Method for assessing the impact of rainfall depth on the stormwater volume in a sanitary sewage network

Sanitary sewage network is relatively rarely considered as the cause of urban floods. Its hydraulic overload can result not only in flooding, but also sanitary contamination of subcatchments. Stormwater is the main reason for this overload. In contrast to the stormwater or combined sewer system, these waters infiltrate into the network in an uncontrolled way, through ventilation holes of covers or structural faults and lack of tightness of manholes. Part of stormwater infiltrates into the soil, where it leaks into pipelines. This greatly hinders assessing the quantity of stormwater influent into the sanitary sewer system. Standard methods of finding correlation between rainfall and the intensity of stormwater flow are ineffective. This is confirmed, i.a. by the studies performed in an existing network, presented in this paper. Only when residuals analysis was performed using the ARIMA and ARIMAX methods, the authors were able to develop a mathematical model enabling to assess the influence of rainfall depth on the stormwater effluent from the sewage network. Owing to the possibility of using the rainfall depth forecasts, the developed mathematical model enables to prepare the local water and sewerage companies for the occurrence of urban floods as well as hydraulic overload of wastewater treatment plants.

ΔCT Value of Amplified Refractory Mutation System Predicts Efficacy of EGFR-TKIs in Advanced Non–Small-Cell Lung Cancer: A Multi-Center Retrospective Study

Purpose: This multi-center retrospective study determines whether the ΔCT value of the Amplified Refractory Mutation System (ARMS) predicts the efficacy of epidermal growth factor receptor (EGFR) tyrosine kinase inhibitors (TKIs) in EGFR-mutant advanced non–small-cell lung cancer (NSCLC).Patients and methods: Patients who harbored an exon 19 deletion (19Del) or L858R mutation detected by the ARMS and previously received treatment of EGFR-TKIs as a monotherapy were enrolled. A total of 108 NSCLC patients in four hospitals were enrolled. We divided the patients into a high ΔCT group (Group H) and a low ΔCT group (Group L) by the Martingale residuals analysis and log-rank test. The primary outcome was progression-free survival (PFS). Univariate analysis and multivariable regression were applied to compare the PFS between the groups.Result: The Martingale residuals analysis and log-rank test were applied to find the cutoff ΔCT value (0.8). In the 108 patients we enrolled, 59 were in group L and 49 were in group H. Patients’ demographics and clinical characteristics, including age, sex, smoking history, pathology, mutation sites, TNM stage, and line of TKIs therapy, were not significantly different between group L and group H. The median PFS was 11.1 months in group L and 6.9 months in group H, and the difference showed statistical significance (p < 0.001). Moreover, the objective response rates (ORRs) in group L was significantly higher than in group H (61.0 vs 34.7%, p = 0.002). The median OS was 25.0 months in group L and 20.0 months in group H (p = 0.046).Conclusion: The ΔCT value of ARMS could be an efficacy predictor for EGFR-TKI treatment in advanced EGFR-mutant NSCLC.

A Comparative Study of Different Machine Learning Algorithms in Predicting the Content of Ilmenite in Titanium Placer

In this study, the ilmenite content in beach placer sand was estimated using seven soft computing techniques, namely random forest (RF), artificial neural network (ANN), k-nearest neighbors (kNN), cubist, support vector machine (SVM), stochastic gradient boosting (SGB), and classification and regression tree (CART). The 405 beach placer borehole samples were collected from Southern Suoi Nhum deposit, Binh Thuan province, Vietnam, to test the feasibility of these soft computing techniques in estimating ilmenite content. Heavy mineral analysis indicated that valuable minerals in the placer sand are zircon, ilmenite, leucoxene, rutile, anatase, and monazite. In this study, five materials, namely rutile, anatase, leucoxene, zircon, and monazite, were used as the input variables to estimate ilmenite content based on the above mentioned soft computing models. Of the whole dataset, 325 samples were used to build the regarded soft computing models; 80 remaining samples were used for the models’ verification. Root-mean-squared error (RMSE), determination coefficient (R2), a simple ranking method, and residuals analysis technique were used as the statistical criteria for assessing the model performances. The numerical experiments revealed that soft computing techniques are capable of estimating the content of ilmenite with high accuracy. The residuals analysis also indicated that the SGB model was the most suitable for determining the ilmenite content in the context of this research.

Investigation and optimization of influencing parameters on the copper extraction from a low-grade oxide deposit by acid leaching

Recovery of copper (II) from a low-grade deposit obtained from the rejects of a crushing circuit was investigated by means of leaching using H2SO4solution. Sulfuric acid concentration was between 30–180 g/L, solid content was set at 10–60%, particles were in the size range of −1 + 2 mm, and the leaching time was 5–60 min. Initially, a screening step was utilized involving a full factorial design (FFD) with four factors at two levels and two replications. Normal probability plot of residuals, analysis of variance (ANOVA), surface plots, and contour plots were used to study the effects of the main operational parameters and their interactions. The results of ANOVA,F-test andt-student test showed that at 95% confidence level, the sulfuric acid, solid content, leaching time, and the 2-way interaction of them considerably affected Cu(II) extraction, and increasing their value positively influenced the Cu(II) extraction. A factorial regression model was developed to relate Cu(II) extraction and the operational variables. The fitted model presented a good agreement between the experimental data and the predicted values with a correlation coefficient (R2) of 0.9989. Optimization of Cu(II) extraction suggested that the optimum conditions for Cu(II) extraction (Re = 72%) were sulfuric acid: 90 g/L, solid content: 50%, particle size: under 2 mm, and leaching time: 30 min.

What are the Most Widely Used and Effective Attack Coverage Systems in Men’s Volleyball?

In volleyball, attack coverage is one of the play actions most neglected in coaching and research. The purpose of this study was to find out which attack coverage systems are used by high-level men’s teams in different game situations and the characteristics of the most effective systems. We analysed 15 matches from the 2010 Men’s Pan-American Volleyball Cup, with a total of 1,415 coverage actions. Chi-square tests for independence, adjusted residuals analysis and calculations of standardised mean difference were performed. The results show that high-level men’s volleyball uses many coverage systems other than the traditional 3-2-0 and 2-3-0. At this level of play, the most frequent systems were 1-3-1 and 1-2-2, which occurred significantly often at the culmination of a third-tempo attack at the wing. The most effective systems consisted of three coverage lines, with fewer than five players covering the spiker and at least one player in the first coverage line, in both the attack and counterattack phases. Given the large number of coverage systems identified in different game situations, we recommend flexible, loosely structured training in these systems, based on a set of guiding principles that all players on a team must internalise for the specific position they are playing. Regarding the systems’ efficacy, the main watchword is that on each coverage line there should always be at least one player, but the first line should not be exposed.