Web service to deliver filtered RSS items to a mobile application

In the past decade there has been massive growth of data on the internet. Many people rely on XML based RSS feeds to receive updates from websites. In this paper, we propose a method for managing the RSS feeds from various news websites. A web service is developed to deliver filtered news items from RSS feeds to a mobile client. Each news item is indexed, subsequently, the indexes are used for filtering news items. Indexing is done in two steps. First, classical text categorization algorithms are used to assign a category to each news item, second, geoparsing is used to assign geolocation data to each news item. An android application is developed to access filtered news items by consuming the proposed web service. A prototype is implemented using Rapid miner 5.0 as the data mining tool and SVM as the classification algorithm. Geoparsing and geocoding web services, and Android API are used to implement location-based access to news items. Experimental results prove that the proposed approach is effective and saves a significant amount of information overload processing time.

The impact of an exercise physiologist coordinated resistance exercise program on the physical function of people receiving hemodialysis: a stepped wedge randomised control study

Background:

Stressors, Recovery Processes, and Manifestations of Training Distress in Dance

Abstract
Dancers are expected to maintain consistently high levels of performance capability and to perform on demand. To meet these expectations, they subject their bodies to long hours of intensive physical training. Such training regimens are often combined with tight rehearsal and performance schedules, which over time, can lead to persistent fatigue, psychological distress, performance decrements, and injury. A similar process has been observed as a consequence of high-intensity training in many different sports, and considerable sport-related research has been devoted to identifying the antecedents, the symptoms that are experienced, and the most cost-effective ways of monitoring symptom development. This paper presents a general heuristic framework for understanding this “training distress process” and discusses the framework with specific reference to dance.

The small molecule, genistein, increases hepcidin expression in human hepatocytes

Hepcidin, a peptide hormone that decreases intestinal iron absorption and macrophage iron release, is a potential drug target for patients with iron overload syndromes because its levels are inappropriately low in these individuals. Endogenous stimulants of Hepcidin transcription include bone morphogenic protein 6 (BMP6) and interleukin-6 (IL-6) by effects on mothers against decapentaplegic homolog (Smad)4 or signal transducer and activator of transcription (Stat)3, respectively. We conducted a small-scale chemical screen in zebrafish embryos to identify small molecules that modulate hepcidin expression. We found that treatment with the isoflavone, genistein, from 28-52 hours postfertilization in zebrafish embryos enhanced Hepcidin transcript levels, as assessed by whole-mount in situ hybridization and quantitative real-time reverse-transcriptase polymerase chain reaction. Genistein's stimulatory effect was conserved in human hepatocytes: Genistein treatment of HepG2 cells increased both Hepcidin transcript levels and promoter activity. We found that genistein's effect on Hepcidin expression did not depend on estrogen receptor signaling or increased cellular iron uptake, but was impaired by mutation of either BMP response elements or the Stat3-binding site in the Hepcidin promoter. RNA sequencing of transcripts from genistein-treated hepatocytes indicated that genistein up-regulated 68% of the transcripts that were up-regulated by BMP6; however, genistein raised levels of several transcripts involved in Stat3 signaling that were not up-regulated by BMP6. Chromatin immunoprecipitation and ELISA experiments revealed that genistein enhanced Stat3 binding to the Hepcidin promoter and increased phosphorylation of Stat3 in HepG2 cells. Conclusion: Genistein is the first small-molecule experimental drug that stimulates Hepcidin expression in vivo and in vitro. These experiments demonstrate the feasibility of identifying and characterizing small molecules that increase Hepcidin expression. Genistein and other candidate molecules may subsequently be developed into new therapies for iron overload syndromes.

“Digital Literacy” in #legaled, begins here?

Discusses issues of interaction, enablement, social justice, flexibility, overload, and industrialisation in legal education and practical legal training, with reference to flexible, online and distance education and "digital literacy".

Applied econometrics and implications for energy economics research

Developments in applied econometrics, particularly with regard to unit root tests and cointegration tests, have motivated a rich empirical literature on energy economics over the last decade. This study reviews recent developments in time series econometrics applications in the energy economics literature. We first consider the literature on the integration properties of energy variables. We begin with a discussion of the implications of whether energy variables contain a unit root and proceed to examine how results differ according to the specific unit root or stationarity test employed. We then proceed to examine recent developments in the literature on cointegration, Granger causality and long-run estimates between (disaggregated) energy consumption and economic growth. We review both single country and panel studies and pay particular attention to studies which have expanded the literature through adding variables such as financial development and trade, in addition to energy consumption to the augmented production function, as well as studies which have extended the literature through examining disaggregated energy consumption by type. In each case we highlight best practice in the literature, point to limitations in the literature, including econometric modeling challenges, and suggest recommendations for future research. A key message of our survey is that the profession needs to guard against 'overload' of research in these areas as most applied studies are no longer adding anything more to what is already known. © 2014 Elsevier B.V. All rights reserved.

An evaluation of the impact of using authentic design and build industry projects in project-based learning

BACKGROUNDChisholm’s ‘first year experience’ is a significant feature of the new industry focused Bachelor of Engineering Technology program delivered in association with the South East Melbourne Manufacturers’ Alliance (SEMMA). This conceive-design-implement-operate (CDIO Initiative) program commenced as a full time program in first semester 2012. Whereas it is common for CDIO Initiative programs to have a first year experience program containing a project typical of the type of industry project they would complete as a graduate engineer or engineering technologist, this goes further by using real industry projects provided by SEMMA members.This design-and-build industry project runs across both semesters supporting project-based learning in three first year subjects. A concern is that the industry involvement of the projects adds substantially to an already heavy student workload. This has been further increased by the addition of two additional first year initiatives: writing workshops, and training in, and substantial use of, student oral presentations. It is recognised that an excessive workload could lead students to adopt surface learning approaches in other subjects.PURPOSEThe goal of the project is to evaluate student perceptions of the value and work load impact of the industry project and the other new first year initiatives.DESIGN/METHODCentral to this project is a student survey-based evaluation of the industry project based learning that is the core of the ‘first year experience’. The participants were limited to the small group of students who, in a single year, completed all three subjects that comprise the ‘first year experience’. To avoid compromising the results the survey was administered by Chisholm Institute’s Department of Strategy and Planning with no engineering technology degree program staff present. The survey included questions to enable responses to be linked with specific student demographics without identifying any of the respondents.RESULTSThe study showed the industry project-based learning had worthwhile outcomes but placed considerable time pressures on most respondents. For some, this also impacted on their other subjects. A first year oral presentation program was also shown to have worthwhile outcomes. However no conclusions could be reliably drawn on the third initiative – writing workshops.CONCLUSIONSThe results confirm that the authentic industry project is considered a worthwhile initiative but contributes significantly to student overload. This applies also – to a lesser extent – to the first year oral presentation program. Both also require new approaches to delivery as student numbers increase. Strategies to address these issues are discussed.

A block-aware hybrid data dissemination with hotspot elimination in wireless sensor network

As a significant milestone in the data dissemination of wireless sensor networks (WSNs), the comb-needle (CN) model was developed to dynamically balance the sensor data pushing and pulling during hybrid data dissemination. Unfortunately, the hybrid push-pull data dissemination strategy may overload some sensor nodes and form the hotspots that consume energy significantly. This usually leads to the collapse of the network at a very early stage. In the past decade, although many energy-aware dynamic data dissemination methods have been proposed to alleviate the hotspots issue, the block characteristic of sensor nodes has been overlooked and how to offload traffic from hot blocks with low energy through long-distance hybrid dissemination remains an open problem. In this paper, we developed a block-aware data dissemination model to balance the inter-block energy and eliminate the spreading of intra-block hotspots. Through the clustering mechanism based on geography and energy, "similar" large-scale sensor nodes can be efficiently grouped into specific blocks to form the global block information (GBI). Based on GBI, the long-distance block-cross hybrid algorithms are further developed by effectively aggregating inter-block and intra-block data disseminations. Extensive experimental results demonstrate the capability and the efficiency of the proposed approach. © 2014 Elsevier Ltd.

SVD-based incremental approaches for recommender systems

Due to the serious information overload problem on the Internet, recommender systems have emerged as an important tool for recommending more useful information to users by providing personalized services for individual users. However, in the “big data“ era, recommender systems face significant challenges, such as how to process massive data efficiently and accurately. In this paper we propose an incremental algorithm based on singular value decomposition (SVD) with good scalability, which combines the Incremental SVD algorithm with the Approximating the Singular Value Decomposition (ApproSVD) algorithm, called the Incremental ApproSVD. Furthermore, strict error analysis demonstrates the effectiveness of the performance of our Incremental ApproSVD algorithm. We then present an empirical study to compare the prediction accuracy and running time between our Incremental ApproSVD algorithm and the Incremental SVD algorithm on the MovieLens dataset and Flixster dataset. The experimental results demonstrate that our proposed method outperforms its counterparts.

The role of general and specific stressors in the health and well-being of call centre operators

The call centre industry has developed a reputation for generating a highly stressful work environment with high absenteeism and turnover rates. Research has identified role ambiguity, role conflict, role overload, and work-family conflict as common stressors in other settings. Call centre research has additionally identified performance monitoring, job design and job opportunities as call centre specific stressors.OBJECTIVE AND METHODS: This study investigated the impact of the identified stressors on burnout, somatic symptomology, and turnover intent among 126 call centre representatives (CCRs) from 11 call centres in metropolitan Melbourne, Australia.

Marginalisation of dental students in a shared medical and dental education programme

OBJECTIVES: Internationally, there are a number of universities at which medical and dental education programmes share common elements. There are no studies about the experiences of medical and dental students enrolled in different programmes who share significant amounts of learning and teaching. METHODS: Semi-structured interviews and focus groups were conducted with 36 students and staff in a learning programme shared between separate medical and dental faculties. They were transcribed and an iterative process of interpretation and analysis within the theoretical framework of the contact hypothesis and social identity theory was used to group data into themes and sub-themes. RESULTS: Dental students felt 'marginalised' and felt they were treated as 'second-class citizens' by medical students and medical staff in the shared aspects of their programmes. Contextual factors such as the geographical location of the two schools, a medical : dental student ratio of almost 3 : 1, along with organisational factors such as curriculum overload, propagated negative attitudes towards and professional stereotyping of the dental students. Lack of understanding by medical students and faculty of dental professional roles contributed further. CONCLUSIONS: Recommendations for reducing the marginalisation of dental students in this setting include improving communication between faculties and facilitating experiential contact. This might be achieved through initiating a common orientation session, stronger social networks and integrated learning activities, such as interprofessional problem-based learning and shared clinical experiences.

A graph-based model for context-aware recommendation using implicit feedback data

Recommender systems have been successfully dealing with the problem of information overload. However, most recommendation methods suit to the scenarios where explicit feedback, e.g. ratings, are available, but might not be suitable for the most common scenarios with only implicit feedback. In addition, most existing methods only focus on user and item dimensions and neglect any additional contextual information, such as time and location. In this paper, we propose a graph-based generic recommendation framework, which constructs a Multi-Layer Context Graph (MLCG) from implicit feedback data, and then performs ranking algorithms in MLCG for context-aware recommendation. Specifically, MLCG incorporates a variety of contextual information into a recommendation process and models the interactions between users and items. Moreover, based on MLCG, two novel ranking methods are developed: Context-aware Personalized Random Walk (CPRW) captures user preferences and current situations, and Semantic Path-based Random Walk (SPRW) incorporates semantics of paths in MLCG into random walk model for recommendation. The experiments on two real-world datasets demonstrate the effectiveness of our approach.

Predictive data mining for converged internet of things: a mobile health perspective

Mobile Health (mHealth) is now emerging with Internet of Things (IoT), Cloud and big data along with the prevalence of smart wearable devices and sensors. There is also the emergence of smart environments such as smart homes, cars, highways, cities, factories and grids. Presently, it is difficult to quickly forecast or prevent urgent health situations in real-time as health data are analyzed offline by a physician. Sensors are expected to be overloaded by demands of providing health data from IoT networks and smart environments. This paper proposes to resolve the problems by introducing an inference system so that life-threatening situations can be prevented in advance based on a short and long term health status prediction. This prediction is inferred from personal health information that is built by big data in Cloud. The inference system can also resolve the problem of data overload in sensor nodes by reducing data volume and frequency to reduce workload in sensor nodes. This paper presents a novel idea of tracking down and predicting a personal health status as well as intelligent functionality of inference in sensor nodes to interface IoT networks

What are the perceived learning needs of Australian general practice registrars for quality prescribing?

BACKGROUND: Little is known about the perceived learning needs of Australian general practice (GP) registrars in relation to the quality use of medicines (QUM) or the difficulties experienced when learning to prescribe. This study aimed to address this gap. METHODS: GP registrars' perceived learning needs were investigated through an online national survey, interviews and focus groups. Medical educators' perceptions were canvassed in semi-structured interviews in order to gain a broader perspective of the registrars' needs. Qualitative data analysis was informed by a systematic framework method involving a number of stages. Survey data were analysed descriptively. RESULTS: The two most commonly attended QUM educational activities took place in the workplace and through regional training providers. Outside of these structured educational activities, registrars learned to prescribe mainly through social and situated means. Difficulties encountered by GP registrars included the transition from hospital prescribing to prescribing in the GP context, judging how well they were prescribing and identifying appropriate and efficient sources of information at the point of care. CONCLUSIONS: GP registrars learn to prescribe primarily and opportunistically in the workplace. Despite many resources being expended on the provision of guidelines, decision-support systems and training, GP registrars expressed difficulties related to QUM. Ways of easing the transition into GP and of managing the information 'overload' related to medicines (and prescribing) in an evidence-guided, efficient and timely manner are needed. GP registrars should be provided with explicit feedback about the process and outcomes of prescribing decisions, including the use of audits, in order to improve their ability to judge their own prescribing.

Dimensions and metrics for evaluating recommendation systems

Recommendation systems support users and developers of various computer and software systems to overcome information overload, perform information discovery tasks, and approximate computation, among others. They have recently become popular and have attracted a wide variety of application scenarios ranging from business process modeling to source code manipulation. Due to this wide variety of application domains, different approaches and metrics have been adopted for their evaluation. In this chapter, we review a range of evaluation metrics and measures as well as some approaches used for evaluating recommendation systems. The metrics presented in this chapter are grouped under sixteen different dimensions, e.g., correctness, novelty, coverage. We review these metrics according to the dimensions to which they correspond. A brief overview of approaches to comprehensive evaluation using collections of recommendation system dimensions and associated metrics is presented. We also provide suggestions for key future research and practice directions.