346 research outputs found
Economic factors affecting obesity: an application in Italy
No full textThe World Health Organization has stated that obesity is spreading around the world like a “global epidemic”. In 2004 the percentage of obese people in the Italian population was 9%, but the trend s increasing in recent years. Focusing on this country, the purpose of the paper is to analyze the socio-economic variables affecting obesity by means of a survey conducted in a consumer sample. Our analysis is based on a survey conducted in Italy, and the sample was composed of 999 consumers. We used a binary logit model and the dependent variable is body mass index (BMI), expressed in a dichotomic way (seriously overweight and obese, value 1, and normal weight, value 0). The results show that the condition of the seriously overweight and obese increases with age, especially in people over 65 of age. Also gender is correlated with the pathology: being seriously overweight and obese is far more likely for men than for women. An inverse relation was shown between obesity and education, and between obesity and the level of food knowledge. The results highlight that disadvantaged social categories are more susceptible to the problem of overweight and obesity. A policy implication of the analysis, to limit the spread of obesity, could lie in programs aimed at improving health and food awareness and focused on these minority groups.economics of obesity, BMI and consumer, logit model, Food Consumption/Nutrition/Food Safety, Health Economics and Policy,
Dealing with indistinguishable particles and their entanglement
No full textHere, we discuss a particle-based approach to deal with systems of many identical quantum objects (particles) that never employs labels to mark them. We show that it avoids both methodological problems and drawbacks in the study of quantum correlations associated with the standard quantum mechanical treatment of identical particles. The core of this approach is represented by the multiparticle probability amplitude, whose structure in terms of single-particle amplitudes we derive here by first principles. To characterize entanglement among the identical particles, this new method uses the same notions, such as partial trace, adopted for non-identical ones. We highlight the connection between our approach and second quantization. We also define spin-exchanged multipartite states which contain a generalization of W states to identical particles. We prove that particle spatial overlap plays a role in the distributed entanglement within multipartite systems and is responsible for the appearance of non-local quantum correlations.This article is part of a discussion meeting issue 'Foundations of quantum mechanics and their impact on contemporary society'
Effects of Indistinguishability in a System of Three Identical Qubits
No full textQuantum correlations of identical particles are important for quantum-enhanced technologies. The recently introduced non-standard approach to treat identical particles is here exploited to show the effect of particle indistinguishability on the characterization of entanglement of three identical qubits. We show that, by spatially localized measurements in separated regions, three independently-prepared separated qubits in a pure elementary state behave as distinguishable ones, as expected. On the other hand, delocalized measurements make it emerge a measurement-induced entanglement. We then find that three independently-prepared boson qubits under complete spatial overlap exhibit genuine three-partite entanglement. These results evidence the effect of spatial overlap on identical particle entanglement and show that the latter depends on both the quantum state and the type of measurement
Analyzing Uncertainty in Microplastic Detection: A Comprehensive CT Scan and Neural Network Approach
No full textMicroplastics (MPs), smaller than 5 mm and often invisible to the naked eye, represent a significant threat to marine ecosystems and human health. Traditional methods for detecting MPs often lack efficiency and may compromise sample integrity. In this study, a novel non-destructive methodology for detecting MPs in fish samples is proposed, utilizing computed tomography (CT) scanning and neural networks (NN). Leveraging recent advancements in uncertainty analysis, the reliability of the innovative approach and its application to fish sample analysis is assessed. Sources of uncertainty originating from both tomographic imaging and NN processing are identified, addressing issues such as false positives/negatives and uncertainties in measured parameters such as particle position, size, and material. Through post-processing techniques and calibration procedures, uncertainties are mitigated, providing insights into epistemic and stochastic uncertainties inherent in the methodology. Our findings demonstrate that the calibration system used in conjunction with tomographic imaging shows good linearity, with a standard deviation of 0.029 kg/m3. Despite inherent noise in the images causing variability in gray levels, the range of variability for the plastics of interest was narrower than the uncertainty of the calibration curve. This indicates that while the calibration was accurate, the inherent noise still posed challenges for precise material classification. This comprehensive analysis enhances the reliability and accuracy of MPs detection in fish samples, contributing to a deeper understanding of uncertainty in CT-based analyses and NN
Virtualized Microphone Array for Moving Sources Mapping
No full textThe acoustic analysis of a moving object, such as in pass-by or fly-over tests, is a very important and demanding issue. These types of analyses make it possible to characterize the machine in quite realistic conditions, but the typical difficulties related to source localization and characterization are usually exacerbated by the need to take into consideration and to compensate for the object movement. In this paper, a technique based on acoustic beamforming is proposed, which is applicable to all those cases where the object under investigation is moving. In the proposed technique, the object’s movement is not regarded as a problem but as a resource, enabling a virtual increase in the number of microphone acquisitions. For a stationary acoustic emission from a moving object, each time segment of the acquired signal is treated as if it is coming from a microphone (virtual) positioned differently relative to the object’s reference system. This paper describes the technique and presents examples of results obtained from both simulated and real signals. Performance analysis is conducted and discussed in detai
SwimmerNET: Underwater 2D Swimmer Pose Estimation Exploiting Fully Convolutional Neural Networks
No full textProfessional swimming coaches make use of videos to evaluate their athletes’ performances. Specifically, the videos are manually analyzed in order to observe the movements of all parts of the swimmer’s body during the exercise and to give indications for improving swimming technique. This operation is time-consuming, laborious and error prone. In recent years, alternative technologies have been introduced in the literature, but they still have severe limitations that make their correct and effective use impossible. In fact, the currently available techniques based on image analysis only apply to certain swimming styles; moreover, they are strongly influenced by disturbing elements (i.e., the presence of bubbles, splashes and reflections), resulting in poor measurement accuracy. The use of wearable sensors (accelerometers or photoplethysmographic sensors) or optical markers, although they can guarantee high reliability and accuracy, disturb the performance of the athletes, who tend to dislike these solutions. In this work we introduce swimmerNET, a new marker-less 2D swimmer pose estimation approach based on the combined use of computer vision algorithms and fully convolutional neural networks. By using a single 8 Mpixel wide-angle camera, the proposed system is able to estimate the pose of a swimmer during exercise while guaranteeing adequate measurement accuracy. The method has been successfully tested on several athletes (i.e., different physical characteristics and different swimming technique), obtaining an average error and a standard deviation (worst case scenario for the dataset analyzed) of approximately 1 mm and 10 mm, respectively
Author response
No full textDetecting pathogens and mounting immune responses upon infection is crucial for animal health. However, these responses come at a high metabolic price (McKean and Lazzaro, 2011, Kominsky et al., 2010), and avoiding pathogens before infection may be advantageous. The bacterial endotoxins lipopolysaccharides (LPS) are important immune system infection cues (Abbas et al., 2014), but it remains unknown whether animals possess sensory mechanisms to detect them prior to infection. Here we show that Drosophila melanogaster display strong aversive responses to LPS and that gustatory neurons expressing Gr66a bitter receptors mediate avoidance of LPS in feeding and egg laying assays. We found the expression of the chemosensory cation channel dTRPA1 in these cells to be necessary and sufficient for LPS avoidance. Furthermore, LPS stimulates Drosophila neurons in a TRPA1-dependent manner and activates exogenous dTRPA1 channels in human cells. Our findings demonstrate that flies detect bacterial endotoxins via a gustatory pathway through TRPA1 activation as conserved molecular mechanism.sponsorship: Vlaams Instituut voor Biotechnologie Alessia Soldano Luis Franco Guangda Liu Natalia Mora Emre Yaksi Bassem A Hassanr Fonds Wetenschappelijk Onderzoek G.0702.12 Alessia Soldano Yeranddy A Alpizar Brett Boonen Alejandro Lopez-Requena Natalia Mora Thomas Voets Rudi Vennekens Bassem A Hassan Karel Talaverar Fonds Wetenschappelijk Onderzoek G.0077.15 Alessia Soldano Yeranddy A Alpizar Brett Boonen Alejandro Lopez-Requena Natalia Mora Thomas Voets Rudi Vennekens Bassem A Hassan Karel Talaverar Fonds Wetenschappelijk Onderzoek G.0680.10 Alessia Soldano Yeranddy A Alpizar Brett Boonen Alejandro Lopez-Requena Natalia Mora Thomas Voets Rudi Vennekens Bassem A Hassan Karel Talaverar Fonds Wetenschappelijk Onderzoek G.0681.10 Alessia Soldano Yeranddy A Alpizar Brett Boonen Alejandro Lopez-Requena Natalia Mora Thomas Voets Rudi Vennekens Bassem A Hassan Karel Talaverar Fonds Wetenschappelijk Onderzoek G.0503.12 Alessia Soldano Yeranddy A Alpizar Brett Boonen Alejandro Lopez-Requena Natalia Mora Thomas Voets Rudi Vennekens Bassem A Hassan Karel Talaverar Fonds Wetenschappelijk Onderzoek G.0654.15 Alessia Soldano Yeranddy A Alpizar Brett Boonen Alejandro Lopez-Requena Natalia Mora Thomas Voets Rudi Vennekens Bassem A Hassan Karel Talaverar Fonds Wetenschappelijk Onderzoek G.0761.10N Alessia Soldano Yeranddy A Alpizar Brett Boonen Alejandro Lopez-Requena Natalia Mora Thomas Voets Rudi Vennekens Bassem A Hassan Karel Talaverar Fonds Wetenschappelijk Onderzoek G.0596.12 Alessia Soldano Yeranddy A Alpizar Brett Boonen Alejandro Lopez-Requena Natalia Mora Thomas Voets Rudi Vennekens Bassem A Hassan Karel Talaverar Fonds Wetenschappelijk Onderzoek G.0565.07 Alessia Soldano Yeranddy A Alpizar Brett Boonen Alejandro Lopez-Requena Natalia Mora Thomas Voets Rudi Vennekens Bassem A Hassan Karel Talaverar KU Leuven GOA/14/011 Alessia Soldano Yeranddy A Alpizar Brett Boonen Luis Franco Alejandro Lopez-Requena Guangda Liu Natalia Mora Emre Yaksi Thomas Voets Rudi Vennekens Bassem A Hassan Karel Talaverar European Commission IUAP P7/13 Alessia Soldano Yeranddy A Alpizar Brett Boonen Luis Franco Alejandro Lopez-Requena Guangda Liu Natalia Mora Emre Yaksi Thomas Voets Rudi Vennekensr KU Leuven OT/12/091 Alessia Soldano Yeranddy A Alpizar Brett Boonen Luis Franco Alejandro Lopez-Requena Guangda Liu Natalia Mora Emre Yaksi Thomas Voets Rudi Vennekens Bassem A Hassan Karel Talaverar KU Leuven PF-TRPLe Alessia Soldano Yeranddy A Alpizar Brett Boonen Luis Franco Alejandro Lopez-Requena Guangda Liu Natalia Mora Emre Yaksi Thomas Voets Rudi Vennekens Bassem A Hassan Karel Talavera (Vlaams Instituut voor Biotechnologie, Fonds Wetenschappelijk Onderzoek|G.0702.12, Fonds Wetenschappelijk Onderzoek|G.0077.15, Fonds Wetenschappelijk Onderzoek|G.0680.10, Fonds Wetenschappelijk Onderzoek|G.0681.10, Fonds Wetenschappelijk Onderzoek|G.0503.12, Fonds Wetenschappelijk Onderzoek|G.0654.15, Fonds Wetenschappelijk Onderzoek|G.0761.10N, Fonds Wetenschappelijk Onderzoek|G.0596.12, KU Leuven|GOA/14/011, KU Leuven|OT/12/091, European Commission|IUAP P7/13, KU Leuven PF-TRPLe)status: Publishe
Indistinguishability as a quantum information resource by localized measurements
No full textQuantum networks are typically made of identical subsystems. Exploiting indistinguishability as a direct quantum resource would thus be highly desirable. We show this is achievable by spatially localized measurements, enabling teleportation and entanglement swapping protocols
Robust entanglement preparation against noise by controlling spatial indistinguishability
No full textInitialization of composite quantum systems into highly entangled states is usually a must to enable their use for quantum technologies. However, unavoidable noise in the preparation stage makes the system state mixed, hindering this goal. Here, we address this problem in the context of identical particle systems within the operational framework of spatially localized operations and classical communication (sLOCC). We define the entanglement of formation for an arbitrary state of two identical qubits. We then introduce an entropic measure of spatial indistinguishability as an information resource. Thanks to these tools we find that spatial indistinguishability, even partial, can be a property shielding nonlocal entanglement from preparation noise, independently of the exact shape of spatial wave functions. These results prove quantum indistinguishability is an inherent control for noise-free entanglement generation
Detection of microplastics in fish using computed tomography and deep learning
No full textMarine organisms have been observed ingesting microplastic particles, with field analyses indicating fibers and fragments as prevalent forms. Current microplastic detection methods are mainly time-consuming, susceptible to cross-contamination, and expensive. Furthermore, these techniques, being disruptive, do not allow for the exact localization of the microplastic in the sample. This study proposes a new approach using Computed Tomography (CT scan) and Artificial Intelligence for the automatic and non-destructive detection of microplastics in fishes and other species based on the combination of several factors, such as density and shape. The advantages of this methodology include accurate identification of plastic localization, a low risk of cross-contamination, rapid processing, automatic tomographic measurement, efficient data processing, cost-effectiveness, and a high cost-benefit ratio. The herein results highlight how artificial intelligence applied to conventional techniques can significantly improve precision and efficiency in microplastic research. Indeed, the semantic segmentation model clearly recognized the presence of 100 % of the plastic particles, both in their location and in their volume, accelerating the identification process and surpassing the limitations of traditional spectral analysis methodologies
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