ethicalabs/Research-Intent-Collab huggingface dataset:
The precautionary principle as multi-period games where players have different thresholds for acceptable uncertainty

Abstract   The precautionary principle (PP) is formalized assuming multiple players (branches of government, interest groups, etc.). Each player has a different threshold for acceptable uncertainty. A flow chart illustrates how a threat impacts uncertainty. Thereafter each player, starting with the most precautionary, assesses whether the uncertainty is below its threshold. Replying yes implies no command and execution of action, and vice versa when replying no. One game is played in each time period through the flow chart, followed by a feedback loop causing a new and possibly lower threat and uncertainty. The process continues as long as at least one player experiences uncertainty above its threshold. The flow chart is illustrated with a 2  ×  2 game displaying equilibria through three time periods where both players, one player, and no players are precautionary. Illustrations are made involving pollution, climate change, genetically modified food, new drugs, and nuclear proliferation. A nuclear power example with three players illustrates equilibria with varying degrees of being precautionary.
Double-loop importance sampling for McKean–Vlasov stochastic differential equation

AbstractThis paper investigates Monte Carlo (MC) methods to estimate probabilities of rare events associated with the solution to the d-dimensional McKean–Vlasov stochastic differential equation (MV-SDE). MV-SDEs are usually approximated using a stochastic interacting P-particle system, which is a set of P coupled d-dimensional stochastic differential equations (SDEs). Importance sampling (IS) is a common technique for reducing high relative variance of MC estimators of rare-event probabilities. We first derive a zero-variance IS change of measure for the quantity of interest by using stochastic optimal control theory. However, when this change of measure is applied to stochastic particle systems, it yields a $$P \times d$$                                        P                     ×                     d                                    -dimensional partial differential control equation (PDE), which is computationally expensive to solve. To address this issue, we use the decoupling approach introduced in (dos Reis et al. 2023), generating a d-dimensional control PDE for a zero-variance estimator of the decoupled SDE. Based on this approach, we develop a computationally efficient double loop MC (DLMC) estimator. We conduct a comprehensive numerical error and work analysis of the DLMC estimator. As a result, we show optimal complexity of $$\mathcal {O}\left( \textrm{TOL}_{\textrm{r}}^{-4}\right) $$                                        O                                                                     TOL                                                    r                                                                             -                           4                                                                                                          with a significantly reduced constant to achieve a prescribed relative error tolerance $$\textrm{TOL}_{\textrm{r}}$$                                        TOL                     r                                    . Subsequently, we propose an adaptive DLMC method combined with IS to numerically estimate rare-event probabilities, substantially reducing relative variance and computational runtimes required to achieve a given $$\textrm{TOL}_{\textrm{r}}$$                                        TOL                     r                                     compared with standard MC estimators in the absence of IS. Numerical experiments are performed on the Kuramoto model from statistical physics.
Book Review: "The Precautionary Principle in Practice — Environmental Decision-Making and Scientific Uncertainty", Jacqueline Peel

No abstract received.
Facilitating bias in cost-effectiveness analysis: CHEERS 2022 and the creation of assumption-driven imaginary value claims in health technology assessment

The current standards for health technology cost-effectiveness assessment rest on the creation of lifetime assumption-driven modeled simulations for imaginary pricing and consequent patient access recommendations. A recent                     BMJ                     paper reports a detailed assessment of 8,192 cost-effectiveness analyses, concluding that industry-sponsored modeled claims were more likely to publish incremental cost-effectiveness ratios (ICERs) below a USD 50,000 threshold than non-industry sponsored studies, supporting the claim that the product was cost-effective. This is unsurprising; indeed, the opposite can occur with a modeled claim deliberately resulting in ICER is excess of USD 50,000. This methodology is well entrenched with the recently published Consolidated Health Economic Evaluation Reporting Standards (CHEERS) 2022 guidance for creating imaginary cost-effectiveness modeled claims ensuring the opportunity for deliberately manipulated cost-effectiveness claims. This overlooks limitations imposed by fundamental measurement, rendering assumption-driven simulations redundant analytical exercises. Manipulation of ICERs and claims for cost-effectiveness are mathematically impossible; including cost-utility thresholds, because the preference or utilities supporting creation of quality-of-life years (QALYs) are ordinal scores. Nevertheless, with the promotion of CHEERS 2022, the belief in imaginary modeled value claims is both facilitated and reinforced. With CHEERS 2022, there is a concerted effort, largely in self-interest, to perpetuate the current belief system. This is a self-defeating strategy. Rather than admitting to the deficiencies of assumption-driven simulated imaginary claims, leaders are maintaining that health system decision makers can ignore standards of normal science and fundamental measurement in value claims for pharmaceutical products. This disregard of standards that are commonplace in the sciences and mainstream social sciences perpetuates the opportunity for self-serving modeled claims; where models are a marketing vehicle leading to sponsored systematic bias in formulary submissions. This supports the need for a NEW START paradigm for health technology assessment, focusing on evaluable single-attribute value claims, meeting the required standards for normal science and fundamental measurement.
Synthesis, Characterization, Electrochemistry, and In Situ X‐ray Diffraction Investigation of Ni<sub>3</sub>(PO<sub>4</sub>)<sub>2</sub> as a Negative Electrode Material for Lithium‐Ion Batteries

AbstractNickel orthophosphate, Ni3(PO4)2, was successfully synthesized using solid‐state reaction at 900 °C. The effect of the calcination temperature on the material purity was investigated at different temperatures (700–900 °C). XRD analysis has shown that Ni3(PO4)2 crystallizes in a monoclinic system with P21/a space group. The electrochemical performances of carbon‐coated Ni3(PO4)2 were investigated for the first time versus Li+/Li and improved by the optimization of the cut‐off voltage, the carbon‐coating source, and the binder used. The best performance was delivered when using 0.01 V cut‐off voltage, sucrose for C‐coating, and carboxymethyl cellulose (CMC) binder. A reversible capacity of 249 mAh g−1 and a capacity retention around 74–79 % after 90 cycles with a good coulombic efficiency of 98 % were obtained at C/5 current rate. In situ XRD measurements demonstrated the irreversible amorphization of the Ni3(PO4)2 crystal structure during the first discharge process, confirming that this phosphate exhibits a pure conversion mechanism.
Kasteran* — Memory Safety in Systems Programming — Compiler, Programming Languages, Type Theory, Sovereign AI, and Post-Cloud Architecture (Kasteran)

Memory safety remains one of the most critical challenges in systems programming, with approximately 70% of security vulnerabilities in low-level code stemming from memory corruption errors. This document surveys the evolution of memory safety techniques—from conservative garbage collection to sophisticated linear type systems and borrow checking—and examines their applicability to the Kasteran* programming language. We argue that a hybrid approach combining affine types with region-based memory management offers the strongest safety guarantees without sacrificing the performance characteristics required for systems programming.    Part of The Anticloud research corpus by Lois-Kleinner Alpasan (ORCID: 0009-0009-2233-6107). This work explores compiler, programming languages in the context of sovereign AI infrastructure, post-cloud computing architectures, and transparent, blackbox-free systems.
Secondary Battery Science: At the Confluence of Electrochemistry and Materials Engineering

Considerations of energy density, power, and calendar life are critical to effectively develop advanced secondary systems. For next generation battery applications requiring multiple features including long life, large cycle count, high energy density and high power, new strategies are needed for the rational design of electroactive materials and electrodes. This article discusses several conceptual approaches under exploration with examples from our research group. The first approach is the systematic synthesis of materials with structures facilitating ion insertion and deinsertion at high voltage and energy density, where we control materials properties such as surface area, particle size and in particular crystallite size. A second approach is the investigation of novel electrode structures and substrates to increase energy density and capacity retention under cycling, where we have developed strategies for minimizing passive components. A third approach is investigation of catalysts for metal air batteries where the cathode active material is drawn from the air rather than carried in the battery.
Failure Pattern and Reliability Analysis of Mobile Biomass Gasifier Engine (Prototype 3) using Weibull Calculation as a Basis for Updating the Preventive Maintenance Activity Schedule

Gasification is a technology that optimistically utilizes biomass to produce syngas (consisting of H2, CO, CO2, CH4) that can be used as an energy source while reducing excess CO2 emissions. However, if there are frequent failures due to unplanned maintenance activities, the engine performance will not be optimal. Therefore, an up-to-date reliability study is needed as a basis for updating preventive maintenance activities to maintain the quality of the engine so that it continues to operate properly and is durable. This research aims to analyze the reliability, unreliability, and availability of the Mobile Biomass Gasifier (Prototype3), as well as study the failure patterns measured using the shape parameter (?) with the Weibull distribution. The focus of this research is on the Suction blower which based on Pareto results is the equipment with the highest failure frequency. The results show that the average reliability is smaller than the failure rate. In addition, the value of the shape parameter (?) &gt; 1, which means that the damage rate increases as the component ages. The implication of this study shows the need to update the preventive maintenance (PM) schedule by considering the addition of new PM activities that are in accordance with the duration of the life of the components that cause machine damage.
A Chinese perspective on artificial intelligence generated content and copyright

In recent years, the application of artificial intelligence (AI) in the field of content generation has become more and more widespread, and the concept of artificial intelligence generated content (AIGC) has gradually entered the public consciousness. Can pieces of AIGC be considered works? Can AI be the author of AIGC? This paper seeks to provide a comprehensive and systematic analysis of the literature of Chinese scholars so as to sort out the different perspectives of Chinese scholars on the relevant issues. This paper uses the China National Knowledge Infrastructure (CNKI) as the data source database and uses Citespace to carry out text-mining work in the retrieved literature. This literature presents twelve main doctrines on the copyrightability of AIGC and three doctrines on its attribution.
Integrating Decentralized Finance (DeFi) Protocols into SAP Systems for Automated Payment Processing

DeFi or Decentralized Finance aims to automate and decentralize any form of traditional finance workflow done by a centralized institution. In this regard, cross border payments and transactions in SAP ERPs can be automated and secured using DeFi protocols. Thus, this study aims to design a payment interface that would fit into SAP ERP frameworks capable of meeting the low-cost, automated, and secure requirements for cross-border payment transactions. Traditionally, payments were made via SWIFT and SEPA. The proposed model intends to replace these with DeFi transactions handled through smart contracts, oracles, and payment middleware. Focusing on results, transaction latency, smart contract auditability, saved costs, and compliance assessments were measured for Ethereum, BNB Smart Chain, and Polygon. Real SAP Business Environment pilots showed over 60% decrease in processing cost while settlement speed increased by up to 90%. The model is designed to handle enterprise risk and compliance by incorporating robust KYC/AML governance, validation, and logging controls. A roadmap for the incorporation of DeFi into enterprise ERPs at a large scale for finance automation will serve as the study’s conclusion.
Peace building from 'Below': Challenging the limitations of conflict resolution in EI Salvador and Colombia?

Yes
Efficient Alignment of Large Language Models via Data Sampling

LLM alignment ensures that large language models behave safely and effectively by aligning their outputs with human values, goals, and intentions. Aligning LLMs employ huge amounts of data, computation, and time. Moreover, curating data with human feedback is expensive and takes time. Recent research depicts the benefit of data engineering in the fine-tuning and pre-training paradigms to bring down such costs. However, alignment differs from the afore-mentioned paradigms and it is unclear if data efficient alignment is feasible. In this work, we first aim to understand how the performance of LLM alignment scales with data. We find out that LLM alignment performance follows an exponential plateau pattern which tapers off post a rapid initial increase. Based on this, we identify data subsampling as a viable method to reduce resources required for alignment. Further, we propose an information theory-based methodology for efficient alignment by identifying a small high quality subset thereby reducing the computation and time required by alignment. We evaluate the proposed methodology over multiple datasets and compare the results. We find that the model aligned using our proposed methodology outperforms other sampling methods and performs comparable to the model aligned with the full dataset while using less than 10% data, leading to greater than 90% savings in costs, resources, and faster LLM alignment.
Geopolitical determinants of the constitutional position of the President of Ukraine as a guarantor of sovereignty and territorial integrity of the state

The author, based on the legal-dogmatic method and the analysis of the constitutional practice, presents the amendments I of the Constitution and legislation of Ukraine from the point of view of the constitutional position of the President as the guarantor of state security. Author, based on it, proposes a new periodization of the last several decades of Ukrainian constitutionalism.
A methodology for part classification with supervised machine learning

AbstractIn this paper, we report on a data analysis process for the automated classification of mechanical components. In particular, here, we describe, how to implement a machine learning system for the automated classification of parts belonging to several sub-categories. We collect models that are typically used in the mechanical industry, and then we represent each object by a collection of features. We illustrate how to set-up a supervised multi-layer artificial neural network with an ad-hoc classification schema. We test our solution on a dataset formed by 2354 elements described by 875 features and spanned among 15 sub-categories. We state the accuracy of classification in terms of average area under ROC curves and the ability to classify 606 unknown 3D objects by similarity coefficients. Our parts’ classification system outperforms a classifier based on the Light Field Descriptor, which, as far as we know, actually represents the gold standard for the identification of most types of 3D mechanical objects.
Reforms of the United Nations’ Security Council to Ensure Equity and Efficiency: An Argument for Selected Emerging Countries' Inclusion

In recent years, policymakers and analysts have called for the UN Security Council to undergo reform as soon as possible to better reflect the current world system and modern realities. The legitimacy, fairness, and efficiency of the Council have been questioned because some academics argue that unless the Council is restructured to consider the geopolitical realities of the modern international system and recent shifts in world politics, its credibility is at risk. This study critically examines the need for reforming the United Nations Security Council (UNSC) to align with the changing global power structure of the 21st century. Created in the aftermath of World War II, the current composition of the UNSC disproportionately favours a few permanent members, marginalising emerging powers and underrepresented regions such as Africa, Latin America, and South Asia. This imbalance has raised questions about the Council’s legitimacy, accountability, and ability to address modern global security challenges. To contribute to this discussion, this paper considers some emerging countries applying for permanent membership in the UNSC, assessing their contributions to the UN and using those contributions to support their eligibility.
1018. Health Technology Assessment of New Long-Acting, Directly-Observed HIV Treatments in Canada: Impact of Real-World Adherence to Daily Oral Therapy on Treatment, Transmission and Cost-Effectiveness

Abstract                                   Background                   Current antiretroviral therapy (ART) has dramatically improved outcomes for people living with HIV (PLWHIV), however adherence to daily oral dosing remains a challenge for some. New, long-acting (LA) ARTs which are directly administered by physicians eliminate the need to adhere to daily oral dosing and may improve clinical outcomes. The study objective was to evaluate costs and QALYs associated with improved adherence achieved via a novel, directly-observed therapy (DOT) of a monthly LA injectable ART, compared to standard of care (SoC), daily oral therapy.                                                   Methods                   A published Markov cohort state-transition model was adapted to model the impact of treatment adherence and subsequent disease transmission. Without the need to adhere to daily dosing, the efficacy of the injectable was modelled independent of adherence whereas virologic suppression in the SoC arm was adjusted to reflect published data on adherence to daily dosing (8.12% below optimal levels observed in clinical trials).                                                   Results                   This evidence-based approach of accounting for adherence revealed an increase in lifetime costs for oral SOC of approximately $850, and QALY loss of 0.109 when compared to results without accounting for adherence. Disease transmission results yielded 3 cases averted of HIV per 1,000 patients with LA’s impact on adherence.                                                   Conclusion                   In the absence of comparative adherence estimates between a LA, injectable DOT and daily oral therapy in the real world, an evidence-based approach provides a method to address the uncertainty around the true impact on costs and QALYs of a novel mode of administration.                                                   Disclosures                   Erin Arthurs, MSc, GlaxoSmithKline (Employee) Ben Parker, MSc, HEOR Ltd. (Employee) Ian Jacob, MSc, HEOR Ltd (Employee) Debbie Becker, MSc, GSK (Consultant) Amy Lee, MSc, PhD, GSK (Consultant) Olivia Hayward, PhD, HEOR ltd. (Employee) Vasiliki Chounta, MSc, GlaxoSmithKline (Shareholder)ViiV Healthcare (Employee) Sarah-Jane Anderson, PhD, GlaxoSmithKline (Employee, Shareholder) Nicolas Van de Velde, PhD, GlaxoSmithKline (Shareholder)ViiV Healthcare (Employee)
Enhanced Recovery After Surgery (ERAS) Protocols and Their Impact on Quality of Care and Patient Safety in Peri-Anaesthesia Nursing: A Narrative Review

This narrative review aimed to critically synthesise current evidence on Enhanced Recovery After Surgery (ERAS) protocols and their impact on quality of care and patient safety in peri-anaesthesia nursing, with particular emphasis on the Post-Anaesthesia Care Unit (PACU) and the immediate postoperative period. A narrative literature review was conducted to identify evidence related to ERAS implementation and peri-anaesthesia nursing practice. International databases, including PubMed and Elsevier, were searched for relevant publications addressing ERAS protocols, perioperative care, patient safety, and quality improvement. The review focused on studies published between 2019 and 2025. Peer-reviewed articles, clinical guidelines, and original studies relevant to peri-anaesthesia nursing and postoperative recovery were included, and the selected literature was screened and synthesised descriptively. The review demonstrated that ERAS protocols are associated with improved quality and patient safety outcomes during the peri-anaesthesia phase of care. Reported benefits included enhanced postoperative recovery and pain management, reduced complication rates, earlier mobilisation, and greater consistency in clinical practice within the PACU. The findings also highlighted the central role of peri-anaesthesia nurses in patient monitoring, education, and early recovery support, underscoring their contribution to successful ERAS implementation. Overall, ERAS protocols provide a structured framework for improving quality and patient safety in peri-anaesthesia nursing care. Strong peri-anaesthesia nursing involvement and effective multidisciplinary collaboration are essential for optimal implementation, particularly during immediate postoperative recovery. Further research focusing on peri-anaesthesia nursing-specific outcomes and experiences with ERAS pathways is warranted.