Dynamics of Plug Formation in a Circular Cylinder Under Low Bond Number Conditions: Experiment and Simulation

The goal of the current study is to investigate the dynamics of two phase interface under a low Bond number condition. Silicone oil is injected into a cylinder under a Bond number of about 0.47 via a side tube forming a T-junction with the former. The time evolution of the interface of silicon oil in a cylinder is captured using a high speed camera. The volume at which the plug is formed is then determined using an image processing tool to analyze the captured images. A numerical simulation is carried out where fluid is injected into a cylinder, under a less than unity Bond number condition, via a side tube. Numerical and experimental results are then compared.     

A superhydrophilic titanium implant functionalized by ozone gas modulates bone marrow cell and macrophage responses

Bone-forming cells and M? play key roles in bone tissue repair. In this study, we prepared a superhydrophilic titanium implant functionalized by ozone gas to modulate osteoconductivity and inhibit inflammatory response towards titanium implants. After 24 h of ozone gas treatment, the water contact angle of the titanium surface became zero. XPS analysis revealed that hydroxyl groups were greatly increased, but carbon contaminants were largely decreased 24 h after ozone gas functionalization. Also, ozone gas functionalization did not alter titanium surface topography. Superhydrophilic titanium (O₃–Ti) largely increased the aspect ratio, size and perimeter of cells when compared with untreated titanium (unTi). In addition, O₃–Ti facilitated rat bone marrow derived MSCs differentiation and mineralization evidenced by greater ALP activity and bone-like nodule formation. Interestingly, O₃–Ti did not affect RAW264.7 M? proliferation. However, naive RAW264.7 M? cultured on unTi produced a two-fold larger amount of TNFα than that on O₃–Ti. Furthermore, O₃–Ti greatly mitigated proinflammatory cytokine production, including TNFα and IL-6 from LSP-stimulated RAW264.7 M?. These results demonstrated that a superhydrophilic titanium prepared by simple ozone gas functionalization successfully increased MSCs proliferation and differentiation, and mitigated proinflammatory cytokine production from both naive and LPS-stimulated M?. This superhydrophilic surface would be useful as an endosseous implantable biomaterials and as a biomaterial for implantation into other tissues.     

Nonlinearity of bituminous mixtures

This paper presents an experimental characterization of the strain dependency of the complex modulus of bituminous mixtures for strain amplitude levels lower than about \(110~\upmu\mbox{m}/\mbox{m}\). A series of strain amplitude sweep tests are performed at different temperatures (8, 10, 12 and 14°C) and frequencies (0.3, 1, 3 and 10 Hz), during which complex modulus is monitored. For each combination of temperature and frequency, four maximum strain amplitudes are targeted (50, 75, 100 and \(110~\upmu\mbox{m}/\mbox{m}\)). For each of them, two series of 50 loading cycles are applied, respectively at decreasing and increasing strain amplitudes. Before each decreasing strain sweep and after each increasing strain sweep, 5 cycles are performed at constant maximum targeted strain amplitude.Experimental results show that the behavior of the studied material is strain dependent. The norm of the complex modulus decreases and phase angle increases with strain amplitude. Results are presented in Black and Cole–Cole plots, where characteristic directions of nonlinearity can be identified. Both the effects of nonlinearity in terms of the complex modulus variation and of the direction of nonlinearity in Black space seem to validate the time–temperature superposition principle with the same shift factors as for linear viscoelasticity.The comparison between results obtained during increasing and decreasing strain sweeps suggests the existence of another phenomenon occurring during cyclic loading, which appears to systematically induce a decrease of the norm of the complex modulus and an increase of the phase angle, regardless of the type of the strain sweep (increasing or decreasing).     

A Minimal Closed-form Solution for the Perspective Three Orthogonal Angles (P3oA) Problem: Application To Visual Odometry

We provide a simple closed-form solution to the Perspective three orthogonal angles (P3oA) problem: given the projection of three orthogonal lines in a calibrated camera, find their 3D directions. Upon this solution, an algorithm for the estimation of the camera relative rotation between two frames is proposed. The key idea is to detect triplets of orthogonal lines in a hypothesize-and-test framework and use all of them to compute the camera rotation in a robust way. This approach is suitable for human-made environments where numerous groups of orthogonal lines exist. We evaluate the numerical stability of the P3oA solution and the estimation of the relative rotation with synthetic and real data, comparing our results to other state-of-the-art approaches.     

Multivariate Median Filters and Partial Differential Equations

Multivariate median filters have been proposed as generalizations of the well-established median filter for gray-value images to multichannel images. As multivariate median, most of the recent approaches use the \(L^1\) median, i.e., the minimizer of an objective function that is the sum of distances to all input points. Many properties of univariate median filters generalize to such a filter. However, the famous result by Guichard and Morel about approximation of the mean curvature motion PDE by median filtering does not have a comparably simple counterpart for \(L^1\) multivariate median filtering. We discuss the affine equivariant Oja median and the affine equivariant transformation–retransformation \(L^1\) median as alternatives to \(L^1\) median filtering. We analyze multivariate median filters in a space-continuous setting, including the formulation of a space-continuous version of the transformation–retransformation \(L^1\) median, and derive PDEs approximated by these filters in the cases of bivariate planar images, three-channel volume images, and three-channel planar images. The PDEs for the affine equivariant filters can be interpreted geometrically as combinations of a diffusion and a principal-component-wise curvature motion contribution with a cross-effect term based on torsions of principal components. Numerical experiments are presented, which demonstrate the validity of the approximation results.     

On negative results when using sentiment analysis tools for software engineering research

Recent years have seen an increasing attention to social aspects of software engineering, including studies of emotions and sentiments experienced and expressed by the software developers. Most of these studies reuse existing sentiment analysis tools such as SentiStrength and NLTK. However, these tools have been trained on product reviews and movie reviews and, therefore, their results might not be applicable in the software engineering domain. In this paper we study whether the sentiment analysis tools agree with the sentiment recognized by human evaluators (as reported in an earlier study) as well as with each other. Furthermore, we evaluate the impact of the choice of a sentiment analysis tool on software engineering studies by conducting a simple study of differences in issue resolution times for positive, negative and neutral texts. We repeat the study for seven datasets (issue trackers and Stack Overflow questions) and different sentiment analysis tools and observe that the disagreement between the tools can lead to diverging conclusions. Finally, we perform two replications of previously published studies and observe that the results of those studies cannot be confirmed when a different sentiment analysis tool is used.     

Top-<Emphasis Type="Italic">k</Emphasis> temporal keyword search over social media data

Social media services have already become main sources for monitoring emerging topics and sensing real-life events. A social media platform manages social stream consisting of a huge volume of timestamped user generated data, including original data and repost data. However, previous research on keyword search over social media data mainly emphasizes on the recency of information. In this paper, we first propose a problem of top-k most significant temporal keyword query to enable more complex query analysis. It returns top-k most popular social items that contain the keywords in the given query time window. Then, we design a temporal inverted index with two-tiers posting list to index social time series and a segment store to compute the exact social significance of social items. Next, we implement a basic query algorithm based on our proposed index structure and give a detailed performance analysis on the query algorithm. From the analysis result, we further refine our query algorithm with a piecewise maximum approximation (PMA) sketch. Finally, extensive empirical studies on a real-life microblog dataset demonstrate the combination of two-tiers posting list and PMA sketch achieves remarkable performance improvement under different query settings.     

Enhancing comparison shopping agents through ordering and gradual information disclosure

The plethora of comparison shopping agents (CSAs) in today’s markets enables buyers to query more than a single CSA when shopping, thus expanding the list of sellers whose prices they obtain. This potentially decreases the chance of a purchase within any single interaction between a buyer and a CSA, and consequently decreases each CSAs’ expected revenue per-query. Obviously, a CSA can improve its competence in such settings by acquiring more sellers’ prices, potentially resulting in a more attractive “best price”. In this paper we suggest a complementary approach that improves the attractiveness of the best result returned based on intelligently controlling the order according to which they are presented to the user, in a way that utilizes several known cognitive-biases of human buyers. The advantage of this approach is in its ability to affect the buyer’s tendency to terminate her search for a better price, hence avoid querying further CSAs, without spending valuable resources on finding additional prices to present. The effectiveness of our method is demonstrated using real data, collected from four CSAs for five products. Our experiments confirm that the suggested method effectively influence people in a way that is highly advantageous to the CSA compared to the common method for presenting the prices. Furthermore, we experimentally show that all of the components of our method are essential to its success.     

Big data in the public sector: Uncertainties and readiness

Big data is being implemented with success in the private sector and science. Yet the public sector seems to be falling behind, despite the potential value of big data for government. Government organizations do recognize the opportunities of big data but seem uncertain about whether they are ready for the introduction of big data, and if they are adequately equipped to use big data. This paper addresses those uncertainties. It presents an assessment framework for evaluating public organizations’ big data readiness. Doing so demystifies the concept of big data, as it is expressed in terms of specific and measureable organizational characteristics. The framework was tested by applying it to organizations in the Dutch public sector. The results suggest that organizations may be technically capable of using big data, but they will not significantly gain from these activities if the applications do not fit their organizations and main statutory tasks. The framework proved helpful in pointing out areas where public sector organizations could improve, providing guidance on how government can become more big data ready in the future.     

When do agents outperform centralized algorithms?

Multi-agent systems (MAS) literature often assumes decentralized MAS to be especially suited for dynamic and large scale problems. In operational research, however, the prevailing paradigm is the use of centralized algorithms. Present paper empirically evaluates whether a multi-agent system can outperform a centralized algorithm in dynamic and large scale logistics problems. This evaluation is novel in three aspects: (1) to ensure fairness both implementations are subject to the same constraints with respect to hardware resources and software limitations, (2) the implementations are systematically evaluated with varying problem properties, and (3) all code is open source, facilitating reproduction and extension of the experiments. Existing work lacks a systematic evaluation of centralized versus decentralized paradigms due to the absence of a real-time logistics simulator with support for both paradigms and a dataset of problem instances with varying properties. We extended an existing logistics simulator to be able to perform real-time experiments and we use a recent dataset of dynamic pickup-and-delivery problem with time windows instances with varying levels of dynamism, urgency, and scale. The OptaPlanner constraint satisfaction solver is used in a centralized way to compute a global schedule and used as part of a decentralized MAS based on the dynamic contract-net protocol (DynCNET) algorithm. The experiments show that the DynCNET MAS finds solutions with a relatively lower operating cost when a problem has all following three properties: medium to high dynamism, high urgency, and medium to large scale. In these circumstances, the centralized algorithm finds solutions with an average cost of 112.3% of the solutions found by the MAS. However, averaged over all scenario types, the average cost of the centralized algorithm is 94.2%. The results indicate that the MAS performs best on very urgent problems that are medium to large scale.