A Simple Direct Method to Obtain Kinetic Parameters for Polymer Thermal Decomposition

In a fire, the polymer combustion occurs when gaseous fuels react with oxygen. The heating of a material could force the release of gaseous fuels during thermal decomposition and pyrolysis. The rate of pyrolysis to define the gaseous fuels is usually interpreted by means of the Arrhenius expression and a reaction model expression, which are characterized by an activation energy, a pre-exponential factor, and a reaction order value. Many methods are available for determining kinetic parameters from thermogravimetric experimental data. However, the most challenging issue is achieving an adequate balance between accuracy and simplicity. This work proposes a direct method for determining the kinetic parameters with only a thermogravimetric experiment at a single heating rate. The method was validated with six polymers, and the results were compared with those from similar procedures, such as the Lyon method and generalized direct method. The results achieved using the simpler approach of the proposed method show a high level of accuracy.

Modeling of Hygro-Mechanical Behaviour through Raffia vinifera Fiber during the Water Diffusion Phenomenon

In an industrial context where the use of friendly materials is encouraged, natural fibers of vegetable origin become more solicited for the reinforcement of composite materials. This work deals with the modeling of the hygro-mechanical behavior through raffia vinifera fiber during the diffusion phenomenon. The modeling of water diffusion through the raffia vinifera fiber is described by Fick’s second law and taking into account the swelling phenomenon of the fiber. The equation obtained is solved numerically by the finite difference method, and the evolution of the fiber radius as a function of time is obtained. By applying the Leibniz integration rule, a mathematical expression to predict the evolution of this radius as a function of time is proposed. It is observed numerically and analytically an increase of the dimensionless fiber radius with time up to a critical value after what one observes the saturation. This model allowed us to propose a mathematical model describing the absorption kinetics of the raffia vinifera fiber through its absorption ratio. By comparing the results of this model with the experimental results from the literature, one observes a good agreement. Moreover, the induced stresses in the fiber during the water diffusion can also be estimated with the proposed mathematical model expression of fiber. These stresses increase with time and can reach between 5 and 7 GPa. The results of this work can be used to predict the behavior of the raffia vinifera fiber inside a composite material during its development.

Synthesis, characterization and adsorption capacity of lead(II) ions of Fe3O4/C core-shell structure materials

Core shell magnetite nanoparticles (Fe3O4@C) as adsorbent for lead ions from aqueous solution were synthesized. The characteristics of the modified materials were analysed. It could also be shown that the adsorption isotherms were well described by the Langmuir model. The kinetic of the adsorption process was found to follow the pseudo-second-order model expression. Thermodynamic studies indicated that the adsorption process was feasible, spontaneous and endothermic.

Big Data Business Actual Analysis: Stock Price Prediction Based on Time Series Model

This paper selects the daily closing price data of the Shanghai Composite Index from January 1, 2016 to December 31, 2017, excluding holidays, and preprocesses the data. After taking the logarithm and converting it into the rate of return data, the first-order difference is performed to make it into a stable time series, and then the ARMA(p,q) model is constructed. Through parameter significance test, residual test and characteristic root test, according to the minimum principle of AIC, the optimal model is finally determined to be ARMA(2,5) of sparse coefficient, and the expression of the model is obtained. The GARCH(1,1) model is established for the residual of ARMA(2,5), and the model expression is obtained. In order to directly predict the return rate of the Shanghai Composite Index, the ARIMA(2,1,5) model of the sparse coefficient is constructed for the return rate of the Shanghai Composite Index, and the model expression is obtained. By predicting the Shanghai Composite Index return data on January 2, 2018, it is found that the prediction error of the model is small, and it can be used for subsequent predictions.

The Scoring Mechanism of Players after Game Based on Cluster Regression Analysis Model

Cluster regression analysis model is an effective theory for a reasonable and fair player scoring game. It can roughly predict and evaluate the performance of athletes after the game with limited data and provide scientific predictions for the performance of athletes. The purpose of this research is to achieve the player’s postmatch scoring through the cluster regression model. Through the research and analysis of past ball games, the comparison and experiment of multiple objects based on different regression analysis theories, the following conclusions are drawn. Different regression models have different standard errors, but if the data in other model categories are put into the centroid model expression, the standard error and the error of the original model are within 0.3, which can replace other models for calculation. In the player’s postmatch scoring, although the expert’s prediction of the result is very accurate, within the error range of 1 copy, the player’s postmatch scoring mechanism based on the cluster regression analysis model is more accurate, and the error formula is in the 0.5 range. It is best to switch the data of the regression model twice to compare the scoring mechanism using different regression experiments.

Atherosclerosis Regression and Cholesterol Efflux in Hypertriglyceridemic Mice

Rationale: Hypertriglyceridemia (HyperTG) and low high-density lipoprotein cholesterol (HDL-C), both of which are regulated by lipoprotein lipase (LpL) activity, associate with increased cardiovascular disease (CVD). Genetic regulators of LpL actions track with CVD risk in humans. Whether this is due to changes in HDL-C or function, or circulating triglyceride (TG) levels is unresolved. Objective: We created HyperTG and HDL-C reduction in atherosclerotic mice to allow the assessment of how HyperTG and reduced HDL-C affect regression of atherosclerosis and the phenotype of plaque macrophages. Methods and Results: Atherosclerosis regression was studied in control LpL floxed ( Lpl fl/fl ) mice and tamoxifen-inducible whole-body LpL KO ( iLpl -/- ) mice with HyperTG (~500mg/dL) and reduced HDL-C (~50% reduction). Atherosclerosis regression was studied using two models in which advanced plaques resulting from hypercholesterolemia are exposed to normal LDL-C levels using aortic transplantation or treatments with oligonucleotides. In a subset of mice, we expressed human cholesterol ester transfer protein (hCETP) to humanize the relationship between apoB-lipoproteins and HDL. HDL particle number (HDL-P), cholesterol efflux capacity (CEC) and HDL proteome were measured in HyperTG mice and humans. Surprisingly, HyperTG and reduced HDL-C levels due to loss of LpL did not affect atherosclerosis lesion size or macrophage content (CD68+ cells) in either model. Expression of hCETP and further reduction of HDL-C did not alter lesions. Sera from iLpl -/- mice had a decrease in total CEC, but not ABCA1-mediated CEC. HyperTG humans, including those with LpL deficiency, had greater ABCA1-mediated CEC and total CEC per HDL-P. Conclusions: Atherosclerosis regression in mice is driven by LDL-C reduction and is not affected by HyperTG and plasma HDL-C levels.

Neuroepithelial cell competition triggers loss of cellular juvenescence

Cell competition is a cell–cell interaction mechanism which maintains tissue homeostasis through selective elimination of unfit cells. During early brain development, cells are eliminated through apoptosis. How cells are selected to undergo elimination remains unclear. Here we aimed to identify a role for cell competition in the elimination of suboptimal cells using an in vitro neuroepithelial model. Cell competition was observed when neural progenitor HypoE-N1 cells expressing RASV12 were surrounded by normal cells in the co-culture. The elimination through apoptosis was observed by cellular changes of RASV12 cells with rounding/fragmented morphology, by SYTOX blue-positivity, and by expression of apoptotic markers active caspase-3 and cleaved PARP. In this model, expression of juvenility-associated genes Srsf7 and Ezh2 were suppressed under cell-competitive conditions. Srsf7 depletion led to loss of cellular juvenescence characterized by suppression of Ezh2, cell growth impairment and enhancement of senescence-associated proteins. The cell bodies of eliminated cells were engulfed by the surrounding cells through phagocytosis. Our data indicates that neuroepithelial cell competition may have an important role for maintaining homeostasis in the neuroepithelium by eliminating suboptimal cells through loss of cellular juvenescence.

The Risk-Averse Static Stochastic Knapsack Problem

This research proposes and analyzes new models for a stochastic resource allocation problem that arises in a variety of operations contexts. One of the primary contributions of the paper lies in providing a succinct, robust, and general model that can address a range of different risk-based objectives and cost assumptions under uncertainty. Although the model expression is relatively simple, it embeds a reasonably high degree of underlying complexity, as the analysis shows. In addition, in-depth analysis of the model, both in its general form and under various specific risk measures, uncovers some interesting and powerful insights regarding the problem trade-offs. Furthermore, this analysis leads to a highly efficient class of heuristic algorithms for solving the problem, which we demonstrate via numerical experimentation to provide close-to-optimal solutions. This computational benefit is a critical element for solving a class of broadly applicable larger problems for which our problem arises as a subproblem that requires repeated solution.

Teaching hydrogeology in the field: the bottleneck in student conceptual model development

Teaching hydrogeology in the field presents unique cognitive difficulties, including the multidisciplinary and hidden nature of the processes. Lecturers commonly encounter large heterogeneity in student backgrounds, and many students harbor pre-existing mental models of the subsurface that differ from reality. In this study, we assess the influence of a student’s prior knowledge on his/her outcome in an inquiry-based learning strategy designed for a hydrogeology field course. We also assess the effectiveness of this strategy in the students’ conceptual model expression for the field site. Statistical results showed that in general lower scores were obtained in the conceptual model expression than in the inquiry-based learning. However, students with a high prior knowledge showed in average a better performance in the conceptual model expression, although with a larger variability, indicating that the prior knowledge is not a guarantee for an adequate conceptual model conception. In general, a learning bottleneck was identified: going from the split information to the integration of it. In the light of these findings, and in order to improve the student’s ability for conceptual model expression, we recommend the inclusion of specific prior-to-field lessons in the classroom to introduce methodologies for the expression of hydrogeological conceptual models to identify and dispel any prior misconceptions.