ethicalabs/Research-Intent-Collab huggingface dataset:
Utilizing AI to unveil the nonlinear interplay of convection, drift, and diffusion on galactic cosmic ray modulation in the inner heliosphere

AbstractGalactic Cosmic Rays (GCRs) are charged particles, originating from galactic and/or extra-galactic Supernova Remnants (SNR), that continuously permeate the Heliosphere. The GCRs are modulated in the heliosphere by convection by solar wind (SW), drift via gradients and curvatures in the Heliospheric Magnetic Field (HMF), diffusion from fluctuations in the HMF, and adiabatic cooling in the expanding SW. An improved understanding of their modulation is imperative as studies on the variations in solar activity levels and solar eruptions in the past rely heavily on the relationship between their modulation and formation of the secondary particles in the Earth’s atmosphere. Here, for the first time, we utilize an AI method, Light Gradient Boosting Machines (LightGBM), to investigate the nonlinear interplay among the modulation processes in different timescales. Our study indicates that the nonlinear interplay among the mechanisms responsible for the GCR modulation in the inner heliosphere are not limited to the scenario of “drift-dominated solar minimum” versus “diffusion-dominated solar maximum”, instead they have dynamic behavior displaying variations in time and in timescales. This study also demonstrates the value of using AI methods to investigate non-linear physical processes in Space Physics in the era of big data.
New instruments of Control over public Corruption : the Italian Reform to restore Transparency and Accountability

The paper analyses recent Italian legislation on the matter of fighting corruption (Law 190/2012) and on administrative transparency (Leg. Dec. 33/2013), describing the new principles introduced, implications on the regulatory system and administrative organisation previously in force, and a number of the critical areas. Specifically, the paper points out how the fight against corruption and illegality must be achieved not through the "culture of performance", which occasionally emerges in the new provisions, but rather through the development of various elements (also present in the new legislation) such as the training and education of public workers and civil society, and differentiation in the measures taken, depending on the administrations involved.
Age-Related Change of the Activity of Autonomic Nervous System Measured by Wearable Heart Rate Sensor for Long Period of Time

We analyzed long period of time (more than 10 h) autonomic nervous system data of 128 subjects (78 males and 50 females in 20’s, 30’s, 40’s and 50’s respectively) by using small wearable heart rate sensors. As a result, we found that there was a significant negative correlation (p value < 0.05) between LnTP (Total-Power as an indicator of comprehensive autonomic nervous system activity) and age for both sexes (genders). Moreover, the negative correlation value for male was higher than for female. The noticeable difference from the preceding study is that our research was based on data measured by many advanced wearable heart rate sensors which enabled to accumulate long period of time data in our daily life for many subjects and that we found the similar correlation between TP and aging comparing to the preceding study.
Mixed incentives: Adopting ICT innovations for transparency, accountability, and anti-corruption

Governments adopt anti-corruption-related ICT innovations for many reasons. Different motivations for adopting these technologies shape the way they are put into practice and the anti-corruption impacts they may have. ICT for anti-corruption should not be understood as a single approach, since different technologies, and different modes of technology adoption, create different dynamics. Whether or not a particular ICT can bring anti-corruption benefits will depend upon the design of a specific implementation, the incentives driving its adoption, and the wider context in which it is applied. This issue paper raises critical questions for policy makers, funders, and advocates to consider when seeking positive anti-corruption impacts from ICTs.
Inferring birth versus death dynamics for ecological interactions in stochastic heterogeneous populations

In this paper, we study the significance of ecological interactions and separation of birth and death dynamics in stochastic heterogeneous populations via general birth-death processes. Interactions can manifest through the birth dynamics, the death dynamics, or some combination of the two. The underlying microscopic mechanisms are important but often implicit in population-level data. We propose an inference method for disambiguating the types of interaction and the birth and death processes from population size time series data of a stochastic $n$-type heterogeneous population. The interspecies interactions considered can be competitive, antagonistic, or mutualistic. We show that different pairs of birth and death rates with the same net growth rate result in different time series statistics. Then, the inference method is validated in the example of a birth-death process inspired by the two-type Lotka-Volterra interaction dynamics. Utilizing stochastic fluctuations enables us to estimate additional parameters in this stochastic Lotka-Volterra model, which are not identifiable in a deterministic model.
Advancement of Emerging Tools in Synthetic Biology for the Designing and Characterization of Genetic Circuits

Bioengineering of synthetic metabolic pathways is a valuable tool for production of useful products and basic understanding of the biological complexity. Thus, we require high-throughput cloning and characterization tools and technologies. Standard cloning techniques have limitation for construct size and slow process. Here, we underline recent advancement and development of high-throughput technologies which can help to accelerate the synthetic biology research. It can be useful for the construction of large gene cassettes for production of drugs, bio-fuels, therapeutics and also rewired the natural systems; and created novel gene networks. In this chapter, we confer the different gene cloning methods for assembly of gene networks and their characterization using recent synthetic biology tools. It can be useful for accelerating the synthetic biology research for human welfare.
Bridging Large Language Models and Graph Structure Learning Models for Robust Representation Learning

Graph representation learning, involving both node features and graph structures, is crucial for real-world applications but often encounters pervasive noise. State-of-the-art methods typically address noise by focusing separately on node features with large language models (LLMs) and on graph structures with graph structure learning models (GSLMs). In this paper, we introduce LangGSL, a robust framework that integrates the complementary strengths of pre-trained language models and GSLMs to jointly enhance both node feature and graph structure learning. In LangGSL, we first leverage LLMs to filter noise in the raw data and extract valuable cleaned information as features, enhancing the synergy of downstream models. During the mutual learning phase in LangGSL, the core idea is to leverage the relatively small language model (LM) to process local attributes and generate reliable pseudo-labels and informative node embeddings, which are then integrated into the GSLM's prediction phase. This approach enriches the global context and enhances overall performance. Meanwhile, GSLM refines the evolving graph structure constructed from the LM's output, offering updated labels back to the LM as additional guidance, thus facilitating a more effective mutual learning process. The LM and GSLM work synergistically, complementing each other's strengths and offsetting weaknesses within a variational information-maximizing framework, resulting in enhanced node features and a more robust graph structure. Extensive experiments on diverse graph datasets of varying scales and across different task scenarios demonstrate the scalability and effectiveness of the proposed approach.
Adjusted competency assessment in technology-mediated higher education: Learning analytics evidence from environmental solutions competence in the Global South

This study examines adjusted competency assessment in technology-mediated higher education, using learning analytics evidence from environmental solutions competence in the Global South. The study addresses a methodological and educational challenge: how to assess complex competencies when students are evaluated under heterogeneous instructional and assessment conditions. A quantitative, non-experimental, retrospective, and explanatory design was applied to institutional academic records. The analysis used 17,818 academic records from 2,688 students and constructed an Adjusted Competency Score (ACS), a composite indicator that integrates assessment coverage and observed achievement. Descriptive analyses, nonparametric group comparisons, effect size estimation, and Spearman correlation tests were conducted. The results show that the original achievement variable had limited discriminatory capacity because of a pronounced ceiling effect. In contrast, the ACS provided a more sensitive measure of competency performance. Significant differences in ACS were found across educational modalities, with a moderate-to-large effect size, suggesting that instructional modality is meaningfully associated with adjusted competency performance. Academic workload, measured by the number of assigned activities, showed a weak but statistically significant negative association with ACS, indicating that a greater number of activities does not necessarily translate into stronger competency development. These findings contribute to competency-based assessment, learning analytics, and institutional decision-making in technology-mediated higher education. The study also highlights the need for more robust indicators to evaluate complex competencies in diverse educational contexts, particularly in institutions from the Global South.
Biodiversity conservation and land use public policy: a review of the implementation of the Green and Blue Infrastructure in France

Introduced in 2007 in France, the Green and Blue infrastructure policy, the "Trame verte et bleue" (TVB), aims to conserve biodiversity by preserving, managing and restoring the ecological network. This paper examines the implementation of TVB based on a review of scientific and technical literature relating to four main topics: the concepts of landscape ecology on which TVB is based, the coherence between TVB different scales of implementation, TVB governance and relationship with other environmental policies. The implementation of landscape ecology concepts in this policy has revisited these concepts and has highlighted the need for new tools and knowledge. The translation of the TVB is variable at different scales. Several difficulties are raised in the implementation of the TVB: (1) lack of operationality of a zonal approach to characterize corridors which are reticular and even shifting, (2) articulation of scales struggles to guarantee regional coherence, (3) modes of governance that barely involve non-technical stakeholders and civil society, and (4) incomplete data to identify continuities. The implementation of the TVB is taking place in an increasingly multifunctional perspective combining ecological, landscape and development issues. It is also increasingly being put into perspective with other environmental policies such as the mitigation hierarchy.
Driver-Centric Design: Empirical Strategies for Optimizing the Visualization of Driving Information on Vehicle Liquid Crystal Display Dashboards

With the continuous advancement of smart car and vehicle display technologies, liquid crystal display (LCD) dashboards have become the mainstream medium for displaying driving information. This makes the reading performance and user research of LCD vehicle dashboards critically important. In this study, we aim to evaluate the impact of the human-machine interface (HMI) design of vehicle LCD dashboards on driver readability and user experience. Twelve experts participated in a clustering experiment of vehicle dashboards, and 32 drivers participated in a simulated driving environment test of resident display information and temporary display information. The results for resident display information indicate that design type makes a significant difference in both reading performance and visual search efficiency. In addition, only the unconventional-shaped (L-shaped) dual-dial design achieves the &#x201C;desired&#x201D; rating in the user experience evaluations. Regarding temporary display information, findings indicate that icons positioned at the top of the dashboard interface are more readable than those at the bottom. High-prioritized or frequently used icons should be placed at the top of the screen. Alternatively, if icons must be positioned at the bottom, they should be no smaller than 8 mm in size. This research will help reduce the driver&#x2019;s visual workload and off-road sight time, reduce the risk of traffic accidents, and improve the driver&#x2019;s user experience.
India on Humanitarian Intervention and Responsibility to Protect: Shifting Nuances

India’s opposition to humanitarian intervention has been influenced by its colonial experience and its predisposition towards the principles of sovereignty and non-intervention. However, India did not adopt a strident opposition in the post-Cold War due to the changed power configuration. The article discusses how India adopted a cautious approach and yet used every opportunity to remind the international community the baleful effect of intervention in the internal affairs. After securing concession to a considerable extent on the ambitious Responsibility to Protect (R2P) and when most of the countries showed an inclination to accept humanitarian intervention in the form of ‘R2P’ at the UN summit in 2005, India grudgingly went along accepting it. India participated in the deliberation on the implementation of R2P and took its stand on various crises in which R2P was evoked. The experience of NATO’s Libya operation under R2P was regarded as substantiation of India’s apprehension of the misuse of the concept, and India reverted its position to the sceptical view of humanitarian intervention/R2P. By mere complaining about the mixing of peace enforcement with peacekeeping, when the United Nations deployed ‘intervention brigade’ for the protection of civilians, India lost the opportunity to take the initiative to propose a new mechanism to deal with the humanitarian crisis in atrocious internal conflicts.
The Reaction RuleML Classification of the Event / Action / State Processing and Reasoning Space

Reaction RuleML is a general, practical, compact and user-friendly XML-serialized language for the family of reaction rules. In this white paper we give a review of the history of event / action /state processing and reaction rule approaches and systems in different domains, define basic concepts and give a classification of the event, action, state processing and reasoning space as well as a discussion of relevant / related work
Diffusion Models at the Drug Discovery Frontier: A Review on Generating Small Molecules Versus Therapeutic Peptides

Diffusion models have emerged as a leading framework in generative modeling, poised to transform the traditionally slow and costly process of drug discovery. This review provides a systematic comparison of their application in designing two principal therapeutic modalities: small molecules and therapeutic peptides. We dissect how the unified framework of iterative denoising is adapted to the distinct molecular representations, chemical spaces, and design objectives of each modality. For small molecules, these models excel at structure-based design, generating novel, pocket-fitting ligands with desired physicochemical properties, yet face the critical hurdle of ensuring chemical synthesizability. Conversely, for therapeutic peptides, the focus shifts to generating functional sequences and designing de novo structures, where the primary challenges are achieving biological stability against proteolysis, ensuring proper folding, and minimizing immunogenicity. Despite these distinct challenges, both domains face shared hurdles: the scarcity of high-quality experimental data, the reliance on inaccurate scoring functions for validation, and the crucial need for experimental validation. We conclude that the full potential of diffusion models will be unlocked by bridging these modality-specific gaps and integrating them into automated, closed-loop Design-Build-Test-Learn (DBTL) platforms, thereby shifting the paradigm from mere chemical exploration to the on-demand engineering of novel therapeutics.
Broad Iron Lines in AGN and X-ray Binaries

Several AGN and black hole X-ray binaries show a clear very broad iron line which is strong evidence that the black holes are rapidly spinning. Detailed analysis of these objects shows that the emission line is not significantly affected by absorption and that the source variability is principally due to variation in amplitude of a power-law. Underlying this is a much less variable, relativistically-smeared, reflection-dominated, component which carries the imprint of strong gravity at a few gravitational radii. The strong gravitational light bending in these regions then explains the power-law variability as due to changes in height of the primary X-ray source above the disc. The reflection component, in particular its variability and the profile of the iron line, enables us to study the innermost regions around an accreting, spinning, black hole.
Relationship Between Social Media Use and Adolescent Mental Health - a Systematic Review

Social media has been inseparably linked to the daily lives of adolescents for several decades. In social and scientific discourse, we often hear that they are considered one of the significant factors responsible for the observed increase in mental health problems among young people in recent decades. Therefore, understanding the impact of these media on the mental well-being of young individuals has become a priority. For this purpose, a systematic review of research on the relationship between adolescents' use of social media and their mental health was conducted. The systematic review was carried out following the PRISMA standards. Its aim was to identify all studies exploring the association between adolescents' engagement in social media use and the state of their mental health, specifically targeting individuals aged 12-18 years. The EBSCOhost search engine was employed for this purpose, and databases such as Academic Search Ultimate, Education Source Ultimate, Applied Science &amp; Technology Source Ultimate, Medline, Eric, and APA PsycArticle were searched from May to June 2023, aiming to identify studies published between 2013 and 2023. A total of 25 articles were identified from 16,787 records and subjected to evaluation. The results presented in these articles did not provide clear evidence for a linear, strong, and positive association between adolescents' use of social media and the occurrence of mental health problems. The majority of the evaluated research projects had several limitations, therefore it seems justified to conduct further studies with significantly improved measurement procedures, which could yield valuable and clinically relevant data on the relationship between social media use and the mental health of young individuals.
Cloud Architectures for Distributed Serverless Computing: A Review of Event-Driven and Function-as-a-Service Paradigms

The advent of serverless computing has revolutionized the cloud computing landscape, providing scalable, cost-effective, and flexible solutions for modern application development. This paper comprehensively reviews cloud architectures for distributed serverless computing, focusing on event-driven and Function-as-a-Service (FaaS) paradigms. This research explores the fundamental principles and benefits of serverless computing, highlighting its impact on development practices and infrastructure management. The review covers key components, including orchestration, scalability, and security, and examines leading serverless platforms and frameworks. Through critically analyzing current research and industry practices, identify challenges and propose future directions for optimizing serverless architectures. This paper aims to explain how event-driven and FaaS paradigms reshape cloud computing, enabling developers to build resilient and efficient applications without server management. Our research found that event-driven architectures in serverless computing offer significant advantages in scalability, real-time processing, and resource utilization. FaaS paradigms provide modularity, granularity, and cost-effectiveness, making them suitable for various applications. Cloud-edge collaborative architectures are crucial for achieving low-latency and high-performance serverless applications but require robust security, privacy, and resource management frameworks.
Predicting Thrombectomy Recanalization from CT Imaging Using Deep Learning Models

For acute ischemic stroke (AIS) patients with large vessel occlusions, clinicians must decide if the benefit of mechanical thrombectomy (MTB) outweighs the risks and potential complications following an invasive procedure. Pre-treatment computed tomography (CT) and angiography (CTA) are widely used to characterize occlusions in the brain vasculature. If a patient is deemed eligible, a modified treatment in cerebral ischemia (mTICI) score will be used to grade how well blood flow is reestablished throughout and following the MTB procedure. An estimation of the likelihood of successful recanalization can support treatment decision-making. In this study, we proposed a fully automated prediction of a patient's recanalization score using pre-treatment CT and CTA imaging. We designed a spatial cross attention network (SCANet) that utilizes vision transformers to localize to pertinent slices and brain regions. Our top model achieved an average cross-validated ROC-AUC of 77.33 $\pm$ 3.9\%. This is a promising result that supports future applications of deep learning on CT and CTA for the identification of eligible AIS patients for MTB.
The Dark Energy Survey

We describe the Dark Energy Survey (DES), a proposed optical-near infrared survey of 5000 sq. deg of the South Galactic Cap to ~24th magnitude in SDSS griz, that would use a new 3 sq. deg CCD camera to be mounted on the Blanco 4-m telescope at Cerro Telolo Inter-American Observatory (CTIO). The survey data will allow us to measure the dark energy and dark matter densities and the dark energy equation of state through four independent methods: galaxy clusters, weak gravitational lensing tomography, galaxy angular clustering, and supernova distances. These methods are doubly complementary: they constrain different combinations of cosmological model parameters and are subject to different systematic errors. By deriving the four sets of measurements from the same data set with a common analysis framework, we will obtain important cross checks of the systematic errors and thereby make a substantial and robust advance in the precision of dark energy measurements.