Search Results (144)

This paper presents a novel EEG-based learning system designed to enhance the efficiency and effectiveness of studying by dynamically adjusting the difficulty level of learning materials based on real-time attention levels. In the training phase, EEG signals corresponding to high and low concentration levels are recorded while participants engage in quizzes to learn and memorize Chinese characters. The attention levels are determined based on performance metrics derived from the quiz results. Following extensive preprocessing, the EEG data undergoes severmal feature extraction steps: removal of artifacts due to eye blinks and facial movements, segregation of waves based on their frequencies, similarity indexing with respect to delay, binary thresholding, and (PCA). These extracted features are then fed into a k-NN classifier, which accurately distinguishes between high and low attention brain wave patterns, with the labels derived from the quiz performance indicating high or low attention. During the implementation phase, the system continuously monitors the user’s EEG signals while studying. When low attention levels are detected, the system increases the repetition frequency and reduces the difficulty of the flashcards to refocus the user’s attention. Conversely, when high concentration levels are identified, the system escalates the difficulty level of the flashcards to maximize the learning challenge. This adaptive approach ensures a more effective learning experience by maintaining optimal cognitive engagement, resulting in improved learning rates, reduced stress, and increased overall learning efficiency. This adaptive approach ensures a more effective learning experience by maintaining optimal cognitive engagement, resulting in improved learning rates, reduced stress, and increased overall learning efficiency. Our results indicate that this EEG-based adaptive learning system holds significant potential for personalized education, fostering better retention and understanding of Chinese characters. Full article

This study examines how memorable tourism experiences (MTEs) influence tourists’ intention to recommend coastal tourism destinations. Using a quantitative approach of PLS-SEM analysis and a disjoint two-stage approach, this study examines MTE as a higher-order construct (HOC) with its seven dimensions and the moderating role of coastal destination competitiveness (CDC) in structural relationships. Data were collected through purposive sampling from 339 tourists who had visited Likupang, one of the priority tourism destinations in Indonesia. The results show that MTE plays a crucial role in increasing perceived economic value (PEV) and place attachment (PLA), and it is directly related to the intention to recommend the destination (ITRD). In addition to the prominent mediation role of PEV, these findings reveal that the CDC can strengthen or weaken the influence of these factors on tourists’ intention to provide recommendations. Specifically, the CDC can strengthen PLA influence towards intention to recommend, whereas, in contrast, it weakens the PEV in driving these intentions. The findings of this study expand the horizon of managing coastal tourism with an understanding of tourist behavior, particularly through a focus on improving MTE from the dynamics of its seven dimensions in encouraging promotion through tourist recommendations while optimizing the natural competitiveness elements of Likupang. Full article

As health and well-being become central concerns in the post-pandemic tourism landscape, health tourism is evolving to prioritize not only physical recovery but also psychological engagement and emotional value. This study explores how gamified design can enhance tourist participation and experience quality within health-related tourism contexts. By integrating theories from tourism psychology and game-based experience design, a structural equation model is proposed to examine the relationships among memorable tourism experiences, tourist motivation, game design elements, flow experience, and perceived value, and their joint influence on behavioral intention. Data collected from tourists who engaged in gamified experiences were analyzed using structural equation modeling (SEM) techniques. The results identify a dynamic “participation–immersion–value” mechanism, in which gameful design fosters flow and perceived value, thereby mediating gamification’s impact on behavioral intention. These findings offer valuable insights for health tourism developers and experience designers seeking to create emotionally engaging, motivating, and sustainable visitor experiences in the context of health and well-being. Full article

Bangiales are photosynthetic organisms that grow in the intertidal zone, a region characterized by fluctuating environmental conditions. The order comprises genera exhibiting two different morphological variations, filamentous and foliose. It was recently demonstrated that the filamentous alga ‘Bangia’ sp. ESS1 possesses the intrinsic ability to “memorize” an experience of prior heat stress to enhance its survival under subsequent, normally lethal, high-temperature conditions via the acquisition of heat stress tolerance. Here, we investigated whether foliose red algae can similarly memorize heat stress to acquire thermotolerance. When Pyropia yezoensis thalli were primed with non-lethal, high-temperature treatments (22 and 25 °C) for 7 days, vegetative cells subsequently triggered with a normally lethal temperature of 30 °C showed dramatically increased survival rates, indicating that P. yezoensis can acquire heat stress tolerance via exposure to non-lethal high temperatures. In addition, when 22 °C-primed thalli were incubated at 15 °C for recovery, vegetative cells survived subsequent incubation at 30 °C; their survival rates varied depending on the duration of recovery. These findings indicate that, like filamentous red algae, the foliose species P. yezoensis memorizes heat stress to acquire tolerance to recurrent thermostress. The identification of heat stress memory in foliose Bangiales lays a foundation for improving the heat stress tolerance of these important algae, supporting the sustainability of the nori mariculture industry. Full article

The growing importance of customer experience management (CEM) in the tourism sector has led to a proliferation of research interests in satisfaction enhancement, loyalty, and value co-creation. This study proposes a systematic and exhaustive thematic and bibliometric analysis of 3874 articles on CEM in the tourism industry published in the Scopus database between 1979 and 2024. Following the guidelines of the PRISMA protocol, the study uses Bibliometrix (version 4.4.1) in R and VOSviewer (version 1.6.20) to map publication trends, author networks, thematic and chronological evolution, and influential contributions. A qualitative content analysis of the most cited works, guided by grounded theory, revealed the main antecedents, consequences, mediators, and moderators of customer experience management. This analysis is embodied in the proposal of a conceptual model that illustrates the dynamic relationship between these elements and provides the basis for future research for theoretical enrichment and empirical validation. The results offer actionable insights for academics and industry practitioners alike, with the aim of promoting authentic and memorable tourism experiences. Full article

Gastronomic event tourism is an emerging phenomenon that attracts an increasing number of tourists seeking a memorable gastronomic experience based on its cultural uniqueness, highlighting the need to understand the behavior of this tourism demand. This study proposes a model to determine the importance of food tourism events in developing the cultural identity of tourist destinations by analyzing the tourist experience concerning their motivation, satisfaction, and loyalty, understood as variables of sustainable tourism behavior. The hypotheses in the model have been tested using non-linear structural equations modeling (SEM), estimated with data from the Resident Tourism Survey of the National Statistics Institute (NSI), on domestic demand for value-added gastronomic tourism in Spain. The data analyzed covers the period from January 2019 to December 2022 (n = 3483). The results show a significant relationship between the variables influencing overall tourist satisfaction with gastronomic events and tourist loyalty. This research provides knowledge on the behavior of gastronomically motivated tourists with practical implications for destinations, aimed at improving decision-making in the definition of tourism strategies, focusing on the preservation and enhancement of their gastronomic offer as a cultural identity through the offer of food events. Full article

Deep learning models have an intrinsic privacy issue as they memorize parts of their training data, creating a privacy leakage. Membership inference attacks (MIAs) exploit this to obtain confidential information about the data used for training, aiming to steal information. They can be repurposed as a measurement of data integrity by inferring whether the data were used to train a machine learning model. While state-of-the-art attacks achieve significant privacy leakage, their requirements render them infeasible, hindering their use as practical tools to assess the magnitude of the privacy risk. Moreover, the most appropriate evaluation metric of MIA, the true positive rate at a low false positive rate, lacks interpretability. We claim that the incorporation of few-shot learning techniques into the MIA field and a suitable qualitative and quantitative privacy evaluation measure should resolve these issues. In this context, our proposal is twofold. We propose a few-shot learning-based MIA, termed the FeS-MIA model, which eases the evaluation of the privacy breach of a deep learning model by significantly reducing the number of resources required for this purpose. Furthermore, we propose an interpretable quantitative and qualitative measure of privacy, referred to as the Log-MIA measure. Jointly, these proposals provide new tools to assess privacy leakages and to ease the evaluation of the training data integrity of deep learning models, i.e., to analyze the privacy breach of a deep learning model. Experiments carried out with MIA over image classification and language modeling tasks, and a comparison to the state of the art, show that our proposals excel in identifying privacy leakages in a deep learning model with little extra information. Full article

Extended-reality (XR) tools are increasingly used to revitalise museum experiences, but typical head-mounted or smartphone solutions tend to fragment audiences and suppress the social dialogue that makes cultural heritage memorable. This article addresses that gap on two fronts. First, it proposes a four-phase design methodology—spanning artifact selection, narrative framing, tangible-interface fabrication, spatial installation, software integration, validation, and deployment—that helps curators, designers, and technologists to co-create XR exhibitions in which co-presence, embodied action, and multisensory cues are treated as primary design goals rather than afterthoughts. Second, the paper reports LanternXR, a proof-of-concept built with the methodology: visitors share a 3D-printed replica of the fourteenth-century Virgin of Boixadors while wielding a tracked “camera” and a candle-like lantern that lets them illuminate, photograph, and annotate the sculpture inside a life-sized Gothic nave rendered on large 4K displays with spatial audio and responsive lighting. To validate the approach, the article presents an analytical synthesis of feedback from curators, museologists, and XR technologists, underscoring the system’s capacity to foster collaboration, deepen engagement, and broaden accessibility. The findings show how XR can move museum audiences from isolated immersion to collective, multisensory exploration. Full article

This paper presents a control framework for the image tracking of two-wheeled self-balancing carts, with the objective of achieving precise tracking control. Exploiting the remarkable memory capacity of the Long Short-Term Memory (LSTM) neural network for sequence signals, the framework conducts image memory judgment and memorization, aiming to enhance control accuracy. After the training phase, comprehensive simulations and real-world experiments are carried out based on the established model to verify the effectiveness and practicality of the proposed control strategy. The system utilizes the TSL1401 linear array CCD lens to detect black tapes on the ground and identify and memorize surrounding images. Through the establishment of a continuous set of training sample points, the LSTM network is trained using Python and TensorFlow. This training process optimizes the network’s weights and generates weight files, which can be readily converted into machine code for physical implementation. Initially, the effectiveness of the control law is verified through simulating the symmetrical steering control of the two-wheeled cart. The simulation results demonstrate the validity of the proposed design method and its superior performance. Finally, a physical two-wheeled self-balancing cart is developed to further validate the feasibility of the framework. Experimental results confirm that this method is highly effective, demonstrating robust image tracking capabilities and optimal tracking performance. Full article

Grouping information into categories enables us to learn, integrate, and apply new information. Presenting items from different categories sequentially (i.e., interleaving) is often more effective than presenting items from a single category sequentially (i.e., blocking), particularly when evaluating learning using memory-based tests. However, blocking can be more effective than interleaving for rule-based learning. Research has investigated optimal sequence (interleaving vs. blocking) for category learning when participants can either memorize or find rules, finding an interaction between learning strategy and sequence; that is, when memorizing, interleaving is more effective than blocking for classifying based on similarity, but when trying to find a rule, blocking is more effective than interleaving for classifying based on the rule. The goal of the present experiments was to replicate and extend this finding by examining transfer immediately after learning and then at a delay of about 48 h. The present results replicate the interaction between sequence and strategy, and show that the pattern persists over a delay. The results also suggest that some rule-based learning may be more resistant than memory-based learning to forgetting. These findings have educational implications for structuring learning as a function of strategy or learning goals. Full article

In the era of artificial intelligence (AI), generative AI tools like ChatGPT 4.5 have greatly improved the ease of obtaining answers to questions, thereby diminishing the importance of memorizing declarative knowledge while increasing the significance of procedural knowledge required in the problem-solving process. However, conventional assessment approaches such as paper-based assessments still focus on assessing declarative knowledge; these are difficult to adapt to the challenges of the AI era. This study aims to explore an innovative approach for the assessment of procedural knowledge, with a specific emphasis on gamification. This study employs a comprehensive approach involving an experimental research method, a case study, and a questionnaire survey. A total of 151 undergraduate students were recruited and randomly assigned to an experimental group and a control group for the experiment. We compared performance outcomes between a gamification-based assessment and paper-based assessment. The results confirmed the effectiveness of the gamification-based assessment, demonstrating its superiority over the paper-based assessment in assessing procedural knowledge. The findings of the paper are not only applicable to assess knowledge in emergency situations such as fire safety but can also be applied to the assessment of procedural knowledge across various academic subjects within educational institutions. Full article

Airport terminals are considered transitional spaces in travelers’ journeys. They tend to be globalized in architectural features, competing to show their superiority by using the latest technologies and contemporary designs, yet it is often forgotten that they are the gateways to their host cities and that they should reflect their identity. This paper aims to investigate the impact of adopting the identity of the host city in the terminal’s design through different identity indicators, asking if it would enhance the user’s experience and motivate the passengers to explore the cultural side of the city. Through the analysis of four major airports in Saudi Arabia, identity indicators in the architectural design and interior settings were explored to understand how identity is reflected in each airport. A questionnaire was distributed to delve more into the impact of these identity indicators on the passengers’ experiences in the different spaces in the four airports. It appeared that these indicators create a sense of attachment and interaction between the users and the spaces, transforming them into places that have the potential to enhance travelers’ journeys and emotions to present a memorable and sustainable cultural user experience, elevating the quality of their trips. Full article

We investigate diffusion models generating synthetic samples from the probability distribution represented by the ground truth (GT) samples. We focus on how GT sample information is encoded in the score function (SF), computed (not simulated) from the Wiener–Ito linear forward process in the artificial time $t\in [0\to \infty ]$, and then used as a nonlinear drift in the simulated Wiener–Ito reverse process with $t\in [\infty \to 0]$. We propose U-Turn diffusion, an augmentation of a pre-trained diffusion model, which shortens the forward and reverse processes to $t\in [0\to T_u]$ and $t\in [T_u\to 0]$. The U-Turn reverse process is initialized at $T_u$ with a sample from the probability distribution of the forward process (initialized at $t=0$ with a GT sample) ensuring a detailed balance relation between the shortened forward and reverse processes. Our experiments on the class-conditioned SF of the ImageNet dataset and the multi-class, single SF of the CIFAR-10 dataset reveal a critical Memorization Time $T_m$, beyond which generated samples diverge from the GT sample used to initialize the U-Turn scheme, and a Speciation Time $T_s$, where for $T_u>T_s>T_m$, samples begin representing different classes. We further examine the role of SF nonlinearity through a Gaussian Test, comparing empirical and Gaussian-approximated U-Turn auto-correlation functions and showing that the SF becomes effectively affine for $t>T_s$ and approximately affine for $t\in [T_m,T_s]$. Full article

Kruger National Park is one of the most well-preserved national parks in the Southern Hemisphere. However, cases of visitors participating in deviant leisure practices (DLPs) are reported in the park, threatening the sustainability of sensitive tourism resources. Adopting a deviant leisure lens, this study assesses the extent to which visitors participate in DLPs at the Kruger National Park (KNP) and the causes of those behaviours. Variables adopted from the KNP codes of conduct for visitors were used to measure the DLPs based on the visitors’ perception of the park. A quantitative survey design, with a sample size of 237 respondents, assessed respondents’ participation in DLPs. The study results reveal that visitors participate in DLPs at KNP. However, the level at which visitors participate in DLPs is inconsistent. The most common DLPs by visitors are getting close to animals to take pictures and driving off-road to see animals. The reasons for visitors participating in these behaviours are the need to create memorable experiences and being in “holiday mode”. The least violated codes of conduct in the park are picking up archaeological objects to keep them as souvenirs and bringing prohibited items into the park without declaring. This study is significant as it is the first to investigate the extent visitors participate in DLPs using a self-reported instrument. Based on the results, park managers may develop effective strategies to reduce the number of visitors getting close to animals to take pictures and driving off-road to observe animals at close range. Full article

Many experiments on distance perception have revealed that there is a difference between perceptual distance and objective distance. It has been accepted that a route with more memorable features will make its perceived distance longer. This study revisited this information storage model and examined how estimations change by repeated journeys in a university campus. While the outcome confirms the existing hypothesis, an unexpected pattern of distance compression by time was found. Spending more years on the campus, the estimation tended to decrease. The rate of decrease was bigger and more distinctively gradual for architecture and female students than non-architecture and male students. At the end, a cognitive threshold hypothesis was suggested as a possible model to explain the complexity of distance perception. Before reaching it, the distance grows along with the knowledge on a route but beyond the point of knowledge saturation, it begins to compress. Full article