Showing papers in "Mathematical Problems in Engineering in 2017"

Bearing Fault Detection by One-Dimensional Convolutional Neural Networks

Abstract: Bearing faults are the biggest single source of motor failures. Artificial Neural Networks (ANNs) and other decision support systems are widely used for early detection of bearing faults. The typical decision support systems require feature extraction and classification as two distinct phases. Extracting fixed features each time may require a significant computational cost preventing their use in real-time applications. Furthermore, the selected features for the classification phase may not represent the most optimal choice. In this paper, the use of 1D Convolutional Neural Networks (CNNs) is proposed for a fast and accurate bearing fault detection system. The feature extraction and classification phases of the bearing fault detection are combined into a single learning body with the implementation of 1D CNN. The raw vibration data (signal) is fed into the proposed system as input eliminating the need for running a separate feature extraction algorithm each time vibration data is analyzed for classification. Implementation of 1D CNNs results in more efficient systems in terms of computational complexity. The classification performance of the proposed system with real bearing data demonstrates that the reduced computational complexity is achieved without a compromise in fault detection accuracy.

Identifying APT Malware Domain Based on Mobile DNS Logging

Abstract: Advanced Persistent Threat (APT) is a serious threat against sensitive information. Current detection approaches are time-consuming since they detect APT attack by in-depth analysis of massive amounts of data after data breaches. Specifically, APT attackers make use of DNS to locate their command and control (C&C) servers and victims’ machines. In this paper, we propose an efficient approach to detect APT malware C&C domain with high accuracy by analyzing DNS logs. We first extract 15 features from DNS logs of mobile devices. According to Alexa ranking and the VirusTotal’s judgement result, we give each domain a score. Then, we select the most normal domains by the score metric. Finally, we utilize our anomaly detection algorithm, called Global Abnormal Forest (GAF), to identify malware C&C domains. We conduct a performance analysis to demonstrate that our approach is more efficient than other existing works in terms of calculation efficiency and recognition accuracy. Compared with Local Outlier Factor (LOF), -Nearest Neighbor (KNN), and Isolation Forest (iForest), our approach obtains more than 99% and for the detection of C&C domains. Our approach not only can reduce data volume that needs to be recorded and analyzed but also can be applicable to unsupervised learning.

A MapReduce Based High Performance Neural Network in Enabling Fast Stability Assessment of Power Systems

Abstract: Transient stability assessment is playing a vital role in modern power systems. For this purpose, machine learning techniques have been widely employed to find critical conditions and recognize transient behaviors based on massive data analysis. However, an ever increasing volume of data generated from power systems poses a number of challenges to traditional machine learning techniques, which are computationally intensive running on standalone computers. This paper presents a MapReduce based high performance neural network to enable fast stability assessment of power systems. Hadoop, which is an open-source implementation of the MapReduce model, is first employed to parallelize the neural network. The parallel neural network is further enhanced with HaLoop to reduce the computation overhead incurred in the iteration process of the neural network. In addition, ensemble techniques are employed to accommodate the accuracy loss of the parallelized neural network in classification. The parallelized neural network is evaluated with both the IEEE 68-node system and a real power system from the aspects of computation speedup and stability assessment.

A Multicriteria Decision Model for Supplier Selection in a Food Industry Based on FITradeoff Method

Abstract: This article puts forward a decision model for solving a supplier selection problem in a food industry by considering multiple objectives that influence the decision-making process. In times of increasing competitiveness, companies strive hard to improve their profitability, and selection of supply sources may help if an appropriate decision is made through a well-structured decision-making process. Preference modeling is conducted in a flexible and interactive elicitation manner with the decision-maker (DM), aided by FITradeoff method. Partial information is gathered about the DM’s preferences in such a way that less effort is spent on finding a final solution for the problem.

Asphalt Pavement Pothole Detection and Segmentation Based on Wavelet Energy Field

Abstract: Potholes are one type of pavement surface distresses whose assessment is essential for developing road network maintenance strategies. Existing methods for automatic pothole detection either rely on expensive and high-maintenance equipment or could not segment the pothole accurately. In this paper, an asphalt pavement pothole detection and segmentation method based on energy field is put forward. The proposed method mainly includes two processes. Firstly, the wavelet energy field of the pavement image is constructed to detect the pothole by morphological processing and geometric criterions. Secondly, the detected pothole is segmented by Markov random field model and the pothole edge is extracted accurately. This methodology has been implemented in a MATLAB prototype, trained, and tested on 120 pavement images. The results show that it can effectively distinguish potholes from cracks, patches, greasy dirt, shadows, and manhole covers and accurately segment the pothole. For pothole detection, the method reaches an overall accuracy of 86.7%, with 83.3% precision and 87.5% recall. For pothole segmentation, the overlap degree between the extracted pothole region and the original pothole region is mostly more than 85%, which accounts for 88.6% of the total detected pavement pothole images.

Using an Integrated Group Decision Method Based on SVM, TFN-RS-AHP, and TOPSIS-CD for Cloud Service Supplier Selection

Abstract: To solve the cloud service supplier selection problem under the background of cloud computing emergence, an integrated group decision method is proposed. The cloud service supplier selection index framework is built from two perspectives of technology and technology management. Support vector machine- (SVM-) based classification model is applied for the preliminary screening to reduce the number of candidate suppliers. A triangular fuzzy number-rough sets-analytic hierarchy process (TFN-RS-AHP) method is designed to calculate supplier’s index value by expert’s wisdom and experience. The index weight is determined by criteria importance through intercriteria correlation (CRITIC). The suppliers are evaluated by the improved TOPSIS replacing Euclidean distance with connection distance (TOPSIS-CD). An electric power enterprise’s case is given to illustrate the correctness and feasibility of the proposed method.

Facial Expression Recognition Using Stationary Wavelet Transform Features

Abstract: Humans use facial expressions to convey personal feelings. Facial expressions need to be automatically recognized to design control and interactive applications. Feature extraction in an accurate manner is one of the key steps in automatic facial expression recognition system. Current frequency domain facial expression recognition systems have not fully utilized the facial elements and muscle movements for recognition. In this paper, stationary wavelet transform is used to extract features for facial expression recognition due to its good localization characteristics, in both spectral and spatial domains. More specifically a combination of horizontal and vertical subbands of stationary wavelet transform is used as these subbands contain muscle movement information for majority of the facial expressions. Feature dimensionality is further reduced by applying discrete cosine transform on these subbands. The selected features are then passed into feed forward neural network that is trained through back propagation algorithm. An average recognition rate of 98.83% and 96.61% is achieved for JAFFE and CK+ dataset, respectively. An accuracy of 94.28% is achieved for MS-Kinect dataset that is locally recorded. It has been observed that the proposed technique is very promising for facial expression recognition when compared to other state-of-the-art techniques.

Optimal Routing and Scheduling of Charge for Electric Vehicles: A Case Study

Abstract: There are increasing interests in improving public transportation systems. One of the proposed strategies for this improvement is the use of Battery Electric Vehicles (BEVs). This approach leads to a new challenge as the BEVs’ routing is exposed to the traditional routing problems of conventional vehicles, as well as the particular requirements of the electrical technologies of BEVs. Examples of BEVs’ routing problems include the autonomy, battery degradation, and charge process. This work presents a differential evolution algorithm for solving an electric vehicle routing problem (EVRP). The formulation of the EVRP to be solved is based on a scheme to coordinate the BEVs’ routing and recharge scheduling, considering operation and battery degradation costs. A model based on the longitudinal dynamics equation of motion estimates the energy consumption of each BEV. A case study, consisting of an airport shuttle service scenario, is used to illustrate the proposed methodology. For this transport service, the BEV energy consumption is estimated based on experimentally measured driving patterns.

Electric Vehicle Routing Problem with Charging Time and Variable Travel Time

Abstract: An electric vehicle routing problem with charging time and variable travel time is developed to address some operational issues such as range limitation and charging demand. The model is solved by using genetic algorithm to obtain the routes, the vehicle departure time at the depot, and the charging plan. Meanwhile, a dynamic Dijkstra algorithm is applied to find the shortest path between any two adjacent nodes along the routes. To prevent the depletion of all battery power and ensure safe operation in transit, electric vehicles with insufficient battery power can be repeatedly recharged at charging stations. The fluctuations in travel time are implemented to reflect a dynamic traffic environment. In conclusion, a large and realistic case study with a road network in the Beijing urban area is conducted to evaluate the model performance and the solution technology and analyze the results.

Dynamical Analysis and FPGA Implementation of a Novel Hyperchaotic System and Its Synchronization Using Adaptive Sliding Mode Control and Genetically Optimized PID Control

Abstract: We announce a new 4D hyperchaotic system with four parameters. The dynamic properties of the proposed hyperchaotic system are studied in detail; the Lyapunov exponents, Kaplan-Yorke dimension, bifurcation, and bicoherence contours of the novel hyperchaotic system are derived. Furthermore, control algorithms are designed for the complete synchronization of the identical hyperchaotic systems with unknown parameters using sliding mode controllers and genetically optimized PID controllers. The stabilities of the controllers and parameter update laws are proved using Lyapunov stability theory. Use of the optimized PID controllers ensures less time of convergence and fast synchronization speed. Finally the proposed novel hyperchaotic system is realized in FPGA.

Corrigendum to “Combining Facility Location and Routing Decisions in Sustainable Urban Freight Distribution under Horizontal Collaboration: How Can Shippers Be Benefited?”

Abstract: This article investigates the potential economic, environmental, and social effects of combining depot location and vehicle routing decisions in urban road freight transportation under horizontal collaboration. We consider a city in which several suppliers decide to joint deliveries to their customers and goods are delivered via intermediate depots. We study a transportation optimization problem from the perspective of sustainability development. This quantitative approach is based on three-objective mathematical model for strategic, tactical, and operational decision-making as a two-echelon location routing problem (2E-LRP). The objectives are to minimize cost and CO2 emissions of the transportation and maximize the created job opportunities. The model was solved with the e-constraint method using extended known instances reflecting the real distribution in urban area to evaluate several goods’ delivery strategies. The obtained results by comparing collaborative and noncollaborative scenarios show that collaboration leads to a reduction in CO2 emissions, transportation cost, used vehicles, and travelled distances in addition to the improvement of the vehicles load rate but collaboration affects negatively social impact. To evaluate the effect of the method used to allocate the total gains to the different partners, we suggest to decision makers a comparison between well-known allocation methods.

Chaotic S-Box: Intertwining Logistic Map and Bacterial Foraging Optimization

Abstract: As the unique nonlinear component of block ciphers, Substitution box (S-box) directly affects the safety of a cryptographic system. It is important and difficult to design strong S-box that simultaneously meets multiple cryptographic criteria such as bijection, nonlinearity, strict avalanche criterion (SAC), bit independence criterion (BIC), differential probability (DP), and linear probability (LP). Though many chaotic S-boxes have been proposed, the cryptographic performance of most of them needs to be further improved. A new chaotic S-box based on the intertwining logistic map and bacterial foraging optimization is designed in this paper. It firstly iterates the intertwining logistic map to construct many S-boxes and then applies a bacterial foraging optimization algorithm to find the optimal S-box. Moreover, bacterial foraging optimization algorithm considers the nonlinearity and differential uniformity as the fitness functions in the optimization process. We experiment that the proposed S-box can effectively resist multiple types of cryptanalysis attacks.

A Multiple Hidden Layers Extreme Learning Machine Method and Its Application

Abstract: Extreme learning machine (ELM) is a rapid learning algorithm of the single-hidden-layer feedforward neural network, which randomly initializes the weights between the input layer and the hidden layer and the bias of hidden layer neurons and finally uses the least-squares method to calculate the weights between the hidden layer and the output layer. This paper proposes a multiple hidden layers ELM (MELM for short) which inherits the characteristics of parameters of the first hidden layer. The parameters of the remaining hidden layers are obtained by introducing a method (make the actual output zero error approach the expected hidden layer output). Based on the MELM algorithm, many experiments on regression and classification show that the MELM can achieve the satisfactory results based on average precision and good generalization performance compared to the two-hidden-layer ELM (TELM), the ELM, and some other multilayer ELM.

An Improved Grey Wolf Optimization Strategy Enhanced SVM and Its Application in Predicting the Second Major

Abstract: In order to develop a new and effective prediction system, the full potential of support vector machine (SVM) was explored by using an improved grey wolf optimization (GWO) strategy in this study. An improved GWO, IGWO, was first proposed to identify the most discriminative features for major prediction. In the proposed approach, particle swarm optimization (PSO) was firstly adopted to generate the diversified initial positions, and then GWO was used to update the current positions of population in the discrete searching space, thus getting the optimal feature subset for the better classification purpose based on SVM. The resultant methodology, IGWO-SVM, is rigorously examined based on the real-life data which includes a series of factors that influence the students’ final decision to choose the specific major. To validate the proposed method, other metaheuristic based SVM methods including GWO based SVM, genetic algorithm based SVM, and particle swarm optimization-based SVM were used for comparison in terms of classification accuracy, AUC (the area under the receiver operating characteristic (ROC) curve), sensitivity, and specificity. The experimental results demonstrate that the proposed approach can be regarded as a promising success with the excellent classification accuracy, AUC, sensitivity, and specificity of 87.36%, 0.8735, 85.37%, and 89.33%, respectively. Promisingly, the proposed methodology might serve as a new candidate of powerful tools for second major selection.

Feature Extraction and Fusion Using Deep Convolutional Neural Networks for Face Detection

Abstract: This paper proposes a method that uses feature fusion to represent images better for face detection after feature extraction by deep convolutional neural network (DCNN). First, with Clarifai net and VGG Net-D (16 layers), we learn features from data, respectively; then we fuse features extracted from the two nets. To obtain more compact feature representation and mitigate computation complexity, we reduce the dimension of the fused features by PCA. Finally, we conduct face classification by SVM classifier for binary classification. In particular, we exploit offset max-pooling to extract features with sliding window densely, which leads to better matches of faces and detection windows; thus the detection result is more accurate. Experimental results show that our method can detect faces with severe occlusion and large variations in pose and scale. In particular, our method achieves 89.24% recall rate on FDDB and 97.19% average precision on AFW.

The Influence of Slip Boundary Condition on Casson Nanofluid Flow over a Stretching Sheet in the Presence of Viscous Dissipation and Chemical Reaction

Abstract: The impacts of multiple slips with viscous dissipation on the boundary layer flow and heat transfer of a non-Newtonian nanofluid over a stretching surface have been investigated numerically. The Casson fluid model is applied to characterize the non-Newtonian fluid behavior. Physical mechanisms responsible for Brownian motion and thermophoresis with chemical reaction are accounted for in the model. The governing nonlinear boundary layer equations through appropriate transformations are reduced into a set of nonlinear ordinary differential equations, which are solved numerically using a shooting method with fourth-order Runge-Kutta integration scheme. Comparisons of the numerical method with the existing results in the literature are made and an excellent agreement is obtained. The heat transfer rate is enhanced with generative chemical reaction and concentration slip parameter, whereas the reverse trend is observed with destructive chemical reaction and thermal slip parameter. It is also noticed that the mass transfer rate is boosted with destructive chemical reaction and thermal slip parameter. Further, the opposite influence is found with generative chemical reaction and concentration slip parameter.

A Deep Learning Prediction Model Based on Extreme-Point Symmetric Mode Decomposition and Cluster Analysis

Abstract: Aiming at the irregularity of nonlinear signal and its predicting difficulty, a deep learning prediction model based on extreme-point symmetric mode decomposition (ESMD) and clustering analysis is proposed. Firstly, the original data is decomposed by ESMD to obtain the finite number of intrinsic mode functions (IMFs) and residuals. Secondly, the fuzzy -means is used to cluster the decomposed components, and then the deep belief network (DBN) is used to predict it. Finally, the reconstructed IMFs and residuals are the final prediction results. Six kinds of prediction models are compared, which are DBN prediction model, EMD-DBN prediction model, EEMD-DBN prediction model, CEEMD-DBN prediction model, ESMD-DBN prediction model, and the proposed model in this paper. The same sunspots time series are predicted with six kinds of prediction models. The experimental results show that the proposed model has better prediction accuracy and smaller error.

An Effective Conversation-Based Botnet Detection Method

Abstract: A botnet is one of the most grievous threats to network security since it can evolve into many attacks, such as Denial-of-Service (DoS), spam, and phishing. However, current detection methods are inefficient to identify unknown botnet. The high-speed network environment makes botnet detection more difficult. To solve these problems, we improve the progress of packet processing technologies such as New Application Programming Interface (NAPI) and zero copy and propose an efficient quasi-real-time intrusion detection system. Our work detects botnet using supervised machine learning approach under the high-speed network environment. Our contributions are summarized as follows: (1) Build a detection framework using PF_RING for sniffing and processing network traces to extract flow features dynamically. (2) Use random forest model to extract promising conversation features. (3) Analyze the performance of different classification algorithms. The proposed method is demonstrated by well-known CTU13 dataset and nonmalicious applications. The experimental results show our conversation-based detection approach can identify botnet with higher accuracy and lower false positive rate than flow-based approach.

Unsteady Micropolar Fluid over a Permeable Curved Stretching Shrinking Surface

Abstract: This work deals with the unsteady micropolar fluid over a permeable curved stretching and shrinking surface. Using similarity transformations, the governing boundary layer equations are transformed into the nonlinear ordinary (similarity) differential equations. The transformed equations are then solved numerically using the shooting method. The effects of the governing parameters on the skin friction and couple stress are illustrated graphically. The results reveal that dual solutions exist for stretching/shrinking surface as well as weak/strong concentration. A comparison with known results from the open literature has been done and it is shown to be in excellent agreement.

On the nanocommunications at THz band in graphene-enabled Wireless Network-on-Chip

Abstract: One of the main challenges towards the growing computation-intensive applications with scalable bandwidth requirement is the deployment of a dense number of on-chip cores within a chip package To this end, this paper investigates the Wireless Network-on-Chip (WiNoC), which is enabled by graphene-based nanoantennas (GNAs) in Terahertz frequency band We first develop a channel model between the GNAs taking into account the practical issues of the propagation medium, such as transmission frequency, operating temperature, ambient pressure, and distance between the GNAs In the Terahertz band, not only dielectric propagation loss but also molecular absorption attenuation (MAA) caused by various molecules and their isotopologues within the chip package constitutes the signal transmission loss We further propose an optimal power allocation to achieve the channel capacity The proposed channel model shows that the MAA significantly degrades the performance at certain frequency ranges compared to the conventional channel model, even when the GNAs are very closely located More specifically, at transmission frequency of 1 THz, the channel capacity of the proposed model is shown to be much lower than that of the conventional model over the whole range of temperature and ambient pressure of up to 268% and 25%, respectively

Mode Choice Model for Public Transport with Categorized Latent Variables

Abstract: Mode choice model for public transport, which integrates structural equation model (SEM) and discrete choice model (DCM) with categorized latent variables, was presented in this paper. Apart from identifying those important latent variables that affect mode choice for public transport, the objective of this study was also to develop an improved disaggregative model that better explains travel behavior of those decision-makers in choosing public transport. After extensive observations, selective latent variable sets which consist of latent variable components were chosen together with explicit variables in formulating the utility functions. Data collected in Chengdu city, China, were used to calibrate and validate the model. Results showed that the impact of fare on mode choice of public transport escalated in the SEM-DCM integrated model compared with the traditional logit model. The goodness of fit for the integrated model with latent variable sets is 0.201 higher than that of the traditional logit model, which proves that latent variables have an obvious impact on mode choice behavior, and the SEM-DCM integrated model has higher accuracy and stronger explanatory ability. The results are especially helpful for public transport operators to achieve higher mode share split by improving the service quality of public transport in terms of providing more convenience and better service environment for public transport users.

Modeling and Identification of Podded Propulsion Unmanned Surface Vehicle and Its Course Control Research

Abstract: The response model of podded propulsion unmanned surface vehicle (USV) is established and identified; then considering the USV has characteristic of high speed, the course controller with fast convergence speed is proposed. The idea of MMG separate modeling is used to establish three-DOF planar motion model of the podded propulsion USV, and then the model is simplified as a response model. Then based on field experiments, the parameters of the response model are obtained by the method of system identification. Unlike ordinary ships, USV has the advantages of fast speed and small size, so the controller needs fast convergence speed and strong robustness. Based on the theory of multimode control, a fast nonsingular terminal sliding mode (FNTSM) course controller is proposed. In order to reduce the chattering of system, disturbance observer is used to compensate the disturbance to reduce the control gain and RBF neural network is applied to approximate the symbolic function. At the same time, fuzzy algorithm is employed to realize the mode soft switching, which avoids the unnecessary chattering when the mode is switched. Finally the rapidity and robustness of the proposed control approach are demonstrated by simulations and comparison studies.

Fuzzy Modeling for Uncertainty Nonlinear Systems with Fuzzy Equations

Abstract: The uncertain nonlinear systems can be modeled with fuzzy equations by incorporating the fuzzy set theory. In this paper, the fuzzy equations are applied as the models for the uncertain nonlinear systems. The nonlinear modeling process is to find the coefficients of the fuzzy equations. We use the neural networks to approximate the coefficients of the fuzzy equations. The approximation theory for crisp models is extended into the fuzzy equation model. The upper bounds of the modeling errors are estimated. Numerical experiments along with comparisons demonstrate the excellent behavior of the proposed method.

Flood Risk Zoning by Using 2D Hydrodynamic Modeling: A Case Study in Jinan City

Abstract: Climate change and rapid urbanization have aggravated the rainstorm flood in Jinan City during the past decades. Jinan City is higher in the south and lower in the north with a steep slope inclined from the south to the north. This results in high-velocity overland flow and deep waterlogging, which poses a tremendous threat to pedestrians and vehicles. Therefore, it is vital to investigate the rainstorm flood and further perform flood risk zoning. This study is carried out in the “Sponge City Construction” pilot area of Jinan City, where the InfoWorks ICM 2D hydrodynamic model is utilized for simulating historical and designed rainfall events. The model is validated with observations, and the causes for errors are analyzed. The simulated water depth and flow velocity are recorded for flood risk zoning. The result shows that the InfoWorks ICM 2D model performed well. The flood risk zoning result shows that rainfalls with larger recurrence intervals generate larger areas of moderate to extreme risk. Meanwhile, the zoning results for the two historical rainfalls show that flood with a higher maximum hourly rainfall intensity is more serious. This study will provide scientific support for the flood control and disaster reduction in Jinan City.

An Improved Traveling-Wave-Based Fault Location Method with Compensating the Dispersion Effect of Traveling Wave in Wavelet Domain

Abstract: The fault generated transient traveling waves are wide band signals which cover the whole frequency range. When the frequency characteristic of line parameters is considered, different frequency components of traveling wave will have different attenuation values and wave velocities, which is defined as the dispersion effect of traveling wave. Because of the dispersion effect, the rise or fall time of the wavefront becomes longer, which decreases the singularity of traveling wave and makes it difficult to determine the arrival time and velocity of traveling wave. Furthermore, the dispersion effect seriously affects the accuracy and reliability of fault location. In this paper, a novel double-ended fault location method has been proposed with compensating the dispersion effect of traveling wave in wavelet domain. From the propagation theory of traveling wave, a correction function is established within a certain limit band to compensate the dispersion effect of traveling wave. Based on the determined arrival time and velocity of traveling wave, the fault distance can be calculated precisely by utilizing the proposed method. The simulation experiments have been carried out in ATP/EMTP software, and simulation results demonstrate that, compared with the traditional traveling-wave fault location methods, the proposed method can significantly improve the accuracy of fault location. Moreover, the proposed method is insensitive to different fault conditions, and it is adaptive to both transposed and untransposed transmission lines well.

Analysis of Drought Characteristics in Xilingol Grassland of Northern China Based on SPEI and Its Impact on Vegetation

Abstract: This research is based on the standardized precipitation evapotranspiration index (SPEI) and normalized difference vegetation index (NDVI) which represent the drought and vegetation condition on land. Take the linear regression method and Pearson correlation analysis to study the spatial and temporal evolution of SPEI and NDVI and the drought effect on vegetation. The results show that (1) during 1961–2015, SPEI values at different time scales showed a downward trend; SPEI-12 has a mutation in 1997 and the SPEI value significantly decreased after this year. (2) During 2000–2015, the annual growing season SPEI has an obvious upward trend in time and the apparent wetting spatially. (3) In the recent 16 years, the growing season NDVI showed an upward trend and more than 80% of the total area’s vegetation increased in Xilingol. (4) Vegetation coverage in Xilingol grew better in humid years and opposite in arid years. SPEI and NDVI had a significant positive correlation; 98% of the region showed positive correlation, indicating that meteorological drought affects vegetation growth more in arid and semiarid region. (5) The effect of drought on vegetation has lag effect, and the responses of different grassland types to different scales of drought were different.

An Exact Solution of MHD Boundary Layer Flow of Dusty Fluid over a Stretching Surface

Abstract: This paper deals with the boundary layer flow of electrically conducting dusty fluid over a stretching surface in the presence of applied magnetic field. The governing partial differential equations of the problem are transformed to nonlinear nondimensional coupled ordinary differential equations using suitable similarity transformations. The problem is now fully specified in terms of characterizing parameters known as fluid particle interaction parameter, magnetic field parameter, and mass concentration of dust particles. An exact analytical solution of the resulting boundary value problem is presented that works for all values of the characterizing parameters. The effects of these parameters on the velocity field and the skin friction coefficient are presented graphically and in the tabular form, respectively. We emphasize that an approximate numerical solution of this problem was available in the literature but no analytical solution was presented before this study.

Implications of a Spatial Multicriteria Decision Analysis for Urban Development in Ulaanbaatar, Mongolia

Abstract: New technology has provided new tools for effective spatial planning. Through the example of locating suitable sites for urbandevelopment in Ulaanbaatar, this paper illustrates how multicriteria de ...

MHD Flow of Micropolar Fluid over an Oscillating Vertical Plate Embedded in Porous Media with Constant Temperature and Concentration

Abstract: The present analysis represents the MHD flow of micropolar fluid past an oscillating infinite vertical plate embedded in porous media. At the plate, free convections are caused due to the differences in temperature and concentration. Therefore, the combined effect of radiative heat and mass transfer is taken into account. Partial differential equations are used in the mathematical formulation of a micropolar fluid. The system of dimensional governing equations is reduced to dimensionless form by means of dimensional analysis. The Laplace transform technique is applied to obtain the exact solutions for velocity, temperature, and concentration. In order to highlight the flow behavior, numerical computation and graphical illustration are carried out. Furthermore, the corresponding skin friction and wall couple stress are calculated.

Unsteady bioconvection squeezing flow in a horizontal channel with chemical reaction and magnetic field effects

Abstract: The time-dependent mixed bioconvection flow of an electrically conducting fluid between two infinite parallel plates in the presence of a magnetic field and a first-order chemical reaction is investigated. The fully coupled nonlinear systems describing the total mass, momentum, thermal energy, mass diffusion, and microorganisms equations are reduced to a set of ordinary differential equations via a set of new similarity transformations. The detailed analysis illustrating the influences of various physical parameters such as the magnetic, squeezing, and chemical reaction parameters and the Schmidt and Prandtl numbers on the distributions of temperature and microorganisms as well as the skin friction and the Nusselt number is presented. The conclusion is drawn that the flow field, temperature, and chemical reaction profiles are significantly influenced by magnetic parameter, heat generation/absorption parameter, and chemical parameter. Some examples of potential applications of such bioconvection could be found in pharmaceutical industry, microfluidic devices, microbial enhanced oil recovery, modeling oil, and gas-bearing sedimentary basins.