Some philosophical enquiries on E-learning: preparing the tomorrow business school

Emerging digital technologies and increasing interest in the computerized delivery of higher education have led to e-learning through electronic mail, the Internet, the World Wide Web (WWW), and multimedia. The major objective of this research outlet is to examine the e-learning evolution in business schools. Our research intentions are to investigate: 1. if universities understand the market dynamics (regarding to segmentation and crossing the chasm); 2. mapping the s-curve to student needs and 3. how business schools will change the value map. From the analysis of existing empirical evidence and our research results from 140 business students of the University of Ioannina (Greece) and 50 business students of the University of Winchester (UK), we can summarize that: a. value is created when new technology is matched to student need; b. but student needs change: as the technology evolves existing students develop new needs and in addition the technology may appeal to new kinds of students, with new kinds of needs and c. understanding the structure of student needs may be particularly important at times of potential discontinuity, when existing students may reject new technologies (for excellent reasons!).  The authors suggest that business schools interested in being productive should invest in implementing performance tools for all educational methods in order to accomplish the educational objectives. Further research in this crucial field of the evolution of e-learning in business schools is the examination of anticipated benefits and the experiences by early e-learning adopters, return on investment and expectations for the future

Towards Quality-Aware Development of Big Data Applications with DICE

© Springer International Publishing Switzerland 2016.Model-driven engineering (MDE) has been extended in recent years to account for reliability and performance requirements since the early design stages of an application. While this quality-aware MDE exists for both enterprise and cloud applications, it does not exist yet for Big Data systems. DICE is a novel Horizon2020 project that aims at filling this gap by defining the first quality-driven MDE methodology for Big Data applications. Concrete outputs of the project will include a data-aware UML profile capable of describing Big Data technologies and architecture styles, data-aware quality prediction methods, and continuous delivery tools

The effect of a mixture of herbal essential oils or á-tocopheryl acetate on performance parameters and oxidation of body lipid in broilers

No Abstract. South African Journal of Animal Science Vol. 34 (1) 2004: pp.52-6

Molecular Cloning and Rare Cleavage Mapping of Human 2P, 6Q, 8Q, 12Q, and 18Q Telomeres

Large terminal fragments of human chromosomes 2p, 6q, 8q, 12q, and 18q were cloned using yeast artificial chromosomes (YACs). RecA-assisted restriction endonuclease (RARE) cleavage analysis of genomic DNA samples from 11 unrelated individuals using YAC-derived probes confirmed the telomeric localizations of the half-YACs studied. The cloned Fragments provide telomeric closure of maps for the respective chromosome arms and will supply the reagents needed for analyzing and sequencing these distal subtelomeric regions

XR-RF Imaging Enabled by Software-Defined Metasurfaces and Machine Learning: Foundational Vision, Technologies and Challenges

We present a new approach to Extended Reality (XR), denoted as iCOPYWAVES, which seeks to offer naturally low-latency operation and cost-effectiveness, overcoming the critical scalability issues faced by existing solutions. iCOPYWAVES is enabled by emerging PWEs, a recently proposed technology in wireless communications. Empowered by intelligent (meta)surfaces, PWEs transform the wave propagation phenomenon into a software-defined process. We leverage PWEs to i) create, and then ii) selectively copy the scattered RF wavefront of an object from one location in space to another, where a machine learning module, accelerated by FPGAs, translates it to visual input for an XR headset using PWEdriven, RF imaging principles (XR-RF). This makes for an XR system whose operation is bounded in the physical layer and, hence, has the prospects for minimal end-to-end latency. Over large distances, RF-to-fiber/fiber-to-RF is employed to provide intermediate connectivity. The paper provides a tutorial on the iCOPYWAVES system architecture and workflow. A proof-of-concept implementation via simulations is provided, demonstrating the reconstruction of challenging objects in iCOPYWAVES produced computer graphics

Marketing analytics: managing incomplete information in consumer markets and the contribution of mathematics to the accountability of marketing decisions

Information risk management in the marketing function will play a significant role in the decision-making process of any modern business. With the development of computer science and technology, innovative ways have been introduced to process information. At the same time, it is realized that the different ways of comprehending information used by humans can be very misleading, since all information contains error or noise and the human reasoning and senses are very limited. Therefore, marketing managers must recognize that mathematical calculations use data as input - not information, as some seem to believe. Information can only be obtained by providing attributes or relevance and purpose to data. Numbers by themselves do not constitute information, which is why information technology (IT) is not really information-oriented by default but data-oriented. Only the human mind can provide purpose and hence relevance to data, which of course can subsequently be built into data systems to provide information systems. For this reason we introduce Marketing Analytics [MA], a new concept that stresses the raising issue of accountability in the marketing literature. We strongly believe that a marketing analytics' philosophy will contribute significantly at the empowerment of the accountability of marketing decisions. But in order this philosophy to be empowered; information risk management systems must be incorporated in the marketing decision-making process. We believe that these systems in order to work effectively, a classification of consumers' informational uncertainties are needed. So, the research scope of this work is to classify consumers' informational uncertainties, in terms of efficient management of incomplete information in consumer markets. In terms of this research try, based on our proposed fundamental formula of "information utility" we will express, mathematically, grey and fuzzy information (relative concepts of "information uncertainty"). We strongly believe that the incorporation of concepts from mathematics in marketing planning will contribute significantly to the accountability of marketing decisions. According to the literature review, accountability in marketing planning is not only a matter of ineffective decision tools, but also a matter of culture

Towards quality-aware development of Big Data applications with DICE

Model-driven engineering (MDE) has been extended in recent years to account for reliability and performance requirements since the early design stages of an application. While this quality-aware MDE exists for both enterprise and cloud applications, it does not exist yet for Big Data systems. DICE is a novel Horizon2020 project that aims at filling this gap by defining the first quality-driven MDE methodology for Big Data applications. Concrete outputs of the project will include a data-aware UML profile capable of describing Big Data technologies and architecture styles, data-aware quality prediction methods, and continuous delivery tools