Validation of three new measure-correlate-predict models for the long-term prospection of the wind resource

The estimation of the long-term wind resource at a prospective site based on a relatively short on-site measurement campaign is an indispensable task in the development of a commercial wind farm. The typical industry approach is based on the measure-correlate-predict �MCP� method where a relational model between the site wind velocity data and the data obtained from a suitable reference site is built from concurrent records. In a subsequent step, a long-term prediction for the prospective site is obtained from a combination of the relational model and the historic reference data. In the present paper, a systematic study is presented where three new MCP models, together with two published reference models �a simple linear regression and the variance ratio method�, have been evaluated based on concurrent synthetic wind speed time series for two sites, simulating the prospective and the reference site. The synthetic method has the advantage of generating time series with the desired statistical properties, including Weibull scale and shape factors, required to evaluate the five methods under all plausible conditions. In this work, first a systematic discussion of the statistical fundamentals behind MCP methods is provided and three new models, one based on a nonlinear regression and two �termed kernel methods� derived from the use of conditional probability density functions, are proposed. All models are evaluated by using five metrics under a wide range of values of the correlation coefficient, the Weibull scale, and the Weibull shape factor. Only one of all models, a kernel method based on bivariate Weibull probability functions, is capable of accurately predicting all performance metrics studied

Información arqueológica y etnográfica sobre el uso de yunques de huesos en el caso de Mallorca (Islas Baleares, España)

After most researchers have agreed to interpret the bone anvils as artefacts used by blacksmiths to cut teeth on metal sickles, present lines of enquiry are focused on drawing the geographic and chronological scope of these tools. Following this path, this paper presents the results of archaeological and ethnographic surveys carried out in Mallorca (Balearic Islands, Spain). The new data recorded not only has enabled us to document the presence and temporal scope of the bone anvils on the island, but also, for the first time, the involvement of women in their use.Una vez establecido que los yunques de hueso fueron objetos utilizados por los herreros para crear el dentado de las hoces de metal, la investigación actual se ha centrado en trazar el alcance geográfico y cronológico de estas herramientas. Siguiendo este camino, se presentan aquí los resultados arqueológicos y etnográficos de la investigación llevada a cabo en Mallorca (Islas Baleares, España). Los nuevos datos obtenidos no sólo nos permiten documentar la presencia y alcance temporal de los yunques de hueso en la isla, sino también, por primera vez, el papel desempeñado por las mujeres en su uso

Artificial Intelligence for breast cancer detection:Technology, challenges, and prospects

Purpose: This review provides an overview of the current state of artificial intelligence (AI) technology for automated detection of breast cancer in digital mammography (DM) and digital breast tomosynthesis (DBT). It aims to discuss the technology, available AI systems, and the challenges faced by AI in breast cancer screening. Methods: The review examines the development of AI technology in breast cancer detection, focusing on deep learning (DL) techniques and their differences from traditional computer-aided detection (CAD) systems. It discusses data pre-processing, learning paradigms, and the need for independent validation approaches. Results: DL-based AI systems have shown significant improvements in breast cancer detection. They have the potential to enhance screening outcomes, reduce false negatives and positives, and detect subtle abnormalities missed by human observers. However, challenges like the lack of standardised datasets, potential bias in training data, and regulatory approval hinder their widespread adoption. Conclusions: AI technology has the potential to improve breast cancer screening by increasing accuracy and reducing radiologist workload. DL-based AI systems show promise in enhancing detection performance and eliminating variability among observers. Standardised guidelines and trustworthy AI practices are necessary to ensure fairness, traceability, and robustness. Further research and validation are needed to establish clinical trust in AI. Collaboration between researchers, clinicians, and regulatory bodies is crucial to address challenges and promote AI implementation in breast cancer screening.</p

Comprehensive Benchmarking and Integration of Tumour Microenvironment Cell Estimation Methods

Various computational approaches have been developed for estimating the relative abundance of different cell types in the tumour microenvironment (TME) using bulk tumour RNA data. However, a comprehensive comparison across diverse data sets that objectively evaluates the performance of these approaches has not been conducted. Here we benchmarked seven widely used tools and gene sets and introduce ConsensusTME, a method that integrates gene sets from all the other methods for relative TME cell estimation of 18 cell types. We collected a comprehensive benchmark dataset consisting of pan-cancer data (DNA-derived purity, leukocyte methylation, and H&E-derived lymphocyte counts) and cell-specific benchmark data sets (peripheral blood cells and tumour tissues). Although none of the methods outperformed others in every benchmark, ConsensusTME ranked top three in all cancer-related benchmarks and was the best performing tool overall. We provide a web resource to interactively explore the benchmark results and an objective evaluation to help researchers select the most robust and accurate method to further investigate the role of the TME in cancer (www.consensusTME.org).A. Jiménez-Sánchez was supported by a doctoral fellowship from the Cancer Research UK Cambridge Institute and the Mexican National Council of Science and Technology (CONACyT). O. Cast and M.L. Miller were supported by the Brown Performance Group, Innovation in Cancer Informatics Discovery Grant (BD523775). M.L. Miller was supported by Cancer Research UK core grant (C14303/A17197) and the Target Ovarian Cancer Translational Project Grant (Cambridge1320 MM18)

High performance reduced order modeling techniques based on optimal energy quadrature: application to geometrically non-linear multiscale inelastic material modeling

A High-Performance Reduced-Order Model (HPROM) technique, previously presented by the authors in the context of hierarchical multiscale models for non linear-materials undergoing infinitesimal strains, is generalized to deal with large deformation elasto-plastic problems. The proposed HPROM technique uses a Proper Orthogonal Decomposition procedure to build a reduced basis of the primary kinematical variable of the micro-scale problem, defined in terms of the micro-deformation gradient fluctuations. Then a Galerkin-projection, onto this reduced basis, is utilized to reduce the dimensionality of the micro-force balance equation, the stress homogenization equation and the effective macro-constitutive tangent tensor equation. Finally, a reduced goal-oriented quadrature rule is introduced to compute the non-affine terms of these equations. Main importance in this paper is given to the numerical assessment of the developed HPROM technique. The numerical experiments are performed on a micro-cell simulating a randomly distributed set of elastic inclusions embedded into an elasto-plastic matrix. This micro-structure is representative of a typical ductile metallic alloy. The HPROM technique applied to this type of problem displays high computational speed-ups, increasing with the complexity of the finite element model. From these results, we conclude that the proposed HPROM technique is an effective computational tool for modeling, with very large speed-ups and acceptable accuracy levels with respect to the high-fidelity case, the multiscale behavior of heterogeneous materials subjected to large deformations involving two well-separated scales of length.Peer ReviewedPostprint (author's final draft

Disputas territoriales y conflictos por la apropiación de la renta turística en San Martín de los Andes

En este trabajo analizamos tres casos conflictivos que tienen como protagonista central a las agrupaciones mapuche de San Martín de los Andes. Intentamos mostrar que, lejos de reducir las disputas a un lineal enfrentamiento de las comunidades indígenas con “los winkas”, los conflictos involucran a múltiples actores cuyo origen, modalidades de accionar e intereses, constelan un escenario complejo muy alejado de cualquier construcción polar. Su selección responde a la intención de mostrar la diversidad de prácticas y estrategias desplegadas por los actores.In this paper we analyze three conflicting cases that have as main protagonist the mapuche groupings of San Martín de los Andes. We try to show that, far from reducing disputes to a linear confrontation of the indigenous communities with "the winkas", disputes involve multiple actors whose origin, modes of action and interests, make up a complex scenario far removed from any polar construction. Its selection responds to the intention to show the diversity of practices and strategies deployed by the actors.Fil: Balazote Oliver, Alejandro Omar. Universidad Nacional de Luján; Argentina. Universidad de Buenos Aires; ArgentinaFil: Cherñavsky, Sasha Camila. No especifica;Fil: Stecher, Gabriel Andre. Consejo Nacional de Investigaciones Científicas y Técnicas; Argentina. Universidad Nacional del Comahue; Argentin