ethicalabs/Research-Intent-Collab huggingface dataset:
Inductive and deductive justification of knowledge: epistemological beliefs and critical thinking at the beginning of studying mathematics

AbstractEpistemological beliefs are considered to play an important role in processes of learning and teaching. However, research on epistemological beliefs is confronted with methodological issues as for traditionally used self-report instruments with closed items, problems with social desirability, validity, and capturing domain-specific aspects of beliefs have been reported. Therefore, a new instrument with open items has been developed to capture mathematics-related epistemological beliefs, focusing on the justification of knowledge. Extending a previous study, the study at hand uses a larger and more diverse sample as well as a refined methodology. In total, 581 mathematics students (Bachelor of Science as well as pre-service teachers) completed the belief questionnaire and a test for mathematical critical thinking. The results confirm that beliefs can empirically be distinguished into belief position and belief argumentation, with only the latter being correlated to critical thinking.
Geo-Alignment of Vague Cognitive Regions: Representing Uneven Cognitive Geographies of Large Language Models

Vague cognitive regions (VCRs) such as Levant or Bible Belt play a central role in how people reason about space and place, despite lacking clear boundaries or formal definitions. Prior studies in behavioral geography and GIScience have used human surveys or crowd-sourced social media data to delineate human perception and cognition of such regions. In this paper, we introduce a new AI-based approach, which uses foundation models, particularly large language models (LLMs), to represent VCRs. Then, we challenge the results by arguing that LLMs exhibit uneven cognitive geographies, representing some VCRs more coherently and stably than others. We introduce geo-alignment as an analytical lens to examine the discrepancies between different representations. Focusing on comparative cases such as the Alps, Northern-Southern California, the Sahara, and Kashmir, we show variations that systematically shape LLM-derived VCRs. Rather than treating misalignment as a modeling error, we conceptualize it as a signal of unequal global cognitive visibility embedded in training data. The paper contributes a methodological framework for analyzing VCRs through the lens of geo-alignment and advances a methodological GIScience perspective on the spatial knowledge encoded in LLMs. &nbsp;Reproducibility review available at: https://doi.org/10.17605/OSF.IO/WH6GD
Relational Graph Representation Learning for Open-Domain Question Answering

We introduce a relational graph neural network with bi-directional attention mechanism and hierarchical representation learning for open-domain question answering task. Our model can learn contextual representation by jointly learning and updating the query, knowledge graph, and document representations. The experiments suggest that our model achieves state-of-the-art on the WebQuestionsSP benchmark.
Effectiveness and Implementation Outcome Measures of Mental Health Curriculum Intervention Using Social Media to Improve the Mental Health Literacy of Adolescents

Hailemariam Mamo Hassen,1 Manas Ranjan Behera,1 Pratap Kumar Jena,1 Rebecca S Dewey,2 Getachew Abeshu Disassa3 1School of Public Health, KIIT Deemed to Be University, Bhubaneswar, Odisha, India; 2School of Medicine, The University of Nottingham, Nottingham, UK; 3College of Education and Behavioural Sciences, Jimma University, Jimma, EthiopiaCorrespondence: Hailemariam Mamo Hassen, School of Public Health, KIIT Deemed to Be University, Email 1781133@kiit.ac.inPurpose: Globally, adolescent mental health problems reportedly manifest more severely in individuals with lower mental health literacy. Mental health curriculum interventions using social media have been associated with positive implementation outcomes. This study aimed to investigate whether such an intervention significantly improves adolescent mental health literacy and is associated with positive implementation outcome measures.Methods: This study employed a quasi-experimental design, comparing the before-and-after difference between the intervention group (n1=77) and the control group (n1=76). A mental health literacy module was delivered using social media. Data were collected using validated questionnaires. Effect size and difference-in-differences model calculations (and associated 95% confidence intervals, CI) were conducted alongside other descriptive analyses.Results: The estimated intervention effect size and difference-in-differences estimates were greater than zero. The effect was greater in the intervention than in the control group (p< 0.05) despite sex and age differences. The effect size estimate provided by Cohen’s d and Hedges’ g values was medium-to-large (d/g=0.429– 0.767, p< 0.05). The difference-in-differences reflected a significant effect (DID=0.348, CI: 0.154– 0.542, p< 0.001) in increasing mental health literacy despite differences associated with sex, age, and school grade. The intervention program was acceptable, appropriate, feasible, and satisfactory, and more than 80% of participants said they agree to completely agree with these implementation outcomes. Resources and personal and family-related factors were among the perceived influencing factors determining the effectiveness and implementation outcome measures.Conclusion: The intervention program effectively improved the mental health literacy of adolescents. It has been highlighted that social media interventions for mental health promotion could be practical and scalable; however, there is a need to take into account socio-demographic differences and barriers to inclusion/compliance.Keywords: adolescent mental health, mental health promotion, difference-in-differences, effect size
SpeechPrompt: Prompting Speech Language Models for Speech Processing Tasks

Prompting has become a practical method for utilizing pre-trained language models (LMs). This approach offers several advantages. It allows an LM to adapt to new tasks with minimal training and parameter updates, thus achieving efficiency in both storage and computation. Additionally, prompting modifies only the LM's inputs and harnesses the generative capabilities of language models to address various downstream tasks in a unified manner. This significantly reduces the need for human labor in designing task-specific models. These advantages become even more evident as the number of tasks served by the LM scales up. Motivated by the strengths of prompting, we are the first to explore the potential of prompting speech LMs in the domain of speech processing. Recently, there has been a growing interest in converting speech into discrete units for language modeling. Our pioneer research demonstrates that these quantized speech units are highly versatile within our unified prompting framework. Not only can they serve as class labels, but they also contain rich phonetic information that can be re-synthesized back into speech signals for speech generation tasks. Specifically, we reformulate speech processing tasks into speech-to-unit generation tasks. As a result, we can seamlessly integrate tasks such as speech classification, sequence generation, and speech generation within a single, unified prompting framework. The experiment results show that the prompting method can achieve competitive performance compared to the strong fine-tuning method based on self-supervised learning models with a similar number of trainable parameters. The prompting method also shows promising results in the few-shot setting. Moreover, with the advanced speech LMs coming into the stage, the proposed prompting framework attains great potential.
Machine Learning in Materials Science : Predicting molecular properties using sequence-based methods

Noggrann förutsägelse av molekylära egenskaper är avgörande för upptäckt och design av nya material inom organisk elektronik. I detta examensarbete undersöks hur djupa neurala nätverk kan användas för att förutsäga energinivåer för högsta ockuperade (HOMO) och lägsta icke ockuperade (LUMO) molekylorbitaler direkt från SMILES-representationer. Detta utgör ett datadrivet alternativ till traditionella kvantmekaniska metoder såsom DFT. Tre olika neurala nätverksarkitekturer (GRU, LSTM, och transformer) tränades på ett utvalt dataset med molekyler med DFT-beräknade orbitalenergier. Deras prestanda utvärderades med avseende på noggrannhet och generaliseringsförmåga. Bland dessa modeller presterade GRU-arkitekturen bäst och användes därefter för att förutsäga orbitalenergier för 45 miljoner molekyler. En high-throughput screening identifierade kandidater med små förutsagda HOMOLUMO gap, vilka därefter validerades genom DFT-beräkningar. Vidare strukturanalys avslöjade återkommande strukturer i överensstämmelse med kända mönster för låggap molekyler, vilket stärker modellens förmåga att identifiera meningsfulla kemiska samband. Sammanfattningsvis visar resultaten att djupinlärningsmodeller, särskilt GRUbaserade arkitekturer, kan användas för att på ett effektivt sätt accelerera arbetsflöden för molekylupptäckter. Detta möjliggör snabb och skalbar screening av stora kemiska bibliotek för tillämpningar inom nästa generations organiska elektronikmaterial.
Ethics in the Age of AI : Principles and Guidelines for Responsible Implementation in Workplace

The integration of artificial intelligence (AI) tools into corporate settings has become increasingly prevalent, with organizations leveraging AI to enhance daily operations. However, as reliance on AI grows, ethical considerations surrounding its use become paramount. This paper examines the ethical dimensions of AI implementation, drawing on insights from industry reports and frameworks such as those by McKinsey, Accenture, and IBM. Key challenges include ensuring system integrity, preventing privacy breaches, and addressing biases. To address these challenges, organizations must prioritize data privacy and security, accuracy and reliability of AI predictions, ethical use and inclusivity, and transparency in decision-making processes. Recommendations for organizations include providing education and training on AI ethics, continuously monitoring and improving ethical guidelines, and regularly updating policies to align with technological advancements and corporate values. Further research is needed to explore evolving ethical considerations in AI usage over time
Research Progress on Carbon Footprint Accounting and Evaluation of Automotive Seats Based on Life Cycle Assessment (LCA)

This paper addresses the challenges of long industrial chains, complex carbon footprint accounting, and high uncertainty in the automotive industry. It systematically reviews studies on carbon footprint accounting of automotive seats based on Life Cycle Assessment (LCA), constructs a framework for carbon footprint evaluation based on LCA, and describes the accounting methods. The study presents the carbon footprint results of a specific automotive driver’s seat, analyzes the causes of carbon emissions at different stages of its life cycle, and concludes that the majority of emissions are concentrated in the raw material acquisition stage. Finally, the paper summarizes the existing issues in automotive seat carbon footprint research and explores future directions, suggesting that measures to reduce the carbon footprint of automotive seats include lightweighting, reducing the use of raw materials such as steel, optimizing manufacturing processes, and increasing the proportion of clean energy.
Digital Strategies for Sustainable Agricultural Outreach: A Model for Food Security Advocacy

Objective: This study explores the role of digital marketing and communication strategies in enhancing agricultural advocacy across Africa, with scalable implications for U.S. rural communities.  It examines how tools such as SMS, WhatsApp, social media campaigns, and mobile apps are being utilized to disseminate agricultural knowledge, drive behavioral change, and influence food policy to enhance food security. Special emphasis is placed on the professional experience of  Aderemi Bunmi Kutelu in sales and digital platform engagement, providing a practitioner lens on the integration of marketing principles into development communication for food security.  Study Design: A structured review of peer-reviewed academic literature from 2018 to 2025 was conducted, focusing on digital agriculture initiatives across Africa and their behavioral, policy, and outreach impacts. After screening and eligibility checks, a total of 30 journal articles were included, spanning empirical interventions, meta-analyses, and case studies relevant to agricultural communication, technology adoption, and rural mobilization.  Methodology: The research draws from peer-reviewed journal articles, government reports, and industry analyses. Through a systematic literature review, the study evaluates SMS, WhatsApp, and mobile applications in agricultural outreach. It follows a qualitative review approach to highlight the need, use, and impact of digital strategies for sustainable agricultural outreach.  Results: Findings reveal that mobile-based campaigns and peer-led content significantly outperform traditional methods in driving adoption of agricultural practices. For example, WhatsApp group interventions in Uganda raised female participation by 36%, while SMS nudges improved input use by up to 20% in Nigeria. Content credibility, language accessibility, and message frequency emerged as core behavioral levers. Infrastructure challenges, shared phone ownership, and literacy gaps persist but can be addressed with offline-first designs and multimedia content.  Conclusions: Drawing from both scholarly evidence and Aderemi Kutelu’s real-world digital engagement experience, the study proposes a framework for people-centered, tech-enabled agricultural advocacy. This approach supports scalable, participatory, and data-driven outreach strategies that align with both African rural realities and emerging needs in U.S. food-insecure zones. The findings contribute to a growing scientific discourse on tech for development and underscore the need for cross-regional knowledge transfer in achieving inclusive food security.
Additional file 1 of Cost-effectiveness of a hybrid emergency room system for severe trauma: a health technology assessment from the perspective of the third-party payer in Japan

Additional file 1:. Table S1. Input Parameters
Impact of the Ce $4f$ states in the electronic structure of the intermediate-valence superconductor CeIr$_3$

The electronic structure of the $f$-based superconductor $\mathrm{CeIr_3}$ was studied by photoelectron spectroscopy. The energy distribution of the $\mathrm{Ce}~4f$ states were revealed by the $\mathrm{Ce}~3d-4f$ resonant photoelectron spectroscopy. The $\mathrm{Ce}~4f$ states were mostly distributed in the vicinity of the Fermi energy, suggesting the itinerant character of the $\mathrm{Ce}~4f$ states. The contribution of the $\mathrm{Ce}~4f$ states to the density of states (DOS) at the Fermi energy was estimated to be nearly half of that of the $\mathrm{Ir}~5d$ states, implying that the $\mathrm{Ce}~4f$ states have a considerable contribution to the DOS at the Fermi energy. The $\mathrm{Ce}~3d$ core-level and $\mathrm{Ce}~3d$ X-ray absorption spectra were analyzed based on a single-impurity Anderson model. The number of the $\mathrm{Ce}~4f$ states in the ground state was estimated to be $0.8-0.9$, which is much larger than the values obtained in the previous studies (i.e., $0-0.4$).
Wavelets for Density-Functional Theory and Post-Density-Functional-Theory Calculations

We give a fairly comprehensive review of wavelets and of their application to density-functional theory (DFT) and to our recent application of a wavelet-based version of linear-response time-dependent DFT (LR-TD-DFT). Our intended audience is quantum chemists and theoretical solid-state and chemical physicists. Wavelets are a Fourier-transform-like approach which developed primarily in the latter half of the last century and which was rapidly adapted by engineers in the 1990s because of its advantages compared to standard Fourier transform techniques for multiresolution problems with complicated boundary conditions. High performance computing wavelet codes now also exist for DFT applications in quantum chemistry and solid-state physics, notably the BigDFT code described in this chapter. After briefly describing the basic equations of DFT and LR-TD-DFT, we discuss how they are solved in BigDFT and present new results on the small test molecule carbon monoxide to show how BigDFT results compare against those obtained with the quantum chemistry gaussian-type orbital (GTO) based code deMon2k. In general, the two programs give essentially the same orbital energies, but the wavelet basis of BigDFT converges to the basis set limit much more rapidly than does the GTO basis set of deMon2k. Wavelet-based LR-TD-DFT is still in its infancy, but our calculations confirm the feasibility of implementing LR-TD-DFT in a wavelet-based code.
Systems-level modeling of antimicrobial utilization patterns to optimize stewardship interventions and suppress emerging resistance pathways

Rising antimicrobial resistance (AMR) presents a profound threat to global health, driven in part by complex and often poorly understood patterns of antimicrobial utilization across healthcare systems. Traditional stewardship approaches focused on guideline adherence, formulary controls, or individual prescribing behaviors are insufficient when viewed against the scale and dynamism of resistance evolution. Systems-level modeling offers a broader analytical lens, enabling researchers and stewardship teams to examine antimicrobial use not as isolated prescribing events but as interconnected patterns shaped by clinical workflows, patient demographics, institutional pressures, and microbial ecology. From a macro perspective, systems models integrate multi-source data including electronic health records, pharmacy distributions, resistance phenotypes, and patient-flow dynamics to map utilization trends across wards, hospitals, and regional care networks. These models reveal latent drivers of inappropriate use, identify clusters of high-risk prescribing, and detect feedback loops that accelerate resistance emergence. Agent-based simulations and compartmental models can further characterize how antimicrobial pressure influences microbial population shifts, revealing pathways through which resistant strains develop, propagate, and persist within healthcare environments. Narrowing the focus, this study emphasizes how advanced modeling techniques, such as machine learning–enhanced forecasting, causal inference frameworks, and network-based transmission analysis, can guide precision stewardship interventions. By simulating the outcomes of alternative policies such as formulary restrictions, diagnostic stewardship, or targeted provider education systems-level models enable proactive selection of strategies most likely to reduce selective pressure while preserving clinical effectiveness. Ultimately, integrating systems-level modeling into antimicrobial stewardship transforms reactive management into anticipatory control. Such an approach not only optimizes utilization patterns but also helps suppress emerging resistance pathways, contributing to a more sustainable, resilient antimicrobial ecosystem across healthcare settings.
Lunaport: Math, Mechanics & Transport

Issues for transport facilities on the lunar surface related to science, engineering, architecture, and human-factors are discussed. Logistic decisions made in the next decade may be crucial to financial success. In addition to outlining some of the problems and their relations with math and computation, the paper provides useful resources for decision-makers, scientists, and engineers.
Predicting Survival Time of Ball Bearings in the Presence of Censoring

Ball bearings find widespread use in various manufacturing and mechanical domains, and methods based on machine learning have been widely adopted in the field to monitor wear and spot defects before they lead to failures. Few studies, however, have addressed the problem of censored data, in which failure is not observed. In this paper, we propose a novel approach to predict the time to failure in ball bearings using survival analysis. First, we analyze bearing data in the frequency domain and annotate when a bearing fails by comparing the Kullback-Leibler divergence and the standard deviation between its break-in frequency bins and its break-out frequency bins. Second, we train several survival models to estimate the time to failure based on the annotated data and covariates extracted from the time domain, such as skewness, kurtosis and entropy. The models give a probabilistic prediction of risk over time and allow us to compare the survival function between groups of bearings. We demonstrate our approach on the XJTU and PRONOSTIA datasets. On XJTU, the best result is a 0.70 concordance-index and 0.21 integrated Brier score. On PRONOSTIA, the best is a 0.76 concordance-index and 0.19 integrated Brier score. Our work motivates further work on incorporating censored data in models for predictive maintenance.
Detection of ultra-high energy cosmic ray showers with a single-pixel fluorescence telescope

Accepted for publication in Astroparticle Physics
A Large Language Model Algorithm for Language Processing Technology Deviation Detection

With the wide application of large language models in natural language processing, problems such as data bias, improper algorithm selection and amplification of social bias have become increasingly prominent, and it is urgent to ensure its fairness and accuracy through technical improvement and ethical consideration. At present, the error detection technology in natural language processing has the problem of insufficient accuracy and efficiency, which seriously affects the reliability of language processing. Therefore, this study aims to build an efficient deviation detection system using large language model algorithm to improve the accuracy and robustness of natural language processing technology. In this paper, a deviation detection system based on speech processing technology is designed and implemented by using the algorithm of large language model in neural network. Its performance is verified by experiments. The experimental results show that the algorithm is easy to operate and the accuracy of deviation detection is as high as 96.76%. This study not only provides a new technical path for the application of large language models in natural language processing, but also provides an important reference for improving the fairness and accuracy of language processing technology. The results show that the large language model algorithm has significant advantages in deviation detection and can effectively solve the technical bottleneck in natural language processing.