Prediction of bulk modulus and volumetric expansion coefficient of water for leak tightness test of pipelines

To determine whether any pressure variation in pipeline hydrostatic test is a result of temperature changes or the presence of leaks, the calculation of pressure/temperature changes is required for test sections. In these calculations, bulk modulus and volumetric expansion coefficient of fresh or sea water must be taken into account. In this study, a simple-to-use correlation is developed to predict the bulk modulus and volumetric expansion coefficient of both fresh and sea water as a function of temperature and pressure. The proposed correlation helps to cover the bulk modulus and volumetric expansion coefficient of both fresh and sea water for temperatures less than 50 °C (40 °C for sea water) as well as pressures up to 55,000 kPa (550 bar). The results can be used in follow-up calculations to determine whether any pressure variation in pipeline hydrostatic test is a result of temperature changes or the presence of leaks. The proposed correlation showed promising results with average absolute deviations for volumetric expansion coefficient and bulk modulus of water being around 0.58% and 0.08% respectively. The novel correlation is easy to use and will prove to be of immense value for project engineers to test the critical limits accurately.     

The effects of food essential oils on cardiovascular diseases: A review

Essential oils (EO) are complex secondary metabolites, which are produced by aromatic plants and identified by their powerful odors. Present studies on EO and their isolated ingredients have drawn the attention of researchers to screen these natural products and evaluate their effect on the cardiovascular system. Some EO, and their active ingredients, have been reported to improve the cardiovascular system significantly by affecting vaso-relaxation, and decreasing the heart rate and exert a hypotension activity. Several mechanisms have been proposed for the role of EO and their main active components in promoting the health of the cardiovascular system. The objective of this review is to highlight the current state of knowledge on the functional role of EO extracted from plants for reducing the risk of cardiovascular diseases and their mechanisms of action. Research on EO has the potential to identify new bioactive compounds and formulate new functional products for the treatment of cardiovascular diseases such as arterial hypertension, angina pectoris, heart failure, and myocardial infarction.     

Incorporating Nano-silica as a Binder to Improve Corrosion Resistance of High Alumina Refractory Castables

In this study, four types of castables as calcium-aluminate cement (CAC)-bonded and nano-silica (NS)-bonded castables based on tabular-alumina and bauxite aggregates were prepared to investigate the replacement of the calcium-aluminate cement by NS. All samples were allowed to dry at 110 °C then fired at 800 and 1200 °C. Bulk density and apparent porosity of samples were measured. The molten aluminum static corrosion test (cup test) results showed that NS-bonded specimens had higher resistance to corrosion compared to CAC-bonded samples (based on the measured average aluminum penetration depth into the refractory texture). However no penetration was observed in bauxite NS-bonded samples. The results were consistent with dynamic corrosion test in aluminum melt carried out at 800 °C for 100 h. The small pore size in NS-bonded castables was found to be the main cause for high corrosion resistance as micro-pores prevented the melt to penetrate into the refractory.     

Development of planning level transportation safety tools using Geographically Weighted Poisson Regression

A common technique used for the calibration of collision prediction models is the Generalized Linear Modeling (GLM) procedure with the assumption of Negative Binomial or Poisson error distribution. In this technique, fixed coefficients that represent the average relationship between the dependent variable and each explanatory variable are estimated. However, the stationary relationship assumed may hide some important spatial factors of the number of collisions at a particular traffic analysis zone. Consequently, the accuracy of such models for explaining the relationship between the dependent variable and the explanatory variables may be suspected since collision frequency is likely influenced by many spatially defined factors such as land use, demographic characteristics, and traffic volume patterns. The primary objective of this study is to investigate the spatial variations in the relationship between the number of zonal collisions and potential transportation planning predictors, using the Geographically Weighted Poisson Regression modeling technique. The secondary objective is to build on knowledge comparing the accuracy of Geographically Weighted Poisson Regression models to that of Generalized Linear Models. The results show that the Geographically Weighted Poisson Regression models are useful for capturing spatially dependent relationships and generally perform better than the conventional Generalized Linear Models.     

Optimization of Water Distribution Networks Using Integer Linear Programming

In this study optimum design of municipal water distribution networks for a single loading condition is determined by the branch and bound integer linear programming technique. The hydraulic and optimization analyses are linked through an iterative procedure. This procedure enables us to design a water distribution system that satisfies all required constraints with a minimum total cost. The constraints include pipe sizes, which are limited to the commercially available sizes, reservoir levels, pipe flow velocities, and nodal pressures. Accuracy of the developed model has been assessed using a network with limited solution alternatives, the optimal solution of which can be determined without employing optimization techniques. The proposed model has also been applied to a network solved by others. Comparison of the results indicates that the accuracy and convergence of the proposed method is quite satisfactory.     

Modeling Multithreaded Applications Using Petri Nets

Since most modern computing systems contain multiple processing elements, applications are relying on multithreaded programming techniques that allow a program to execute multiple tasks concurrently to take advantage of the processing capabilities. Multithreaded programs are more difficult to design and test because of the nondeterministic execution orders and synchronization among the threads. Different approaches can be used to test Multithreaded Applications. In our approach we use Petri nets to represent the key elements of interactions among threads to identify potential problems such as race conditions, lost signals, and deadlocks. A tool called C2Petri has been developed which converts C-Pthreads programs to the equivalent Petri net model. This tool helps verification of Pthread-based programs. At present the tool has limited capabilities and we hope to expand the capabilities of our tool in the near future.     

Multireference object pose retrieval with volume feedback

Pose retrieval of a rigid object from monocular video sequences or images is addressed. Initially, the object pose is estimated in each image assuming flat depth maps. Shape-from-silhouette is then applied to make a 3-D model (volume), which is used for a new round of pose estimations, this time by a model-based method that gives better estimates. Before repeating this process by building a new volume, pose estimates are adjusted to reduce error by maximizing a novel quality factor for shape-from-silhouette volume reconstruction. The feedback loop is terminated when pose estimates do not change much, as compared with those produced by the previous iteration. Based on a theoretical study of the proposed system, a test of convergence to a given set of poses is devised. Reliable performance of the system is also proved by several experiments on both synthetic and real image sequences. No model is assumed for the object and no feature point is detected or tracked as there is no problematic feature matching or correspondence. Our method can be used for 3-D object tracking in video, 3-D modeling, and volume reconstruction from video.     

A hybrid multi-objective shuffled frog-leaping algorithm for a mixed-model assembly line sequencing problem

In this paper, a mixed-model assembly line (MMAL) sequencing problem is studied. This type of production system is used to manufacture multiple products along a single assembly line while maintaining the least possible inventories. With the growth in customers’ demand diversification, mixed-model assembly lines have gained increasing importance in the field of management. Among the available criteria used to judge a sequence in MMAL, the following three are taken into account: the minimization of total utility work, total production rate variation, and total setup cost. Due to the complexity of the problem, it is very difficult to obtain optimum solution for this kind of problems by means of traditional approaches. Therefore, a hybrid multi-objective algorithm based on shuffled frog-leaping algorithm (SFLA) and bacteria optimization (BO) are deployed. The performance of the proposed hybrid algorithm is then compared with three well-known genetic algorithms, i.e. PS-NC GA, NSGA-II, and SPEA-II. The computational results show that the proposed hybrid algorithm outperforms the existing genetic algorithms, significantly in large-sized problems.     

Critical heat flux prediction by using radial basis function and multilayer perceptron neural networks: A comparison study

Critical heat flux (CHF) is an important parameter for the design of nuclear reactors. Although many experimental and theoretical researches have been performed, there is not a single correlation to predict CHF because it is influenced by many parameters. These parameters are based on fixed inlet, local and fixed outlet conditions. Artificial neural networks (ANNs) have been applied to a wide variety of different areas such as prediction, approximation, modeling and classification. In this study, two types of neural networks, radial basis function (RBF) and multilayer perceptron (MLP), are trained with the experimental CHF data and their performances are compared. RBF predicts CHF with root mean square (RMS) errors of 0.24%, 7.9%, 0.16% and MLP predicts CHF with RMS errors of 1.29%, 8.31% and 2.71%, in fixed inlet conditions, local conditions and fixed outlet conditions, respectively. The results show that neural networks with RBF structure have superior performance in CHF data prediction over MLP neural networks. The parametric trends of CHF obtained by the trained ANNs are also evaluated and results reported.