Search Results (310)

Designers of digital rehabilitation experiences can accommodate error-prone input devices like brain–computer interfaces (BCIs) by incorporating virtual help mechanisms to adjust the difficulty, but it is unclear on what grounds users are willing to accept such help. To study users’ experience of virtual help mechanisms, we used three help mechanisms in a blink-controlled game simulating a BCI-based stroke rehabilitation exercise. A mixed-method, simulated BCI study was used to evaluate game help by 19 stroke patients who rated their frustration and perceived control when experiencing moderately high input recognition. None of the help mechanisms affected ratings of frustration, which were low throughout the study, but two mechanisms affected patients’ perceived control ratings positively and negatively. Patient ratings were best explained by the amount of positive feedback, including game help, which increased perceived control ratings by 8% and decreased frustration ratings by 3%. The qualitative analysis revealed appeal, interference, self-blame, and prominence as deciding experiential factors of help, but it was unclear how they affected frustration and perceived control ratings. Building upon the results, we redesigned and tested self-reported measures of help quantity, help appeal, irritation, and pacing with game-savvy adults in a follow-up study using the same game. Help quantity appeared larger when game help shielded players from negative feedback, but this did not necessarily appeal to them. Future studies should validate or control for the constructs of perceived help quantity and appeal. Full article

The development of artificial intelligence has revolutionized language learning approaches with AI-assisted language applications (AiLAs) like Grammarly, Siri, and ChatGPT 3.5, offering self-paced learning, tailored feedback, and increased engagement. There is, however, not much understanding about AI’s precise effects on perceived English learning improvements among students, as the majority of current research concentrates on the fact that AI is generally regarded as a language support tool. This study investigates the relation between students’ beliefs of using AiLA in terms of duration, frequency, familiarity, and user satisfaction to improve their learning of English. Fifty-five (55) undergraduate students between the ages of 21 and 24 participated in the survey. The results showed that the duration of use and perceived English learning improvements had a moderate positive relationship, indicating that extensive use of AiLA aids in language acquisition. Frequency of use, however, had little effect, suggesting that frequent use of AiLA may not be enough. There was a small and statistically insignificant correlation between students’ perceived English learning improvement and their familiarity with AiLA. Additionally, there was a minimal to no significant correlation between user pleasure and perceived improvements in English learning, indicating that enjoyment of AiLA is not closely related to the use of the tools. These findings demonstrate that AiLA needs to be systematically incorporated into instruction, with a focus on interactive and adaptable features rather than passive engagement. To maximize language acquisition, developers should improve AI-driven feedback and adaptive learning pathways, while educators should integrate AiLA into collaborative learning. Full article

According to recent appraisals, despite its pathological aspects, melancholy can be a psychological impetus for spiritual creativity and utopianism. Drawing on those appraisals, this article examines some religious implications of mourning and melancholy in novels of Yoon Dae-nyeong and Kim Hoon in the context of Korean society in the 1990s and the early 2000s. Firstly, Yoon Dae-nyeong’s early works depict an intense sense of loss arising from the compressed pace of Korean modernity, and, throughout religious imagery, they express an aspiration for spiritual renewal. However, in Yoon’s works, spiritual aspiration soon gives way to a sense of resignation. Next, this article explores melancholy in Kim Hoon’s novels. Although Kim’s first two novels share with Yoon’s works an intense sense of loss, the melancholic traits in their characters are sublimated thanks to the characters’ openness to others and patient utopianism. They thus avoid the spiritual trap induced by melancholy’s self-destructive aspect. Kim’s utopianism is expressed again in his more recent works, such as Black Mountain and Harbin, which illustrate the Korean people’s present aspiration toward a spiritual utopia. Full article

Linguistic expressions of respectful terms are shaped by social status. Previous studies have shown respectful term usage affects online language processing. This study investigates its impact on semantic integration through three self-pace reading experiments, manipulating Respect Consistency (Respect vs. Disrespect) and Semantic Consistency (Semantic Consistent vs. Semantic Inconsistent). In Experiment 1, disrespect was manipulated by using the plain form of pronouns instead of the respectful form when addressing individuals of higher social status. The results showed longer reading times for semantically inconsistent sentences compared to consistent ones, reflecting the classic semantic integration effect. Nevertheless, this effect was only detected when respectful pronouns were employed. For Experiments 2 and 3, disrespect was operationalized by directly addressing individuals of higher social status by their personal names. A comparable interaction to that in Experiment 1 was identified solely in Experiment 3, which involved an appropriateness judgment task. In contrast, no such interaction was observed in Experiment 2, which involved a reading comprehension task. These results indicated that both disrespectful pronouns and addressing individuals by their personal names hinder semantic integration, but through different mechanisms. These findings provide important insights into the role of respectful term usage on semantic integration during discourse comprehension. Full article

Introduction: Arrhythmias are a frequent complication of pulmonary hypertension (PH). Supraventricular tachycardias (SVT) are predominantly reported and are associated with clinical deterioration and an increased mortality. In contrast, the prevalence and clinical relevance of bradycardias is largely unclear. Therefore, the aim of the present study was to determine a prevalence of bradycardias in PH patients and to outline their clinical relevance. Material and methods: Between January 2000 and June 2013, consecutive PH patients were pro- and retrospectively enrolled in two cohorts. Patients received either a 24 h or 72 h Holter ECG. Results: A total of 314 patients (58% female, mean age: 63 years) from PH groups 1–5 (39%, 11%, 19%, 28%, 3%) were included. Basic heart rhythm was sinus rhythm in 87% of patients (9% atrial fibrillation, 2% atrial flutter and 2% paced rhythm). Further arrhythmias were detected in 34% of patients (SVT: 12%, non-sustained ventricular tachycardia: 16%) with a 6% prevalence of relevant bradycardias. Atrioventricular block was revealed in 5% of patients (seven first-degree, one and three second-degree Wenckebach and Mobitz type, respectively, four third-degree), and 1% revealed sinoatrial block (one second-degree, third-degree and unspecified each). Conclusions: The prevalence of bradycardias appears to be about 5–10% in PH patients. Most of them are short and self-limiting. However, some patients experience syncope or clinical deterioration and, therefore, need specific treatment. To find these patients, long-term ECG monitoring combined with ECG-symptom correlation may be useful. Bradycardic medication should be excluded as a cause. Full article

Background: The ongoing conflict in Ukraine has forced numerous migrants into neighboring countries, many suffering from pre-existing or newly acquired physical and mental health conditions. Addressing these complex challenges in humanitarian settings requires innovative, evidence-based interventions that are cost-effective and easy to administer. Drawing upon research highlighting the vagus nerve’s role in regulating well-being, we hypothesized that vagal nerve activation could offer a promising therapeutic approach. Method: We conducted a proof-of-concept study in which 21 Ukrainian forced migrants were trained in a biofeedback-guided paced breathing intervention designed to stimulate the vagus nerve and promote self-regulation of stress response systems. Changes in pain perception, perceived stress, blood pressure, and heart rate were assessed before and after the vagal breathing intervention using a t-test. Correlations were examined at baseline. Results: Statistically significant improvements were observed in all measures except systolic blood pressure, providing preliminary evidence for the efficacy of vagal nerve activation in alleviating stress-related health symptoms. Conclusions: This study demonstrates the feasibility and therapeutic potential of a vagal nerve-activating intervention in a humanitarian setting. These findings warrant replication in larger, controlled trials. If substantiated, this low-cost, scalable intervention could help mitigate health burdens among forced migrant populations worldwide. Full article

This commentary examines a recent study that challenges the view that cognitive control supports the resolution of linguistic ambiguities. We critique the study’s methodological limitations, particularly its reliance on self-paced reading, which lacks the sensitivity to detect the effects of cognitive control on language processing. Furthermore, we address theoretical issues with the proposal that visual attention, rather than cognitive control, explains prior findings from the visual-world paradigm. By highlighting the linking assumptions behind the visual-world paradigm, we argue that eye movement patterns reflect syntactic parsing decisions and cannot be explained by visual attention alone. Considering these factors and the broader body of evidence, we maintain that cognitive control remains a key mechanism in language comprehension, despite the alternative account presented in the target study. Full article

Functional near-infrared spectroscopy (fNIRS) is particularly suitable for measuring brain activity during motor tasks, due to its portability and good motion tolerance. In such cases, the trials’ duration may vary depending on the experimental conditions or the participant’s response, therefore a comparison of hemodynamic responses across repetitions cannot be properly performed. In this work, we present a MATLAB (R2023a) function (TaskNorm.m) developed for time-normalizing fNIRS data recorded during trials with different durations. It is based on a spline interpolation method that rescales the time -axis to the percentage of the trial with a fixed number of samples. This allows us to successively average across repetitions to obtain the mean hemodynamic responses and complete the standard data processing. The algorithm was tested on eight subjects (four with developmental coordination disorder, age: 9.78 ± 0.30 and four typically developing children, age: 9.02 ± 0.30) performing three different tasks. The results show that the TaskNorm function works as expected, allowing both a comparison and averaging of the data across multiple repetitions. The performance of the function is independent of the task or the pre-processing pipeline applied. The proposed function is publicly available and importable into the HomER3 package (v1.72.0), representing a further step in the ongoing standardization process of fNIRS data analysis. Full article

As malware continues to evolve, AI-based malware classification methods have shown significant promise in improving the malware classification performance. However, these methods lead to a substantial increase in computational complexity and the number of parameters, increasing the computational cost during the training process. Moreover, the maintenance cost of these methods also increases, as frequent retraining and transfer learning are required to keep pace with evolving malware variants. In this paper, we propose an efficient knowledge distillation technique for AI-based malware classification methods called Self-MCKD. Self-MCKD transfers output logits that are separated into the target class and non-target classes. With the separation of the output logits, Self-MCKD enables efficient knowledge transfer by assigning weighted importance to the target class and non-target classes. Also, Self-MCKD utilizes small and shallow AI-based malware classification methods as both the teacher and student models to overcome the need to use large and deep methods as the teacher model. From the experimental results using various malware datasets, we show that Self-MCKD outperforms the traditional knowledge distillation techniques in terms of the effectiveness and efficiency of its malware classification. Full article

Self-evaluation empowers students to progress independently and adapt their pace according to their unique circumstances. A critical facet of self-assessment and personalized learning lies in furnishing learners with formative feedback. This feedback, dispensed following their responses to self-assessment questions, constitutes a pivotal component of formative assessment systems. We hypothesize that it is possible to generate explanations that are useful as formative feedback using different techniques depending on the type of self-assessment question under consideration. This study focuses on a subject taught in a computer science program at a Spanish distance learning university. Specifically, it delves into advanced data structures and algorithmic frameworks, which serve as overarching principles for addressing complex problems. The generation of these explanatory resources hinges on the specific nature of the question at hand, whether theoretical, practical, related to computational cost, or focused on selecting optimal algorithmic approaches. Our work encompasses a thorough analysis of each question type, coupled with tailored solutions for each scenario. To automate this process as much as possible, we leverage natural language processing techniques, incorporating advanced methods of semantic similarity. The results of the assessment of the feedback generated for a subset of theoretical questions validate the effectiveness of the proposed methods, allowing us to seamlessly integrate this feedback into the self-assessment system. According to a survey, students found the resulting tool highly useful. Full article

Breast cancer survivors experience numerous chronic symptoms linked to autonomic dysfunction including anxiety, stress, insomnia, menopausal symptoms, and cognitive impairment. Effective non-pharmacological solutions to address these are currently lacking. Methods: Our three-armed longitudinal randomized controlled trial assessed the effectiveness of a 4-week remote smartphone-based heart rate variability biofeedback intervention which involved daily paced breathing at 6 breaths p/min; active (12 breaths p/min) and waitlist controls were included. Heart rate variability and self-reported cancer-related symptoms were assessed at baseline, post-, and 6 months-post intervention. Participants were 60 UK-based women with primary breast cancer history (6 to 60 months post-active treatment). Results: The intervention group showed significant increases in low-frequency heart rate variability over time (F (4, 103.89) = 2.862, p = 0.027, d = 0.33), long-lasting improvement in sleep quality (F (4, 88.04) = 4.87, p = 0.001, d = 0.43) and cessations in night sweats (X² (2, N = 59) = 6.44, p = 0.04, Cramer’s V = 0.33), and reduced anxiety post-intervention compared to the active and waitlist controls (F (4, 82.51) = 2.99, p = 0.023, d = 0.44). Other findings indicated that the intervention and active control participants reported lasting improvements in cognitive function, fatigue, and stress-related symptoms (all ps < 0.05). The waitlist group reported no symptom changes across time. Conclusion: Heart rate variability biofeedback is a feasible intervention for addressing diverse chronic symptoms commonly reported by breast cancer survivors. Full article

Research on deep learning (DL)-based channel estimation for massive multiple-input multiple-output (MIMO) communication systems has attracted considerable interest in recent years. In this paper, we propose a DL-assisted channel estimation algorithm that transforms the original channel estimation problem into an image denoising problem, contrasting it with traditional experience-based channel estimation methods. We establish a new polarized self-attention-aided channel estimation neural network (PACE-Net) to achieve efficient channel estimation. This approach addresses the limitations of the conventional methods, particularly their low accuracy and high computational complexity. In addition, we construct a channel dataset to facilitate the training and testing of PACE-Net. The simulation results show that the proposed DL-assisted channel estimation algorithm has better normalization mean square error (NMSE) performance compared with the traditional algorithms and other DL-assisted algorithms. Furthermore, the computational complexity of the proposed DL-assisted algorithm is significantly lower than that of the traditional minimum mean square error (MMSE) channel estimation algorithm. Full article

The expansion of the use of artificial intelligence (AI) across different stages of production and distribution in journalism is opening a debate on its applications within newsrooms and in business models. This research studies how different media outlets, media groups and institutions are beginning to create internal regulations for the use of AI, both from a technical and an ethical perspective. To do so, an international sample (N = 45) of editorial stylebooks and internal self-regulatory guidelines published between 2023 and early 2025 have been compiled—all links are openly available here—and put through a process of content analysis. The results indicate that the self-regulatory guidelines emerge from an individual initiative of the media themselves, with a focus on limiting the use of generative AI, particularly in text creation. The guidelines emphasize ethical commitments such as transparency, content verification, and respect for data and copyright while underlining the importance of human oversight. Key objectives include avoiding bias, ensuring information quality, and strengthening audience trust. Despite progress, regulation remains in its early stages and requires continuous adaptation to keep pace with technological advancements. Full article

Background: Tinnitus can significantly impact a patient’s quality of life. As no evidence-based curative treatments exist, therapies such as cognitive behavioral therapy, tinnitus retraining therapy, acceptance and commitment therapy, and mindfulness-based interventions aim to minimize tinnitus severity and have been shown effective. Since traditional delivery can be costly and time-consuming and often has limited accessibility, therapies might also be provided via eHealth. This study investigates the change in tinnitus severity measured by the Tinnitus Functional Index (TFI) score after participation in an online self-paced tinnitus (“Still Tinnitus”) course. The secondary aim was to identify predictors for the clinically relevant improvement after participation in this course. Methods: This retrospective record study included patients from Still Tinnitus course between March 2023 and July 2024. Patients were recruited via the Still Tinnitus website. Differences in the TFI scores from baseline and after completing the fifth (last) module of the course were calculated to investigate the change in tinnitus over time. Multivariate logistic analyses were performed to identify the possible predictors for the clinically relevant improvement after completion of the Still Tinnitus course. Results: In total, 122 patients were included in the study. The analysis revealed a clinically relevant reduction in the TFI score of 27.2 points. Multiple regression analyses showed that the “duration of the tinnitus” (OR 5.0; 95%CI: 1.537–16.240; p = 0.007) and “female sex” (OR 1.9; 95%CI 0.111–7.637; p = 0.030) are predictors for a clinically relevant improvement. Conclusions: In a convenience sample of tinnitus patients, the Still Tinnitus course may contribute to a clinically relevant reduction in tinnitus severity. A shorter duration of tinnitus and female sex were identified as significant predictors. Full article

This study evaluates “I Learn with Prompt Engineering”, a self-paced, self-regulated elective course designed to equip university students with skills in prompt engineering to effectively utilize large language models (LLMs), foster self-directed learning, and enhance academic English proficiency through generative AI applications. By integrating prompt engineering concepts with generative AI tools, the course supports autonomous learning and addresses critical skill gaps in language proficiency and market-ready capabilities. The study also examines EnSmart, an AI-driven tool powered by GPT-4 and integrated into Canvas LMS, which automates academic test content generation and grading and delivers real-time, human-like feedback. Performance evaluation, structured questionnaires, and surveys were used to evaluate the course’s impact on prompting skills, academic English proficiency, and overall learning experiences. Results demonstrated significant improvements in prompt engineering skills, with accessible patterns like “Persona” proving highly effective, while advanced patterns such as “Flipped Interaction” posed challenges. Gains in academic English were most notable among students with lower initial proficiency, though engagement and practice time varied. Students valued EnSmart’s intuitive integration and grading accuracy but identified limitations in question diversity and adaptability. The high final success rate demonstrated that proper course design (taking into consideration Panadero’s four dimensions of self-regulated learning) can facilitate successful autonomous learning. The findings highlight generative AI’s potential to enhance autonomous learning and task automation, emphasizing the necessity of human oversight for ethical and effective implementation in education. Full article