ethicalabs/Research-Intent-Collab huggingface dataset:
AI-ENABLED PREDICTIVE ANALYTICS FOR CLIMATE ADAPTATION AND COMMUNITY RESILIENCE: A SCALABLE FRAMEWORK FOR DATA-DRIVEN DECISION-MAKING IN VULNERABLE U.S. COASTAL POPULATIONS

Climate change has become a major problem to the populations in the coast of the United States and the rising sealevels; extreme weather conditions and floods are threatening the lives and infrastructure. Although the risks havebeen raised, there is still a lack of scalable, real-time, data-driven structures to support climate adaptation, as well asimprove community resilience. The article discusses the opportunity of AI-driven predictive analytics to deliveruseful information in climate adaptation on vulnerable coasts. With the combination of machine learning models,geospatial data, and real-time environmental data, AI is able to enhance the accuracy of the climate risks predictionand enable more effective decision-making processes. This paper focuses on AI-based flood risk managementsystems, early warning, and distribution of resources on coastal communities. Results show that AI models have thepotential to become important in terms of timeliness and accuracy of predictions, enabling communities to executeadaptive actions and enhance overall insights to climate-related dangers. Nevertheless, including data privacy,model accuracy, and the necessity to collaborate across sectors are still present. The article ends with the realizationof the significance of scalable AI platforms to promote climate resilience in coastal areas and proposes the directionof future research to expand the role of AI in climate action
Application of Human Factors Analysis and Classification System (HFACS-MA) vs Root Cause Analysis (RCA) in Investigation of Maritime Incident within Marine Ports

Abstract:  This study compares the Human Factors Analysis and Classification System (HFACS) and Root Cause Analysis (RCA) in the context of marine incident investigations within the Marine Port. HFACS focuses on human error and organizational influences, while RCA identifies the underlying causes of incidents through a systematic process. The comparison highlights HFACS’s detailed approach to human and systemic factors and RCA’s efficiency in pinpointing direct causes. The findings suggest that both methods have unique strengths and, when used together, can provide a more comprehensive framework for improving safety and risk management in port operations.
Public communication about science in 68 countries: Global evidence on how people encounter information about science and engage with it

This 68-country survey (n = 71,922) examines how people encounter information about science and communicate about it with others, identifies cross-country differences, and tests the extent to which economic and sociopolitical conditions predict such differences. We find that social media are the most used sources of science information in most countries, except those with democratic-corporatist media systems where news media tend to be used more widely. People in collectivist societies are less outspoken about science in daily life, whereas low education is associated with higher outspokenness. Limited access to digital media is correlated with participation in public protests on science matters.
Digital Phenotyping in Mental Health - What can it mean for the future of Psychiatry?

IntroductionSmartphones, central to modern life, offer a cost-effective tool for gaining patient insights outside the consultation room. Through passive data collection (e.g., sensor data) and active questioning, smartphones enable ecological assessments of psychiatric symptoms and self-reported experiences. This “moment-by-moment quantification of the individual-level human phenotype in situ using data from personal digital devices” via digital phenotyping (DP) has garnered significant research attention, showing potential for early detection and intervention in mental health.ObjectivesWe explore recent DP developments in mental health, highlighting its potential to transform clinical practice while acknowledging challenges and risks.MethodsNarrative literature review resorting to PubMed and Google Scholar using keywords such as “digital phenotyping”, “digital phenotype”, “digital biomarker” and “mobile sensing”.ResultsDP studies, particularly in Mood Disorders and Schizophrenia, mostly rely machine learning for data analysis. Biomarkers from passive data (e.g., GPS, social connectivity, physical activity) correlate with self-reports and clinical measures of depression, anxiety, mania, and psychosis. Speech and text analysis through Natural Language Processing (NLP) offers new research avenues. DP promises early detection, relapse prevention, and treatment monitoring but faces challenges, including privacy concerns, and low user engagement - that could be solved by closing the loop by returning individual research results or a tailor-made intervention. Nevertheless, regulation and good practice standards are still lacking, posing the threat of diagnostic inaccuracy and undeniable iatrogenic risk.ConclusionsFor DP to fully realize its potential, integration with standard care and existing systems is essential. While risks exist, when comparing DP with other medical interventions currently under research, perils are minor. Mental health care urgently needs disruptive innovation to improve access and quality.Disclosure of InterestNone Declared
Missed Opportunities: Participation of NGOs in Advisory Proceedings of the African Court on Human and Peoples’ Rights

Abstract                This article scrutinizes the participation of NGOs in advisory proceedings of the African Court on Human and Peoples’ Rights. Contrary to its regional counterparts, the Court allows NGOs to petition for advisory opinions. In addition, NGOs can contribute to its advisory proceedings as amici curiae. Surprisingly, the Court has only delivered two advisory opinions requested by an NGO within the last 15 years. Moreover, the rate of amici interventions by NGOs in this Court’s advisory cases has been exceptionally infrequent. This article views that the less productive use of ACtHPR’s advisory function by NGOs is a loss to the African human rights system and is attributable not only to the Court itself but also to NGOs and State parties to the ACtHPR. It also holds that amici NGOs have been very instrumental to advisory proceedings and having greater participation of amici NGOs would provide concrete benefits to the proceedings.
Harmonised Standards and Conformity Assessments in the AI Act

Adopted by the European Union in 2024, the Artificial Intelligence Act (AIA) constitutes a landmark framework for the regulation of AI systems within the EU’s internal market. While recognising its significance for fostering accountability and trustworthy AI, this position paper focuses on two central mechanisms for its effective implementation: (1) technical standardisation and (2) conformity assessments. It argues that, in their current formulation, both mechanisms risk an excessive reliance on private governance, thereby constraining democratic oversight and the effective protection of fundamental rights. The AIA implements its legal obligations through technical standardisation under Article 40, delegating detailed requirements to the European standardisation organisations. Compliance with these harmonised standards creates a presumption of conformity, giving companies strong incentives to follow them. Although the AIA introduces broader stakeholder consultation via the AI Board and Advisory Forum, participation remains non-binding, allowing large corporations to dominate standard-setting and raising concerns about democratic legitimacy and fundamental rights protection. Under Article 43, conformity assessments verify whether high-risk AI systems meet AIA re- quirements before market entry. Most rely on internal self-assessment; external control is only required in very limited cases. While this reduces administrative burdens, it also risks weakening oversight, transparency, and protection of fundamental rights.
Incommensurate Moiré Stacking and Landau Quantization Without External Magnetic Field in Turbostratic Graphene

Turbostratic multilayer graphene, composed of randomly twisted and stacked graphene sheets, offers a naturally disordered yet tunable platform for exploring moiré physics beyond tedious artificial stacking. Using scanning tunneling microscopy/spectroscopy (STM/STS) and Raman analysis, we uncover a wide distribution of twist angles and stacking configurations spontaneously formed across large-area turbostratic films. In several regions, we identify overlapping incommensurate moiré patterns consistent with locally chiral trilayer stacking. We observe van Hove singularities and reconstructed Dirac-like spectra whose angle dependence supports strong interlayer electronic coherence. In the highly strained trilayered regions, we observe peaks in the local density-of states with characteristic scaling of the quantized Landau levels strikingly even in the absence of a magnetic field. They arise from the strain-induced pseudo-magnetic fields (~ 26 T), making turbostratic graphene a single natural platform to explore the physics of moiré structures as well as of the pseudo-electromagnetic fields.
Generative Artificial Intelligence and Usage in Academia

Artificial intelligence is not a new concept. However, it has reached an important point with technological development. Today, there are many software developed using artificial intelligence and various application areas where they are used. Generative artificial intelligence, one of these areas, is a technology in machine learning of artificial intelligence, aiming to generate new content by training on large data sets. Generative artificial intelligence is used in fields such as health, business, finance, e-commerce, academic studies, and R&amp;D. This study evaluates the use of generative artificial intelligence applications in the academic field. In this context, the differences and similarities between texts generated by ChatGPT, Claude Sonet, and Google Gemini generative artificial intelligence applications and texts prepared by human intelligence were analyzed regarding subject integrity, language, ethics, and plagiarism rate. Descriptive content analysis, one of the qualitative methods, was used in the study. As a result, it was concluded that texts generated by generative artificial intelligence applications and texts prepared by human intelligence are similar in subject integrity and content, and plagiarism rates of texts generated by generative artificial intelligence vary according to language.
Beyond likes and follows understanding social media's grip on adolescent mental health

This study aims to examine the psychological effects of social media use on adolescents, with particular attention to how patterns of digital interaction influence symptoms of anxiety, depression, and self-esteem. It seeks to understand not only the extent of these effects but also the underlying mechanisms—especially the role of social comparison and the pursuit of online validation within the broader context of the attention economy. Adopting a mixed-methods approach, this research integrates quantitative data from a survey of 500 high school students aged 13–18 with qualitative insights drawn from in-depth interviews with 20 adolescents. The survey measures the frequency and intensity of social media use, emotional responses to online interactions, and self-reported mental health outcomes using validated psychological instruments. The qualitative component enriches the findings by exploring how adolescents interpret their online experiences, internalize digital norms, and navigate the pressures of social media culture. Findings indicate a significant correlation between high-frequency social media use and increased levels of anxiety and depressive symptoms, especially among female participants. Respondents frequently reported feelings of inadequacy, social pressure, and sleep disturbances linked to online comparison and fear of missing out (FOMO). The contribution of this research lies in its comprehensive and context-sensitive examination of adolescent social media engagement, offering both empirical evidence and theoretical insight into the psychosocial vulnerabilities exacerbated by digital platforms. By combining statistical trends with personal narratives, the study adds depth to ongoing discussions about youth mental health in the digital age. It further advocates for the development of digital literacy education and mental health interventions that are responsive to the lived realities of adolescents growing up in algorithmically curated social environments.
The Cosmic Microwave Background: Beyond the Power Spectrum

Much recent work on the cosmic microwave background (CMB) has focussed on the angular power spectrum of temperature anisotropies and particularly on the recovery of cosmological parameters from acoustic peaks in the power spectrum. However, there is more that can conceivably be done with CMB measurements. Here I briefly survey a few such ideas: cross-correlation with other cosmic backgrounds as a probe of the density of the Universe; CMB polarization as a gravitational-wave detector; secondary anisotropies and the ionization history of the Universe; tests of alternative-gravity theories; polarization, the Sunyaev-Zeldovich effect, and cosmic variance; and tests for a neutrino mass.
Living Up to the Ideal of Respectability : Sexual and Reproductive Health and Rights Implications for Unmarried Migrant Workers, Single Mothers, and Women in Prostitution in Sri Lanka

This thesis aims to gain a deeper understanding of relationships and sexuality of women at risk of social exclusion in Sri Lanka and the risk of violations of their sexual and reproductive health and rights (SRHR) that they might face. Individual qualitative interviews with migrant women workers (n=18) and men (n=18) in the Free Trade Zone (FTZ), women facing single motherhood (n=28) and women formerly involved in prostitution (n=15) were conducted. Conceptual approaches included gender, social navigation and SRHR. The interviews were analyzed using thematic analysis, qualitative content analysis and discourse analysis. Findings revealed that the migrant women workers negotiated norms of respectability in a society that highly stigmatizes FTZ women workers, while the men identified conflicting constructions of masculinity existing in the FTZ. The women facing single motherhood navigated oppressive and stigmatizing social forces, and the women in prostitution constructed themselves as respectable in opposition to their societal disvalue and marginalization. In order to retain an image of sexual innocence, unmarried women are likely to refrain from demanding or demonstrating SRHR knowledge and accessing services. Furthermore, gender power imbalances leave the women vulnerable to sexual persuasion, coercion and violence. Once pregnant, social, legal, and knowledge barriers hinder or delay them in accessing abortion services. Unmarried pregnant women are thus left with the alternatives of adoption, infanticide, and suicide or become stigmatized single mothers with risks of health and social exclusion for mother and child. Extreme marginalization and limited power make women in prostitution vulnerable to unsafe sex, rape and violence. In conclusion, these women are likely to face numerous and serious SRHR hazards. The complexity of gendered social circumstances and the SRHR implications demonstrated in this thesis, add to the SRHR knowledge in Sri Lanka, and should inform politicians and policy makers about the need to improve the situation of all women in Sri Lanka.
The Impact of Monetary Policy on Macroeconomic Stability in Indonesia: an Analysis of Inflation, Interest Rates, and Economic Growth

This research analyzes the impact of monetary policy on macroeconomic stability in Indonesia, focusing on inflation, interest rates, and economic growth. The study uses secondary data from the Central Bureau of Statistics (BPS). Model selection estimation testing indicates that the Fixed Effect Model (FEM) is the most appropriate approach, and panel data regression analysis is applied. The results show that the money supply does not significantly affect economic growth, indicating that growth is more strongly influenced by structural and non-monetary factors such as productivity, institutions, and fiscal policy. Interest rates are also found to have no significant impact on economic growth. This may reflect the low responsiveness of the real sector to interest rate changes, particularly when economic actors prioritize non-monetary factors, including political stability, business regulations, and market demand. Limited access to banking services for communities and MSMEs further weakens monetary policy transmission. Meanwhile, inflation has a significant effect on economic growth in Indonesia. However, high and uncontrolled inflation can reduce purchasing power and weaken long-term economic stability. This research can make a positive contribution to addressing economic growth issues in Indonesia and promoting sustainable development.
The ‘majesty of the law’: depoliticisation, the Rule of Law and judicial independence

The concept of depoliticisation is an important analytical tool in the study of British politics, although there remains a disproportionate focus on ‘successful’ examples of the strategy. This article provides an account of the failure of the Industrial Relations Act, focusing specifically on the National Industrial Relations Court. By presenting the Act as an attempt to insulate government by relying on the ‘majesty of the law’ as a disciplinary tool, this article argues that contradictions associated with the Rule of Law supporting capitalist social relations made the court an inappropriate vehicle for governing at arm’s-length in this context. The article explores how the lines of judicial independence became blurred, undermining perceptions of governmental disengagement. This resulted in the court becoming the most controversial aspect of the 1970–1974 Conservative government’s reforms. The analysis demonstrates how the interplay between formal rules and informal norms associated with ‘depoliticised institutions’ can produce unexpected outcomes and undermine a governing strategy. Contrary to intentions, the rules-based strategy embodied in the Act was anything but predictable and fuelled its untimely demise.
Modelling aperiodic X-ray variability in black hole binaries as propagating mass accretion rate fluctuations: a short review

Black hole binary systems can emit very bright and rapidly varying X-ray signals when material from the companion accretes onto the black hole, liberating huge amounts of gravitational potential energy. Central to this process of accretion is turbulence. In the propagating mass accretion rate fluctuations model, turbulence is generated throughout the inner accretion flow, causing fluctuations in the accretion rate. Fluctuations from the outer regions propagate towards the black hole, modulating the fluctuations generated in the inner regions. Here, I present the theoretical motivation behind this picture before reviewing the array of statistical variability properties observed in the light curves of black hole binaries that are naturally explained by the model. I also discuss the remaining challenges for the model, both in terms of comparison to data and in terms of including more sophisticated theoretical considerations.
Seeing without Looking: Do Vision-Language Benchmarks Really Test Vision?

Benchmark accuracy is often implicitly assumed to reflect grounded visual understanding in vision-language models (VLMs), yet it remains unclear to what extent such scores truly reflect reliance on visual evidence. Motivated by a surprising observation that removing a substantial fraction of image tokens only degrades model performance very slightly on a widely used hallucination benchmark, we systematically investigate this mismatch in a set of open-source VLMs. Our analysis spans multiple levels of granularity, spanning global visual degradation, localized occlusion, question reformulation, answer-space expansion, and decision-level analyses beyond standard accuracy. We further complement these behavioral results with a layer-wise analysis of vision-token geometry. Throughout the experiments, we find that although VLMs do incorporate visual input, their predictions are less sensitive to the loss of fine-grained visual evidence that standard accuracy should have suggested. Even when the final prediction remains unchanged, the model's internal support for the correct answer may already be weakened. We further complement a representation-level analysis, which shows increasing similarity among visual tokens in deeper layers, providing a possible explanation for our findings. Together, these results suggest that current benchmarks are not sufficient to reliably evaluate fine-grained visual grounding in VLMs.
Convergence of web design and spatial, statistical, genomic and epidemiological information: the case of geo-dashboards in the Covid-19 crisis

In response to the Covid-19 outbreak several organizations, as well as private citizens, considered the opportunity to design and publish online geospatial dashboards (geo-dashboard), blending geographical, graphical and statistical representation approaches. In particular, in this paper geo-dashboards are reviewed and conceptualized in the framework of online explanatory data visualization tools and GIS. The study presents the analysis of a significant number of relevant publicly available geo-dashboards to track, visualize, and communicate indicators related to the epidemic outbreak. Covid-19 is a significant case study on how and how much public communication in a pandemic crisis uses spatially related data and technique.
Knowledge-aware Evolutionary Graph Neural Architecture Search

Graph neural architecture search (GNAS) can customize high-performance graph neural network architectures for specific graph tasks or datasets. However, existing GNAS methods begin searching for architectures from a zero-knowledge state, ignoring the prior knowledge that may improve the search efficiency. The available knowledge base (e.g. NAS-Bench-Graph) contains many rich architectures and their multiple performance metrics, such as the accuracy (#Acc) and number of parameters (#Params). This study proposes exploiting such prior knowledge to accelerate the multi-objective evolutionary search on a new graph dataset, named knowledge-aware evolutionary GNAS (KEGNAS). KEGNAS employs the knowledge base to train a knowledge model and a deep multi-output Gaussian process (DMOGP) in one go, which generates and evaluates transfer architectures in only a few GPU seconds. The knowledge model first establishes a dataset-to-architecture mapping, which can quickly generate candidate transfer architectures for a new dataset. Subsequently, the DMOGP with architecture and dataset encodings is designed to predict multiple performance metrics for candidate transfer architectures on the new dataset. According to the predicted metrics, non-dominated candidate transfer architectures are selected to warm-start the multi-objective evolutionary algorithm for optimizing the #Acc and #Params on a new dataset. Empirical studies on NAS-Bench-Graph and five real-world datasets show that KEGNAS swiftly generates top-performance architectures, achieving 4.27% higher accuracy than advanced evolutionary baselines and 11.54% higher accuracy than advanced differentiable baselines. In addition, ablation studies demonstrate that the use of prior knowledge significantly improves the search performance.