A novel methodology to study shape and surface tension of drops in Electric Fields

A novel methodology is introduced that can be used to study the behavior of conducting drops in electrostatic fields, when gravity effects are negligible. This methodology, called Axisymmetric Drop Shape Analysis — Electric Field (ADSA-EF), generates numerical drop profiles in the electrostatic field, for a given surface tension. Then, it calculates the true value of the surface tension by matching the theoretical profiles with the shape of the experimental drops, with the surface tension as an adjustable parameter. ADSA-EF can be employed for simulating drop shapes in the electric field, detecting the effect of an electric field on liquid surface tensions, and measuring surface tensions in microgravity, where current drop-shape techniques are not applicable. The predicted drop shapes in the electric field were compared with experimental images, indicating good agreement. Preliminary experiments according to ADSA-EF methodology suggested that the surface tension of water increases by about one percent in the electric field.     

Modeling the oxidative coupling of methane using artificial neural network and optimizing of its operational conditions using genetic algorithm

The effect of some operating conditions such as temperature, gas hourly space velocity (GHSV), CH₄/O₂ ratio and diluents gas (mol% N₂) on ethylene production by oxidative coupling of methane (OCM) in a fixed bed reactor at atmospheric pressure was studied over Mn/Na₂WO₄/SiO₂ catalyst. Based on the properties of neural networks, an artificial neural network was used for model developing from experimental data. To prevent network complexity and effective data input to the network, principal component analysis method was used and the number of output parameters was reduced from 4 to 2. A feed-forward back-propagation network was used for simulating the relations between process operating conditions and those aspects of catalytic performance including conversion of methane, C₂ products selectivity, C2 yielding and C₂H₄/C₂H₆ ratio. Levenberg-Marquardt method is presented to train the network. For the first output, an optimum network with 4-9-1 topology and for the second output, an optimum network with 4-6-1 topology was prepared. After simulating the process as well as using ANNs, the operating conditions were optimized and a genetic algorithm based on maximum yield of C₂ was used. The average error in comparing the experimental and simulated values for methane conversion, C₂ products selectivity, yield of C₂ and C₂H₄/C₂H₆ ratio, was estimated as 2.73%, 10.66%, 5.48% and 10.28%, respectively.     

Approximation Algorithms for the Directed k-Tour and k-Stroll Problems

We consider two natural generalizations of the Asymmetric Traveling Salesman problem: the k-Stroll and the k-Tour problems. The input to the k-Stroll problem is a directed n-vertex graph with nonnegative edge lengths, an integer k, as well as two special vertices s and t. The goal is to find a minimum-length s-t walk, containing at least k distinct vertices (including the endpoints s,t). The k-Tour problem can be viewed as a special case of k-Stroll, where s=t. That is, the walk is required to be a tour, containing some pre-specified vertex s. When k=n, the k-Stroll problem becomes equivalent to Asymmetric Traveling Salesman Path, and k-Tour to Asymmetric Traveling Salesman. Our main result is a polylogarithmic approximation algorithm for the k-Stroll problem. Prior to our work, only bicriteria (O(log² k),3)-approximation algorithms have been known, producing walks whose length is bounded by 3OPT, while the number of vertices visited is Ω(k/log² k). We also show a simple O(log² n/loglogn)-approximation algorithm for the k-Tour problem. The best previously known approximation algorithms achieved min(O(log³ k),O(log² n?logk/loglogn)) approximation in polynomial time, and O(log² k) approximation in quasipolynomial time.     

Cobalt plating of high temperature stainless steel interconnects

To limit oxidation and protect against loss of conductivity in stainless steel for applications such as high temperature fuel cell interconnects, a layer of cobalt was electroplated on the surface. When heated in air at 800 °C, the cobalt was converted into spinel phases containing cobalt, chromium and iron. The oxide layer was characterized by X-ray diffraction and energy dispersive spectrometry. The area specific resistance (ASR) of the samples was measured by a new technique. After 1900 h oxidation at 800 °C in air, the cobalt-coated UNS430 stainless steel had a clean surface and a stable ASR of 0.026 Ω cm² compared to uncoated UNS430 which showed a thick, porous oxide growth and an ASR more than 10-fold higher.     

Performance evaluation of silty sand reinforced with fibres

Triaxial compression tests were conducted to evaluate the response of randomly distributed fibre on the strength of reinforced silty sand. In this study, oil palm empty fruit bunch (OPEFB) fibre was mixed with silty sand soil to investigate the increase of shear strength during triaxial compression. The specimens were tested under drained and undrained conditions with 0.25% and 0.5% content of OPEFB fibres of different lengths (i.e. 15 mm, 30 mm and 45 mm). In addition, OPEFB fibres coated with acrylic butadiene styrene thermoplastic were tested to determine the effect of coating on reinforcement. Inclusion of randomly distributed discrete fibres significantly improved the shear strength of silty sand. Coated OPEFB fibres increased the shear strength of silty sand much more compared to uncoated fibres. Coating fibres increases interface friction between fibre and soil particles by increasing the surface area. Reinforced silty sand containing 0.5% coated fibres of 30 mm length exhibited approximately 25% increase in friction angle and 35% in cohesion under undrained loading conditions compared to those of unreinforced silty sand. The results indicate that the shear strength parameters of the soil-fibre mixture (i.e. Φ′ and c′) can be improved significantly.     

Euclidean Prize-Collecting Steiner Forest

In this paper, we consider Steiner forest and its generalizations, prize-collecting Steiner forest and k-Steiner forest, when the vertices of the input graph are points in the Euclidean plane and the lengths are Euclidean distances. First, we present a simpler analysis of the polynomial-time approximation scheme (PTAS) of Borradaile et?al. (Proceedings of the 49th annual IEEE symposium on foundations of computer science, FOCS, pp.?115–124, 2008) for the Euclidean Steiner forest problem. This is done by proving a new structural property and modifying the dynamic programming by adding a new piece of information to each dynamic programming state. Next we develop a PTAS for a well-motivated case, i.e., the multiplicative case, of prize-collecting and budgeted Steiner forest. The ideas used in the algorithm may have applications in design of a broad class of bicriteria PTASs; see Sect.?1.3. At the end, we demonstrate why PTASs for these problems can be hard in the general Euclidean case (and thus for PTASs we cannot go beyond the multiplicative case). In fact, this conjecture was later proved by Bateni, Hajiaghayi and Marx?(1006.4339 [abs], 2009).     

Performance of oil palm empty fruit bunch fibres coated with acrylonitrile butadiene styrene

Natural fibres are suitable for reinforcement of soils due to their availability, low cost and environment-friendly nature. Oil palm empty fruit bunches (OPEFB), solid waste produced during refinement of oil from oil palm fruit, provide fibres which have been used as reinforcement material for soil improvements. To protect the fibres from biodegrading in reinforced soil, OPEFB fibre is coated with non-biodegradable material. The effect of coating OPEFB fibres with acrylonitrile butadiene styrene (ABS) was evaluated. Morphological characterization, mechanical and physical properties of the coated fibres exhibited improved fibre performance. The ABS treatment protected fibres from water absorption and decreased the biodegradation potential of the fibres in contact with soil. The tensile strength and elasticity moduli of the OPEFB fibres were also improved with the coating. Coating fibres increases interface friction between fibre and soil particles due to increased surface area. The results were shown that the shear strength parameters of the fibre-reinforced soils can be improved significantly.     

Artificial intelligence-based estimation of flushing half-cone geometry

An accurate estimation of half-cone geometry (i.e., volume and length) created by pressure flushing operation in dam reservoirs is required for sediment management in the reservoir storage. In this study, two artificial intelligence techniques namely, Artificial Neural Network (ANN) and Adaptive Neuro-fuzzy Inference System (ANFIS) were utilized to estimate the volume and length of flushing half-cone based on influential variables, i.e., mean flow velocity through bottom outlet (u), water depth in reservoir (H_w), mean grain diameter of deposited sediments (d₅₀), thickness of deposited sediment (H_s) and bottom outlet diameter (D). Experimental data in both dimensional and non-dimensional forms were used to train and test ANN and ANFIS models. The results of the intelligence-based models were also compared with those of existing studies. The outcomes indicated that both ANN and ANFIS models predict the volume and length of flushing half-cone more accurately than existing studies. Also, it was found that the ANN model provides a better estimation of the geometry of flushing half-cone compared to the ANFIS model. Finally, sensitivity analysis was conducted to determine the most and the least influential variables affecting the flushing half-cone geometry. It was found that the sediment characteristics (H_s and d₅₀) and fluids properties (H_w and u) have respectively the most and the least effect on flushing half-cone volume and length.     

Estimation of soil dispersivity using soft computing approaches

The accurate estimation of soil dispersivity (α) is required for characterizing the transport of contaminants in soil. The in situ measurement of α is costly and time-consuming. Hence, in this study, three soft computing methods, namely adaptive neuro-fuzzy inference system (ANFIS), artificial neural network (ANN), and gene expression programming (GEP), are used to estimate α from more readily measurable physical soil variables, including travel distance from source of pollutant (L), mean grain size (D ₅₀), soil bulk density (ρ _b), and contaminant velocity (V _c). Based on three statistical metrics [i.e., mean absolute error, root-mean-square error (RMSE), and coefficient of determination (R ²)], it is found that all approaches (ANN, ANFIS, and GEP) can accurately estimate α. Results also show that the ANN model (with RMSE = 0.00050 m and R ² = 0.977) performs better than the ANFIS model (with RMSE = 0.00062 m and R ² = 0.956), and the estimates from GEP are almost as accurate as those from ANFIS. The performance of ANN, ANFIS, and GEP models is also compared with the traditional multiple linear regression (MLR) method. The comparison indicates that all of the soft computing methods outperform the MLR model. Finally, the sensitivity analysis shows that the travel distance from source of pollution (L) and bulk density (ρ _b) have, respectively, the most and the least effect on the soil dispersivity.

     

The Effect of 80 dB Environmental Noise on Control of Posture in Healthy Young Adults

The ability to control movement of the center of mass (COM) plays a vital role in control of posture. Traditionally, visual, vestibular, and somatosensory systems are considered sources of information provided to the central nervous system to control posture. Recently, other environmental stimuli that can indirectly affect postural control have been taken into consideration. One in particular is excessive environmental noise that may exist in some working conditions (e.g., construction sites, assembly lines, food‐processing plants). We examined postural performance of 16 healthy young adults (21 to 26 years old) while being exposed to a computer‐generated 80‐dB intermittent white noise. Participants were tested for their ability to keep their COM static in three conditions of eyes open‐static platform (SEO), eyes closed‐static platform (SEC), and eyes open‐dynamic platform (DYN). Their limits of stability (LOS) also were tested to identify their ability to shift their COM to the borders of an area representing 50% of their base of support. Our results indicated that white noise at the level of 80 dB does not influence either the ability to hold the COM static or the precision to shift COM toward the borders of the base of support. We concluded that environmental noise at the level of 80 dB does not have a short‐term effect on postural control in a normal standing posture. © 2011 Wiley Periodicals, Inc.