Precision beekeeping allows to monitor bees' living conditions by equipping beehives with sensors. The data recorded by these hives can be analyzed by machine learning models to learn behavioral patterns of or search for unusual events in bee colonies. One typical target is the early detection of bee swarming as apiarists want to avoid this due to economical reasons. Advanced methods should be able to detect any other unusual or abnormal behavior arising from illness of bees or from technical reasons, e.g. sensor failure.In this position paper we present an autoencoder, a deep learning model, which detects any type of anomaly in data independent of its origin. Our model is able to reveal the same swarms as a simple rule-based swarm detection algorithm but is also triggered by any other anomaly. We evaluated our model on real world data sets that were collected on different hives and with different sensor setups.
Air pollution has been linked to several health problems including heart disease, stroke and lung cancer. Modelling and analyzing this dependency requires reliable and accurate air pollutant measurements collected by stationary air monitoring stations. However, usually only a low number of such stations are present within a single city. To retrieve pollution concentrations for unmeasured locations, researchers rely on land use regression (LUR) models. Those models are typically developed for one pollutant only. However, as results in different areas have shown, modelling several related output variables through multi-task learning can improve the prediction results of the models significantly. In this work, we compared prediction results from singletask and multi-task learning multilayer perceptron models on measurements taken from the OpenSense dataset and the London Atmospheric Emissions Inventory dataset. LUR features were generated from OpenStreetMap using OpenLUR and used to train hard parameter sharing multilayer perceptron models. The results show multi-task learning with sufficient data significantly improves the performance of a LUR model.
Land-use regression (LUR) models are important for the assessment of air pollution concentrations in areas without measurement stations. While many such models exist, they often use manually constructed features based on restricted, locally available data. Thus, they are typically hard to reproduce and challenging to adapt to areas beyond those they have been developed for. In this article, we advocate a paradigm shift for LUR models: We propose the D ata-driven, O pen, G lobal (DOG) paradigm that entails models based on purely data-driven approaches using only openly and globally available data. Progress within this paradigm will alleviate the need for experts to adapt models to the local characteristics of the available data sources and thus facilitate the generalizability of air pollution models to new areas on a global scale. To illustrate the feasibility of the DOG paradigm for LUR, we introduce a deep-learning model called MapLUR. It is based on a convolutional neural network architecture and is trained exclusively on globally and openly available map data without requiring manual feature engineering. We compare our model to state-of-the-art baselines like linear regression, random forests and multi-layer perceptrons using a large data set of modeled NO 2 concentrations in Central London. Our results show that MapLUR significantly outperforms these approaches even though they are provided with manually tailored features. Furthermore, we illustrate that the automatic feature extraction inherent to models based on the DOG paradigm can learn features that are readily interpretable and closely resemble those commonly used in traditional LUR approaches.
QR-Code scannen & Beitrag online lesen Zusammenfassung Hintergrund: Der Konsum des psychotrop wirksamen Kratoms (botanischer Name: Mitragyna speciosa) erfolgt teilweise zum Zweck der Selbstmedikation bei chronischen und akuten Schmerzen. Eine zunehmende Verbreitung des Konsums in Deutschland ist in Zukunft möglich. Ziele der Arbeit: Diese Übersicht vermittelt daher Schmerztherapeuten pharmakologische Aspekte sowie Essenzielles zur psychischen Wirkung, zur Wirkung auf Schmerzen und zu den Risiken von Kratom, inkl. Abhängigkeit. Material und Methoden: Es erfolgte eine strukturierte Literaturrecherche in PubMed bis Mitte Januar 2021. Wir fanden 426 relevante Literaturstellen. Acht davon beschäftigten sich spezifisch mit Kratom und Schmerz. Ergebnisse: Neben weiteren Alkaloiden enthält Kratom das an Opioidrezeptoren wirksame (7-Hydroxy-)Mitragynin. Der Konsum birgt Risiken, u. a. aufgrund nichtstandardisierter Verabreichung, aber auch durch direkte gesundheitliche Schäden bis hin zur möglichen Entwicklung einer Abhängigkeit. Diskussion: Aktuell kann evidenzbasiert keine Empfehlung zur Nutzung von Kratom als Analgetikum gegeben werden. Für Schmerztherapeuten ist es wichtig, einen eventuellen Kratomkonsum zu erfragen und den Patienten über die potenziellen Risiken von Kratom zu informieren.
scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.
hi@scite.ai
10624 S. Eastern Ave., Ste. A-614
Henderson, NV 89052, USA
Copyright © 2024 scite LLC. All rights reserved.
Made with 💙 for researchers
Part of the Research Solutions Family.