ethicalabs/Research-Intent-Collab huggingface dataset:
Designing Inclusive AI Learning Tools: Insights from Women in Tech on AI-Augmented Creativity for Equitable Computing

This study explores how AI-augmented creativity tools can support girls’ engagement in computing by integrating the lived experiences of women in technology. Drawing on ten semi-structured interviews, we present key themes that reflect emotional, cultural, and social dimensions of learning. The findings offer critical design implications for inclusive AI learning environments that are participatory, reflexive, and experience-centered—while acknowledging both benefits and risks of technological mediation.
Homological stability for classical groups

Associated to every group with a weak spherical Tits system of rank n+1 with an appropriate rank n subgroup, we construct a relative spectral sequence involving group homology of Levi subgroups of both groups. Using the fact that such Levi subgroups frequently split as semidirect products of smaller groups, we prove homological stability results for unitary groups over division rings with infinite centre as well as for special linear and special orthogonal groups over infinite fields.
A New Occupational Safety and Health Administration Directive Regarding H1N1 Influenza in the Workplace

This article addresses the following aspects of a new Occupational Safety and Health Administration (OSHA) directive associated with H1N1 in the workplace: the recordability of H1N1 exposure in the workplace; the need for employers to have a pandemic plan that addresses the identification and management of H1N1 exposure risk to employees; the respiratory protection requirements of an exposure control plan; OSHA workplace inspections in response to exposure complaints or fatalities associated with H1N1; and the availability of OSHA compliance guidance for employers.
Congressional Districting: "Rocks-Pebbles-Sand"

As a case study into an algorithmic approach to congressional districting, North Carolina provides a lot to explore. Statistical modeling has called into question whether recent North Carolina district plans are unbiased. In particular, the literature suggests that the district plan used in the 2016 U.S. House of Representatives election yields outlier results that are statistically unlikely to be obtained without the application of bias. Therefore, methods for creating strong and fair district plans are needed. Informed by previous districting models and algorithms, we build a model and algorithm to produce an ensemble of viable Congressional district plans. Our work contributes a ``Rocks-Pebbles-Sand'' concept and procedure facilitating reasonable population equity with a small overall number of county splits among districts. Additionally, our methodology minimizes the initial need for granular, precinct-level data, thereby reducing the risk of covert gerrymandering. This case study indicates plausibility of an approach built upon an easy-to-understand intuition.
Phase Transitions in Financial Markets Using the Ising Model: A Statistical Mechanics Perspective

This dissertation investigates the ability of the Ising model to replicate statistical characteristics, or stylized facts, commonly observed in financial assets. The study specifically examines in the S&amp;P500 index the following features: volatility clustering, negative skewness, heavy tails, the absence of autocorrelation in returns, and the presence of autocorrelation in absolute returns. A significant portion of the dissertation is dedicated to Ising model-based simulations. Due to the lack of an analytical or deterministic solution, the Monte Carlo method was employed to explore the model's statistical properties. The results demonstrate that the Ising model is capable of replicating the majority of the statistical features analyzed.
Choosing the Best Model for Crop Yield Prediction by the Means of Regression Analysis on the Example of “Crop Yield – Water Use” Simulations Through Best Subsets Approach

Crop yield prediction is relevant subject of current agricultural science. There are various mathematical approaches to crop yield prediction, and regression analysis, notwithstanding the fact that it is somewhat outdated, is still one of the most used ones in this purpose. The quality of predictive model is of great importance, and it is strongly dependent on the rational choice of the target function. The goal of this study is to find out the best regression model for winter wheat, soybeans, and grain corn yield prediction depending on the crops’ water use. The data on true crops’ yields and water use were collected within 1970-2020 at the experimental fields of the Institute of Climate-Smart Agriculture, Kherson region, Ukraine. In total, 145 data pairs were processed by the best subsets regression to find out the best model in terms of fitting quality (assessed by the Pearson’s coefficient of correlation), and prediction accuracy (assessed by the values of the minimum and maximum absolute errors and mean average percentage error). As a result, it was established that the best fitting quality for all the studied crops is attributed to cubic function, while the best accuracy is recorded for hyperbolic (reverse) function in soybeans (mean absolute percentage error is 12.27%), quadratic and hyperbolic functions in winter wheat (mean absolute percentage error is 20.54%), and cubic function in grain corn (mean absolute percentage error is 14.92%). To sum up the results of the study, it is recommended to apply cubic regression function for modeling crops’ yields in agricultural studies.
Additional file 2 of Health insurance coverage and access to child and maternal health services in West Africa: a study protocol for a systematic review

Additional file 2:. List of eligible countries.
Research on the Protection Path of Minority Shareholders’ Rights and Interests Under the Mandatory Delisting System

With the continuous development of the capital market, the mandatory delisting system has become an important means to maintain market health. Since the implementation of the Opinions on Strictly Implementing the Delisting System, the number of listed enterprises subject to mandatory delisting has increased sharply, significantly enhancing the market’s capacity for clearing out inefficient entities. However, the issue of minority shareholders’ rights and interests being impaired during the delisting process has become increasingly prominent, making the protection of these rights a major social concern. Taking the mandatory delisting case of Guanghui Automotive, whose stock closed below 1 yuan for 20 consecutive trading days, as the research object, this paper explores the internal and external causes of the company’s mandatory delisting against the backdrop of the current delisting system. It further analyzes the root causes of the infringement on minority shareholders’ rights and interests, and proposes targeted counter-measures including improving the information disclosure system, strengthening accounting supervision, implementing compensation mechanisms, perfecting the representative litigation system, and enhancing investor education. This study aims to provide theoretical support and practical reference for protecting minority shareholders’ rights and interests under the capital market delisting system, and promote the construction of a more fair, transparent, and efficient capital market environment.
Solid State Chemistry and Electrochemistry of Five-Volt Lithium Insertion Materials for Advanced Lithium-Ion Batteries

論文の内容の要旨及び論文審査の結果の要旨
Predicting drug–drug interactions and optimal dosing of betrixaban with physiologically based pharmacokinetic modeling in patients with renal impairment who were coadministered with P-glycoprotein inhibitors

Objective                     To optimize dosing adjustments in patients with renal impairment who were coadministered with P-glycoprotein inhibitors—verapamil or amiodarone—using a physiologically based pharmacokinetic model.                                                           Methods                     The developed physiologically based pharmacokinetic model was corroborated using previously reported pharmacokinetic parameters across multiple doses, ratio values in drug–drug interactions, and pharmacokinetic ratio values in patients with renal impairment.                                                           Results                     The physiologically based pharmacokinetic model exhibited fold errors between 0.7 and 1.3 and effectively illustrated changes in pharmacokinetic ratios in patients with renal impairment. The model suggested that the dosing regimen of betrixaban should be reduced to 40 mg once daily in patients with mild renal impairment who were administered with verapamil or amiodarone. Additionally, it is advised that the dosing regimen of betrixaban be reduced to 40 mg once daily in patients with moderate renal impairment who were administered with amiodarone, whereas no suitable dosing is recommended for those administered with verapamil. For patients with severe renal impairment, it may be reasonable to avoid concurrent use of betrixaban with P-glycoprotein inhibitors.                                                           Conclusions                     The coadministration of P-glycoprotein inhibitors elevates the exposure of betrixaban and may heighten the risk of major bleeding. Reduced dosing regimens are recommended for patients with renal impairment when betrixaban is coadministered with P-glycoprotein inhibitors.
A Reproducible and Unified Benchmark of Deep Learning Feature Selection Across Simulations and Multi-Omics datasets

Abstract                 Reliable feature selection is critical for extracting small, interpretable biomarker panels from high-dimensional omics, yet deep learning–based feature selection methods have rarely been compared systematically. We present a unified and reproducible benchmark of post-hoc explainers—attribution (GradientSHAP, and DeepLIFT), perturbation (LIME, Feature Ablation, and Occlusion) and embedded selectors (CancelOut, EAR-FS, and GRACES). Using standardized preprocessing, a shared neural network, and Optuna tuning, we evaluate feature selection performance and computational efficiency in simulations and downstream predictive accuracy on diverse real datasets, including gene expression datasets with binary outcomes, five TCGA projects spanning mRNA, methylation, and SNP modalities with multi-class outcomes, and ADNI gene expression with continuous neuroimaging phenotypes. EAR-FS and GRACES consistently perform best: GRACES is most robust but computationally intensive, whereas EAR-FS achieves similar accuracy with much lower computational cost. Classical methods remain competitive when signals are sparse and near-linear. Post-hoc explainers contribute most as interpretability tools and model auditors rather than as primary subset selectors. To enable reproducibility and broad adoption, we provide a software hub and website implementing all these methods with standardized pipelines and evaluation routines, facilitating efficient feature selection under typical constraints of budget, sample size, and turnaround time.
OPTIMIZING RECOMMENDER SYSTEMS:  INTEGRATING COLLABORATIVE FILTERING WITH BAYESIAN  AND GAUSSIAN TECHNIQUES

One frequently utilized method in recommendation systems is collaborative filtering (CF). Fine-tuning the hyperparameters of CF algorithms remains a difficult task even with developments in modeling consumers and products/services. This work explores an alternative approach for this aim by means of Bayesian optimization using Gaussian processes during hyperparameter alteration. This method reduces the time and effort usually needed for manual tuning by autonomously adjusting hyperparameters for two basic and simple CF algorithms on three popular datasets, yielding competitive results: Netflix Prize, Movielens 1M, and Movielens 10M. Therefore, it could enable practitioners to improve the performance of their recommendation systems whilst greatly shortening the time and effort spent on tuning their systems.
Unjustified Discrimination: Is the Moratorium on the use of Genetic Test Results by Insurers a Contradiction in Terms?

This paper considers the legal position of genetic test results in insurance law in England and Wales. The strict position is that this information is material to the decision of the insurer to offer insurance cover and should be disclosed by insurance applicants. However, the British Government and the Association of British Insurers have agreed to a moratorium on the use of genetic test results in insurance, which will run until 2014. The moratorium prohibits unfavourable treatment of insurance clients on their basis of their genetics, unless it can be justified. In this paper, I consider the notion of genetic discrimination and ask whether it is possible to justify the concept in such a way that its existence should be accepted. The paper suggests that the insurance industry and the general public have different viewpoints on the concept of discrimination, and that this causes much of the disagreement over the issue of using genetic test results in insurance.
Interpretability versus Explainability: Classification for Understanding Deep Learning Systems and Models

The techniques of explainability and interpretability are not alternatives for many realworld problems, as recent studies often suggest. Interpretable machine learning is not a subset of explainable artificial intelligence or vice versa. While the former aims to build glass-box predictive models, the latter seeks to understand a black box using an explanatory model, a surrogate model, an attribution approach, relevance importance, or other statistics. There is concern that definitions, approaches, and methods do not match, leading to the inconsistent classification of deep learning systems and models for interpretation and explanation. In this paper, we attempt to systematically evaluate and classify the various basic methods of interpretability and explainability used in the field of deep learning. One goal of this paper is to provide specific definitions for interpretability and explainability in Deep Learning. Another goal is to spell out the various research methods for interpretability and explainability through the lens of the literature to create a systematic classifier for interpretability and explainability in deep learning. We present a classifier that summarizes the basic techniques and methods of explainability and interpretability models. The evaluation of the classifier provides insights into the challenges of developing a complete and unified deep learning framework for interpretability and explainability concepts, approaches, and techniques.
Cosmological Simulation for Fuzzy Dark Matter Model

10 pages, 2 tables, published on Front. Astron. Space Sci. under the topic: Dark Matter - Where is it? What is it?
PENERAPAN FRAMEWORK LARAVEL PADA APLIKASI HRIS (HUMAN RESOURCE INFORMATION SYSTEM)

Sumber daya manusia merupakan sumber daya organisasi yang perlu dikelola dengan efisien dan efektif. Banyaknya informasi yang harus dikelola terkait sumber daya manusia memunculkan permasalahan tingkat kompleksitas yang tinggi. Pentingnya pengaturan sumber daya manusia yang sangat kompleks pada suatu perusahaan membuat perusahaan diharuskan membentuk sebuah divisi baru yaitu Human Resource Department atau Personalia. Tanpa sebuah aplikasi, bagian HRD mempunyai beberapa kesulitan berupa pengelompokkan data, penghitungan jumlah kehadiran, uang transport dan makan kemudian terakhir slip gaji karyawan dan pencetakkan laporan slip gaji. Untuk menangani permasalahan tersebut perlu diterapkan aplikasi Human Resources Information System (HRIS). Sebuah modul yang penting yang terdapat pada sistem ini adalah payroll atau pengajian. PT Octagon Studio Ltd merupakan startup asal Bandung, salah satu perusahaan yang sering kali mengalami masalah terkait pengelolaan manajemen sumber daya manusia yang kurang baik. Selama ini proses perhitungan gaji yang dilakukan oleh sistem yang ada, masih tergolong belum efektif. Jadi proses perhitungan gaji dilakukan satu persatu tiap pegawai. Sehingga waktu yang diperlukan untuk melakukan perhitungan gaji membutuhkan waktu lama. Pada pengajuan cuti, masih dilakukan secara manual sehingga pengelolaannya belum optimal dan prosesnya cukup lambat. Selain itu sulitnya mendapatkan informasi seperti data kepegawaian dan jumlah cuti yang tersedia untuk masing-masing pegawai. Aplikasi HRIS ini dibuat menggunakan model waterfall yang mencakup: analisis, desain, pengkodean dan pengujian dengan mengunakan PHP Framework Laravel dan MySQL sebagai database. Terkait masalah tersebut diperlukan aplikasi HRIS yang diharapkan dapat membantu perusahaan dalam perhitungan gaji karyawan secara bersamaan dan pengelolaan data pegawai.
Linking Science Data and Research to Enable Data and Knowledge Discovery

NASA Earth Science Data Centers contain enormous amounts of remote sensing digital data. It is often a significant challenge for users to find data suitable for their research topic in these vast archives. One of the approaches is the usage-driven data discovery, where users seek publications on projects similar to their intended study. Tools and methodologies that can help facilitate and organize these connections are therefore valuable for creating improved knowledge mappings, which can be further used by search engines to suggest data or publications best tailored to a user’s specific research goal. To create knowledge representations of science carried out in these publications, we use existing ontologies such as GCMD and SWEET. These ontologies together encompass term dictionaries that include measured variables, names of molecules or radicals, mission and instrument names, locations, realms, among many others. A database of these terms can be used to enhance the automation of knowledge discovery and facilitate machine learning and artificial intelligence algorithms or applications. This poster was presented at the 2021 Earth Science Information Partners (ESIP) Winter Meeting held virtually in January 2021.
About the poor decay of certain cross-correlation functions in the statistical mechanics of phase transitions in the static and dynamical regime

We introduce a method to prove poor decay of certain cross-correlation functions which are closely related to the phase transition. The methods apply both to equal and nonequal times, which gives access to the dynamical regime. We establish a criterion which displays openly what happens when the Goldstone picture breaks down. Since no rudiments of translation invariance are needed the treatment covers phases in coexistence like, e.g., liquid-gas interfaces and completely inhomogeneous systems. Furthermore a perhaps surprising connection with the breaking of time reflection invariance of the equilibrium state is established.
Shaping corporate social responsibility management and reporting through engagement

Advocacy organisations have traditionally played a prominent role in shaping corporate social responsibility (CSR) management and reporting practices through organisational-level and institutional-level engagement. Recent years have seen advocacy organisations expand the nature and content of their engagement interactions by (1) endeavouring to influence the emergence of institutional-level CSR frameworks and (2) placing enhanced emphasis on challenging the nature of companies’ implementation of CSR frameworks through direct engagement processes. Together, these actions have sought to guide companies’ adoption of CSR management and reporting mechanisms in order to ensure greater accountability is discharged for the social and environmental impact of their economic activities.This thesis comprises three research papers and two case studies that examine how two influential advocacy organisations sought to shape the nature and content of the CSR management and reporting mechanisms implemented by companies in the United Kingdom and The Netherlands. The research papers are interpreted and theoretically framed using aspects of Bourdieu’s theory of practice, the concepts of strategic frames and frame resonance, and institutional work. The thesis provides several empirical contributions that deepen our understanding of the role advocacy organisations play during processes of organisational-level and institutional-level engagement and the role forms of accounting play therein. The thesis also offers a number of theoretical contributions by advancing the application of the three aforementioned theoretical framings within the accounting and organisational studies literature streams.
PathGAN: Visual Scanpath Prediction with Generative Adversarial Networks

We introduce PathGAN, a deep neural network for visual scanpath prediction trained on adversarial examples. A visual scanpath is defined as the sequence of fixation points over an image defined by a human observer with its gaze. PathGAN is composed of two parts, the generator and the discriminator. Both parts extract features from images using off-the-shelf networks, and train recurrent layers to generate or discriminate scanpaths accordingly. In scanpath prediction, the stochastic nature of the data makes it very difficult to generate realistic predictions using supervised learning strategies, but we adopt adversarial training as a suitable alternative. Our experiments prove how PathGAN improves the state of the art of visual scanpath prediction on the iSUN and Salient360! datasets. Source code and models are available at https://imatge-upc.github.io/pathgan/
Recent advances in stem cell therapy: Translational applications in regenerative medicine

Stem cell research represents one of the most transformative fields in modern biomedical science, offering groundbreaking potential in regenerative medicine and disease treatment. Stem cells possess unique self-renewal and differentiation capabilities, allowing them to replace damaged or diseased cells in tissues and organs. Various types of stem cells embryonic, mesenchymal, hematopoietic, neural, induced pluripotent, and tissue-resident play crucial roles in tissue regeneration and therapy development. Their applications span a wide range of medical disciplines, including cardiovascular, neurological, endocrine, metabolic, hepatic, renal, and cancer-related disorders. advances have integrated stem cell technology with innovative approaches such as gene editing (CRISPR-Cas9), organoid formation, and biomaterial scaffolds, significantly enhancing therapeutic precision and clinical translation. Stem cell-based therapies are currently being evaluated in numerous clinical trials for conditions like Parkinson’s disease, diabetes, myocardial infarction, and spinal cord injuries. Moreover, the development of Advanced Therapy Medicinal Products (ATMPs) has provided structured pathways for using gene, cell, and tissue-engineered drugs derived from stem cells.Despite these promising developments, several limitations persist, including tumorigenicity risks, immune rejection, reproducibility challenges, high production costs, and ethical issues, particularly regarding embryonic stem cells. Addressing these barriers through standardized procedures, genetic screening, and ethical frameworks remains essential for clinical success.Looking forward, stem cells are expected to play a pivotal role in personalized medicine by enabling patient-specific therapies tailored to genetic and environmental factors. Collaborative research models, such as the Triple Helix approach linking academia, industry, and government, are vital to accelerating innovation. Overall, stem cell therapy stands as a cornerstone for next-generation medical treatments, holding immense promise for restoring health and improving quality of life worldwide.