mdpi-ai/citelens huggingface dataset:
Personalized Non-Player Characters: A Framework for Character-Consistent Dialogue Generation
Field: AI

Generating character-consistent and personalized dialogue for Non-Player Characters (NPCs) in Role-Playing Games (RPGs) poses significant challenges, especially due to limited memory retention and inconsistent character representation. This paper proposes a framework for generating personalized dialogues based on character-specific knowledge. By combining static knowledge fine-tuning and dynamic knowledge graph technology, the framework generates dialogue content that is more aligned with character settings and is highly personalized. Specifically, the paper introduces a protective static knowledge fine-tuning approach to ensure that the language model does not generate content beyond the character’s cognitive scope during conversations. Additionally, dynamic knowledge graphs are employed to store and update the interaction history between NPCs and players, forming unique “experience-response” patterns. During dialogue generation, the paper first parses player input into an Abstract Meaning Representation (AMR) graph, retrieves relevant memory nodes from the knowledge graph, and constructs a fused graph structure. This integrated graph is encoded via a graph neural network to generate high-dimensional semantic vectors, which are then used to retrieve and supplement knowledge from the vector database. Ultimately, the model generates personalized responses consistent with the NPC’s identity. Experimental results demonstrate that the framework significantly enhances the authenticity of NPC dialogues and player immersion and performs well on multiple large-scale language models.
Precision enhancement in wireless capsule endoscopy: a novel transformer-based approach for real-time video object detection
Field: AI

Background: Wireless Capsule Endoscopy (WCE) enables non-invasive imaging of the gastrointestinal tract but generates vast video data, making real-time and accurate abnormality detection challenging. Traditional detection methods struggle with uncontrolled illumination, complex textures, and high-speed processing demands.Methods: This study presents a novel approach using Real-Time Detection Transformer (RT-DETR), a transformer-based object detection model, specifically optimized for WCE video analysis. The model captures contextual information between frames and handles variable image conditions. It was evaluated using the Kvasir-Capsule dataset, with performance assessed across three RT-DETR variants: Small (S), Medium (M), and X-Large (X).Results: RT-DETR-X achieved the highest detection precision. RT-DETR-M offered a practical trade-off between accuracy and speed, while RT-DETR-S processed frames at 270 FPS, enabling real-time performance. All three models demonstrated improved detection accuracy and computational efficiency compared to baseline methods.Discussion: The RT-DETR framework significantly enhances precision and real-time performance in gastrointestinal abnormality detection using WCE. Its clinical potential lies in supporting faster and more accurate diagnosis. Future work will focus on further optimization and deployment in endoscopic video analysis systems.
Robust Single-Cell RNA-Seq Analysis Using Hyperdimensional Computing: Enhanced Clustering and Classification Methods
Field: AI

Background. Single-cell RNA sequencing (scRNA-seq) has transformed genomics by enabling the study of cellular heterogeneity. However, its high dimensionality, noise, and sparsity pose significant challenges for data analysis. Methods. We investigate the use of Hyperdimensional Computing (HDC), a brain-inspired computational framework recognized for its noise robustness and hardware efficiency, to tackle the challenges in scRNA-seq data analysis. We apply HDC to both supervised classification and unsupervised clustering tasks. Results. Our experiments demonstrate that HDC consistently outperforms established methods such as XGBoost, Seurat reference mapping, and scANVI in terms of noise tolerance and scalability. HDC achieves superior accuracy in classification tasks and maintains robust clustering performance across varying noise levels. Conclusions. These results highlight HDC as a promising framework for accurate and efficient single-cell data analysis. Its potential extends to other high-dimensional biological datasets including proteomics, epigenomics, and transcriptomics, with implications for advancing bioinformatics and personalized medicine.
Effect of an Educational Intervention on Pupil’s Knowledge, Attitudes, Perceptions, and Behavior on Air Pollution in Public Schools in Pristina
Field: PSYCHOLOGY

This interventional study aimed to assess the effectiveness of a school-based environmental education program on improving knowledge, attitudes, perceptions, and behavior related to air pollution among pupils in low-middle schools in Pristina, Kosovo. Air pollution is a pressing issue in Kosovo, particularly in urban areas, making it essential to raise awareness from an early age. As one of the first initiatives of its kind in the country, this study offers valuable insights into the impact of educational interventions on students’ understanding of environmental issues. The study involved an intervention group of fifth to ninth grade students who participated in a structured environmental education program, with data collected through pre-test, post-test, and follow-up assessment. We used a quantitative questionnaire with four sections—demographics, knowledge, perceptions, attitudes, and behavior. The findings revealed a significant improvement in knowledge and perceptions about air pollution among students in the intervention group, highlighting the crucial role of education in raising environmental awareness. However, the intervention had limited impact on changing attitudes and no significant effect on pro-environmental behavior, echoing challenges found in previous studies. Parental education, particularly maternal education, was found to play a substantial role in shaping attitudes, while gender and parental education positively influenced perceptions. The study also identified a negative association between higher grade levels and both knowledge and perception scores. Despite its success in enhancing knowledge, the short intervention period and challenges in participant engagement limited the program’s ability to drive long-term behavioral change. These findings emphasize the need for more sustained and comprehensive interventions to address the complex relationship between knowledge, attitudes, and environmental behaviors.
Automated Pruning Framework for Large Language Models Using Combinatorial Optimization
Field: AI

Currently, large language models (LLMs) have been utilized in many aspects of natural language processing. However, due to their significant size and high computational demands, large computational resources are required for deployment. In this research, we focus on the automated approach for size reduction of such a model. We propose the framework to perform the automated pruning based on combinatorial optimization. Two techniques were particularly studied, i.e., particle swarm optimization (PSO) and whale optimization algorithm (WOA). The model pruning problem was modeled as a combinatorial optimization task whose the goal is to minimize model size while maintaining model accuracy. The framework systematically explores the search space to identify the most optimal pruning configurations, removing redundant or non-contributory parameters. The two optimizations, PSO and WOA, were evaluated for their ability to efficiently navigate the search space. As a result, with PSO, the proposed framework can reduce the model size of Llama-3.1-70B by 13.44% while keeping the loss of model accuracy at 19.25%; with WOA, the model size reduction is 12.07% with 22.81% loss of model accuracy. Since accuracy degradation may occur during pruning process, the framework integrates the post-process to recover the model accuracy. After this process, the pruned model loss can reduce to 12.72% and 14.83% using PSO and WOA, respectively.
The Influence of Loneliness, Social Support and Income on Mental Well-Being
Field: PSYCHOLOGY

Mental well-being is a multifaceted concept that reflects emotional stability, psychological resilience and social connectedness. This study examines how demographic factors, perceived loneliness, and social support influence mental well-being in Spain. Participants were surveyed online and provided personal information along with responses to the University of California, Los Angeles (UCLA) Loneliness Scale, the Medical Outcomes Study Social Support Survey (MOS-SSS), and the Warwick–Edinburgh Mental Well-Being Scale (WEMWBS). Our findings support previous research on mental well-being in Spain and again show significant associations between income, loneliness, social support and overall mental health. In particular, perceived loneliness was found to be a strong predictor of mental well-being. Furthermore, income and social support were found to partially mediate the relationship between loneliness and mental well-being. These findings highlight the critical role of social connections and financial stability in promoting mental health. Overall, this research contributes to the growing understanding of the factors influencing mental well-being and provides valuable insights for improving mental health outcomes.
Min3GISG: A Synergistic Feature Selection Framework for Industrial Control System Security with the Integrating Genetic Algorithm and Filter Methods
Field: AI

Industrial control systems (ICS) are crucial for automating and optimizing industrial operations but are increasingly vulnerable to cyberattacks due to their interconnected nature. High-dimensional ICS datasets pose challenges for effective anomaly detection and classification. This study aims to enhance ICS security by improving attack detection through an optimized feature selection framework that balances dimensionality reduction and classification accuracy. The study utilizes the HAI dataset, comprising 54,000 time series records with 225 features representing normal and anomalous ICS behaviors. A hybrid feature selection approach integrating wrapper and filter methods was employed. Initially, a Genetic Algorithm (GA) identified 118 relevant features. Further refinement was conducted using filter-based methods—Symmetrical Uncertainty (SU), Information Gain (IG), and Gain Ratio (GR)—leading to a final subset of 104 optimal features. These features were used to train classification models (Naive Bayes (NB), Random Forest (RF), and Support Vector Machine (SVM)) with a 70:30 train-test split and tenfold cross-validation. The proposed feature selection method significantly improved classification accuracy, achieving 98.86% (NB), 99.91% (RF), and 97.97% (SVM). Compared to the full dataset (225 features), which yielded 97.51%, 99.93%, and 96.17%, respectively, our optimized feature subset maintained or enhanced classification performance while reducing computational complexity. This research demonstrates the effectiveness of a hybrid feature selection approach in improving ICS anomaly detection. By reducing feature dimensionality without compromising accuracy, the proposed method enhances ICS security, offering a scalable and efficient solution for real-time attack detection.
The Illusory Health Beliefs Scale: validation using exploratory structural equation modeling and multidimensional Rasch analysis
Field: PSYCHOLOGY

The Illusory Health Beliefs Scale (IHBS) is a multidimensional instrument that evaluates endorsement of scientifically unsubstantiated, illusory health-oriented notions. These beliefs are important because they potentially influence attitudes/actions to the detriment of personal wellbeing/health. Preceding research examining IHBS item performance at the unidimensional subscale level identified five dimensions (Religious/Spiritual, Superstition, Precognitive, Health Myths, Skepticism), and an independent Health Pseudoscience subscale. The present paper extended latent structure analysis by employing exploratory structural equation modeling (ESEM) and multidimensional Rasch analysis. Concurrently, statistical appraisal tested convergent validity via relationships with related health-based constructs (i.e., health locus of control, HLC and beliefs about complementary and alternative medicine, CAM). A sample of 2,138 completed the IHBS (1,016 males, 1,113 females, seven non-binary, two preferred not to disclose). Following minor scale modification, ESEM reported good data-fit for a six-factor model. With the exception of Skepticism, which was negatively associated, IHBS subfactors correlated positively with HLC and CAM. These outcomes supported the supposition that the IHBS measures perceived and illusory health control. Rasch analysis designated sufficient multidimensionality and satisfactory subscale functioning. Strong associations indicated that IHBS dimensions assessed related but discrete aspects of illusory health beliefs. High associations among paranormal-based dimensions (Religious/Spiritual, Superstition, and Precognitive) suggested the need for greater content separation. Moreover, the poor reliability of Skepticism designated the need to develop a more efficacious assessment of this dimension.
Molecular Mechanisms of Drug Resistance in Clear Cell Renal Cell Carcinoma
Field: ONCOLOGY

Renal cell carcinoma (RCC) accounts for about 3% of all human tumors. Alterations of oxygen, lipids, iron, and energy metabolism are involved in carcinogenesis, development, and expansion. Thirty percent of patients affected by clear cell renal cell carcinoma (ccRCC) will develop relapses or distance metastases (mRCC), dramatically reducing their life expectancy. Current first-line therapies for mRCC patients are based on treatment with immune checkpoint inhibitors (ICIs) alone and in combination with each other or with tyrosine kinase inhibitors (TKIs). However, only 20% of patients show a mild response because of innate or acquired drug resistance during long-term treatment; therefore, resistant patients need alternative first-line or second-line therapies. Pharmacological resistance represents a big problem that counteracts the efficacy of treatment by reducing overall survival (OS) in mRCC patients. Investigating the molecular mechanisms underlying drug resistance is crucial to overcoming drug insensitivity and enhancing therapeutic outcomes. In this review, we emphasize the latest and most significant studies on the molecular mechanisms that drive drug resistance in ccRCC carcinoma. Particular attention is given to the key signaling pathways involved in resistance, including those mediated by HIF, p53, Akt-mTOR, MEK–ERK cascades, Wnt signaling, autophagy, membrane transporters, ferroptosis, and non-coding RNAs. Understanding these resistance mechanisms is essential for developing new therapeutic strategies aimed to enhancing overall OS and improving the quality of life for mRCC patients. This review also discusses recent clinical trial findings on the use of specific inhibitors able to circumvent drug resistance. The data presented here could be valuable for clinicians in understanding the mechanisms of drug resistance, ultimately aiding in the management of ccRCC patients.
Thoracoscopy-guided thoracic paravertebral block using dexmedetomidine in combination with ropivacaine for postoperative analgesia after thoracoscopic radical resection of lung cancer: a randomized controlled trial
Field: ONCOLOGY

Purpose The aim of this trial was to evaluate the analgesic effect of dexmedetomidine combined with ropivacaine for thoracoscopic-guided thoracic paravertebral block (TTPB) after thoracoscopic radical resection (TRR) of lung cancer. Methods A total of 60 patients were enrolled from our hospital who underwent elective TRR of lung cancer and randomized into either a control group (group C) or a dexmedetomidine group (group D). Prior to incisional suturing, group C received ropivacaine alone for TTPB, while group D received dexmedetomidine combined with ropivacaine for TTPB. The primary outcome was the time to the first analgesic request (TFAR). The secondary outcomes included heart rate (HR), mean arterial pressure (MAP), Ramsay sedation score, and Numerical Rating Scale (NRS) scores (both at rest and during coughing) at the following time points: before the TTPB operation (T0), 1 h postoperatively (T1), 2 h postoperatively (T2), 6 h postoperatively (T3), 12 h postoperatively (T4), 24 h postoperatively (T5), as well as 48 h postoperatively (T6). Additional secondary outcomes included the patient-controlled intravenous analgesia (PCIA) sufentanil dosage at 48 h postoperatively, the incidence of adverse reactions, and postoperative recovery. Results Compared to group C, group D showed a longer TFAR, lower total PCIA sufentanil dosage at 48 h postoperatively, and lower NRS scores at all time points; Group D also had lower MAP and HR, higher Ramsay sedation scores from T1 to T3 after surgery, a higher incidence of drowsiness, and better postoperative recovery. Conclusions As an adjuvant in combination with ropivacaine, dexmedetomidine enhanced the analgesic effect of TTPB, prolonged the duration of analgesia, and reduced the time to first ambulation and hospital stay. Clinical Trial Registration ChiCTR2400086347, Registered 28/06/2024.
Financial literacy and educational level in Ecuadorian students: a structural analysis
Field: EDUCATION

Background: Financial literacy has been recognized as a key competency for making in-formed economic decisions, particularly in contexts where access to financial products exceeds the population’s literacy level. However, in Ecuador, persistent gaps remain be-tween formal educational attainment and applied financial knowledge. In this context, the objective of this study was to analyze the relationship between educational level and financial literacy among Ecuadorian students.Methods: A quantitative approach was adopted, with a descriptive-correlational level, non-experimental type, and cross-sectional design. The sample consisted of 2,021 participants, selected through non-probabilistic convenience sampling. A structured questionnaire of 33 items was administered, distributed across four analytical dimensions. Statistical analysis was performed using SPSS and AMOS, including reliability testing, factorial validity, and structural model fit.Results: The results revealed that educational level has a significant effect on financial literacy. Individuals with higher education exhibited the highest levels, while those who completed only primary education showed the lowest. Four latent factors were validated: technical knowledge, socioeconomic impact of financial education, practical application of knowledge, and financial self-management.Conclusion: The correlations between these factors were strong and statistically significant, highlighting the pivotal role of educational level in shaping financial literacy. The proposed model presents a valid and consistent structure, effectively reflecting the relationships between the key variables. These findings emphasize the necessity for tailored and context-specific educational interventions that address the diverse needs of different population segments, thereby enhancing financial literacy across varying educational levels.
Everyone Is Reading and Playing! A Participatory Theatre Project to Promote Reading Competence
Field: EDUCATION

This study explores the use of a theatre project to enhance reading competencies among students with special educational needs (SENs) in inclusive classrooms. The project, titled “Stop Bullying! A Theatre Project”, aimed to improve students’ reading skills through dramatised engagement with texts, with a particular focus on promoting literacy and social interaction. Employing a Design-Based Research (DBR) methodology, the study involved iterative cycles of implementation and data collection. Participants, including students with varying reading abilities, engaged in theatrical activities that incorporated reading strategies such as reading aloud, paired reading, and choral reading—each designed to support comprehension, fluency, and reading confidence. Findings from multiple cycles indicated improvements in students’ social dynamics, including stronger peer interactions and increased group cohesion. While quantitative reading assessment data showed only modest gains in reading performance, qualitative observations revealed significant improvements in reading skills and social interactions during collaborative performances. The study concludes that a theatre-based approach can effectively support reading development while fostering a more inclusive and supportive classroom environment.
Developing Elite Strength and Conditioning Coaches’ Practice Through Facilitated Reflection
Field: EDUCATION

Recent research has suggested that strength and conditioning (S&C) coach development should consider constructivist learning theories to promote coach development and learning of psychosocial coaching competencies. Reflective practice can encourage holistic learning through promoting an internal dialogue of the meaningfulness of an individual’s experiences. Our study aimed to examine the efficacy of a facilitated, guided, and longitudinal reflective process to promote coach learning of psychosocial coaching practice using Moon’s reflective framework. Over a four-week period, six elite S&C coaches engaged in a guided process reflection process with a facilitator. This included daily journaling in an e-diary with the facilitator providing feedback at the end of each week. At the end, each S&C coach participated in an exit interview. Data were analysed using interpretative phenomenological analysis. Findings revealed that there were potential benefits for the S&C coach’s process of reflection such as providing accountability through developing a close relationship with the facilitator, which enabled the S&C coaches to more critically link learning to behaviour change. Furthermore, S&C coaches’ learning resulted in developing awareness of self/athlete’s needs, increased flexibility, and enhanced confidence. This resulted in S&C coaches developing psychosocial coaching competencies that enabled them to change their practice to become more athlete centred.
Using website creation as a hub, promoted collaborative learning in teacher education
Field: EDUCATION

Introduction: The article presents a teaching design used in the first year of a teacher education in Norway. The teacher educators designed, taught and researched the project, evaluating it in collaboration with students. This is in line with a practitioner/action research approach and formative dialogue research. The teaching design was centered around the making of websites as a form of wiki learning. 143 students participated in the project. This article focuses on the student perspective and the data material was gathered through a survey and reflection papers.Results: The students pointed out the collaboration as the most important learning and how that prepared them for the future teacher profession. The student statements were classified into three categories of professional growth and discussed using the theory of professional capital. Using the students’ comments and the teacher educators’ experience, the teaching design is discussed up against earlier designs of wiki learning. The discussion elaborates on 6 possible success factors.