Towards more predictive clarification models via experimental determination of flocculent settling coefficient values

Intrinsic and extrinsic techniques for quantification uncertainty of an interpretable GRU deep learning model used to predict atmospheric total suspended particulates (TSP) in Zabol, Iran during the dusty period of 120-days wind

Owing to the importance of octanol-air partition coefficients (KOA) in describing the partition of organic pollutants from air to environmental organic phases, the paucity of KOA data at different environmental temperatures, and the difficulty or high expenditures involved in experimental determination, the development of predictive models for KOA is necessary. Approaches such as this are greatly needed to evaluate the environmental fate of the ever-increasing list of production chemicals. Partial least squares (PLS) regression with 18 molecular structural descriptors was used to develop predictive models based on directly measured KOA values of selected chlorobenzenes, polychlorinated biphenyls (PCBs), polychlorinated naphthalenes, polychlorinated dibenzo-p-dioxins/dibenzofurans, polybrominated diphenyl ethers, polycyclic aromatic hydrocarbons, and organochlorine pesticides (OPs). An optimization procedure resulted in two temperature-dependent universal predictive models that explained at least 91 % of the variance of log KOA. Model 1 was the more general of the two models that could be used for all the persistent organic pollutant (POP) classes investigated. Although model 1 performed poorly for select OPs, this was attributed to wide variability in structural types within this subset of POPs and their diversity compared to the other POP classes that were investigated. The exclusion of the structurally complex OP subset resulted in a more precise model, model 5. Intermolecular dispersive interactions (induced dipole-induced dipole forces) between octanol and solute molecules play a decisive role in governing KOA and its temperature dependence. Further investigations are needed to better characterize the steric structures of the POPs under study, especially of OPs.

The diffusion of five flavor organic molecules, including D-limonene, myrcene, ethyl hexanoate, 2-nonanone, and linalool in low density polyethylene (LDPE) film were investigated by combined experimental and molecular dynamics (MD) simulation studies. The diffusion coefficients derived from the prediction model, experimental determination, and MD simulation were compared, and the related microscopic diffusion mechanism was investigated. The effects of the presence of the flavor organic molecules on the diffusion of O2 and H2O in polyethylene (PE) were also studied by MD simulation. Results show that: The diffusion of five flavor molecules in LDPE is well followed to Fick’s second law by the immersion experiment; MD simulation indicates the dual mode diffusion mechanism of the flavor molecules is in LDPE; the diffusion coefficients from MD simulation are close to those from the experimental determination, but are slightly larger than those values; the presence of the flavor organic molecules hinders the diffusion of O2 and H2O, which can be well explained from the fraction of free volume (FFV) and interaction energy calculation results derived from MD simulation.

The COVID-19 outbreak has changed the world at large since it was announced by the World Health Organization (WHO). Many policies in various countries were then implemented to control its spread. Most aspects of human life and the environment are affected by this pandemic. This paper aims to determine the prediction model for the COVID-19 outbreak in Indonesia. The approach used for this modelling employs a logistic regression model. The data modeled in this paper is data on the distribution of COVID-19 sufferers and data on patients who have recovered from COVID-19 in Indonesia. The data obtained as research material were taken from March 2, 2020, to November 12, 2020. From the results of this paper, this prediction model obtained logistic regression coefficient values for data on COVID-19 sufferers in Indonesia of 8.114748 and 0.750743, while the coefficient values for data on sufferers who recovered from COVID-19 were 9.360925 and 0.788334. The results of the prediction model for sufferers and people who have recovered from COVID-19 have the same accuracy value, namely mean absolute error (MAE) of 0.02, mean square error (MSE) of 0.00, and R2 of 0.99.

Connectome-based model predicts episodic memory performance in individuals with subjective cognitive decline and amnestic mild cognitive impairment

Prediction capabilities of mathematical models in producing a renewable fuel from waste cooking oil for sustainable energy and clean environment

A predictive model is developed for the pressure loss coefficient for a viscous flow through a rectangular orifice on a pipe-installed thick plate. The model is developed based on the 1-dimensional Navier-Stokes equation and an asymptotic increase in velocity modeled to have a direct relation with the flow convergence in the near-inlet region. Here, the flow velocity increases asymptotically from the steady mean upstream value to the orifice velocity. This phenomenon is represented by a convergence parameter, ϕ, used in the velocity transition model to quantify the length of the convergence zone. The static pressure drop is measured experimentally for varying orifice aspect ratio, AR, at creeping Reynolds numbers (0.01 ≤ Re ≤ 0.1). A significantly wider range of AR is covered (1 ≤ AR ≤ 250), compared to related works in the literature. Results show that the relative dominance of the convergence phenomenon is affected by AR. The maximum length of convergence is for the square orifice (AR = 1), as the flow experiences comparable convergence from all directions, whereas for higher AR, convergence becomes less dominant in one of the two midplanes of investigation. The loss coefficient thus decreases as AR increases. At constant Re, higher AR generally leads to higher pressure drop but lower values of the loss coefficient. The velocity gradient in the convergence zone is also determined as a function of AR and Re which verifies that lower AR takes a longer distance for the velocity transition due to increased convergence.

Hypertensive disorders in pregnancy remain important problem of modern obstetrics, their frequency being 2-8% with no tendency of decreasing. To identify pregnant women with high risk and to conduct preventive treatment is very important for the practitioner. The aim of the study was to develop and justify differential management of pregnants depending on the risk of preeclampsia. 131 pregnant women in I trimester were tested to determine the risk of hypertensive disorders. Prediction model allows to identify pregnant women with risk of hypertensive disorders according to the results of testing thrombophilia genes (presence of 455G → A polymorphism in the gene for fibrinogen β and 4G/5G in gene of plasminogen activator inhibitor - type 1), the level of antibodies to β2 glycoprotein-1, the level of D-dimer and the value of atherogenicity coefficient. Pregnant women with high risk of hypertensive disorders, received prophylactic treatment complex. This complex included antiplatelet therapy, correction of hyperho­mo­cysteinemia and hypercholesterolemia levels. Etio-pathogenic concept of prediction and prevention of pre-eclampsia associated with thrombophilia was proposed. The proposed scheme of preventive treatment allowed to normalize blood clotting parameters, lipid metabolism, to reduce the number of thrombophilia markers. The necessity of early and long-term administration of prophylactic complex was proved. This management prevents endothelial damage and deve­lopment of pathological range of pre-eclampsia. The results of prophylactic treatment were the reduction of preeclampsia cases by 6,5 times (p < 0.05), number of complicated deliveries by 3 times (p <0.05), number of preterm birth by 6,57 times (p <0.05), cases of growth retardation by 9,8 times (p = 0.003), increase of newborns’ weight by 1,24 times (p = 0.02).

Citrullination, an important post-translational modification of proteins, alters the molecular weight and electrostatic charge of the protein side chains. Citrulline, in protein sequences, is catalyzed by a class of Peptidyl Arginine Deiminases (PADs). Dependent on Ca2+, PADs include five isozymes: PAD 1, 2, 3, 4/5, and 6. Citrullinated proteins have been identified in many biological and pathological processes. Among them, abnormal protein citrullination modification can lead to serious human diseases, including multiple sclerosis and rheumatoid arthritis. It is important to identify the citrullination sites in protein sequences. The accurate identification of citrullination sites may contribute to the studies on the molecular functions and pathological mechanisms of related diseases. In this study, after an encoded training set (containing 116 positive and 348 negative samples) into the feature matrix, the mRMR method was used to analyze the 941- dimensional features which were sorted on the basis of their importance. Then, a predictive model based on a self-normalizing neural network (SNN) was proposed to predict the citrullination sites in protein sequences. Incremental Feature Selection (IFS) and 10-fold cross-validation were used as the model evaluation method. Three classical machine learning models, namely random forest, support vector machine, and k-nearest neighbor algorithm, were selected and compared with the SNN prediction model using the same evaluation methods. SNN may be the best tool for citrullination site prediction. The maximum value of the Matthews Correlation Coefficient (MCC) reached 0.672404 on the basis of the optimal classifier of SNN. The results showed that the SNN-based prediction methods performed better when evaluated by some common metrics, such as MCC, accuracy, and F1-Measure. SNN prediction model also achieved a better balance in the classification and recognition of positive and negative samples from datasets compared with the other three models.

To quantitatively reflect the relationship between dust and plant spectral reflectance. Dust from different sources in the city were selected to simulate the spectral characteristics of leaf dust. Taking Euonymus japonicus as the research object. Prediction model of leaf dust deposition was established based on spectral parameters. Results showed that among the three different dust pollutants, the reflection spectrum has 6 main reflection peaks and 7 main absorption valleys in 350–2500 nm. A steep reflection platform appears in the 692–763 nm band. In 760–1400 nm, the spectral reflectance gradually decreases with the increase of leaf dust coverage, and the variation range was coal dust > cement dust > pure soil dust. The spectral reflectance in 680–740 nm gradually decreases with the increase of leaf dust coverage. In the near infrared band, the fluctuation amplitude and slope of its first derivative spectrum gradually decrease with the increase of leaf dust. The biggest amplitude of variation was cement dust. With the increase of dust retention, the red edge position generally moves towards short wave direction, and the red edge slope generally decreases. The blue edge position moved to the short wave direction first and then to the long side direction, while the blue edge slope generally shows a decreasing trend. The yellow edge position moved to the long wave direction first and then to the short wave direction (coal dust, cement dust), and generally moved to the long side direction (pure soil dust). The yellow edge slope increases first and then decreases. The R2 values of the determination coefficients of the dust deposition prediction model have reached significant levels, which indicated that there was a relatively stable correlation between the spectral reflectance and dust deposition. The best prediction model of leaf dust deposition was leaf water content index model (y = 1.5019x − 1.4791, R2 = 0.7091, RMSE = 0.9725).

BackgroundIn-hospital mortality in patients with ST-segment elevation myocardial infarction (STEMI) is higher in those with COVID-19 than in those without COVID-19. The factors that predispose to this mortality rate and their relative contribution are poorly understood. This study developed a risk score inclusive of clinical variables to predict in-hospital mortality in patients with COVID-19 and STEMI.MethodsBaseline demographic, clinical, and procedural data from patients in the North American COVID-19 Myocardial Infarction registry were extracted. Univariable logistic regression was performed using candidate predictor variables, and multivariable logistic regression was performed using backward stepwise selection to identify independent predictors of in-hospital mortality. Independent predictors were assigned a weighted integer, with the sum of the integers yielding the total risk score for each patient.ResultsIn-hospital mortality occurred in 118 of 425 (28%) patients. Eight variables present at the time of STEMI diagnosis (respiratory rate of >35 breaths/min, cardiogenic shock, oxygen saturation of <93%, age of >55 ​years, infiltrates on chest x-ray, kidney disease, diabetes, and dyspnea) were assigned a weighted integer. In-hospital mortality increased exponentially with increasing integer risk score (Cochran-Armitage χ2, P ​< ​.001), and the model demonstrated good discriminative power (c-statistic ​= ​0.81) and calibration (Hosmer-Lemeshow, P ​= ​.40). The increasing risk score was strongly associated with in-hospital mortality (3.6%-60% mortality for low-risk and very high–risk score categories, respectively).ConclusionsThe risk of in-hospital mortality in patients with COVID-19 and STEMI can be accurately predicted and discriminated using readily available clinical information.

Skin is the most challenging problem in oil and gas production resulting from near wellbore mechanical damage or non-Darcy effect due to gas turbulence. Mechanical skin is introduced to the pay zone during drilling and completion phase while rate-dependent skin (non-Darcy effect) comes into play as gas production commences. Also turbulence effect may cause a huge pressure drop for oil wells but it is more sensitive in gas production. Rate-dependent skin is caused by contravening basic Darcy's assumptions in gas reservoir and would be sensible as gas starts rushing to the wellbore. It can be used to have an idea about fluid compressibility and flow path tortuosity near wellbore caused by high gas flow rate. Lots of attempts have been directed for computation of rate-dependent skin. Most of them proposed a power mode equation for estimation of non-mechanical skin in gas reservoirs. This includes turbulence coefficient, squared drawdown pressure, flow rate, and deviation coefficient. The turbulence coefficient (D) to be determined needs running a gas well test or at least two pressure flow rate data points. Other conventional methods also can be used for prediction of this parameter. But as a matter of fact, every predictor model may ignore some effective parameters for simplicity, which might deviate the result from reality. Thus, using a better approach including as much as possible effective parameters such as artificial intelligence can result in more accurate results. The authors propose a new technique to estimate value of the turbulence coefficient (D) by using neural networks based on skin factor, reservoir rock, and fluid properties. It is easy to apply and evaluate. The proposed method is validated using field data under variety of conditions. A computed value of D from neural network matches real data pretty well in comparison with conventional correlations.

To investigate whether MRI features could preoperatively predict local tumor progression (LTP) in patients with hepatocellular carcinoma (HCC) treated with radiofrequency ablation (RFA) as the first-line treatment and improve a novel predictive model through developing a nomogram including various conventional MRI parameters. 105 patients with HCCs who had received RFA were enrolled. All patients had undergone conventional MRI before RFA. Uni- and multivariable analyses for LTP were assessing using a Cox proportional hazards model. The developed MRI-based nomogram was further designed based on multivariable logistic analysis in our study and the usefulness of the developed model was validated according to calibration curves and the C-index. Rim enhancement (hazard ratio: 2.689, P = .044) and the apparent diffusion coefficient (ADC) values (hazard ratio: 0.055, P = .038) were statistically significant independent predictors of LTP after RFA at multivariable analysis. The performance of the nomogram incorporating two MRI parameters (with a C-index of 0.782) was improved compared with that based on rim enhancement and ADC alone (with C-index values of 0.630 and 0.728, respectively). The calibration curve of the MRI-based nomogram showed good conformance between evaluation and observation at 0.5, 1, and 1.5 years after RFA. The preliminary predictive model based on MRI findings including rim enhancement and ADC value could be used preoperatively to estimate the risk of LTP of HCC after RFA as the first-line treatment.

Heat exchanger fouling model and preventive maintenance scheduling tool

Cytotoxicity and 2D-QSAR study of some heterocyclic compounds