Effects of entrepreneurial knowledge on entrepreneurial intentions: a longitudinal study of selected South-east Asian business students

Drawing on the theory of planned behaviour, this study examines the direct and indirect effects of knowledge gained from a formal entrepreneurship education programme on an individual’s entrepreneurial intentions (EI). It tracks the changes in students’ entrepreneurial knowledge (EK), perceptions of desirability of, and self-efficacy in, engaging in entrepreneurship and the impact of those changes on students’ EI upon completion of an entrepreneurship course. It uses longitudinal survey data of 245 business students in a Philippine university. Using cross-lagged panel method and partial-least squares-based structural equation modelling, the study builds and tests the measurement and structural models to examine the hypothesised interactions of EK, perceived desirability of, self-efficacy towards entrepreneurship, and EI. The findings underscore the importance of developing knowledge to nurture students’ self-confidence and attitudinal propensity to engage in entrepreneurship.

Empirical investigation of decision tree ensembles for monitoring cardiac complications of diabetes

Cardiac complications of diabetes require continuous monitoring since they may lead to increased morbidity or sudden death of patients. In order to monitor clinical complications of diabetes using wearable sensors, a small set of features have to be identified and effective algorithms for their processing need to be investigated. This article focuses on detecting and monitoring cardiac autonomic neuropathy (CAN) in diabetes patients. The authors investigate and compare the effectiveness of classifiers based on the following decision trees: ADTree, J48, NBTree, RandomTree, REPTree, and SimpleCart. The authors perform a thorough study comparing these decision trees as well as several decision tree ensembles created by applying the following ensemble methods: AdaBoost, Bagging, Dagging, Decorate, Grading, MultiBoost, Stacking, and two multi-level combinations of AdaBoost and MultiBoost with Bagging for the processing of data from diabetes patients for pervasive health monitoring of CAN. This paper concentrates on the particular task of applying decision tree ensembles for the detection and monitoring of cardiac autonomic neuropathy using these features. Experimental outcomes presented here show that the authors' application of the decision tree ensembles for the detection and monitoring of CAN in diabetes patients achieved better performance parameters compared with the results obtained previously in the literature.

A data mining framework for electricity consumption analysis from meter data

This paper presents a novel data mining framework for the exploration and extraction of actionable knowledge from data generated by electricity meters. Although a rich source of information for energy consumption analysis, electricity meters produce a voluminous, fast-paced, transient stream of data that conventional approaches are unable to address entirely. In order to overcome these issues, it is important for a data mining framework to incorporate functionality for interim summarization and incremental analysis using intelligent techniques. The proposed Incremental Summarization and Pattern Characterization (ISPC) framework demonstrates this capability. Stream data is structured in a data warehouse based on key dimensions enabling rapid interim summarization. Independently, the IPCL algorithm incrementally characterizes patterns in stream data and correlates these across time. Eventually, characterized patterns are consolidated with interim summarization to facilitate an overall analysis and prediction of energy consumption trends. Results of experiments conducted using the actual data from electricity meters confirm applicability of the ISPC framework.

Expression of muscarinic receptor subtypes in tree shrew ocular tissues and their regulation during the development of myopia

Muscarinic receptors are known to regulate several important physiologic processes in the eye. Antagonists to these receptors such as atropine and pirenzepine are effective at stopping the excessive ocular growth that results in myopia. However, their site of action is unknown. This study details ocular muscarinic subtype expression within a well documented model of eye growth and investigates their expression during early stages of myopia induction. Total RNA was isolated from tree shrew corneal, iris/ciliary body, retinal, choroidal, and scleral tissue samples and was reverse transcribed. Using tree shrew-specific primers to the five muscarinic acetylcholine receptor subtypes (CHRM1-CHRM5), products were amplified using polymerase chain reaction (PCR) and their identity confirmed using automated sequencing. The expression of the receptor proteins (M1-M5) were also explored in the retina, choroid, and sclera using immunohistochemistry. Myopia was induced in the tree shrew for one or five days using monocular deprivation of pattern vision, and the expression of the receptor subtypes was assessed in the retina, choroid, and sclera using real-time PCR. All five muscarinic receptor subtypes were expressed in the iris/ciliary body, retina, choroid, and sclera while gene products corresponding to CHRM1, CHRM3, CHRM4, and CHRM5 were present in the corneal samples. The gene expression data were confirmed by immunohistochemistry with the M1-M5 proteins detected in the retina, choroid, and sclera. After one or five days of myopia development, muscarinic receptor gene expression remained unaltered in the retinal, choroidal, and scleral tissue samples. This study provides a comprehensive profile of muscarinic receptor gene and protein expression in tree shrew ocular tissues with all receptor subtypes found in tissues implicated in the control of eye growth. Despite the efficacy of muscarinic antagonists at inhibiting myopia development, the genes of the muscarinic receptor subtypes are neither regulated early in myopia (before measurable axial elongation) nor after significant structural change.

Managing the implementation of business intelligence systems : a critical success factors framework

The implementation of a BI system is a complex undertaking requiring considerable resources. Yet there is a limited authoritative set of CSFs for management reference. This article represents a first step of filling in the research gap. The authors utilized the Delphi method to conduct three rounds of studies with 15 BI system experts in the domain of engineering asset management organizations. The study develops a CSFs framework that consists of seven factors and associated contextual elements crucial for BI systems implementation. The CSFs are committed management support and sponsorship, business user-oriented change management, clear business vision and well-established case, business-driven methodology and project management, business-centric championship and balanced project team composition, strategic and extensible technical framework, and sustainable data quality and governance framework. This CSFs framework allows BI stakeholders to holistically understand the critical factors that influence implementation success of BI systems.

An empirical study of business intelligence impact on corporate performance management

Business intelligence technologies have received much attention recently from both academics and practitioners. However, the impact of business intelligence (BI) on corporate performance management (CPM) has not yet been investigated. To address this gap, we conducted a large-scale survey collecting data from 337 senior managers. Partial least square method was employed to analyse the survey data. Findings suggest that the more effective the BI implementation, the more effective the CPM-related planning and analytic practices. Interestingly, size and industry sector do not influence the relationships between BI effectiveness and the CPM. This research offers a number of implications for theory and practice.

Rapid discrimination and determination of polyunsaturated fatty acid composition in marine oils by FTIR Spectroscopy and Multivariate Data Analysis

A rapid analytical approach for discrimination and quantitative determination of polyunsaturated fatty acid (PUFA) contents, particularly eicosapentaenoic acid (EPA) and docosahexaenoic acid (DHA), in a range of oils extracted from marine resources has been developed by using attenuated total reflection Fourier transform infrared spectroscopy and multivariate data analysis. The spectral data were collected without any sample preparation; thus, no chemical preparation was involved, but data were rather processed directly using the developed spectral analysis platform, making it fast, very cost effective, and suitable for routine use in various biotechnological and food research and related industries. Unsupervised pattern recognition techniques, including principal component analysis and unsupervised hierarchical cluster analysis, discriminated the marine oils into groups by correlating similarities and differences in their fatty acid (FA) compositions that corresponded well to the FA profiles obtained from traditional lipid analysis based on gas chromatography (GC). Furthermore, quantitative determination of unsaturated fatty acids, PUFAs, EPA and DHA, by partial least square regression analysis through which calibration models were optimized specifically for each targeted FA, was performed in both known marine oils and totally independent unknown n - 3 oil samples obtained from an actual commercial product in order to provide prospective testing of the developed models towards actual applications. The resultant predicted FAs were achieved at a good accuracy compared to their reference GC values as evidenced through (1) low root mean square error of prediction, (2) good coefficient of determination close to 1 (i.e., R 2≥ 0.96), and (3) the residual predictive deviation values that indicated the predictive power at good and higher levels for all the target FAs. © 2014 Springer Science+Business Media New York.

Turning Gigabytes into Gigs: “Songification” and Live Music Data

Complex data is challenging to understand when it is represented as written communication even when it is structured in a table. How- ever, choosing to represent data in creative ways can aid our under- standing of complex ideas and patterns. In this regard, the creative industries have a great deal to offer data-intensive scholarly disci- plines. Music, for example, is not often used to interpret data, yet the rhythmic nature of music lends itself to the representation and anal- ysis of temporal data.Taking the music industry as a case study, this paper explores how data about historical live music gigs can be analysed, extend- ed and re-presented to create new insights. Using a unique process called ‘songification’ we demonstrate how enhanced auditory data design can provide a medium for aural intuition. The case study also illustrates the benefits of an expanded and inclusive view of research; in which computation and communication, method and media, in combination enable us to explore the larger question of how we can employ technologies to produce, represent, analyse, deliver and exchange knowledge.

Informal settlements and placemaking : The case of Caracoli barrio in Bogotá

Placemaking in the developed world can be understood as a concept where through a social and political process, value and meaning in a particular setting is created. This focus of placemaking revolves around a setting in the urban environment, its role as a unique setting and, importantly, the people that make up this place: all of which is focused on a highly structured and formal participatory planning process.The role of placemaking in Latin America’s informal settlements, however, is largelyuntested. With more than 75% of Latin America’s population living in cities since 2001and over 30% (128 million people) of the urban population estimated to reside in what the United Nations define as slums; these informal settlements can offer alternative ways of thinking about urban space and the transformation of spaces people live in. In essence, informal settlements are, to a large extent, what people make of them through their own initiative and imagination. What they achieve is remarkable considering their limited resources and sometimes nonexistent participation in formal planning.Through empirical data collected in 2013 and 2014, this paper discusses how in theabsence of a formal participatory planning process (as the west or developed worldmay perceive it) and lack of resources the barrio of Caracoli, in Bogotá has been ableto create value and meaning in their place. This has been possible, despite social and economic difficulties –which are not to be forgotten-, through inventiveness and the richness of community members’ lives. In this sense, it can be argued that informal settlements can offer a different path to understanding the concept of placemaking currently dominating the developed world.

Applying a multi-agent classifier system with a novel trust measurement method to classifying medical data

In this paper, we present the application of a Multi-Agent Classifier System (MACS) to medical data classification tasks. The MACS model comprises a number of Fuzzy Min-Max (FMM) neural network classifiers as its agents. A trust measurement method is used to integrate the predictions from multiple agents, in order to improve the overall performance of the MACS model. An auction procedure based on the sealed bid is adopted for the MACS model in determining the winning agent. The effectiveness of the MACS model is evaluated using the Wisconsin Breast Cancer (WBC) benchmark problem and a real-world heart disease diagnosis problem. The results demonstrate that stable results are produced by the MACS model in undertaking medical data classification tasks. © 2014 Springer Science+Business Media Singapore.

Motor fault detection and diagnosis using a hybrid FMM-CART model with online learning

In this paper, a hybrid online learning model that combines the fuzzy min-max (FMM) neural network and the Classification and Regression Tree (CART) for motor fault detection and diagnosis tasks is described. The hybrid model, known as FMM-CART, incorporates the advantages of both FMM and CART for undertaking data classification (with FMM) and rule extraction (with CART) problems. In particular, the CART model is enhanced with an importance predictor-based feature selection measure. To evaluate the effectiveness of the proposed online FMM-CART model, a series of experiments using publicly available data sets containing motor bearing faults is first conducted. The results (primarily prediction accuracy and model complexity) are analyzed and compared with those reported in the literature. Then, an experimental study on detecting imbalanced voltage supply of an induction motor using a laboratory-scale test rig is performed. In addition to producing accurate results, a set of rules in the form of a decision tree is extracted from FMM-CART to provide explanations for its predictions. The results positively demonstrate the usefulness of FMM-CART with online learning capabilities in tackling real-world motor fault detection and diagnosis tasks. © 2014 Springer Science+Business Media New York.