mdpi-ai/citelens huggingface dataset:
The S in STEM: gender differences in science anxiety and its relations with science test performance-related variables
Field: EDUCATION

STEM education has experienced significant growth due to its pivotal role in innovation and economic development. While cognitive factors like prior knowledge are known predictors of STEM success, non-cognitive factors, including attitudes and demographics, also play vital roles. However, there is a notable scarcity of research focusing on the "S" in STEM—science—compared to extensive studies in fields like mathematics. This study aims to address this gap by exploring gender differences in science test performance and related attitudes, providing insights into this under-researched aspect of STEM education. The effective sample comprised 1839 Estonian 12th-grade students who took a computer-assisted science test. The test consisted of tasks combining chemistry, physics, biology, and geography, and a post-test survey was also administered. Across the total sample, the results showed that test performance positively correlated with test-taking duration, effort, and test importance. Test performance was negatively correlated with perceived test difficulty. Interestingly, while general science anxiety was not associated with test performance, subject-specific anxiety, especially chemistry anxiety had a negative association with test performance. While there were no gender differences in test performance, female students scored consistently higher on all science anxiety measures, compared to male students. Furthermore, female students assessed the science test to be more difficult, and they also took more time to complete the test. The correlations in gender subsamples mirrored those observed in the total sample. The association between science test performance and test-related variables is nuanced: students might not necessarily have a “general” STEM anxiety but it may be associated with a specific subject. Moreover, the findings imply that although there are no gender differences in test performance, girls have a greater anxiety when it comes to natural sciences subjects. These findings indicate the need for investigating the origin of such anxieties, which do not seem to stem from aptitude.
Enhancing public dialogue about inclusion in school education: a citizens’ panel pilot
Field: EDUCATION

Introduction: This paper reports on a small-scale Citizens’ Panel pilot project using deliberative democratic methods to produce policy ideas about inclusion in school education of young people with special educational needs and disabilities (SEN/D) in England. The project had two aims: (i) to obtain information about modifying a Citizens’ Panel process to enhance the effective participation of young people with SEN/D; and (ii) to generate more nuanced, grounded and integrated policy ideas about inclusion than can be found in recent English school education policy.Methods: The Citizens’ Panel was a two phase deliberative process. Phase 1 involved working with six young people with SEN/D and their parents/carers to shape the Citizens’ Panel agenda, and to obtain information about how they could participate and communicate their perspectives during the events. Phase 2 involved the delivery of the Citizens’ Panel itself, which comprised 28 people: the six young people from phase 1, plus four young people without SEN/D, 13 parents/carers, and five education professionals.Results: The process evaluation revealed the need for and impact of meticulous planning using a differentiated and strengths-based approach to design. While participants reported that taking part in the Citizens’ Panel was overall, a positive and worthwhile experience, the differentiated approach involved trade-offs that affected the experiences of some participants without SEN/D, though not detrimentally. The panel produced distinctive ideas about more inclusive schools, where almost all of the themes were about general school changes for everyone. Most general themes involved some specific SEN/D aspects, with only one theme being SEN/D specific. This paper illustrates how these ideas are more nuanced, grounded and integrated than those in current national policy.Discussion: This paper provides evidence of how deliberative approaches can be used within and between schools, groups of schools (e.g., multi academy trusts), local networks (including local authorities), as well as at the national level. Lessons drawn show how deliberative methods used by advocacy groups, protest movements and non-governmental organisations in support of more transformational change can be developed in ways that enable young people with SEN/D to participate and have their voices heard.
Associations of HALP score with serum prostate-specific antigen and mortality in middle-aged and elderly individuals without prostate cancer
Field: ONCOLOGY

Background: The association between the Hemoglobin, Albumin, Lymphocyte, and Platelet (HALP) score and serum prostate-specific antigen (PSA) and all-cause mortality remains underexplored. We aimed to investigate the relationship between HALP score and these outcomes among middle-aged and elderly individuals without prostate cancer (PCa).Methods: This cross-sectional study included participants aged 40 years and older from National Health and Nutrition Examination Survey (NHANES) 2001–2010. HALP score was calculated using the formula: HALP score = (Hemoglobin × Albumin × Lymphocytes)/Platelets. High PSA level was defined as a percentage free PSA (%fPSA) less than or equal to 25% and a total PSA (tPSA) level equal to or higher than 4.0 ng/mL. Mortality data were obtained through December 30, 2019 by linking to the National Death Index.Results: Among 7,334 participants, 6,826 were classified as having low PSA level, while 508 were categorized as having high PSA level. Logistic regression revealed lower odds of high PSA level with higher HALP quartiles (Ptrend<0.001). Among 508 participants with high PSA level, over a median follow-up period of 10.13 years (IQR: 5.42-13.17 years), a total of 268 all-cause deaths were recorded. Cox regression analysis showed that participants in the highest HALP quartile had the lowest risk of all-cause mortality (HR = 0.527, 95% CI: 0.368-0.754) in participants with high PSA level. Restricted cubic spline analysis indicated a non-linear and negative correlation between HALP score and all-cause mortality, with an inflection point at 43.98 (P for non-linearity = 0.009). Random survival forest analysis ranked HALP score as the most significant predictor for all-cause mortality.Conclusion: Our study highlights that the HALP score the HALP score is associated with high PSA level and all-cause mortality among middle-aged and elderly individuals without PCa. Further research is warranted to validate these findings and elucidate underlying mechanisms.
The relationship between physical activity and mental health of middle school students: the chain mediating role of negative emotions and self-efficacy
Field: PSYCHOLOGY

Objective: To explore the relationship between mental health and physical activity (PA) in middle school students, and examining the roles of negative emotions and self-efficacy in the relationship.Methods: Data from 1,134 Chinese middle school students (50.2% females, 49.8% males; Mage = 15.18, SDage = 2.00) were collected using the Physical Activity Rating Scale (PARS-3), Positive and Negative Affect Scale (PANAS), General Self-Efficacy Scale (GSES), and Middle School Student Mental Health Scale (MSSMHS).Results: (1) There is a significant positive correlation between PA and mental health (r = 0.16, p < 0.01), and the direct path of PA on mental health is significant (t = 2.101, p < 0.01). (2) PA negatively predicts negative emotions (r = −0.12, p < 0.01), and is significantly positively correlated with self-efficacy (r = 0.24, p < 0.01). Negative emotions negatively predict self-efficacy (r = −0.23, p < 0.01) and mental health (r = −0.67, p < 0.01). Self-efficacy positively predicts mental health (r = 0.30, p < 0.01). (3) Negative emotions and self-efficacy play a significant mediating role between PA and mental health. The mediating effect includes three paths: PA → negative emotion → mental health (effect value: 0.130); PA → self-efficacy → mental health (effect size: 0.052); PA → negative emotions → self-efficacy → mental health (effect size: 0.006).Conclusion: PA among middle school students can indirectly affect mental health through negative emotions and self-efficacy. Middle school students should be encouraged to participate in PA to reduce their negative emotions and increase their self-efficacy, thus improving their mental health.
Towards enhanced creativity in fashion: integrating generative models with hybrid intelligence
Field: AI

Introduction: This study explores the role and potential of large language models (LLMs) and generative intelligence in the fashion industry. These technologies are reshaping traditional methods of design, production, and retail, leading to innovation, product personalization, and enhanced customer interaction.Methods: Our research analyzes the current applications and limitations of LLMs in fashion, identifying challenges such as the need for better spatial understanding and design detail processing. We propose a hybrid intelligence approach to address these issues.Results: We find that while LLMs offer significant potential, their integration into fashion workflows requires improvements in understanding spatial parameters and creating tools for iterative design.Discussion: Future research should focus on overcoming these limitations and developing hybrid intelligence solutions to maximize the potential of LLMs in the fashion industry.
Image restoration in frequency space using complex-valued CNNs
Field: AI

Real-valued convolutional neural networks (RV-CNNs) in the spatial domain have outperformed classical approaches in many image restoration tasks such as image denoising and super-resolution. Fourier analysis of the results produced by these spatial domain models reveals the limitations of these models in properly processing the full frequency spectrum. This lack of complete spectral information can result in missing textural and structural elements. To address this limitation, we explore the potential of complex-valued convolutional neural networks (CV-CNNs) for image restoration tasks. CV-CNNs have shown remarkable performance in tasks such as image classification and segmentation. However, CV-CNNs for image restoration problems in the frequency domain have not been fully investigated to address the aforementioned issues. Here, we propose several novel CV-CNN-based models equipped with complex-valued attention gates for image denoising and super-resolution in the frequency domains. We also show that our CV-CNN-based models outperform their real-valued counterparts for denoising super-resolution structured illumination microscopy (SR-SIM) and conventional image datasets. Furthermore, the experimental results show that our proposed CV-CNN-based models preserve the frequency spectrum better than their real-valued counterparts in the denoising task. Based on these findings, we conclude that CV-CNN-based methods provide a plausible and beneficial deep learning approach for image restoration in the frequency domain.
Anomaly detection via Gumbel Noise Score Matching
Field: AI

We propose Gumbel Noise Score Matching (GNSM), a novel unsupervised method to detect anomalies in categorical data. GNSM accomplishes this by estimating the scores, i.e., the gradients of log likelihoods w.r.t. inputs, of continuously relaxed categorical distributions. We test our method on a suite of anomaly detection tabular datasets. GNSM achieves a consistently high performance across all experiments. We further demonstrate the flexibility of GNSM by applying it to image data where the model is tasked to detect poor segmentation predictions. Images ranked anomalous by GNSM show clear segmentation failures, with the anomaly scores strongly correlating with segmentation metrics computed on ground-truth. We outline the score matching training objective utilized by GNSM and provide an open-source implementation of our work.
Genetic Analysis of Biopsy Tissues from Colorectal Tumors in Patients with Ulcerative Colitis
Field: ONCOLOGY

Background/Objectives: Colorectal neoplasia developing from ulcerative colitis mucosa (CRNUC) can be divided into ulcerative colitis-associated neoplasia (UCAN) and non-UCAN; however, it is often difficult to distinguish UCAN from non-UCAN during a biopsy diagnosis. We investigated whether a genomic analysis could improve the diagnostic accuracy of UCAN using biopsy specimens. Methods: In step 1, 14 CRNUCs were used to examine whether the genomic landscape of biopsy and resection specimens matched. In step 2, we investigated the relationship between the genomic landscapes and the pathological diagnosis of 26 CRNUCs. The cancer genome was analyzed by deep sequencing using a custom panel of 27 genes found to be mutated in our previous CRNUC analysis. Results: In step 1, of the 27 candidate genes, 14 were mutated. The concordance rate of the pathogenic mutations in these 14 genes between the biopsy and resection specimens was 29% (4/14), while that of the pathogenic mutations in TP53 and KRAS was 79% (11/14). In step 2, the pathological diagnosis of biopsy specimens using only hematoxylin and eosin (HE) staining had a sensitivity of 33% and an accuracy of 38% for UCAN diagnosis. On the other hand, the combination of the HE pathology and p53 immunohistochemical staining had a sensitivity of 73% and an accuracy of 85% for UCAN diagnosis, while the combination of HE staining and a TP53 mutation had a sensitivity of 87% and an accuracy of 88% for UCAN diagnosis. Conclusions: An evaluation of TP53 mutations in biopsy specimens may be useful for diagnosing UCAN. However, further studies with larger sample sizes are required before this can be applied in clinical practice.
First-Line Use of Daratumumab in Patients with Multiple Myeloma Shows Delayed Neutrophil and Platelet Engraftment after Autologous Stem Cell Transplantation: Results from a Real-Life Single-Center Study
Field: ONCOLOGY

Background: This real-life study aimed to investigate the possible impact of D-VTd induction therapy on hematopoietic engraftment after autologous stem cell transplantation (auto-SCT). Methods: Sixty consecutive NDMM patients received four cycles of induction therapy with D-VTd. The conditioning regimen consisted of melphalan 200 mg/m2. These patients were compared with a historical control group of 80 patients who received four cycles of VTd as induction therapy. Results: The median days to reach neutrophil and platelet engraftment significantly differed between patients treated with D-VTd (11 and 13 days, respectively) and VTd (10 and 12 days). Univariate Cox analyses show that patients treated with D-VTd had a hazard ratio of neutrophil engraftment that was 42% significantly lower than those in the VTd arm (HR: 0.58, p = 0.002), and a multivariate model confirmed this result. Patients treated with D-VTd developed FN more frequently. Univariate and multivariate logistic regressions revealed an association between D-VTd and FN. Delayed engraftment did not correlate with more extended hospitalization. No patients died in the first six months after transplantation. Conclusions: Our real-life study showed that a four-drug induction therapy containing DARA does not impact transplant safety outcomes.
Integration of AI Training in the Field of Higher Education in the Republic of Bulgaria: An Overview
Field: EDUCATION

The presented work provides a comprehensive evaluation of the current availability of education programs and courses related to of AI the field of Information Technologies and Computer Science in higher education institutions (HIEs) in the Republic of Bulgaria. More specifically, this study examines 163 bachelor’s and 239 master’s degree programs from 28 HEIs available during the 2023/24 academic year in four professional fields: (1) Electrical Engineering, Electronics, and Automation; (2) Communication and Computer Technologies; (3) Informatics and Computer Science; and (4) Mathematics. The conducted evaluation shows that 41.1% of evaluated BSc programs and 26.4% of MSc programs include at least one AI-dedicated course. Results indicate a significant presence of AI-focused education, particularly in degrees related to Informatics and Computer Science, where 47.8% of AI courses are concentrated. However, a notable disparity exists in the inclusion of AI subjects across other technical fields, particularly in Electrical Engineering and related degrees, which contain only 8% of the identified AI courses for BSc degree programs. The findings highlight the need for a broader and more accelerated integration of AI education to meet the evolving demands of both students and the labor market. This work underscores the importance of strategic curriculum adaptation to enhance the readiness of Bulgarian HEIs to support the development and application of AI technologies, addressing the skills gap and fostering a workforce capable of navigating the AI-driven future.
Advancing Persistent Character Generation: Comparative Analysis of Fine-Tuning Techniques for Diffusion Models
Field: AI

In the evolving field of artificial intelligence, fine-tuning diffusion models is crucial for generating contextually coherent digital characters across various media. This paper examines four advanced fine-tuning techniques: Low-Rank Adaptation (LoRA), DreamBooth, Hypernetworks, and Textual Inversion. Each technique enhances the specificity and consistency of character generation, expanding the applications of diffusion models in digital content creation. LoRA efficiently adapts models to new tasks with minimal adjustments, making it ideal for environments with limited computational resources. It excels in low VRAM contexts due to its targeted fine-tuning of low-rank matrices within cross-attention layers, enabling faster training and efficient parameter tweaking. DreamBooth generates highly detailed, subject-specific images but is computationally intensive and suited for robust hardware environments. Hypernetworks introduce auxiliary networks that dynamically adjust the model’s behavior, allowing for flexibility during inference and on-the-fly model switching. This adaptability, however, can result in slightly lower image quality. Textual Inversion embeds new concepts directly into the model’s embedding space, allowing for rapid adaptation to novel styles or concepts, but is less effective for precise character generation. This analysis shows that LoRA is the most efficient for producing high-quality outputs with minimal computational overhead. In contrast, DreamBooth excels in high-fidelity images at the cost of longer training. Hypernetworks provide adaptability with some tradeoffs in quality, while Textual Inversion serves as a lightweight option for style integration. These techniques collectively enhance the creative capabilities of diffusion models, delivering high-quality, contextually relevant outputs.
Aircraft Skin Damage Visual Testing System Using Lightweight Devices with YOLO: An Automated Real-Time Material Evaluation System
Field: AI

Inspection and material evaluation are some of the critical factors to ensure the structural integrity and safety of an aircraft in the aviation industry. These inspections are carried out by trained personnel, and while effective, they are prone to human error, where even a minute error could result in a large-scale negative impact. Automated detection devices designed to improve the reliability of inspections could help the industry reduce the potential effects caused by human error. This study aims to develop a system that can automatically detect and identify defects on aircraft skin using relatively lightweight devices, including mobile phones and unmanned aerial vehicles (UAVs). The study combines an internet of things (IoT) network, allowing the results to be reviewed in real time, regardless of distance. The experimental results confirmed the effective recognition of defects with the mean average precision (mAP@0.5) at 0.853 for YOLOv9c for all classes. However, despite the effective detection, the test device (mobile phone) was prone to overheating, significantly reducing its performance. While there is still room for further enhancements, this study demonstrates the potential of introducing automated image detection technology to assist the inspection process in the aviation industry.
Sequential therapy for extramedullary plasmacytoma of the palate: a rare case report with seven years of follow-up and literature review
Field: ONCOLOGY

Background Extramedullary plasmacytoma (EMP) is a rare solitary malignancy that accounts for 3% of plasma cell neoplasms, and EMP with a primary occurrence in the palate is extremely uncommon. Owing to the long course of EMP and the limited available data on treatment outcomes, there are no definitive guidelines for its management, especially for high-risk patients who are more susceptible to early progression to multiple myeloma. Case presentation In this study, we review nine relevant studies and describe a 54-year-old woman who presented with an asymptomatic nonulcerative mass localized in the palate. After initial radical surgical resection of the lesion, the patient was definitively diagnosed with EMP with minimal plasmacytosis in the bone marrow, and adjuvant intensity-modulated radiation therapy with a minimum dose of 39.6 Gy was administrated in the surgical area. There was no evidence of local recurrence, nodal metastasis or progression to multiple myeloma (MM) during the seven-year follow-up period. Conclusion Given the atypical clinical features of palate EMP reported in the literature and the encouraging results of our patient, sequential therapy involving surgery and adjuvant radiotherapy for primary palatal lesions in high-risk EMP patients without nodal involvement could be an effective treatment modality.
A Bio-Psycho-Social Approach to Understanding Optimism and Pessimism in Response to Stress
Field: PSYCHOLOGY

Stress is widely known to have debilitating effects on physical health and mental wellbeing, particularly on one’s coping styles, personality traits, and outlook on life. Cumulative and chronic stress, which can serve as a triggering or aggravating factor for many pathological disorders if left unaddressed, has been linked to many life-threatening diseases. While many studies have looked at how optimism and pessimism are used as a form of coping mechanism, few have examined how different bio-psycho-social reactions to stress shape the level of optimism and pessimism. Using a sample of adult individuals aged 18 and older in the United States (n = 3361), this study addressed the following research questions: (1) What types of stress are predictive of optimism and pessimism? (2) Which responses to stress and coping mechanisms are most predictive of optimism and pessimism? (3) Do optimism and pessimism share the same stress-related risk and protective factors? Overall, this study found that while optimism and pessimism share conceptual similarities, they are not necessarily influenced by the same stress mechanisms. Stress, whether personal or financial, was associated with a negative outlook on life. This study showed that having good sleep quality and a lower number of psychological stress symptoms was linked to increasing optimism and reducing pessimism, while overeating or eating unhealthily was connected to both optimism and pessimism. Additionally, this study found that exercise/walking and emotional support mediated the effects of the responses to stress on the respondents’ level of optimism and pessimism.