IDENTIFICATION AND USE OF INFORMATION AND COMMUNICATION TECHNOLOGY IN THE PROMOTION OF CROATIAN TOURISM

The development of information and communication technology has created a solid technological framework for the application of new technologies in different environments. The implementation of cyberspace tools has changed social interactions in general as well as the technologies and advertising methods. Today, information and communication technology allows users to access information via telecommunications. The means whereby messages are conveyed include, among others, the Internet, wireless networks, advanced mobile devices, and other communication media. Numerous studies have shown that an appropriate use of electronic marketing activities can be useful in achieving various tourist promotion goals. However, today’s level of technological development allows modern consumers, i.e. users, to search for information on a tourist destination in real time, and also before they arrive at the destination through mobile technology. The paper starts from the assumption that in the near future tourist destinations that provide information to consumers through information and communication technology will benefit the most. The aim of the paper is to define key factors conducive to increased online presence and use of online services. The research conducted will give a clear picture of the current situation in terms of the use of information and communication infrastructure in the promotion of tourism which can be used to develop and propose an appropriate strategy for the management of electronic marketing activities aimed at promoting Croatian tourism

Add and Thin: Diffusion for Temporal Point Processes

Autoregressive neural networks within the temporal point process (TPP) framework have become the standard for modeling continuous-time event data. Even though these models can expressively capture event sequences in a one-step-ahead fashion, they are inherently limited for long-term forecasting applications due to the accumulation of errors caused by their sequential nature. To overcome these limitations, we derive ADD-THIN, a principled probabilistic denoising diffusion model for TPPs that operates on entire event sequences. Unlike existing diffusion approaches, ADD-THIN naturally handles data with discrete and continuous components. In experiments on synthetic and real-world datasets, our model matches the state-of-the-art TPP models in density estimation and strongly outperforms them in forecasting

Modeling Temporal Data as Continuous Functions with Process Diffusion

Temporal data like time series are often observed at irregular intervals which is a challenging setting for existing machine learning methods. To tackle this problem, we view such data as samples from some underlying continuous function. We then define a diffusion-based generative model that adds noise from a predefined stochastic process while preserving the continuity of the resulting underlying function. A neural network is trained to reverse this process which allows us to sample new realizations from the learned distribution. We define suitable stochastic processes as noise sources and introduce novel denoising and score-matching models on processes. Further, we show how to apply this approach to the multivariate probabilistic forecasting and imputation tasks. Through our extensive experiments, we demonstrate that our method outperforms previous models on synthetic and real-world datasets

Dose-volume derived nomogram as a reliable predictor of radiotherapy-induced hypothyroidism in head and neck cancer patients

Background The aim of this study was to determine the possible predictive value of various dosimetric parameters on the development of hypothyroidism (HT) in patients with head and neck squamous cell carcinoma (HNSCC) treated with (chemo)radiotherapy. ----- Patients and methods This study included 156 patients with HNSCC who were treated with (chemo)radiotherapy in a primary or postoperative setting between August 2012 and September 2017. Dose-volume parameters as well as V10 toV70, D02 to D98, and the VS10 to VS70 were evaluated. The patients’ hormone status was regularly assessed during follow-up. A nomogram (score) was constructed, and the Kaplan-Maier curves and Log-Rank test were used to demonstrate the difference in incidence of HT between cut-off values of specific variables. ----- Results After a median follow-up of 23.0 (12.0–38.5) months, 70 (44.9%) patients developed HT. In univariate analysis, VS65, Dmin, V50, and total thyroid volume (TTV) had the highest accuracy in predicting HT. In a multivariate model, HT was associated with lower TTV (OR 0.31, 95% CI 0.11–0.87, P = 0.026) and Dmin (OR 9.83, 95% CI 1.89–108.08, P = 0.042). Hypothyroidism risk score (HRS) was constructed as a regression equation and comprised TTV and Dmin. HRS had an AUC of 0.709 (95% CI 0.627–0.791). HT occurred in 13 (20.0%) patients with a score 7.1. ----- Conclusions The dose volume parameters VS65, Dmin, V50, and TTV had the highest accuracy in predicting HT. The HRS may be a useful tool in detecting patients with high risk for radiation-induced hypothyroidism