ethicalabs/Research-Intent-Collab huggingface dataset:
The precautionary principle and international conflict over domestic regulation: mitigating uncertainty and improving adaptive capacity

Disputes over invocation of precaution in the presence of uncertainty are building. This essay finds: (1) analysis of past WTO panel decisions and current EU-US regulatory conflicts suggests that appeals to scientific risk assessment will not resolve emerging conflicts; (2) Bayesian updating strategies, with commitments to modify policies as information emerges, may ameliorate conflicts over precaution in environmental and security affairs.
In Pursuit of Compliance: Lessons from the World Trade Organization's Dispute Settlement Mechanism

The purpose of this thesis is to examine if there is a problem with compliance in the World Trade Organization, to investigate the validity of the managerial and the political economy approaches to compliance and to analyze reform proposals that tackle the issue of compliance, pursuing improvement of the system. Drawing on the scenario of increasing legalization and cooperation in trade, the first question is examined by way of interviewing trade experts and officials as well as analyzing case studies that are pertinent to the research at hand. The second question – if management is preferred to enforcement as to induce compliance – is answered by analyzing official WTO Dispute Settlement reports, interviews, case reviews and articles on retaliation and compliance written by different authors. The third question is answered as a reflection of the findings of the first two questions. Analysis on the managerial theory of compliance examine whether enforcement plays a minor role in inducing compliance in the WTO, if there is a propensity to comply amongst states and if noncompliance is inadvertent rather than a result of calculation of interests. In the other hand, tests conducted on the enforcement approach to compliance investigate the importance of retaliation in WTO Dispute Settlement, the necessity of an enforcement tool and the claim that noncompliance is a political decision. Tests conducted suggest that the enforcement school of compliance is correct when stating that noncompliance is a political decision, resulted from careful calculation of interests. The research indicates that the WTO Dispute Settlement presents a dual facet of compliance, in which the enforcement tool is responsible for allowing the managerial effects to take place. In this regard, the enforcement tool alone is seen as inappropriate, especially if economic asymmetries are present. An approach that accommodates both enforcement and managerial aspects is prescribed. The research has indicated that successful reform proposals should aim at increasing the credibility of the threat of retaliation as to follow the diagnosis verified by the tests conducted.
Places of pharmaceutical knowledge-making: Global health, postcolonial science, and hope in South African drug discovery

This article draws on ethnographic research at iThemba Pharmaceuticals, a small South African startup pharmaceutical company with an elite international scientific board. The word ‘iThemba’ is Zulu for ‘hope’, and so far drug discovery at the company has been essentially aspirational rather than actual. Yet this particular place provides an entry point for exploring how the location of the scientific knowledge component of pharmaceuticals – rather than their production, licensing, or distribution – matters. The article explores why it matters for those interested in global health and postcolonial science, and why it matters for the scientists themselves. Consideration of this case illuminates limitations of global health frameworks that implicitly posit rich countries as the unique site of knowledge production, and thus as the source of unidirectional knowledge flows. It also provides a concrete example for consideration of the contexts and practices of postcolonial science, its constraints, and its promise. Although the world is not easily bifurcated, it still matters who makes knowledge and where.
Medical Knowledge-Guided Deep Curriculum Learning for Elbow Fracture Diagnosis from X-Ray Images

Elbow fractures are one of the most common fracture types. Diagnoses on elbow fractures often need the help of radiographic imaging to be read and analyzed by a specialized radiologist with years of training. Thanks to the recent advances of deep learning, a model that can classify and detect different types of bone fractures needs only hours of training and has shown promising results. However, most existing deep learning models are purely data-driven, lacking incorporation of known domain knowledge from human experts. In this work, we propose a novel deep learning method to diagnose elbow fracture from elbow X-ray images by integrating domain-specific medical knowledge into a curriculum learning framework. In our method, the training data are permutated by sampling without replacement at the beginning of each training epoch. The sampling probability of each training sample is guided by a scoring criterion constructed based on clinically known knowledge from human experts, where the scoring indicates the diagnosis difficultness of different elbow fracture subtypes. We also propose an algorithm that updates the sampling probabilities at each epoch, which is applicable to other sampling-based curriculum learning frameworks. We design an experiment with 1865 elbow X-ray images for a fracture/normal binary classification task and compare our proposed method to a baseline method and a previous method using multiple metrics. Our results show that the proposed method achieves the highest classification performance. Also, our proposed probability update algorithm boosts the performance of the previous method.
Monitoring the ecological status of rivers with diatom eDNA metabarcoding: A comparison of taxonomic markers and analytical approaches for the inference of a molecular diatom index

AbstractRecently, several studies demonstrated the usefulness of diatom eDNA metabarcoding as an alternative to assess the ecological quality of rivers and streams. However, the choice of the taxonomic marker as well as the methodology for data analysis differ between these studies, hampering the comparison of their results and effectiveness. The aim of this study was to compare two taxonomic markers commonly used in diatom metabarcoding and three distinct analytical approaches to infer a molecular diatom index. We used the values of classical morphological diatom index as a benchmark for this comparison. We amplified and sequenced both a fragment of the rbcL gene and the V4 region of the 18S rRNA gene for 112 epilithic samples from Swiss and French rivers. We inferred index values using three analytical approaches: by computing it directly from taxonomically assigned sequences, by calibrating de novo the ecovalues of all metabarcodes, and by using a supervised machine learning algorithm to train predictive models. In general, the values of index obtained using the two “taxonomy‐free” approaches, encompassing molecular assignment and machine learning, were closer correlated to the values of the morphological index than the values based on taxonomically assigned sequences. The correlations of the three analytical approaches were higher in the case of rbcL compared to the 18S marker, highlighting the importance of the reference database which is more complete for the rbcL marker. Our study confirms the effectiveness of diatom metabarcoding as an operational tool for rivers ecological quality assessment and shows that the analytical approaches by‐passing the taxonomic assignments are particularly efficient when reference databases are incomplete.
Climate-Resilient Irrigation Pathways: A Systematic Review of Global Hydrological Modeling, Vulnerabilities, and Adaptation Strategies

Irrigation is the largest user of freshwater globally and is increasingly exposed to climate variability, groundwater depletion, and growing competition from domestic, industrial, and environmental sectors (FAO, 2017; IWMI, 2019). This review synthesizes evidence from peer-reviewed studies published between 2010 and 2025 to examine how climate change is influencing irrigation water demand, hydrological processes, regional vulnerability, and adaptation responses (Page et al., 2021). The literature indicates that altered precipitation regimes, increasing evaporative demand, groundwater stress, and more frequent hydrological extremes are intensifying irrigation-related risks, particularly in already water-scarce regions (IPCC, 2021; Green et al., 2011). The review further shows that climate-resilient irrigation depends on integrated adaptation pathways that combine technological improvements, demand management, groundwater governance, crop adjustment, and institutional reform (Wada et al., 2014; Darko et al., 2025). Particular vulnerabilities are evident in semi-arid areas, smallholder systems, transboundary basins, and regions with weak monitoring and governance capacity (M&uuml;ller et al., 2017; Wada et al., 2013). The principal contribution of this review lies in linking hydrological modeling, regional vulnerability, and adaptation planning within a single analytical framework that is directly relevant to researchers, irrigation managers, and policymakers. The review concludes by identifying major research gaps in integrated modeling, groundwater sustainability, adaptation performance, and equity-centred implementation.
Central Bank digital currency: Challenges and opportunities

Digital assets have launched a real revolution, bringing about the necessary changes in payments and banking services. Traditional banking actors and technology firms are taking a serious interest in these assets and want to embrace this movement to stay caught up. In the current developments in the broad spectrum of digital currencies, particularly CBDCs, the article aims to provide a global overview from different perspectives: the central bank and the regulators, the financial companies, and the citizens. Issues like competition in the banking sector with the possibility of disintermediation and the monetary policy options that the central banks explore by the valuation of their CBDCs through a policy rate different from cash, along with strategic technology decisions to be taken related to the infrastructure architecture and implementation and information privacy are only some of the challenges that the current pilot programs run by various countries are trying to address. The benefits include improved access to domestic and cross-border payments and the financial system, reduced costs, substitutes for undesirable cryptocurrencies and risky stable coins, counter tax evasion and illegal currency use, and competition between CBDCs suppliers worldwide.
The Identity of the EU Legal Order as a “Shield” for Judicial Independence in the (Polish) Rule of Law Crisis

In its recent case law the CJEU stated that the values contained in Article 2 TEU define the “very identity of the European Union as a common legal order”. The CJEU made also some further important statements on the perception and the functioning of Article 2 TEU values in the European legal space. This working paper takes a closer look at how the CJEU has used the value of rule of law in Art. 2 TEU to build a “shield” that serves as a defense for national judges against interference with their independence. This “shield” has been created on the basis of the principle of effective judicial protection under Article 19 (1)(2) TEU and Article 47 EUCFR. Such a “shield” shield is particularly useful in those EU Member States where material threats to the rule of law emerged, such as Poland. It benefits from the strong possibilities of the EU supranational legal system, i.e. the principle of direct effect and the principle of primacy. However, such a “shield” has also its limits, restrictions, and weak points. These are primarily due, on the one hand, to the procedural limitations of the preliminary ruling procedure, which is the main interface of cooperation between the national courts and the CJEU, and, on the other hand, to the fact that, ultimately, EU law must be effectively implemented and adopted into the national legal order by national authorities. However, during an ongoing rule of law crisis, not all state authorities will always be interested in correctly and effectively implementing European standards.
Handling Catastrophic Forgetting: Online Continual Learning for Next Activity Prediction

Predictive business process monitoring focuses on predicting future process trajectories, including next-activity predictions. This is crucial in dynamic environments where processes change or face uncertainty. However, current frameworks often assume a static environment, overlooking dynamic characteristics and concept drifts. This results in catastrophic forgetting, where training while focusing merely on new data distribution negatively impacts the performance on previously learned data distributions. Continual learning addresses, among others, the challenges related to mitigating catastrophic forgetting. This paper proposes a novel approach called Continual Next Activity Prediction with Prompts (CNAPwP) which adapts the DualPrompt algorithm for next-activity prediction to improve accuracy and adaptability while mitigating catastrophic forgetting. New datasets with recurring concept drifts are introduced, alongside a task-specific forgetting metric that measures the prediction accuracy gap between initial and subsequent task encounters. Extensive testing on both synthetic and real-world datasets shows that this approach outperforms five competing methods, demonstrating its potential applicability in real-world scenarios. An open source implementation of our method together with datasets and results are available under: https://github.com/TamaraVerbeek/CNAPwP.
Rural sexual and reproductive health and rights: An exploration of the experiences of consumers and providers navigating a rural health system for unintended pregnancy and abortion care in Australia

Background Access to safe abortion and pregnancy planning are indicates of sexual and reproductive health and rights (SRHR) Despite Australia’s sophisticated public health system, equitable access to reproductive health services including abortion care remains a significant challenge for rural populations. Methods This PhD uses multiple methods to explore the complexities of health service access for unintended pregnancy and abortion care in rural Australia. A quantitative study uses the London Measure of Unplanned Pregnancy (LMUP) tool and online surveys to estimate the prevalence of unintended pregnancy and abortion consideration among antenatal care recipients in rural NSW. Two parallel qualitative studies explore the experiences of rural consumers and primary care providers in negotiating the local health system for early pregnancy and abortion care, employing semi-structured interviews and inductive thematic analysis to identify themes. Results The quantitative study findings indicate 32% of all antenatal care participants had an unintended pregnancy, and statistically significant association between unintended pregnancy and abortion consideration. The consumer-focused qualitative study describes complex, compounding and uniquely rural challenges to accessing abortion care, and strong consumer expectations about SRHR. The third study highlights the structural and professional obstructions within the health system that either inhibit or diminish the provision of abortion care in rural Australia. Conclusion This thesis demonstrate equivalent prevalence of unintended pregnancy compared to other Australian settings, yet a far more complex and fragmented health service environment where rural abortion care is both hard to find and hard to provide. Findings demonstrate how abortion access in rural Australia is entangled by two sets of intersecting challenges: abortion exceptionalism in mainstream health services and a severely under-resourced rural health system.
Environment, animals, and food as reservoirs of antibiotic-resistant bacteria for humans: One health or more?

Antimicrobial resistance (AMR) is a major public health challenge. For several years, AMR has been addressed through a One Health approach that links human health, animal health, and environmental quality. In this review, we discuss AMR in different reservoirs with a focus on the environment. Anthropogenic activities produce effluents (sewage, manure, and industrial wastes) that contaminate soils and aquatic environments with antibiotic-resistant bacteria (ARB), antibiotic-resistant genes (ARGs), and selective agents such as antibiotics, biocides, and heavy metals. Livestock treated with antibiotics can also contaminate food with ARB. In high-income countries (HICs), effective sanitation infrastructure and limited pharmaceutical industries result in more controlled discharges associated with human activities. Hence, studies using genome-based typing methods have revealed that, although rare inter-reservoir transmission events have been reported, human acquisition in HICs occurs primarily through person-to-person transmission. The situation is different in low- and middle-income countries (LMICs) where high population density, poorer sanitation and animal farming practices are more conducive to inter-reservoir transmissions. In addition, environmental bacteria can be a source of ARGs that, when transferred to pathogenic species under antibiotic selection pressure in environmental hotspots, produce new antibiotic-resistant strains that can potentially spread in the human community through human-to-human transmission. The keys to reducing AMR in the environment are (i) better treatment of human waste by improving wastewater treatment plants (WWTPs) in HICs and improving sanitation infrastructure in LMICs, (ii) reducing the use of antibiotics by humans and animals, (iii) prioritizing the use of less environmentally harmful antibiotics, and (iv) better control of pharmaceutical industry waste.
Quantum machine learning for the quantum lattice Boltzmann method: Trainability of variational quantum circuits for the nonlinear collision operator across multiple time steps

This study investigates the application of quantum machine learning (QML) to approximate the nonlinear component of the collision operator within the quantum lattice Boltzmann method (QLBM). To achieve this, we train a variational quantum circuit (VQC) to construct an operator $U$. When applied to the post-linear-collision quantum state $\ket{Ψ_i}$, this operator yields a final state $\ket{Ψ_f} = U\ket{Ψ_i}$ that successfully replicates the nonlinear collision dynamics derived from the Bhatnagar-Gross-Krook (BGK) approximation. Within this framework, we present two distinct architectures: the R1 model and the R2 model. The R1 model is designed for quantum simulations that involve multiple time steps without intermediate measurements, focusing on accurately capturing nonlinear dynamics in continuous evolution. In contrast, the R2 model is tailored to achieve the high-precision reconstruction of the nonlinear operator for a single time step with an unitary operator.
Corporate social responsibility reporting of the banks in Bosnia and Herzegovina, Croatia and Montenegro

The interest in corporate social responsibility has grown considerably and so is the practice of its reporting. The CSR reporting is an output of sustainability accounting. The aim of this study is to determine the factors of corporate social reporting of the banks in Bosnia & Herzegovina, Croatia and Montenegro and to point out the differences between the practices in those countries. The empirical research reveals that very few banks publish the CSR report. Disclosure of CSR on the Internet is associated with the higher value of assets and profits which is statistically confirmed.
AgriSen-COG, a Multicountry, Multitemporal Large-Scale Sentinel-2 Benchmark Dataset for Crop Mapping Using Deep Learning

With the increasing volume of collected Earth observation (EO) data, artificial intelligence (AI) methods have become state-of-the-art in processing and analyzing them. However, there is still a lack of high-quality, large-scale EO datasets for training robust networks. This paper presents AgriSen-COG, a large-scale benchmark dataset for crop type mapping based on Sentinel-2 data. AgriSen-COG deals with the challenges of remote sensing (RS) datasets. First, it includes data from five different European countries (Austria, Belgium, Spain, Denmark, and the Netherlands), targeting the problem of domain adaptation. Second, it is multitemporal and multiyear (2019–2020), therefore enabling analysis based on the growth of crops in time and yearly variability. Third, AgriSen-COG includes an anomaly detection preprocessing step, which reduces the amount of mislabeled information. AgriSen-COG comprises 6,972,485 parcels, making it the most extensive available dataset for crop type mapping. It includes two types of data: pixel-level data and parcel aggregated information. By carrying this out, we target two computer vision (CV) problems: semantic segmentation and classification. To establish the validity of the proposed dataset, we conducted several experiments using state-of-the-art deep-learning models for temporal semantic segmentation with pixel-level data (U-Net and ConvStar networks) and time-series classification with parcel aggregated information (LSTM, Transformer, TempCNN networks). The most popular models (U-Net and LSTM) achieve the best performance in the Belgium region, with a weighted F1 score of 0.956 (U-Net) and 0.918 (LSTM).The proposed data are distributed as a cloud-optimized GeoTIFF (COG), together with a SpatioTemporal Asset Catalog (STAC), which makes AgriSen-COG a findable, accessible, interoperable, and reusable (FAIR) dataset.
AKARI and BLAST Observations of the Cassiopeia A Supernova Remnant and Surrounding Interstellar Medium

We use new large area far infrared maps ranging from 65 - 500 microns obtained with the AKARI and the Balloon-borne Large Aperture Submillimeter Telescope (BLAST) missions to characterize the dust emission toward the Cassiopeia A supernova remnant (SNR). Using the AKARI high resolution data we find a new tepid dust grain population at a temperature of ~35K and with an estimated mass of 0.06 solar masses.  This component is confined to the central area of the SNR and may represent newly-formed dust in the unshocked supernova ejecta.  While the mass of tepid dust that we measure is insufficient by itself to account for the dust observed at high redshift, it does constitute an additional dust population to contribute to those previously reported.  We fit our maps at 65, 90, 140, 250, 350, and 500 microns to obtain maps of the column density and temperature of cold dust (near 16 K) distributed throughout the region. The large column density of cold dust associated with clouds seen in molecular emission extends continuously from the surrounding interstellar medium to project on the SNR, where the foreground component of the clouds is also detectable through optical, X-ray, and molecular extinction.  At the resolution available here, there is no morphological signature to isolate any cold dust associated only with the SNR from this confusing interstellar emission. Our fit also recovers the previously detected hot dust in the remnant, with characteristic temperature 100 K.