Statistical Models of Network Traffic

Model-based approaches have been applied successfully to a wide range of tasks such as specification, simulation, testing, and diagnosis. But one bottleneck often prevents the introduction of these ideas: Manual modeling is a non-trivial, time-consuming task. Automatically deriving models by observing and analyzing running systems is one possible way to amend this bottleneck. To derive a model automatically, some a-priori knowledge about the model structure–i.e. about the system–must exist. Such a model formalism would be used as follows: (i) By observing the network traffic, a model of the long-term system behavior could be generated automatically, (ii) Test vectors can be generated from the model, (iii) While the system is running, the model could be used to diagnose non-normal system behavior. The main contribution of this paper is the introduction of a model formalism called 'probabilistic regression automaton' suitable for the tasks mentioned above.

A Learning-Community Recommendation Approach for Web-Based Cooperative Learning

Cooperative learning has been defined as learners working together as a team to solve a problem to complete a task or to accomplish a common goal, which emphasizes the importance of interactions among members to promote the whole learning performance. With the popularity of society networks, cooperative learning is no longer limited to traditional classroom teaching activities. Since society networks facilitate to organize online learners, to establish common shared visions, and to advance learning interaction, the online community and online learning community have triggered the establishment of web-based societies. Numerous research literatures have indicated that the collaborative learning community is a critical issue to enhance learning performance. Hence, this paper proposes a learning community recommendation approach to facilitate that a learner joins the appropriate learning communities, which is based on k-nearest neighbor (kNN) classification. To demonstrate the viability of the proposed approach, the proposed approach is implemented for 117 students to recommend learning communities. The experimental results indicate that the proposed approach can effectively recommend appropriate learning communities for learners.

Effect of Particle Size in Aviation Turbine Fuel-Al2O3 Nanofluids for Heat Transfer Applications

The effect of Alumina nanoparticle size on thermophysical properties, heat transfer performance and pressure loss characteristics of Aviation Turbine Fuel (ATF)-Al2O3 nanofluids is studied experimentally for the proposed application of regenerative cooling of semi-cryogenic rocket engine thrust chambers. Al2O3 particles with mean diameters of 50 nm or 150 nm are dispersed in ATF. At 500C and 0.3% particle volume concentration, the bigger particles show increases of 17% in thermal conductivity and 55% in viscosity, whereas the smaller particles show corresponding increases of 21% and 22% for thermal conductivity and viscosity respectively. Contrary to these results, experiments to study the heat transfer performance and pressure loss characteristics show that at the same pumping power, the maximum enhancement in heat transfer coefficient at 500C and 0.3% concentration is approximately 47% using bigger particles, whereas it is only 36% using smaller particles.

Retrospective Synthetic Focusing with Correlation Weighting for Very High Frame Rate Ultrasound

The need of high frame-rate imaging has been triggered by the new applications of ultrasound imaging to transient elastography and real-time 3D ultrasound. Using plane wave excitation (PWE) is one of the methods to achieve very high frame-rate imaging since an image can be formed with a single insonification. However, due to the lack of transmit focusing, the image quality with PWE is lower compared with those using conventional focused transmission. To solve this problem, we propose a filter-retrieved transmit focusing (FRF) technique combined with cross-correlation weighting (FRF+CC weighting) for high frame-rate imaging with PWE. A restrospective focusing filter is designed to simultaneously minimize the predefined sidelobe energy associated with single PWE and the filter energy related to the signal-to-noise-ratio (SNR). This filter attempts to maintain the mainlobe signals and to reduce the sidelobe ones, which gives similar mainlobe signals and different sidelobes between the original PWE and the FRF baseband data. Normalized cross-correlation coefficient at zero lag is calculated to quantify the degree of similarity at each imaging point and used as a weighting matrix to the FRF baseband data to further suppress sidelobes, thus improving the filter-retrieved focusing quality.

Cyprus- Offshore Aquaculture Mooring Systems: Current Status and Future Development

Cyprus- offshore aquaculture industry has promising prospects taking into account that Cyprus is an island. Its production trend is increasing overtaking bigger countries such Greece and Italy. However, current mooring systems seem to be under-performing acting as obstacles for its future development. Furthermore, shallow coastal waters scarcity due to competing industries dictates future development to come by moving further from shore exposing fish farms and subsequently mooring systems to harsher environmental loadings. It is, therefore, of paramount importance to design mooring systems based on engineering and scientific principles and leave behind the present “trial and error" methods. This paper presents the current state of Cyprus- offshore aquaculture industry and focuses of its mooring designs by proposing a new methodology for designing more reliable systems, hence ensuring its future.

Effects of Upflow Liquid Velocity on Performance of Expanded Granular Sludge Bed (EGSB) System

The effects of upflow liquid velocity (ULV) on performance of expanded granular sludge bed (EGSB) system were investigated. The EGSB reactor, made from galvanized steel pipe 0.10 m diameter and 5 m height, had been used to treat piggery wastewater, after passing through acidification tank. It consisted of 39.3 l working volume in reaction zone and 122 l working volume in sedimentation zone, at the upper part. The reactor was seeded with anaerobically digested sludge and operated at the ULVs of 4, 8, 12 and 16 m/h, consecutively, corresponding to organic loading rates of 9.6 – 13.0 kg COD/ (m3.d). The average COD concentrations in the influent were 9,601 – 13,050 mg/l. The COD removal was not significantly different, i.e. 93.0% - 94.0%, except at ULV 12 m/h where SS in the influent was exceptionally high so that VSS washout had occurred, leading to low COD removal. The FCOD and VFA concentrations in the effluent of all experiments were not much different, indicating the same range of treatment performance. The biogas production decreased at higher ULV and ULV of 4 m/h is suggested as design criterion for EGSB system.

Chaos Theory and Application in Foreign Exchange Rates vs. IRR (Iranian Rial)

Daily production of information and importance of the sequence of produced data in forecasting future performance of market causes analysis of data behavior to become a problem of analyzing time series. But time series that are very complicated, usually are random and as a result their changes considered being unpredictable. While these series might be products of a deterministic dynamical and nonlinear process (chaotic) and as a result be predictable. Point of Chaotic theory view, complicated systems have only chaotically face and as a result they seem to be unregulated and random, but it is possible that they abide by a specified math formula. In this article, with regard to test of strange attractor and biggest Lyapunov exponent probability of chaos on several foreign exchange rates vs. IRR (Iranian Rial) has been investigated. Results show that data in this market have complex chaotic behavior with big degree of freedom.

Bone Generation through Mechanical Loading

Bones are dynamic and responsive organs, they regulate their strength and mass according to the loads which they are subjected. Because, the Wnt/β-catenin pathway has profound effects on the regulation of bone mass, we hypothesized that mechanical loading of bone cells stimulates Wnt/β-catenin signaling, which results in the generation of new bone mass. Mechanical loading triggers the secretion of the Wnt molecule, which after binding to transmembrane proteins, causes GSK-3β (Glycogen synthase kinase 3 beta) to cease the phosphorylation of β-catenin. β-catenin accumulation in the cytoplasm, followed by its transport into the nucleus, binding to transcription factors (TCF/LEF) that initiate transcription of genes related to bone formation. To test this hypothesis, we used TOPGAL (Tcf Optimal Promoter β-galactosidase) mice in an experiment in which cyclic loads were applied to the forearm. TOPGAL mice are reporters for cells effected by the Wnt/β-catenin signaling pathway. TOPGAL mice are genetically engineered mice in which transcriptional activation of β- catenin, results in the production of an enzyme, β-galactosidase. The presence of this enzyme allows us to localize transcriptional activation of β-catenin to individual cells, thereby, allowing us to quantify the effects that mechanical loading has on the Wnt/β-catenin pathway and new bone formation. The ulnae of loaded TOPGAL mice were excised and transverse slices along different parts of the ulnar shaft were assayed for the presence of β-galactosidase. Our results indicate that loading increases β-catenin transcriptional activity in regions where this pathway is already primed (i.e. where basal activity is already higher) in a load magnitude dependent manner. Further experiments are needed to determine the temporal and spatial activation of this signaling in relation to bone formation.

Robust Steam Temperature Regulation for Distillation of Essential Oil Extraction Process using Hybrid Fuzzy-PD plus PID Controller

This paper presents a hybrid fuzzy-PD plus PID (HFPP) controller and its application to steam distillation process for essential oil extraction system. Steam temperature is one of the most significant parameters that can influence the composition of essential oil yield. Due to parameter variations and changes in operation conditions during distillation, a robust steam temperature controller becomes nontrivial to avoid the degradation of essential oil quality. Initially, the PRBS input is triggered to the system and output of steam temperature is modeled using ARX model structure. The parameter estimation and tuning method is adopted by simulation using HFPP controller scheme. The effectiveness and robustness of proposed controller technique is validated by real time implementation to the system. The performance of HFPP using 25 and 49 fuzzy rules is compared. The experimental result demonstrates the proposed HFPP using 49 fuzzy rules achieves a better, consistent and robust controller compared to PID when considering the test on tracking the set point and the effects due to disturbance.

Analysis of Explosive Shock Wave and its Application in Snow Avalanche Release

Avalanche velocity (from start to track zone) has been estimated in the present model for an avalanche which is triggered artificially by an explosive devise. The initial development of the model has been from the concept of micro-continuum theories [1], underwater explosions [2] and from fracture mechanics [3] with appropriate changes to the present model. The model has been computed for different slab depth R, slope angle θ, snow density ¤ü, viscosity μ, eddy viscosity η*and couple stress parameter η. The applicability of the present model in the avalanche forecasting has been highlighted.

Average Current Estimation Technique for Reliability Analysis of Multiple Semiconductor Interconnects

Average current analysis checking the impact of current flow is very important to guarantee the reliability of semiconductor systems. As semiconductor process technologies improve, the coupling capacitance often become bigger than self capacitances. In this paper, we propose an analytic technique for analyzing average current on interconnects in multi-conductor structures. The proposed technique has shown to yield the acceptable errors compared to HSPICE results while providing computational efficiency.

Novel PES Membrane Reinforced by Nano-WS2 for Enhanced Fouling Resistance

Application of nanoparticles as additives in membrane synthesis for improving the resistance of membranes against fouling has triggered recent interest in new membrane types. However, most nanoparticle-enhanced membranes suffer from the tradeoff between permeability and selectivity. In this study, nano-WS2 was explored as the additive in membrane synthesis by non-solvent induced phase separation. Blended PES-WS2 flat-sheet membranes with the incorporation of ultra-low concentrations of nanoparticles (from 0.025 to 0.25%, WS2/PES ratio) were manufactured and investigated in terms of permeability, fouling resistance and solute rejection. Remarkably, a significant enhancement in the permeability was observed as a result of the incorporation of ultra-low fractions of nano-WS2 to the membrane structure. Optimal permeability values were obtained for modified membranes with 0.10% nanoparticle/polymer concentration ratios. Furthermore, fouling resistance and solute rejection were significantly improved by the incorporation of nanoparticles into the membrane matrix. Specifically, fouling resistance of modified membrane can increase by around 50%.

The Role Played by Swift Change of the Stability Characteristic of Mean Flow in Bypass Transition

The scenario of bypass transition is generally described as follows: the low-frequency disturbances in the free-stream may generate long stream-wise streaks in the boundary layer, which later may trigger secondary instability, leading to rapid increase of high-frequency disturbances. Then possibly turbulent spots emerge, and through their merging, lead to fully developed turbulence. This description, however, is insufficient in the sense that it does not provide the inherent mechanism of transition that during the transition, a large number of waves with different frequencies and wave numbers appear almost simultaneously, producing sufficiently large Reynolds stress, so the mean flow profile can change rapidly from laminar to turbulent. In this paper, such a mechanism will be figured out from analyzing DNS data of transition.

Decision Trees for Predicting Risk of Mortality using Routinely Collected Data

It is well known that Logistic Regression is the gold standard method for predicting clinical outcome, especially predicting risk of mortality. In this paper, the Decision Tree method has been proposed to solve specific problems that commonly use Logistic Regression as a solution. The Biochemistry and Haematology Outcome Model (BHOM) dataset obtained from Portsmouth NHS Hospital from 1 January to 31 December 2001 was divided into four subsets. One subset of training data was used to generate a model, and the model obtained was then applied to three testing datasets. The performance of each model from both methods was then compared using calibration (the χ2 test or chi-test) and discrimination (area under ROC curve or c-index). The experiment presented that both methods have reasonable results in the case of the c-index. However, in some cases the calibration value (χ2) obtained quite a high result. After conducting experiments and investigating the advantages and disadvantages of each method, we can conclude that Decision Trees can be seen as a worthy alternative to Logistic Regression in the area of Data Mining.