Analysis and forecasting of IPO underpricing

Probability of withdrawal is a feature of initial public offering (IPOs), which can be an important parameter in decisions of investors and issuers. Considering the probability of offering withdrawal facilitates more precise estimation of underpricing. In this paper, the effective factors on probability of IPO withdrawal and underpricing in Tehran Stock Exchange have been characterized using regression, and then neural network is applied to estimate the probability of IPO withdrawal and underpricing. To evaluate the performance of our applied method, fuzzy regression is employed and compared with neural network. According to the obtained empirical results, neural network demonstrates better accuracy than fuzzy regression. The results indicate that there is a meaningful relationship between underpricing and probability of withdrawal, and the probability of IPO withdrawal plays an important role in precise evaluation of underpricing.     

Using hash tables to manage the time-storage complexity in a point location problem: Application to explicit model predictive control

The online computational burden of linear model predictive control (MPC) can be moved offline by using multi-parametric programming, so-called explicit MPC. The solution to the explicit MPC problem is a piecewise affine (PWA) state feedback function defined over a polyhedral subdivision of the set of feasible states. The online evaluation of such a control law needs to determine the polyhedral region in which the current state lies. This procedure is called point location; its computational complexity is challenging, and determines the minimum possible sampling time of the system. A new flexible algorithm is proposed which enables the designer to trade off between time and storage complexities. Utilizing the concept of hash tables and the associated hash functions, the proposed method solves an aggregated point location problem that overcomes prohibitive complexity growth with the number of polyhedral regions, while the storage–processing trade-off can be optimized via scaling parameters. The flexibility and power of this approach is supported by several numerical examples.     

Reserve estimation of central part of Choghart north anomaly iron ore deposit through ordinary kriging method

This paper is devoted to application of ordinary kriging method in Choghart north anomaly iron ore deposit in Yazd province, Iran. In order to estimate the deposit, 2329 input data gained from 26 boreholes were used. Fe grade was selected as the major regional variable on which the present research has focused. All of the available data were changed to 12.5 m composites so that statistical regularization could be reached. Studies indicated that iron grade input data had single-population characteristics. To carry out ordinary kriging, a spherical model was fitted over empirical variogram. Then the model was verified through cross validation method and proved to be valid with a coherence coefficient of 0.773 between the estimated and real data. Plotting the empirical variogram in different directions showed no geometric anisotropy for the deposit. To estimate the Iron grade, ordinary kriging method was used according to which, all of the exploitable blocks with dimensions 20 m × 20 m × 12.5 m were block estimated within the estimation space. Finally tonnage-grade curve has been drawn and reserve classified into measured, indicated and inferred.     

Sequence-dependent group scheduling problem on unrelated-parallel machines

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

Deep features-based speech emotion recognition for smart affective services

Emotion recognition from speech signals is an interesting research with several applications like smart healthcare, autonomous voice response systems, assessing situational seriousness by caller affective state analysis in emergency centers, and other smart affective services. In this paper, we present a study of speech emotion recognition based on the features extracted from spectrograms using a deep convolutional neural network (CNN) with rectangular kernels. Typically, CNNs have square shaped kernels and pooling operators at various layers, which are suited for 2D image data. However, in case of spectrograms, the information is encoded in a slightly different manner. Time is represented along the x-axis and y-axis shows frequency of the speech signal, whereas, the amplitude is indicated by the intensity value in the spectrogram at a particular position. To analyze speech through spectrograms, we propose rectangular kernels of varying shapes and sizes, along with max pooling in rectangular neighborhoods, to extract discriminative features. The proposed scheme effectively learns discriminative features from speech spectrograms and performs better than many state-of-the-art techniques when evaluated its performance on Emo-DB and Korean speech dataset.

     

A Study of Web Services Projects in the Financial Services Industry

This study examines the significance of technological, methodological, and business factors in contributing to the success of initial Web Services projects. Focusing on four case studies from the financial services sector, the authors' findings suggest that a strong focus on business factors is associated with successful Web Services strategies.     

Submicron nanoporous polyacrylamide beads with tunable size for verapamil imprinting

Submicron sized polyacrylamide particles were prepared via modified precipitation polymerization method. Experimental design based on Taguchi approach was employed to study the influence of the polymerization composition including monomer (acrylamide), crosslinker (methylenebisacrylamide), initiator (azobisisobutyronitrile), and modifier (polyvinylpyrrolidone, K-30), on the size and morphology of the particles. Varying the polymerization composition, submicron-particles with sizes ranging between 100 and 600 nm were achieved. In all the cases, polydispersity index (PDI) of the particle size was found to be almost 1 indicating uniformity of the particle size. The concentration of crosslinker was found to be the most influential parameter on the particles size and the modifier concentration as an extra tunable parameter was believed to affect the nucleation mechanism and the viscosity of the medium to help controlling the particle size. To validate the optimization, particles with a preset diameter, i.e., 500 nm, were synthesized based on the composition predicted by the mathematical correlation. The polymer with the preset particle size was also imprinted with verapamil and characterized by FTIR, DSC, SEM, physisorption, elemental analysis, swelling, and batch rebinding experiments. The verapamil imprinted polymers bearing nano-cavities exhibited high affinity with imprinting factor 2.17 towards the target molecule. © 2011 Wiley Periodicals, Inc. J Appl Polym Sci, 2012     

Analytical overview of DNA interaction with Morin and its metal complexes

Flavonoids play an important role in pharmaceutical and biomedical sciences. Various flavonoids, especially Morin, have potential to interact with DNA. Their prospective clinical and pharmacological utilities have attracted researchers?? attention in last two decades. Due to their interaction with DNA and their anticancer effects, a lot of investigations have been carried out all over the world. In this paper, we will review the properties of Morin and its different metal complexes as well as their interactions with DNA that have been reported by several research groups. Also, further details have been provided about binding mechanisms of these materials with DNA using analytical techniques such as UV?CVisible and fluorescence spectroscopies, viscosity and voltammetry. Structural analysis showed Morin binds to DNA through non-intercalation mode, but its metal complexes interact with DNA via intercalation mode and binding mode of Morin, and its metal complexes with DNA will be helpful in the determination of anticancer drugs binding mechanism to DNA. Therefore, it will be useful in new drug designs.