mdpi-ai/citelens huggingface dataset:
The sorrow comes when I’m having moments of joy—experiences of parenting a live baby following a previous stillbirth: an interpretative phenomenological analysis
Field: PSYCHOLOGY

Stillbirth can lead to complex and varied psychological outcomes for parents. Many choose to have another pregnancy following a stillbirth; however, little is known about the experience of parenting and bonding with the subsequent baby. Couples, who were the biological parents of a stillborn baby and at least one subsequent live baby aged under five, were recruited and interviewed individually. Data were analysed using interpretative phrenomenological analysis. Twelve individual interviews (of six couples) were conducted and four themes with nine subthemes were developed. Theme 1 “Back to the starting line: pregnancy as a means to an end” captured parents’ desire to bring a live baby home with pregnancy being experienced alongside fear, trauma, and grief. Theme 2 “Reality hits” encapsulated the experience of arriving home and feeling overwhelmed by the demands of a new-born baby. Theme 3 “Being a living and loss parent” captured the experience of being a parent to both a living and non-living baby with conflicting emotions. Theme 4 “Protection: ‘I need him there next to me, so I know he’s alive’” represented the fear some parents felt when parenting their live baby and included parents’ strategies to manage this anxiety. This study presents novel insight into the complexities of being a parent to a stillborn baby in tandem with a live baby, with difficulties arising in bonding, and managing emotional distress linked to trauma and grief. Potential implications for care includes a need for increased training for professionals providing postnatal care.
The Efficacy of a Lower Dose of Everolimus in Patients with Advanced Neuroendocrine Tumors
Field: ONCOLOGY

Background: Everolimus at 10 mg daily is approved to treat patients with advanced grade 1/2 neuroendocrine tumors (NETs), although it may lead to significant toxicity. Grade 3 or higher drug-related adverse events and drug discontinuation occur in approximately one-fourth of cases. However, phase I trials have demonstrated that doses from 5 mg daily efficiently inhibit NET cell signaling. Objectives and Methods: This multicenter retrospective study compared the time to treatment failure (TTF) in patients with NETs who received a mean daily dose of 7–10 mg (higher dose [HD]) or ≤6 mg (lower dose [LD]) of everolimus. Results: Ninety-two patients were included: 74 (80%) in the HD group and 18 (20%) in the LD group. At a median follow-up of 4.2 years, the median time to treatment failure (TTF) was 9.2 months for the HD and 7.2 months for the LD groups (p = 0.85). The TTF did not significantly differ between the LD and the HD groups (HR: 1.24; 95% CI: 0.68–2.25; p = 0.47), even after adjusting for age at treatment initiation, the NET grade, and the treatment line. Conclusion: Everolimus doses from 5 to 6 mg/day seem to be equally as effective as higher doses, but lower doses are potentially associated with less toxicity and lower costs. These findings support validation through a randomized clinical trial.
Inpainting of damaged temple murals using edge- and line-guided diffusion patch GAN
Field: AI

Mural paintings are vital cultural expressions, enriching our lives by beautifying spaces, conveying messages, telling stories, and evoking emotions. Ancient temple murals degrade over time due to natural aging, physical damage, etc. Preserving these cultural treasures is challenging. Image inpainting is often used for digital restoration, but existing methods typically overlook naturally degraded areas, using randomly generated binary masks or small, narrow regions for repair. This study proposes a novel architecture to reconstruct large areas of naturally degraded murals, maintaining intrinsic details, avoiding color bias, and preserving artistic excellence. The architecture integrates generative adversarial networks (GANs) and the diffusion model, including a whole structure formation network (WSFN), a semantic color network (SCN), and a diffusion mixture distribution (DIMD) discriminator. The WSFN uses the original image, a line drawing, and an edge map to capture mural details, which are then texturally inpainted in the SCN using gated convolution for enhanced results. Special attention is given to globally extending the receptive field for large-area inpainting. The model is evaluated using custom-degraded mural images collected from Tamil Nadu temples. Quantitative analysis showed superior results than state-of-the-art methods, with SSIM, MSE, PSNR, and LPIPS values of 0.8853, 0.0021, 29.8826, and 0.0426, respectively.
Employing automatic analysis tools aligned to learning progressions to assess knowledge application and support learning in STEM
Field: EDUCATION

We discuss transforming STEM education using three aspects: learning progressions (LPs), constructed response performance assessments, and artificial intelligence (AI). Using LPs to inform instruction, curriculum, and assessment design helps foster students’ ability to apply content and practices to explain phenomena, which reflects deeper science understanding. To measure the progress along these LPs, performance assessments combining elements of disciplinary ideas, crosscutting concepts and practices are needed. However, these tasks are time-consuming and expensive to score and provide feedback for. Artificial intelligence (AI) allows to validate the LPs and evaluate performance assessments for many students quickly and efficiently. The evaluation provides a report describing student progress along LP and the supports needed to attain a higher LP level. We suggest using unsupervised, semi-supervised ML and generative AI (GAI) at early LP validation stages to identify relevant proficiency patterns and start building an LP. We further suggest employing supervised ML and GAI for developing targeted LP-aligned performance assessment for more accurate performance diagnosis at advanced LP validation stages. Finally, we discuss employing AI for designing automatic feedback systems for providing personalized feedback to students and helping teachers implement LP-based learning. We discuss the challenges of realizing these tasks and propose future research avenues.
Assessing Student Teachers’ Motivation and Learning Strategies in Digital Inquiry-Based Learning
Field: EDUCATION

Over the past two decades, teachers have adopted several teaching and learning strategies for motivating students to learn chemistry. Learning chemistry in context enables students to develop richer crosscutting learning experiences relevant to contributing to solving problems. A qualitative case study method was adopted to examine student teachers’ experiences in digital inquiry-based learning. Questionnaires with closed-ended and open-ended questions were used to evaluate student teachers’ motivational orientations and learning strategies during a general chemistry course for one month. The results show that student teachers utilized varied perspectives such as self-efficacy, task value, and intrinsic goals to elaborate their learning for knowledge construction and application when performing collaborative tasks. The approach enables students to receive maximum support and feedback from instructors who use pedagogical styles to self-direct them during class discussions, which enhances their active participation in learning with the learning materials. The findings provide a practical insight into instructional strategies in delivering chemistry concepts when students are motivated to use and adopt varied learning strategies.
The effectiveness of multimedia mental health self-care program based on cyber space on the mental health of infertile women: a randomized controlled trial
Field: PSYCHOLOGY

So far, some training interventions have been carried out to improve the mental health in women with infertility, but designing the need and evidence-based, as well as multimedia mental health self-care interventions based on cyber space has received less attention. Due to the spread of the internet and the role of self-care in improving mental disorders, this study was conducted to evaluate the effectiveness of the multimedia mental health self-care program on mental health and to assess the users' satisfaction. This study is a randomized controlled trial with pretest–posttest follow-up design. The sample was selected using a convenience sampling method (n = 90). The random number function was used to assign random numbers. The research instruments include a demographic, psychological Well-being, depression, anxiety, perceived stress, fertility problems and satisfaction with training questionnaire. Six weeks of intervention was conducted following the pre-test and the link of each session's content was sent to the participants, based on the training schedule, through Eitaa Messenger. The post-test and follow-up were conducted 1 week and 1 month post intervention. The data were analyzed using independent t-test and repeated measures ANOVA. A statistically significant difference was observed between the intervention and control group in the mean score of psychological well-being, perceived stress and infertility stress 1 week and 1 month post intervention and in the mean score of depression and anxiety 1 month post intervention. The intervention group scored higher than the control in psychological well-being but lower in perceived stress, depression, anxiety and infertility stress. The intervention had a positive effect and reduced the score of perceived stress, depression, anxiety and infertility in the intervention group over time. The score reduction continued until the follow-up stage. No significant time-interaction effect was observed on psychological well-being and on the control group. Satisfaction with the program and subscales was desirable. This program could significantly reduce the depression, anxiety, perceived stress and infertility stress and desirable satisfaction with the program was observed among users. This program can be used in designing the experimental and therapeutic interventions to improve mental-health self-care behaviors. RCT Registry: Iranian Registry of Clinical Trials; RCT registration number: IRCT20210526051410N1; Registration date: 2022–11-06. Last update: 2023–01-28
SIBILA: Automated Machine-Learning-Based Development of Interpretable Machine-Learning Models on High-Performance Computing Platforms
Field: AI

As machine learning (ML) transforms industries, the need for efficient model development tools using high-performance computing (HPC) and ensuring interpretability is crucial. This paper presents SIBILA, an AutoML approach designed for HPC environments, focusing on the interpretation of ML models. SIBILA simplifies model development by allowing users to set objectives and preferences before automating the search for optimal ML pipelines. Unlike traditional AutoML frameworks, SIBILA is specifically designed to exploit the computational capabilities of HPC platforms, thereby accelerating the model search and evaluation phases. The emphasis on interpretability is particularly crucial when model transparency is mandated by regulations or desired for stakeholder understanding. SIBILA has been validated in different tasks with public datasets. The results demonstrate that SIBILA consistently produces models with competitive accuracy while significantly reducing computational overhead. This makes it an ideal choice for practitioners seeking efficient and transparent ML solutions on HPC infrastructures. SIBILA is a major advancement in AutoML, addressing the rising demand for explainable ML models on HPC platforms. Its integration of interpretability constraints alongside automated model development processes marks a substantial step forward in bridging the gap between computational efficiency and model transparency in ML applications. The tool is available as a web service at no charge.
Evaluating Anomaly Explanations Using Ground Truth
Field: AI

The widespread use of machine and deep learning algorithms for anomaly detection has created a critical need for robust explanations that can identify the features contributing to anomalies. However, effective evaluation methodologies for anomaly explanations are currently lacking, especially those that compare the explanations against the true underlying causes, or ground truth. This paper aims to address this gap by introducing a rigorous, ground-truth-based framework for evaluating anomaly explanation methods, which enables the assessment of explanation correctness and robustness—key factors for actionable insights in anomaly detection. To achieve this, we present an innovative benchmark dataset of digital circuit truth tables with model-based anomalies, accompanied by local ground truth explanations. These explanations were generated using a novel algorithm designed to accurately identify influential features within each anomaly. Additionally, we propose an evaluation methodology based on correctness and robustness metrics, specifically tailored to quantify the reliability of anomaly explanations. This dataset and evaluation framework are publicly available to facilitate further research and standardize evaluation practices. Our experiments demonstrate the utility of this dataset and methodology by evaluating common model-agnostic explanation methods in an anomaly detection context. The results highlight the importance of ground-truth-based evaluation for reliable and interpretable anomaly explanations, advancing both theory and practical applications in explainable AI. This work establishes a foundation for rigorous, evidence-based assessments of anomaly explanations, fostering greater transparency and trust in AI-driven anomaly detection systems.
Speech characteristics that differentiate stuttering and cluttering in Japanese speakers
Field: PSYCHOLOGY

Background: Cluttering is a speech disorder distinct from stuttering. Despite this distinction, there is no established method to clearly differentiate the two disorders. This study aimed to use objective criteria to differentiate cluttering from stuttering in Japanese speakers.Methods: Participants were 32 consecutive native-Japanese speakers who visited the Keio University Hospital between July 2020 and January 2023 with a chief complaint of speech disfluency. One physician and two speech-language-hearing therapists concurred on a stuttering or cluttering diagnosis of the 32 patients based on recordings of the Kitsuon kensa-ho test. The frequencies of stuttering-like disfluencies (SDF) and normal disfluencies (NDF) were calculated from the Kitsuon kensa-ho, and the ratio of disfluencies (RDF) was calculated as the ratio of SDF to NDF. Differences between the cluttering and stuttering groups in the RDF and the mean articulatory rate (MAR) for oral reading and a monologue task were tested using the Mann–Whitney U test. ROC curves were used to determine the sensitivity and specificity that well-distinguished subjects with cluttering from those with stuttering; the experts’ diagnosis was the gold standard.Results: Of the 32 participants, 12 (38%) were diagnosed with cluttering and 20 (62%) with stuttering. The cluttering and stuttering groups were comparable in demographic characteristics. The RDF on monologue task had the highest sensitivity in diagnosing cluttering, and the MAR on monologue task had the highest specificity. Adopting provisional criteria of a monologue RDF greater than 1.2 and a monologue MAR greater than 7.5 produced a sensitivity of 0.92 and a specificity of 0.95.Conclusion: We conclude that combining monologue RDF and monologue MAR well-distinguished cluttering from stuttering. This method provides new objective diagnostic criteria, which can aid clinicians, therapists, and basic researchers.
Learning and Evolution: Factors Influencing an Effective Combination
Field: AI

(1) Background: The mutual relationship between evolution and learning is a controversial argument among the artificial intelligence and neuro-evolution communities. After more than three decades, there is still no common agreement on the matter. (2) Methods: In this paper, the author investigates whether combining learning and evolution permits finding better solutions than those discovered by evolution alone. In further detail, the author presents a series of empirical studies that highlight some specific conditions determining the success of such combination. Results are obtained in five qualitatively different domains: (i) the 5-bit parity task, (ii) the double-pole balancing problem, (iii) the Rastrigin, Rosenbrock and Sphere optimization functions, (iv) a robot foraging task and (v) a social foraging problem. Moreover, the first three tasks represent benchmark problems in the field of evolutionary computation. (3) Results and discussion: The outcomes indicate that the effect of learning on evolution depends on the nature of the problem. Specifically, when the problem implies limited or absent agent–environment conditions, learning is beneficial for evolution, especially with the introduction of noise during the learning and selection processes. Conversely, when agents are embodied and actively interact with the environment, learning does not provide advantages, and the addition of noise is detrimental. Finally, the absence of stochasticity in the experienced conditions is paramount for the effectiveness of the combination. Furthermore, the length of the learning process must be fine-tuned based on the considered task.
Machine Learning in Active Power Filters: Advantages, Limitations, and Future Directions
Field: AI

Machine learning (ML) techniques have permeated various domains, offering intelligent solutions to complex problems. ML has been increasingly explored for applications in active power filters (APFs) due to its potential to enhance harmonic compensation, reference signal generation, filter control optimization, and fault detection and diagnosis. This paper reviews the most recent applications of ML in APFs, highlighting their abilities to adapt to nonlinear load conditions, improve fault detection and classification accuracy, and optimize system performance in real time. However, this paper also highlights several limitations of these methods, such as the high computational complexity, the need for extensive training data, and challenges with real-time deployment in distributed power systems. For example, the marginal improvements in total harmonic distortion (THD) achieved by ML-based methods often do not justify the increased computational overhead compared to traditional control methods. This review then suggests future research directions to overcome these limitations, including lightweight ML models for faster and more efficient control, federated learning for decentralized optimization, and digital twins for real-time system monitoring. While traditional methods remain effective, ML-based solutions have the potential to significantly enhance APF performance in future power systems.
The impact of AI on education and careers: What do students think?
Field: AI

Introduction: Providing one-on-one support to large cohorts is challenging, yet emerging AI technologies show promise in bridging the gap between the support students want and what educators can provide. They offer students a way to engage with their course material in a way that feels fluent and instinctive. Whilst educators may have views on the appropriates for AI, the tools themselves, as well as the novel ways in which they can be used, are continually changing.Methods: The aim of this study was to probe students' familiarity with AI tools, their views on its current uses, their understanding of universities' AI policies, and finally their impressions of its importance, both to their degree and their future careers. We surveyed 453 psychology and sport science students across two institutions in the UK, predominantly those in the first and second year of undergraduate study, and conducted a series of five focus groups to explore the emerging themes of the survey in more detail.Results: Our results showed a wide range of responses in terms of students' familiarity with the tools and what they believe AI tools could and should not be used for. Most students emphasized the importance of understanding how AI tools function and their potential applications in both their academic studies and future careers. The results indicated a strong desire among students to learn more about AI technologies. Furthermore, there was a significant interest in receiving dedicated support for integrating these tools into their coursework, driven by the belief that such skills will be sought after by future employers. However, most students were not familiar with their university's published AI policies.Discussion: This research on pedagogical methods supports a broader long-term ambition to better understand and improve our teaching, learning, and student engagement through the adoption of AI and the effective use of technology and suggests a need for a more comprehensive approach to communicating these important guidelines on an on-going basis, especially as the tools and guidelines evolve.
CT-defined muscle density as a prognostic factor in multiple myeloma undergoing autologous stem cell therapy: a retrospective single center study
Field: ONCOLOGY

Purpose: Skeletal muscle quality assessment can be performed by cross-sectional imaging. Skeletal muscle density (SMD) identified to be of prognostic relevance of several clinically outcomes in patients with hematological diseases. The purpose of the present study was to establish the effect of SMD on overall survival (OS) and progression-free survival (PFS) in patients with multiple myeloma (MM). Methods: All patients with MM were retrospectively analyzed between 2009 and 2019. 127 patients were included into the analysis. Whole-body computed tomography (CT) was used to calculate skeletal muscle index (SMI), SMD, albumin-gauge score and intramuscular adipose tissue content (IMAC). Results: Overall, 28 patients (22.0%) of the patient sample died. In the discrimination analysis muscle density was higher in non-survivors compared to survivors (mean 30.8 ± 12.5 versus 24.1 ± 15.8, p = 0.03) and IMAC was lower in non-survivors (− 0.66 ± 1.8 versus − 0.25 ± 0.21, p = 0.01). These differences, however, were not demonstrated in the logistic regression analysis, which could not show prognostic relevance for the investigated muscle density parameters on PFS or OS. Conclusion: CT-defined muscle density parameters have no prognostic relevance on survival in patients with MM undergoing autologous stem cell therapy, which was demonstrated in a comprehensive analysis. These results corroborate previous smaller studies that body composition might have a limited role in this tumor entity.