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Found 6374 publications

DIGITALIZATION AND TOURISM: HOW X, Y, AND Z GENERATIONS MAKE TRAVEL DECISIONS IN THE ONLINE ERA

Zsuzsa Ivancsóné Horváth Marcell Kupi Viktória Kundi

Publication Name: Geojournal of Tourism and Geosites

Publication Date: 2025-01-01

Volume: 60

Issue: Unknown

Page Range: 1302-1314

Description:

This study aims to explore the impact of digitalisation on the travel decision-making process of Generation X, Y, and Z, as well as to identify generational differences in tourism consumer behaviour. The research places particular emphasis on the role of information sources and variations in decision-making preferences. The study employs a quantitative research approach, analysing the travel decision-making habits of different generations through survey-based data collection. A literature review was conducted to examine intergenerational differences and the relationship between digitalisation and travel-related decision-making. Results and discussions: The findings indicate that digitalisation influences travel decisions across all generations, albeit in different ways. Trust and personal recommendations play a crucial role for Generation X, whereas Generation Y relies more heavily on online reviews. In the case of Generation Z, social media and digital channels have a decisive influence. The study also highlights that price remains a key factor for all generations; however, expectations regarding quality and information-seeking behaviours differ. Generation Y and Z exhibit a higher demand for visual content and real-time information, while Generation X tends to value reliability and detailed planning. Furthermore, the results show a clear trend toward mobile-first decision-making among younger users, especially within Generation Z. The study's findings contribute to the development of tourism marketing strategies by enabling the more effective application of generation-specific communication tools. Based on the results, recommendations can be made for tourism service providers regarding the optimisation of their digital presence and customer communication. The insights gained can also support designing personalised digital campaigns, enhance customer engagement, and foster loyalty among different generational segments in the evolving digital tourism landscape.

Open Access: Yes

DOI: 10.30892/gtg.602spl26-1502

DISTRIBUTION-FREE DATA UNCERTAINTY FOR NEURAL NETWORK REGRESSION

Péter Kersch Domokos Kelen Ádám Jung András Benczúr

Publication Name: 13th International Conference on Learning Representations Iclr 2025

Publication Date: 2025-01-01

Volume: Unknown

Issue: Unknown

Page Range: 67459-67490

Description:

Quantifying uncertainty is an essential part of predictive modeling, especially in the context of high-stakes decision-making. While classification output includes data uncertainty by design in the form of class probabilities, the regression task generally aims only to predict the expected value of the target variable. Probabilistic extensions often assume parametric distributions around the expected value, optimizing the likelihood over the resulting explicit densities. However, using parametric distributions can limit practical applicability, making it difficult for models to capture skewed, multi-modal, or otherwise complex distributions. In this paper, we propose optimizing a novel nondeterministic neural network regression architecture for loss functions derived from a sample-based approximation of the continuous ranked probability score (CRPS), enabling a truly distribution-free approach by learning to sample from the target's aleatoric distribution, rather than predicting explicit densities. Our approach allows the model to learn well-calibrated, arbitrary uni- and multivariate output distributions. We evaluate the method on a variety of synthetic and real-world tasks, including uni- and multivariate problems, function inverse approximation, and standard regression uncertainty benchmarks. Finally, we make all experiment code publicly available.

Open Access: Yes

DOI: DOI not available

Xenillus meridaensis sp. nov. (Acari, Oribatida, Liacaridae) from Venezuela, including a key to all species of the genus from the Neotropical region

Sergey G. Ermilov Jenő Kontschán

Publication Name: International Journal of Acarology

Publication Date: 2025-01-01

Volume: 51

Issue: 1

Page Range: 34-41

Description:

A new species of Xenillus (Oribatida, Liacaridae)–X. meridaensissp. nov.–is described, based on adults collected from sweep samples in the cloud forest in Venezuela. The new species differs from its closely related species, X. capitatus, by its far larger body size, the morphology of the cusp of the lamella, the morphology of the lamellar seta, the surface of the notogaster, and the number of genital setae. An identification key to the Neotropical Xenillus species is given.

Open Access: Yes

DOI: 10.1080/01647954.2024.2423920

Adaptive few-shot tiny neural systems for real-time traffic intensity prediction in smart cities

Dragan Pamucar Aanjankumar Sureshkumar Rajesh Kumar Dhanaraj Maragatharajan Muthusamy Aanjankumar Sureshkumar Maragatharajan Muthusamy Rajesh Kumar Dhanaraj Balakannan sirumolasi Paramasivam

Publication Name: ICT Express

Publication Date: 2025-01-01

Volume: Unknown

Issue: Unknown

Page Range: Unknown

Description:

The rapid evolution of urban mobility and smart city demands an intelligent transportation system which can make real-time decisions using lightweight and adaptive AI models. This research introduces a novel application of tiny machine learning which will combine the features of Few-shot learning algorithm and it will classify the traffic intensity levels on regional traffic data. By converting the traffic volume into three dynamic classes (Low/ Medium/ High), a compact neural network model is trained on episodic few-shot tasks that can mimic real-world low-data learning conditions. The proposed work supports open set classification which is more suitable for detecting unknown traffic behavior analysis by considering the previous day traffic level and how the future traffic intensity level can be predicted effectively. The accuracy of the proposed method is compared with the existing methods which lie with the baseline CNN (90 %) and SVM (89 %). But the average episode accuracy achieved through the proposed model is 95.2 % which makes this model promising for low-power edge deployment in intelligent transportation system.

Open Access: Yes

DOI: 10.1016/j.icte.2025.08.010

Driver Focused Comparison of Field Oriented Control and Direct Torque Control using MATLAB Simulink Simulations

Istvan Szalay Tamás Horváth Denes Fodor

Publication Name: Edpe 2025 37th International Conference on Electrical Drives and Power Electronics and 12th Joint Croatia Slovakia Conference

Publication Date: 2025-01-01

Volume: Unknown

Issue: Unknown

Page Range: Unknown

Description:

This paper presents a comparison of two widely used motor control strategies namely field oriented control (FOC) and direct torque control (DTC). They both have their advantages and disadvantages that make them a suitable choice for use in a vehicle drive system. This paper compares these two implemented in a MATLAB Simulink simulation using an automotive Permanent Magnet Synchronous Motor (PMSM). The motor and test parameters are chosen to be realistic from a human driver point-ofview. Simulation results are analyzed to highlight the differences between the two strategies and identify cases where one outperforms the other. In conclusion, the paper shows the effects of the differences and the general characteristics of the simulation results in a realistic case on a human driver as the user of the PMSM in an automotive drive. The paper contributes valuable insight in the classic comparison of these two strategies for automotive use.

Open Access: Yes

DOI: 10.1109/EDPE66853.2025.11224164

Chatbot assistant based on Large-Language Models for University students

Csaba Hajdu Katalin Kovács Ami Otsuka

Publication Name: Ines 2025 29th IEEE International Conference on Intelligent Engineering Systems 2025 Proceedings

Publication Date: 2025-01-01

Volume: Unknown

Issue: Unknown

Page Range: 77-82

Description:

Large-language models (LLMs) have recently gained significant traction in natural language processing (NLP) by accurately modeling and imitating human-like conversations. One standout application area involves chatbots, which leverage LLMs to provide context-aware, natural language interactions. However, existing solutions often target English and rely on external cloud-based platforms, raising concerns about data privacy and language coverage. In contrast, this paper presents a locally deployable, Hungarian-language chatbot developed to assist university students with education and examination regulations. The proposed system ensures in-house deployment, facilitating compliance with institutional data policies and offering cost-effective scalability. Beyond offering straightforward answers on deadlines and academic rules, our chatbot is designed to handle more nuanced student inquiries, enhancing user experience and administrative efficiency. Preliminary testing demonstrates robust performance in Hungarian context. Future plans include extending the chatbot's domain to more complex subjects, broader document sets, and additional institutions, as well as integrating high-performance computing resources for large-scale deployments.

Open Access: Yes

DOI: 10.1109/INES67149.2025.11078205

The Most Significant Case of Military Supplier Fraud in Hungary During the First World War: The Criminal Judgement of the Cloth-Fraud Case Based on the Judgements of the Military Courts of the Time

Róbert Bartkó Roland Kelemen

Publication Name: Journal on European History of Law

Publication Date: 2025-01-01

Volume: 16

Issue: 1

Page Range: 154-161

Description:

In the first year of the First World War, several cases of war transport abuse came to light in Hungary, which were of great social interest and caused extremely significant material and moral damage. One of the largest of these was the so-called baized fraud case. The special feature of these cases is that some of the defendants are military personnel, while others have civilian status. In Hungary, the practice has developed that both categories of persons are tried by military courts. This study aims to provide a comprehensive overview of the historical facts of the case, the legal context of the case and the decisions of the courts at first and second instance.

Open Access: Yes

DOI: DOI not available

Case Report: From disordered eating to an eating disorder—a case study of an orienteering athlete with anorexia nervosa and the shortcomings of the multidisciplinary approach

Réka Erika Kovács Szilvia Boros

Publication Name: Frontiers in Psychology

Publication Date: 2025-01-01

Volume: 16

Issue: Unknown

Page Range: Unknown

Description:

This case study explores the transition from disordered eating (DE) to an eating disorder (ED) in a 23-year-old female orienteer. Despite her talent as an athlete, her eating habits and training practices led to significant health concerns. After following an ovo-lacto vegetarian diet for 3 years, she exhibited symptoms of DE, including low energy intake (1,200 kcal/day), low body weight (50.1 kg, BMI: 16.9), and amenorrhea. Her condition deteriorated over 2 years, resulting in a diagnosis of anorexia nervosa (AN) by February 2023. During the treatment process, the athlete utilized a multidisciplinary approach that included dietitians, psychologists, and physicians. Despite achieving some initial progress, including a slight increase in body weight and the return of menstruation in July 2022, her health declined after psychological consultations were halted, leading to a further decrease in body fat and persistent low serum iron levels. This case highlights the importance of continuous monitoring, timely intervention, and a coordinated multidisciplinary team in addressing DE and ED in athletes. It also highlights the significance of effective communication among healthcare professionals and the need for comprehensive treatment strategies that include psychological, nutritional, and medical support. This study highlights the significance of early detection, suitable intervention, and the prevention of long-term health complications, such as decreased bone density and cardiovascular issues, in athletes with DE and ED.

Open Access: Yes

DOI: 10.3389/fpsyg.2025.1537844

Microalgal and cyanobacterial biostimulants used in wheat and maize production

I. Gergely G. Takács Vince Ördög Wendy A. Stirk Johannes van Staden

Publication Name: Biostimulants for Improving Reproductive Growth and Crop Yield

Publication Date: 2025-01-01

Volume: Unknown

Issue: Unknown

Page Range: 169-218

Description:

Wheat and maize are staple cereals that are each cultivated on about 200 million hectares globally. Microalgae and cyanobacteria have potential to be developed as biostimulants for wheat and maize production. This review focuses on biostimulating effects of various microalgae and cyanobacteria on seed priming, soil and foliar treatments applied in pot experiments and field trials. Two case studies on wheat and maize field trials are included. Seed priming with selected microalgal extracts is a promising method to promote plant growth but still needs validation in field trials. Soil biofertilizers based on living N2-fixing cyanobacteria (algalization) applied alone or in combination with plant growth promoting rhizobacteria modulate soil microbial composition and enhance nutrient uptake. However, this requires application of tens or hundreds kg/ha biomass to substitute for N-fertilizers so is not yet an economically viable option. The case studies indicated that a single foliar treatment of wheat at tillering and maize at the V6 growth stage with Chlorella vulgaris or Tetracystis sp. suspensions (0.1–1g DW/L applied at 400L/ha) increased grain yield, grain protein content and improve stress tolerance. These results indicated that certain microalgae could be effective biostimulants. However, producing sufficient microalgae biomass on a commercial scale is still a challenge. Monoalgal mass production in closed photobioreactors is expensive. A promising approach is the cultivation of mixed algal cultures in nutrient rich wastewater using open raceway reactors.

Open Access: Yes

DOI: 10.1016/B978-0-443-13207-0.00011-1