MobilEuropan Campus Rottal-Inn
Katerina Volchek, A. Liu, H. Song, D. Buhalis
Forecasting tourist arrivals at attractions: Search engine empowered methodologies
Tourism Economics, vol. 25, no. 3, pp. 425-447
Tourist decision to visit attractions is a complex process influenced by multiple factors of individual context. This study investigates how the accuracy of tourism demand forecasting can be improved at the micro level. The number of visits to five London museums is forecast and the predictive powers of Naïve I, seasonal Naïve, seasonal autoregressive moving average, seasonal autoregressive moving average with explanatory variables, SARMAX-mixed frequency data sampling and artificial neural network models are compared. The empirical findings extend understanding of different types of data and forecasting algorithms to the level of specific attractions. Introducing the Google Trends index on pure time-series models enhances the forecasts of the volume of arrivals to attractions. However, none of the applied models outperforms the others in all situations. Different models’ forecasting accuracy varies for short- and long-term demand predictions. The application of higher frequency search query data allows for the generation of weekly predictions, which are essential for attraction- and destination-level planning.
A. Al Lily, J. Foland, D. Stoloff, A. Gogus, I. Erguvan, M. Awshar, J. Tondeur, M. Hammond, I. Venter, P. Jerry, A. Oni, Y. Liu, R. Badosek, López de la Madrid, M.C., E. Mazzoni, D. Vlachopoulos, H. Lee, K. Kinley, M. Kalz, U. Sambuu, T. Bushnaq, N. Pinkwart, N. Adedokun-Shittu, P.-O. Zander, K. Oliver, L. Teixeira Pombo, J. Balaban Sali, S. Gregory, S. Tobgay, M. Joy, J. Elen, Odeh Helal Jwaifell, M., M.N.H.M. Said, Y. Al-Saggaf, A. Naaji, J. White, K. Jordan, J. Gerstein, İ. Umit Yapici, C. Sanga, P. Nleya, B. Sbihi, M. Rocha Lucas, V. Mbarika, S. Schön, L. Sujo-Montes, M. Santally, P. Häkkinen, A. Al Saif, Andreas Gegenfurtner, S. Schatz, V. Padilla Vigil, C. Tannahill, S. Padilla Partida, Z. Zhang, K. Charalambous, A. Moreira, M. Coto, et al.
Academic domains as political battlegrounds
A global enquiry by 99 academics in the fields of education and technology
Information Development, vol. 33, no. 3, pp. 270-288
This article theorizes the functional relationship between the human components (i.e., scholars) and non-human components (i.e., structural configurations) of academic domains. It is organized around the following question: in what ways have scholars formed and been formed by the structural configurations of their academic domain? The article uses as a case study the academic domain of education and technology to examine this question. Its authorship approach is innovative, with a worldwide collection of academics (99 authors) collaborating to address the proposed question based on their reflections on daily social and academic practices. This collaboration followed a three-round process of contributions via email. Analysis of these scholars’ reflective accounts was carried out, and a theoretical proposition was established from this analysis. The proposition is of a mutual (yet not necessarily balanced) power (and therefore political) relationship between the human and non-human constituents of an academic realm, with the two shaping one another. One implication of this proposition is that these non-human elements exist as political ‘actors’, just like their human counterparts, having ‘agency’ – which they exercise over humans. This turns academic domains into political (functional or dysfunctional) ‘battlefields’ wherein both humans and non-humans engage in political activities and actions that form the identity of the academic domain.
NachhaltigElektrotechnik und MedientechnikIQMA
Y. Ji, H. Fei, Y. Shi, V. Igelsias, D. Lewis, N. Jiebin, S. Long, M. Liu, Alexander Hofer, Werner Frammelsberger, Günther Benstetter, A. Scheuermann, P. McIntyre, M. Lanza
Characterization of the photocurrents generated by the laser of atomic force microscopes
Review of Scientific Instruments, vol. 87, no. 8
The conductive atomic force microscope (CAFM) has become an essential tool for the nanoscale electronic characterization of many materials and devices. When studying photoactive samples, the laser used by the CAFM to detect the deflection of the cantilever can generate photocurrents that perturb the current signals collected, leading to unreliable characterization. In metal-coated semiconductor samples, this problem is further aggravated, and large currents above the nanometer range can be observed even without the application of any bias. Here we present the first characterization of the photocurrents introduced by the laser of the CAFM, and we quantify the amount of light arriving to the surface of the sample. The mechanisms for current collection when placing the CAFM tip on metal-coated photoactive samples are also analyzed in-depth. Finally, we successfully avoided the laser-induced perturbations using a two pass technique: the first scan collects the topography (laser ON) and the second collects the current (laser OFF). We also demonstrate that CAFMs without a laser (using a tuning fork for detecting the deflection of the tip) do not have this problem.