Computation, Volume 7, Issue 1 (March 2019) – 19 articles

In recent times, the subject of effective cooling have become an interesting research topic for electronic and mechanical engineers due to the increased miniaturization trend in modern electronic systems. However, fins are useful for cooling various low and high power electronic systems. For improved thermal management of electronic systems, porous fins of functionally graded materials (FGM) have been identified as a viable candidate to enhance cooling. The present study presents an analysis of a convective–radiative porous fin of FGM. For theoretical investigations, the thermal property of the functionally graded material is assumed to follow linear and power-law functions. In this study, we investigated the effects of inhomogeneity index of FGM, convective and radiative variables on the thermal performance of the porous heatsink. The results of the present study show that an increase in the inhomogeneity index of FGM, convective and radiative parameter improves fin efficiency. Moreover, the rate of heat transfer in longitudinal FGM fin increases as β increases. The temperature prediction using the Adomian decomposition method is in excellent agreement with other analytical and method. Full article

Thermophysical properties of nanofluids play a key role in their heat transfer capability and can be significantly affected by several factors, such as temperature and concentration of nanoparticles. Developing practical and simple-to-use predictive models to accurately determine these properties can be advantageous when numerous dependent variables are involved in controlling the thermal behavior of nanofluids. Artificial neural networks are reliable approaches which recently have gained increasing prominence and are widely used in different applications for predicting and modeling various systems. In the present study, two novel approaches, Genetic Algorithm-Least Square Support Vector Machine (GA-LSSVM) and Particle Swarm Optimization- artificial neural networks (PSO-ANN), are applied to model the thermal conductivity and dynamic viscosity of Fe₂O₃/EG-water by considering concentration, temperature, and the mass ratio of EG/water as the input variables. Obtained results from the models indicate that GA-LSSVM approach is more accurate in predicting the thermophysical properties. The maximum relative deviation by applying GA-LSSVM was found to be approximately ±5% for the thermal conductivity and dynamic viscosity of the nanofluid. In addition, it was observed that the mass ratio of EG/water has the most significant impact on these properties. Full article

A precise estimation of the bubble size distribution (BSD) is required to understand the fluid dynamics in gas-liquid bubble columns at the “bubble scale,” evaluate the heat and mass transfer rate, and support scale-up approaches. In this paper, we have formulated a population balance model, and we have validated it against a previously published experimental dataset. The experimental dataset consists of BSDs obtained in the “pseudo-homogeneous” flow regime, in a large-diameter and large-scale bubble column. The aim of the population balance model is to predict the BSD in the developed region of the bubble column using as input the BSD at the sparger. The proposed approach has been able to estimate the BSD correctly and is a promising approach for future studies and to estimate bubble size in large-scale gas–liquid bubble columns. Full article

We analyze the theoretical properties of the recently proposed objective function for efficient online construction and training of multiclass classification trees in the settings where the label space is very large. We show the important properties of this objective and provide a complete proof that maximizing it simultaneously encourages balanced trees and improves the purity of the class distributions at subsequent levels in the tree. We further explore its connection to the three well-known entropy-based decision tree criteria, i.e., Shannon entropy, Gini-entropy and its modified variant, for which efficient optimization strategies are largely unknown in the extreme multiclass setting. We show theoretically that this objective can be viewed as a surrogate function for all of these entropy criteria and that maximizing it indirectly optimizes them as well. We derive boosting guarantees and obtain a closed-form expression for the number of iterations needed to reduce the considered entropy criteria below an arbitrary threshold. The obtained theorem relies on a weak hypothesis assumption that directly depends on the considered objective function. Finally, we prove that optimizing the objective directly reduces the multi-class classification error of the decision tree. Full article

In semi-supervised label propagation (LP), the data manifold is approximated by a graph, which is considered as a similarity metric. Graph estimation is a crucial task, as it affects the further processes applied on the graph (e.g., LP, classification). As our knowledge of data is limited, a single approximation cannot easily find the appropriate graph, so in line with this, multiple graphs are constructed. Recently, multi-metric fusion techniques have been used to construct more accurate graphs which better represent the data manifold and, hence, improve the performance of LP. However, most of these algorithms disregard use of the information of label space in the LP process. In this article, we propose a new multi-metric graph-fusion method, based on the Flexible Manifold Embedding algorithm. Our proposed method represents a unified framework that merges two phases: graph fusion and LP. Based on one available view, different simple graphs were efficiently generated and used as input to our proposed fusion approach. Moreover, our method incorporated the label space information as a new form of graph, namely the Correlation Graph, with other similarity graphs. Furthermore, it updated the correlation graph to find a better representation of the data manifold. Our experimental results on four face datasets in face recognition demonstrated the superiority of the proposed method compared to other state-of-the-art algorithms. Full article

This study adopts the flow shop concept used in industrial production to schedule repetitive non-linear construction projects, where specialized groups of workers execute processes in work zones (buildings) in a predefined order common to all groups. This problem is characteristic of construction projects that involve erecting multiple buildings. As the duration of the project heavily depends upon the sequence of the work zones, this study aims at providing a model and a practical approach for finding the optimal solution that assures the shortest duration of the project, allows the contractor to complete particular work zones (buildings) as soon as possible (without idle time), and conforms to a predefined sequence of work zone completion. This last constraint may arise from the client’s requirements or physical conditions of the project and has not been addressed by existing scheduling methods. Reducing the duration of the entire project brings the benefit of lower indirect costs and, if accompanied by a reduced duration of completing particular buildings (i.e., work zones), may also provide the opportunity to sell project deliverables sooner, thus improving the economic efficiency of the project. In search of optimal schedules, the authors apply the algorithms of Minimum Hamiltonian Cycle/Asymmetric Traveling Salesman Problem (ATSP). Full article

The development of detection methodologies for reliable drowsiness tracking is a challenging task requiring both appropriate signal inputs and accurate and robust algorithms of analysis. The aim of this research is to develop an advanced method to detect the drowsiness stage in electroencephalogram (EEG), the most reliable physiological measurement, using the promising Machine Learning methodologies. The methods used in this paper are based on Machine Learning methodologies such as stacked autoencoder with softmax layers. Results obtained from 62 volunteers indicate 100% accuracy in drowsy/wakeful discrimination, proving that this approach can be very promising for use in the next generation of medical devices. This methodology can be extended to other uses in everyday life in which the maintaining of the level of vigilance is critical. Future works aim to perform extended validation of the proposed pipeline with a wide-range training set in which we integrate the photoplethysmogram (PPG) signal and visual information with EEG analysis in order to improve the robustness of the overall approach. Full article

Due to the increment in hand motion types, electromyography (EMG) features are increasingly required for accurate EMG signals classification. However, increasing in the number of EMG features not only degrades classification performance, but also increases the complexity of the classifier. Feature selection is an effective process for eliminating redundant and irrelevant features. In this paper, we propose a new personal best (Pbest) guide binary particle swarm optimization (PBPSO) to solve the feature selection problem for EMG signal classification. First, the discrete wavelet transform (DWT) decomposes the signal into multiresolution coefficients. The features are then extracted from each coefficient to form the feature vector. After which pbest-guide binary particle swarm optimization (PBPSO) is used to evaluate the most informative features from the original feature set. In order to measure the effectiveness of PBPSO, binary particle swarm optimization (BPSO), genetic algorithm (GA), modified binary tree growth algorithm (MBTGA), and binary differential evolution (BDE) were used for performance comparison. Our experimental results show the superiority of PBPSO over other methods, especially in feature reduction; where it can reduce more than 90% of features while keeping a very high classification accuracy. Hence, PBPSO is more appropriate for application in clinical and rehabilitation applications. Full article

Parameter estimation is of paramount importance in every digital receiver. This is not only true for radio, but also for optical links; otherwise, subsequent processing stages, like detector units or error correction schemes, could not be operated reliably. However, for a bandlimited optical intensity channel, the problem of parameter estimation is strongly related to non-negative pulse shapes satisfying also the Nyquist criterion to keep the detection process as simple as possible. To the best of the author’s knowledge, it is the first time that both topics—parameter estimation on the one hand and bandlimited intensity modulation on the other—are jointly investigated. Since symbol timing and signal amplitude are the parameters of interest in this case, the corresponding Cramer–Rao lower bounds are derived as the theoretical limit of the jitter variance generated by the related estimator algorithms. In this context, a maximum likelihood solution is developed for the recovery of both timing and amplitude. Since this approach requires a receiver matched filter destroying the Nyquist criterion of the non-negative pulse shape, we compare it to a flat receiver filter preserving the required orthogonality property. It turned out that the jitter performance of the matched filter method is close to the Cramer–Rao lower bound in the medium-to-low SNR range, but due to inter-symbol interference effects an error floor emerges at higher SNR values. The flat filter solution avoids this drawback, although the price to be paid is a larger noise level at the filter output, so that a somewhat increased jitter variance is observed. Full article

Welding alloy 617 with other metals and alloys has been receiving significant attention in the last few years. It is considered to be the benchmark for the development of economical hybrid structures to be used in different engineering applications. The differences in the physical and metallurgical properties of dissimilar materials to be welded usually result in weaker structures. Fatigue failure is one of the most common failure modes of dissimilar material welded structures. In this study, fatigue life prediction of dissimilar material weld was evaluated by the accelerated life method and artificial neural network approach (ANN). The accelerated life testing approach was evaluated for different distributions. Weibull distribution was the most appropriate distribution that fits the fatigue data very well. Acceleration of fatigue life test data was attained with 95% reliability for Weibull distribution. The probability plot verified that accelerating variables at each level were appropriate. Experimental test data and predicted fatigue life were in good agreement with each other. Two training algorithms, Bayesian regularization (BR) and Levenberg–Marquardt (LM), were employed for training ANN. The Bayesian regularization training algorithm exhibited a better performance than the Levenberg–Marquardt algorithm. The results confirmed that the assessment methods are effective for lifetime prediction of dissimilar material welded joints. Full article

Parallel multiphysics simulations often suffer from load imbalances originating from the applied coupling of algorithms with spatially and temporally varying workloads. It is, thus, desirable to minimize these imbalances to reduce the time to solution and to better utilize the available hardware resources. Taking particulate flows as an illustrating example application, we present and evaluate load balancing techniques that tackle this challenging task. This involves a load estimation step in which the currently generated workload is predicted. We describe in detail how such a workload estimator can be developed. In a second step, load distribution strategies like space-filling curves or graph partitioning are applied to dynamically distribute the load among the available processes. To compare and analyze their performance, we employ these techniques to a benchmark scenario and observe a reduction of the load imbalances by almost a factor of four. This results in a decrease of the overall runtime by 14% for space-filling curves. Full article

This research answers the following question: What is the fluid dynamic behavior of a supercritical fluid (SCF) inside a membrane module? At this time, there is very little or no reported information that can provide an answer to this question. The research studies related to the themes of supercritical CO₂ (SC-CO₂), hollow fiber membrane contactors (HFMCs), and numerical simulations have mainly reported on 2D simulations, but in this work, 3D profiles are presented. Simulations were performed based on the experimental results and other simulations, using the geometry of a commercial module. The results were mainly based on the different operating conditions and geometric dimensions. A mesh study was performed to ensure the mesh non-dependence of the results presented here. It was observed that the velocity profile developed at 10 mm from the wall of the supercritical CO₂ entrance pipe. A profile equilibrium around the fiber close to the entrance of the module was achieved in the experimental hollow fiber membrane contactor when compared to the case of the commercial hollow fiber membrane contactor. The results of this research provided a visualization of the boundary layer, which did not cover the entire fiber length. Finally, the results of this paper are interesting for technical applications and contribute to our understanding of the hydrodynamics of SCFs. Full article

Overlap of footprints of light emitting diodes (LEDs) increases the positioning accuracy of wearable LED indoor positioning systems (IPS) but such an approach assumes that the footprint boundaries are defined. In this work, we develop a mathematical model for defining the footprint boundaries of an LED in terms of a threshold angle instead of the conventional half or full angle. To show the effect of the threshold angle, we compare how overlaps and receiver tilts affect the performance of an LED-based IPS when the optical boundary is defined at the threshold angle and at the full angle. Using experimental measurements, simulations, and theoretical analysis, the effect of the defined threshold angle is estimated. The results show that the positional time when using the newly defined threshold angle is 12 times shorter than the time when the full angle is used. When the effect of tilt is considered, the threshold angle time is 22 times shorter than the full angle positioning time. Regarding accuracy, it is shown in this work that a positioning error as low as 230 mm can be obtained. Consequently, while the IPS gives a very low positioning error, a defined threshold angle reduces delays in an overlap-based LED IPS. Full article

The ability for DFT: B3LYP calculations using the 6-31g and lanl2dz basis sets to predict the electrochemical properties of twenty (20) 3-aryl-quinoxaline-2-carbonitrile 1,4-di-N-oxide derivatives with varying degrees of cytotoxic activity in dimethylformamide (DMF) was investigated. There was a strong correlation for the first reduction and moderate-to-low correlation of the second reduction of the diazine ring between the computational and the experimental data, with the exception of the derivative containing the nitro functionality. The four (4) nitro group derivatives are clear outliers in the overall data sets and the derivative E4 is ill-behaved. The remaining three (3) derivatives containing the nitro groups had a strong correlation between the computational and experimental data; however, the computational data falls substantially outside of the expected range. Full article

Stock market prediction and trading has attracted the effort of many researchers in several scientific areas because it is a challenging task due to the high complexity of the market. More investors put their effort to the development of a systematic approach, i.e., the so called “Trading System (TS)” for stocks pricing and trend prediction. The introduction of the Trading On-Line (TOL) has significantly improved the overall number of daily transactions on the stock market with the consequent increasing of the market complexity and liquidity. One of the most main consequence of the TOL is the “automatic trading”, i.e., an ad-hoc algorithmic robot able to automatically analyze a lot of financial data with target to open/close several trading operations in such reduced time for increasing the profitability of the trading system. When the number of such automatic operations increase significantly, the trading approach is known as High Frequency Trading (HFT). In this context, recently, the usage of machine learning has improved the robustness of the trading systems including HFT sector. The authors propose an innovative approach based on usage of ad-hoc machine learning approach, starting from historical data analysis, is able to perform careful stock price prediction. The stock price prediction accuracy is further improved by using adaptive correction based on the hypothesis that stock price formation is regulated by Markov stochastic propriety. The validation results applied to such shares and financial instruments confirms the robustness and effectiveness of the proposed automatic trading algorithm. Full article

The dysregulation of inflammation, normally a self-limited response that initiates healing, is a critical component of many diseases. Treatment of inflammatory disease is hampered by an incomplete understanding of the complexities underlying the inflammatory response, motivating the application of systems and computational biology techniques in an effort to decipher this complexity and ultimately improve therapy. Many mathematical models of inflammation are based on systems of deterministic equations that do not account for the biological noise inherent at multiple scales, and consequently the effect of such noise in regulating inflammatory responses has not been studied widely. In this work, noise was added to a deterministic system of the inflammatory response in order to account for biological stochasticity. Our results demonstrate that the inflammatory response is highly dependent on the balance between the concentration of the pathogen and the level of biological noise introduced to the inflammatory network. In cases where the pro- and anti-inflammatory arms of the response do not mount the appropriate defense to the inflammatory stimulus, inflammation transitions to a different state compared to cases in which pro- and anti-inflammatory agents are elaborated adequately and in a timely manner. In this regard, our results show that noise can be both beneficial and detrimental for the inflammatory endpoint. By evaluating the parametric sensitivity of noise characteristics, we suggest that efficiency of inflammatory responses can be controlled. Interestingly, the time period on which parametric intervention can be introduced efficiently in the inflammatory system can be also adjusted by controlling noise. These findings represent a novel understanding of inflammatory systems dynamics and the potential role of stochasticity thereon. Full article

Energy consumption in the building sector is responsible for a very large amount of electricity consumption worldwide. The reduction of this consumption is a crucial issue in order to achieve sustainability. The objective of this work is to investigate the use of phase change materials (PCMs) in the building walls in order to reduce the heating and the cooling loads. The novelty of this work is based on the investigation of different scenarios about the position of the PCM layer in the south and the north walls. PCMs can improve the thermal performance and the thermal comfort of a building due to their ability to store large amounts of thermal energy in latent form and so to reduce the temperature fluctuations of the structural components, keeping them within the desired temperature levels. More specifically, this work presents and compares the heating loads, the cooling loads and the temperature distribution of a building in Athens (Greece), with and without PCMs in different positions in the south wall and in the north walls. The simulation is performed with the commercial software TRNSYS 17, using the TRNSYS component: type 1270 (PCM Wall). The results proved that the maximum energy savings per year were achieved by the combination of the insulation and the PCM layer in the north and south walls. More specifically, the reductions in the heating and the cooling loads were found to be 1.54% and 5.90%, respectively. Furthermore, the temperature distribution with the use of a PCM layer is the most acceptable, especially during the summer period. Full article

In this paper, the performance of the optimal beam radius indoor positioning (OBRIP) and two-receiver indoor positioning (TRIP) algorithms are analysed by varying system parameters in the presence of an indoor optical wireless channel modelled in line of sight configuration. From all the conducted simulations, the minimum average error value obtained for TRIP is 0.61 m against 0.81 m obtained for OBRIP for room dimensions of 10 m × 10 m × 3 m. In addition, for each simulated condition, TRIP, which uses two receivers, outperforms OBRIP and reduces position estimation error up to 30%. To get a better understanding of error in position estimation for different combinations of beam radius and separation between light emitting diodes, the 90th percentile error is determined using a cumulative distribution frequency (CDF) plot, which gives an error value of 0.94 m for TRIP as compared to 1.20 m obtained for OBRIP. Both algorithms also prove to be robust towards change in receiver tilting angle, thus providing flexibility in the selection of the parameters to adapt to any indoor environment. In addition, in this paper, a mathematical model based on the concept of raw moments is used to confirm the findings of the simulation results for the proposed algorithms. Using this mathematical model, closed-form expressions are derived for standard deviation of uniformly distributed points in an optical wireless communication based indoor positioning system with circular and rectangular beam shapes. Full article