ethicalabs/Research-Intent-Collab huggingface dataset:
Accelerated Knowledge Discovery: A Vision for NASA Science

Abstract                   This paper introduces the sixth paradigm of scientific discovery: accelerated knowledge discovery (AKD). This paradigm is defined by the full integration of artificial intelligence (AI) into the research workflow as a tool augmenting cognitive capabilities of human scientists. AKD emerges from the convergence of advanced AI models, autonomous agentic systems, and human‐AI collaboration. AKD accelerates the research cycle by reducing the time from conceptualization to discovery. It automates labor‐intensive tasks such as literature review, hypothesis generation, experimental design, data analysis, modeling, simulation, and manuscript drafting. In well‐defined domains, AKD can transform the scientific method into a continuously adaptive cycle, where outputs from each phase inform the next. These closed‐loop scientific workflows shorten discovery timelines and reduce overhead. In addition to the scientific speed up, AKD targets an increase in the quality of research allowing for more systematic discovery of knowledge. However, AKD's success depends on principled, trustworthy design. This requires a holistic approach that emphasizes explainability, reproducibility, robustness, adaptability, and transparency. Key requirements include alignment with open science principles, required human oversight, scientific accountability, and rigorous provenance tracking. Human researchers must remain ultimately responsible for scientific integrity, ethical reasoning, and interpretation, with AI serving as an augmentative partner. Although the proposed approach applies to any domain focused on scientific discovery, this paper highlights AKD's potential to advance NASA's science mission, given the agency's vast data assets, complex objectives, and interdisciplinary challenges. By integrating NASA's data, foundation models, scalable computing, and knowledge frameworks, AKD can accelerate discovery and foster innovation across its scientific portfolio.
Hu, Y., et al., Generating Business Intelligence Through Social Media Analytics: Measuring Brand Personality with Consumer-, Employee-, and Firm-Generated Content. Journal of Management Information Systems, 2019. 36(3): p. 893-930.

{"references": ["Hu, Y., et al., Generating Business Intelligence Through Social Media Analytics: Measuring Brand Personality with Consumer-, Employee-, and Firm-Generated Content. Journal of Management Information Systems, 2019. 36(3): p. 893-930.", "Kietzmann, J. and L.F. Pitt, Computerized content analysis of online data \u2013 opportunities for marketing scholars and practitioners. European journal of marketing, 2020. 54(3): p. 473-477.", "Balducci, B. and D. Marinova, Unstructured data in marketing. Journal of the Academy of Marketing Science, 2018. 46(4): p. 557-590.", "Bruce, K.L., Assessing Brand-Consumer Personality Congruence on Twitter: A Computational Textual Analysis Approach. 2020, The University of Alabama", "Seok, H., Y. Joo, and Y. Nam, An Analysis of the Sustainable Tourism Value of Graffiti Tours through Social Media: Focusing on TripAdvisor Reviews of Graffiti Tours in Bogota, Colombia. Sustainability, 2020. 12(11): p. 4426", "Blazquez, D. and J. Domenech, Big Data sources and methods for social and economic analyses. Technological forecasting &amp; social change, 2018. 130: p. 99-113.", "Neto, A.P., et al., Discovering the sustainable hotel brand personality on TripAdvisor. Journal of Hospitality and Tourism Technology, 2020.", "Provalis Research, P.R., Brand personality dictionary. 2019."]}
How Do Family Caregivers Providing Care To Older People Experience Palliative/End Of Life Nursing Care In The Old People's Home Setting And Do Their Experiences Affect Their Quality Of Life?

This study focuses upon the area of Bremen and Lower Saxony (Niedersachsen), Germany. It aims to ascertain the nursing care experiences of the Family caregivers of older people at the end of life in a nursing home setting.These are Family caregivers whose loved ones are faced with chronic life-threatening diseases, including cancer and dementia, and includes Family caregivers who have been bereaved within one year of this study. Both structured and semi-structured Interviews, including a quesstionaire, were used to provide an overview of how family caregivers experience end of life care in nursing homes.
A Study of Additive Manufacturing Using 3D Printing Machines and Pens

3D printing (3DP), also known as additive manufacturing (AM), is a popular method used in Industry 4.0 that involves using machines and pens to create customized and complex 3D objects from a digital file by layering materials. This technology is widely used in industries such as aerospace, automotive, healthcare, and consumer goods for prototyping, rapid tooling, and production of end-use parts. 3DP offers companies a low-risk, low-cost, and fast way to produce prototypes, allowing them to test new products and speed up development without expensive models or specialized tools. Machines are more suitable for creating larger and more complex objects with high accuracy, while pens are better for smaller and simpler designs. Machines can be expensive and require training, while pens are more affordable and easy to use. Overall, AM with 3DP has provided new possibilities for designers and manufacturers. This chapter will further explore different printing methods, materials, advantages, limitations, software applications, potential uses, and future prospects of this technology.
Detection of the Power Spectrum of Cosmic Microwave Background Lensing by the Atacama Cosmology Telescope

We report the first detection of the gravitational lensing of the cosmic microwave background through a measurement of the four-point correlation function in the temperature maps made by the Atacama Cosmology Telescope. We verify our detection by calculating the levels of potential contaminants and performing a number of null tests. The resulting convergence power spectrum at 2-degree angular scales measures the amplitude of matter density fluctuations on comoving length scales of around 100 Mpc at redshifts around 0.5 to 3. The measured amplitude of the signal agrees with Lambda Cold Dark Matter cosmology predictions. Since the amplitude of the convergence power spectrum scales as the square of the amplitude of the density fluctuations, the 4-sigma detection of the lensing signal measures the amplitude of density fluctuations to 12%.
SOUNDING ROCKET MICROGRAVITY EXPERIMENTS ELUCIDATING DIFFUSIVE AND RADIATIVE TRANSPORT EFFECTS ON FLAME SPREAD OVER THERMALLY THICK SOLIDS

A series of 6-minute microgravity combustion experiments of opposed flow flame spread over thermally-thick PMMA has been conducted to extend data previously reported at high opposed flows to almost two decades lower in flow. The effect of flow velocity on flame spread shows a square root power law dependence rather than the linear dependence predicted by thermal theory. The experiments demonstrate that opposed flow flame spread is viable to very low velocities and more robust than expected from the numerical model, which predicts that at very low velocities (less than 5 centimeters per second), flame spread rates fall off more rapidly as flow is reduced. It is hypothesized that the enhanced flame spread observed in the experiments may be due to three- dimensional hydrodynamic effects, which are not included in the zero-gravity, two-dimensional hydrodynamic model. The effect of external irradiation was found to be more complex that the model predicted over the 0-2 Watts per square centimeter range. In the experiments, the flame compensated for the increased irradiation by stabilizing farther from the surface. A surface energy balance reveals that the imposed flux was at least partially offset by a reduced conductive flux from the increased standoff distance, so that the effect on flame spread was weaker than anticipated.
Improving randomized controlled trial analysis via data-adaptive borrowing

In recent years, real-world external controls have grown in popularity as a tool to empower randomized placebo-controlled trials, particularly in rare diseases or cases where balanced randomization is unethical or impractical. However, as external controls are not always comparable to the trials, direct borrowing without scrutiny may heavily bias the treatment effect estimator. Our paper proposes a data-adaptive integrative framework capable of preventing unknown biases of the external controls. The adaptive nature is achieved by dynamically sorting out a comparable subset of the external controls via bias penalization. Our proposed method can simultaneously achieve (a) the semiparametric efficiency bound when the external controls are comparable and (b) selective borrowing that mitigates the impact of the existence of incomparable external controls. Furthermore, we establish statistical guarantees, including consistency, asymptotic distribution, and inference, providing type-I error control and good power. Extensive simulations and two real-data applications show that the proposed method leads to improved performance over the trial-only estimator across various bias-generating scenarios.
The uses of unmanned aerial vehicles –UAV’s- (or drones) in social logistic: Natural disasters response and humanitarian relief aid

Abstract   This paper evaluates the crucial role of unmanned aerial vehicles –UAV’s- (or Drones) in the case of natural disasters response and humanitarian relief aid. The primary objective of this paper is to evaluate how unmanned aerial vehicles –UAV’s- (or Drones) in the present or near future can help survivors in the case of a tsunami, earthquake, flooding, and any natural disaster. Initially, we assume that in any natural disaster always exist the high possibility of damage to the infrastructure, transportation systems, telecommunications systems access, and basic services immediately. This research proposes three areas the uses of unmanned aerial vehicles –UAV’s- (or Drones) in the case of natural disasters response and humanitarian relief aid. These are (i) the aerial monitoring post-natural disaster damage evaluation, (ii) the natural disaster logistic and cargo delivery, (iii) the post-natural disaster aerial assessment.
Stem Cells: A Revolution in Therapeutics—Recent Advances in Stem Cell Biology and Their Therapeutic Applications in Regenerative Medicine and Cancer Therapies

Basic and clinical research accomplished during the last few years on embryonic, fetal, amniotic, umbilical cord blood, and adult stem cells has constituted a revolution in regenerative medicine and cancer therapies by providing the possibility of generating multiple therapeutically useful cell types. These new cells could be used for treating numerous genetic and degenerative disorders. Among them, age-related functional defects, hematopoietic and immune system disorders, heart failures, chronic liver injuries, diabetes, Parkinson's and Alzheimer's diseases, arthritis, and muscular, skin, lung, eye, and digestive disorders as well as aggressive and recurrent cancers could be successfully treated by stem cell-based therapies. This review focuses on the recent advancements in adult stem cell biology in normal and pathological conditions. We describe how these results have improved our understanding on critical and unique functions of these rare sub-populations of multipotent and undifferentiated cells with an unlimited self-renewal capacity and high plasticity. Finally, we discuss some major advances to translate the experimental models on ex vivo and in vivo expanded and/or differentiated stem cells into clinical applications for the development of novel cellular therapies aimed at repairing genetically altered or damaged tissues/organs in humans. A particular emphasis is made on the therapeutic potential of different tissue-resident adult stem cell types and their in vivo modulation for treating and curing specific pathological disorders.
Genetic Testing of Employees: New Technologies, New Challenges, and New Laws for Employers in the Nineties

In their bookMegatrends 2000, John Naisbitt and Patricia Aburdene (1990) stated that one of the ten “megatrends for the 1990's would be the rise of “The Age of Biology.” One of the central forces behind this societal shift which is occurring right now, they say, is research into understanding human genetics and the rise of biotechnology. The scientific knowledge regarding human genetics and the technology to examine an individual's genetic makeup have grown at a rapid pace, especially in the last decade as a result of the Human Genome Project. This venture has been labelled alternatively as “mediocre science” (Roberts, 1990b: p. 804) and as “biology's Holy Grail,” (Nelkin and Tancredi, 1989: p. 14). It is indisputably a monumental scientific undertaking, likened to the drive to put a man on the moon in the sixties (“The Geography of Genes,” 1989). This knowledge and the resultant trends will likely prove to be important factors not only in our future economy, but also in the nature of how we understand ourselves.
Toward Universal Health Coverage: What Socioeconomic and Clinical Factors Influence Health Insurance Coverage and Restrictions in Access to Viral Hepatitis Services in Nasarawa State, Nigeria?

Background: Viral hepatitis B and C (HBV and HCV) pose significant public health concern in Nigeria, where access to healthcare and treatment affordability are limited. This study investigated sociodemographic and clinical predictors of health insurance coverage and access to care among patients with HBV and HCV in Nasarawa State, Nigeria. Methods: A cross-sectional facility-based study was conducted at two secondary hospitals in Nasarawa State, Nigeria. Participants included patients diagnosed with HBV, HCV, or both who were ≥18 years old. Data were collected using a structured questionnaire covering sociodemographic and clinical information, health insurance details, and economic impact. Binary logistic regression was used to analyze the relationship between sociodemographic/clinical factors and health insurance status. Results: Out of 303 participants, 68% had health insurance, which mostly covered hepatitis screening and vaccination. Significant predictors of health insurance coverage included being aged 36–40 years (adjusted odds ratio [aOR]: 11.01, 95% confidence interval [CI]: 2.38–50.89, p = 0.002), having post-secondary education (aOR: 25.2, 95% CI: 9.67–65.68, p &lt; 0.001), being employed (aOR: 27.83, 95% CI: 8.85–87.58, p &lt; 0.001), and being HIV-positive (aOR: 4.06, 95% CI: 1.55–10.61, p = 0.004). Nearly all those insured (99%) faced restrictions in insurance coverage for viral hepatitis services. Conclusions: This study reveals that while health insurance coverage is relatively high among viral hepatitis patients in Nasarawa State, significant restrictions hinder access to comprehensive services, especially for vulnerable groups like younger adults, the unemployed, and PLHIV. Key factors influencing coverage include age, education, employment, and HIV status. Expanding benefit packages to include viral hepatitis diagnosis and treatment, raising awareness about viral hepatitis as part of insurance strategy, improving access for underserved populations, and integrating hepatitis services into existing HIV programs with strong policy implementation monitoring frameworks are crucial to advancing universal health coverage and meeting the WHO’s 2030 elimination goals.
Improving Evaluation of Recombination-based Cartesian Genetic Programming

Cartesian Genetic Programming has traditionally been using mutation as its main and often sole genetic operator to drive evolutionary search. Despite advancements in recent years, recombinationbased approaches have long been avoided, due to apparent lack of performance gains. This study examines two recently suggested recombination-based operators, subgraph crossover and discrete phenotypic recombination on SRBench, a benchmarking platform for symbolic regression. Using the implementations provided in the TinyverseGP framework, we perform hyperparameter optimisation of the respective representations with these two operators. Our work demonstrates that hyperparameter optimisation can lead to improvements in performance for recombination-based Cartesian Genetic Programming.
Cultivating Helpful, Personalized, and Creative AI Tutors: A Framework for Pedagogical Alignment using Reinforcement Learning

The integration of large language models (LLMs) into education presents unprecedented opportunities for scalable personalized learning. However, standard LLMs often function as generic information providers, lacking alignment with fundamental pedagogical principles such as helpfulness, student-centered personalization, and creativity cultivation. To bridge this gap, we propose EduAlign, a novel framework designed to guide LLMs toward becoming more effective and responsible educational assistants. EduAlign consists of two main stages. In the first stage, we curate a dataset of 8k educational interactions and annotate them-both manually and automatically-along three key educational dimensions: Helpfulness, Personalization, and Creativity (HPC). These annotations are used to train HPC-RM, a multi-dimensional reward model capable of accurately scoring LLM outputs according to these educational principles. We further evaluate the consistency and reliability of this reward model. In the second stage, we leverage HPC-RM as a reward signal to fine-tune a pre-trained LLM using Group Relative Policy Optimization (GRPO) on a set of 2k diverse prompts. We then assess the pre- and post-finetuning models on both educational and general-domain benchmarks across the three HPC dimensions. Experimental results demonstrate that the fine-tuned model exhibits significantly improved alignment with pedagogical helpfulness, personalization, and creativity stimulation. This study presents a scalable and effective approach to aligning LLMs with nuanced and desirable educational traits, paving the way for the development of more engaging, pedagogically aligned AI tutors.
Artificial Intelligence Generated Content Empowering Cultural Creative Shoe Design for the Xunpu Zhanhuawei Intellectual Property

The paper discussed the application of Artificial Intelligence Generated Content (AIGC) technology in the design of cultural and creative footwear. It first described the cultural background and IP value of Xunpu Zanhuawei, and then evaluated the feasibility and application pathways of AIGC technology in the design of cultural footwear. Based on Alibaba Cloud's Qwen and the Midjourney platform, this study explored an efficient collaboration mechanism between designers and AIGC technologies, and generated footwear design concepts that align with the cultural characteristics of Xunpu Zanhuawei. The results show that, under the guidance and leadership of designers, AIGC technology as a powerful supporting tool can significantly enhance design efficiency and expand designers' creative boundaries, thus demonstrate great potential in the field of cultural and creative footwear design.
Improved Quality of Life at End of Life Related to Home-Based Palliative Care in Children with Cancer

Nearly 2000 children die due to a malignancy in the United States annually. Emerging data suggest that home is the desired location of care for children with cancer at end of life. However, one obstacle to enrollment in a pediatric palliative care (PPC) home care program may be fear that distressing symptoms at end of life cannot be adequately managed outside the hospital.To compare the symptom distress and quality-of-life experience for children who received concurrent end-of-life care from a PPC home care program (PPC/Oncology) with that of those who died without exposure to the PPC program (Oncology).We conducted a retrospective survey study of a cohort of bereaved parents of children who died of cancer between 2002 and 2008 at a U.S. tertiary pediatric institution.Sixty bereaved parents were surveyed (50% PPC/Oncology). Prevalence of constipation and high distress from fatigue were more common in the PPC/Oncology group; other distressing symptoms were similar between groups, showing room for improvement. Children who received PPC/Oncology were significantly more likely to have fun (70% versus 45%), to experience events that added meaning to life (89% versus 63%), and to die at home (93% versus 20%).This is the first North American study to assess outcomes among children with cancer who received concurrent oncology and palliative home care compared with those who received oncology care alone. Symptom distress experiences were similar in groups. However, children enrolled in a PPC home care program appear to have improved quality of life and are more likely to die at home.
Legal Protection of Right to Privacy in India Regarding Phone Tapping and Surveillance

The research paper titled "Legal Protection of the Right to Privacy in India regarding Phone tapping and Surveillance" leaves an exhaustive examination of the intricate interplay between the right to privacy and state surveillance rehearsed in the Indian legal framework. In a period portrayed by quickly evolving correspondence technologies and elevated worries over privacy infringements, this study digs profound into the legal protections, judicial interpretations, and legislative improvements that delineate the limits of privacy in the context of phone tapping and surveillance. Through fastidious examination of landmark court cases, statutory arrangements, and emerging patterns in surveillance innovation, the research paper plans to reveal insight into the sensitive harmony between national security goals and individual privacy rights within the Indian legal landscape.
Federated Learning With Individualized Privacy Through Client Sampling

With growing concerns about user data collection, individualized privacy has emerged as a promising solution to balance protection and utility by accounting for diverse user privacy preferences. Instead of enforcing a uniform level of anonymization for all users, this approach allows individuals to choose privacy settings that align with their comfort levels. Building on this idea, we propose an adapted method for enabling Individualized Differential Privacy (IDP) in Federated Learning (FL) by handling clients according to their personal privacy preferences. By extending the SAMPLE algorithm from centralized settings to FL, we calculate client-specific sampling rates based on their heterogeneous privacy budgets and integrate them into a modified IDP-FedAvg algorithm. We test this method under realistic privacy distributions and multiple datasets. The experimental results demonstrate that our approach achieves clear improvements over uniform DP baselines, reducing the trade-off between privacy and utility. Compared to the alternative SCALE method in related work, which assigns differing noise scales to clients, our method performs notably better. However, challenges remain for complex tasks with non-i.i.d. data, primarily stemming from the constraints of the decentralized setting.
AI-Enabled Climate Adaptation and Resilience for Vulnerable Populations in the Global South

Climate change poses disproportionate risks to vulnerable populations in the Global South, where social inequality, limited adaptive capacity, and fragile institutions intensify climate impacts. Artificial intelligence (AI) has emerged as a promising tool for enhancing climate adaptation and resilience through improved forecasting, early warning systems, risk mapping, and decision support. However, the effectiveness of AI-enabled climate adaptation depends on how inclusively and ethically these technologies are designed and governed. This chapter examines the role of AI in supporting climate adaptation and resilience for vulnerable populations in the Global South, with particular reference to developing-country contexts. Adopting a human-centered and policy-oriented perspective, the chapter critically analyzes opportunities, equity and governance challenges, and sustainability concerns associated with AI deployment.