New divisors in the boundary of the instanton moduli space

Let ${\mathcal I}(n)$ denote the moduli space of rank $2$ instanton bundles of charge $n$ on ${\mathbb P}^3$. It is known that ${\mathcal I}(n)$ is an irreducible, nonsingular and affine variety of dimension $8n-3$. Since every rank $2$ instanton bundle on ${\mathbb P}^3$ is stable, we may regard ${\mathcal I}(n)$ as an open subset of the projective Gieseker-Maruyama moduli scheme ${\mathcal M}(n)$ of rank $2$ semistable torsion free sheaves $F$ on ${\mathbb P}^3$ with Chern classes $c_1=c_3=0$ and $c_2=n$, and consider the closure $\overline{{\mathcal I}(n)}$ of ${\mathcal I}(n)$ in ${\mathcal M}(n)$. We construct some of the irreducible components of dimension $8n-4$ of the boundary $\partial{\mathcal I}(n):=\overline{{\mathcal I}(n)}\setminus{\mathcal I}(n)$. These components generically lie in the smooth locus of ${\mathcal M}(n)$ and consist of rank $2$ torsion free instanton sheaves with singularities along rational curves

Brazilian monitoring programs for pesticide residues in food – Results from 2001 to 2010

AbstractA total of 13,556 samples of 22 fruit and vegetable crops, rice, and beans were analyzed within two Brazilian pesticide residue monitoring programs between 2001 and 2010. Pesticide residues were found in 48.3% of the samples, and 13.2% presented some irregularity, mostly non-authorized active ingredient use. Less than 3% of the samples had residue levels above the MRL. Apple, papaya, sweet pepper and strawberry were the crops with the higher percentages of positive samples (about 80%). Dithiocarbamates and organophosphorus compounds were found in 41.6% and 30.8% of the samples, respectively. Carbendazim and chlorpyrifos were the pesticides most found (26.7 and 16.1% of positive samples, respectively). Almost half of the samples analyzed had multiple residues (up to 10 residues), with multiple residues most common in samples of apple, sweet pepper and tomato. About 8% of positive samples contained up to four residues of the same chemical class, mainly organophosphorus compounds (18.6%, mostly in apple) and triazoles (16.1%, mostly in papaya and grape). In general, the scenario of pesticide residues in foods investigated within the Brazilian governmental monitoring programs in the last decade is similar to what has been found in other countries. However, the use of non-authorized active ingredients is a common practice among the farmers in the country, a problem that the government authorities have been trying to solve. A preliminary cumulative acute exposure assessment for organophosphates and carbamates in apple has shown that the intake by individuals ≥10 years old accounts for 100% of the acephate ARfD, indicating a need to further investigate the exposure through the consumptions of other crops and group of pesticides, mainly for children

Impact of automated action labeling in classification of human actions in RGB-D videos

For many applications it is important to be able to detect what a human is currently doing. This ability is useful for applications such as surveillance, human computer interfaces, games and healthcare. In order to recognize a human action, the typical approach is to use manually labeled data to perform supervised training. This paper aims to compare the performance of several supervised classifiers trained with manually labeled data versus the same classifiers trained with data automatically labeled. In this paper we propose a framework capable of recognizing human actions using supervised classifiers trained with automatically labeled data in RGB-D videos.info:eu-repo/semantics/publishedVersio

Predicting human activities in sequences of actions in RGB-D videos

In our daily activities we perform prediction or anticipation when interacting with other humans or with objects. Prediction of human activity made by computers has several potential applications: surveillance systems, human computer interfaces, sports video analysis, human-robot-collaboration, games and health-care. We propose a system capable of recognizing and predicting human actions using supervised classifiers trained with automatically labeled data evaluated in our human activity RGB-D dataset (recorded with a Kinect sensor) and using only the position of the main skeleton joints to extract features. Using conditional random fields (CRFs) to model the sequential nature of actions in a sequence has been used before, but where other approaches try to predict an outcome or anticipate ahead in time (seconds), we try to predict what will be the next action of a subject. Our results show an activity prediction accuracy of 89.9% using an automatically labeled dataset.info:eu-repo/semantics/acceptedVersio

Human activity recognition from automatically labeled data in RGB-D videos

Human Activity Recognition (HAR) is an interdisciplinary research area that has been attracting interest from several research communities specialized in machine learning, computer vision, medical and gaming research. The potential applications range from surveillance systems, human computer interfaces, sports video analysis, digital shopping assistants, video retrieval, games and health-care. Several and diverse approaches exist to recognize a human action. From computer vision techniques, modeling relations between human motion and objects, marker-based tracking systems and RGB-D cameras. Using a Kinect sensor that provides the position of the main skeleton joints we extract features based solely on the motion of those joints. This paper aims to compare the performance of several supervised classifiers trained with manually labeled data versus the same classifiers trained with data automatically labeled. We propose a framework capable of recognizing human actions using supervised classifiers trained with automatically labeled data.info:eu-repo/semantics/acceptedVersio

Can the Visits of Dogs (Canis lupus familiaris) Influence the Mental Health (Anxiety and Depression) of Male Aging Patients Institutionalized with Dementia in Health Care Units? A Pilot Study of Madeira Island, Portugal

Despite the fact that in the last decades, several mental health studies have shown that companion animals contribute to psychological and social well- being in humans (e.g., positive impacts have been observed in the elderly medicated for chronic diseases such as anxiety, dementia, and depression), bonds between humans and other animals continue to be under-estimated. The aim of this study is to assess the impact of an animal’s visits (twice a week, N = 30) in depression and anxiety levels of an institutionalized male population diagnosed with dementia. While some of these patients are being partially medicated with antidepressants and/or anxiolytics, others are not subject to any medication (control group). The GAI and GDS measuring instruments were used and there were differences in anxiety and depression levels between the first and last dog visit, statistically significant in depression levels of nonmedicated patients. Such findings allow us to conclude that the effects of the visits of an animal near nonmedicated patients are greater than near medicated ones. The complementary role of animals in mental health institutions where patients are being treated for psychiatric disorders (in the particular case of dementia) should be considered

Measurement of the 0.511 MeV gamma ray line from the Galactic Center

The detection of the 0.511 MeV electron positron annihilation line coming from the Galactic Center to provide the means to estimate the rate of positron production and to test some theoretical sources of positrons is addressed. The results of the measurements of the 0.511 MeV line flux made with a gamma ray experiment on board a stratospheric balloon are presented. The detector field of view looked at the galactic longitude range -31 deg l(II) +41 deg. The observed flux is 0.0067 (+ or - 0.0005) photons 1/cm(2)5 which is in very good agreement with the expected flux when assuming that the Galactic Center is a line source emitting uniformly

Automatic human activity segmentation and labeling in RGBD videos

Human activity recognition has become one of the most active research topics in image processing and pattern recognition. Manual analysis of video is labour intensive, fatiguing, and error prone. Solving the problem of recognizing human activities from video can lead to improvements in several application fields like surveillance systems, human computer interfaces, sports video analysis, digital shopping assistants, video retrieval, gaming and health-care. This paper aims to recognize an action performed in a sequence of continuous actions recorded with a Kinect sensor based on the information about the position of the main skeleton joints. The typical approach is to use manually labeled data to perform supervised training. In this paper we propose a method to perform automatic temporal segmentation in order to separate the sequence in a set of actions. By measuring the amount of movement that occurs in each joint of the skeleton we are able to find temporal segments that represent the singular actions.We also proposed an automatic labeling method of human actions using a clustering algorithm on a subset of the available features.info:eu-repo/semantics/acceptedVersio

Stent-armed kyphoplasty in osteoporotic thoracolumbar fractures—clinical and functional results and a center experience over 10 years

Background: The optimal treatment of osteoporotic vertebral fractures is still a controversial and under discussion topic. Armed kyphoplasty with expansive intravertebral implants is an emerging procedure, which, in theory, it not only makes it possible to achieve instant analgesia, and to get stabilization gains of benefits of kyphoplasty and vertebroplasty, but also, allows for a more effective maintenance of the restored vertebral height. Methods: A retrospective observational study is presented, in which 30 patients participated, including a total of 33 osteoporotic thoracolumbar compression burst vertebral fractures with involvement of one or both vertebral platforms and of more than one fifth of the posterior wall. These individuals underwent armed kyphoplasty with VBS® stents (or stentoplasty) filled with bone cement over 10 years (between 2012 and 2022) at the same center. Clinical (visual analogue scale, Oswestry Disability Index and Patient Global Impression of Change) and imaging results (restoration and maintenance of vertebral body heights) achieved were investigated. The mean follow-up time was 4.5 years (range, 1-10 years). Results: There was a statistically significant improvement in all clinical and functional parameters evaluated, as well as a statistically significant difference in the various vertebral body heights between preoperative and end of follow-up time [increase of 10.7-15.2-5.0 mm (anterior-median-posterior) in the sagittal plane and 6.7-11.6-9.7 mm (right-median-left) in the coronal plane]. There was a statistically significant direct correlation between vertebral heights in the coronal plane, and between the Beck index assessed at the end of the follow-up period and the improvement in functional disability. Conclusions: The percutaneous transpedicular posterior approach, the ability to anatomically restore the fractured vertebra and to maintain it in the medium-long term, as well as the reduced risk of adverse effects, make stent-armed kyphoplasty a very attractive treatment option for osteoporotic compressive thoracolumbar fractures. A clinical-morphological correlation was demonstrated regarding the surgical treatment of these fractures, it was found that a more effective morphological restoration of vertebral heights in both the sagittal and coronal planes is associated with superior satisfactory clinical functional parameters.info:eu-repo/semantics/publishedVersio