Petrochemical wastewater treatment by means of clean electrochemical technologies

The removal of chemical oxygen demand (COD), turbidity, phenol, hydrocarbon and grease from petrochemical wastewater (PCWW) was experimentally done by using electroflotation (EF) and electrocoagulation (EC). In the EF unit, a graphite anode and a stainless steel mesh as cathode were used. In the EC unit, iron and aluminium were used simultaneously as materials for two blocks of alternating electrodes. The reactor voltage was 12 V, current density (CD) was varied from 5 to 15 mA cm^–2, and the residence time varied in the limits of 2–20 min for EF and 1–10 min for EC. The results have shown that EC removes the mentioned contaminants from PCWW more effectively than EF. Turbidity removal in the process of PCWW purification was estimated as 83% for EF and 88% for EC. The yields of phenol, hydrocarbon and grease removal by EC were examined under different values of residence time, CD, and with iron and aluminium as materials for electrodes.     

Antithrombin Activity and Association with Risk of Thrombosis and Mortality in Patients with Cancer

Venous and arterial thromboembolism (VTE/ATE) are common complications in cancer patients. Antithrombin deficiency is a risk factor for thrombosis in the general population, but its connection to risk of cancer-associated thrombosis is unclear. We investigated the association of antithrombin activity levels with risk of cancer-associated VTE/ATE and all-cause mortality in an observational cohort study including patients with cancer, the Vienna Cancer and Thrombosis Study. In total, 1127 patients were included (45% female, median age: 62 years). Amongst these subjects, 110 (9.7%) patients were diagnosed with VTE, 32 (2.8%) with ATE, and 563 (49.9%) died. Antithrombin was not associated with a risk of VTE (subdistribution hazard ratio (SHR): 1.00 per 1% increase in antithrombin level; 95% CI: 0.99–1.01) or ATE (SHR: 1.00; 95% CI: 0.98–1.03). However, antithrombin showed a u-shaped association with the risk of all-cause death, i.e., patients with very low but also very high levels had poorer overall survival. In the subgroup of patients with brain tumors, higher antithrombin levels were associated with ATE risk (SHR: 1.02 per 1% increase; 95% CI: 1.00–1.04) and mortality (HR: 1.01 per 1% increase; 95% CI: 1.00–1.02). Both high and low antithrombin activity was associated with the risk of death. However, no association with cancer-associated VTE and ATE across all cancer types was found, with the exception of in brain tumors.     

Functionalization of carbon nanotubes and carbon nanofibers used in epoxy/amine matrices that avoid partitioning of the monomers at the fiber interface

Single‐wall carbon nanotubes (SWCNT), multiwall carbon nanotubes (MWCNT), and carbon nanofibers (CNF) were oxidized with a conventional H₂SO₄/HNO₃ treatment, and the resulting COOH groups were esterified with phenyl glycidyl ether (PGE). Pristine, oxidized and esterified SWCNT, MWCNT, and CNF (1 wt%) were dispersed in diglycidyl ether of bisphenol A/polyether triamine, and cured to produce epoxy nanocomposite. The quality of the dispersion varied with surface modification in the order: COOH > unmodified > PGE, as assessed for SWCNT/epoxy composites using radial breathing modes in Raman spectra and scanning electron microscopy. Local bundling of PGE‐modified nanofillers was explained by a polymerization‐induced phase‐separation process. Dynamic mechanical analysis showed the presence of two relaxation peaks in nanocomposites prepared with unmodified and COOH‐modified SWCNT, MWCNT, and CNF, assigned to a partitioning of monomers at the carbon interface. This produced a significant decrease of the glass transition temperature. However, the esterification with PGE led to a single relaxation peak close to the one of the neat epoxy, for the three types of nanofillers without any penalty in the glassy and rubbery elastic moduli. The effect was explained by matrix exclusion from phase‐separated domains. POLYM. ENG. SCI., 2010. © 2009 Society of Plastics Engineers     

Experimental and Numerical Investigation on Hydraulic Performance of Slit-check Dams in Subcritical Flow Condition

Water Resources Management - Slit-check dams have a flow regulation function under normal flow conditions as well as holding large solid (debris flow control). However, the effects of such...     

3D Multi-Layered Normal Distribution Transform for Fast and Long Range Scan Matching

Simultaneously Localization and Mapping (SLAM) problem requires a sophisticated scan matching algorithm, in which two consecutive point clouds belonging to highly correlated scene are registered by finding the rigid body transformation parameters when an initial relative pose estimate is available. A well-known scan matching method is the Iterative Closest Point (ICP) algorithm, and the basis of the algorithm is the minimization of an error function that takes point correspondences into account. Another 3D scan matching method called Normal Distribution Transform (NDT) has several advantages over ICP such as the surface representation capability, accuracy, and data storage. On the other hand, the performance of the NDT is directly related to the size of the cell, and there is no proved way of choosing an optimum cell size. In this paper, a novel method called Multi-Layered Normal Distribution Transform (ML-NDT) using various cell sizes in a structured manner is introduced. In this structure a number of layers are used, where each layer contains different but regular cell sizes. In the conventional NDT, the score function is chosen as Gaussian probability function which is minimized iteratively by Newton optimization method. However, the ML-NDT score function is described as the Mahalanobis distance function, and in addition to Newton optimization method, Levenberg–Marquardt algorithm is also adapted to the proposed method for this score function. The performance of the proposed method is compared to the original NDT, and the effects of the optimization methods are discussed. Moreover, an important issue in a scan matching algorithms is the subsampling strategy since the point cloud contains huge amount of data which has a non-uniform distribution. Therefore, the application of a sampling strategy is a must for fast and robust scan matching. In the performance analysis, two sampling strategies are investigated which are random sampling and grid based sampling. The method is successfully applied to experimentally obtained datasets, and the results show that ML-NDT with grid based sampling provides a fast and long range scan matching capability.     

Ensemble of Evolving Neural Networks in Classification

The ensemble of evolving neural networks, which employs neural networks and genetic algorithms, is developed for classification problems in data mining. This network meets data mining requirements such as smart architecture, user interaction, and performance. The evolving neural network has a smart architecture in that it is able to select inputs from the environment and controls its topology. A built-in objective function of the network offers user interaction for customized classification. The bagging technique, which uses a portion of the training set in multiple networks, is applied to the ensemble of evolving neural networks in order to improve classification performance. The ensemble of evolving neural networks is tested by various data sets and produces better performance than both classical neural networks and simple ensemble methods.     

Fabrication of Patterned Magnetic Nanomaterials for Data Storage Media

Patterned media (PM) for magnetic information storage have received increased attention in recent years as the primary candidate for 1?Terabit/in² or higher recording density for computer hard disk drives. A PM consists of a periodic array of well-defined magnetic islands, each of which can store one bit of data. In the simplest scheme, the structures could be magnetic pillars and dots with a single easy axis of magnetization. The direction of detected magnetization by the read/write head is interpreted as a binary signal 1 or 0. Some of the main technical issues in the PM include the difficulty in fabricating small nanoisland arrays in a periodic fashion over large areas, reliability/reproducibility of magnetic bit characteristics, as well as wear and head flyability issues associated with the media surface roughness, and processing cost. This article deals with a recent investigation of various fabrication approaches, nanostructural features, and magnetic properties for the bit PM.     

Elasto-plastic finite element analysis of orthotropic rotating discs with holes

The elasto-plastic stress analysis of orthotropic rotating discs with holes has been carried out by the finite element method (FEM). An isoparametric rectangular element with nine nodes has been chosen and the Lagrange polynomial has been used as interpolation function. Steel-aluminium composite has been manufactured by upsetting under the pressure and the temperature. Mechanical properties and yield strengths of composite material have been obtained experimentally by using a strain gauge in the tensile testing machine. The expansions of plastic regions have been illustrated for various cases. Residual stresses and tangential stresses have been shown on the elasto-plastic boundaries of the disc. The limit of angular velocities of the orthotropic disc have been increased by using tangential residual stresses and tangential stresses in the disc subjected to the centrifugal force.     

Hybrid Intelligent Packing System (HIPS) through integration of Artificial Neural Networks,Artificial Intelligence,and mathematical programming

A successful solution to the packing problem is a major step toward material savings on the scrap that could be avoided in the cutting process and therefore money savings. Although the problem is of great interest, no satisfactory algorithm has been found that can be applied to all the possible situations. This paper models a Hybrid Intelligent Packing System (HIPS) by integrating Artificial Neural Networks (ANNs), Artificial Intelligence (AI), and Operations Research (OR) approaches for solving the packing problem. The HIPS consists of two main modules, an intelligent generator module and a tester module. The intelligent generator module has two components: (i) a rough assignment module and (ii) a packing module. The rough assignment module utilizes the expert system and rules concerning cutting restrictions and allocation goals in order to generate many possible patterns. The packing module is an ANN that packs the generated patterns and performs post-solution adjustments. The tester module, which consists of a mathematical programming model, selects the sets of patterns that will result in a minimum amount of scrap.