ethicalabs/Research-Intent-Collab huggingface dataset:
Isotopic geochemistry (Nd, Sr) and trace element geochemistry Of the basalts of the French Massif Central: Constraints And arguments for metasomatismmantle.

Les roches basiques ( basaltes, kimberlites, komatiites, etc ) proviennent par fusion partielle du manteau terrestre. L'étude géochimique de ces roches peut donc nous aider à comprendre la composition chimique et la structure de leurs sources mantelliques. Malgré les variations de composition chimique, les basaltes terrestres modernes peuvent être grossièrement classés en deux familles. La première, la famille des tholéiites, regroupe les basaltes de rides et de planchers océaniques (les différents types de ''MORB", Schilling, 1975) et certains basaltes continentaux (les "continentalflood basalts"). Cette famille est beaucoup plus représentée que la seconde, qui regroupe sous le nom de famille des basaltes alcalins, les basaltes alcalins, les basanites, les néphélinites, les kimberlites, les lamprophyres, etc •.• Les roches de cette famille apparaissent généralement dans des sites tectoniques tout à fait différents de ceux des tholéiites, c'est-à-dire principalement sous forme de volcanisme intra-plaques océaniques (basaltes d'îles océaniques) ou continentales (basaltes alcalins continentaux) mais aussi au niveau des failles transformantes. Ces sites géotectoniques peuvent être liés à l'activité de "hot spots" (Morgan, 1972 ; Girot et al, 1978). Le problème de la genèse des basaltes est l'objet de nombreuses études depuis quelques années que ce soit par les pétrologues ou les géochimistes. Les études de pétrologie expérimentale qui portent sur les relations entre les phases au liquidus dans les basaltes et au solidus dans leurs sources potentielles ont permis l'élaboration de modèles pétrogénétiques. Ceux-ci expliquent la diversité de composition des basaltes par une variation de la pression, de la température et du taux de fusion. L'intervention d'unephase fluide, ainsi que sa nature, jouent également un rôle important dans la composition du liquide basaltique.
Capturing Knowledge of User Preferences: ontologies on recommender systems

Tools for filtering the World Wide Web exist, but they are hampered by the difficulty of capturing user preferences in such a dynamic environment. We explore the acquisition of user profiles by unobtrusive monitoring of browsing behaviour and application of supervised machine-learning techniques coupled with an ontological representation to extract user preferences. A multi-class approach to paper classification is used, allowing the paper topic taxonomy to be utilised during profile construction. The Quickstep recommender system is presented and two empirical studies evaluate it in a real work setting, measuring the effectiveness of using a hierarchical topic ontology compared with an extendable flat list.
Teaching Machine Learning to Computer Science Preservice Teachers

Machine learning is a fast-growing field with various applications in artificial intelligence and data science. Recently, a new machine learning program have been integrated into the Israeli high school computer science curriculum and thus we added a new machine learning module to the Methods of Teaching Computer Science (MTCS) course, which is part of the teachers' preparation program. This machine learning module provides us a unique opportunity to teach both pedagogy and content with the same subject matter. After teaching the basics of machine learning, we asked the students to find similarities between human learning theories and machine learning algorithms. Students identified several interesting parallels: (a) Supervised learning is similar to behavioral learning as the machine learns to connect training examples (stimuli) with labels (behavior). Also, the learning is based on minimizing error (punishment) function, (b) Reinforcement learning is similar to behavioral learning as learning is based on feedback from the environment, (c) Constructivism can be identified in the iterative convergence of the algorithm; the inner model improves each iteration based on the current knowledge, and (d) Social learning is reflected in clustering as each cluster affects the learning of the other clusters. In our talk, we present the idea that computational mental models may be used to reinforce pedagogical mental models and vice versa.
CLIMATE CHANGE ADAPTATION STRATEGIES AMONG SMALLHOLDER  FARMERS IN OBAFEMI-OWODE, NIGERIA: PERCEPTION, VULNERABILITY,  AND RESILIENCE

Climate change continues to threaten agricultural productivity, food security, and rural livelihoods across sub-Saharan Africa. In Nigeria, smallholder farmers who constitute the majority of agricultural producers are especially susceptible to climate-related risks. This study investigated the adaptation strategies employed by smallholder farmers in Obafemi-Owode, Nigeria, with specific objectives to assess their awareness of climate change, identify factors influencing their perceptions, examine its effects on farming activities, and explore the adaptation strategies adopted. A mixed-methods approach was utilized, combining quantitative data from a survey of 120 farmers with qualitative insights from focus group discussions and key informant interviews. The study revealed a high level of climate change awareness (78%) among farmers, with radio (70.8%) and peer communication (72.5%) as primary information sources. Key factors shaping farmers’ perceptions include access to irrigation infrastructure (59.2%), cultural beliefs and knowledge (50.8%), irregular rainfall patterns (48.3%), and rising temperatures (44.2%). Among the impacts, delayed planting (40.8%) emerged as a significant effect of climate variability. In terms of adaptation, the most commonly reported strategy was redirecting water flow into farmlands (47.5%). The study underscores the need to enhance farmer capacity through targeted awareness campaigns, practical training, and improved access to extension services. It recommends that adaptation strategies be context-specific, accessible, and grounded in local realities to effectively build resilience among vulnerable farming communities.
Error Correction in Ensemble Registers for Quantum Repeaters and Quantum Computers

We propose to use a collective excitation blockade mechanism to identify errors that occur due to disturbances of single atoms in ensemble quantum registers where qubits are stored in the collective population of different internal atomic states. A simple error correction procedure and a simple decoherence-free encoding of ensemble qubits in the hyperfine states of alkali atoms are presented.
Preclusion of switch behavior in reaction networks with mass-action kinetics

We provide a Jacobian criterion that applies to arbitrary chemical reaction networks taken with mass-action kinetics to preclude the existence of multiple positive steady states within any stoichiometric class for any choice of rate constants. We are concerned with the characterization of injective networks, that is, networks for which the species formation rate function is injective in the interior of the positive orthant within each stoichiometric class. We show that a network is injective if and only if the determinant of the Jacobian of a certain function does not vanish. The function consists of components of the species formation rate function and a maximal set of independent conservation laws. The determinant of the function is a polynomial in the species concentrations and the rate constants (linear in the latter) and its coefficients are fully determined. The criterion also precludes the existence of degenerate steady states. Further, we relate injectivity of a chemical reaction network to that of the chemical reaction network obtained by adding outflow, or degradation, reactions for all species.
Autonomous Databases: Leveraging Machine Learning and Neural Networks for Predictive Query Optimization, Self-Tuning, and Index Optimization in Multi-RDBMS Systems

Autonomous databases are the new fad in modern database systems. The database systems are managed by machine learning and neural networks for query prediction, self-tuning, and self-indexing. These systems decrease intervention in multi-relational database management systems (multi-RDBMS). This paper analyses the relevance of ML and NN in optimizing the queries and automating the working of databases. Either simulation results of the tested benchmark queries or real-time use cases show the extent of the query processing speed increase and its accuracy. However, problems like implementing these technologies into current structures and dealing with high-velocity data persist. The proposed solutions are using graph neural networks to solve scalability problems. In conclusion, this research enshrines the prospects for AI autonomous databases to improve performance in multi-RDBMS architecture.
Genetic Testing, Insurance and Employment in the UK: Is the Regulatory Regime Fit for Purpose?

This chapter provides an overview of the current legal and policy landscape governing genetic testing in the context of insurance and employment in the UK. It focuses on the characteristic features of the UK regulatory regime, namely the decision to avoid including “genetic characteristics” within the general anti-discrimination legislation, and instead to regulate the use of genetic test results by insurers and employers through soft law guidance documents. Other relevant guidance documents and statutes are also considered. The overall adequacy of the UK regime, and the degree to which it satisfactorily reconciles the divergent interests of the various stakeholders, are examined critically.
Lie symmetry analysis and similarity solutions for the Camassa-Choi equations

The method of Lie symmetry analysis of differential equations is applied to determine exact solutions for the Camassa-Choi equation and its generalization. We prove that the Camassa-Choi equation is invariant under an infinite-dimensional Lie algebra, with an essential five-dimensional Lie algebra. The application of the Lie point symmetries leads to the construction of exact similarity solutions.
The Infinity of Science: A Comprehensive Defense of the Inexhaustibility of Scientic Discovery and the Unbounded Nature of Human Knowledge

This paper presents a comprehensive philosophical, empirical, and historical defense of the thesis that science is infinite: it possesses a temporal beginning but no epistemic terminus. We argue that scientific inquiry—including mathematics and all empirical sciences—while rooted in historical origins, exhibits no intrinsic limit to discovery, understanding, or refinement.  The central argument is structured around four fundamental pillars: (1) the distinction between temporal initiation and epistemic boundedness; (2) the historical trajectory of scientific development, which consistently demonstrates perpetual expansion rather than convergence toward closure; (3) the self-referential and recursive nature of knowledge generation, wherein each solution gives rise to new questions; and (4) the logical incoherence inherent in claims of a final or complete state of knowledge.  We contend that asserting the existence of an endpoint to science constitutes a profound epistemological error—comparable in magnitude to the historical assertion that the Earth is flat. In both cases, such claims reflect the limitations of contemporary perspective rather than genuine boundaries of the domain itself.  Through rigorous analysis of the history of science, the structural properties of mathematical and empirical inquiry, and the cognitive conditions underlying human understanding, we demonstrate that infinity is not merely a speculative abstraction but a necessary consequence of how knowledge itself operates. This, ultimately, is the beauty of the evolution of human knowledge: it has no end.
Climate activism as a form of science communication and public engagement

The current state and future projections of the climate and environmental crises call for science to be able to have a deep impact on society, and to have it quickly. Here we discuss how scientists engaging in climate activism can contribute to educating the general public and press for urgent action, as well as under which conditions such scientific activism can be most effective.Traditionally, science has mostly interacted with society by making scientific results public, without interfering in how politicians, business and the general public would make use of them. Similarly, the role of science educators has been often confined to spreading knowledge to students and broader audiences, independently of how this knowledge affects society. However, such communication and education efforts appear insufficient considering unambitious policies with regards to the current climate and ecological crises. As a result, many scientists, both within and outside academia, have been looking for other ways to communicate the urgency of the climate crisis. Notably, communication efforts have increasingly extended to public support of environmental action movements and participation in protests and civil disobedience actions.In this work, we discuss how activism can be complementary to classical approaches of science communication and public education on the urgency of the climate and environmental crises. We will highlight recent examples of civil disobedience by scientists with a focus on the Netherlands. We also present the reaction from stakeholders such as politicians and representatives of companies, as well as the reception of such actions by the scientific community. We discuss the place of activism in the broader scientific community, where our viewpoint is that scientific activism can only become an efficient way of science communication and public engagement if (i) it is accepted and respected within the scientific community, and (ii) it adheres to rules allowing such communication to maintain or increase scientific reputation in society. We also stress the supportive role of universities and research institutes in enabling the engagement with activism, especially for early career scientists. Scientific institutions should emphasize that climate activism and advocacy is welcome among both researchers and educators, that their freedom of speech is protected, and that such activities are recognized as valuable.
The Deep Arbitrary Polynomial Chaos Neural Network or how Deep Artificial Neural Networks could benefit from Data-Driven Homogeneous Chaos Theory

Artificial Intelligence and Machine learning have been widely used in various fields of mathematical computing, physical modeling, computational science, communication science, and stochastic analysis. Approaches based on Deep Artificial Neural Networks (DANN) are very popular in our days. Depending on the learning task, the exact form of DANNs is determined via their multi-layer architecture, activation functions and the so-called loss function. However, for a majority of deep learning approaches based on DANNs, the kernel structure of neural signal processing remains the same, where the node response is encoded as a linear superposition of neural activity, while the non-linearity is triggered by the activation functions. In the current paper, we suggest to analyze the neural signal processing in DANNs from the point of view of homogeneous chaos theory as known from polynomial chaos expansion (PCE). From the PCE perspective, the (linear) response on each node of a DANN could be seen as a $1^{st}$ degree multi-variate polynomial of single neurons from the previous layer, i.e. linear weighted sum of monomials. From this point of view, the conventional DANN structure relies implicitly (but erroneously) on a Gaussian distribution of neural signals. Additionally, this view revels that by design DANNs do not necessarily fulfill any orthogonality or orthonormality condition for a majority of data-driven applications. Therefore, the prevailing handling of neural signals in DANNs could lead to redundant representation as any neural signal could contain some partial information from other neural signals. To tackle that challenge, we suggest to employ the data-driven generalization of PCE theory known as arbitrary polynomial chaos (aPC) to construct a corresponding multi-variate orthonormal representations on each node of a DANN to obtain Deep arbitrary polynomial chaos neural networks.
Hacia una moneda global: blockchain, stablecoins y central‐bank digital currencies

Grado en Administración y Dirección de Empresas
THE BUSINESS ETHICS - SUSTAINABILITY - CORPORATE SOCIAL RESPONSIBILITY TRIPTYCH

This paper explored the concepts of business ethics, sustainability and corporate social responsibility from a perspective where the development of the organizations should be seen as significant in the context of a general paradigm of sustainable development. The main purpose of the paper is that of identifying the connection between the business ethics, sustainability and the corporate social responsibility in the context of the exigencies of the organizations’ sustainable development. The importance of sustainability at an organization level is given by the desire to be competitive in an as much as large horizon, to survive in a more and more fierce market when also considering that sustainable competitiveness takes place while respecting the business ethics, the environment and the society as a whole.
Neural Networks and Schramm-Loewner Evolutions

In this manuscript, we explore the application of neural networks to predict the natural parameter $κ\geq 0$ of Schramm-Loewner Evolution (SLE$_κ$) theory. SLE$_κ$ is a family of random fractal curves that has significant implications in Statistical Mechanics and Conformal Field Theory. This parameter $κ\geq 0$ plays an important role in the theory as there are models of Planar Statistical Physics that are proven to have SLE as scaling limits as well as others that are conjectured to have this limit for various choices of the parameter $κ\geq 0$. In addition, there are three different statistical behaviors of the SLE curves as the parameter $κ$ changes in $[0, \infty).$ Leveraging the powerful pattern recognition capabilities of neural networks, this study aims to develop a predictive model that can estimate the $κ$ parameter with good accuracy.
Substitution and electrochemistry in layered oxide cathode materials for sodium-ion batteries

Substitution is a vital strategy for developing high-performance sodium layered oxides (SLOs), which demonstrates great potential for making sodium-ion batteries a viable alternative to lithium-ion batteries. Numerous studies have been conducted on substituted SLOs; however, each substitute exhibits varied effects on the structure and electrochemical performance of the SLOs, and no clear design principles have been established. Clarifying the relationship among substitution, structure and performance is therefore important to enable a rational design strategy for high-performance SLOs. In this Review, the up-to-date substitution guidelines and the current understanding of how substitution affects the structure and electrochemistry in SLOs are discussed, and the site preference and characteristic redox features of different types of substitutes are outlined. The inherent challenges and opportunities for the innovation of better-performing SLOs are summarized, paving the way for accelerating the commercialization of SLO-based sodium-ion batteries and the realization of their applications ranging from electric vehicles to grid energy storage systems.
Importance of individual residues in hydrophobic patch PLVIVGL (1481-1487) in FV-Short for synergistic TFPIα cofactor activity with protein S, an alanine-scanning study: AlphaFold-mediated prediction of FV-Short/TFPIα/protein S trimolecular complex structure

In the splice variant factor (F)V-Short, 702 residues are deleted from the B domain, resulting in exposure of an acid region (AR2; 1493-1537) that binds TFPIα. FV-Short and protein S serve as synergistic TFPIα cofactors in inhibition of FXa. In the preAR2 region, a hydrophobic patch PLVIVGL (1481-1487) is crucial for synergistic TFPIα-cofactor activity and assembly of FV-Short, TFPIα, and protein S.To elucidate the importance of individual residues in the PLVIVGL patch for synergism between FV-Short and protein S as TFPIα cofactors.An alanine scanning of the hydrophobic patch was performed in which 7 FV-Short variants were created. The synergistic TFPIα-cofactor activity was analyzed by FXa inhibition and a microtiter-based assay tested binding between the proteins. AlphaFold 3 was used to predict protein-protein interactions between FV-Short, protein S, and TFPIα.Five of the 7 variants (V1483A, I1484A, V1485A, G1486A, and L1487A) demonstrated decreased synergistic TFPIα cofactor activity; in particular, G1486A and L1487A were severely affected. Neither wild-type FV-Short nor any of the mutants bound protein S in the absence of TFPIα. In the presence of TFPIα, wild-type FV-Short, P1481A, L1482A, and V1485A bound protein S, whereas V1483A, I1484A, G1486A, and L1487A did not. AlphaFold predicted an interaction between the hydrophobic patch in FV-Short and a hydrophobic patch in protein S involving residues 268-276 and 422-426.Individual residues (V1483, I1484, G1486, and L1487) in the hydrophobic patch are demonstrated to be important for the synergistic TFPIα-cofactor activity and for the assembly of a trimolecular FXa-inhibitory complex.
Cherenkov light Extrapolation at Ultra High Energy Cosmic Rays in Extensive Air Showers

6 Pages, 3 figures
Exploring AI identity: The media framing of communicative artificial intelligence in Singapore’s news sites

Implementing artificial intelligence also requires examinations of public attitudes and perceptions. One approach is by examining media framing of artificial intelligence, including news coverage, which is a reflection of societal perceptions and a key influence over people’s understanding. As such, this study examines the framing of communicative artificial intelligence in Singapore, looking at how the news media frame communicative artificial intelligence and characterize it as a social actor. Through a manual content analysis of 336 news articles from three major news websites in Singapore, this study found that the news media in Singapore tend to focus on the benefits and advances of communicative artificial intelligence and portray communicative artificial intelligence as a tool rather than social actor. However, when comparing news coverage of communicative artificial intelligence after the advent of ChatGPT, the news framed communicative artificial intelligence more in terms of risks, regulations, responsibilities, and conflict.