Land Cover Image Classification

Land Cover (LC) image classification has become increasingly significant in understanding environmental changes, urban planning, and disaster management. However, traditional LC methods are often labor-intensive and prone to human error. This paper explores state-of-the-art deep learning models for enhanced accuracy and efficiency in LC analysis. We compare convolutional neural networks (CNN) against transformer-based methods, showcasing their applications and advantages in LC studies. We used EuroSAT, a patch-based LC classification data set based on Sentinel-2 satellite images and achieved state-of-the-art results using current transformer models.Comment: 7 pages, 4 figures, 1 table, published in conferenc

Procesos de integración cooperativa en el Mercado Común del Sur (Mercosur)

El presente trabajo sobre la Federación de Cooperativas de la Región Sur Ltda. (FE.CO.R.SUR) y la Cooperativa de Segundo Grado Cabal –administradora de medios de pago–, se inscribe dentro del Proyecto de Investigación –Impactos de la Integración Regional del Mercosur en el Sector Cooperativo. Según el marco de la investigación, el alcance de los Estudios de Caso nos permitirán dar cuenta de experiencias exitosas en el marco de la integración regional y analizar situaciones concretas de relacionamiento en el sector, en particular en sus modalidades agropecuaria y de ahorro y crédito. (Párrafo extraído del texto a modo de resumen)Facultad de Ciencias Económica

Procesos de integración cooperativa en el Mercado Común del Sur (Mercosur)

El presente trabajo sobre la Federación de Cooperativas de la Región Sur Ltda. (FE.CO.R.SUR) y la Cooperativa de Segundo Grado Cabal –administradora de medios de pago–, se inscribe dentro del Proyecto de Investigación –Impactos de la Integración Regional del Mercosur en el Sector Cooperativo. Según el marco de la investigación, el alcance de los Estudios de Caso nos permitirán dar cuenta de experiencias exitosas en el marco de la integración regional y analizar situaciones concretas de relacionamiento en el sector, en particular en sus modalidades agropecuaria y de ahorro y crédito. (Párrafo extraído del texto a modo de resumen)Facultad de Ciencias Económica

Chemical Analysis of Nutritional Content of Prickly Pads (Opuntia ficus indica) at Varied Ages in an Organic Harvest

Opuntia ficus indica, also known as prickly pads, are an important part of the human diet and are also used as forage for livestock. This is an interesting vegetable due the environmental conditions in which it grows and its resistance to climatic extremes; however, little is known about its nutritional properties, especially in the later stages of maturity. The objective of this study was to determine the composition of organic prickly pads (Opuntia ficus indica) at differing stages of growth maturity. Chemical proximate analysis and mineral constituent analysis at different maturation stages were carried out in this investigation. As a result, older prickly pads were found to be an important source of nutritional components such as calcium

Impact of previous tobacco use with or without cannabis on first psychotic experiences in patients with first-episode psychosis

Objective: There is high prevalence of cigarette smoking in individuals with first-episode psychosis (FEP) prior to psychosis onset. The purpose of the study was to determine the impact of previous tobacco use with or without cannabis on first psychotic experiences in FEP and the impact of this use on age of onset of symptoms, including prodromes. Methods: Retrospective analyses from the naturalistic, longitudinal, multicentre, “Phenotype-Genotype and Environmental Interaction. Application of a Predictive Model in First Psychotic Episodes (PEPs)” Study. The authors analysed sociodemographic/clinical data of 284 FEP patients and 231 matched healthy controls, and evaluated first psychotic experiences of patients using the Symptom Onset in Schizophrenia Inventory. Results: FEP patients had significantly higher prevalence of tobacco, cannabis, and cocaine use than controls. The FEP group with tobacco use only prior to onset (N = 56) had more sleep disturbances (42.9% vs 18.8%, P = 0.003) and lower prevalence of negative symptoms, specifically social withdrawal (33.9% vs 58%, P = 0.007) than FEP with no substance use (N = 70), as well as lower prevalence of ideas of reference (80.4% vs 92.4%, P = 0.015), perceptual abnormalities (46.4% vs 67.4%, P = 0.006), hallucinations (55.4% vs 71.5%, P = 0.029), and disorganised thinking (41.1% vs 61.1%, P = 0.010) than FEP group with previous tobacco and cannabis use (N = 144). FEP patients with cannabis and tobacco use had lower age at first prodromal or psychotic symptom (mean = 23.73 years [SD = 5.09]) versus those with tobacco use only (mean = 26.21 [SD = 4.80]) (P = 0.011). Conclusions: The use of tobacco alone was not related to earlier age of onset of a first psychotic experience, but the clinical profile of FEP patients is different depending on previous tobacco use with or without cannabis. © 2021 The Author

The Cell Tracking Challenge: 10 years of objective benchmarking

The Cell Tracking Challenge is an ongoing benchmarking initiative that has become a reference in cell segmentation and tracking algorithm development. Here, we present a signifcant number of improvements introduced in the challenge since our 2017 report. These include the creation of a new segmentation-only benchmark, the enrichment of the dataset repository with new datasets that increase its diversity and complexity, and the creation of a silver standard reference corpus based on the most competitive results, which will be of particular interest for data-hungry deep learning-based strategies. Furthermore, we present the up-to-date cell segmentation and tracking leaderboards, an in-depth analysis of the relationship between the performance of the state-of-the-art methods and the properties of the datasets and annotations, and two novel, insightful studies about the generalizability and the reusability of top-performing methods. These studies provide critical practical conclusions for both developers and users of traditional and machine learning-based cell segmentation and tracking algorithms.Web of Science2071020101