Martel

Background. There is a complex interaction of endogenous and exogenous factors in rosacea pathogenesis. Existing methods of analysis often do not consider the multifactorial nature of dermatosis, which complicates effective management of patients. Objective. To identify the main relationships between the development factors and rosacea severity building a Bayesian network model original for this dermatosis, as well as to evaluate the results of topical therapy with metronidazole. Material and methods. The study was performed from 2023 to 2025 in Kazan. It involved 117 patients with papulopustular rosacea, median age 35.0 [24; 45] years. The studied parameters included: unchangeable (general characteristics, anamnestic data) and changeable (laboratory indicators, morphofunctional parameters and microbiota content of the facial skin) factors. Obtained parameters — clinical characteristics and dermatological indices. At the first stage of the study, a reconstruction of non-parametric Bayesian network was carried out using a modified algorithm that ensures reproducibility of the model due to clear threshold criteria (Spearman’s rank correlation coefficient equal 0.7 and above) and cycle elimination stages. At the second stage, a group of patients (n=52) with papulopustular subtype of rosacea of mild and moderate course was formed from the total sample, and these subjects underwent 12-week topical therapy with 1% metronidazole gel. Results. With the use of a new network approach for rosacea, key factors influencing the course of dermatosis were identified, 4 clusters of features describing the influence of hormonal, biochemical, immunohematologic and microbial pathogenesis factors were formed. Topical therapy of patients (n=52) with 1% metronidazole gel was effective in 86.5% (n=45) cases. Adverse local reactions were characterized by a mild degree of severity and did not require withdrawal of the drug. Conclusion. The proposed multicomponent Bayesian rosacea model with methodological reproducibility is useful for understanding the relationships between pathogenesis factors and clinical manifestations of dermatosis. Topical therapy of papulopustular rosacea with 1% metronidazole gel has shown high effectiveness, safety and favorable tolerability.

We present ongoing and forthcoming developements for building an integrated model capable of propagating solar storms from the Sun to Earth, together with high-energy particle emissions from the solar corona by coupling different models. The resulting global model will be based on several world-renowned solar wind, CME and SEP models, some of which are already available to users 24/7 via the STORMS weather forecasting service (storms-service.irap.omp.eu) accredited by the CNRS and maintained by Infor'Marty and IRAP/University of Toulouse.The PARASOL project will introduce new coupling and data-driving methodologies in order to obtain continuous simulations of the complex interactions between solar storms and the solar wind, as well as the acceleration and propagation of high-energy particles between the Sun and the Earth. Solar imagery and in-situ data obtained in real time is use to not only to initialise the models, but also to constraint them continuously. We will discuss the layout of the new operational systems, together with the strategies for validation of each individual component and of the full chain. The end goal of the project is to lay out a complete operational space weather forecasting framework ready to be exploited by end-users noth in the public and private sectors.We acknowledge funding by ANR/AID (grant ANR-25-ASTR-0025, PARASOL).

To examine risk factors associated with fall events among hospitalized individuals with cancer. 188 individuals with cancer from the inpatient oncology unit of a large, urban, public hospital in New York satisfied requirements for inclusion. A retrospective case-control analysis of electronic health records was conducted from January to December 2022. Descriptive statistics were used to investigate factors associated with fall events and falls with injuries. Bivariate analyses showed that being male, having a preference for the English language, having a solid tumor diagnosis, having metastatic cancer, receiving chemotherapy, using psychotropic medications, and having any isolation precautions were associated with falls among individuals with cancer. High Morse Fall Scale scores, English-language preference, and the combination of receiving chemotherapy and any isolation precautions were found to be highly predictive of a fall event during hospitalization among individuals with cancer. Incorporating oncology-specific patient risk factors into screening or assessment tools and individualized interventions enhances safety.

General-purpose large language models are increasingly used by employees to interpret standards, troubleshoot systems, and draft or refine engineering artefacts. This routine assistance creates bidirectional flows: proprietary documentation is occasionally externalised as prompts, uploads, screenshots, or connector-mediated retrieval, while model outputs are pasted back into internal tickets, runbooks, and repositories. This integrative review synthesises heterogeneous evidence (peer-reviewed research, provider and regulator materials, and structured incident reporting) to map employee-driven leakage mechanisms along the documentation lifecycle and to derive a governance approach that is auditable under policy drift and multi-vendor toolchains. We identify a recurrent set of boundary-crossing transition points such as copy-paste, upload/OCR, connector invocation, and paste-back, where risk concentrates and where observability is often weakest. Across these pathways, four cross-cutting risk dimensions recur: confidentiality and competitive exposure, compliance and cross-border transfer, model-side effects (including extraction, spillover, and contamination risks), and incentive-driven governance gaps that sustain shadow workflows. Building on the mechanism map, we propose a proportionate "minimal guardrail stack" and an organisational evaluation framework combining qualitative risk scoring, rule-based escalation, and simple, trackable metrics (e.g., consolidation onto sanctioned channels, blocking effectiveness, inspection false positives, policy-drift lag, and time to a compliant alternative). The paper does not assert prevalence. Instead, it aims to make assumptions explicit and support cautious, workflow-compatible adoption decisions.

Künstliche Intelligenz (KI) hat sich in den letzten Jahrzehnten rapid entwickelt und in den letzten Jahren sogar noch beschleunigt. Die Europäische Union versucht, mit dem KI-Gesetz auf die regulatorischen Herausforderungen der Technologie zu reagieren, und Ungarn versucht, durch Rechtsvorschriften im Zusammenhang mit dem Gesetz darauf zu reagieren. Der Artikel befasst sich mit der Auslegung von KI in der EU und Ungarn, den Unterschieden in der akzeptierten Definition zwischen den beiden und den Definitionsumständen, insbesondere zwischen generativen Modellen, universeller und allgemeiner KI. Der Artikel stellt die wichtigsten Bestimmungen der KI-Verordnung vor, die zum Zeitpunkt der Erstellung dieses Artikels in Kraft treten werden, unter besonderer Berücksichtigung verbotener KI-Praktiken und der Verordnungen zu universellen KI-Modellen aufgrund des schrittweisen Inkrafttretens. Darüber hinaus stellt er, auch aufgrund seiner Aktualität, das institutionelle System der KI-Verordnung auf EU-Ebene und die geplanten inländischen Organisationen vor, die damit verbunden sind und es ergänzen, aufbauend auf den verfügbaren Formularen der neuen ungarischen KI-Strategie (MIS 2.0). Ziel des Artikels ist es, dem Leser einen Einblick in die Regulierungsbemühungen der EU und Ungarns KI zu geben, mit besonderem Fokus auf die Antworten des Gesetzgebers auf die sozialen und wirtschaftlichen Fragen der Technologie.

Rural productive units represent an alternative for improving the quality of life in rural communities, but they require relevant training for their implementation and sustainability. During the COVID-19 pandemic, the business training processes offered by the National Learning Service (SENA) underwent significant transformations, which in many cases were not effective in consolidating these productive units. The objective of this study is to analyze the effectiveness of SENA training programs during the social confinement in Colombia and their impact on the development of rural productive units in municipalities of Cundinamarca. A qualitative approach with descriptive scope and an action research design was used, employing interviews and focus groups with 22 participants of the SENA Emprende Rural program. The findings indicate that the insufficient accompaniment by tutors and the lack of adequate training hindered the consolidation of the productive units, which led to the closure of seven of them by the beneficiaries. Therefore, it is concluded that it is necessary to offer training programs adjusted to the needs of the rural context, in addition to providing continuous support and financing to ensure the sustainability of the productive units.

This paper presents a next-generation architecture that focuses on the advancement of edge computing and Internet of Things (IoT) technologies in the context of the automotive supply value chain for electric vehicles (EVs). First, we outline the general architecture design, the specific layers and their goals. Based on the principles of the proposed architecture, we also give a use case for improving the traceability, monitoring and efficiency of EV battery transportation using innovative approaches in federated data spaces, AI-powered inference and orchestration of a multi-objective computational continuum. The automotive supply chain use case is presented with potential Key Performance Indicators (KPIs) while emphasizing the potential impact on operational efficiency, cost reduction and sustainability. By addressing the current limitations in distributed intelligence, data governance, and cross-domain interoperability, we emphasize the importance of real-time data processing, dynamic field governance, and energy-efficient machine learning in the context of the electric vehicle supply chain. At the end of the paper, a discussion and comparative analysis highlights the advances over existing technologies and frameworks and identifies future directions to further improve innovations and applications in this area.

We study the impact of nuclear structure uncertainties on a measurement of the weak charge of C12 at the future Mainz Energy Recovering Superconducting Accelerator (MESA) facility in Mainz. Information from a large variety of nuclear models, accurately calibrated to the ground-state properties of selected nuclei, suggest that a 0.3% precision measurement of the parity-violating asymmetry at forward angles will not be compromised by nuclear structure effects, thereby allowing a world-leading determination of the weak charge of C12. Furthermore, we show that a combination of measurements of the parity-violating asymmetry at forward and backward angles for the same electron-beam energy can be used to extract information on the nuclear weak charge distribution. We conclude that a 0.34% precision on the weak radius of C12 may be achieved by performing a 3% precision measurement of the parity-violating asymmetry at backward angles. Published by the American Physical Society 2024

<strong class="journal-contentHeaderColor">Abstract.</strong> Oil palm is a controversial crop, primarily because it is associated with negative environmental impacts such as tropical deforestation. Mapping the crop and its characteristics, such as age, is crucial for informing public and policy discussions regarding these impacts. Oil palm has received substantial mapping efforts, but up-to-date accurate oil palm maps for both extent and age are essential for monitoring impacts and informing concomitant debate. Here, we present a 10-meter resolution global map of industrial and smallholder oil palm, developed using Sentinel-1 data for the years 2016&ndash;2021 and a deep learning model based on convolutional neural networks. In addition, we used Landsat-5, -7, and -8 to estimate the planting year from 1990 to 2021 at a 30-meter spatial resolution. The planting year indicates the year of establishment for an oil palm plantation as of 2021, either newly planted or replanted oil palm in an existing plantation. We validated the oil palm extent layer using 17,812 randomly distributed reference points. The accuracy of the planting year layer was assessed using field data collected from 5,831 industrial parcels and 1,012 smallholder plantations distributed throughout the oil palm growing area. We found oil palm plantations covering a total mapped area of 23.98 Mha, and our area estimates are 16.66 &plusmn; 0.25 Mha of industrial and 7.59 &plusmn; 0.29 Mha of smallholder oil palm worldwide. The producers&rsquo; and users&rsquo; accuracy is 91.9 &plusmn; 3.4 % and 91.8 &plusmn; 1.0 % for industrial plantations, and 72.7 &plusmn; 1.3 % and 75.7 &plusmn; 2.5 % for smallholders, which improves upon a previous global oil palm dataset, particularly in terms of omission of oil palm. The overall mean error between estimated planting year and field data was -0.24 years and the root-mean-square error was 2.65 years, but the agreement was lower for smallholders. Mapping the extent and planting year of smallholder plantations remains challenging, particularly for wild and sparsely planted oil palm, and future mapping efforts should focus on these specific types of plantations. The average oil palm plantation age was 14.1 years, and the area of oil palm over 20 years was 6.28 Mha. Given that oil palm plantations are typically replanted after 25 years, our findings indicate that this area will require replanting within the coming decade, starting from 2021. Our dataset provides valuable input for optimal land use planning to meet the growing global demand for vegetable oils. The global oil palm extent layer for the year 2021 and the planting year layer from 1990 to 2021 can be found at <a href="https://doi.org/10.5281/zenodo.11034131" target="_blank" rel="noopener">https://doi.org/10.5281/zenodo.11034131</a> (Descals, 2024).

Oil palm is a controversial crop, primarily because it is associated with negative environmental impacts such as tropical deforestation. Mapping the crop and its characteristics, such as age, is crucial for informing public and policy discussions regarding these impacts. Oil palm has received substantial mapping efforts, but up-to-date accurate oil palm maps for both extent and age are essential for monitoring impacts and informing concomitant debate. Here, we present a 10-meter resolution global map of industrial and smallholder oil palm, developed using Sentinel-1 data for the years 2016-2021 and a deep learning model based on convolutional neural networks. In addition, we used Landsat-5, -7, and -8 to estimate the planting year from 1990 to 2021 at a 30-meter spatial resolution. The planting year indicates the year of establishment for an oil palm plantation as of 2021, either newly planted or replanted oil palm in an existing plantation. We validated the oil palm extent layer using 17,812 randomly distributed reference points. The accuracy of the planting year layer was assessed using field data collected from 5,831 industrial parcels and 1,012 smallholder plantations distributed throughout the oil palm growing area. We found oil palm plantations covering a total mapped area of 23.98 Mha, and our area estimates are 16.66 ± 0.25 Mha of industrial and 7.59 ± 0.29 Mha of smallholder oil palm worldwide. The producers' and users' accuracy is 91.9 ± 3.4% and 91.8 ± 1.0% for industrial plantations, and 72.7 ± 1.3% and 75.7 ± 2.5% for smallholders, which improves upon a previous global oil palm dataset, particularly in terms of omission of oil palm. The overall mean error between estimated planting year and field data was -0.24 years and the root-mean-square error was 2.65 years, but the agreement was lower for smallholders. Mapping the extent and planting year of smallholder plantations remains challenging, particularly for wild and sparsely planted oil palm, and future mapping efforts should focus on these specific types of plantations. The average oil palm plantation age was 14.1 years, and the area of oil palm over 20 years was 6.28 Mha. Given that oil palm plantations are typically replanted after 25 years, our findings indicate that this area will require replanting within the coming decade, starting from 2021. Our dataset provides valuable input for optimal land use planning to meet the growing global demand for vegetable oils. The global oil palm extent layer for the year 2021 and the planting year layer from 1990 to 2021 can be found at