Determining the Main Factors in Declining the Urmia Lake Level by Using System Dynamics Modeling

Urmia Lake in Iran is the second largest saline lake in the world. This ecosystem is the home for different species. Due to various socio-economical and ecological criteria, Urmia Lake has important role in the Northwestern part of the country but it has faced many problems in recent years. Because of droughts, overuse of surface water resources and dam constructions, water level has decreased in such a way that one quarter of the lake has changed to saline area in the last 10 years. The purpose of this research is to determine the main factors which reduce the lake’s water level. To this end, a simulation model, based on system dynamics method, is developed for the Urmia Lake basin to estimate the lake’s level. After successful verification of the model, results show that (among the proposed factors) changes in inflows due to the climate change and overuse of surface water resources is the main factor for 65% of the effect, constructing four dams is responsible for 25% of the problem, and less precipitation on lake has 10% effect on decreasing the lake’s level in the recent years. In the future, the model also can be used by managers as a decision support system to find the effects of building new dams or other infrastructures.     

A high-performance and scalable multi-core aware software solution for network monitoring

In recent years, the need for high-performance network monitoring tools, which can cope with rapidly increasing network bandwidth, has become vital. A possible solution is to utilize the processing power of multi-core processors that nowadays are available as commercial-off-the-shelf (COTS) hardware. In this paper, we introduce a software solution for wire-speed packet capturing and transmission for TCP/IP networks under Linux operating system, called DashCap. The results of our experimental evaluations show that the proposed solution causes more than two times performance boost for packet capturing in comparison to the existing software solutions under Linux. We have proposed a scalable software architecture for network monitoring tools called DashNMon, which is based on DashCap. Multi-core awareness is a distinguished property of this architecture. Comparing to the existing cluster-based solutions, DashNMon can be used with COTS multi-core processors. In order to evaluate the proposed solutions, we have developed several prototype tools. The results of the experiments carried out using these tools show the scalability and high performance of the network monitoring tools that are based on the proposed architecture. Using the proposed architecture, it is possible to design and implement high-performance multi-threaded network intrusion detection systems (NIDSs) or application-layer firewalls, completely in the user space and with better utilization of the computational resources of multi-processor/multi-core systems.     

Adaptive Control for State Synchronization of Nonlinear Haptic Telerobotic Systems with Asymmetric Varying Time Delays

In this paper, we introduce a new adaptive controller design scheme for nonlinear telerobotic systems with varying time delays where the delays and their variation rates are unknown. The designed controller has the ability to synchronize the state behaviors of the local and the remote robots. In this paper, asymptotic stability in the presence of varying time delays is of interest. Using the proposed controller, asymptotic stability of the bilateral telerobotic system subject to any bounded yet unknown varying delay with a bounded yet unknown rate of change can be guaranteed. Besides the varying time delay, the proposed adaptive controller has the ability to adapt to the parameter variations in the local and the remote robots’ dynamics. It is shown that position and velocity errors between the local and the remote manipulators converge to the zero asymptotically, thus ensuring teleoperation transparency. Experimental and simulation results with a pair of PHANToM haptic devices and a pair of planar manipulators under varying time delays in the communication channel demonstrate the effectiveness of the proposed scheme.     

Implementation of genetic algorithm in an embedded microcontroller-based polarization control system

Implementation of genetic algorithm in a PIC32MX microcontroller-based polarization control system is proposed and demonstrated. The controller measures the signal intensity that is used to estimate the genetic value. This process is controlled by the genetic algorithm rather than referring to the Look-Up-Table as implemented in existing solutions. To reach optimum performance, the code is optimized by using the best genetic parameters so that the fastest execution time can be achieved. An ability of genetic algorithm to work efficiently in polarization control system possesses many advantages including easy code construction, low memory consumption and fast control speed. Genetic algorithm is intelligent enough to be used for endless polarization stabilization and in the worst case, able to stabilize the polarization changes in 300 μs. In the best case the response time can reach 17 μs.     

Classification of non-alcoholic beer based on aftertaste sensory evaluation by chemometric tools

Sensory evaluation is the application of knowledge and skills derived from several different scientific and technical disciplines, physiology, chemistry, mathematics and statistics, human behavior, and knowledge about product preparation practices. This research was aimed to evaluate aftertaste sensory attributes of commercial non-alcoholic beer brands (P1, P2, P3, P4, P5, P6, P7) by several chemometric tools. These attributes were bitter, sour, sweet, fruity, liquorice, artificial, body, intensity and duration. The results showed that the data are in a good consistency. Therefore, the brands were statistically classified in several categories. Linear techniques as Principal Component Analysis (PCA) and Linear Discriminant Analysis (LDA) were performed over the data that revealed all types of beer are well separated except a partial overlapping between zones corresponding to P4, P6 and P7. In this research, for the confirmation of the groups observed in PCA and in order to calculate the errors in calibration and in validation, PLS-DA technique was used. Based on the quantitative data of PLS-DA, the classification accuracy values were ranked within 49-86%. Moreover, it was found that the classification accuracy of LDA was much better than PCA. It shows that this trained sensory panel can discriminate among the samples except an overlapping between two types of beer. Also, two types of artificial networks were used: Probabilistic Neural Networks (PNN) with Radial Basis Functions (RBF) and FeedForward Networks with Back Propagation (BP) learning method. The highest classification success rate (correct predicted number over total number of measurements) of about 97% was obtained for RBF followed by 94% for BP. The results obtained in this study could be used as a reference for electronic nose and electronic tongue in beer quality control.     

Sequence-dependent group scheduling problem on unrelated-parallel machines

In this research we address a sequence-dependent group scheduling problem on a set of unrelated-parallel machines where the run time of each job differs on different machines. To benefit both producer and customers we attempt to minimize a linear combination of total weighted completion time and total weighted tardiness. Since the problem is shown to be NP-hard, meta-heuristic algorithms based on tabu search are developed to find the optimal/near optimal solution. For some small size yet complex problems, the results from these algorithms are compared to the optimal solutions found by CPLEX. The result obtained in all of these problems is that the tabu search algorithms could find solutions at least as good as CPLEX but in drastically shorter computational time, thus signifying the high degree of efficiency and efficacy attained by the former.     

Probabilistic characterisation of model error using Gaussian mixture model—With application to Charpy impact energy prediction for alloy steel

A novel approach to characterise the model prediction errors using a Gaussian mixture model is proposed. The motivation for this work lies behind many data models that are developed through prediction error minimisation with the assumption of a normal noise distribution. When the noise is non-normal, which may often be the case in complicated data modelling scenarios, the model prediction errors may contain rich information, which can be further exploited for model refinement and improvement. The key contents presented in this paper include: choosing the relevant variables to form the error data, optimising the number of Gaussian components required for the error data modelling, and fitting the Gaussian mixture parameters using an expectation-maximisation algorithm. Application of the proposed method for further model improvement, within the framework of hybrid deterministic/stochastic modelling, is also discussed. Preliminary results on the real industrial Charpy impact energy data for heat-treated steels show its effectiveness for model error characterisation, and the potential for model performance improvement in terms of prediction accuracy as well as providing accurate prediction confidence intervals.     

Effect of Multi-Wall Carbon Nanotubes on the Mechanical Properties of Natural Rubber

Multi-walled carbon nanotubes (MWNTs) were used to prepare natural rubber (NR) nanocomposites. Our first efforts to achieve nanostructures in MWNTs/NR nanocomposites were formed by incorporating carbon nanotubes in a polymer solution and subsequently evaporating the solvent. Using this technique, nanotubes can be dispersed homogeneously in the NR matrix in an attempt to increase the mechanical properties of these nanocomposites. The properties of the nanocomposites such as tensile strength, tensile modulus, elongation at break and hardness were studied. Mechanical test results show an increase in the initial modulus for up to 12 times in relation to pure NR. In addition to mechanical testing, the dispersion state of the MWNTs into NR studied by Transmission Electron Microscopy (TEM) in order to understand the morphology of the resulting system     

Light composite elliptic springs for vehicle suspension

For composites to compete in vehicle suspension applications, it is essential to control their failure by utilising their strength in principal direction instead of shear. This can be achieved efficiently by employing a new configuration instead of existing one. This study marries between an elliptical configuration and the woven roving composites.

In this paper, the influence of ellipticity ratio on performance of woven roving wrapped composite elliptical springs has been investigated both experimentally and numerically. A series of experiments was conducted for composite elliptical springs with ellipticity ratios (a/b) ranging from one to two. Typical failure histories of their failure mechanism are presented and discussed. In general, this study demonstrated that composites elliptical spring can be used for light and heavy trucks and meet the requirements, together with substantial weight saving. The results showed that the ellipticity ratio significantly influenced the spring rate and failure loads. Composite elliptic spring with ellipticity ratios of a/b 2.0 displayed the highest spring rate.