ethicalabs/Research-Intent-Collab huggingface dataset:
Modelling of cosmic ray modulation in the heliosphere by stochastic processes

The transport of cosmic rays in the heliosphere is studied by making use of a newly developed modulation model. This model employes stochastic differential equations to numerically solve the relevant transport equation, making use of this approach's numerical advantages as well as the opportunity to extract additional information regarding cosmic ray transport and the processes responsible for it. The propagation times and energy losses of galactic electrons and protons are calculated for different drift cycles. It is confirmed that protons and electrons lose the same amount of rigidity when they experience the same transport processes. These particles spend more time in the heliosphere, and also lose more energy, in the drift cycle where they drift towards Earth mainly along the heliospheric current sheet. The propagation times of galactic protons from the heliopause to Earth are calculated for increasing heliospheric tilt angles and it is found that current sheet drift becomes less effective with increasing solar activity. Comparing calculated propagation times of Jovian electrons with observations, the transport parameters are constrained to find that 50% of 6 MeV electrons measured at Earth are of Jovian origin. Charge-sign dependent modulation is modelled by simulating the proton to anti-proton ratio at Earth and comparing the results to recent PAMELA observations. A hybrid cosmic ray modulation model is constructed by coupling the numerical modulation model to the heliospheric environment as simulated by a magneto-hydrodynamic model. Using this model, it is shown that cosmic ray modulation persists beyond the heliopause. The level of modulation in this region is found to exhibit solar cycle related changes and, more importantly, is independent of the magnitude of the individual diffusion coefficients, but is rather determined by the ratio of parallel to perpendicular diffusion.
Aspects of density functional theory in ab initio quantum chemistry: external correlation for free

The restricted Hartree-Fock method is subject to a first-order perturbation theory regularization of the electron repulsion integrals representation, to mimic the electron-electron correlation hole effect. The efficiency of this regularization is demonstrated for electron affinities and ionization energies of atoms, and on atomization energies of molecules. The accuracy of the method is comparable to that of modern density functional approaches as it provides significant improvement on standard Hartree-Fock results. Conceptual similarities with density functional theory are discussed, as are the implications for ab initio calculations on large systems.
A monthly mean dataset of global oceanic temperature and salinity derived from Argo float observations

We produced a global monthly dataset of temperature and salinity using data from Argo floats, Triangle Trans-Ocean Buoy Network (TRITON), and available conductivity-temperature-depth (CTD) casts. The Argo project started from 2000, and the number of floats is increasing yearly throughout the global ocean. The Argo floats observe pressure, temperature and salinity from the sea surface down to 2000 dbar and enable the production of monthly global datasets of temperature and salinity. We have created Grid Point Value of the Monthly Objective Analysis using Argo float data (MOAA GPV) that contains optimally interpolated temperature and salinity values at selected standard pressure levels on a 1˚ grid in the global ocean. The dataset is freely available on the Argo website of the Japan Agency for Marine-Earth Science and Technology (JAMSTEC) at http://www.jamstec.go.jp/ARGO/J_ARGOe.html.
Policy Integration for Overcoming Fragmented Government Action in South Africa: The Case of the National Policy on Food and Nutrition Security

ABSTRACT                                                             In South Africa, as globally, scholars in public policy, administration, and governance have acknowledged the necessity of integrated policy approaches to address complex, cross‐sectoral, and multilevel government issues. However, the complex of problems and fragmented government structures and actions hinder effective policy implementation and governance. Despite the potential of policy integration to overcome these challenges, there has been little focus on the concept in South African literature, which is widespread in official speeches and documents by the government—a scholarship‐practice mismatch. Using the case of the National Policy on Food Security and Nutrition Security, persistent fragmented government action was demonstrated using a policy integration analytical framework. The policy's ineffectiveness stems from a lack of coordination, decisional platforms, and stakeholder participation, with implications for governance. This article provides theoretical and practical insights for improving policy making, integration, decision making, and governance, and suggests research areas for South African Public Administration scholarship.                                                           Related Articles                                            Asare‐Nuamah, P., A. Amoah, and S. A. Asongu. 2023. “Achieving Food Security in Ghana: Does Governance Matter?”                       Politics &amp; Policy                       51, no. 4: 614–635.                       https://doi.org/10.1111/polp.12540                       .                                                                 Djeufack Dongmo, A., and D. Avom. 2024. “Urbanization, Civil Conflict, and the Severity of Food Insecurity in Africa.”                       Politics &amp; Policy                       52, no. 1: 140–168.                       https://doi.org/10.1111/polp.12572                       .                                                                 Oehmke, J. F., S. L. Young, G. Bahiigwa, B. B. Keizire, and L. A. Post. 2018. “The Behavioral‐Economics Basis of Mutual Accountability to Achieve Food Security.”                       Politics &amp; Policy                       46, no. 1: 32–57.                       https://doi.org/10.1111/polp.12244                       .
Isotopic (O, Sr, Nd) and trace element geochemistry of the Laouni layered intrusions (Pan-African belt, Hoggar, Algeria): evidence for post-collisional continental tholeiitic magmas variably contaminated by continental crust

The three layered intrusions studied in the Laouni area have been emplaced within syn-kinematic Pan-African granites and older metamorphic rocks. They have crystallized at the end of the regional high-temperature metamorphism, but are free from metamorphic recrystallization, revealing a post-collisional character. The cumulate piles can be interpreted in terms of two magmatic liquid lines of descent: one is tholeiitic and marked by plagioclase-olivine-clinopyroxene cumulates .troctolites or olivine bearing gabbros , while the other is calc-alkaline and produced orthopyroxene-plagioclase rich . . . cumulates norites . One intrusion WL West Laouni -troctolitic massif , shows a Lower Banded Zone where olivine-chro- mite orthocumulates are interlayered with orthopyroxene-rich and olivine-plagioclase-clinopyroxene cumulates, whereas the Upper Massive Zone consists mainly of troctolitic and gabbroic cumulates. The other two massifs are more . homogeneous: the WL-noritic massif has a calc-alkaline differentiation trend whereas the EL East Laouni -troctolitic massif has a tholeiitic one. Separated pyroxene and plagioclase display similar incompatible trace element patterns, regardless of the cumulate type. Calculated liquids in equilibrium with the two pyroxenes for both noritic and troctolitic cumulates are characterized by negative Nb, Ta, Zr and Hf anomalies and light REE enrichment inherited from the parental
From Humanitarian Intervention to the Responsibility to Protect

Since the Treaty of Westphalia of 1648, states have traditionally claimed exclusive jurisdiction over their citizens and have been averse to any external criticism of how they treat their nationals as undue interference in their internal affairs. However, that has been altered by the structure of the modern world of interdependence which has raised the level of international concerns with human rights. The international community is developing more and more intrusive norms and mechanisms for monitoring and protecting human rights within the territory of states. The doctrine of humanitarian intervention is probably the oldest of such doctrines. This doctrine did not have a precise meaning in the writings of early jurists and for a very long time, its scope and content remained unclear. Early state practice of humanitarian intervention was based on ‘religious solidarity’ and it was only in the nineteenth century that it began to acquire a specific and technical meaning. Yet, its legal validity before and after the UN Charter came into force remains controversial. In this chapter, I briefly track the origin and debates in the different stages of the normative evolution of the doctrine of humanitarian intervention and relevant international law norms that impact the doctrine. The purpose is to set out the scope and trajectory of the development of humanitarian intervention as a broad context within which to explore the legal validity of the AU–ECOWAS regional military intervention legal regimes under international law.
Why Machine Learning Integrated Patient Flow Simulation?

Patient flow analysis can be studied from a clinical and or operational perspective using simulation. Traditional statistical methods such as stochastic distribution methods have been used to construct patient flow simulation submodels such as patient inflow, Length of Stay (LoS), Cost of Treatment (CoT) and Clinical Pathway (CP) models. However, patient inflow demonstrates seasonality, trend and variation over time. LoS, CoT and CP are significantly determined by attributes of patients and clinical and laboratory test results. For this reason, patient flow simulation models constructed using traditional statistical methods are criticized for ignoring heterogeneity and their contribution to personalized and value based healthcare. On the other hand, machine learning methods have proven to be efficient to study and predict admission rate, LoS, CoT, and CP. This paper, hence, describes why coupling machine learning with patient flow simulation is important and proposes a conceptual architecture that shows how to integrate machine learning with patient flow simulation.
Quantum Computing and Error Correction

The main ideas of quantum error correction are introduced. These are encoding, extraction of syndromes, error operators, and code construction. It is shown that general noise and relaxation of a set of 2-state quantum systems can always be understood as a combination of Pauli operators acting on the system. Each quantum error correcting code allows a subset of these errors to be corrected. In many situations the noise is such that the remaining uncorrectable errors are unlikely to arise, and hence quantum error correction has a high probability of success. In order to achieve the best noise tolerance in the presence of noise and imprecision throughout the computer, a hierarchical construction of a quantum computer is proposed.
From Promise to Serendipity: A Breath of Fresh Air Beyond Bronchodilation: Early Data on Hemodynamic and Functional Outcomes with Ensifentrine Nebulization in Severe COPD Associated Pulmonary Hypertension–A Six-Month Retrospective Observational Study

Background:  Pulmonary hypertension (PH) is a frequent and serious complication of advanced chronic obstructive pulmonary disease (COPD) and is associated with poor prognosis and limited treatment options. Current management focuses on optimizing COPD care, while the role of pulmonary vasodilators remains controversial. Ensifentrine, a dual phosphodiesterase (PDE) 3/4 inhibitor with bronchodilatory and anti-inflammatory properties, may offer therapeutic benefit in COPD-associated PH. Objective:  To retrospectively evaluate the effects of 6 months of nebulized ensifentrine on pulmonary hemodynamics and functional outcomes in patients with severe COPD and associated pre-capillary PH. Methods:  We conducted a single-center, retrospective chart review of patients with GOLD stage III and IV COPD and confirmed pre-capillary PH (mean pulmonary artery pressure [mPAP] ≥20 mmHg, pulmonary vascular resistance [PVR] &amp;gt; 2 Wood Units, pulmonary capillary wedge pressure ≤15 mmHg). Five patients treated with nebulized ensifentrine (3 mg twice daily) between September 2024 and May 2025 were analyzed. Hemodynamics via right heart catheterization (RHC) and 6-minute walk distance (6MWD) were assessed at baseline and at 6 months. Results:  The cohort (mean age 76 years, 60% male, mean FEV₁ 33.4% predicted) demonstrated severe emphysema and PH (baseline mPAP 53.2 mmHg, PVR 9.5 WU, cardiac index 2.5 L/min/m²). After 6 months, mean changes were: mPAP: decrease by 1.6 mmHg Cardiac index: increase by 0.1 L/min/m² PVR: decrease by 1.0 WU 6MWD: Improved by 31 m mMRC dyspnea score improved from 3.6 to 3.2, CAT score from 23 to 21.6, and DLCO increased modestly (29%→31.2% predicted). No adverse events, exacerbations, or hospitalizations were observed during this period. The combination of ensifentrine with roflumilast was well tolerated. Conclusions:  In this retrospective study, ensifentrine was safe and associated with modest improvements in pulmonary hemodynamics and functional status in severe COPD with pre-capillary PH. These preliminary findings warrant confirmation in larger, prospective controlled trials.
Challenging exclusion: bringing the human rights to water and sanitation to Europe

ABSTRACT                Research and advocacy on water and sanitation in low-income countries often consider the human rights to water and sanitation as the basis for improving access to these essential services. But how often is the spotlight turned on high-income countries? Do the predominantly global North researchers, advocates, donors and development partners who explore failures to meet the human rights in Accra or Jakarta ever ask if the same rights are realized at home in Berlin, London or Paris? Exclusion and marginalization similar to that faced by various populations in low-income countries are present in the lived experiences of individuals and groups in Europe, including Roma communities, refugees and people facing homelessness or incarceration. Yet, the same human rights that are invoked in low-income countries are all too often ignored in Europe. Meanwhile, high-income countries cannot point to insufficient financial resources to justify failures in human rights realization. Against this background, we outline some of the inequalities in access to water and sanitation that exist and persist in high-income countries through the lens of human rights, and propose that invoking these human rights can support the ideals of equity and justice contributing to re-imagining universal access to services across Europe.
Using classical bit-flip correction for error mitigation in quantum computations including 2-qubit correlations

We present an error mitigation scheme which corrects readout errors on Noisy Intermediate-Scale Quantum (NISQ) computers [1,2]. After a short review of applying the method to one qubit, we proceed to discuss the case when correlations between different qubits occur. We demonstrate how the readout error can be mitigated in this case. By performing experiments on IBMQ hardware, we show that such correlations do not have a strong effect on the results, justifying to neglect them.
Artificial Intelligence and Non-Contractual Liability: Will the Insurance System Replace the Tort System in the Future? An example of autonomous vehicles

The rapid advancement of artificial intelligence (AI) technologies, particularly in the development of autonomous vehicles, presents a fundamental shift in the distribution of liability for traffic accidents. This article examines the future of non-contractual liability in the context of fully autonomous vehicles and explores whether the insurance system could eventually replace traditional tort-based liability frameworks. It analyses the classification of vehicle autonomy levels, the specific risks posed by autonomous systems (software failures, hacking, etc.), and discusses who may be held liable under existing and proposed EU regulations. Particular attention is given to the applicability of the Motor Insurance Directive (MID), the limits of civil liability in different Member States, and the feasibility of introducing compulsory cyber or producer liability insurance. The authors argue against the fragmentation of liability systems and propose that existing motor third-party liability insurance frameworks be adapted to encompass the new risks presented by autonomous technologies. Such an approach would avoid the creation of parallel compensation systems and ensure consistent victim protection throughout the EU.
Statistical inference via conditional Bayesian posteriors in high-dimensional linear regression

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Using Natural Language Processing of Clinical Notes to Supplement Structured Electronic Health Record Data for Phenotyping Smoking and Obesity in a Healthcare System

Abstract                                    Purpose                   Studies based on electronic health records (EHR) often rely on structured data, which may incompletely capture important clinical phenotypes in EHR notes. The purpose of this study was to assess two natural language processing (NLP) tools to extract phenotypes from unstructured EHR notes, and to evaluate the added value of integrating NLP-derived phenotypes with structured EHR data at a health system scale.                                                     Methods                   This retrospective study is based on inpatient and outpatient EHR data from the Mass General Brigham healthcare system between January 1, 2019 and December 31, 2020. Two established rule-based NLP tools were applied to extract smoking and obesity information from 19,215,303 clinical notes of 503,025 patients. NLP performance was evaluated through manual review of stratified samples. Phenotype prevalence was estimated using structured EHR data alone and compared with prevalence estimates obtained by supplementing structured data with NLP-derived features.                                                     Results                   Both NLP tools exhibited high performance, with both accuracy and F1 score of 0.99 for smoking, and 0.92 and 0.91 for obesity, respectively. The combination of NLP and structured data identified 220,714 patients (43.88%) with smoking, compared with 170,396 patients (33.87%) identified using structured data alone, representing a 29.5% relative increase. For obesity, NLP identified 121,360 patients (24.13%) from EHR notes, and 169,905 patients (33.78%) were documented in structured data; inclusion of NLP-derived features contributed additional 32,823 patients, corresponding to a 19.3% relative increase.                                                     Conclusion                   NLP-derived phenotypes from unstructured EHR notes substantially improve patient identification for both smoking and obesity compared with structured EHR data alone at scale.
ENHANCING QUANTUM ERROR CORRECTION: OPTIMIZING  NOISE REDUCTION TECHNIQUES FOR RELIABLE QUANTUM  COMPUTATION

Quantum computing holds the potential to revolutionize computation by solving complex problems that classical computers cannot. However, quantum systems are highly susceptible to errors due to noise and decoherence. In this paper, we propose a novel hybrid AI-Quantum approach for Quantum Error Correction (QEC) to optimize noise reduction techniques. By leveraging deep learning and reinforcement learning, we develop a method to predict and correct quantum noise in real time. Our experiments, conducted on IBM Qiskit simulators and actual quantum processors, demonstrate a significant reduction in quantum gate errors compared to traditional QEC codes. Our method achieves improved fault tolerance with reduced qubit overhead, paving the way for scalable and reliable quantum computation. Furthermore, we make our research publicly available with open-source Python code and an interactive Jupyter notebook, enabling others to replicate and extend our work.
Geochemistry of Quaternary basaltic lavas in the Norikura area, central Japan: Influence of the subcontinental upper mantle on the trace elements and Sr isotope compositions

Abstract   The trend of across-arc major- and trace-element compositions and 87Sr/ 86Sr ratios for Quaternary lavas from the western part of central Japan are different from those from NE Japan. Basalts from the Norikura area, located at the backarc-side of the western part of central Japan are characterized by lower K/Zr and higher 87Sr/ 86Sr (~0.705–0.706) than those of the backarc-side of NE Japan. Beneath the Norikura area, the Pacific plate is subducting at the depth where dehydration of phlogopite in the down-dragged peridotite occurs. Through dehydration, fluid is added to the mantle wedge which consists of a N-MORB-like source, triggering partial melting, and producing uprising mantle diapirs. These diapirs have high K/Zr and low 87Sr/ 86Sr as indicated by basalts in the backarc-side of NE Japan. Subcontinental mantle materials which have low LIL/HFS and high 87Sr/ 86Sr exist in the uppermost portions of the mantle wedge beneath the Norikura area, and interact geochemically with the uprising diapirs. The geochemical characteristics of the Norikura area may be attributed to the involvement of the subcontinental upper mantle.
Virtual element methods for the neutron diffusion and neutron transport equations with applications in nuclear reactor physics

In this thesis we propose a new spatial discretisation for the solution of the multigroup neutron diffusion equation (NDE) and the first-order form of the multigroup, discrete-ordinates, neutron transport equation (SN-NTE). The proposed spatial discretisation is called the NURBS- Enhanced virtual element method (NE-VEM). The NE-VEM is a generalisation of the Galerkin finite element method which expands its applicability to meshes comprised of arbitrary polygonal elements with curved edges. Non-uniform rational B-splines (NURBS) are used as the geometric description of curved edges as they are the standard geometry encoding used by the computer-aided geometric design (CAGD) industry. We developed a continuous-Galerkin VEM (NECG-VEM) to discretise the NDE and a discon- tinuous Galerkin VEM (NEDG-VEM) to discretise the SN-NTE. For both of these discretisatons we prove the well-posedness and derive a priori error estimates. We also present parallelisation strategies for both methods: a distributed memory strategy for the NECG-VEM and shared- memory strategy for the NEDG-VEM. The benefits of maintaining exact geometry are demon- strated through numerical examples. We show that the NE-VEM attains the theoretical order of convergence for problems with curved geometry for both the NDE and the SN-NTE. The convergence rate of conventional, straight-sided VEM however, saturates due to the geometric error induced by approximating curved geometry with line segments. Additionally, we present a 2D geometric modelling kernel and mesh generator to both model nuclear reactor core geometries and to create polygonal meshes which exactly represent the geometry of the reactor from the lowest level of mesh refinement.