ethicalabs/Research-Intent-Collab huggingface dataset:
Small molecule lead generation processes for drug discovery

In response to the growing economic pressure to accelerate drug discovery processes, there have been numerous reports of attempts to improve the quality of leads through computational methods and combinatorial chemistry. With the reports of drug-likeness concepts and metrics, there has been a substantial increase in awareness of the types of molecules that are likely to be useful leads for drug discovery. The concepts of greater drug-likeness are used routinely to refine compound library designs for lead generation. Challenges still remain to develop chemistry amenable to the introduction of multiple diversity components while still creating molecules of lead-like and drug-like properties. Despite the common capability to synthesize and screen thousands of molecules, the current focus has shifted to smaller libraries targeted with greater computational sophistication according to putatively smarter target-drug premises. The need to target the right drug-like lead generation library will only grow in importance in order to exploit the information advantage of the chemical genomics approach. Organizations that develop efficient lead generation capabilities will likely find increased competitive advantage in the drug discovery field.
Balancing Innovation, Access, and Equity in Drug Pricing: Comparative Institutional Lessons for Advancing Universal Health Coverage

Context: Global efforts to reform prescription drug pricing must navigate the trade-offs between four core objectives: cost containment, innovation incentives, equitable access, and administrative feasibility. In the United States, the Most Favored Nation (MFN) pricing rule and emerging value-based care (VBC) models reflect divergent strategies for aligning prices with therapeutic value. However, comparative analyses across different health systems are limited.                                                                                                                  Methods: A structured trade-off matrix was employed to compare pharmaceutical pricing strategies in the U.S., Canada, and the UK across four dimensions: cost containment, innovation incentives, equity in access, and implementation feasibility. The comparison is drawn from peer-reviewed scholarly literature, policy studies, and regulatory publications spanning the period 2007 to 2025.                                                                                                                                                                                                                                         Findings: The MFN model offers short-term savings but can hinder innovation and outcome-based reimbursement in fragmented systems such as the U.S. The UK achieves significant value and pricing alignment through centralized institutions, such as NICE and the VPAG scheme. Canada, via the PMPRB, enforces price controls and international referencing, yet lacks integration of value-based principles and risk-sharing mechanisms. These findings have implications for the pursuit of Universal Health Coverage, illustrating how institutional design influences equity in access to essential medicines.                                                                                                                                                               Conclusions: Global models offer guiding principles, whereas sustainable pricing reforms must be designed to fit local political, institutional, and market conditions. Value-based incentives combined with cross-country yardsticks in hybrid models, though flexible and transparent, can create a more feasible reform approach for the U.S. Achieving Universal Health Coverage will require institutional designs that balance equity, innovation, and feasibility in pharmaceutical pricing.
The Impact of Workplace Reform on Occupational Health and Safety: A Study of Five Workplaces

No abstract supplied
A proactive GDPR-compliant solution for fostering medical scientific research as a secondary use of per-sonal health data

The secondary processing of personal health data for scientific research in the medical field is fundamental for fostering innovation and growing knowledge that improves individual and public health. Personal health data that are primarily processed for healthcare purposes by healthcare providers may be secondarily used by researchers for scientific purposes. However, the data controller shall assess the applicable grounds and conditions and then comply with the data protection framework to safeguard fundamental rights and freedoms. In this paper we analyse the legal requirements laid down on these aspects by the General Data Protection Regulation at the European Union level, which harmonises the general rules, and by two national implementations at the Member State level, Italy and France, which further regulate with specific conditions. After this comparative investigation, we propose a proactive, legal-technical e-health solution that complies with the rules and principles of the legal frameworks and empowers the individual’s control over personal health data while promoting medical research. To this end, the data protection by design concept plays a central role, and an interdisciplinary approach is fundamental in combining legal and technical perspectives.
Human Resource Management and Transformational Leadership: Strategies for Improving Innovation Behavior in MSMEs in West Sumatra with Organizational Culture as a Mediating Variable

This study aims to analyze the effect of human resource management and transformational leadership on innovation behavior in MSMEs in West Sumatra with organizational culture as a mediating variable. The research was conducted by taking a sample of 400 respondents who are MSMEs in West Sumatra. The data collection tool used was a questionnaire. The data obtained was processed with the SEM PLS (Structure Equation Modeling Partial Least Square) analysis technique using the SmartPLS 3.0 program. The results of this study explain that there is a positive and significant effect of HR Management variables on innovative behavior, there is a positive and significant effect of transformational leadership variables on innovative behavior, there is a positive and significant effect of HR Management variables on organizational culture, there is a positive and significant effect of transformational leadership variables on organizational culture, there is a positive and significant effect of organizational culture variables on innovative behavior, there is a positive and significant effect of HR Management on innovative behavior through organizational culture and there is a positive and significant effect of transformational leadership on innovative behavior through organizational culture.
A Study on the Changes in the Production Process of Publishing Contents by Generative Artificial Intelligence Technology

Artificial intelligence technology is being applied to the publishing industry in various forms. This study focused on content among the C-P-N-D perspectives that use artificial intelligence technology that is affecting the publishing ecosystem as a media ecosystem analysis framework. As a result of the study, visual intelligence technology was being applied to image generation areas such as book covers and book design generation of publications. Language intelligence technology was being applied to content planning, automation content generation, content editing and revision areas in connection with text generation. The expert system used artificial intelligence technology in the publishing process to optimize various areas such as content planning, creation, editing, and marketing, and bring about changes in the efficiency of work and reduction of production cost. This study discussed changes and major issues, which were grafted in the publishing area and artificial intelligence technology focused on content (C). Understanding the environmental changes and applications that artificial intelligence technology will bring to the publishing industry can strengthen the understanding and direction of the publishing industry.
Conceptual Study of Neutron Physics of Nuclear Fuel Cycle for Ceramic Fast Reactor

Abstract                To make the nuclear fuel cycle more economical and convenient, as well as prevent nuclear proliferation, the conceptual study of a simple high-temperature dry reprocessing of spent nuclear fuel (SNF) for a ceramic fast reactor is proposed in this paper. This simple high-temperature dry (HT-dry) reprocessing includes the Atomics International Reduction Oxidation (AIROX) process and purification method for rare-earth elements. After removing the part of fission products from SNF by a HT-dry reprocessing without fine separation, the remaining nuclides and some uranium are fabricated into fresh fuel which can be used back to the ceramic fast reactor. Based on the ceramic coolant fast reactor, we studied neutron physics of nuclear fuel cycle which consists operation of ceramic reactor, removing part of fission products from SNF and preparation of fresh fuels for many time. The parameters of the study include effective multiplication factor (Keff), beam density, and nuclide mass for different ways to remove the fission products from SNF. With the increase in burnup time, the trend of increasing 239Pu gradually slows down, and the trend of 235U gradually decreases and become balanced. For multiple removal of part of fission products in the nuclear fuel cycle, the higher the removal, the larger the initial Keff.
Access to Behavioral Health Services: Supply, Coverage, and Health Insurance Literacy

VCU Theses and Dissertations
Bridging community-engaged research and implementation science methods to advance public health practice

Addressing public health challenges through equitable, evidence-based strategies remains a pressing global need. Implementation science bridges research and practice, offering methods to improve intervention design, delivery, and evaluation. Concurrently, community-engaged research embeds equity and trust into public health initiatives. This paper introduces selected community engagement methods and related implementation science approaches to address characteristic common constraints in public health practice, illustrated with real-world examples. Adapting the Knowledge to Action framework, we present a discussion of community-engaged methods and accompanying case studies. The discussion follows four phases present in applied public health research: (1) identifying and defining the problem, (2) designing and modifying the intervention, (3) evaluating the intervention, and (4) communicating and disseminating findings. For each phase, we highlight common barriers, recommend methods, and present case studies such as rapid qualitative methods, human-centered design, and participatory data analysis. Case studies demonstrate these methods' effectiveness in addressing common constraints. These approaches ensured that interventions were culturally tailored, contextually relevant, and evidence-based.
The Responsibility to Protect by Military Means : Emerging Norms on Humanitarian Intervention?

This dissertation is an interdisciplinary study on the external ‘Responsibility to Protect’ (R2P) and international law. It focuses on the legal customary process on jus ad bellum by which states try to address the gap between the legitimacy and legality of humanitarian intervention to protect human security within a state against genocide, war crimes and crimes against humanity. The development of specific legal rights for the Security Council, regional organisations and ‘coalitions of the willing’ to protect by military means is examined through case studies of humanitarian interventions after the Cold War. Constructivist perspectives on security and norms are contrasted with legal positivist analyses of customary law, the applicable law on the use of force, and evolutionary interpretation and informal modification of treaties. Feminist theories and gender perspectives on human security, the R2P and humanitarian intervention are also integrated into the thesis. The decisions to authorise humanitarian interventions in Bosnia, Somalia, Rwanda, East Timor and Darfur confirm an external R2P for the Security Council in the form of a lex lata right to protect by military means where states have manifestly failed to do so and where peaceful means are considered inadequate. Furthermore, a similar customary right for regional organisations may also be emerging, when the state concerned manifestly fails to protect, the Security Council is unable or unwilling to act and peaceful means found inadequate. Finally, support is found for a lex ferenda right of regional organisations, coalitions of the willing and individual states to intervene by military means in ‘exceptional circumstances’ in accordance with the theories on ex post facto or implied authority, or the principle of necessity, to protect people in humanitarian crises where the criteria of the R2P doctrine are met.
Critical Infrastructures, Protection and Resilience

This chapter introduces the concept of Critical Infrastructure (CI). Although old civilisations had CI, the protection and resilience of CI has come to the fore again in the last two decades. The risk to society due to inadvertent and deliberate CI disruptions has largely increased due to interrelation, complexity, and dependencies of these infrastructures. The increased use of information and telecommunication technologies (ICT) to support, monitor, and control CI functionalities has contributed to this. The interest in CI and complex systems is strongly related to initiatives by several governments that from the end of the 90s of the previous century recognised the relevance of the undisturbed functioning of CI for the wellbeing of their population, economy, and so on. Their policies highlighted early the increasing complexity of CI and the challenges of providing such CI services without disruption, especially when accidental or malicious events occur. In recent years, most national policies have evolved following a direction from protection towards resilience. The need for this shift in perspective and these concepts are also analysed in this chapter. © The Author(s) 2016.
Veteran Monitoring Initiative for Noninvasive Physiology and Depression (V-MIND) Exploring Physical Activity and Mental Health in UK Veterans: Protocol for an Observational Digital Phenotyping Study (Preprint)

BACKGROUND                   Veterans face an increased risk of common mental disorders when compared to civilian groups. However, veteran disengagement from treatment is a concern among health care providers, resulting in a need to explore novel ways of managing veteran mental health. Wearable devices, such as fitness trackers and smartwatches, have been explored for their potential to assess, monitor, and predict mental health outcomes in the general population. Such devices provide continuous data on metrics including physical activity, heart rate, sleep quality, and stress levels, offering a comprehensive view of the lifestyle and physiological factors influencing mental health.                                                     OBJECTIVE                   This study aims to explore the feasibility of using wearable technology as a data collection and potential health monitoring tool among UK veterans. It also aims to explore the associations between mental health, physical activity, and functioning factors among UK veterans.                                                     METHODS                   This is an observational feasibility study measuring mental health via validated questionnaires completed at baseline (T0), day 28 (T1), day 56 (T2), and day 84 (T3), and physiological metrics measured continuously via wrist-worn fitness trackers (Garmin vívosmart-5 watches) over 3 months (84 days). UK veterans will be recruited through convenience sampling methods. Statistical analysis will be exploratory, and machine learning models will be trained to detect changes in mental health and well-being outcomes.                                                     RESULTS                   Data collection was conducted between February 2025 and October 2025, and data analysis is scheduled to begin in January 2026.                                                     CONCLUSIONS                   This study will provide information on the feasibility of using wearable technology devices within a UK veteran population and may inform potential future interventions seeking to integrate wearable-derived data alongside the management of common mental disorders in veterans experiencing mental health difficulties. Findings would also enhance understanding of the relationship between mental health and physiological factors (eg, physical activity and sleep) in UK veterans.                                                     INTERNATIONAL REGISTERED REPORT                   PRR1-10.2196/73060
Beyond current boundaries: Integrating deep learning and AlphaFold for enhanced protein structure prediction from low-resolution cryo-EM maps

Constructing atomic models from cryo-electron microscopy (cryo-EM) maps is a crucial yet intricate task in structural biology. While advancements in deep learning, such as convolutional neural networks (CNNs) and graph neural networks (GNNs), have spurred the development of sophisticated map-to-model tools like DeepTracer and ModelAngelo, their efficacy notably diminishes with low-resolution maps beyond 4 Å. To address this shortfall, our research introduces DeepTracer-LowResEnhance, an innovative framework that synergizes a deep learning-enhanced map refinement technique with the power of AlphaFold. This methodology is designed to markedly improve the construction of models from low-resolution cryo-EM maps. DeepTracer-LowResEnhance was rigorously tested on a set of 37 protein cryo-EM maps, with resolutions ranging between 2.5 to 8.4 Å, including 22 maps with resolutions lower than 4 Å. The outcomes were compelling, demonstrating that 95.5\% of the low-resolution maps exhibited a significant uptick in the count of total predicted residues. This denotes a pronounced improvement in atomic model building for low-resolution maps. Additionally, a comparative analysis alongside Phenix's auto-sharpening functionality delineates DeepTracer-LowResEnhance's superior capability in rendering more detailed and precise atomic models, thereby pushing the boundaries of current computational structural biology methodologies.
Continual learning and catastrophic forgetting

Preprint of a book chapter; 21 pages, 4 figures
Rethinking the Rise of Global Central Bank Digital Currencies: A Policy Perspective

This article discusses the impact of central bank digital currency(CBDC) from the perspective of monetary and fiscal policy. We mainly discuss how retail CBDC can improve traditional policy performance. We argue that A CBDC helps existing monetary policies to break through the zero lower bound, shorten the policy time lag, realize selective policy interventions, and make possible the price level target policy system. Similarly, the combination of CBDC and smart contracts can directly affect the performance of the fiscal policy. Compared with the convenience and impact of CDBC as a medium of exchange, its impact as a policy tool is more profound. The current monetary policy is facing a zero lower bound. The fiscal policy is facing a huge sovereign debt crisis and the impact of other private digital currencies on fiat money, which makes the importance of CBDC as a new policy tool more prominent. However, the research on CBDC as a policy tool has many fundamental questions that remain to be answered. This paper contextualizes CBDC with global challenges in policies and new technology advances, and the authors rethink CBDC from a policy perspective, which may provide many new thoughts and have implications for the rise of global CBDC.
Quantum computation and optimized error correction

Two subjects in the area of quantum computation are considered here. In the first chapter I present a universal model for a quantum Robot. Chapters two, three, and four are dedicated to the problem of quantum error correction/protection.
Trust computational models for mobile ad hoc networks: recommendation based trustworthiness evaluation using multidimensional metrics to secure routing protocol in mobile ad hoc networks.

Distributed systems like e-commerce and e-market places, peer-to-peer networks, social networks, and mobile ad hoc networks require cooperation among the participating entities to guarantee the formation and sustained existence of network services. The reliability of interactions among anonymous entities is a significant issue in such environments. The distributed entities establish connections to interact with others, which may include selfish and misbehaving entities and result in bad experiences. Therefore, trustworthiness evaluation using trust management techniques has become a significant issue in securing these environments to allow entities decide on the reliability and trustworthiness of other entities, besides it helps coping with defection problems and stimulating entities to cooperate. Recent models on evaluating trustworthiness in distributed systems have heavily focused on assessing trustworthiness of entities and isolate misbehaviours based on single trust metrics. Less effort has been put on the investigation of the subjective nature and differences in the way trustworthiness is perceived to produce a composite multidimensional trust metrics to overcome the limitation of considering single trust metric. In the light of this context, this thesis concerns the evaluation of entities’ trustworthiness by the design and investigation of trust metrics that are computed using multiple properties of trust and considering environment. Based on the concept of probabilistic theory of trust management technique, this thesis models trust systems and designs cooperation techniques to evaluate trustworthiness in mobile ad hoc networks (MANETs). A recommendation based trust model with multi-parameters filtering algorithm, and multidimensional metric based on social and QoS trust model are proposed to secure MANETs. Effectiveness of each of these models in evaluating trustworthiness and discovering misbehaving nodes prior to interactions, as well as their influence on the network performance has been investigated. The results of investigating both the trustworthiness evaluation and the network performance are promising.
Does AlphaFold predict the spatial structure of a protein from physics or recognize it (its main parts and their association) using databases?

ABSTRACT                                    The great success of the AlphaFold programs poses the following questions: (i) What is the main reason for this success? (ii) What exactly do AlphaFolds do:                   prediction                   of the 3D protein structure based on its amino acid sequence and knowledge of the protein physics or                   recognition                   of this 3D structure, based on the similarity between some parts of its amino acid sequence and parts of sequences with already known 3D structures? The answers given in this paper are: The main reason for the tremendous success of the AlphaFold is (i) the usage of huge protein databases, which already cover all or almost all of the protein superfamilies existing in nature; (ii) using these databases and the resulting multiple sequence alignments and coevolutionary information (like correlations in pairs and especially in triplets of amino acid residues in the contacting chain regions), AlphaFold                   recognizes                   a 3D structure of the examined amino acid sequence by a similarity of this sequence (or its parts) to related sequences with already known 3D structures. Concluding, I have to emphasize that this paper does not diminish the merit and utility of AlphaFold; it only explains the basis of its success.