bluuebunny/crossref_metadata_2025 huggingface dataset:
CHALLENGES FOR PRODUCT AND SERVICE PROVIDERS IN OPEN INNOVATION WITH CUSTOMERS IN BUSINESS-TO-BUSINESS MARKETS
Authors: PAASI JAAKKO , LAPPALAINEN INKA , RANTALA TUIJA , PIKKARAINEN MINNA
Published: April 2014

The purpose of this paper is to empirically study challenges faced by product and service providers when innovating openly with customers in business-to-business (B-to-B) markets. The study aims to fill the research gap of how the challenges vary in different types of customer involvement in innovation. The work applied the qualitative methodology of multiple case studies, and the material was collected in semi-structured interviews with management personnel at 48 companies in Finland and in the Netherlands that are practicing open innovation with their customers in one form or another. For the analysis of challenges identified from the empirical data, a typology of open innovation with customers was built, resulting in four distinct types to categorise product and service providers through their dominant business and innovation logics and related customer involvement. The typology gave the context in which strategic and operational challenges of open innovation with customers were explored. Each of the four types seems to have specific and characteristic challenges of open innovation. The findings of the study will help managers of product and service providers in guiding their open innovation practices with customers in B-to-B markets.
Analysis of a delayed epidemic model with non-monotonic incidence rate and vertical transmission
Authors: Xu Jinhu , Xu Wenxiong , Zhou Yicang
Published: April 2014

A delayed SEIR epidemic model with vertical transmission and non-monotonic incidence is formulated. The equilibria and the threshold of the model have been determined on the bases of the basic reproduction number. The local stability of disease-free equilibrium and endemic equilibrium is established by analyzing the corresponding characteristic equations. By comparison arguments, it is proved that, if R0 < 1, the disease-free equilibrium is globally asymptotically stable. Whereas, the disease-free equilibrium is unstable if R0 > 1. Moreover, we show that the disease is permanent if the basic reproduction number is greater than one. Furthermore, the sufficient conditions are obtained for the global asymptotic stability of the endemic equilibrium when R0 > 1.
Invertible Dirac operators and handle attachments on manifolds with boundary
Authors: Dahl Mattias , Grosse Nadine
Published: April 2014

For spin manifolds with boundary we consider Riemannian metrics which are product near the boundary and are such that the corresponding Dirac operator is invertible when half-infinite cylinders are attached at the boundary. The main result of this paper is that these properties of a metric can be preserved when the metric is extended over a handle of codimension at least two attached at the boundary. Applications of this result include the construction of non-isotopic metrics with invertible Dirac operator, and a concordance existence and classification theorem.
A linguistic analysis of group support systems interactions for uncovering social realities of organizations
Authors: Kuo Feng-Yang , Yin Chun-Po
Published: April 2014

Talks are actions, and language represents the medium through which we encounter reality, carry out practical reasoning, and construct social actions. This study applies the speech act theory to analyze the data collected in a study by Trauth and Jessup [2000] and confirms previous research findings that both the topic and the group size influence the pattern of discussion, especially when issues are threatening. It also shows that the abundance of speech acts like assertives, directives, and expressives can be accounted for by a few simple recurring patterns, indicating participants are rather close-minded. More important, linguistic analysis helps uncovering defensive speech routines that inhibit the generation of valid information and create self-sealing patterns of escalating error. Linguistic analysis may therefore complement positivist and interpretive analysis by examining if participants' engagement is superficial or profound, if consensus is reached or blocked, and if certain speech acts lead to dysfunctional organizational learning. Hence, in the era of participatory Web in which language is the primary medium for interactive sharing and dynamic collaboration, linguistic analysis can be applied to study the promises and declarations that people rely on to initiate, coordinate, and complete social actions.
Risk hedging in storage grid markets
Authors: Du Anna Ye , Das Sanjukta , Gopal Ram D. , Ramesh R.
Published: April 2014

Internet storage services allow businesses to move away from maintaining their own internal storage networks. Service providers currently follow a utility pricing model which translates to them absorbing all the risk that arises from the fluctuating storage needs of their customers. The risk borne by the Internet storage service providers has large revenue implications as Internet startups and smaller companies, which face significant demand stochasticity, constitute an important segment of their clientele. We develop an option pricing mechanism to hedge against this risk and evaluate its effectiveness vis-à-vis forward contracts. We obtain the conditions under which options dominate forward contracts and the trade-offs involved when the provider has to decide on appropriate pricing mechanisms. Our empirical study uses publicly obtainable traffic data of Amazon S3 clients to validate the analytical results. We show that providers can significantly benefit from including options in their risk-hedging portfolio, especially when there is less variation in the costs faced by the buyers in building their own data networks as opposed to using cloud services.
Trust in a specific technology
Authors: Mcknight D. Harrison , Carter Michelle , Thatcher Jason Bennett , Clay Paul F.
Published: April 2014

Trust plays an important role in many Information Systems (IS)-enabled situations. Most IS research employs trust as a measure of interpersonal or person-to-firm relations, such as trust in a Web vendor or a virtual team member. Although trust in other people is important, this article suggests that trust in the Information Technology (IT) itself also plays a role in shaping IT-related beliefs and behavior. To advance trust and technology research, this article presents a set of trust in technology construct definitions and measures. We also empirically examine these construct measures using tests of convergent, discriminant, and nomological validity. This study contributes to the literature by providing: (a) a framework that differentiates trust in technology from trust in people, (b) a theory-based set of definitions necessary for investigating different kinds of trust in technology, and (c) validated trust in technology measures useful to research and practice.
Knowledge management revisited
Authors: Tuzhilin Alexander
Published: April 2014

The article considers the (old) field of Knowledge Management (KM) and examines the factors that led to its decline over the last several years. It also argues that the demise of knowledge management is premature and that it is time to revisit the field and take it to the “next level” of academic research and business practice. Then the article presents a proposal of how to do it by deploying certain “new tricks,” that is, various modern developments in the fields related to KM that can make a difference and solve the old problems of knowledge management.
From telesales to tele-advisory in travel agencies
Authors: Schmidt-Rauch Susanne , Schwabe Gerhard
Published: April 2014

This article describes a multiloop design approach, demonstrating two concatenated build-and-evaluate loops of a project that aims at enhancing and improving tele-counseling at travel agency call-centers. The socio-technical system design intends to implement and support a collaborative travel counseling concept which equalizes travel agents and customers within a value co-created service encounter to resolve the specific problems in travel agency call-centers. The design is finally guided by four instrumental goals: (1) increase transparency, (2) improve information quality, (3) support joint problem solving, and (4) create advisory experience. While the first build-and-evaluate loop mainly enhanced the workplace picture, the second build-and-evaluate loop primarily revealed organizational demands. This indicates that the proposed design approach enables sequentially broadening the problem space for design and supports iteratively moving forward the utility of the emerging artifact. Design is informed by concatenating insights from the previous loops which enrich understanding of the instrumental goals. The presented goals as well as the design approach are encouraging candidates for additional testing as a base for general design principles.
Designing a social-broadcasting-based business intelligence system
Authors: Rui Huaxia , Whinston Andrew
Published: April 2014

The rise of social media has fundamentally changed the way information is produced, disseminated, and consumed in the digital age, which has profound economic and business effects. Among many different types of social media, social broadcasting networks such as Twitter in the U.S. and “Weibo” in China are particularly interesting from a business perspective. In the case of Twitter, the huge amounts of real-time data with extremely rich text, along with valuable structural information, makes Twitter a great platform to build Business Intelligence (BI) systems. We propose a framework of social-broadcasting-based BI systems that utilizes real-time information extracted from these data with text mining techniques. To demonstrate this framework, we designed and implemented a Twitter-based BI system that forecasts movie box office revenues during the opening weekend and forecasts daily revenue after 4 weeks. We found that incorporating information from Twitter could reduce the Mean Absolute Percentage Error (MAPE) by 44% for the opening weekend and by 36% for total revenue. For daily revenue forecasting, including Twitter information into a baseline model could reduce forecasting errors by 17.5% on average. On the basis of these results, we conclude that social-broadcasting-based BI systems have great potential and should be explored by both researchers and practitioners.
Text mining and probabilistic language modeling for online review spam detection
Authors: Lau Raymond Y. K. , Liao S. Y. , Kwok Ron Chi-Wai , Xu Kaiquan , Xia Yunqing , Li Yuefeng
Published: April 2014

In the era of Web 2.0, huge volumes of consumer reviews are posted to the Internet every day. Manual approaches to detecting and analyzing fake reviews (i.e., spam) are not practical due to the problem of information overload. However, the design and development of automated methods of detecting fake reviews is a challenging research problem. The main reason is that fake reviews are specifically composed to mislead readers, so they may appear the same as legitimate reviews (i.e., ham). As a result, discriminatory features that would enable individual reviews to be classified as spam or ham may not be available. Guided by the design science research methodology, the main contribution of this study is the design and instantiation of novel computational models for detecting fake reviews. In particular, a novel text mining model is developed and integrated into a semantic language model for the detection of untruthful reviews. The models are then evaluated based on a real-world dataset collected from amazon.com. The results of our experiments confirm that the proposed models outperform other well-known baseline models in detecting fake reviews. To the best of our knowledge, the work discussed in this article represents the first successful attempt to apply text mining methods and semantic language models to the detection of fake consumer reviews. A managerial implication of our research is that firms can apply our design artifacts to monitor online consumer reviews to develop effective marketing or product design strategies based on genuine consumer feedback posted to the Internet.
Discovery and diagnosis of behavioral transitions in patient event streams
Authors: Robinson William N. , Akhlaghi Arash , Deng Tianjie , Syed Ali Raza
Published: April 2014

Users with cognitive impairments use assistive technology (AT) as part of a clinical treatment plan. As the AT interface is manipulated, data stream mining techniques are used to monitor user goals. In this context, real-time data mining aids clinicians in tracking user behaviors as they attempt to achieve their goals. Quality metrics over stream-mined models identify potential changes in user goal attainment, as the user learns his or her personalized emailing system. When the quality of some data-mined models varies significantly from nearby models—as defined by quality metrics—the user's behavior is then flagged as a significant behavioral change. The specific changes in user behavior are then characterized by differencing the data-mined decision tree models. This article describes how model quality monitoring and decision tree differencing can aid in recognition and diagnoses of behavioral changes in a case study of cognitive rehabilitation via emailing. The technique may be more widely applicable to other real-time data-intensive analysis problems.
Process Mining
Authors: van der Aalst Wil
Published: April 2014

Over the last decade, process mining emerged as a new research field that focuses on the analysis of processes using event data. Classical data mining techniques such as classification, clustering, regression, association rule learning, and sequence/episode mining do not focus on business process models and are often only used to analyze a specific step in the overall process. Process mining focuses on end-to-end processes and is possible because of the growing availability of event data and new process discovery and conformance checking techniques.

Process models are used for
analysis
(e.g., simulation and verification) and
enactment
by BPM/WFM systems. Previously, process models were typically made by hand without using event data. However, activities executed by people, machines, and software leave trails in so-called
event logs
. Process mining techniques use such logs to discover, analyze, and improve business processes.

Recently, the Task Force on Process Mining released the Process Mining Manifesto. This manifesto is supported by 53 organizations and 77 process mining experts contributed to it. The active involvement of end-users, tool vendors, consultants, analysts, and researchers illustrates the growing significance of process mining as a bridge between data mining and business process modeling. The practical relevance of process mining and the interesting scientific challenges make process mining one of the “hot” topics in Business Process Management (BPM). This article introduces process mining as a new research field and summarizes the guiding principles and challenges described in the manifesto.
Analyzing Online Review Helpfulness Using a Regressional ReliefF-Enhanced Text Mining Method
Authors: Ngo-Ye Thomas L. , Sinha Atish P.
Published: April 2014

Within the emerging context of Web 2.0 social media, online customer reviews are playing an increasingly important role in disseminating information, facilitating trust, and promoting commerce in the e-marketplace. The sheer volume of customer reviews on the web produces information overload for readers. Developing a system that can automatically identify the most helpful reviews would be valuable to businesses that are interested in gathering informative and meaningful customer feedback. Because the target variable---review helpfulness---is continuous, common feature selection techniques from text classification cannot be applied. In this article, we propose and investigate a text mining model, enhanced using the Regressional ReliefF (RReliefF) feature selection method, for predicting the helpfulness of online reviews from Amazon.com. We find that RReliefF significantly outperforms two popular dimension reduction methods. This study is the first to investigate and compare different dimension reduction techniques in the context of applying text regression for predicting online review helpfulness. Another contribution is that our analysis of the keywords selected by RReliefF reveals meaningful feature groupings.
Optimal Adapter Creation for Process Composition in Synchronous vs. Asynchronous Communication
Authors: Shan Zhe , Kumar Akhil
Published: April 2014

A key issue in process-aware e-commerce collaboration is to orchestrate business processes of multiple business partners throughout a supply chain network in an automated and seamless way. Since each partner has its own internal processes with different control flow structures and message interfaces, the real challenge lies in verifying the correctness of process collaboration, and reconciling conflicts in an automated manner to make collaboration successful. The purpose of business process adaptation is to mediate the communication between independent processes to overcome their mismatches and incompatibilities. The goal of this article is to develop and compare efficient approaches of optimal adapter (i.e. one that minimizes the number of messages to be adapted) creation for multiple interacting processes under both synchronous and asynchronous communication. We start with an analysis of interactions of each message pair, and show how to identify incompatible cases and their adaptation elements for both types of communication. Then, we show how to extend this analysis into more general cases involving
M
messages and
N
processes (
M, N
> 2). Further, we present optimal adapter creation algorithms for both scenarios based on our analysis technique. The algorithms were implemented in a Java-based prototype system, and results of two experiments are reported. We compare and discuss the insights gained about adapter creation in these two scenarios.
Who is Retweeting the Tweeters? Modeling, Originating, and Promoting Behaviors in the Twitter Network
Authors: Achananuparp Palakorn , Lim Ee-Peng , Jiang Jing , Hoang Tuan-Anh
Published: April 2014

Real-time microblogging systems such as Twitter offer users an easy and lightweight means to exchange information. Instead of writing formal and lengthy messages, microbloggers prefer to frequently broadcast several short messages to be read by other users. Only when messages are interesting, are they propagated further by the readers. In this article, we examine user behavior relevant to information propagation through microblogging. We specifically use retweeting activities among Twitter users to define and model
originating
and
promoting
behavior. We propose a basic model for measuring the two behaviors, a
mutual dependency
model, which considers the mutual relationships between the two behaviors, and a
range-based
model, which considers the
depth
and
reach
of users’ original tweets. Next, we compare the three behavior models and contrast them with the existing work on modeling influential Twitter users. Last, to demonstrate their applicability, we further employ the behavior models to detect interesting events from sudden changes in aggregated information propagation behavior of Twitter users. The results will show that the proposed behavior models can be effectively applied to detect interesting events in the Twitter stream, compared to the baseline tweet-based approaches.
Credit Rating Change Modeling Using News and Financial Ratios
Authors: Lu Hsin-Min , Tsai Feng-Tse , Chen Hsinchun , Hung Mao-Wei , Li Shu-Hsing
Published: April 2014

Credit ratings convey credit risk information to participants in financial markets, including investors, issuers, intermediaries, and regulators. Accurate credit rating information plays a crucial role in supporting sound financial decision-making processes. Most previous studies on credit rating modeling are based on accounting and market information. Text data are largely ignored despite the potential benefit of conveying timely information regarding a firm’s outlook. To leverage the additional information in news full-text for credit rating prediction, we designed and implemented a news full-text analysis system that provides firm-level coverage, topic, and sentiment variables. The novel topic-specific sentiment variables contain a large fraction of missing values because of uneven news coverage. The missing value problem creates a new challenge for credit rating prediction approaches. We address this issue by developing a missing-tolerant multinomial probit (MT-MNP) model, which imputes missing values based on the Bayesian theoretical framework. Our experiments using seven and a half years of real-world credit ratings and news full-text data show that (1) the overall news coverage can explain future credit rating changes while the aggregated news sentiment cannot; (2) topic-specific news coverage and sentiment have statistically significant impact on future credit rating changes; (3) topic-specific negative sentiment has a more salient impact on future credit rating changes compared to topic-specific positive sentiment; (4) MT-MNP performs better in predicting future credit rating changes compared to support vector machines (SVM). The performance gap as measured by macroaveraging F-measure is small but consistent.
Using a Network Analysis Approach for Organizing Social Bookmarking Tags and Enabling Web Content Discovery
Authors: Wei Wei , Ram Sudha
Published: April 2014

This article describes an innovative approach to reorganizing the tag space generated by social bookmarking services. The objective of this work is to enable effective search and discovery of Web content using social bookmarking tags. Tags are metadata generated by users for Web content annotation. Their potential as effective Web search and discovery tool is hindered by challenges such as, the tag space being untidy due to ambiguity, and hidden or implicit semantics. Using a novel analytics approach, we conducted network analyses on tags and discovered that tags are generated for different purposes and that there are inherent relationships among tags. Our approach can be used to extract the purposes of tags and relationships among the tags and this information can be used as facets to add structure and hierarchy to reorganize the flat tag space. The semantics of relationships and hierarchy in our proposed faceted model of tags enable searches on annotated Web content in an effective manner. We describe the implementation of a prototype system called FASTS to demonstrate feasibility and effectiveness of our approach.
Business Intelligence and Analytics
Authors: Lim Ee-Peng , Chen Hsinchun , Chen Guoqing
Published: April 2014

Business intelligence and analytics (BIA) is about the development of technologies, systems, practices, and applications to analyze critical business data so as to gain new insights about business and markets. The new insights can be used for improving products and services, achieving better operational efficiency, and fostering customer relationships. In this article, we will categorize BIA research activities into three broad research directions: (a) big data analytics, (b) text analytics, and (c) network analytics. The article aims to review the state-of-the-art techniques and models and to summarize their use in BIA applications. For each research direction, we will also determine a few important questions to be addressed in future research.
Network Effects in Health Information Exchange Growth
Authors: Yaraghi Niam , Du Anna Ye , Sharman Raj , Gopal Ram D. , Ramesh R.
Published: April 2014

The importance of the Healthcare Information Exchange (HIE) in increasing healthcare quality and reducing risks and costs has led to greater interest in identifying factors that enhance adoption and meaningful use of HIE by healthcare providers. In this research we study the interlinked network effects between two different groups of physicians -- primary care physicians and specialists -- as significant factors in increasing the growth of each group in an exchange. An analytical model of interlinked and intragroup influences on adoption is developed using the Bass diffusion model as a basis. Adoption data on 1,060 different primary and secondary care physicians over 32 consecutive months was used to test the model. The results indicate not only the presence of interlinked effects, but also that their influence is stronger than that of the intragroup. Further, the influence of primary care physicians on specialists is stronger than that of specialists on primary care physicians. We also provide statistical evidence that the new model performs better than the conventional Bass model, and the assumptions of diffusion symmetry in the market are statistically valid. Together, the findings provide important guidelines on triggers that enhance the overall growth of HIE and potential marketing strategies for HIE services.
A Systematic Review of Business and Information Technology Alignment
Authors: Ullah Azmat , Lai Richard
Published: April 2014

Business organizations have become heavily dependent on information technology (IT) services. The process of alignment is defined as the mutual synchronization of business goals and IT services. However, achieving mature alignment between business and IT is difficult due to the rapid changes in the business and IT environments. This article provides a systematic review of studies on the alignment of business and IT. The research articles reviewed are based on topics of alignment, the definition of alignment, history, alignment challenges, phases of alignment, alignment measurement approaches, the importance of alignment in business industries, how software engineering helps in better alignment, and the role of the business environment in aligning business with IT. It aims to present a thorough understanding of business-IT alignment and to provide a list of future research directions regarding alignment. To perform the systematic review, we used the guidelines developed by Kitchenham for reviewing the available research papers relevant to our topic.
Bayesian Inference in Trust Networks
Authors: Orman Levent V.
Published: April 2014

Trust has emerged as a major impediment to the success of electronic markets and communities where interaction with the strangers is the norm. Social Networks and Online Communities enable interaction with complete strangers, and open up new commercial, political, and social possibilities. But those promises are rarely achieved because it is difficult to trust the online contacts. A common approach to remedy this problem is to compute trust values for the new contacts from the existing trust values in the network. There are two main methods: aggregation and transitivity. Yet, neither method provides satisfactory results because trust networks are sparse and transitivity may not hold. This article develops a Bayesian formulation of the problem, where trust is defined as a conditional probability, and a Bayesian Network analysis is employed to compute the unknown trust values in terms of the known trust values. The algorithms used to propagate conditional probabilities through the network are theoretically sound and based on a long-standing literature on probability propagation in Bayesian networks. Moreover, the context information that is typically ignored in trust literature is included here as a major factor in computing new trust values. These changes have led to significant improvements over existing approaches in the accuracy of computed trust, and with some modifications to the algorithm, in its reach. Real data acquired from Advogato network is used to do extensive testing, and the results confirm the practical value of a theoretically sound Bayesian approach.
A Random Walk Model for Item Recommendation in Social Tagging Systems
Authors: Zhang Zhu , Zeng Daniel D. , Abbasi Ahmed , Peng Jing , Zheng Xiaolong
Published: April 2014

Social tagging, as a novel approach to information organization and discovery, has been widely adopted in many Web 2.0 applications. Tags contributed by users to annotate a variety of Web resources or items provide a new type of information that can be exploited by recommender systems. Nevertheless, the sparsity of the ternary interaction data among users, items, and tags limits the performance of tag-based recommendation algorithms. In this article, we propose to deal with the sparsity problem in social tagging by applying random walks on ternary interaction graphs to explore transitive associations between users and items. The transitive associations in this article refer to the path of the link between any two nodes whose length is greater than one. Taking advantage of these transitive associations can allow more accurate measurement of the relevance between two entities (e.g., user-item, user-user, and item-item). A PageRank-like algorithm has been developed to explore these transitive associations by spreading users’ preferences on an item similarity graph and spreading items’ influences on a user similarity graph. Empirical evaluation on three real-world datasets demonstrates that our approach can effectively alleviate the sparsity problem and improve the quality of item recommendation.
Detecting Deceptive Chat-Based Communication Using Typing Behavior and Message Cues
Authors: Derrick Douglas C. , Meservy Thomas O. , Jenkins Jeffrey L. , Burgoon Judee K. , Nunamaker Jay F.
Published: April 2014

Computer-mediated deception is prevalent and may have serious consequences for individuals, organizations, and society. This article investigates several metrics as predictors of deception in synchronous chat-based environments, where participants must often spontaneously formulate deceptive responses. Based on cognitive load theory, we hypothesize that deception influences response time, word count, lexical diversity, and the number of times a chat message is edited. Using a custom chatbot to conduct interviews in an experiment, we collected 1,572 deceitful and 1,590 truthful chat-based responses. The results of the experiment confirm that deception is positively correlated with response time and the number of edits and negatively correlated to word count. Contrary to our prediction, we found that deception is not significantly correlated with lexical diversity. Furthermore, the age of the participant moderates the influence of deception on response time. Our results have implications for understanding deceit in chat-based communication and building deception-detection decision aids in chat-based systems.
An Inference Engine for Estimating Outside States of Clinical Test Items
Authors: Sakata Masato , Yücel Zeynep , Shinozawa Kazuhiko , Hagita Norihiro , Imai Michita , Furutani Michiko , Matsuoka Rumiko
Published: April 2014

Common periodical health check-ups include several clinical test items with affordable cost. However, these standard tests do not directly indicate signs of most lifestyle diseases. In order to detect such diseases, a number of additional specific clinical tests are required, which increase the cost of the health check-up. This study aims to enrich our understanding of the common health check-ups and proposes a way to estimate the signs of several lifestyle diseases based on the standard tests in common examinations without performing any additional specific tests. In this manner, we enable a diagnostic process, where the physician may prefer to perform or avoid a costly test according to the estimation carried out through a set of common affordable tests. To that end, the relation between standard and specific test results is modeled with a multivariate kernel density estimate. The condition of the patient regarding a specific test is assessed following a Bayesian framework. Our results indicate that the proposed method achieves an overall estimation accuracy of 84%. In addition, an outstanding estimation accuracy is achieved for a subset of high-cost tests. Moreover, comparison with standard artificial intelligence methods suggests that our algorithm outperforms the conventional methods.

Our contributions are as follows: (i) promotion of affordable health check-ups, (ii) high estimation accuracy in certain tests, (iii) generalization capability due to ease of implementation on different platforms and institutions, (iv) flexibility to apply to various tests and potential to improve early detection rates.
The Role of Sound Source Perception in Gestural Sound Description
Authors: Caramiaux B. , Bevilacqua F. , Bianco T. , Schnell N. , Houix O. , Susini P.
Published: April 2014

We investigated gesture description of sound stimuli performed during a listening task. Our hypothesis is that the strategies in gestural responses depend on the level of identification of the sound source and specifically on the identification of the action causing the sound. To validate our hypothesis, we conducted two experiments. In the first experiment, we built two corpora of sounds. The first corpus contains sounds with identifiable causal actions. The second contains sounds for which no causal actions could be identified. These corpora properties were validated through a listening test. In the second experiment, participants performed arm and hand gestures synchronously while listening to sounds taken from these corpora. Afterward, we conducted interviews asking participants to verbalize their experience while watching their own video recordings. They were questioned on their perception of the listened sounds and on their gestural strategies. We showed that for the sounds where causal action can be identified, participants mainly mimic the action that has produced the sound. In the other case, when no action can be associated with the sound, participants trace contours related to sound acoustic features. We also found that the interparticipants’ gesture variability is higher for causal sounds compared to noncausal sounds. Variability demonstrates that, in the first case, participants have several ways of producing the same action, whereas in the second case, the sound features tend to make the gesture responses consistent.
Embodying Care in Matilda
Authors: Khosla Rajiv , Chu Mei-Tai
Published: April 2014

Ageing population is at the center of the looming healthcare crisis in most parts of the developed and developing world. Australia, like most of the western world, is bracing up for the looming ageing population crisis, spiraling healthcare costs, and expected serious shortage of healthcare workers. Assistive service and companion (social) robots are being seen as one of the ways for supporting aged care facilities to meet this challenge and improve the quality of care of older people including mental and physical health outcomes, as well as to support healthcare workers in personalizing care. In this article, the authors report on the design and implementation of first-ever field trials of Matilda, a human-like assistive communication (service and companion) robot for improving the emotional well-being of older people in three residential care facilities in Australia involving 70 participants. The research makes several unique contributions including Matilda’s ability to break technology barriers, positively engage older people in group and one-to-one activities, making these older people productive and useful, helping them become resilient and cope better through personalization of care, and finally providing them sensory enrichment through Matilda’s multimodal communication capabilities.
Modeling Throughput of Emergency Departments via Time Series
Authors: Wang Zidong , Eatock Julie , McClean Sally , Liu Dongmei , Liu Xiaohui , Young Terry
Published: April 2014

In this article, the expectation maximization (EM) algorithm is applied for modeling the throughput of emergency departments via available time-series data. The dynamics of emergency department throughput is developed and evaluated, for the first time, as a stochastic dynamic model that consists of the noisy measurement and first-order autoregressive (AR) stochastic dynamic process. By using the EM algorithm, the model parameters, the actual throughput, as well as the noise intensity, can be identified simultaneously. Four real-world time series collected from an emergency department in West London are employed to demonstrate the effectiveness of the introduced algorithm. Several quantitative indices are proposed to evaluate the inferred models. The simulation shows that the identified model fits the data very well.
Septic Shock Prediction for Patients with Missing Data
Authors: Ho Joyce C. , Lee Cheng H. , Ghosh Joydeep
Published: April 2014

Sepsis and septic shock are common and potentially fatal conditions that often occur in intensive care unit (ICU) patients. Early prediction of patients at risk for septic shock is therefore crucial to minimizing the effects of these complications. Potential indications for septic shock risk span a wide range of measurements, including physiological data gathered at different temporal resolutions and gene expression levels, leading to a nontrivial prediction problem. Previous works on septic shock prediction have used small, carefully curated datasets or clinical measurements that may not be available for many ICU patients. The recent availability of a large, rich ICU dataset called MIMIC-II has provided the opportunity for more extensive modeling of this problem. However, such a large clinical dataset inevitably contains a substantial amount of missing data. We investigate how different imputation selection criteria and methods can overcome the missing data problem. Our results show that imputation methods in conjunction with predictive modeling can lead to accurate septic shock prediction, even if the features are restricted primarily to noninvasive measurements. Our models provide a generalized approach for predicting septic shock in any ICU patient.
Automated quantification of pancreatic β-cell mass
Authors: Golson Maria L. , Bush William S. , Brissova Marcela
Published: April 2014

β-Cell mass is a parameter commonly measured in studies of islet biology and diabetes. However, the rigorous quantification of pancreatic β-cell mass using conventional histological methods is a time-consuming process. Rapidly evolving virtual slide technology with high-resolution slide scanners and newly developed image analysis tools has the potential to transform β-cell mass measurement. To test the effectiveness and accuracy of this new approach, we assessed pancreata from normal C57Bl/6J mice and from mouse models of β-cell ablation (streptozotocin-treated mice) and β-cell hyperplasia (leptin-deficient mice), using a standardized systematic sampling of pancreatic specimens. Our data indicate that automated analysis of virtual pancreatic slides is highly reliable and yields results consistent with those obtained by conventional morphometric analysis. This new methodology will allow investigators to dramatically reduce the time required for β-cell mass measurement by automating high-resolution image capture and analysis of entire pancreatic sections.
Treating Postlaparoscopic Surgery Shoulder Pain with Acupuncture
Authors: Kreindler Gur , Attias Samuel , Kreindler Anna , Hen Haim , Haj Bassel , Matter Ibrahim , Ben-Arye Eran , Schiff Elad
Published: April 2014

Objective. The purpose of this study was to examine the effect of acupuncture on postlaparoscopic shoulder pain (PLSP) which is a common side effect in patients undergoing abdominal laparoscopic surgery. Methods. Patients with moderate to severe PLSP in spite of analgesic treatment, which were referred by the medical staff to the Complementary‐Integrative Surgery Service (CISS) at our institution, were provided with acupuncture treatment. The severity of PLSP and of general pain was assessed using a Visual Analogue Scale (VAS) from 0 to 10. Pain assessment was conducted prior to and two hours following acupuncture treatment. Acupuncture treatment was individualized based on traditional Chinese medicine diagnosis. Results. A total of 25 patients were evaluated during a 14‐month period, from March 2011 to May 2012. A significant reduction in PLSP (mean reduction of 6.4 ± 2.3  P < 0.0001) and general pain (mean reduction 6.4 ± 2.1  P < 0.0001) were observed, and no significant side effects were reported. Conclusion. Individualized acupuncture treatments according to traditional Chinese medicine principles may improve postlaparoscopic shoulder pain and general pain when used in conjunction with conventional therapy. The primary findings of this study warrant verification in controlled studies.
Production of High Viscosity Chitosan from Biologically Purified Chitin Isolated by Microbial Fermentation and Deproteinization
Authors: Ploydee Ekkalak , Chaiyanan Saipin
Published: April 2014

The objective of this study was to produce high viscosity chitosan from shrimp chitin prepared by using a two-step biological treatment process: decalcification and deproteinization. Glucose was fermented withLactobacillus pentosusL7 to lactic acid. At a pH of3.9±0.1, the calcium carbonate of the shells was solubilized in 48 hours. The amounts of residual calcium in the form of ash (1.4±0.5%) and crude protein (23.2±2.5%) were further eliminated by the activity of proteolyticBacillus thuringiensisSA. After decalcification and deproteinization of the shrimp shells, residual calcium and crude protein of shrimp chitin flakes were1.7±0.4% and3.8±1.3%, respectively. Chitin was deacetylated with 50% NaOH at 121°C for 5 hours. After deacetylation, the chitosan had residual calcium, crude protein content, and degree of acetylation of1.6±0.6%,0.4±0.3%, and83.2±1.5%, respectively. The viscosity of chitosan prepared from chitin extracted by this two-step biological process was1,007±14.7 mPa·s, whereas chitosan prepared from chemically processed chitin had a viscosity of323±15.6  mPa·s, indicating that biologically purified chitin gave chitosan with a high quality.
Controlled Sink Mobility Algorithms for Wireless Sensor Networks
Authors: Koç Metin , Korpeoglu Ibrahim
Published: April 2014

A wireless sensor network (WSN) consists of hundreds or thousands of sensor nodes organized in an ad hoc manner to achieve a predefined goal. Although WSNs have limitations in terms of memory and processors, the main constraint that makes WSNs different from traditional networks is the battery problem which limits the lifetime of a network. Different approaches are proposed in the literature for improving the network lifetime, including data aggregation, energy efficient routing schemes, and MAC protocols. Sink node mobility is also an effective approach for improving the network lifetime. In this paper, we investigate controlled sink node mobility and present a set of algorithms for deciding where and when to move a sink node to improve network lifetime. Moreover, we give a load-balanced topology construction algorithm as another component of our solution. We did extensive simulation experiments to evaluate the performance of the components of our mobility scheme and to compare our solution with static case and random movement strategy. The results show that our algorithms are effective in improving network lifetime and provide significantly better lifetime compared to static sink case and random movement strategy.
Analysis of General Input State Dependent Working Vacation Queue with Changeover Time
Authors: Pikkala Vijaya Laxmi , Vepada Suchitra
Published: April 2014

We consider a finite buffer GI/M(n)/1 queue with multiple working vacations and changeover time, where the server can keep on working but at a slower speed during the vacation period. Moreover, the amount of service demanded by a customer is conditioned by the queue length at the moment service is begun for that customer. We provide a recursive algorithm using the supplementary variable technique to numerically compute the stationary queue length distribution of the system. Finally, some numerical results of the model are presented to show the parameter effect on various performance measures.
Robotic Upper Limb Rehabilitation after Acute Stroke by NeReBot: Evaluation of Treatment Costs
Authors: Stefano Masiero , Patrizia Poli , Mario Armani , Ferlini Gregorio , Rizzello Roberto , Rosati Giulio
Published: April 2014

Stroke is the first cause of disability. Several robotic devices have been developed for stroke rehabilitation. Robot therapy by NeReBot is demonstrated to be an effective tool for the treatment of poststroke paretic upper limbs, able to improve the activities of daily living of stroke survivors when used both as additional treatment and in partial substitution of conventional rehabilitation therapy in the acute and subacute phases poststroke. This study presents the evaluation of the costs related to delivering such therapy, in comparison with conventional rehabilitation treatment. By comparing several NeReBot treatment protocols, made of different combinations of robotic and nonrobotic exercises, we show that robotic technology can be a valuable and economically sustainable aid in the management of poststroke patient rehabilitation.
Effects of Canonical NF-κB Signaling Pathway on the Proliferation and Odonto/Osteogenic Differentiation of Human Stem Cells from Apical Papilla
Authors: Li Junjun , Yan Ming , Wang Zilu , Jing Shuanglin , Li Yao , Liu Genxia , Yu Jinhua , Fan Zhipeng
Published: April 2014

Background Information. NF-κB signaling pathway plays a complicated role in the biological functions of mesenchymal stem cells. However, the effects of NF-κB pathway on the odonto/osteogenic differentiation of stem cells from apical papilla (SCAPs) remain unclear. The present study was designed to evaluate the effects of canonical NF-κB pathway on the osteo/odontogenic capacity of SCAPsin vitro.Results. Western blot results demonstrated that NF-κB pathway in SCAPs was successfully activated by TNF-αor blocked by BMS-345541. NF-κB pathway-activated SCAPs presented a higher proliferation activity compared with control groups, as indicated by dimethyl-thiazol-diphenyl tetrazolium bromide assay (MTT) and flow cytometry assay (FCM). Wound scratch assay revealed that NF-κB pathway-activated SCAPs presented an improved migration capacity, enhanced alkaline phosphatase (ALP) activity, and upregulated mineralization capacity of SCAPs, as compared with control groups. Meanwhile, the odonto/osteogenic markers (ALP/ALP,RUNX2/RUNX2,OSX/OSX,OCN/OCN,OPN/OPN,BSP/BSP,DSPP/DSP, andDMP-1/DMP-1) in NF-κB pathway-activated SCAPs were also significantly upregulated as compared with control groups at both protein and mRNA levels. However, NF-κB pathway-inhibited SCAPs exhibited a lower proliferation/migration capacity, and decreased odonto/osteogenic ability in comparison with control groups.Conclusion. Our findings suggest that classical NF-κB pathway plays a paramount role in the proliferation and committed differentiation of SCAPs.