State-of-the-art review and critical success factors for mobile business intelligence

Due to ubiquitous information requirements, market interest in mobile business intelligence (BI) has grown markedly. However, mobile BI market is a relatively new area that has been driven primarily by the IT industry. Yet, there is a lack of systematic study on the critical success factors for mobile BI. This research reviews the state-of-the-art of mobile BI, and explores the critical success factors based on a rigorous examination of the academic and practitioner literature. The study reveals that critical success factors of mobile BI generally fall into four key dimensions, namely security, mobile technology, system content and quality, and organisational support perspectives. The various research findings will be useful to organisations which are considering or undertaking mobile business intelligence initiatives.

The brachyspira hyodysenteriae ftnA gene : DNA vaccination and real-time PCR quantification of bacteria in a mouse model of disease

The nucleotide sequence of the Brachyspira hyodysenteriae ftnA gene, encoding a putative ferritin protein (FtnA), was determined. Analysis of the sequence predicted that this gene encoded a protein of 180 amino acids. RT-PCR and Western blot showed that the ftnA gene was expressed in B. hyodysenteriae, and evidence suggests that FtnA stores iron rather than haem. ftnA was delivered as DNA and recombinant protein vaccines in a mouse model of B. hyodysenteriae infection. Vaccine efficacy was monitored by caecal pathology and quantification of B. hyodysenteriae numbers in the caeca of infected mice by real-time PCR.

A decision-making model to choose business intelligence platforms for organizations

In recent time, technology applications in different fields, especially Business Intelligence (BI) have been developed rapidly and considered to be one of the most significant uses of information technology with special position reserved. The application of BI systems provides organizations with a sense of superiority in the competitive environment. Despite many advantages, the companies applying such systems may also encounter problems in decision-making process because of the highly diversified interactions within the systems. Hence, the choice of a suitable BI platform is important to take the great advantage of using information technology in all organizational fields. The current research aims at addressing the problems existed in the organizational decision-making process, proposing and implementing a suitable BI platform using Iranian companies as case study. The paper attempts to present a solitary model based on studying different methods in BI platform choice and applying the chosen BI platform for different decisionmaking processes. The results from evaluating the effectiveness of subsequently implementing the model for Iranian Industrial companies are discussed.

Classifying human actions using an incomplete real-time pose skeleton

Currently, most human action recognition systems are trained with feature sets that have no missing data. Unfortunately, the use of human pose estimation models to provide more descriptive features also entails an increased sensitivity to occlusions, meaning that incomplete feature information will be unavoidable for realistic scenarios. To address this, our approach is to shift the responsibility for dealing with occluded pose data away from the pose estimator and onto the action classifier. This allows the use of a simple, real-time pose estimation (stick-figure) that does not estimate the positions of limbs it cannot find quickly. The system tracks people via background subtraction and extracts the (possibly incomplete) pose skeleton from their silhouette. Hidden Markov Models modified to handle missing data are then used to successfully classify several human actions using the incomplete pose features.

Supporting business intelligence usage: an integrated framework with automatic weighting

This paper presents an integrated framework that comprises an automatic weighting method for assessing data quality (DQ) of the framework so as to better support the business intelligence (BI) usage. Specifically, we utilize business process modeling (BPM) notation and information product map and frame them into a hierarchical mapping structure. Furthermore, we develop and demonstrate an automatic weight-assignment method for evaluating critical dimensions (i.e., completeness and accuracy) of DQ of the integrated framework. Through a design science paradigm, the effectiveness of the framework and the associated DQ weighting method has been rigorously validated by faculty management users of a university. The framework together with the DQ weighting method builds user confidence by enhancing the traceability of a BI product. The automatic DQ weight assignment also provides better time efficiency because the weight of each data attribute is determined automatically based on its usage on the BI dashboard.

Effect of creatine supplementation on housekeeping genes in human skeletal muscle using real-time RT-PCR

The present study examined the validity and reliability of measuring the expression of various genes in human skeletal muscle using quantitative real-time RT-PCR on a GeneAmp 5700 sequence detection system with SYBR Green 1 chemistry. In addition, the validity of using some of these genes as endogenous controls (i.e., housekeeping genes) when human skeletal muscle was exposed to elevated total creatine levels and exercise was also examined. For all except 28S, linear relationships between the logarithm of the starting RNA concentrations and the cycle threshold (C_T) values were established for ß-actin, ß2-microglobulin (ß₂M), cyclophilin (CYC), and glyceraldehyde-3-phosphate dehydrogenase (GAPDH). We found a linear response between C_T values and the logarithm of a given amount of starting cDNA for all the genes tested. The overall intra-assay coefficient of variance for these genes was 1.3% and 21% for raw C_T values and the linear value of 2^-C_T, respectively. Interassay variability was 2.3% for raw C_T values and 34% for the linear value of 2^-C_T. We also examined the expression of various housekeeping genes in human skeletal muscle at days 0, 1, and 5 following oral supplementation with either creatine or a placebo employing a double-blind crossover study design. Treatments were separated by a 5-wk washout period. Immediately following each muscle sampling, subjects performed two 30-s all-out bouts on a cycle ergometer. Creatine supplementation increased (P < 0.05) muscle total creatine content above placebo levels; however, there were no changes (P > 0.05) in C_T values across the supplementation periods for any of the genes. Nevertheless, 95% confidence intervals showed that GAPDH was variable, whereas ß-actin, ß₂M, and CYC were the least varying genes. Normalization of the data to these housekeeping genes revealed variable behavior for ß₂M with more stable expressions for both ß-actin and CYC. We conclude that, using real-time RT-PCR, ß-actin or CYC may be used as housekeeping genes to study gene expression in human muscle in experiments employing short-term creatine supplementation combined with high-intensity exercise.

Providing real-time information services for public bus commuters

This paper investigates RTIS for public bus commuters; identifying their needs and proposing the architecture and design for this service. Considering that the service needs to be extensible, adaptable and interoperative, the new system is based on the use of XML Web Services. This gives the structure flexibility for further development and integration with other like services.

Miscibility, crystallization κinetics and real-time small-Angle x-ray scattering investigation of the semicrystalline morphology in thermosetting polymer blends of epoxy resin and poly(ethylene oxide)

Thermosetting polymer blends of poly(ethylene oxide) (PEO) and bisphenol-A-type epoxy resin (ER) were prepared using 4,4′-methylenebis(3-chloro-2,6-diethylaniline) (MCDEA) as curing agent. The miscibility and crystallization behavior of MCDEA-cured ER/PEO blends were investigated by differential scanning calorimetry (DSC). The existence of a single composition-dependent glass transition temperature (Tg) indicates that PEO is completely miscible with MCDEA-cured ER in the melt and in the amorphous state over the entire composition range. Fourier-transform infrared (FTIR) investigations indicated hydrogen-bonding interaction between the hydroxyl groups of MCDEA-cured ER and the ether oxygens of PEO in the blends, which is an important driving force for the miscibility of the blends. The average strength of the hydrogen bond in the cured ER/PEO blends is higher than in the pure MCDEA-cured ER. Crystallization kinetics of PEO from the melt is strongly influenced by the blend composition and the crystallization temperature. At high conversion, the time dependence of the relative degree of crystallinity deviated from the Avrami equation. The addition of a non-crystallizable ER component into PEO causes a depression of both the overall crystallization rate and the melting temperature. The surface free energy of folding σe displays a minimum with variation of composition. The spherulitic morphology of PEO in the ER/PEO blends exhibits typical characteristics of miscible crystalline/amorphous blends, and the PEO spherulites in the blends are always completely volume-filling. Real-time small-angle X-ray scattering (SAXS) experiments reveal that the long period L increases drastically with increasing ER content at the same temperatures. The amorphous cured ER component segregates interlamellarly during the crystallization process of PEO because of the low chain mobility of the cured ER. A model describing the semicrystalline morphology of MCDEA-cured ER/PEO blends is proposed based on the SAXS results. The semicrystalline morphology is a stack of crystalline lamellae; the amorphous fraction of PEO, the branched ER chains and imperfect ER network are located between PEO lamellae.

Real time, perceived time and time online : a review of student experiences of time over thirty years of distance and online education

This paper examines how students use and perceive time when studying in distance education modes and what affects this perception of time and the reality of time. We examine 30 years of student involvement on distance and online education, their comments on both their learning experiences, and the technology requirements of distance education/online learning. Our University has been involved in distance education since its formation in 1974. The online technologies offer increasingly sophisticated and immersive experiences for our students, both on campus and off campus, but many of our students continue to complain of time squeeze, and fail to predict the time it will take them to complete our subjects. We research how the technologies we use for online learning are contributing to this time squeeze perception and the student's "real" time to learn.
Research is drawn from both the Australian Bureau of Statistics and surveys of our students' experiences (we have 32,000 students online, with single online classes of over 1300 students), to examine student use and perceptions of their available time to study and how the technologies used in online learning affect this.

Poesis in diegesis : real-time interactive dance performance

This paper describes the processes of creating a hybrid experience of live performance and virtual imagery in 1 + x: Mid-range Projections, a dance work that uses real-time processing of live feed video to create a duet between dancers and their virtual images. 1 + x is about creating experiences of interiority and intimacy, ironically, through the juxtaposition of virtual images of performers with their real selves. This paper examines how the ideal of reinserting intimacy, humanity and 'presence' into a technologically generated image space, in the case of 1 + x, depends on the precise and delicate manipulation of live-feed video in collaboration with the audience's experience of interactivity. The result is a performance work that creates a poesis of 'presence', through a diegesis that melds real and virtual images on the same screen and the same conceptual and spatial plane - a 'becoming virtual'.

A video-based real-time vehicle detection method by classified background learning

A new two-level real-time vehicle detection method is proposed in order to meet the robustness and efficiency requirements of real world applications. At the high level, pixels of the background image are classified into three categories according to the characteristics of Red, Green, Blue (RGB) curves. The robustness of the classification is further enhanced by using
line detection and pattern connectivity. At the lower level, an exponential forgetting algorithm with adaptive parameters for different categories is utilised to calculate the background and reduce the distortion by the small motion of video cameras. Scene tests show that the proposed method is more robust and faster than previous methods, which is very suitable for real-time vehicle detection in outdoor environments, especially concerning locations where the level of illumination changes frequently and speed detection is important.

RFID-based real-time smart waste management system

In an environmental context, the use of RFID (radio frequency identification) and load cell sensor technology can be employed for not only bringing down waste management costs, but also to facilitate automating and streamlining waste (e.g., garbage, recycling, and green) identification and weight measurement processes for designing smart waste management systems. In this paper, we outline a RFID and sensor model for designing a system in real-time waste management. An application of the architecture is described in the area of RFID and sensor based automatic waste identity, weight, and stolen bins identification system (WIWSBIS).

Delay control and parallel admission algorithms for real-time anycast flow

An anycast flow is a flow that can be connected to any one of the members in a group of designated (replicated) servers (called anycast group). In this paper, we derive a set of formulas for calculating the end-to-end delay bound for the anycast flows and present novel admission control algorithms for anycast flows with real-time constraints. Given such an anycast group, our algorithms can effectively select the paths for anycast flows' admission and connection based on the least end-to-end delay bounds evaluated. We also present a parallel admission control algorithm that can effectively calculate the available paths with a short delay bound for different destinations in the anycast group so that a best path with the shortest delay bound can be chosen.