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.     

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.     

Positron emission tomography in partial epilepsies: the clinical point of view

Epilepsy research using positron emission tomography (PET) has advanced our understanding of the pathophysiology and neurochemical correlates of both focal and generalized epilepsies, but from the clinical viewpoint its major contribution has been in the presurgical evaluation of patients with medically intractable partial seizures. Depending on the tracer used, PET may provide information on regional cerebral blood flow and glucose metabolism, and the binding of specific ligands to receptors that are thought to be related to the genesis and propagation of epileptic activity. In this communication, we discuss the diagnostic yield, limitations and perspectives of 18F-fluorodeoxyglucose (FDG) and 11C-flumazenil (FMZ) PET in partial epilepsies. The current evidence regarding the pathophysiology of the focal changes is also presented, with an emphasis on issues which must be carefully addressed for effective and reliable clinical research.     

Genetic mapping of a major susceptibility locus for juvenile myoclonic epilepsy on chromosome 15q

The epilepsies are a group of disorders characterised by recurrent seizures caused by episodes of abnormal neuronal hyperexcitability involving the brain. Up to 60 million people are affected worldwide and genetic factors may contribute to the aetiology in up to 40% of patients. The most common human genetic epilepsies display a complex pattern of inheritance. These are categorised as idiopathic in the absence of detectable structural or metabolic abnormalities. Juvenile myoclonic epilepsy (JME) is a distinctive and common variety of familial idiopathic generalised epilepsy (IGE) with a prevalence of 0.5-1.0 per 1000 and a ratio of sibling risk to population prevalence (lambda(s)) of 42. The molecular genetic basis of these familial idiopathic epilepsies is entirely unknown, but a mutation in the gene CHRNA4, encoding the alpha4 subunit of the neuronal nicotinic acetylcholine receptor (nAChR), was recently identified in a rare Mendelian variety of idiopathic epilepsy. Chromosomal regions harbouring genes for nAChR subunits were therefore tested for linkage to the JME trait in 34 pedigrees. Significant evidence for linkage with heterogeneity was found to polymorphic loci encompassing the region in which the gene encoding the alpha7 subunit of nAChR (CHRNA7) maps on chromosome 15q14 (HLOD = 4.4 at alpha = 0.65; Z(all) = 2.94, P = 0.0005). This major locus contributes to genetic susceptibility to JME in a majority of the families studied.     

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.     

Is HLA-DRW13 (W6) associated with juvenile myoclonic epilepsy in Arab patients?

In a study of 32 unrelated Arab patients with juvenile myoclonic epilepsy (JME), we compared the frequencies of human leukocyte antigen (HLA) class I and II alleles with those of unrelated healthy controls. A significant difference between the phenotypic frequencies in JME patients and controls was observed for DRW13, the split of DRW6 (37.5 vs 11% of controls). The strength of association as measured by the relative risk was 4.85 for this antigen (p = 0.002). The possible association of JME with HLA-DRW6 recently reported in Caucasians was confirmed in this study. This finding speaks for the homogeneity of the disease among Arabic and Caucasian JME patients. The existence of this association is evidence of a locus in the HLA region that influences expression of JME.     

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.

The Dynamic Lot-Sizing Problem for Multiple Items Under Limited Capacity

This paper addresses the problem of scheduling the timing and quantities of production of n different products over m periods for a single production facility with a prespecified capacity. We assume that the demand is deterministic and can vary from one period to another and from one product to another. The objective is to minimize the sum of production setup and inventory holding costs. For medium-size problems, optimal solution algorithms do not yet exist and therefore heuristic solution algorithms are of interest. Most of the existing heuristics make use of the “forward-pass” concept in one way or the other. Forward pass means we begin by determining the lot sizes for earlier periods before moving to study the later periods. In this paper we study the forward-pass approach as well as a different solution approach which we call the four-step algorithm. We develop the feasibility conditions for pure forward-pass algorithms. Finally, we perform a comparative evaluation study.     

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.