Magneto-Acoustic Properties of UCuGe Single Crystal

We report on results of sound velocity and sound-attenuation measurements performed on the antiferromagnetic (T _N =48 K) UCuGe. The measurements have been done on a UCuGe single crystal at different frequencies for longitudinal ultrasound waves propagating along the [001] direction in static (up to 18 T) and pulsed (up to 60 T) magnetic fields applied along the same direction. The temperature dependences of the sound velocity and attenuation display a pronounced anomaly at T _N , which is evidence for a strong magneto-elastic interaction. The pulse-field measurements at 4.2 K show a minimum in the sound velocity followed by a jump-like anomaly at 37 T, and another kink-like anomaly at 48–49 T. These anomalies are due to field-induced spin rearrangements as measured in magnetization studies. In the paramagnetic state (T>T _N ), both acoustic characteristics show large frequency-dependent changes revealing the presence of an unusual relaxation mechanism which might be due to vacancy dynamics.     

The importance of economies of scale,transport costs and demand patterns in optimising hydrogen fuelling infrastructure: An exploration with SHIPMod (Spatial hydrogen infrastructure planning model)

Hydrogen is widely recognised as an important option for future road transportation, but a widespread infrastructure must be developed if the potential for hydrogen is to be achieved. This paper and related appendices which can be downloaded as Supplementary material present a mixed-integer linear programming model (called SHIPMod) that optimises a hydrogen supply chains for scenarios of hydrogen fuel demand in the UK, including the spatial arrangement of carbon capture and storage infrastructure. In addition to presenting a number of improvements on past practice in the literature, the paper focuses attention on the importance of assumptions regarding hydrogen demand. The paper draws on socio-economic data to develop a spatially detailed scenario of possible hydrogen demand. The paper then shows that assumptions about the level and spatial dispersion of hydrogen demand have a significant impact on costs and on the choice of hydrogen production technologies and distribution mechanisms.     

A negative dual rectangle cancellation algorithm for the linear assignment problem

In this paper, we consider three alternative primal models and their corresponding alternative dual models for the linear assignment problem. We then define the concept of Negative Dual Rectangle (NDR) and suggest an algorithm that solves two of these dual problems by repeatedly finding and cancelling NDRs until it yields an optimal solution to the assignment problem. The algorithm is simple, flexible, efficient, and unified. We also introduce the notion of partial zero cover as an interpretation of an NDR. We then introduce some heuristic methods for finding NDRs. We also state and prove a lemma to establish the optimal use of an NDR. Furthermore, we show that on a new class of benchmark instances that is introduced in this paper the running time of our algorithm is highly superior to a well-known pure shortest path algorithm.     

Lagrangian relaxation and constraint generation for allocation and advanced scheduling

Diverse applications in manufacturing, logistics, health care, telecommunications, and computing require that renewable resources be dynamically scheduled to handle distinct classes of job service requests arriving randomly over slotted time. These dynamic stochastic resource scheduling problems are analytically and computationally intractable even when the number of job classes is relatively small. In this paper, we formally introduce two types of problems called allocation and advanced scheduling, and formulate their Markov decision process (MDP) models. We establish that these MDPs are “weakly coupled” and exploit this structural property to develop an approximate dynamic programming method that uses Lagrangian relaxation and constraint generation to efficiently make good scheduling decisions. In fact, our method is presented for a general class of large-scale weakly coupled MDPs that we precisely define. Extensive computational experiments on hundreds of randomly generated test problems reveal that Lagrangian decisions outperform myopic decisions with a statistically significant margin. The relative benefit of Lagrangian decisions is much higher for advanced scheduling than for allocation scheduling.     

A coarse-to-fine deformable contour optimization framework

This paper introduces a novel coarse-to-fine deformable contour optimization framework, which is composed of two main components. The first component uses scale-space and information theories to produce a coarser representation of the input image to be used in a coarse-to-fine optimization scheme. The employment of information theory ensures that maximal image information is propagated to the coarse images and employment of scale spaces provides a mechanism to change the image coarseness locally based on the deformable contour model definition. The second component of this framework uses a novel combination of dynamic programming and gradient descent methods to optimize the contour energy on coarser representations and then use the obtained coarse contour positions in finer optimizations. The motivation in using a combination of dynamic programming and gradient descent method is to take advantage of each method's efficiency and avoid their drawbacks. In order to verify the performance of this framework, we constructed a deformable contour model for the spatiotemporal tracking of closed contours and optimized the model energy under this framework. Experiments on this system performed using synthetic images and real world echocardiographic sequences demonstrated the effectiveness and practicality of this framework.     

Can end-user feedback in social media be trusted for software evolution: Exploring and analyzing fake reviews

End-user feedback in social media platforms, particularly in the app stores, is increasing exponentially with each passing day. Software researchers and vendors started to mine end-user feedback by proposing text analytics methods and tools to extract useful information for software evolution and maintenance. In addition, research shows that positive feedback and high-star app ratings attract more users and increase downloads. However, it emerged in the fake review market, where software vendors started incorporating fake reviews against their corresponding applications to improve overall software ratings. For this purpose, we conducted an exploratory study to understand how end-users register and write fake reviews in the Google Play Store. We curated a research data set containing 68,000 end-user comments from the Google Play Store and a fake review generator, that is, the Testimonial generator (TG). Its purpose is to understand fake reviews on these platforms and identify the common patterns potential end-users and professionals use to report fake reviews by critically analyzing the end-user feedback. We conducted a detailed survey at the University of Science and Technology Bannu, Pakistan, to identify the intelligence and accuracy of crowd-users in manually identifying fake reviews. In addition, we developed a ground truth to be compared with the results obtained from the automated machine and deep learning (M&DL) classifier experiment. In the survey, 512 end-users participated and recorded their responses in identifying fake reviews. Finally, various M&DL classifiers are employed to classify and identify end-user reviews into real and fake to automate the process. Unlike humans, the M&DL classifiers performed well in automatically classifying reviews into real and fake by obtaining much higher accuracy, precision, recall, and f-measures. The accuracy of manually identifying fake reviews by the crowd-users is 44.4%. In contrast, the M&DL classifiers obtained an average accuracy of 96%. The experimental results obtained with various M&DL classifiers are encouraging. It is the first step towards identifying fake reviews in the app store by studying its implications in software and requirements engineering.     

Improved transesterification of waste cooking oil into biodiesel using calcined goat bone as a catalyst

In this study, potassium hydroxide-treated animal bones were employed? as a solid heterogeneous catalyst in transesterification of waste cooking oil. This catalyst was characterized by the Fourier-transform infrared spectroscopy (FTIR), and it displayed high-catalytic activity for biodiesel production. Optimum conditions for biodiesel production were catalyst loading 6.0% (w/w) of oil, methanol/oil molar ratio 9:1, calcination temperature 800°C, reaction temperature 65°C, and reaction time of 5 h, which gave maximum biodiesel yield of 84%. Reusability of the catalyst was also confirmed by repeated use of the same catalyst three times without losing much of its activity. Hence, calcined goat bones were found to be a potentially applicable catalyst for biodiesel production at industrial scale.     

A two phase multi-attribute decision-making approach for new product introduction

This study aims at improving the quality and effectiveness of decision-making in new product introduction. New product development has long been recognized as one of the corporate core functions to be competitive on an increasingly competitive global market. However, developing new products is a process involving risk and uncertainty. In order to solve this stochastic problem, companies need to evaluate their new product initiatives carefully and make accurate decisions. For this reason, a systematic decision process for selecting more rational new product ideas is proposed. Basically, two stages of decision-making are described: the identification of nondominated new product candidates and the selection of the best new product idea. These stages are composed of an integrated approach based on a fuzzy heuristic multi-attribute utility method and a hierarchical fuzzy TOPSIS method. Finally, an application is given to demonstrate the potential of the methodology.