ethicalabs/Research-Intent-Collab huggingface dataset:
The effects of salinity and inclination on the morphology of melting ice

The salinity of water and the slope of ice significantly influence the melt rate and surface morphology of ice, both highly relevant in the context of glacier and iceberg melting in oceanic environments. In this study, we conducted experiments on vertical and sloped ice blocks melting in quiescent saline water. Through the use of fringe projection profilometry, we measured the morphology of the ice's front face. In particular, we combine the spatio-temporal phase shifting and orthogonal sampling moire methods. The far field salinity in the experiments ranged from 0 g/kg to 35 g/kg, and angles were between -18° and 50°. The ice block sizes were 32 cm $\times$ 23 cm $\times$ 12 cm high, wide, and long respectively, leading to Ra = $\mathcal{O}(10^7)$. We identified and classify five surface morphologies and regimes arising from the flow regimes imposed by salinity and inclination, namely scalloped, channelized, top-melting, bottom-melting, and incurved. The channelized morphology consists of vertical channels carved along the ice surface, whose development originates from a Rayleigh--Bénard type instability, and which are enhanced by bubbles released from the melting ice and rising along the interface. The scalloped regime is characterize by a rough dimpled pattern commonly referred to as scallops. We observe that increasing the salinity leads to scallops that are smaller, shallower, and more uniform in size. Additionally, a salinity dependence of the melt rate is found, showing a non-monotonic behavior, while the inclination angle shows little influence on the overall melt rate.
Quantum-classical hybrid algorithm using an error-mitigating  <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"><mml:mi>N</mml:mi></mml:math> -representability condition to compute the Mott metal-insulator transition

Quantum algorithms for molecular electronic structure have been developed with lower computational scaling than their classical counterparts, but emerging quantum hardware is far from being capable of the coherence,connectivity and gate errors required for their experimental realization. Here we propose a class of quantum-classical hybrid algorithms that compute the energy from a two-electron reduced density matrix (2-RDM). The 2-RDM is constrained by $N$-representability conditions, conditions for representing an $N$-electron wavefunction, that mitigates noise from the quantum circuit. We compute the strongly correlated dissociation of doublet H$_{3}$ into three hydrogen atoms. The hybrid quantum-classical computer matches the energies from full configuration interaction to 0.1 kcal/mol, one-tenth of "chemical accuracy," even in the strongly correlated limit of dissociation. Furthermore, the spatial locality of the computed one-electron RDM reveals that the quantum computer accurately predicts the Mott metal-insulator transition.
The Role of Civil Society in Protecting Judicial Independence in Times of Rule of Law Backsliding in Poland

The article discusses approaches to defending the independence of the judiciary – one of the main institutional requirements of the rule of law – adopted by civil society organisations (CSOs) in Poland since 2015. Beginning by describing the rule of law backsliding in context, this article shows how civil society organisations reacted to the orchestrated threats to judicial independence in Poland. The article highlights the negative consequences of rule of law backsliding for the civic space, proving that the role of CSOs was not only to protect the rule of law (judicial independence, in particular), but also to fight the shrinking civic space. This article surveys the concept of the ‘rule of law from below’ by critically analysing the actions of CSOs in response to attempts to undermine judicial independence, providing a part of a broader ‘rule of law backsliding’ picture. The article concludes with a detailed analysis of two case studies – reactions from CSOs to Poland’s so-called ‘reform of the judiciary’.
The cosmic microwave background and mass power spectrum profiles for a novel and efficient model of dark energy

&#x0D; &#x0D; &#x0D; In a previous work [1] it was shown that by considering the quantum nature of the gravitational field mediator, it is possible to introduce the momentum energy of the graviton into the Einstein equations as an effective cosmological constant. The Compton Mass Dark Energy (CMaDE) model proposes that this momentum can be interpreted as dark energy, with a Compton wavelength given by the size of the observable universe RH, implying that the dark energy varies depending on this size. The main result of this previous work is the existence of an effective cosmological constant Λ = 2π2/λ2 that varies very slowly, being λ = (c/H0)RH the graviton Compton wavelength. In the present work we use that the dark energy density rate is given by ΩΛ = 2π2/3/RH2 , it only has the curvature Ωk as a free constant and depends exclusively on the radiation rate Ωr. Using Ωr = 9.54×10−5, the theoretical prediction for a flat universe of the dark energy rate is ΩΛ = 0.6922. We perform a general study for a non-flat universe, using the Planck data and a modified version of the CLASS code we find an excellent concordance with the Cosmic Microwave Background and Mass Power Spectrum profiles, provided that the Hubble parameter today is H0 = 72.6 km/s/Mpc for an universe with curvature Ωk = −0.003. We conclude that the CMaDE model provides a natural explanation for the accelerated expansion and the coincidence problem of the universe.&#x0D; &#x0D; &#x0D;
Combining Electron-Phonon and Dynamical Mean-Field Theory Calculations of Correlated Materials: Transport in the Correlated Metal Sr$_2$RuO$_4$

Electron-electron ($e$-$e$) and electron-phonon ($e$-ph) interactions are challenging to describe in correlated materials, where their joint effects govern unconventional transport, phase transitions, and superconductivity. Here we combine first-principles $e$-ph calculations with dynamical mean field theory (DMFT) as a step toward a unified description of $e$-$e$ and $e$-ph interactions in correlated materials. We compute the $e$-ph self-energy using the DMFT electron Green's function, and combine it with the $e$-$e$ self-energy from DMFT to obtain a Green's function including both interactions. This approach captures the renormalization of quasiparticle dispersion and spectral weight on equal footing. Using our method, we study the $e$-ph and $e$-$e$ contributions to the resistivity and spectral functions in the correlated metal Sr$_2$RuO$_4$. In this material, our results show that $e$-$e$ interactions dominate transport and spectral broadening in the temperature range we study (50$-$310~K), while $e$-ph interactions are relatively weak and account for only $\sim$10\% of the experimental resistivity. We also compute effective scattering rates, and find that the $e$-$e$ interactions result in scattering several times greater than the Planckian value $k_BT$, whereas $e$-ph interactions are associated with scattering rates lower than $k_BT$. Our work demonstrates a first-principles approach to combine electron dynamical correlations from DMFT with $e$-ph interactions in a consistent way, advancing quantitative studies of correlated materials.
The analysis of topological structure in data using persistent homology: applications to lexical word association networks

Final report for undergraduate research project
Radio Observations of Supernova Remnants and the Surrounding Interstellar Medium

Supernova Remnants (SNRs) play essential roles in the dynamics of galaxies by injecting large amounts of energy which can accelerate particles generating cosmic radiation, create large, hot, low density bubbles, and maintain turbulent cloud motions. SNRs shock waves can also compress nearby dense clouds, inducing fragmentation and perhaps collapse into protostars, thus joining the extremes in the life-cycle of stars. The present review illustrates how high quality low frequencies radio observations of galactic SNRs and the surrounding interstellar medium (ISM), can help in the understanding of the many aspects of the nature of the remnants, as well as the consequences of their interaction with the ambient gas.
Documenting and disseminating agricultural indigenous knowledge for sustainable food security in Uganda

There is a wealth of agricultural indigenous knowledge (AIK) in Uganda, which is useful in livestock keeping, crop management and food processing and storage as well as soil and water management. Unfortunately, this AIK is becoming less visible and irrelevant in some communities because of the adoption of modern methods of farming. In fact, a lot of AIK has remained largely undocumented which threatens its sustained utilisation. One of the bottlenecks of the effective utilisation of AIK is access to relevant and usable indigenous knowledge for the diverse stakeholders in the agricultural sector including farmers. It seems farmers in Uganda are adopting modern methods of agriculture at the expense of the AIK because of the less perceived benefits that AIK promises because crops planted using AIK have often faced pests and diseases and not yielded much. The problem is perhaps compounded because of increasing population growth, land fragmentation as well as migration to urban areas. This phenomenon raises the question of how AIK can be conserved. This paper is based on a study that investigated how Agricultural Indigenous Knowledge (AIK) is documented and disseminated in addition to identifying the challenges faced in its management for sustainable food security in Uganda’s district of Soroti. Data in this study was collected through interviews, focus group discussions, document reviews and participant observation. The study findings revealed that despite the advent of modern farming methods, many small-scale farmers in the Soroti district continue to embrace indigenous knowledge in farming such as in managing soil fertility, controlling pests and diseases, controlling weeds, soil preparation, planting materials, harvesting and storage of indigenous root crops and animals. The study concludes that indigenous knowledge is still widely used but most of it is not documented nor fully understood by some members of the community; and that the Iteso and Kumam cultures have some restrictions on who acquires the knowledge. Thus the study recommends that AIKs be recorded for posterity, AIK should be researched upon further, be thoroughly documented and made freely available to anyone who needs it. On the whole, AIK in Soroti district requires attitudinal, behavioural, and methodological changes to give it a scientific touch. Moreover, small-scale farmers should be involved in agricultural extension services rather than leaving the work to formally trained officers who may have little attachment to specific cultural practices in the areas they operate.Keywords: Documenting, Disseminating, Agricultural Indigenous Knowledge, Sustainable Food security, Uganda
BenchFaaS: Benchmarking Serverless Functions in an Edge Computing Network Testbed

The serverless computing model has evolved as one of the key solutions in the cloud for fast autoscaling and capacity planning. In edge computing environments, however, the serverless model is challenged by the system heterogeneity and performance variability. In this paper, we introduce BenchFaaS, an open-source edge computing network testbed which automates the deployment and benchmarking of serverless functions. Our edge computing network considers a cluster of virtual machines and Raspberry Pis, and is designed to benchmark serverless functions under different hardware and network conditions. We measure and evaluate: (i) overhead incurred by testbed, (ii) performance of compute intensive tasks, (iii) impact of application payload size, (iv) scalability, and (v) performance of chained serverless functions. We share the lessons learnt in engineering and implementing the testbed. We present the measurement results and analyze the impact of networked infrastructure on serverless performance. The measurements indicate that a properly dimensioned edge computing network can effectively serve as a serverless infrastructure.
Multiple cosmic collisions and the microwave background power spectrum

Collisions between cosmic bubbles of different vacua are a generic feature of false vacuum eternal inflation scenarios. While previous studies have focused on the consequences of a single collision event in an observer's past, we begin here an investigation of the more general scenario allowing for many mild collisions intersecting our past light cone (and one another). We discuss the general features of multiple collision scenarios and consider their impact on the cosmic microwave background (CMB) temperature power spectrum, treating the collisions perturbatively. In a large class of models, one can approximate a multiple collision scenario as a superposition of individual collision events governed by nearly isotropic and scale-invariant distributions, most appearing to take up less than half of the sky. In this case, the shape of the expected CMB temperature spectrum maintains statistical isotropy and typically features a dramatic increase in power in the low multipoles relative to that of the best-fit $Λ$CDM model, which is in tension particularly with the observed quadrupole. We argue that this predicted spectrum is largely model-independent and can be used to outline features of the underlying statistical distributions of colliding bubbles consistent with CMB temperature measurements.
Minimal cellular automaton model with heterogeneous cell sizes predicts epithelial colony growth

Regulation of cell proliferation is a crucial aspect of tissue development and homeostasis and plays a major role in morphogenesis, wound healing, and tumor invasion. A phenomenon of such regulation is contact inhibition, which describes the dramatic slowing of proliferation, cell migration and individual cell growth when multiple cells are in contact with each other. While many physiological, molecular and genetic factors are known, the mechanism of contact inhibition is still not fully understood. In particular, the relevance of cellular signaling due to interfacial contact for contact inhibition is still debated. Cellular automata (CA) have been employed in the past as numerically efficient mathematical models to study the dynamics of cell ensembles, but they are not suitable to explore the origins of contact inhibition as such agent-based models assume fixed cell sizes. We develop a minimal, data-driven model to simulate the dynamics of planar cell cultures by extending a probabilistic CA to incorporate size changes of individual cells during growth and cell division. We successfully apply this model to previous in-vitro experiments on contact inhibition in epithelial tissue: After a systematic calibration of the model parameters to measurements of single-cell dynamics, our CA model quantitatively reproduces independent measurements of emergent, culture-wide features, like colony size, cell density and collective cell migration. In particular, the dynamics of the CA model also exhibit the transition from a low-density confluent regime to a stationary postconfluent regime with a rapid decrease in cell size and motion. This implies that the volume exclusion principle, a mechanical constraint which is the only inter-cellular interaction incorporated in the model, paired with a size-dependent proliferation rate is sufficient to generate the observed contact inhibition.
The COBRA Project: a community-based approach to public engagement in science.

Scientific research and communications is dominated by a command-and-control approach which lacks the ability to engage the public in managing and adapting to surprises and rapid change. These initiatives emerge from higher-scale structures e.g. national institutions, which are not always compatible with the realities and perspectives of communities. The failure of top-down, 'deficit model' approaches to science communication have encouraged communities to support an alternative, bottom-up, culturally and ecologically sensitive approach to communication for addressing complex socio-ecological problems. This paper explores the development and promotion of a 'community-expertise' model of public engagement through the COBRA Project, a participatory project involving indigenous communities of South America. The project’s aim is to significantly scale up the sharing of indigenous expertise and knowledge through photography, video and online platforms. We will present the results of how this expertise is identified, recorded and shared with national and international scientists and policymakers. We report on the conflict between the principles behind participatory community engagement and the demands of policymakers for scientific, empirically validated data, which clearly require an imposition on the type and process of data collection, analysis and modes of communication. We argue that participatory methods that engage local indigenous communities are empowering for these involved, but it is in the end up to the scientific and policy-making establishment to accept the validity of these ‘non-standard’ forms of science communication.
Sub-Nanometer Channels Embedded in Two-Dimensional Materials

Two-dimensional (2D) materials are among the most promising candidates for next-generation electronics due to their atomic thinness, allowing for flexible transparent electronics and ultimate length scaling. Thus far, atomically-thin p-n junctions, metal-semiconductor contacts, and metal-insulator barriers have been demonstrated. While 2D materials achieve the thinnest possible devices, precise nanoscale control over the lateral dimensions is also necessary. Here, we report the direct synthesis of sub-nanometer-wide 1D MoS2 channels embedded within WSe2 monolayers, using a dislocation-catalyzed approach. The 1D channels have edges free of misfit dislocations and dangling bonds, forming a coherent interface with the embedding 2D matrix. Periodic dislocation arrays produce 2D superlattices of coherent MoS2 1D channels in WSe2. Using molecular dynamics simulations, we have identified other combinations of 2D materials where 1D channels can also be formed. The electronic band structure of these 1D channels offer the promise of carrier confinement in a direct-gap material and charge separation needed to access the ultimate length scales necessary for future electronic applications.
Genetic information and testing in insurance and employment: technical, social and ethical issues

The present paper examines the professional and scientific views on the social, ethical and legal issues that impact on genetic information and testing in insurance and employment in Europe. For this purpose, many aspects have been considered, such as the concerns of medical geneticists, of the insurers and employers, of the public, as well as the regulatory frameworks and unresolved issues. The method used was primarily the review of the technical, social, economical and ethical aspects of advances in genetics and the concerns of parties who are involved, that is, the insurers, the employers and the public. The existing guidelines and legislation on this topic were also reported. Then, the method was to examine the issues debated by these parties in Europe, as well as by 47 experts from 14 European countries invited to an international workshop organized by the European Society of Human Genetics Public and Professional Policy Committee in Manchester, UK, 25-27 February 2000. The result of this was that the most important issues raised by genetic information and testing in insurance and employment in Europe include a need for clear definitions of terms used in genetics, declaring the grounds on which genetic information is or is not used, and promoting confidence between the public and the insurance industry. There is currently very little use of genetic information in relation to employment, but the situation should be kept under review.
Explainable AI for Railway Fault Diagnosis: A Comparative Study of Deep Learning Models with Grad-CAM Interpretability

Ensuring the safety of railway networks requires dependable, scalable fault detection systems. Conventional manual inspection methods are labor-intensive and error prone. Such visual track surveys by railway workers are inadequate for modern infrastructure. This article presents a deep learning framework for multi-class railway fault classification. The framework addresses these challenges by using transfer learning to adapt pre-trained models to specialized railway tasks. We rigorously assess several convolutional neural network (CNN) architectures including EfficientNet-B0, ResNet50, MobileNetV3, and SeresNext26t_32x4d optimized on a carefully selected dataset of authentic railway track photos. The framework accurately classifies images into three categories: non-defective, fastener-defective, and rail-defective. The SeresNext26t\_32x4d model achieves the highest accuracy of 96.07%.The main contribution of this work is the use of Explainable Artificial Intelligence (XAI) through Gradient-weighted Class Activation Mapping (Grad-CAM), a technique that shows which image regions most influence model predictions. This improves visual interpretability by highlighting areas most important to the model&amp;apos;s decisions, ensuring those decisions rely on meaningful, physically relevant features, not misleading correlations. Pairing accuracy with interpretability offers a strong basis for real-time, automated railway inspection systems. This method can improve safety and maintenance efficiency in railway systems, especially where infrastructure develops rapidly.
3D Face Reconstruction with Deep Learning: Architectures, Datasets, and Benchmark Analysis

Three-Dimensional (3D) face reconstruction from monocular Red-Green-Blue (RGB) imagery remains a fundamental yet ill-posed challenge in computer vision, with applications in biometrics, augmented reality/virtual reality (AR/VR), and intelligent visual sensing systems. While deep learning has significantly improved reconstruction fidelity and realism, existing surveys primarily focus on network architectures in isolation, often overlooking how sensing conditions, data acquisition protocols, and geometric calibration influence reconstruction reliability and evaluation outcomes. This paper presents a sensor-aware, end-to-end review of deep learning-based 3D face reconstruction and introduces a unified modular framework that connects sensing hardware, data acquisition, calibration, representation learning, and geometric refinement within a coherent pipeline. The reconstruction process is organized into four stages: sensor-driven acquisition and calibration, landmark estimation and feature extraction, 3D representation and parameter regression, and iterative refinement via differentiable rendering. Within this framework, we examine how sensor characteristics, calibration accuracy, representation models, and supervision strategies affect reconstruction accuracy, perceptual quality, robustness, and computational efficiency. We further synthesize the reported results across widely used benchmarks using both geometric and perceptual metrics, highlighting trade-offs between reconstruction fidelity and deployment constraints. By integrating sensing-aware analysis with architectural evaluation, this survey provides practical insights for developing scalable and reliable 3D face reconstruction systems under real-world conditions.