A smart sensing platform for the classification of ambulatory patterns

This paper describes an innovative sensing approach allowing capture, discrimination, and classification of transients automatically in gait. A walking platform is described, which offers an alternative design to that of standard force plates with advantages that include mechanical simplicity and less restriction on dimensions. The scope of the work is to investigate as an experiment the sensitivity of the distributive tactile sensing method with the potential to address flexibility on gait assessment, including patient targeting and the extension to a variety of ambulatory applications. Using infrared sensors to measure plate deflection, gait patterns are compared with stored templates using a pattern recognition algorithm. This information is input into a neural network to classify normal and affected walking events, with a classification accuracy of just under 90 per cent achieved. The system developed has potential applications in gait analysis and rehabilitation, whereby it can be used as a tool for early diagnosis of walking disorders or to determine changes between pre- and post-operative gait.

Classification of bacterial strains based on DNA profiles

The analysis of bacterial genomes for epidemiological purposes often results in the production of a banding profile of DNA fragments characteristic of the genome under investigation. These may be produced using various methods, many of which involve the cutting or amplification of DNA into defined and reproducible characteristic fragments. It is frequently of interest to enquire whether the bacterial isolates are naturally classifiable into distinct groups based on their DNA profiles. A major problem with this approach is whether classification or clustering of the data is even appropriate. It is always possible to classify such data but it does not follow that the strains they represent are ‘actually’ classifiable into well-defined separate parts. Hence, the act of classification does not in itself answer the question: do the strains consist of a number of different distinct groups or species or do they merge imperceptibly into one another because DNA profiles vary continuously? Nevertheless, we may still wish to classify the data for ‘convenience’ even though strains may vary continuously, and such a classification has been called a ‘dissection’. This Statnote discusses the use of classificatory methods in analyzing the DNA profiles from a sample of bacterial isolates.

Hierarchical classification of G-protein-coupled receptors with data-driven selection of attributes and classifiers

We address the important bioinformatics problem of predicting protein function from a protein's primary sequence. We consider the functional classification of G-Protein-Coupled Receptors (GPCRs), whose functions are specified in a class hierarchy. We tackle this task using a novel top-down hierarchical classification system where, for each node in the class hierarchy, the predictor attributes to be used in that node and the classifier to be applied to the selected attributes are chosen in a data-driven manner. Compared with a previous hierarchical classification system selecting classifiers only, our new system significantly reduced processing time without significantly sacrificing predictive accuracy.

Market orientation of service networks:direct and indirect effects on sustained competitive advantage

Market orientation is an organization-wide concept that helps explain sustained competitive advantage (SCA). Since networks become ever more important, especially in the service sector, there is need to expand the concept of MO to a network setting. In line with Narver and Slater (1990), the concept of Market Orientation of Networks (MONW) is developed. This study indicates how MONW relates to the resource-based view (RBV) of the firm and the industrial organization (IO) view in explaining SCA. It is argued that MONW has direct and indirect effects on SCA. More precisely, the antecedent effect of MONW to resources and industry structure is considered.

On the 'classification' of neurodegenerative disorders:discrete entities, overlap or continuum?

The traditional method of classifying neurodegenerative diseases is based on the original clinico-pathological concept supported by 'consensus' criteria and data from molecular pathological studies. This review discusses first, current problems in classification resulting from the coexistence of different classificatory schemes, the presence of disease heterogeneity and multiple pathologies, the use of 'signature' brain lesions in diagnosis, and the existence of pathological processes common to different diseases. Second, three models of neurodegenerative disease are proposed: (1) that distinct diseases exist ('discrete' model), (2) that relatively distinct diseases exist but exhibit overlapping features ('overlap' model), and (3) that distinct diseases do not exist and neurodegenerative disease is a 'continuum' in which there is continuous variation in clinical/pathological features from one case to another ('continuum' model). Third, to distinguish between models, the distribution of the most important molecular 'signature' lesions across the different diseases is reviewed. Such lesions often have poor 'fidelity', i.e., they are not unique to individual disorders but are distributed across many diseases consistent with the overlap or continuum models. Fourth, the question of whether the current classificatory system should be rejected is considered and three alternatives are proposed, viz., objective classification, classification for convenience (a 'dissection'), or analysis as a continuum.

Novel speech signal processing algorithms for high-accuracy classification of Parkinson's disease

There has been considerable recent research into the connection between Parkinson's disease (PD) and speech impairment. Recently, a wide range of speech signal processing algorithms (dysphonia measures) aiming to predict PD symptom severity using speech signals have been introduced. In this paper, we test how accurately these novel algorithms can be used to discriminate PD subjects from healthy controls. In total, we compute 132 dysphonia measures from sustained vowels. Then, we select four parsimonious subsets of these dysphonia measures using four feature selection algorithms, and map these feature subsets to a binary classification response using two statistical classifiers: random forests and support vector machines. We use an existing database consisting of 263 samples from 43 subjects, and demonstrate that these new dysphonia measures can outperform state-of-the-art results, reaching almost 99% overall classification accuracy using only ten dysphonia features. We find that some of the recently proposed dysphonia measures complement existing algorithms in maximizing the ability of the classifiers to discriminate healthy controls from PD subjects. We see these results as an important step toward noninvasive diagnostic decision support in PD.

Pragmatic development of service based real-time change data capture

This thesis makes a contribution to the Change Data Capture (CDC) field by providing an empirical evaluation on the performance of CDC architectures in the context of realtime data warehousing. CDC is a mechanism for providing data warehouse architectures with fresh data from Online Transaction Processing (OLTP) databases. There are two types of CDC architectures, pull architectures and push architectures. There is exiguous data on the performance of CDC architectures in a real-time environment. Performance data is required to determine the real-time viability of the two architectures. We propose that push CDC architectures are optimal for real-time CDC. However, push CDC architectures are seldom implemented because they are highly intrusive towards existing systems and arduous to maintain. As part of our contribution, we pragmatically develop a service based push CDC solution, which addresses the issues of intrusiveness and maintainability. Our solution uses Data Access Services (DAS) to decouple CDC logic from the applications. A requirement for the DAS is to place minimal overhead on a transaction in an OLTP environment. We synthesize DAS literature and pragmatically develop DAS that eciently execute transactions in an OLTP environment. Essentially we develop effeicient RESTful DAS, which expose Transactions As A Resource (TAAR). We evaluate the TAAR solution and three pull CDC mechanisms in a real-time environment, using the industry recognised TPC-C benchmark. The optimal CDC mechanism in a real-time environment, will capture change data with minimal latency and will have a negligible affect on the database's transactional throughput. Capture latency is the time it takes a CDC mechanism to capture a data change that has been applied to an OLTP database. A standard definition for capture latency and how to measure it does not exist in the field. We create this definition and extend the TPC-C benchmark to make the capture latency measurement. The results from our evaluation show that pull CDC is capable of real-time CDC at low levels of user concurrency. However, as the level of user concurrency scales upwards, pull CDC has a significant impact on the database's transaction rate, which affirms the theory that pull CDC architectures are not viable in a real-time architecture. TAAR CDC on the other hand is capable of real-time CDC, and places a minimal overhead on the transaction rate, although this performance is at the expense of CPU resources.

Spillover effects of service failures in coalition loyalty programs:the buffering effect of special treatment benefits

Coalition loyalty programs are on the rise, yet few studies investigate the impact of service failures in such programs. Using data from a retail context, the authors show that a program partner deemed responsible for a service failure suffers negative customer responses. However, customers' perceptions of the benefits of the coalition loyalty program buffer these consequences. Perhaps most importantly, when customers perceive the program's special treatment benefits as low, direct and indirect spillover effects occur, such that a service failure by one program partner has a negative effect on customer loyalty toward the program itself. © 2013 New York University.

A simulation study of service differentiation for the bandwidth request scheme in fixed IEEE 802.16 network

Various flexible mechanisms related to quality of service (QoS) provisioning have been specified for uplink traffic at the medium access control (MAC) layer in the IEEE 802.16 standards. Among the mechanisms, contention based bandwidth request scheme can be used to indicate bandwidth demands to the base station for the non-real-time polling and best-effort services. These two services are used for most applications with unknown traffic characteristics. Due to the diverse QoS requirements of those applications, service differentiation (SD) is anticipated over the contention based bandwidth request scheme. In this paper we investigate the SD with the bandwidth request scheme by means of assigning different channel access parameters and bandwidth allocation priorities at different packets arrival probability. The effectiveness of the differentiation schemes is evaluated by simulations. It is observed that the initial backoff window can be efficient in SD, and if combined with the bandwidth allocation priority, the SD performances will be better.

The role of working memory in compulsive checking and OCD:a systematic classification of 58 experimental findings

Despite the large body of research regarding the role of memory in OCD, the results are described as mixed at best (Hermans et al., 2008). For example, inconsistent findings have been reported with respect to basic capacity, intact verbal, and generally affected visuospatial memory. We suggest that this is due to the traditional pursuit of OCD memory impairment as one of the general capacity and/or domain specificity (visuospatial vs. verbal). In contrast, we conclude from our experiments (i.e., Harkin & Kessler, 2009, 2011; Harkin, Rutherford, & Kessler, 2011) and recent literature (e.g., Greisberg & McKay, 2003) that OCD memory impairment is secondary to executive dysfunction, and more specifically we identify three common factors (EBL: Executive-functioning efficiency, Binding complexity, and memory Load) that we generalize to 58 experimental findings from 46 OCD memory studies. As a result we explain otherwise inconsistent research – e.g., intact vs. deficient verbal memory – that are difficult to reconcile within a capacity or domain specific perspective. We conclude by discussing the relationship between our account and others', which in most cases is complementary rather than contradictory.

On the hierarchical classification of G protein-coupled receptors

MOTIVATION: G protein-coupled receptors (GPCRs) play an important role in many physiological systems by transducing an extracellular signal into an intracellular response. Over 50% of all marketed drugs are targeted towards a GPCR. There is considerable interest in developing an algorithm that could effectively predict the function of a GPCR from its primary sequence. Such an algorithm is useful not only in identifying novel GPCR sequences but in characterizing the interrelationships between known GPCRs. RESULTS: An alignment-free approach to GPCR classification has been developed using techniques drawn from data mining and proteochemometrics. A dataset of over 8000 sequences was constructed to train the algorithm. This represents one of the largest GPCR datasets currently available. A predictive algorithm was developed based upon the simplest reasonable numerical representation of the protein's physicochemical properties. A selective top-down approach was developed, which used a hierarchical classifier to assign sequences to subdivisions within the GPCR hierarchy. The predictive performance of the algorithm was assessed against several standard data mining classifiers and further validated against Support Vector Machine-based GPCR prediction servers. The selective top-down approach achieves significantly higher accuracy than standard data mining methods in almost all cases.

The classification of HLA supertypes by GRID/CPCA and hierarchical clustering methods

Biological experiments often produce enormous amount of data, which are usually analyzed by data clustering. Cluster analysis refers to statistical methods that are used to assign data with similar properties into several smaller, more meaningful groups. Two commonly used clustering techniques are introduced in the following section: principal component analysis (PCA) and hierarchical clustering. PCA calculates the variance between variables and groups them into a few uncorrelated groups or principal components (PCs) that are orthogonal to each other. Hierarchical clustering is carried out by separating data into many clusters and merging similar clusters together. Here, we use an example of human leukocyte antigen (HLA) supertype classification to demonstrate the usage of the two methods. Two programs, Generating Optimal Linear Partial Least Square Estimations (GOLPE) and Sybyl, are used for PCA and hierarchical clustering, respectively. However, the reader should bear in mind that the methods have been incorporated into other software as well, such as SIMCA, statistiXL, and R.

What do students really think? Understanding postgraduate students' perception of service quality

This paper explores the components of Service Quality in HE from the Business School Postgraduate student perspective. A six-dimensional scale measuring Service Quality is developed based on focus group and survey data. Our findings highlight that postgraduate students are highly outcome oriented; the award of a reputable degree to gain employment is more important than learning for life. Whilst developing employable graduates, Business Schools must not neglect the core service; teaching & learning. In the long-term this contributes to employability rates and the reputation of institutions. However, as student satisfaction is an increasingly paramount objective, balancing the core service and factors perceived as important by postgraduate students is key.

The impact of service characteristics on the switching costs-customer loyalty link

This research investigates the interrelationship between service characteristics and switching costs and makes two contributions to the service retailing literature: (1) As a means of better understanding the effectiveness of switching costs, the study suggests a two-dimensional typology of switching costs, including internal and external switching costs and (2) it reveals that the effect of these switching costs on customer loyalty is contingent upon four service characteristics (the IHIP characteristics of service). We carried out a meta-analytic review of the literature on the switching costs-customer loyalty link and created a hierarchical linear model using a sample of 1,694 customers from 51 service industries. Results reveal that external switching costs have a stronger average effect on customer loyalty than do internal switching costs. Moreover, we find that IHIP characteristics moderate the links between switching costs and customer loyalty. Thus, the link between external switching costs and customer loyalty is weaker in industries higher in the four service characteristics (as compared to industries lower in these characteristics), while the opposite moderating effect of service characteristics for the internal switching costs-loyalty link is noted. © 2014 New York University.

Debate : arm's- length bodies and alternative models of service delivery

This article reflects on the UK coalition government’s ‘alternative models’ agenda, specifically in terms of the adoption of new models of service delivery by arm’s-length bodies (ALBs). It provides an overview of the alternative models agenda and discusses barriers to implementation. These include practical challenges involved in the set up of alternative models, the role of sponsor departments, and the effective communication of best practice. Finally, the article highlights some issues for further discussion.