Development of Perez-Du Mortier Calibration Algorithm for Ground-Based Aerosol Optical Depth Measurement with Validation using SMARTS Model

Aerosols are small particles suspended in air that have wide varying spatial and temporal distributions. The concentration of aerosol in total columnar atmosphere is normally measured using aerosol optical depth (AOD). In long-term monitoring stations, accurate AOD retrieval is often difficult due to the lack of frequent calibration. To overcome this problem, a near-sea-level Langley calibration algorithm is developed using the combination of clear-sky detection model and statistical filter. It attempts to produce a dataset that consists of only homogenous and stable atmospheric condition for the Langley calibration purposes. In this paper, a radiance-based validation method is performed to further investigate the feasibility and consistency of the proposed algorithm at different location, day, and time. The algorithm is validated using SMARTS model based n DNI value. The overall results confirmed that the proposed calibration algorithm feasible and consistent for measurements taken at different sites and weather conditions.

Issues in the User Interface Design of a Content Rich Vocational Training Application for Digitally Illiterate Users

This paper discusses our preliminary experiences in the design of a user interface of a computerized content-rich vocational training courseware meant for users with little or no computer experience. In targeting a growing population with limited access to skills training of any sort, we faced numerous challenges, including language and cultural differences, resource limits, gender boundaries and, in many cases, the simple lack of trainee motivation. With the size of the unskilled population increasing much more rapidly than the numbers of sufficiently skilled teachers, there is little choice but to develop teaching techniques that will take advantage of emerging computer-based training technologies. However, in striving to serve populations with minimal computer literacy, one must carefully design the user interface to accommodate their cultural, social, educational, motivational and other differences. Our work, which uses computer based and haptic simulation technologies to deliver training to these populations, has provided some useful insights on potential user interface design approaches.

Septic B-Spline Collocation Method for Numerical Solution of the Kuramoto-Sivashinsky Equation

In this paper the Kuramoto-Sivashinsky equation is solved numerically by collocation method. The solution is approximated as a linear combination of septic B-spline functions. Applying the Von-Neumann stability analysis technique, we show that the method is unconditionally stable. The method is applied on some test examples, and the numerical results have been compared with the exact solutions. The global relative error and L∞ in the solutions show the efficiency of the method computationally.

Absolute Cross Sections of Multi-Photon Ionization of Xenon by the Comparison with Process of its Electron-Impact Ionization

Comparison of electron- and photon-impact processes as a method for determination of photo-ionization cross sections is described, discussed and shown to have many attractive features.

Limit State of Trapezoidal Metal Sheets Exposed to Concentrated Load

In most industrial compounds are used trapezoidal metal sheets like a roof decks. These trapezoidal metal sheets are exposed by concentrated loads, usually by service loads arise from installation of air distribution, sanitary distribution, sprinkler system or wiring installation. In objects of public facilities (like shopping centre, tennis hall, etc.) they can be used for hanging advertising posters etc, too. These systems work as “building kit”. These anchoring systems are represented by clamps in shape of “V”. This paper is occupy with recapitulation of installation systems available in trade with focus on load-bearing capacity specified by producer and on possible methods, how exactly define load bearing capacity of trapezoidal sheet loaded by concentrated load. The load bearing capacity was verified at experimental samples to determine real behavior of trapezoidal metal sheets exposed to concentrated loads.

Creative Teaching of New Product Development to Operations Managers

New Product Development (NPD) has got its roots on an Engineering background. Thus, one might wonder about the interest, opportunity, contents and delivery process, if students from soft sciences were involved. This paper addressed «What to teach?» and «How to do it?», as the preliminary research questions that originated the introduced propositions. The curriculum-developer model that was purposefully chosen to adapt the coursebook by pursuing macro/micro strategies was found significant by an exploratory qualitative case study. Moreover, learning was developed and value created by implementing the institutional curriculum through a creative, hands-on, experiencing, problem-solving, problem-based but organized teamwork approach. Product design of an orange squeezer complying with ill-defined requirements, including drafts, sketches, prototypes, CAD simulations and a business plan, plus a website, written reports and presentations were the deliverables that confirmed an innovative contribution towards research and practice of teaching and learning of engineering subjects to non-specialist operations managers candidates.

Proximal Parallel Alternating Direction Method for Monotone Structured Variational Inequalities

In this paper, we focus on the alternating direction method, which is one of the most effective methods for solving structured variational inequalities(VI). In fact, we propose a proximal parallel alternating direction method which only needs to solve two strongly monotone sub-VI problems at each iteration. Convergence of the new method is proved under mild assumptions. We also present some preliminary numerical results, which indicate that the new method is quite efficient.

Investments Attractiveness via Combinatorial Optimization Ranking

The paper proposes an approach to ranking a set of potential countries to invest taking into account the investor point of view about importance of different economic indicators. For the goal, a ranking algorithm that contributes to rational decision making is proposed. The described algorithm is based on combinatorial optimization modeling and repeated multi-criteria tasks solution. The final result is list of countries ranked in respect of investor preferences about importance of economic indicators for investment attractiveness. Different scenarios are simulated conforming to different investors preferences. A numerical example with real dataset of indicators is solved. The numerical testing shows the applicability of the described algorithm. The proposed approach can be used with any sets of indicators as ranking criteria reflecting different points of view of investors. 

Revision of Genus Polygonum L. s.l. in Flora of Armenia

The account of genus Polygonum L. in "Flora of Armenia" was made more than five decades ago. After that many expeditions have been carried out in different regions of Armenia and a huge herbarium material has been collected. The genus included 5 sections with 20 species. Since then many authors accepted the sections as separate genera on the basis of anatomical, morphological, palynological and molecular data. According to the above mentioned it became clear, that the taxonomy of Armenian representatives of Polygonum s. l. also needs revision. New literature data and our investigations of live and herbarium material (ERE, LE) with specification of the morphological characters, distribution, ecology, flowering and fruiting terms brought us to conclusion, that genus Polygonum s. l. has to be split into 5 different genera (Aconogonon (Meisn.) Reichenb., Bistorta (L.) Scop., Fallopia Adans., Persicaria Mill., Polygonum L. s. s.). The number of species has been reduced to 16 species. For each genus new determination keys has been created. 

Exponential Stability of Periodic Solutions in Inertial Neural Networks with Unbounded Delay

In this paper, the exponential stability of periodic solutions in inertial neural networks with unbounded delay are investigated. First, using variable substitution the system is transformed to first order differential equation. Second, by the fixed-point theorem and constructing suitable Lyapunov function, some sufficient conditions guaranteeing the existence and exponential stability of periodic solutions of the system are obtained. Finally, two examples are given to illustrate the effectiveness of the results.

Spline Basis Neural Network Algorithm for Numerical Integration

A new basis function neural network algorithm is proposed for numerical integration. The main idea is to construct neural network model based on spline basis functions, which is used to approximate the integrand by training neural network weights. The convergence theorem of the neural network algorithm, the theorem for numerical integration and one corollary are presented and proved. The numerical examples, compared with other methods, show that the algorithm is effective and has the characteristics such as high precision and the integrand not required known. Thus, the algorithm presented in this paper can be widely applied in many engineering fields.

Automatic Detection of Breast Tumors in Sonoelastographic Images Using DWT

Breast Cancer is the most common malignancy in women and the second leading cause of death for women all over the world. Earlier the detection of cancer, better the treatment. The diagnosis and treatment of the cancer rely on segmentation of Sonoelastographic images. Texture features has not considered for Sonoelastographic segmentation. Sonoelastographic images of 15 patients containing both benign and malignant tumorsare considered for experimentation.The images are enhanced to remove noise in order to improve contrast and emphasize tumor boundary. It is then decomposed into sub-bands using single level Daubechies wavelets varying from single co-efficient to six coefficients. The Grey Level Co-occurrence Matrix (GLCM), Local Binary Pattern (LBP) features are extracted and then selected by ranking it using Sequential Floating Forward Selection (SFFS) technique from each sub-band. The resultant images undergo K-Means clustering and then few post-processing steps to remove the false spots. The tumor boundary is detected from the segmented image. It is proposed that Local Binary Pattern (LBP) from the vertical coefficients of Daubechies wavelet with two coefficients is best suited for segmentation of Sonoelastographic breast images among the wavelet members using one to six coefficients for decomposition. The results are also quantified with the help of an expert radiologist. The proposed work can be used for further diagnostic process to decide if the segmented tumor is benign or malignant.

Bifurcations for a FitzHugh-Nagumo Model with Time Delays

In this paper, a FitzHugh-Nagumo model with time delays is investigated. The linear stability of the equilibrium and the existence of Hopf bifurcation with delay τ is investigated. By applying Nyquist criterion, the length of delay is estimated for which stability continues to hold. Numerical simulations for justifying the theoretical results are illustrated. Finally, main conclusions are given.

Coordinated Design of PSS and STATCOM for Power System Stability Improvement Using Bacteria Foraging Algorithm

This paper presents the coordinated controller design of static synchronous compensator (STATCOM) and power system stabilizers (PSSs) for power system stability improvement. Coordinated design problem of STATCOM-based controller with multiple PSSs is formulated as an optimization problem and optimal controller parameters are obtained using bacteria foraging optimization algorithm. By minimizing the proposed objective function, in which the speed deviations between generators are involved; stability performance of the system is improved. The nonlinear simulation results show that coordinated design of STATCOM-based controller and PSSs improve greatly the system damping oscillations and consequently stability improvement.

Green-Reduction of Covalently Functionalized Graphene Oxide with Varying Stoichiometry

Graphene-based materials were prepared by chemical reduction of covalently functionalized graphene oxide with environmentally friendly agents. Two varying stoichiometry of graphene oxide (GO) induced by using different chemical preparation conditions, further covalent functionalization of the GO materials with 1-ethyl-3-(3-dimethylaminopropyl) carbodiimide hydrochloride / N-hydroxysuccinimide and ascorbic acid and sodium bisulfite as reducing agents were exploited in order to obtain controllable properties of the final solution-based graphene materials. The obtained materials were characterized by thermo-gravimetric analysis, Fourier transform infrared and Raman spectroscopy and X-ray diffraction. The results showed successful functionalization of the GO materials, while a comparison of the deoxygenation efficiency of the two-type functionalized graphene oxide suspensions by the different reducing agents has been made, revealing the strong dependence of their properties on the GO structure and reducing agents.

An Analytical Method to Analysis of Foam Drainage Problem

In this study, a new reliable technique use to handle the foam drainage equation. This new method is resulted from VIM by a simple modification that is Reconstruction of Variational Iteration Method (RVIM). The drainage of liquid foams involves the interplay of gravity, surface tension, and viscous forces. Foaming occurs in many distillation and absorption processes. Results are compared with those of Adomian’s decomposition method (ADM).The comparisons show that the Reconstruction of Variational Iteration Method is very effective and overcome the difficulty of traditional methods and quite accurate to systems of non-linear partial differential equations.

An Educational Data Mining System for Advising Higher Education Students

Educational  data mining  is  a  specific  data   mining field applied to data originating from educational environments, it relies on different  approaches to discover hidden knowledge  from  the  available   data. Among these approaches are   machine   learning techniques which are used to build a system that acquires learning from previous data. Machine learning can be applied to solve different regression, classification, clustering and optimization problems. In  our  research, we propose  a “Student  Advisory  Framework” that  utilizes  classification  and  clustering  to  build  an  intelligent system. This system can be used to provide pieces of consultations to a first year  university  student to  pursue a  certain   education   track   where  he/she  will  likely  succeed  in, aiming  to  decrease   the  high  rate   of  academic  failure   among these  students.  A real case study  in Cairo  Higher  Institute  for Engineering, Computer  Science  and  Management  is  presented using  real  dataset   collected  from  2000−2012.The dataset has two main components: pre-higher education dataset and first year courses results dataset. Results have proved the efficiency of the suggested framework.

Heat Transfer Characteristics and Fluid Flow past Staggered Flat-Tube Bank Using CFD

A computational fluid dynamic (CFD-Fluent 6.2) for two-dimensional fluid flow is applied to predict the pressure drop and heat transfer characteristics of laminar and turbulent flow past staggered flat-tube bank. Effect of aspect ratio ((H/D)/(L/D)) on pressure drop, temperature, and velocity contour for laminar and turbulent flow over staggered flat-tube bank is studied. The theoretical results of the present models are compared with previously published experimental data of different authors. Satisfactory agreement is demonstrated. Also, the comparison between the present study and others analytical methods for the Re number with Nu number is done. The results show as the Reynolds number increases the maximum velocity in the passage between the upper and lower tubes increases. The comparisons show a fair agreement especially in the turbulent flow region. The good agreement of the data of this work with these recommended analytical methods validates the current study.

Determination and Comparison of Fabric Pills Distribution Using Image Processing and Spatial Data Analysis Tools

This work deals with the determination and comparison of pill patterns in 2 sets of fabric samples which differ in way of pill creation. The first set contains fabric samples with the pills created by simulation on a Martindale abrasion machine, while pills in the second set originated during normal wearing and maintenance. The goal of the study is to determine whether the pattern of the fabric pills created by simulation is the same as the pattern of naturally occurring pills. The system of determination and comparison of the pills is based on image processing and spatial data analysis tools. Firstly, 3D reconstruction of the fabric surfaces with the pills is realized with using a gradient fields method. The gradient fields method creates a 3D fabric surface from a set of 4 images. Thereafter, the pills are detected in 3D fabric surfaces using image-processing tools in the MATLAB software. Determination and comparison of the pills patterns of two sets of fabric samples is based on spatial data analysis using tools in R software.

Changes in EEG and HRV during Event-Related Attention

Determination of attentional status is important because working performance and an unexpected accident is highly related with the attention. The autonomic nervous and the central nervous systems can reflect the changes in person’s attentional status. Reduced number of suitable pysiological parameters among autonomic and central nervous systems related signal parameters will be critical in optimum design of attentional devices. In this paper, we analyze the EEG (Electroencephalography) and HRV (Heart Rate Variability) signals to demonstrate the effective relation with brain signal and cardiovascular signal during event-related attention, which will be later used in selecting the minimum set of attentional parameters. Time and frequency domain parameters from HRV signal and frequency domain parameters from EEG signal are used as input to the optimum feature parameters selector.