Regional tourist destinations - the role of information and communications technology (ICT) in collaboration amongst tourism providers

The tourism industry can be seen as one of the first business sectors where business functions are almost exclusively using information and communications technologies (ICT). This has impacted on the way in which regional tourism destinations are promoted. The method of promoting regions via the development of regional tourist destination websites or portals using Internet technologies is increasingly being adopted both in Australia and around the world. This paper investigates whether this approach is the most effective to achieve increased awareness and subsequent visitation of a region. Are there other ways to achieve a similar outcome? One such alternative is via a bottom up approach achieved through co-opetition or collaboration established within the group of local tourism industry operators. This cooperative networking is made possible via the use of ICT to facilitate the establishment of virtual business networks amongst tourism operators in a local community, cascading into an informal secondary tourism network within that region. In many Australian regional areas the tourism bureau has been the key node for local tourism, but this structure has been fraught with many problems. Little is known about their effectiveness in delivering services to local small and medium tourism enterprises (SMTEs). The role of tourism bureaus in local tourism networks is changing and a study of this dynamic is provided here as an example of the interaction between top down and bottom up approaches. Published case studies from around the world are considered demonstrating alternative approaches to using ICT to promote a region and communicate with potential visitors. Future empirical research is required to more fully understand the effectiveness of the different approaches

The effect of ph and applied electrical potential on oil removal from a solid surface in the presence of four types of surfactant solutions

Effective aqueous-based cleaning depends on the appropriate selection of surfactant(s). and pH conditions. Experiments involving the detachment of oil droplets from a metal surface m the presence of surfactant solutions are undertaken to observe the variation of droplet shape, particularly contact angle, and the time required for droplet removal. In parallel, tests of oil removal from the same metal surface in an industrial ultrasonic bath have been conducted under similar conditions. Similar trends are found for both types of tests, that is, conditions for which droplets detach more quickly also correspond to conditions of greater oil removal in an ultrasonic bath. Experiments of drop removal time and cleaning effectiveness in surfactant solutions of altered pH are conducted to better understand the role surfactants adsorbed at the surface play in the detachment process. Negatively charged oil/aqueous interfaces exhibit more efficient cleaning as well as drop removal kinetics at high surfactant solution pH, while positively charged oil/water interfaces exhibit faster detachment at low pH. Experiments are conducted, in parallel, in which the surface to be cleaned is connected directly to a low voltage power supply. A similar cleaning procedure is performed in which the dependent variable is applied electrical potential. Applied electrical potential ranges from 0 to ±4 volts (current is limited to near zero) with respect to the surfactant solution. Trends indicate that surfactant solutions containing nonionic (Triton X-100) surfactants exhibit better cleaning and faster oil drop detachment as the applied electrical potential increases in the positive direction. Cationic (CTAB) surfactant solutions perform better cleaning as voltage increases in the negative direction. Amonic (SDS) surfactant solutions perform better as voltage increases regardless of the polanty. Tests involving a zwitterionic (Chaps) surfactant support the three previous case trends. The pH and applied electrical potential is interpreted by a mechanistic model focusing on the adsorption of surfactant ions at the aqueous/solid interface and the electrostatic repulsion/attraction to the oil drop

Diffusion of Innovation - the adoption of e-commerce by small to medium enterprises (SME\u27s) - a comparative analysis

This paper explores the issues that influence the diffusion of innovation as it relates to the adoption of e-commerce by Small and Medium Enterprises (SMEs). It seeks to identify factors facilitating and inhibiting such adoption across contexts – regional, small city and large city. This analysis is cross cultural so the impact of differing economic and cultural issues also will be identified in this research. Whilst it is generally accepted that the strategic use of information technology (IT) is vital in the marketplace, the rate of such uptake between small and large businesses varies. This research seeks to identify the reasons for this variation. It is critical to understand such factors so that steps can be taken to redress inequity of uptake that might be identified. The paper endeavours to explore factors that are needed to facilitate and encourage IT adoption and so positively influence user acceptance and use of IT innovations in SMEs. Reasons for such uptake as well as strategic approach to the adoption of e-commerce, and variations regarding same also will be considered. The paper examines existing theory as it pertains to the diffusion of innovation acknowledging the perspective of regional and urban SMEs in various cultural contexts. Empirical investigation exploring this diffusion, the rate of and approach to the uptake by SMEs is planned using a case study methodolog

NA

http://archive.org/details/executiveselecti00roweNAN

Deep Learning Methods for Device Identification Using Symbols Trace Plot

Devices authentication is one crucial aspect of any communication system. Recently, the physical layer approach radio frequency (RF) fingerprinting has gained increased interest as it provides an extra layer of security without requiring additional components. In this work, we propose an RF fingerprinting based transmitter authentication approach density trace plot (DTP) to exploit device-identifiable fingerprints. By considering IQ imbalance solely as the feature source, DTP can efficiently extract device-identifiable fingerprints from symbol transition trajectories and density center drifts. In total, three DTP modalities based on constellation, eye and phase traces are respectively generated and tested against three deep learning classifiers: the 2D-CNN, 2D-CNN+biLSTM and 3D-CNN. The feasibility of these DTP and classifier pairs is verified using a practical dataset collected from the ADALM-PLUTO software-defined radios (SDRs)

Advancements in Automotive Antennas

Not availabl

Deep learning forecasting and statistical modeling for Q/V-band LEO satellite channels

As the number of satellite networks increases, the radio spectrum is becoming more congested, prompting the need to explore higher frequencies. However, it is more difficult to operate at higher frequencies due to severe impairments caused by varying atmospheric conditions. Hence, radio channel forecasting is crucial for operators to adjust and maintain the link’s quality. This paper presents a practical approach for Q/V-band modeling for low Earth orbit satellite channels based on tools from machine learning and statistical modeling. The developed Q/V-band LEO satellite channel model is composed of: 1) forecasting method using model-based deep learning, intended for real-time operation of satellite terminals; and 2) statistical channel simulator that generates a time-series path-loss random process, intended for system design and research. Both approaches capitalize on real-measurements obtained from AlphaSat’s Q/V-band transmitter at different geographic latitudes. The results show that model-based deep learning can outperform simple statistical and deep learning methods by at least 50%. Moreover, the model is capable of incorporating varying rain and elevation angle profilesUnited Kingdom (U.K.)-Australia Space Bridge, | Ref. Grant P4-22Agencia Estatal de Investigación | Ref. PID2020-113240RB-I0