ethicalabs/Research-Intent-Collab huggingface dataset:
Social Media, Science Communication and Academic Super Users in the UK

The Internet and social media tools have created new opportunities for open science including communicating in more interactive ways and sharing research data. Drawing on evidence from interviews and a survey of academics in the UK our research suggests that most academics recognised the value and importance of more open science communication and data sharing, but many had concerns about the potential risks. A small group of academics, who can be termed super users, were frequently communicating updates of their ongoing research. It is clear that there are increasing opportunities for more open science and public engagement but challenges remain.
Graphene oxides doped MIL-101(Cr) as anode materials for enhanced electrochemistry performance of lithium ion battery

Abstract   MIL-101(Cr)/graphene oxides composites has been successfully synthesized via a facile route using a solvothermal treatment and fully characterized using powder X-ray diffraction (PXRD), scanning electron microscope (SEM), thermal gravimetric analysis (TGA) and infrared spectroscopy (IR). The specific capacity of MIL-101(Cr)/GO are almost twice larger than that of MIL-101(Cr) at the first discharge process and the multiple relationship maintained over 40 cycles, indicating that the electrochemical performance of MIL-101(Cr) as anode material has been improved through doping by GO.
The Effects of State Mental Health Parity Legislation on Perceived Quality of Insurance Coverage, Perceived Access to Care, and Use of Mental Health Specialty Care

Objective.  To assess the impacts of recent state mental health parity legislation on perceived quality of health insurance coverage, perceived access to needed health care, and use of mental health specialty services by individuals with likely need for mental health care.Data Sources.  The study sample came from two waves of a national household survey first fielded in 1997–1998 and then in 2000–2001. The analysis used a subset of the sample.Study Design.  The study took the Difference‐in‐Difference‐in‐Difference approach to investigate changes in self‐perceived quality of health insurance coverage and access to needed health care, and use of mental health specialty care by the group with mental disorders (relative to those without) in states with parity legislation of different comprehensiveness (relative to the nonparity states) in the years after the law (relative to before the law).Principal Findings.  Overall, there were no significant or consistent effects of the parity legislation. Descriptive statistics showed significant changes in some (but not all) outcome variables, but these results disappeared in detailed statistical analyses by controlling for important covariates.Conclusions.  The null findings of the effects of state mental health parity mandates suggest that under ERISA (Employee Retirement Income Security Act), the scope of state parity legislation may have been restricted because of large proportion of self‐insured employers. Furthermore, comprehensiveness of state legislation appears to be related to the traditional level of use of mental health specialty care, which becomes another confounder for the potential policy effects.
The Sloan Digital Sky Survey quasar catalog: tenth data release

We present the Data Release 10 Quasar (DR10Q) catalog from the Baryon Oscillation Spectroscopic Survey (BOSS) of the Sloan Digital Sky Survey III. The catalog includes all BOSS objects that were targeted as quasar candidates during the first 2.5 years of the survey and that are confirmed as quasars via visual inspection of the spectra. The catalog also includes known quasars (mostly from SDSS-I and II) that were reobserved by BOSS. The catalog contains 166,583 quasars (74,454 are new discoveries since SDSS-DR9) detected over 6,373 deg$^{2}$ with robust identification and redshift measured by a combination of principal component eigenspectra. The number of quasars with $z>2.15$ (117,668) is $\sim$5 times greater than the number of $z>2.15$ quasars known prior to BOSS. Redshifts and FWHMs are provided for the strongest emission lines (CIV, CIII, MgII). The catalog identifies 16,461 broad absorption line quasars and gives their characteristics. For each object, the catalog presents five-band (u, g, r, i, z) CCD-based photometry with typical accuracy of 0.03 mag and information on the optical morphology and selection method. The catalog also contains X-ray, ultraviolet, near-infrared, and radio emission properties of the quasars, when available, from other large-area surveys. The calibrated digital spectra cover the wavelength region 3,600-10,500Å at a spectral resolution in the range 1,300$<$R$<$2,500; the spectra can be retrieved from the SDSS Catalog Archive Server. We also provide a supplemental list of an additional 2,376 quasars that have been identified among the galaxy targets of the SDSS-III/BOSS.
Enhancing clinical decision support with physiological waveforms -- a multimodal benchmark in emergency care

Background: AI-driven prediction algorithms have the potential to enhance emergency medicine by enabling rapid and accurate decision-making regarding patient status and potential deterioration. However, the integration of multimodal data, including raw waveform signals, remains underexplored in clinical decision support. Methods: We present a dataset and benchmarking protocol designed to advance multimodal decision support in emergency care. Our models utilize demographics, biometrics, vital signs, laboratory values, and electrocardiogram (ECG) waveforms as inputs to predict both discharge diagnoses and patient deterioration. Results: The diagnostic model achieves area under the receiver operating curve (AUROC) scores above 0.8 for 609 out of 1,428 conditions, covering both cardiac (e.g., myocardial infarction) and non-cardiac (e.g., renal disease, diabetes) diagnoses. The deterioration model attains AUROC scores above 0.8 for 14 out of 15 targets, accurately predicting critical events such as cardiac arrest, mechanical ventilation, ICU admission, and mortality. Conclusions: Our study highlights the positive impact of incorporating raw waveform data into decision support models, improving predictive performance. By introducing a unique, publicly available dataset and baseline models, we provide a foundation for measurable progress in AI-driven decision support for emergency care.
The Role Of African Women In Peace Building And Conflict Resolution: The Case Of Burundi

Africa nations over the years have been ravaged by conflict which has torn apart the social fabric and also weakened the solidarity and human characteristics of the Africa society. This has resulted in destabilization, displacement and infrastructural destruction which have gender-specific impact on the affected population. This is why accurate information on peace, conflict and gender issues is essential for well informed planning, policy making and action in order to build a culture of peace in Africa and the world at large. Women constitute majority of the world’s refugees and internally displaced persons. Protecting the world from the destructive cost of war requires not only international communities, but women also, who must work together to prevent the start and spread of violent conflict.This paper examines the root causes of conflict in Burundi at different levels, institutions and practices that propagates conflicts. The paper examines the progress made so far by women with the introduction of resolution 1325 by the United Nations Security Council. The paper identifies some needs of women that need to be met to engender post-conflict resolution and peace building in order to build a culture of peace in Africa. Finally, the paper will suggest a few ideas to integrate a gender-perspective into conflict resolution and peace building effortsso that women’s contribution can be seen as most important and their voice heard.
Stability of solutions to stochastic partial differential equations

We provide a general framework for the stability of solutions to stochastic partial differential equations with respect to perturbations of the drift. More precisely, we consider stochastic partial differential equations with drift given as the subdifferential of a convex function and prove continuous dependence of the solutions with regard to random Mosco convergence of the convex potentials. In particular, we identify the concept of stochastic variational inequalities (SVI) as a well-suited framework to study such stability properties. The generality of the developed framework is then laid out by deducing Trotter type and homogenization results for stochastic fast diffusion and stochastic singular p-Laplace equations. In addition, we provide an SVI treatment for stochastic nonlocal p-Laplace equations and prove their convergence to the respective local models.
Synthetic Training Data for Artificial Intelligence-Based Li-Ion Diagnosis and Prognosis

Accurate lithium battery diagnosis and prognosis is critical to increase penetration of electric vehicles and grid-tied storage systems. Both diagnosis and prognosis are complex due to the intricate, nonlinear, and path-dependent nature of battery degradation. Data-driven models are anticipated to play a significant role in the behavioral prediction of dynamical systems such as batteries. However, they are often limited by the amount of training data available.                In this work, we will present a proof-of-concept methodology to generate big data training datasets for intercalation batteries. It will use a mechanistic approach that combines both modeling and experimental techniques to provide a universal tool for the creation of synthetic voltage curves practically indistinguishable from real data. This approach offers the benefits of the broad applicability of the model to various cell chemistries, designs, and operating modes, as well as the high fidelity as inhered from the detailed extraction of the experimental data. The methodology could be applied to the generation of training datasets encompassing the entire degradation spectrum as well as different operating conditions such as rate and temperature. Scenarios leading to lithium plating with adjustable reversibility can be added and the addition of random noise on the data could be considered to make the datasets even more realistic and consider the impact of real-life testing machines' precision and accuracy                The proof-of-concept datasets are over three orders of magnitude larger than what is currently available in the literature. With benchmark datasets, results from different studies could be easily equated, and the performance of different algorithms can be compared, enhanced, and analyzed extensively. This will expend critical capabilities of current AI algorithms, tools, and techniques to predict scientific data.
Thermodynamic Recycling of Algorithmic Failure Branches: Quantum-Computer Demonstration with Quantum Error Correction

Thermodynamic trade-off relations dictate fundamental limits on the performance of thermodynamic tasks through costs such as heat dissipation. Here, we propose a framework called thermodynamic recycling to circumvent these limits in quantum processors by exploiting failure branches of quantum algorithms, which are usually discarded. The key component is an athermal bath naturally generated during the resetting of a failure branch. By coupling this bath to a target system prior to relaxation, thermodynamic tasks can be performed beyond conventional thermodynamic limits. We apply this framework to information erasure and derive the reduction in heat dissipation analytically. As a demonstration, we implement our framework on IBM's superconducting quantum processor by combining the Harrow--Hassidim--Lloyd algorithm with three-qubit quantum error correction, thereby reducing the heat dissipated in erasing syndrome information. Despite substantial noise and errors in current hardware, our method achieves erasure with heat dissipation below the Landauer limit. This work establishes an operational connection between quantum computing and quantum thermodynamics for resource-efficient quantum computation.
Photonic Neural Networks And Optical AI Accelerators: A Comprehensive Review Of Architectures, Material Platforms, And System-Level Challenges

The rapid advancement of artificial intelligence (AI), particularly deep learning and large-scale neural networks, has created significant demand for high-performance and energy-efficient computing architectures. Conventional electronic processors such as GPUs and TPUs are increasingly constrained by power consumption, memory bandwidth limitations, and data movement bottlenecks. In response, photonic neural networks and optical AI accelerators have emerged as promising alternatives that exploit the properties of light to perform computation at high speed and low energy consumption. This paper presents a comprehensive systematic narrative review of photonic neural networks and optical AI accelerators, focusing on their architectures, material platforms, and key engineering challenges. The methodology employed involves structured literature collection from recent peer-reviewed studies, thematic classification of photonic architectures (including Mach–Zehnder interferometer meshes, microring resonator networks, and diffractive optical systems), and comparative analysis of material platforms and performance metrics such as energy efficiency, scalability, and computational latency. The results of the review indicate that photonic systems offer significant advantages over electronic computing, particularly in terms of energy per multiply–accumulate operation (femtojoule-level), ultra-high bandwidth (terahertz range), and low-latency computation. However, practical deployment remains limited by challenges in scalability, fabrication variability, noise sensitivity, and the lack of efficient optical training mechanisms. The analysis further shows that hybrid photonic–electronic architectures currently represent the most viable pathway toward near-term implementation, while heterogeneous material integration is essential for achieving fully functional photonic AI systems. The contribution of this research lies in providing a structured and critical synthesis of recent advancements in photonic AI hardware, identifying key technological bottlenecks, and outlining future research directions toward scalable and commercially viable optical computing systems. This work serves as a reference framework for researchers working at the intersection of photonics, electronics, and artificial intelligence.
The quality of end-of-life care for Danish cancer patients who have received non-specialized palliative care: a national survey using the Danish version of VOICES-SF

About half of Danish patients dying from cancer have never been in contact with specialized palliative care. Non-specialized palliative care in Denmark, i.e., somatic hospital departments, community nurses, and general practitioners, has rarely been described or evaluated. We aim to assess how non-specialized palliative care was evaluated by bereaved spouses, and to test whether distress when completing the questionnaire and ratings of aspects of end-of-life care was associated with satisfaction with place of death and overall quality of end-of-life care.Bereaved spouses of 792 cancer patients who had received non-specialized palliative care were invited to answer the Views of Informal Carers-Evaluation of Services-Short Form (VOICES-SF) and the Hospital Anxiety and Depression Scale (HADS) 3-9 months after the patient's death.A total of 280 (36%) of invited spouses participated. In the last 3 months of the patient's life, the quality of all services taken together was rated as good, excellent, or outstanding in 70% of the cases. Satisfaction was associated with respondent's current distress (p = 0.0004). Eighty percent of bereaved spouses believed that the patient had died in the right place. Satisfaction with place of death was associated with place of death (p = 0.012) and the respondent's current distress (p = 0.0016).Satisfaction with place of death and overall quality of services was generally high but was rated lower by spouses reporting higher levels of distress when completing the questionnaire. Distress should be taken into account whenever services are evaluated by bereaved relatives.
Legal regulation of cyber warfare: reviewing the contribution of the Tallinn manual to the advancement of international law

The development of modern technology is inevitably bound to change the conduct of warfare. It is also self-evident that the mode, typology and participants in current armed conflicts do not fit within the structures of traditional international law on the use of armed force. Indeed, in some cases the new conflicts pose intractable challenges to the existing law. This is particularly true with regard to the military use of cyber operations either in the context of armed self-defence or in the conduct of hostilities in time of armed conflict. The establishment of the worldwide computer network and the increasing reliance on digital services has brought about a new type of clear and present danger: the threat of cyber attack. The fact that cyber operations are a relatively novel phenomenon in the history of international law automatically raises some important questions regarding whether the existing rules of international law apply to them.6 Consider the evidence indicating that there have been Chinese government-backed cyber operations, including espionage, targeting State and corporate computer networks in the United States. The question that arises in regard to cyber incidents, like the one illustrated above, is whether international law governs them, and if so which specific rules apply, and the circumstances in which they apply. With the aim of clarifying the uncertainty as to the specific rules pertinent to cyber warfare, the Tallinn Manual on the International Law Applicable to Cyber Warfare was developed by a group of twenty renowned international law scholars and practitioners. It provides a useful basis on which to identify how and evaluate the extent to which international law applies to cyber operations. This research seeks to critically appraise both the current and prospective contribution of the Tallinn Manual to the advancement of international law. In particular, it focuses on how international law as enunciated in the Tallinn Manual governs cyber operations in general and how it applies to cyber-unique aspects of this form of warfare. The research then reviews the achievements of the Tallinn Manual as well as its shortfalls in relation to the development of a coherent framework of international law that can be used to govern cyber operations. After this, the research turns to the increasing role of non-binding instruments of international law in the process of international law-making. The case is then made for the possibility of the Tallinn Manual being the basis on which future binding norms may be crafted to provide specific legal regulation for cyber operations.
Development of Primary Palliative Care End-of-Life Quality Measures: A Modified Delphi Process

BACKGROUND AND OBJECTIVES                   Children with complex chronic conditions (CCCs) and their families deserve high-quality pediatric palliative care (PPC) throughout their illness trajectory, including at end of life (EOL). Standard EOL quality measures (QM) have only recently been proposed, require surveys and/or manual chart review, and focus on children with cancer. Therefore, we aimed to develop expert-endorsed, hospital-based, primary PPC quality measures for EOL care for all children with CCCs that could be automatically abstracted from the electronic health record (EHR).                                                   METHODS                   We followed a modified Delphi approach for expert opinion gathering, including: (1) a comprehensive literature review of existing adult and pediatric measures (&amp;gt;200 measures); (2) formation of a multidisciplinary expert panel (n = 9); (3) development of a list of candidate measures (20 measures); (4) national survey to assess each QM’s importance and abstraction feasibility and propose new measures (respondents = 95); and (5) final expert panel endorsement.                                                   RESULTS                   Seventeen EHR-abstractable QM were endorsed in 5 domains: (1) health care utilization: 4 measures (eg, &amp;lt;2 emergency department visits in the last 30 days of life); (2) interprofessional services: 4 measures (eg, PPC in the last 30 days of life); (3) medical intensity: 5 measures (eg, death outside the ICU); (4) symptom management: 2 measures (eg, documented pain score within 24 hours of admission); and (5) communication: 2 measures (eg, code status documentation).                                                   CONCLUSIONS                   This study developed a list of EHR-abstractable, hospital-based primary PPC EOL QM, providing a foundation for quality improvement initiatives and further measure development in the future.
DeepXplainer: An interpretable deep learning based approach for lung cancer detection using explainable artificial intelligence

Artificial intelligence (AI) has several uses in the healthcare industry, some of which include healthcare management, medical forecasting, practical making of decisions, and diagnosis. AI technologies have reached human-like performance, but their use is limited since they are still largely viewed as opaque black boxes. This distrust remains the primary factor for their limited real application, particularly in healthcare. As a result, there is a need for interpretable predictors that provide better predictions and also explain their predictions.This study introduces "DeepXplainer", a new interpretable hybrid deep learning-based technique for detecting lung cancer and providing explanations of the predictions. This technique is based on a convolutional neural network and XGBoost. XGBoost is used for class label prediction after "DeepXplainer" has automatically learned the features of the input using its many convolutional layers. For providing explanations or explainability of the predictions, an explainable artificial intelligence method known as "SHAP" is implemented.The open-source "Survey Lung Cancer" dataset was processed using this method. On multiple parameters, including accuracy, sensitivity, F1-score, etc., the proposed method outperformed the existing methods. The proposed method obtained an accuracy of 97.43%, a sensitivity of 98.71%, and an F1-score of 98.08. After the model has made predictions with this high degree of accuracy, each prediction is explained by implementing an explainable artificial intelligence method at both the local and global levels.A deep learning-based classification model for lung cancer is proposed with three primary components: one for feature learning, another for classification, and a third for providing explanations for the predictions made by the proposed hybrid (ConvXGB) model. The proposed "DeepXplainer" has been evaluated using a variety of metrics, and the results demonstrate that it outperforms the current benchmarks. Providing explanations for the predictions, the proposed approach may help doctors in detecting and treating lung cancer patients more effectively.