Fuzzy spectral and spatial feature integration for classification of nonferrous materials in hyperspectral data

Hyperspectral data allows the construction of more elaborate models to sample the properties of the nonferrous materials than the standard RGB color representation. In this paper, the nonferrous waste materials are studied as they cannot be sorted by classical procedures due to their color, weight and shape similarities. The experimental results presented in this paper reveal that factors such as the various levels of oxidization of the waste materials and the slight differences in their chemical composition preclude the use of the spectral features in a simplistic manner for robust material classification. To address these problems, the proposed FUSSER (fuzzy spectral and spatial classifier) algorithm detailed in this paper merges the spectral and spatial features to obtain a combined feature vector that is able to better sample the properties of the nonferrous materials than the single pixel spectral features when applied to the construction of multivariate Gaussian distributions. This approach allows the implementation of statistical region merging techniques in order to increase the performance of the classification process. To achieve an efficient implementation, the dimensionality of the hyperspectral data is reduced by constructing bio-inspired spectral fuzzy sets that minimize the amount of redundant information contained in adjacent hyperspectral bands. The experimental results indicate that the proposed algorithm increased the overall classification rate from 44% using RGB data up to 98% when the spectral-spatial features are used for nonferrous material classification

Biologically-inspired data decorrelation for hyperspectral imaging

Hyper-spectral data allows the construction of more robust statistical models to sample the material properties than the standard tri-chromatic color representation. However, because of the large dimensionality and complexity of the hyper-spectral data, the extraction of robust features (image descriptors) is not a trivial issue. Thus, to facilitate efficient feature extraction, decorrelation techniques are commonly applied to reduce the dimensionality of the hyper-spectral data with the aim of generating compact and highly discriminative image descriptors. Current methodologies for data decorrelation such as principal component analysis (PCA), linear discriminant analysis (LDA), wavelet decomposition (WD), or band selection methods require complex and subjective training procedures and in addition the compressed spectral information is not directly related to the physical (spectral) characteristics associated with the analyzed materials. The major objective of this article is to introduce and evaluate a new data decorrelation methodology using an approach that closely emulates the human vision. The proposed data decorrelation scheme has been employed to optimally minimize the amount of redundant information contained in the highly correlated hyper-spectral bands and has been comprehensively evaluated in the context of non-ferrous material classificatio

Base de datos de abejas ibéricas

Las abejas son un grupo extremadamente diverso con más de 1000 especies descritas en la península ibérica. Además, son excelentes polinizadores y aportan numerosos servicios ecosistémicos fundamentales para la mayoría de ecosistemas terrestres. Debido a los diversos cambios ambientales inducidos por el ser humano, existen evidencias del declive de algunas de sus poblaciones para ciertas especies. Sin embargo, conocemos muy poco del estado de conservación de la mayoría de especies y de muchas de ellas ignoramos cuál es su distribución en la península ibérica. En este trabajo presentamos un esfuerzo colaborativo para crear una base de datos de ocurrencias de abejas que abarca la península ibérica e islas Baleares que permitirá resolver cuestiones como la distribución de las diferentes especies, preferencia de hábitat, fenología o tendencias históricas. En su versión actual, esta base de datos contiene un total de 87 684 registros de 923 especies recolectados entre 1830 y 2022, de los cuales un 87% presentan información georreferenciada. Para cada registro se incluye información relativa a la localidad de muestreo (89%), identificador y colector de la especie (64%), fecha de captura (54%) y planta donde se recolectó (20%). Creemos que esta base de datos es el punto de partida para conocer y conservar mejor la biodiversidad de abejas en la península ibérica e Islas Baleares. Se puede acceder a estos datos a través del siguiente enlace permanente: https://doi.org/10.5281/zenodo.6354502ABSTRACT: Bees are a diverse group with more than 1000 species known from the Iberian Peninsula. They have increasingly received special attention due to their important role as pollinators and providers of ecosystem services. In addition, various rapid human-induced environmental changes are leading to the decline of some of its populations. However, we know very little about the conservation status of most species and for many species, we hardly know their true distributions across the Iberian Peninsula. Here, we present a collaborative effort to collate and curate a database of Iberian bee occurrences to answer questions about their distribution, habitat preference, phenology, or historical trends. In total we have accumulated 87 684 records from the Iberian Peninsula and the Balearic Islands of 923 different species with 87% of georeferenced records collected between 1830 and 2022. In addition, each record has associated information such as the sampling location (89%), collector and person who identified the species (64%), date of the capture (54%) and plant species where the bees were captured (20%). We believe that this database is the starting point to better understand and conserve bee biodiversity in the Iberian Peninsula. It can be accessed at: https://doi.org/10.5281/zenodo.6354502Esta base de datos se ha realizado con la ayuda de los proyectos EUCLIPO (Fundação para a Ciência e a Tecnologia, LISBOA-01-0145-FEDER-028360/EUCLIPO) y SAFEGUARD (ref. 101003476 H2020 -SFS-2019-2).info:eu-repo/semantics/publishedVersio

Mortality from gastrointestinal congenital anomalies at 264 hospitals in 74 low-income, middle-income, and high-income countries: a multicentre, international, prospective cohort study

Summary Background Congenital anomalies are the fifth leading cause of mortality in children younger than 5 years globally. Many gastrointestinal congenital anomalies are fatal without timely access to neonatal surgical care, but few studies have been done on these conditions in low-income and middle-income countries (LMICs). We compared outcomes of the seven most common gastrointestinal congenital anomalies in low-income, middle-income, and high-income countries globally, and identified factors associated with mortality. Methods We did a multicentre, international prospective cohort study of patients younger than 16 years, presenting to hospital for the first time with oesophageal atresia, congenital diaphragmatic hernia, intestinal atresia, gastroschisis, exomphalos, anorectal malformation, and Hirschsprung’s disease. Recruitment was of consecutive patients for a minimum of 1 month between October, 2018, and April, 2019. We collected data on patient demographics, clinical status, interventions, and outcomes using the REDCap platform. Patients were followed up for 30 days after primary intervention, or 30 days after admission if they did not receive an intervention. The primary outcome was all-cause, in-hospital mortality for all conditions combined and each condition individually, stratified by country income status. We did a complete case analysis. Findings We included 3849 patients with 3975 study conditions (560 with oesophageal atresia, 448 with congenital diaphragmatic hernia, 681 with intestinal atresia, 453 with gastroschisis, 325 with exomphalos, 991 with anorectal malformation, and 517 with Hirschsprung’s disease) from 264 hospitals (89 in high-income countries, 166 in middleincome countries, and nine in low-income countries) in 74 countries. Of the 3849 patients, 2231 (58·0%) were male. Median gestational age at birth was 38 weeks (IQR 36–39) and median bodyweight at presentation was 2·8 kg (2·3–3·3). Mortality among all patients was 37 (39·8%) of 93 in low-income countries, 583 (20·4%) of 2860 in middle-income countries, and 50 (5·6%) of 896 in high-income countries (p<0·0001 between all country income groups). Gastroschisis had the greatest difference in mortality between country income strata (nine [90·0%] of ten in lowincome countries, 97 [31·9%] of 304 in middle-income countries, and two [1·4%] of 139 in high-income countries; p≤0·0001 between all country income groups). Factors significantly associated with higher mortality for all patients combined included country income status (low-income vs high-income countries, risk ratio 2·78 [95% CI 1·88–4·11], p<0·0001; middle-income vs high-income countries, 2·11 [1·59–2·79], p<0·0001), sepsis at presentation (1·20 [1·04–1·40], p=0·016), higher American Society of Anesthesiologists (ASA) score at primary intervention (ASA 4–5 vs ASA 1–2, 1·82 [1·40–2·35], p<0·0001; ASA 3 vs ASA 1–2, 1·58, [1·30–1·92], p<0·0001]), surgical safety checklist not used (1·39 [1·02–1·90], p=0·035), and ventilation or parenteral nutrition unavailable when needed (ventilation 1·96, [1·41–2·71], p=0·0001; parenteral nutrition 1·35, [1·05–1·74], p=0·018). Administration of parenteral nutrition (0·61, [0·47–0·79], p=0·0002) and use of a peripherally inserted central catheter (0·65 [0·50–0·86], p=0·0024) or percutaneous central line (0·69 [0·48–1·00], p=0·049) were associated with lower mortality. Interpretation Unacceptable differences in mortality exist for gastrointestinal congenital anomalies between lowincome, middle-income, and high-income countries. Improving access to quality neonatal surgical care in LMICs will be vital to achieve Sustainable Development Goal 3.2 of ending preventable deaths in neonates and children younger than 5 years by 2030

3D Active Surfaces for Liver Segmentation in Multisequence MRI Images

Biopsies for diagnosis can sometimes be replaced by non-invasive techniques such as CT and MRI. Surgeons require accurate and efficient methods that allow proper segmentation of the organs in order to ensure the most reliable intervention planning. Automated liver segmentation is a difficult and open problem where CT has been more widely explored than MRI. MRI liver segmentation represents a challenge due to the presence of characteristic artifacts, such as partial volumes, noise and low contrast. In this paper, we present a novel method for multichannel MRI automatic liver segmentation. The proposed method consists of the minimization of a 3D active surface by means of the dual approach to the variational formulation of the underlying problem. This active surface evolves over a probability map that is based on a new compact descriptor comprising spatial and multisequence information which is further modeled by means of a liver statistical model. This proposed 3D active surface approach naturally integrates volumetric regularization in the statistical model. The advantages of the compact visual descriptor together with the proposed approach result in a fast and accurate 3D segmentation method. The method was tested on 18 healthy liver studies and results were compared to a gold standard made by expert radiologists. Comparisons with other state-of-the-art approaches are provided by means of nine well established quality metrics. The obtained results improve these methodologies, achieving a Dice Similarity Coefficient of 98.59.HEPATOOL project - Spanish Department of Science and Innovation, IPT-2011-1514-90000

Neue Dimensionen von Mensch-Maschine-Interfaces: Entwicklung eines Scoring-Systems zur Beschreibung und Evaluation von Mensch-Maschine-Interfaces für digitalisierte industrielle Anwendungen

Hohe Produktivität und niedrige Kosten sind zentrale Kriterien von Produktionsmaschinen. Doch auch die Qualität und Leistungsfähigkeit der Mensch-Maschine-Schnittstelle (humanmachine interface – HMI) gewinnt an Relevanz bei der Gesamtbewertung einer Maschine. Das liegt auch daran, dass der Mensch als Maschinenbediener oder -überwacher in digitalisierten und Hochgeschwindigkeitsproduktionsprozessen schnell zum Flaschenhals der Informationsverarbeitung und damit ein limitierender Faktor für die Produktivität werden kann. Denn mit der Digitalisierung der Produktionssysteme erweitert sich die Menge an verfügbaren Informationen drastisch und neue Komplexfunktionalitäten wie Assistenzsysteme und dezentrale Überwachungsaufgaben führen zu neuen vielschichtigen Bedienfunktionen. Moderne HMIs müssen dabei nicht nur ergonomisch, sondern auch intuitiv und leistungsfähig sein und mit den richtigen Bedienfunktionen ein reibungsloses und sicheres Bedienen der Maschinen unterstützen