Identification of the thermal growth characteristics of coagulated tumor tissue in laser-induced thermotherapy

We consider an inverse problem arising in laser-induced thermotherapy, a minimally invasive method for cancer treatment, in which cancer tissues is destroyed by coagulation. For the dosage planning numerical simulation play an important role. To this end a crucial problem is to identify the thermal growth kinetics of the coagulated zone. Mathematically, this problem is a nonlinear and nonlocal parabolic heat source inverse problem. The solution to this inverse problem is defined as the minimizer of a nonconvex cost functional. The existence of the minimizer is proven. We derive the Gateaux derivative of the cost functional, which is based on the adjoint system, and use it for a numerical approximation of the optimal coefficient

Occurrence and distribution of neonicotinoid insecticides in surface water and sediment of the Guangzhou section of the Pearl River, South China

Little information is available about the occurrence of neonicotinoid insecticides in surface water and sediment of the metropolitan regions around the rivers in China. Here we investigate the residual level of neonicotinoids in the Guangzhou section of the Pearl River. At least one or two neonicotinoids was detected in each surface water and sediment, and the total amount of neonicotinoids (Sigma(5)neonics) in surface water ranged from 92.6 to 321 ng/L with a geometric mean (GM) of 174 ng/L. Imidacloprid, thiamethoxam and acetamiprid were three frequently detected neonicotinoids (100%2 from surface water. As for the sediment, total concentration was varied between 0.40 and 2.59 ng/g dw with a GM of 1.12 ng/g dw, and acetamiprid and thiacloprid were the common sediment neonicotinoids. Western and Front river-route of the Guangzhou section of the Pearl River suffered a higher neonicotinoids contamination than the Rear river-route, resulting from more effluents of WWTP5 receiving, and intensive commercial and human activities. Level of residual neonicotinoids in surface water was significantly correlated with the water quality (p < 0.01), especially items of pH, DO and ORP, and nitrogen and phosphorus contaminants. Compared with reports about residual neonicotinoids in water and sediment previously, the metropolitan regions of the Guangzhou could be confronted with a moderate contamination and showed serious ecological threats (even heavier than the Pearl Rivers). Our results will provide valuable data for understanding of neonicotinoids contamination in the Pearl River Delta and be helpful for further assessing environmental risk of neonicotinoids. (C) 2019 Elsevier Ltd. All rights reserved

A Hybrid Fuzzy Wavelet Neural Network Model with Self-Adapted Fuzzy c-Means Clustering and Genetic Algorithm for Water Quality Prediction in Rivers

Water quality prediction is the basis of water environmental planning, evaluation, and management. In this work, a novel intelligent prediction model based on the fuzzy wavelet neural network (FWNN) including the neural network (NN), the fuzzy logic (FL), the wavelet transform (WT), and the genetic algorithm (GA) was proposed to simulate the nonlinearity of water quality parameters and water quality predictions. A self-adapted fuzzy c-means clustering was used to determine the number of fuzzy rules. A hybrid learning algorithm based on a genetic algorithm and gradient descent algorithm was employed to optimize the network parameters. Comparisons were made between the proposed FWNN model and the fuzzy neural network (FNN), the wavelet neural network (WNN), and the neural network (ANN). The results indicate that the FWNN made effective use of the self-adaptability of NN, the uncertainty capacity of FL, and the partial analysis ability of WT, so it could handle the fluctuation and the nonseasonal time series data of water quality, while exhibiting higher estimation accuracy and better robustness and achieving better performances for predicting water quality with high determination coefficients R2 over 0.90. The FWNN is feasible and reliable for simulating and predicting water quality in river

Association between STH infection and cognitive ability, nutritional indicators, school absence and performance (infected with any of the 3 types of STHs).

<p>Notes:</p><p>^a Estimated with multivariate regressions adjusted for student characteristics (gender, age, boarding status, ethnicity); student eating and sanitation habits (ever eats uncooked meat / vegetables, ever drinks unboiled water); as well as household and family characteristics (household size, number of siblings, pieces of durable assets, parental migrant status, parental education). Standard errors are adjusted for clustering at the township level. Coefficients are reported in cases of continuous outcome variables (namely, WMI, PSI, Hb, HAZ, WAZ, BmiAZ, Standardized math test score) whereas odds ratio reported in cases of binary outcome variables (namely, anemic and school absence).</p><p>^b Confidence intervals reported here are based on significance level adjusted for multiple hypotheses testing by the Bonferroni method, which adjusted the customary significance level of alpha (i.e., 0.05) downward to 0.006.</p><p>^c eta^2 are reported in cases of continuous outcome variables (namely, WMI, PSI, Hb, HAZ, WAZ, BmiAZ, Standardized math test score) whereas odds ratio reported in cases of binary outcome variables (namely, anemic and school absence).</p><p>^d The Bonferroni method adjusted the customary significance level of 0.05 and 0.01 downward to 0.006 and 0.001, respectively.</p><p>Source: Authors’ survey.</p><p>Association between STH infection and cognitive ability, nutritional indicators, school absence and performance (infected with any of the 3 types of STHs).</p

Background characteristics by STH infection status, values are mean (SD) [95% Confidence Interval].

<p>Source: authors’ survey.</p><p>Background characteristics by STH infection status, values are mean (SD) [95% Confidence Interval].</p