Integrated model-driven dashboard development

Business performance modeling and model-driven business transformation are two research directions that are attracting much attention lately. In this study, we propose an approach for dashboard development that is model-driven and can be integrated with the business performance models. We adopt the business performance modeling framework, and we extend it in order to capture the reporting aspect of the business operation. We describe models that can effectively represent all the elements necessary for the business performance reporting process, and the interactions among them. We demonstrate how all these models can be combined and automatically generate the final solution. We further extend the proposed framework with mechanisms that can detect changes in the models and incrementally update the deployed solutions. Finally, we discuss our experience from the application of our technique in a real-world scenario. This case study shows that our technique can be efficiently applied to and handle changes in the underlying business models, delivering significant benefits in terms of both development time and flexibility.     

An optimized sequential pattern matching methodology for sequence classification

In this paper we present a novel methodology for sequence classification, based on sequential pattern mining and optimization algorithms. The proposed methodology automatically generates a sequence classification model, based on a two stage process. In the first stage, a sequential pattern mining algorithm is applied to a set of sequences and the sequential patterns are extracted. Then, the score of every pattern with respect to each sequence is calculated using a scoring function and the score of each class under consideration is estimated by summing the specific pattern scores. Each score is updated, multiplied by a weight and the output of the first stage is the classification confusion matrix of the sequences. In the second stage an optimization technique, aims to finding a set of weights which minimize an objective function, defined using the classification confusion matrix. The set of the extracted sequential patterns and the optimal weights of the classes comprise the sequence classification model. Extensive evaluation of the methodology was carried out in the protein classification domain, by varying the number of training and test sequences, the number of patterns and the number of classes. The methodology is compared with other similar sequence classification approaches. The proposed methodology exhibits several advantages, such as automated weight assignment to classes using optimization techniques and knowledge discovery in the domain of application.

Survey on mining subjective data on the web

In the past years we have witnessed Sentiment Analysis and Opinion Mining becoming increasingly popular topics in Information Retrieval and Web data analysis. With the rapid growth of the user-generated content represented in blogs, wikis and Web forums, such an analysis became a useful tool for mining the Web, since it allowed us to capture sentiments and opinions at a large scale. Opinion retrieval has established itself as an important part of search engines. Ratings, opinion trends and representative opinions enrich the search experience of users when combined with traditional document retrieval, by revealing more insights about a subject. Opinion aggregation over product reviews can be very useful for product marketing and positioning, exposing the customers’ attitude towards a product and its features along different dimensions, such as time, geographical location, and experience. Tracking how opinions or discussions evolve over time can help us identify interesting trends and patterns and better understand the ways that information is propagated in the Internet. In this study, we review the development of Sentiment Analysis and Opinion Mining during the last years, and also discuss the evolution of a relatively new research direction, namely, Contradiction Analysis. We give an overview of the proposed methods and recent advances in these areas, and we try to layout the future research directions in the field.     

Investigation of HgCdTe p-n device structures grown by liquid-phase epitaxy

The microstructure of p-n device structures grown by liquid-phase epitaxy (LPE) on CdZnTe substrates has been evaluated using transmission electron microscopy (TEM). The devices consisted of thick (∼21-μm) n-type layers and thin (∼1.6-μm) p-type layers, with final CdTe (∼0.5 μm) passivation layers. Initial observations revealed small defects, both within the n-type layer (doped with 8×10¹⁴/cm³ of In) and also within the p-type layer but at a much reduced level. These defects were not visible, however, in cross-sectional samples prepared by ion milling with the sample held at liquid nitrogen temperature. Only isolated growth defects were observed in samples having low indium doping levels (2×10¹⁴/cm³). The CdTe passivation layers were generally columnar and polycrystalline, and interfaces with the p-type HgCdTe layers were uneven. No obvious structural changes were apparent in the region of the CdTe/HgCdTe interfaces as a result of annealing at 250°C.     

Bulk metallic glasses for biomedical applications

The selection criteria for biomaterials include the material’s properties and biocompatibility, and the ability to fabricate the desired shapes. Bulk metallic glasses (BMGs) are relative newcomers in the field of biomaterials but they exhibit an excellent combination of properties and processing capabilities desired for versatile implant applications. To further evaluate the suitability of BMGs for biomedical applications, we analyzed the biological responses they elicited in vitro and in vivo. The BMGs promoted cell adhesion and growth in vitro and induced improved foreign body responses in vivo suggesting their potential use as biomaterials. Because of the BMGs’ flexible chemistry, atomic structure, and surface topography, they offer a unique opportunity to fabricate complex implants and devices with a desirable biological response from a material with superior properties over currently used metallic biomaterials.     

A simple analytical model for the dependence of the propagation delay of the polycrystalline silicon CMOS inverter on temperature

We present a simple analytical model that accounts for the effects of temperature on the propagation delay of the CMOS inverter, implemented with polycrystalline silicon (polysilicon) thin film transistors. We have also fabricated polysilicon CMOS inverters with a variety of crystallization methods. We measure their switching speed as a function of temperature, and compare the results with the analytical model’s predictions. The experimental data are shown to be in excellent agreement with the developed model, which constitutes an effective tool for modeling the impact of temperature on polysilicon digital circuits.     

Serum and follicular fluid leptin levels are correlated with human embryo quality

This prospective study was undertaken to reassess the prognostic value of leptin during critical stages of in vitro fertilization-embryo transfer (IVF-ET) and address its role in the functional staging of assisted reproductive technologies at the level of embryo quality. Serum and follicular fluid samples of 100 selected women undergoing the long IVF-ET protocol were collected for leptin and embryo quality determination. The highest serum leptin concentration (52.11 +/- 4.27 ng/ml) was observed on ovum pick up day, while follicular fluid leptin was higher than all serum samples examined (62.59 +/- 5.73 ng/ml). Serum leptin above 59.48 +/- 7.6 ng/ml was associated with 'poor' embryo quality and above 56.87 +/- 5.52 ng/ml with pregnancy failure. Elevated leptin concentrations were associated with reduced ovarian stimulation and response, follicle maturation, embryo quality and pregnancy success. Our findings suggest that leptin modulates embryo quality and may serve as a sensitive marker of IVF outcome.     

Glycocalyx‐Like Hydrogel Coatings for Small Diameter Vascular Grafts

Novel biological vascular conduits, such as decellularized tissue engineered vascular grafts (TEVGs) are hindered by high thrombogenicity. To mimic the antithrombogenic surface of native vessels with a continuous glycosaminoglycan layer that is present on endothelial cells (ECs), a hyaluronic acid (HA) modified surface is established, to effectively shield blood platelets from collagen‐triggered activation. Using the amine groups present on 4 mm diameter decellularized TEVGs, a continuous HA hydrogel coating is built via a bifunctional thiol‐reactive cross‐linker, thereby avoiding nonspecific collagen matrix cross‐linking. The HA hydrogel layer recreates a luminal wall, “hiding” exposed collagen from the bloodstream. In vitro blood tests show that adhered platelets, fibrinogen absorption, and fibrin formation on HA‐coated decellularized TEVGs are significantly lower than on uncoated decellularized TEVGs. The HA surface also inhibits macrophage adhesion in vitro. HA‐coated decellularized syngeneic rat aortae (≈1.5 mm diameter), and TEVGs in rat and canine models, respectively, are protected from aggressive thrombus formation, and preserve normal blood flow. Re‐endothelialization is also observed. HA‐coated TEVGs may be an off‐the‐shelf small‐diameter vascular graft with dual benefits: antithrombogenic protection and promotion of endothelium.     

Lost at the Edge of Uncertainty: Measuring Player Uncertainty in Digital Games

Uncertainty has previously been identified as an important ingredient of engaging games. Design in games can create different levels of uncertainty in players that they can recognize and describe as being either attributable to external forces, such as chance or hidden information, or internal to their own understanding of what to do in relation to their own goals. While it appears that uncertainty can contribute both positive and negative play experiences, there is little work in trying to operationalize and measure this concept as a component of player experience. Reported in this article is an analysis of data from over 700 players using modern bi-factor analysis techniques resulting in a five factor psychometric scale which captures the broad feelings of players about uncertainty in games. Three of these specific factors appear to point toward a single generic factor of uncertainty that is internal to the players, one captures experiences relating to external uncertainty, with the final factor relating to player’s experience of exploring the game to resolve uncertainty. In order to further validate the scale, we conducted an experiment with a commercial puzzle game manipulating the duration of play with predicted outcomes on the different specific factors of the scale. Overall the scale shows promise with good statistical reliability and construct validity of the separate factors and so will be a useful tool for further investigating player experiences in digital games.