech Cormier posted an update 6 months, 2 weeks ago
Five candidate sources of non-conventional water were evaluated in terms of quantity and quality, namely rainfall/stormwater runoff, industrial cooling water, hydraulic fracturing wastewater, process wastewater, and domestic sewage. Water quality issues, such as suspended solids, biochemical/chemical oxygen demand, total dissolved solids, total nitrogen, bacteria, and emerging contaminates, were assessed. Available technologies for improving the quality of non-conventional water were comprehensively investigated. The potential risks to plants, human health, and the environment posed by non-conventional water reuse for irrigation are also discussed. Lastly, three priority research directions, including efficient collection of non-conventional water, design of fit-for-purpose treatment, and deployment of energy-efficient processes, were proposed to provide guidance on the potential for future research.
To train and validate a predictive model of mortality for hospitalized COVID-19 patients based on lung densitometry.
Two-hundred-fifty-one patients with respiratory symptoms underwent CT few days after hospitalization. learn more “Aerated” (AV), “consolidated” (CV) and “intermediate” (IV) lung sub-volumes were quantified by an operator-independent method based on individual HU maximum gradient recognition. AV, CV, IV, CV/AV, IV/AV, and HU of the first peak position were extracted. Relevant clinical parameters were prospectively collected. The population was composed by training (n=166) and validation (n=85) consecutive cohorts, and backward multi-variate logistic regression was applied on the training group to build a CT_model. Similarly, models including only clinical parameters (CLIN_model) and both CT/clinical parameters (COMB_model) were developed. Model’s performances were assessed by goodness-of-fit (H&L-test), calibration and discrimination. Model’s performances were tested in the validation group.
Fortation.
Few automatically extracted densitometry parameters with clear functional meaning predicted mortality of COVID-19 patients. Combined with clinical features, the resulting predictive model showed higher discrimination/calibration.Adults infer that resources that become scarce over time are in higher demand, and use this “demand inference” to guide their own economic decisions. However, it is unclear when children begin to understand and use economic demand. In six experiments, we investigated the development of demand inference and demand-based economic decisions in 4- to 10-year-old children and adults in the United States. In Experiments 1-5, we showed children two boxes with the same number of compartments but containing different numbers of face-down stickers and varied the information provided about how those differences arose (e.g. that other children had taken the stickers). In separate experiments, we asked children to buy or trade to get a sticker for themselves or to predict what other children would do. We also asked them which set of stickers they thought the other children had preferred to assess their ability to make a demand inference separately from their own choice. Across experiments, children were able to make a demand inference about children’s past preferences by 6 years of age. However, children did not use this demand information when making choices for themselves or when predicting what another child would select in the future. In Experiment 6, we adapted the task for adults and found that adult participants inferred that the set containing fewer resources was in higher demand, and selected the higher demand resource for themselves at rates significantly above chance. The overall pattern of results suggests a dissociation between economic inference and economic decisions during early-to-middle childhood. We discuss implications for our understanding of the development of economic reasoning.The ability to estimate proportions informs our immediate impressions of social environments (e.g., of the diversity of races or genders within a crowded room). This study examines how the distribution of attention during brief glances shapes estimates of group gender proportions. Performance-wise, subjects exhibit a canonical pattern of judgment errors small proportions are overestimated while large values are underestimated. Subjects’ eye movements at sub-second timescales reveal that these biases follow from a tendency to visually oversample members of the gender minority. Rates of oversampling dovetail with average levels of error magnitudes, response variability, and response times. Visual biases are thus associated with the inherent difficulty in estimating particular proportions. All results are replicated at a within-subjects level with non-human ensembles using natural scene stimuli; the observed attentional patterns and judgment biases are thus not exclusively guided by face-specific visual properties. Our results reveal the biased distribution of attention underlying typical judgment errors of group proportions.Multi-component detection of insulin and glucose in serum is of great importance and urgently needed in clinical diagnosis and treatment due to its economy and practicability. However, insulin and glucose can hardly be determined by traditional electrochemical detection methods. Their mixed oxidation currents and rare involvement in the reaction process make it difficult to decouple them. In this study, AI algorithms are introduced to power the electrochemical method to conquer this problem. First, the current curves of insulin, glucose, and their mixed solution are obtained using cyclic voltammetry. Then, seven features of the cyclic voltammetry curve are extracted as characteristic values for detecting the concentrations of insulin and glucose. Finally, after training using machine learning algorithms, insulin and glucose concentrations are decoupled and regressed accurately. The entire detection process only takes three minutes. It can detect insulin at the pmol level and glucose at the mmol level, which meets the basic clinical requirements. The average relative error in predicting insulin concentrations is around 6.515%, and that in predicting glucose concentrations is around 4.36%. To verify the performance and effectiveness of the proposed method, it is used to determine the concentrations of insulin and glucose in fetal bovine serum and real clinical serum samples. The results are satisfactory, demonstrating that the method can meet basic clinical needs. This multi-component testing system delivers acceptable detect limit and accuracy and has the merits of low cost and high efficiency, holding great potential for use in clinical diagnosis.
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