(Approximate) Uncertain Skylines

Given a set of points with uncertain locations, we consider the problem of computing the probability of each point lying on the skyline, that is, the probability that it is not dominated by any other input point. If each point’s uncertainty is described as a probability distribution over a discrete set of locations, we improve the best known exact solution. We also suggest why we believe our solution might be optimal. Next, we describe simple, near-linear time approximation algorithms for computing the probability of each point lying on the skyline. In addition, some of our methods can be adapted to construct data structures that can efficiently determine the probability of a query point lying on the skyline.     

Real-Time Global Illumination for VR Applications

Real-time global illumination in VR systems enhances scene realism by incorporating soft shadows, reflections of objects in the scene, and color bleeding. The Virtual Light Field (VLF) method enables real-time global illumination rendering in VR. The VLF has been integrated with the Extreme VR system for real-time GPU-based rendering in a Cave Automatic Virtual Environment.     

Optimal job sequence determination and operation machine allocation in flexible manufacturing systems: an approach using adaptive hierarchical ant colony algorithm

In this paper, an Adaptive Hierarchical Ant Colony Optimization (AHACO) has been proposed to resolve the traditional machine loading problem in Flexible Manufacturing Systems (FMS). Machine loading is one of the most important issues that is interlinked with the efficiency and utilization of FMS. The machine loading problem is formulated in order to minimize the system unbalance and maximize the throughput, considering the job sequencing, optional machines and technological constraints. The performance of proposed AHACO has been tested over a number of benchmark problems taken from the literature. Computational results indicate that the proposed algorithm is more effective and produces promising results as compared to the existing solution methodologies in the literature. The evaluation and comparison of system efficiency and system utilization justifies the supremacy of the algorithm. Further, results obtained from the proposed algorithm have been compared with well known random search algorithm viz. genetic algorithm, simulated annealing, artificial Immune system, simple ant colony optimization, tabu search etc. In addition, the algorithm has been tested over a randomly generated problem set of varying complexities; the results validate the robustness and scalability of the algorithm utilizing the concepts of ‘heuristic gap’ and ANOVA analysis.     

Change point detection for compositional multivariate data

Change point detection algorithms have numerous applications in areas of medical condition monitoring, fault detection in industrial processes, human activity analysis, climate change detection, and speech recognition. We consider the problem of change point detection on compositional multivariate data (each sample is a probability mass function), which is a practically important sub-class of general multivariate data. While the problem of change-point detection is well studied in univariate setting, and there are few viable implementations for a general multivariate data, the existing methods do not perform well on compositional data. In this paper, we propose a parametric approach for change point detection in compositional data. Moreover, using simple transformations on data, we extend our approach to handle any general multivariate data. Experimentally, we show that our method performs significantly better on compositional data and is competitive on general data compared to the available state of the art implementations.

     

Biosynthesis of zinc oxide nanoparticles by petals extract of Rosa indica L., its formulation as nail paint and evaluation of antifungal activity against fungi causing onychomycosis

Aim: The authors report the biological synthesis of zinc oxide nanoparticles (ZnO‐NPs) from the petals extract of Rosa indica L. (rose). Its efficacy was evaluated against two dermatophytes: namely: Trichophyton mentagrophytes and Microsporum canis which cause onychomycosis. The activity of antibiotics against the tested dermatophytes was enhanced, when evaluated in combination with ZnO‐NPs. Methods and results: The synthesised ZnO‐NPs were preliminary detected by using ultraviolet UV visible spectroscopy, which showed specific absorbance. The ZnO‐NPs were further characterised by nanoparticle tracking analysis (NTA), Fourier transform infrared spectroscopy (FTIR), transmission electron microscopy (TEM), X‐ray diffraction and Zetasizer. Moreover, nanoparticles containing nail paint (nanopaint) was formulated and its antifungal activity was also assessed against T. mentagrophytes and M. canis. ZnO‐NPs and formulated nanopaint containing ZnO‐NPs, both showed significant antifungal activity. The maximum activity was noted against M. canis and lesser against T. mentagrophytes. Minimum inhibitory concentration of ZnO‐NPs was also determined against the dermatophytes causing onychomycosis infection. Conclusion: ZnO‐NPs can be utilised as a potential antifungal agent for the treatment of onychomycosis after more experimental trials.Inspec keywords: diseases, zinc compounds, nanoparticles, nanofabrication, antibacterial activity, microorganisms, nanomedicine, ultraviolet spectra, visible spectra, Fourier transform infrared spectra, transmission electron microscopy, X‐ray diffraction, biomedical materials, patient treatmentOther keywords: zinc oxide nanoparticle biosynthesis, Rosa indica L petals extract, nail paint, antifungal activity evaluation, dermatophyte, Trichophyton mentagrophytes, Microsporum canis, antibiotics activity, ultraviolet‐visible spectroscopy, nanoparticle tracking analysis, Fourier transform infrared spectroscopy, transmission electron microscopy, X‐ray diffraction, zetasizer, antifungal agent, onychomycosis treatment     

VMI versus information sharing: an analysis under static uncertainty strategy with fill rate constraints

A vendor-managed inventory (VMI) relationship between a downstream retailer and an upstream vendor consists of two distinct components: (i) information sharing (IS) and (ii) a shift in decision-making responsibility. This study compares these two components of VMI in a two-stage serial supply chain based on the ‘static uncertainty’ strategy under dynamic and random demand with fill rate constraints. Numerical experiments are conducted using analytical models to identify the conditions where the incremental value of VMI over IS is significant. The results provide guidelines relevant to academia and supply chain practitioners in taking VMI adoption decision above and beyond IS according to their specific business environment.     

Experimental deformation analysis of an adhesively bonded multi-material joint for marine applications

The strength and deformation of full-scale adhesively bonded multi-material joints is studied in this paper. Four joints with a thick layer of methyl methacrylate adhesive (MMA) have been manufactured in shipyard conditions. In two specimens, cracks have been introduced at steel–adhesive and composite–adhesive interfaces. One cracked and one un-cracked specimen were subjected to quasi-static tensile testing; the two remaining specimens were stepwise loaded/unloaded with increasing load until failure. The strain in the adhesive layers was measured with digital image correlation (DIC). This showed a predominant shear deformation and dissimilar shear strain patterns for different bond lines. Fibre Bragg (FBG) sensors were used to monitor strains at steel and composite constituents and to detect the onset and evolution of damage in the un-cracked specimen. Strains measured by FBG sensors correspond well with DIC results at nearby regions. All specimens failed by delamination of the composite panel near the composite–adhesive interface.     

A hybrid approach to asset allocation with simultaneous consideration of suitability and optimality

In this paper, we develop mathematical models for simultaneous consideration of suitability and optimality in asset allocation. We use a hybrid approach that combines behavior survey, cluster analysis, analytical hierarchy process and fuzzy mathematical programming.     

Fusion of probabilistic A* algorithm and fuzzy inference system for robotic path planning

Robotic Path planning is one of the most studied problems in the field of robotics. The problem has been solved using numerous statistical, soft computing and other approaches. In this paper we solve the problem of robotic path planning using a combination of A* algorithm and Fuzzy Inference. The A* algorithm does the higher level planning by working on a lower detail map. The algorithm finds the shortest path at the same time generating the result in a finite time. The A* algorithm is used on a probability based map. The lower level planning is done by the Fuzzy Inference System (FIS). The FIS works on the detailed graph where the occurrence of obstacles is precisely known. The FIS generates smoother paths catering to the non-holonomic constraints. The results of A* algorithm serve as a guide for FIS planner. The FIS system was initially generated using heuristic rules. Once this model was ready, the fuzzy parameters were optimized using a Genetic Algorithm. Three sample problems were created and the quality of solutions generated by FIS was used as the fitness function of the GA. The GA tried to optimize the distance from the closest obstacle, total path length and the sharpest turn at any time in the journey of the robot. The resulting FIS was easily able to plan the path of the robot. We tested the algorithm on various complex and simple paths. All paths generated were optimal in terms of path length and smoothness. The robot was easily able to escape a variety of obstacles and reach the goal in an optimal manner.     

Balancing task allocation in multi-robot systems using K-means clustering and auction based mechanisms

This paper aims to solve the balanced multi-robot task allocation problem. Multi-robot systems are becoming more and more significant in industrial, commercial and scientific applications. Effectively allocating tasks to multi-robots i.e. utilizing all robots in a cost effective manner becomes a tedious process. The current attempts made by the researchers concentrate only on minimizing the distance between the robots and the tasks, and not much importance is given to the balancing of work loads among robots. It is also found from the literature that the multi-robot system is analogous to Multiple Travelling Salesman Problem (MTSP). This paper attempts to develop mechanism to address the above two issues with objective of minimizing the distance travelled by ‘m’ robots and balancing the work load between ‘m’ robots equally. The proposed approach has two fold, first develops a mathematical model for balanced multi-robot task allocation problem, and secondly proposes a methodology to solve the model in three stages. Stage I groups the ‘N’ tasks into ‘n’ clusters of tasks using K-means clustering technique with the objective of minimizing the distance between the tasks, stage II calculates the travel cost of robot and clusters combination, stage III allocates the robot to the clusters in order to utilise all robot in a cost effective manner.