Understanding the value of social media in organisations : a taxonomic approach

While organizations strive to leverage the vast information generated daily from social media platforms and both decision makers and consultants are keen to identify and exploit this information’s value, there has been little research into social media in the business context. Social media are diverse, varying in scope and functionality, this diversity entailing a complex of attributes and characteristics, resulting in confusion for both researchers and organizations. Taxonomies are important precursors in emerging fields and are foundational for rigorous theory building. Though aspects of social media have been studied from various discipline perspectives, this work has been largely descriptive. Thus, while the need for a rigorous taxonomy of social media is strong, previous efforts to classify social media suffer limitations – e.g. lack of a systematic taxonomic method, overreliance on intuition, disregard for the users’ perspective, and inadequate consideration of purpose. Thus, this study was mainly initiated by the overarching question “How can social media in the business context be usefully classified?” In order to address this gap, the current paper proposes a systematic method for developing a taxonomy appropriate to study social media in organizations context, combining Nickerson et al,’s (2012) IS taxonomy building guidelines and a Repertory grid (RepGrid) approach.

Electrical insulation of high voltage inductor with co-axial electrode at floating voltage

Inductive fault current limiters (FCLs) have several advantages, such as significant current limitation, immediate triggering and relatively low losses. Despite these advantages, saturated core FCLs have not been commercialized due to its large size and associated high costs. A major remaining challenge is to reduce the footprint of the device. In this paper, a solution to reduce the overall footprint is proposed and discussed. In arrangements of windings on a core in reactors such as FCLs, the core is conventionally grounded. The electrical insulation distance between high voltage winding and core can be reduced if the core is left at floating potential. This paper shows the results of the investigation carried out on the insulation of such a coil-core assembly. Two experiments were conducted. In the first, the behavior of the apparatus under high voltage conditions was assessed by performing power frequency and lightning impulse tests. In the second experiment, a low voltage test was conducted during which voltages of different frequencies and pulses with varying rise times were applied. A finite element simulation was also carried out for comparison and further investigation

Expression profile of endothelin 1 and its receptor endothelin receptor A in papillary thyroid carcinoma and their correlations with clinicopathologic characteristics

The endothelin axis is a group of signaling molecules and their receptors that have been implicated in vascularization of cancers, with their expression being observed to change in different cancer types. In this research, we examined the expression of endothelin 1 and endothelin receptor A at the protein and messenger RNA (mRNA) levels in 123 papillary thyroid carcinomas and 40 matched lymph nodes with metastatic papillary thyroid carcinomas. We found altered endothelin axis mRNA expression in several clinicopathologic parameters with increased endothelin 1 expression in thyroid papillary carcinoma showing stromal calcification, cancers in men, and primary cancers with lymph node metastases. Increased endothelin receptor A mRNA expression was noted in the larger cancers. There is a significant correlation between expression of endothelin receptor A and endothelin 1 in papillary thyroid carcinoma. Both endothelin receptor A and endothelin 1 mRNA expressions were significantly higher in metastatic carcinoma in the lymph node than in primary thyroid cancer. The metastatic carcinoma in the lymph node had increased expression compared with matched primary thyroid carcinoma. Expressions of endothelin 1 and endothelin receptor A were also documented as being high at the protein level. Our results indicate that in thyroid cancer, endothelin 1 and endothelin receptor A are associated with growth in advanced stages and lymph node metastases, likely through known angiogenic linkages. Targeting the endothelin axis may be useful in planning angiogenesis therapy for thyroid cancer.

The impact of employment-level characteristics on work-life interference in school aged children

Work–life interference is important for school-aged workers because it influences their educational outcomes/career aspirations. Although research highlights the role of work hours in determining work–life interference for these workers, work/job-level characteristics have received limited attention. Using survey data from Queensland school students who work part-time, we assess the influence of a range of employment-level variables on work–life interference. The results of multiple regression analysis indicate work–life interference is exacerbated by having low trust in managers and limited scope to refuse work hours and stability in work hours, emphasising the importance of organisational variables in integrating work and non-work spheres for school-aged workers.

A loop cancellation based active damping solution for constant power instability in vehicular power systems

In recent years, electric propulsion systems have increasingly been used in land, sea and air vehicles. The vehicular power systems are usually loaded with tightly regulated power electronic converters which tend to draw constant power. Since the constant power loads (CPLs) impose negative incremental resistance characteristics on the feeder system, they pose a potential threat to the stability of vehicular power systems. This effect becomes more significant in the presence of distribution lines between source and load in large vehicular power systems such as electric ships and more electric aircrafts. System transients such as sudden drop of converter side loads or increase of constant power requirement can cause complete system instability. Most of the existing research work focuses on the modeling and stabilization of DC vehicular power systems with CPLs. Only a few solutions are proposed to stabilize AC vehicular power systems with non-negligible distribution lines and CPLs. Therefore, this paper proposes a novel loop cancellation technique to eliminate constant power instability in AC vehicular power systems with a theoretically unbounded system stability region. Analysis is carried out on system stability with the proposed method and simulation results are presented to validate its effectiveness.

High mobility top-gate and dual-gate polymer thin-film transistors based on diketopyrrolopyrrole-naphthalene copolymer

In this letter, the performance characteristics of top-gate and dual-gate thin-film transistors (TFTs) with active semiconductor layers consisting of diketopyrrolopyrrole-naphthalene copolymer are described. Optimized top-gate TFTs possess mobilities of up to 1 cm 2 /V s with low contact resistance and reduced hysteresis in air. Dual-gate devices possess higher drive currents as well as improved subthreshold and above threshold characteristics compared to single-gate devices. We also describe the reasons that dual-gate devices result in improved performance. The good stability of this polymer combined with their promising electrical properties make this material a very promising semiconductor for printable electronics.

3D-hybrid networks with controllable electrical conductivity from the electrochemical deposition of terthiophene-functionalized polyphenylene dendrimers

(Figure Presented) Unusual conductivity effects: Suitably functionalized dendrimers (see picture) are capable of forming truly covalent three-dimensional networks with remarkably high conductivity on electrochemical doping. Depending on the charging level of the electroactive components used as building blocks for the dendrimer core and the perimeter, two separated regimes of electrical conductivity can be observed.

A study of the effects metal residues in poly(9,9-dioctylfluorene) have on field-effect transistor device characteristics

The amount of metal residues from organometallic reagents used in preparation of poly(9,9-dioctylfluorene) by palladium catalysed Suzuki and nickel-induced Yamamoto polycondensations have been determined, and their effect upon the behaviour of the polymer in field-effect transistors (FETs) has been measured. The metal levels from material polymerised by Suzuki method were found to be much higher than from that made by the Yamamoto procedure. Simple treatment of the polymers with suitable metal trapping reagents lowered the metal levels significantly, with EDTA giving best results for nickel and triphenylphosphine for palladium. Comparison of the behaviour of FETs using polyfluorenes with varying levels of metal contamination, showed that the metal residues have little effect upon the mobility values, but often affect the degree of hysteresis, possibly acting as charge traps. Satisfactory device performances were obtained from polymer with palladium levels of 2000 μg/g suggesting that complete removal of metal residues may not be necessary for satisfactory device performance.

Domain-specific application analysis for customized instruction identification

With the increasing importance of Application Domain Specific Processor (ADSP) design, a significant challenge is to identify special-purpose operations for implementation as a customized instruction. While many methodologies have been proposed for this purpose, they all work for a single algorithm chosen from the target application domain. Such algorithm-specific approaches are not suitable for designing instruction sets applicable to a whole family of related algorithms. For an entire range of related algorithms, this paper develops a methodology for identifying compound operations, as a basis for designing “domain-specific” Instruction Set Architectures (ISAs) that can efficiently run most of the algorithms in a given domain. Our methodology combines three different static analysis techniques to identify instruction sequences common to several related algorithms: identification of (non-branching) instruction sequences that occur commonly across the algorithms; identification of instruction sequences nested within iterative constructs that are thus executed frequently; and identification of commonly-occurring instruction sequences that span basic blocks. Choosing different combinations of these results enables us to design domain-specific special operations with different desired characteristics, such as performance or suitability as a library function. To demonstrate our approach, case studies are carried out for a family of thirteen string matching algorithms. Finally, the validity of our static analysis results is confirmed through independent dynamic analysis experiments and performance improvement measurements.

A review selection method using product feature taxonomy

As of today, online reviews have become more and more important in decision making process. In recent years, the problem of identifying useful reviews for users has attracted significant attentions. For instance, in order to select reviews that focus on a particular feature, researchers proposed a method which extracts all associated words of this feature as the relevant information to evaluate and find appropriate reviews. However, the extraction of associated words is not that accurate due to the noise in free review text, and this affects the overall performance negatively. In this paper, we propose a method to select reviews according to a given feature by using a review model generated based upon a domain ontology called product feature taxonomy. The proposed review model provides relevant information about the hierarchical relationships of the features in the review which captures the review characteristics accurately. Our experiment results based on real world review dataset show that our approach is able to improve the review selection performance according to the given criteria effectively.

Automated diagnosis of Age-related Macular Degeneration using greyscale features from digital fundus images

Age-related Macular Degeneration (AMD) is one of the major causes of vision loss and blindness in ageing population. Currently, there is no cure for AMD, however early detection and subsequent treatment may prevent the severe vision loss or slow the progression of the disease. AMD can be classified into two types: dry and wet AMDs. The people with macular degeneration are mostly affected by dry AMD. Early symptoms of AMD are formation of drusen and yellow pigmentation. These lesions are identified by manual inspection of fundus images by the ophthalmologists. It is a time consuming, tiresome process, and hence an automated diagnosis of AMD screening tool can aid clinicians in their diagnosis significantly. This study proposes an automated dry AMD detection system using various entropies (Shannon, Kapur, Renyi and Yager), Higher Order Spectra (HOS) bispectra features, Fractional Dimension (FD), and Gabor wavelet features extracted from greyscale fundus images. The features are ranked using t-test, Kullback–Lieber Divergence (KLD), Chernoff Bound and Bhattacharyya Distance (CBBD), Receiver Operating Characteristics (ROC) curve-based and Wilcoxon ranking methods in order to select optimum features and classified into normal and AMD classes using Naive Bayes (NB), k-Nearest Neighbour (k-NN), Probabilistic Neural Network (PNN), Decision Tree (DT) and Support Vector Machine (SVM) classifiers. The performance of the proposed system is evaluated using private (Kasturba Medical Hospital, Manipal, India), Automated Retinal Image Analysis (ARIA) and STructured Analysis of the Retina (STARE) datasets. The proposed system yielded the highest average classification accuracies of 90.19%, 95.07% and 95% with 42, 54 and 38 optimal ranked features using SVM classifier for private, ARIA and STARE datasets respectively. This automated AMD detection system can be used for mass fundus image screening and aid clinicians by making better use of their expertise on selected images that require further examination.

Decision support system for age-related macular degeneration using discrete wavelet transform

Age-related macular degeneration (AMD) affects the central vision and subsequently may lead to visual loss in people over 60 years of age. There is no permanent cure for AMD, but early detection and successive treatment may improve the visual acuity. AMD is mainly classified into dry and wet type; however, dry AMD is more common in aging population. AMD is characterized by drusen, yellow pigmentation, and neovascularization. These lesions are examined through visual inspection of retinal fundus images by ophthalmologists. It is laborious, time-consuming, and resource-intensive. Hence, in this study, we have proposed an automated AMD detection system using discrete wavelet transform (DWT) and feature ranking strategies. The first four-order statistical moments (mean, variance, skewness, and kurtosis), energy, entropy, and Gini index-based features are extracted from DWT coefficients. We have used five (t test, Kullback–Lieber Divergence (KLD), Chernoff Bound and Bhattacharyya Distance, receiver operating characteristics curve-based, and Wilcoxon) feature ranking strategies to identify optimal feature set. A set of supervised classifiers namely support vector machine (SVM), decision tree, k -nearest neighbor ( k -NN), Naive Bayes, and probabilistic neural network were used to evaluate the highest performance measure using minimum number of features in classifying normal and dry AMD classes. The proposed framework obtained an average accuracy of 93.70 %, sensitivity of 91.11 %, and specificity of 96.30 % using KLD ranking and SVM classifier. We have also formulated an AMD Risk Index using selected features to classify the normal and dry AMD classes using one number. The proposed system can be used to assist the clinicians and also for mass AMD screening programs.

Topological localization using optical flow descriptors

We propose a topological localization method based on optical flow information. We analyse the statistical characteristics of the optical flow signal and demonstrate that the flow vectors can be used to identify and describe key locations in the environment. The key locations (nodes) correspond to significant scene changes and depth discontinuities. Since optical flow vectors contain position, magnitude and angle information, for each node, we extract low and high order statistical moments of the vectors and use them as descriptors for that node. Once a database of nodes and their corresponding optical flow features is created, the robot can perform topological localization by using the Mahalanobis distance between the current frame and the database. This is supported by field trials, which illustrate the repeatability of the proposed method for detecting and describing key locations in indoor and outdoor environments in challenging and diverse lighting conditions.

An iterative speaker re-diarization scheme for improving speaker-based entity extraction in multimedia archives

In this paper we present a novel scheme for improving speaker diarization by making use of repeating speakers across multiple recordings within a large corpus. We call this technique speaker re-diarization and demonstrate that it is possible to reuse the initial speaker-linked diarization outputs to boost diarization accuracy within individual recordings. We first propose and evaluate two novel re-diarization techniques. We demonstrate their complementary characteristics and fuse the two techniques to successfully conduct speaker re-diarization across the SAIVT-BNEWS corpus of Australian broadcast data. This corpus contains recurring speakers in various independent recordings that need to be linked across the dataset. We show that our speaker re-diarization approach can provide a relative improvement of 23% in diarization error rate (DER), over the original diarization results, as well as improve the estimated number of speakers and the cluster purity and coverage metrics.