Classification of proteins multiple-labelled and single-labelled with protein functional classes

Advances in high-throughput genome sequencing technology have led to an explosion in the amount of sequence data that are available. The determination of protein function using experimental techniques is time-consuming and expensive; the use of machine-learning techniques rapidly to assess protein function may be useful in streamlining this process. The problem of assigning functional classes to proteins is complicated by the fact that a single protein can participate in several different pathways and thus can have multiple functions. We have developed a tree-based classifier that is capable of handling multiple-labelled data and gaining an insight into the multi-functional nature of proteins. We call the resulting tree a recursive maximum contrast tree (RMCT) and the resulting classifier a multiple-labelled instance classifier (MLIC). We investigate the synergy of machine-learning-based ensemble methods and physiochemical-based feature augments. We test our algorithm on protein phylogenetic profiles generated from 60 completely sequenced genomes and we compare our results with those achieved by algorithms such as support vector machines and decision trees.     

The State of the Art in HDR Deghosting: A Survey and Evaluation

Obtaining a high quality high dynamic range (HDR) image in the presence of camera and object movement has been a long‐standing challenge. Many methods, known as HDR deghosting algorithms, have been developed over the past ten years to undertake this challenge. Each of these algorithms approaches the deghosting problem from a different perspective, providing solutions with different degrees of complexity, solutions that range from rudimentary heuristics to advanced computer vision techniques. The proposed solutions generally differ in two ways: (1) how to detect ghost regions and (2) what to do to eliminate ghosts. Some algorithms choose to completely discard moving objects giving rise to HDR images which only contain the static regions. Some other algorithms try to find the best image to use for each dynamic region. Yet others try to register moving objects from different images in the spirit of maximizing dynamic range in dynamic regions. Furthermore, each algorithm may introduce different types of artifacts as they aim to eliminate ghosts. These artifacts may come in the form of noise, broken objects, under‐ and over‐exposed regions, and residual ghosting. Given the high volume of studies conducted in this field over the recent years, a comprehensive survey of the state of the art is required. Thus, the first goal of this paper is to provide this survey. Secondly, the large number of algorithms brings about the need to classify them. Thus the second goal of this paper is to propose a taxonomy of deghosting algorithms which can be used to group existing and future algorithms into meaningful classes. Thirdly, the existence of a large number of algorithms brings about the need to evaluate their effectiveness, as each new algorithm claims to outperform its precedents. Therefore, the last goal of this paper is to share the results of a subjective experiment which aims to evaluate various state‐of‐the‐art deghosting algorithms.     

Pricing decisions in a strategic single retailer/dual suppliers setting under order size constraints

In this paper, we study a duopolistic market of suppliers competing for the business of a retailer. The retailer sets the order cycle and quantities from each supplier to minimize its annual costs. Different from other studies in the literature, our work simultaneously considers the order size restriction and the benefit of order consolidation, and shows non-trivial pricing behaviour of the suppliers under different settings. Under asymmetric information setting, we formulate the pricing problem of the preferred supplier as a non-linear programming problem and use Karush–Kuhn–Tucker conditions to find the optimal solution. In general, unless the preferred supplier has high-order size limit, it prefers sharing the market with its competitor when retailer’s demand, benefit of order consolidation or fixed cost of ordering from the preferred supplier is high. We model the symmetric information setting as a two-agent non-zero sum pricing game and establish the equilibrium conditions. We show that a supplier might set a ‘threshold price’ to capture the entire market if its per unit fixed ordering cost is sufficiently small. Finally, we prove that there exists a joint-order Nash equilibrium only if the suppliers set identical prices low enough to make the retailer place full-size orders from both.     

Fatigue limit prediction and analysis of nano-structured AISI 304 steel by severe shot peening via ANN

AISI 304 stainless steel is very widely used for industrial applications due to its good integrated performance and corrosion resistance. However, shot peening (SP) is known as one of the effectual surface treatments processes to provide superior properties in metallic materials. In the present study, a comprehensive study on SP of AISI 304 steel including 42 different SP treatments with a wide range of Almen intensities of 14–36 A and various coverage of 100–2000% was carried out. Varieties of experiments were accomplished for the investigation of the microstructure, grain size, surface topography, hardness and residual stresses as well as axial fatigue behavior. After experimental investigations, artificial neural networks modeling was carried out for parametric analysis and optimization. The results indicated that, treated specimens with higher severity had more desirable properties and performances.