ethicalabs/Research-Intent-Collab huggingface dataset:
Nanosciences and Nanotechnology of Vietnamese Women: Fabrication, Characterization, and Application of Rare Earth Nanocompounds for Photonics

Nanorods Y(OH)3:Eu, nanotubes Tb (OH)3, and nanocomposites silica‐titania‐ and silica‐zirconia‐doped erbium and ytterbium were prepared by liquid‐phase methods. Structures and photonic properties such as long decay time (7.4 ms) and waveguide loss were studied in detail in the concentration and temperature dependence. The application of the nanocomposites for planar waveguide systems is presented.
Next-Generation Artificial Intelligence for ADME Prediction in Drug Discovery : From Small Molecules to Biologics

Pharmacokinetic (PK) behavior, which emerges from the underlying processes of absorption, distribution, metabolism, and excretion (ADME), is central to drug discovery and development, dose optimization, and safety assessment. Despite decades of experimental and computational research, early-stage prediction of human PK remains a major challenge, contributing to clinical attrition and inefficiency in pharmaceutical pipelines. Advances in artificial intelligence (AI) and machine learning (ML) have significantly improved ADME predictions, particularly for small molecules. Traditional descriptor-based quantitative structure–activity relationship and classical ML methods offer interpretability and robust performance on standardized datasets. In contrast, graph neural networks, deep learning architectures, and chemical language models facilitate the learning of complex nonlinear structure–property relationships and multitask predictions. Multimodal frameworks further integrate experimental measurements, structural data, and biological contexts, enhancing predictive accuracy under low-data and heterogeneous conditions. Emerging modalities, including peptides, oligonucleotides, and antibody-based therapeutics, pose additional challenges owing to their sequence-dependent stability, conformational flexibility, and mechanistically distinct determinants of ADME and toxicity (ADMET). AI approaches that incorporate sequence-, structure-, and mechanism-aware representations combined with multimodal data integration have demonstrated improved predictability for medium- and large-molecule therapeutics. Recent developments in foundation-model architectures offer unified representations across chemical, biological, and biophysical domains, enabling cross-modality ADMET modeling with enhanced generalization and mechanistic interpretability. In this review, we summarize the evolution of computational ADME- and PK-oriented prediction frameworks from small molecules to complex biologics, highlighting methodological advances, representative studies, and emerging trends in multimodal and foundation-model approaches. We also discuss the limitations and future perspectives of the practical implementation of AI-driven ADMET predictions to support rational drug design and development.
Error suppression and error correction in adiabatic quantum computation: non-equilibrium dynamics

40 pages, 8 figures. Companion paper to "Error suppression and error correction in adiabatic quantum computation I: techniques and challenges". Changes in version 4: updated references. Published version
Modelling of `barrier' modulation for cosmic ray protons in the outer heliosphere

Abstract   Voyager and Pioneer spacecraft observations over 22 years and out to ∼82 AU have found two particular features of cosmic ray proton modulation in the heliosphere: (1) Markedly different behavior for minimum modulation conditions between the radial intensity profiles for periods of opposite magnetic polarities. (2) Most of the residual modulation for these periods was found to take place in the outer heliosphere, near and beyond where the termination shock (TS) is expected to be, and seems to differ for the two polarity cycles. Since contemporary models do not provide satisfactory quantitative explanations for these features, they are studied with a numerical model including a solar wind TS, a heliosheath and drifts. Results show interesting features caused by the TS and the heliosheath which differ markedly for the two polarity cycles, with energy and with increasing solar activity.
Identifying artificial intelligence–generated content in online Q&amp;A communities through interpretable machine learning

This study aims to construct a comprehensive feature system for identifying artificial intelligence–generated content (AIGC) in online Q&amp;A communities, thus uncovering the key factors and mechanisms influencing the identification of AIGC. First, based on the theory of systemic functional linguistics (SFL) and information quality (IQ), this article extracts vocabulary, content, structure, and emotional features from the text, and identifies the AIGC through nine mainstream machine learning algorithms. Subsequently, three widely used resampling strategies are exploited to address the category imbalance problem. The grid search optimisation algorithm fine-tunes different combinations of parameters to improve the performance of the identification classifier. Finally, SHAP values are introduced to evaluate and elucidate the global feature importance and feature influence mechanism. A Chinese corpus from the Zhihu Q&amp;A community is constructed to verify the validity of these methods. The experimental results show that the eXtreme Gradient Boosting (XGBoost) model optimised with hybrid sampling and grid search parameters exhibits excellent performance in identifying AI-generated text, which achieves an F                     1                     -score of 0.9935, an improvement of 0.11 percentage points over the original model. In addition, all four dimensions of features constructed in this article contribute to AI-generated text identification, and the results of feature interpretability analysis show the greatest impact of features that focus on content readability. The study facilitates the identification and labelling of AIGC in online Q&amp;A communities, thereby enhancing transparency and accountability of information shared online.
Compatibility of <i>Atheta coriaria</i> with other biological control agents and reduced-risk insecticides used in greenhouse floriculture integrated pest management programs for fungus gnats

AbstractFungus gnats (FG) (Diptera: Sciaridae: Bradysia spp.) are economically important pests of greenhouse flowers. Larvae feed on root tissue and transmit a variety of phytopathogens. Atheta coriaria (Kraatz) (Coleoptera: Staphylinidae) is a new biological control agent (BCA) for FG. To support its successful use by the greenhouse industry, its compatibility with current integrated pest management (IPM) programs used in floriculture was assessed. This included investigations of prey preference, possible detrimental interactions with other soil-dwelling BCAs, and the toxicity to A. coriaria of registered and novel insecticides. Atheta coriaria showed little preference among eggs of different pest species or between pest eggs and eggs of the intraguild predator Hypoaspis aculeifer (Canestrini) (Acari: Mesostigmata: Laelapidae). It preferred FG 1st-instar larvae to larvae and pupae of other soil-dwelling pests. The entomopathogenic nematode Steinernema feltiae (Filipjev) (Rhabditida: Steinernematidae) was compatible with A. coriaria, but H. aculeifer mites fed on A. coriaria larvae. Insect growth regulators with limited contact activity (e.g., diflubenzuron) were compatible with adult A. coriaria and had minimal effects on larvae compared with other insecticides. Atheta coriaria can be incorporated into an IPM program for FG if harsh insecticides are avoided, but interactions with predatory mites, as well as its effectiveness against other greenhouse pests when FG are present, require further investigation.
Using Natural Language Processing and Machine Learning to Identify Opioids in Electronic Health Record Data

Sean P McDermott, Ajay D Wasan Division of Pain Medicine, Department of Anesthesiology and Perioperative Medicine, University of Pittsburgh Medical Center, Pittsburgh, PA, USACorrespondence: Sean P McDermott, UPMC Pain Medicine, 5750 Centre Ave, Suite 400, Pittsburgh, PA, 15232, USA, Tel +1 412-665-8030, Fax +1 412-647-2993, Email seanmcd@umich.eduPurpose: This study evaluates the utility of machine learning (ML) and natural language processing (NLP) in the processing and initial analysis of data within the electronic health record (EHR). We present and evaluate a method to classify medication names as either opioids or non-opioids using ML and NLP.Patients and Methods: A total of 4216 distinct medication entries were obtained from the EHR and were initially labeled by human reviewers as opioid or non-opioid medications. An approach incorporating bag-of-words NLP and supervised ML classification was implemented in MATLAB and used to automatically classify medications. The automated method was trained on 60% of the input data, evaluated on the remaining 40%, and compared to manual classification results.Results: A total of 3991 medication strings were classified as non-opioid medications (94.7%), and 225 were classified as opioid medications by the human reviewers (5.3%). The algorithm achieved a 99.6% accuracy, 97.8% sensitivity, 94.6% positive predictive value, F1 value of 0.96, and a receiver operating characteristic (ROC) curve with 0.998 area under the curve (AUC). A secondary analysis indicated that approximately 15– 20 opioids (and 80– 100 non-opioids) were needed to achieve accuracy, sensitivity, and AUC values of above 90– 95%.Conclusion: The automated approach achieved excellent performance in classifying opioids or non-opioids, even with a practical number of human reviewed training examples. This will allow a significant reduction in manual chart review and improve data structuring for retrospective analyses in pain studies. The approach may also be adapted to further analysis and predictive analytics of EHR and other “big data” studies.Keywords: retrospective, classification, machine learning, natural language processing, translational, opioids
A Deep Reinforcement Learning Framework with Explainable AI for Personalized and Interpretable Treatment Recommendations in Healthcare

The integration of Explainable Artificial Intelligence (XAI) into healthcare has significantly advanced clinical decision-making by enhancing the transparency and trustworthiness of AI-driven recommendations. This study introduces a novel Deep Reinforcement Learning (DRL) framework designed to generate personalized treatment recommendations tailored to individual patient profiles. The framework combines Deep Q-Learning and Policy Gradient methods to dynamically model and optimize treatment pathways, utilizing historical clinical data, patient demographics, and treatment response patterns. To ensure interpretability, an explainability layer incorporating SHAP (SHapley Additive exPlanations) and LIME (Local Interpretable Model-Agnostic Explanations) provides clinicians with actionable insights into the model’s decision-making process. The proposed framework was rigorously evaluated on a real-world dataset comprising 50,000 electronic health records (EHRs) from patients with cardiovascular disease and diabetes. Experimental results demonstrated a 28% improvement in treatment success rates, a 35% reduction in adverse effects, and a 20% increase in clinician acceptance compared to conventional rule-based methods. Additionally, the explainability module achieved an average accuracy of 92% in attributing model decisions to key patient features, reinforcing its reliability in clinical settings. These findings underscore the potential of the DRL-XAI framework to enhance patient outcomes while fostering trust in AI-assisted healthcare systems. By balancing predictive accuracy with interpretability, this approach addresses critical challenges in AI adoption, paving the way for more transparent and personalized clinical decision support tools. Future research will focus on extending the framework to additional medical conditions and integrating multi-modal patient data for broader applicability.
Assessing the Influence of Human Resource Information Systems (HRIS) on Organizational Efficiency

ABSTRACT  With the rise of globalization and technology, corporations have increasingly implemented information systems across several departments, revolutionizing processes such as recruitment and development. Management techniques have been transformed by IT, which has presented both opportunities and difficulties. Although HRIS adoption is essential for efficient HRM operations, acceptance, and implementation present difficulties. For organizational operations to run smoothly, a supportive management environment and appropriate HRIS integration are crucial. This study examines the difficulties in putting HRIS into practice and offers strategies for its effective implementation in businesses.     Keywords: HRIS, Payroll, Human Resources, Software, HR strategy, Human Resource Information System
A Smart E-commerce Business Keywords Generation and Content Visibility Enhancement System using Artificial Intelligence and Data Science

This paper presents a "Keyword Generator" web application designed to streamline the process of generating relevant keywords for URLs or descriptions using OpenAI's Generative AI model [1]. The project addresses the challenge of keyword generation, crucial for SEO and content categorization, by integrating an AI model that processes input and generates accurate keywords in real time. Experiments were conducted to evaluate the model's accuracy and consistency, revealing high levels of performance, with some limitations in variability. Improvements, such as fine-tuning the model and integrating multilingual capabilities, are suggested to enhance effectiveness. The results highlight the potential of AI-driven keyword generation as an efficient and valuable tool for digital marketing and content creation, demonstrating the system's ability to adapt to different input types and deliver relevant results [2].
A Symposium on David Myer Temin’s Remapping Sovereignty: Decolonization and Self-Determination in North American Indigenous Political Thought

Remapping Sovereignty                     examines how activist-thinkers from Indigenous societies in North America recast the relationship between decolonization and sovereignty over the course of the twentieth century. While political theorists have criticized sovereignty as the dominant paradigm of political authority, alternatives to sovereignty remain elusive. Recasting these debates, Temin argues that activists-intellectuals in the long Red Power movement of the twentieth century engaged in complex acts of contesting and remapping the logic of sovereignty. If logics of Westphalian sovereignty revolve around “the normative centrality and perceived necessity of the claim to final and ultimate authority over a bounded space” (6) then central to its institutional practice is a refusal of the webs of relationality and interdependence on the land and human and non-human others. Rather than upholding these sovereign logics, Indigenous claims to self-determination, Temin shows, are premised not the assertion of territorial control but on the cultivation of reciprocal relations of care for the earth. Creating alternatives to both the institutions of the sovereign-state and the very conceptual framework of sovereignty entails dismantling and repairing the structural hierarchies and conceptual frameworks that stem from the constitutive disavowal of these relationalities embedded in both the concept and practice of state sovereignty.
Unmasking the Men Who Produce Child Sexual Abuse Material (CSAM): Criminal Diversity and Sociodemographic Characteristics

Child sexual abuse material (CSAM) production poses a grave and evolving threat, causing revictimization through the circulation of material for years. The availability of online technology has enabled sophisticated methods of CSAM production and police evasion. The aims of this study were to explore the sociodemographic features of those with a history of CSAM production, explore criminal sub-types of those who produce CSAM, and explore the sociodemographic and criminal features of possible sub-types. Men who came to police attention for CSAM production offenses between 2004–2019 in Victoria, Australia (                     n                     = 741) were included. A hierarchical cluster analysis revealed two distinct criminal profiles: (1) a group with                     specialist                     offense histories, which were sexually focused (                     n =                     455) and (2) a group with a                     generalist                     (                     n                     = 286) offending history, including sexual and non-sexual offenses. The frequency of CSAM production offenses across the sample period almost doubled with an increasing number of individuals with a                     generalist                     criminal history coming to the attention of police, whilst the number of those with a                     specialist                     criminal history remained largely stable. Uncovering these two distinct criminal profiles is a pivotal step toward understanding the nuanced characteristics of individuals responsible for producing CSAM.
Fighting for public water: the first successful European Citizens’ Initiative, “Water and Sanitation are a Human Right”

Between May 2012 and September 2013 the European Citizens’ Initiative (ECI) ‘Water and Sanitation are a Human Right’ successfully collected close to 1.9 million signatures across the European Union (EU), forcing the Commission into an official position on the role of water in the EU and wider world. Based on a historical materialist approach to social movement struggles, the purpose of this article is threefold. First it will analyse the reasons for why the ECI, initiated and co-ordinated by the European Federation of Public Service Unions (EPSU), was so successful. Second, the article will assess the impact of the ECI on EU policy-making. Finally, the article will reflect on the wider lessons to be learned for the struggle against neo-liberal restructuring. It will be argued that a combined focus on the commons as well as new forms of participatory democracy may provide the basis for a broader transformative project.
From Rhetoric to Action: Scaling Up Community and Indigenous Peoples' Land and Resource Rights

The aim of this conference was to take stock of efforts to scale up Indigenous Peoples’ and community land and resource rights worldwide and to develop strategies and actions for moving from rhetoric to action. The conference followed a similar meeting in Interlaken, Switzerland, in 2013, which also aimed to strength  This is the final report for a conference held on 30 September – 1 October, 2015 in Bern, Switzerland.
An analysis of policy-oriented jurisprudence at the International Criminal Tribunal for the former Yugoslavia (ICTY): A lemniscate of natural law, legal positivism and two liberalisms?

Policy-oriented jurisprudence was used to substantively support certain arguments rendered by the International Criminal Tribunal for the former Yugoslavia (ICTY) in two cases, namely those against Dražen Erdemović and Pavle Strugar. Based on its limited use, the policy-oriented approach was clearly not a popular source of justification for arguments at the ICTY. However, its inclusion in structures of argument reveals at least some support for it. In the process of suggesting possible reasons why this particular theoretical approach was utilised in the justification of argument, this article aims to briefly discuss the broader philosophical tensions visible in the reliance on policy-oriented jurisprudence at the ICTY, viz, natural law/legal positivism, on the one hand, and the ‘two liberalisms’, identified by Darryl Robinson as inherent to international criminal law (ICL), on the other hand. Given that policy-oriented jurisprudence’s inclusion in ICL judgments makes its future invocation in ICL literature and judgments possible, the purpose of delving into the broader theoretical paradigms involved is important for the emergence of a coherent jurisprudence.
Central bank digital currency: Implications for liquidity management and monetary policy

The paper discusses potential effects of a central bank digital currency (CBDC) on liquidity and monetary policy. CBDC could benefit the payment system but could also pose macroeconomic/financial challenges if demand for CBDC becomes high. High CBDC demand can affect banks' balance sheets, funding costs and the money and credit creation process, as well as the central bank's balance sheet and risk, and the implementation of liquidity and monetary policy. This may entail a substantial shock to the financial system as it functions today. These macroeconomic/financial costs are discussed in the literature. Much of the discussion focuses on how CBDC can be designed to contribute to a more efficient payment system, while avoiding or at least reducing the macroeconomic/financial drawbacks substantially. Proposed solutions include limiting the amount of CBDC that can be held, a multi-interest rate system for CBDC, a system where CBDC can be lost and where the central bank issues CBDC only in exchange for government securities. The benefits and costs of the first two in particular are discussed in this paper. We believe that both limits on CBDC holdings and multi-interest rates can help curb CBDC demand, but at the same time they will reduce the applicability of CBDC. (...)
Secondary use of routinely collected administrative health data for epidemiologic research: Answering research questions using data collected for a different purpose

The use of routinely collected administrative health data for research can provide unique insights to inform decision-making and, ultimately, support better public health outcomes. Yet, since these data are primarily collected to administer healthcare service delivery, challenges exist when using such data for secondary purposes, namely epidemiologic research. Many of these challenges stem from the researcher's lack of control over the quality and consistency of data collection, and - furthermore - a lessened understanding of the data being analyzed. That said, we assert that these challenges can be partly mitigated through careful, systematic use of these data in epidemiologic research. This article presents considerations derived from experiences analyzing administrative health data (e.g., healthcare practitioner billings, hospitalizations, and prescription medication data) in the Canadian province of British Columbia (population of over 5 million in 2024), though we believe the underlying principles generalize beyond this region. Key considerations were organized around four themes: 1) Know the data and their primary use (understand their scope and limitations); 2) Understand classification and coding systems (appreciate the nuances regarding classification systems, versions, how they are employed in the primary uses of the data, and querying the values); 3) Transform data into meaningful forms (process data and apply identification algorithms, when necessary); 4) Recognize the importance of validity when defining analytic variables (make meaningful inferences based on data/algorithms). Although this article is not an exhaustive list of all considerations, we believe that it will provide pragmatic insights for those interested in leveraging administrative health data for epidemiologic research.