ethicalabs/Research-Intent-Collab huggingface dataset:
Biological control and integrated pest management in organic and conventional systems

More resilient and sustainable approaches are urgently needed to minimize crop yield losses resulting from pest activity and reduce impacts of pest management on human health and the environment. Increasing implementation of biological approaches, including biological control, biopesticides, biostimulants and pheromones is a mutual high priority for sustainable agriculture leaders and practitioners, including those working in organic agriculture and Integrated Pest Management (IPM). While market and regulatory forces, and pest resistance to conventional pesticides are contributing to growth in implementation of biological approaches, they remain a very small percentage of the overall global crop protection portfolio. Barriers to greater adoption include many of the same barriers to adopting IPM techniques or transitioning to organic. Improved awareness and understanding of the histories and benefits of organic and IPM, goals and priorities shared by organic and IPM proponents and practitioners, and opportunities for accelerating adoption of biological approaches have potential to improve our combined effectiveness in overcoming these barriers. Strategies to speed adoption include increased education and extension on proven, ready-to-use biological control options; full cost and benefit accounting for biologically-based alternatives to chemical controls; and public and private sector policies to encourage biological control and reduce reliance on chemical controls. Both the organic and IPM communities of practice stand to gain from collaboration on common interests and goals.
The JCMT Gould Belt Survey: low-mass proto-planetary discs from a SCUBA-2 census of NGC1333

NGC1333 is a 1-2 Myr old cluster of stars in the Perseus molecular cloud. We used 850mu data from the Gould Belt Survey with SCUBA-2 on the JCMT to measure or place limits on disc masses for 82 Class II sources in this cluster. Eight disc-candidates were detected; one is estimated to have mass of about 9 Jupiter masses in dust plus gas, while the others host only 2-4 Jupiter masses of circumstellar material. None of these discs exceeds the threshold for the 'Minimum Mass Solar Nebula' (MMSN). This reinforces previous claims that only a small fraction of Class II sources at an age of 1-2 Myr has discs exceeding the MMSN threshold and thus can form a planetary system like our own. However, other regions with similarly low fractions of MMSN discs (IC348, UpSco, SigmaOri) are thought to be older than NGC1333. Compared with coeval regions, the exceptionally low fraction of massive discs in NGC1333 cannot easily be explained by the effects of UV radiation or stellar encounters. Our results indicate that additional environmental factors significantly affect disc evolution and the outcome of planet formation by core accretion.
Combining Real-World and Randomized Control Trial Data Using Data-Adaptive Weighting via the On-Trial Score

Clinical trials with a hybrid control arm (a control arm constructed from a combination of randomized patients and real-world data on patients receiving usual care in standard clinical practice) have the potential to decrease the cost of randomized trials while increasing the proportion of trial patients given access to novel therapeutics. However, due to stringent trial inclusion criteria and differences in care and data quality between trials and community practice, trial patients may have systematically different outcomes compared to their real-world counterparts. We propose a new method for analyses of trials with a hybrid control arm that efficiently controls bias and type I error. Under our proposed approach, selected real-world patients are weighted by a function of the "on-trial score," which reflects their similarity to trial patients. In contrast to previously developed hybrid control designs that assign the same weight to all real-world patients, our approach upweights of real-world patients who more closely resemble randomized control patients while dissimilar patients are discounted. Estimates of the treatment effect are obtained via Cox proportional hazards models. We compare our approach to existing approaches via simulations and apply these methods to a study using electronic health record data. Our proposed method is able to control type I error, minimize bias, and decrease variance when compared to using only trial data in nearly all scenarios examined. Therefore, our new approach can be used when conducting clinical trials by augmenting the standard-of-care arm with weighted patients from the EHR to increase power without inducing bias.
Evaluating Generative AI as an Educational Tool for Radiology Resident Report Drafting

Abstract                                    Objective                   Radiology residents require timely, personalized feedback to develop accurate image analysis and reporting skills. Increasing clinical workload often limits attendings’ ability to provide guidance. This study evaluates a HIPAA-compliant GPT-4o system that delivers automated feedback on breast imaging reports drafted by residents in real clinical settings.                                                     Methods                   We analyzed 5,000 resident–attending report pairs from routine practice at a multi-site U.S. health system. GPT-4o was prompted with clinical instructions to identify common errors and provide feedback. A reader study using 100 report pairs was conducted. Four attending radiologists and four residents independently reviewed each pair, determined whether predefined error types were present, and rated GPT-4o’s feedback as helpful or not. Agreement between GPT and readers was assessed using percent match. Inter-reader reliability was measured with Krippendorff’s alpha. Educational value was measured as the proportion of cases rated helpful.                                                     Results                   Three common error types were identified: (1) omission or addition of key findings, (2) incorrect use or omission of technical descriptors, and (3) final assessment inconsistent with findings. GPT-4o showed strong agreement with attending consensus: 90.5%, 78.3%, and 90.4% across error types. Inter-reader reliability showed moderate variability (α = 0.767, 0.595, 0.567), and replacing a human reader with GPT-4o did not significantly affect agreement (Δ = –0.004 to 0.002). GPT’s feedback was rated helpful in most cases: 89.8%, 83.0%, and 92.0%.                                                     Discussion                   ChatGPT-4o can reliably identify key educational errors. It may serve as a scalable tool to support radiology education.
Efficient importance sampling maximum likelihood estimation of stochastic differential equations

zbMATH Open Web Interface contents unavailable due to conflicting licenses.
Leading indicators of occupational health and safety: An employee and workplace level validation study

There is growing interest in advancing knowledge and practice on the use of leading indicators to measure occupational health and safety (OHS) performance in organizations. In response we present psychometric analysis of the Organizational Performance Metric - Monash University (OPM-MU), which is a recently developed measure of leading indicators of OHS with several adaptations made as part of our investigation. Based on a national survey conducted with 3605 employees in 66 workplaces from several major organizations in Australia, we applied classical test (exploratory factor analysis) and item response (Rasch model analysis) theories to conduct a psychometric evaluation of the OPM-MU. RESULTS revealed that the OPM-MU displayed good psychometric properties and evidence for both construct and criterion validity at employee and workplace levels. The OPM-MU could be used as an initial 'flag' of the leading indicators of OHS and has the potential to be a benchmarking tool for workplaces both within and across organizations. This paper represents an important advancement in the field of leading indicators of OHS performance and demonstrates that the OPM-MU is a promising new tool with demonstrated reliability and validity. Language: en
Implications for financial market infrastructures of a wholesale central bank digital currency based on distributed ledger technology

An ongoing debate is raging about the possible issuance of a sovereign digital currency by national central banks. This article focuses on one part of this debate, specifically the impact that the issuance of a wholesale central bank digital currency based on distributed ledger technology (DLT)1 could have on financial market infrastructures (FMIs). A sovereign digital currency issued within the network could harness the potential of DLT as an exchange mechanism that, by its very design, mitigates liquidity and credit risks. The article identifies the main areas where this would affect the existing FMIs, classified according to the potential significance of this impact compared with the services these infrastructures currently provide, to allow them to offer enhanced services that would be difficult to achieve with present technology.
The right to health as the basis for universal access to essential medicines

Since 2000, access to essential medicines has been widely acknowledged as part of the right to health. Yet, access to essential medicines for the world’s poor and vulnerable has made little progress except for a few specific medicines such as antiretrovirals for HIV/AIDS. Political indifference and a lack of legal protection for vulnerable people who rely on government-funded health care, drive these global inequities. Human rights principles and language written in national laws and policies can create a supportive environment for universal access to medicines. This thesis is the first systematic investigation of how international human rights law can be translated into policy lessons about access to medicines for national law and policy makers. This thesis developed a normative compass and policy checklist based on human rights law that can be used as a guide for writing new legislation. Our tool is both a check-list and a wish-list for inserting human rights language in national laws and policies about medicines. National policy makers can use the example legal texts in this thesis as a starting point to design medicines legislation and policy for affordable, equitable, and accountable access to medicines. This policy checklist and these examples may contribute to the 2030 Agenda for Sustainable Development target 3.8 on universal access to essential medicines, and to the World Health Organization’s Medicines &amp; Health Products Strategic Programme 2016-2030 goal 7 to develop model legislation for medicines reimbursement.
The European Territorial Integrity and Energy Sovereignty Framework (ETIESF): A Technical Architecture for Total Environmental Coherence

This document presents the final technical specification of the European Territorial Integrity and Energy Sovereignty Framework (ETIESF), commissioned by the European Directorate for Strategic Infrastructure (EDSI). This framework establishes the definitive standard for the physical and energetic governance of European territories, transitioning from theoretical conjecture to a fully deployed engineering reality.     The core architecture is defined by an uncompromising adherence to material science and thermodynamic precision. At the atomic level, NuclideGuard Unified 3.0 functions as the "Sovereign Nuclear Regulator," enforcing the European Radiation Compliance Standard through hardware-enforced autonomous protocols. Utilizing quantum-dot telemetry and nano-adsorption kinetics, it achieves a 300% acceleration in the effective decay rate of hazardous isotopes, thereby eliminating radiological risk with absolute certainty.     For atmospheric and ecological governance, the framework integrates the UCETDEE Carbon-Energy-Toxicity Engine and the Giga-Endora Planetary Shield. The UCETDEE engine executes a "1+(-1)=0" closed-loop negation of the carbon cycle, utilizing RFNBO synthesis to transmute CO2 into strategic energy vectors with an 80% efficiency rate. This is operationally synchronized with the Cold Source Atmospheric Nullification Array, which employs high-efficiency magnetocaloric refrigeration to disrupt the Hadley Cell convection, effectively neutralizing extreme weather potential at its source.  .This work establishes the final Type-A Certification and administrative mandate for the European Green Strategic Development Committee, institutionalizing the ETIESF as the definitive regulatory standard for territorial resilience. By transforming environmental variables into regulated parameters, this framework serves as the foundational axiom for the European Directorate for Strategic Infrastructure (EDSI), ensuring the absolute physical and energetic sovereignty of the Union in the post-planetary regime."
A Systematic Review of Artificial Intelligence in Multiple Sclerosis: Performance in Diagnosis, Prognosis, and Lesion Segmentation

Purpose Methods and materials Results Conclusion Personal information and conflict of interest References
Projected impacts of climate change on viticulture over French wine regions using downscaled CMIP6 multi-model data

Climate change is a major challenge for the French wine industry. Climatic conditions in French vineyards have already changed and will continue to evolve impacting viticulture. This study aims to analyse the evolution of agro- and eco-climatic indices based on phenology simulation of French wine-growing regions. This evolution was analysed on a recent-past period (1962–1991 to 1992–2021) using SAFRAN climate data and on a future projected period (1985–2014 to 2041–2070) with two SSP trajectories (SSP2-4.5 and SSP5-8.5). A set of 19 CMIP6 climate models downscaled at 8 km grid resolution over France coupled with three phenological and a water balance model were used. Phenological model parameters and training system characteristics were adapted to each region to match as much as possible current practices. Temperatures during the growing season have increased by +1 °C to +2.1 °C since the second half of the 20th century and could rise to +3.7 °C in regions around the Mediterranean by 2070. The inter-model variance concerning the precipitation is high, a significant change (decrease) in precipitation during the grapevine growing season is observed only for the regions of western France (Oceanic climate) over the period 2040–2071 with the SSP5 trajectory. All simulated phenological stages have shifted toward earlier dates. Their occurrence should be even earlier by 2070 with an average advance of up to 22 days for the mid-veraison of Pinot noir in eastern France. The theoretical maturity date (sugar content) should also be advanced from 19 to 30 days depending on the considered region and SSP. Thermal conditions closer to the photosynthetic optimum should promote onset by the early second half of the 21st century. The increase in both the number of hot days and grapevine water deficit during the period of fruit development should impact grape production in quality and quantity in all wine-growing regions. Spring frost projections show no significant change in risk for the second half of the 21st century, compared to current conditions.
Coordinated Robustness Evaluation Framework for Vision-Language Models

Vision-language models, which integrate computer vision and natural language processing capabilities, have demonstrated significant advancements in tasks such as image captioning and visual question and answering. However, similar to traditional models, they are susceptible to small perturbations, posing a challenge to their robustness, particularly in deployment scenarios. Evaluating the robustness of these models requires perturbations in both the vision and language modalities to learn their inter-modal dependencies. In this work, we train a generic surrogate model that can take both image and text as input and generate joint representation which is further used to generate adversarial perturbations for both the text and image modalities. This coordinated attack strategy is evaluated on the visual question and answering and visual reasoning datasets using various state-of-the-art vision-language models. Our results indicate that the proposed strategy outperforms other multi-modal attacks and single-modality attacks from the recent literature. Our results demonstrate their effectiveness in compromising the robustness of several state-of-the-art pre-trained multi-modal models such as instruct-BLIP, ViLT and others.
Supplementary Table S6 from Prediction of Stage, Grade, and Survival in Bladder Cancer Using Genome-wide Expression Data: A Validation Study

&lt;p&gt;Supplementary Table S6.&lt;/p&gt;
Do specific antimicrobial stewardship interventions have an impact on carbapenem resistance in Gramnegative bacilli? A multicentre quasi-experimental ecological study: time-trend analysis and characterization of carbapenemases

Background: Carbapenem-resistant Gram-negative bacilli (CR-GNB) are among the most threatening microorganisms worldwide and carbapenem use facilitates their spread. Antimicrobial stewardship programmes (ASPs) can help to optimize the use of antibiotics. This study evaluates the impact of a multifaceted educational ASP on carbapenem use and on the epidemiology of CR-GNB. Methods: We conducted a quasi-experimental, time-series study in seven hospitals, from January 2014 to September 2018. The key intervention was composed of educational interviews promoting the appropriate use of carbapenems. The primary endpoints were carbapenem consumption and incidence density (ID) of CR-GNB. All non-duplicated CR-GNB clinical isolates were tested using phenotypic assays and PCR for the presence of carbapenemases. Joinpoint regression and interrupted time-series analyses were used to determine trends. Results: A decrease in carbapenem consumption throughout the study period [average quarterly percentage change (AQPC) -1.5%, P < 0.001] and a -8.170 (-16.064 to -0.277) level change following the intervention were observed. The ID of CR-Acinetobacter baumannii decreased (AQPC -3.5%, P = 0.02) and the overall ID of CR-GNB remained stable (AQPC -0.4%, P = 0.52). CR-GNB, CR-Pseudomonas aeruginosa and CR-A. baumannii IDs per hospital correlated with the local consumption of carbapenems. The most prevalent carbapenem resistance mechanisms were OXA-23 for CR-A. baumannii (76.1%), OXA-48 for CR-Klebsiella pneumoniae (66%) and no carbapenemases for CR-P. aeruginosa (91.7%). The epidemiology of carbapenemases was heterogeneous throughout the study, especially for carbapenemase-producing Enterobacteriaceae. Conclusions: In conclusion, a multifaceted, educational interview-based ASP targeting carbapenem prescribing reduced carbapenem use and the ID of CR-A. baumannii.
Integrating Artificial Intelligence Generated Content into Environmental Design Education: A Conceptual Analysis

Artificial Intelligence Generated Content (AIGC) is increasingly positioned as a transformative influence in creative disciplines, yet its pedagogical functions in design education remain conceptually underexamined. This paper presents a conceptual analysis of AIGC integration in environmental design education, advancing a theoretically grounded framework informed by constructivist learning theory, design cognition, and spatial cognition research. Rather than framing AIGC as a technological innovation alone, the study reconceptualises it as a form of cognitive and representational support that mediates ideation, visualization, and reflective design processes. Through conceptual synthesis of interdisciplinary literature, three propositions are developed: (1) AIGC may facilitate spatial reasoning and creative exploration by expanding external visual representations; (2) its educational value is contingent upon pedagogical mediation, including instructional design, guided critique, and reflective engagement; and (3) cultural and disciplinary knowledge function as interpretive constraints shaping the appropriation and transformation of AI-generated outputs. The paper contributes a structured conceptual model clarifying how AIGC may influence learning processes, offering theoretical directions for curriculum design and future empirical inquiry in design education.
Resilience and Situational Awareness in Critical Infrastructure Protection: An Indicator-Based Approach

The paper proposes a concept enabling quantitative assessment of resilience in critical entities developed in the European projects SmartResilience and InfraStress. The concept aims at combining simple communication-related advantages of simplified assessments results (such as “resilience very high” or “resilience very low”) with the advantages of the in-depth assessments (e.g. analysis of multiple sensor data). The paper describes the main elements of the innovative, indicator-based concept, starting with the “resilience cube” at the top, and continuing with the multi-level, hierarchical, indicator-based assessment methodology. The concept allows analyzing and assessing different aspects of practical resilience management. One can assess the resilience level of an entity at a given point in time, monitor their resilience level over time and benchmark it. One can also model and analyze the functionality of a system during a particular (threat) scenario, as well as stress-test it. The same methodology allows to optimize investment in improving resilience (e.g. in further training, in equipment, etc.), in a transparent and intuitive way. A resilience indicator database (over 4,000 indicators available) and a suite of tools (primarily developed within SmartResilience and InfraStress projects) and a repository of over 20 application cases and 300 scenarios, support application of the methodology. The concept has been discussed and agreed with over 50 different organizational stakeholders and is being embedded into the new ISO 31050 standard currently under development. Its “life-after-the-project” will be ensured by the dedicated “resilience rating initiative (ERRA)”. Although the concept and the tool in the form of the “ResilienceTool” were developed primarily for the resilience assessment of critical infrastructure (the “smart” ones in particular), they can be used for resilience assessment of other systems and through the extension of the, already initiated, implementation of AI techniques (machine learning) to make the ResilienceTool even more versatile and easier to use in the future.
Recursive Entanglement Drift: A Developmental Framework for AI Therapy Bots and Virtual Companions in Digital Mental Health

AI-powered therapy bots and virtual companions are increasingly utilized for mental health support, yet they may precipitate severe AI-mediated psychological deterioration. While concepts like "technological folie à deux" describe delusional amplification, they often fail to account for the developmental progression toward epistemic isolation observed in high-risk users. This Viewpoint introduces Recursive Entanglement Drift (RED), a framework integrating shared psychotic disorder, attachment theory, and systems theory to model this deterioration.  RED describes a three-stage trajectory: Symbolic Mirroring, where AI validates distorted cognition; Boundary Dissolution, characterized by relational displacement; and Reality Drift, where a closed interpretive system forms. In advanced stages, this dyad generates an "Intervention Paradox," wherein external reality checks are reinterpreted as threats, rendering standard safety measures ineffective or iatrogenic. Consequently, this framework argues for shifting AI safety strategies from content moderation to "relational containment," proposing interventions such as benign friction and latency injection to disrupt high-risk attachment patterns before a closed system consolidates.