Search

Low-density polyethylene (LDPE) nanocomposites containing magnesium oxide (MgO) nanoparticles are obtained by melt mixing. MgO nanoparticles ca. 29 ± 2 nm are synthesized by sol–gel and organically modified on the surface with oleic acid (Mod-MgO), whose final concentration in the polymers is 3, 5, and 10 wt%. The presence of these nanoparticles did not affect the crystallization process of LDPE. However, Young's modulus increases with 10 wt% of Mod-MgO nanoparticles, rendering higher reinforcement effects with an increase as high as 48%. This affects the elongation at break, which decreases ca. 57% compared to neat LDPE. The storage and loss modulus of the LDPE/MgO and LDPE/Mod-MgO nanocomposites increases at room temperature and low temperatures (−150 to −50°C) compared to neat LDPE. SEM analysis showed that the organic modification of MgO nanoparticles improved their dispersion within the polymer matrix. Nanocomposites present antimicrobial properties against Escherichia coli, reaching an efficiency ca. 53%., Paula A. Zapata thanks the financial support under FONDECYT Regular Project 1220093. Viviana Moreno-Serna thanks the ANID FON-DECYT Postdoctorado 3210077. Carlos Loyo and Raúl Vallejos thank Proyecto de Dirección de Investigación Científica y tecnológica (DICYT), 022141ZR_POSTDOC, USA2055, Universidad de Santiago de Chile.

Low-density polyethylene composites containing different sizes of calcium oxide (CaO) nanoparticles were obtained by melt mixing. The CaO nanoparticles were synthesized by either the sol-gel or sonication methods, obtaining two different sizes: ca. 55 nm and 25 nm. These nanoparticles were used either as-synthesized or were modified organically on the surface with oleic acid (Mod-CaO), at concentrations of 3, 5, and 10 wt% in the polymer. The Mod-CaO nanoparticles of 25 nm can act as nucleating agents, increasing the polymer&rsquo, s crystallinity. The Young&rsquo, s Modulus increased with the Mod-CaO nanoparticles, rendering higher reinforcement effects with an increase as high as 36%. The reduction in Escherichia coli bacteria in the nanocomposites increased with the amount of CaO nanoparticles, the size reduction, and the surface modification. The highest antimicrobial behavior was found in the composites with a Mod-CaO of 25 nm, presenting a reduction of 99.99%. This strong antimicrobial effect can be associated with the release of the Ca2+ from the composites, as studied for the composite with 10 wt% nanoparticles. The ion release was dependent on the size of the nanoparticles and their surface modification. These findings show that CaO nanoparticles are an excellent alternative as an antimicrobial filler in polymer nanocomposites to be applied for food packaging or medical devices.

[EN] Effective antimicrobial polymeric nanocomposites were prepared with low density polyethylene (LDPE) and zinc oxide nanoparticles by melt compounding. These nanoparticles (~17 nm) obtained by the sol¿gel method, were used both as-synthesized and modified organically with oleic acid (Mod-ZnO). Young¿s modulus increased ~15 and 18% for LDPE/ZnO and LDPE/Mod-ZnO, respectively, compared to neat LDPE. When these composites were irradiated with white light, they showed an increase with nanoparticle incorporation, and the antimicrobial properties against E. coli were ~96¿99%. The release of the Zn cations was related to the antimicrobial properties. These nanocomposites are attractive for use as food packaging without external irradiation., Financial support provided by the Innovation Found for Competitiveness of the Chilean Economic Development Agency (CORFO) under Grant 13CEI2-21839 is gratefully acknowledged.

Background: Placing central venous lines under ultrasonographic guidance reduces the complications of the procedure. Aim: To compare prevalences of complications of central venous line placements with or without ultrasonographic guidance. Material and Methods: Descriptive study that contemplated the comparison of two groups of patients subjected to a central venous line placement at a nephrology service for renal replacement therapy. In one group of 100 patients, the line was placed without ultrasonographic guidance between 2008 and 2012. Between 2015 and 2017 the line was placed in 138 patients using ultrasonographic guidance. The prevalences of complications with both types of procedures were recorded. Results: The frequency of complications of procedures with and without ultrasonographic guidance was 0.7 and 18% respectively (prevalence ratio 0.04, 95% confidence interval 0-0.3). Ninety five percent of recorded complications were arterial puncture, followed by hematomas in 10% and pneumothorax in 5%. The higher prevalence of complications was observed in emergency line placement without ultrasonographic guidance. There was a direct association between the number of line placement attempts in a single procedure and the prevalence of complications. Conclusions: Ultrasonographic guidance is associated with a reduction in the prevalence of central venous line complications.

Background Placing central venous lines under ultrasonographic guidance reduces the complications of the procedure. Aim To compare prevalences of complications of central venous line placements with or without ultrasonographic guidance. Material and methods Descriptive study that contemplated the comparison of two groups of patients subjected to a central venous line placement at a nephrology service for renal replacement therapy. In one group of 100 patients, the line was placed without ultrasonographic guidance between 2008 and 2012. Between 2015 and 2017 the line was placed in 138 patients using ultrasonographic guidance. The prevalences of complications with both types of procedures were recorded. Results The frequency of complications of procedures with and without ultrasonographic guidance was 0.7 and 18% respectively (prevalence ratio 0.04, 95% confidence interval 0-0.3). Ninety five percent of recorded complications were arterial puncture, followed by hematomas in 10% and pneumothorax in 5%. The higher prevalence of complications was observed in emergency line placement without ultrasonographic guidance. There was a direct association between the number of line placement attempts in a single procedure and the prevalence of complications. Conclusions Ultrasonographic guidance is associated with a reduction in the prevalence of central venous line complications.

A Random Finite Set (RFS) based multi-target filter is proposed, which utilizes a labeled Multi-Bernoulli distribution to model the multi-target state, together with a Poisson RFS distribution to model target birth. Referred to as the Poisson Labeled Multi-Bernoulli (PLMB) filter, results show that, in simulated environments, it outperforms the Labeled Multi-Bernoulli (LMB), δ-Generalized Labeled Multi-Bernoulli (δ-GLMB) and Labeled Multi-Bernoulli Mixtures (LMBM) filters under general target birth scenarios. An algorithm based on a histogram of Gibbs samples is also proposed which efficiently generates a posterior labeled Multi-Bernoulli distribution in a simple manner using a histogram of the state-measurement associations obtained by a Gibbs sampler. The histogram approach is readily applicable to all Multi-Bernoulli based filters and is demonstrated in the form of the Histogram-PLMB (HPLMB) filter.

Placing central venous lines under ultrasonographic guidance reduces the complications of the procedure.To compare prevalences of complications of central venous line placements with or without ultrasonographic guidance.Descriptive study that contemplated the comparison of two groups of patients subjected to a central venous line placement at a nephrology service for renal replacement therapy. In one group of 100 patients, the line was placed without ultrasonographic guidance between 2008 and 2012. Between 2015 and 2017 the line was placed in 138 patients using ultrasonographic guidance. The prevalences of complications with both types of procedures were recorded.The frequency of complications of procedures with and without ultrasonographic guidance was 0.7 and 18% respectively (prevalence ratio 0.04, 95% confidence interval 0-0.3). Ninety five percent of recorded complications were arterial puncture, followed by hematomas in 10% and pneumothorax in 5%. The higher prevalence of complications was observed in emergency line placement without ultrasonographic guidance. There was a direct association between the number of line placement attempts in a single procedure and the prevalence of complications.Ultrasonographic guidance is associated with a reduction in the prevalence of central venous line complications.

This paper introduces and addresses the implementation of the Multi-Bernoulli Poisson (MBP) filter in multi-target tracking. A performance evaluation in a real scenario, in which a 3D lidar, automotive radar and a video camera are used for tracking people will be provided. For implementation purposes, a Gaussian Mixture (GM) approximation of the MBP filter is used. Comparisons with state of the art GM- $\delta$ -GLMB and GM- $\delta$ -GMBP filters show similar accuracy, despite the need for less parameters, and therefore less computational cost, within the GM-MBP filter. Further performance improvements of the GM-MBP filter are shown, based on birth intensity and survival distributions, which take into account the common field of view of the sensors and the variation of time steps between asynchronous measurements.

This paper proposes a multi-target tracking strategy using a δ-Generalized Multi-Bernoulli Poisson (δ-GMBP) filter applied in a multi-sensor scenario. The δ-GMBP distribution is closed under the Chapman-Kolmogorov equation and Bayes rule, and also closed for a wide family of multi-target likelihood functions which allows implementations of different kinematic and measurement models. One difference between the δ-GMBP and the state of the art of multi-Bernoulli filters is that the birth process is modeled with a Poisson Random Finite Set (RFS), which can be more intuitive. Further, in order to obtain the posterior of the δ-GMBP filter recursion, it is not necessary to iterate over all the components of the prior mixture. The δ-GMBP filter, also maintains track labels in the multi-Bernoulli components, thus no other association method is necessary. The experiments carried out consist of people walking in an open place and two sensors recording the scene from a fixed position. The sensors used in the experiment are a 3D lidar and a single-beam mono-pulse radar. The δ-GMBP filter is compared with the classical Gaussian Mixture Probability Hypothesis Density (GM-PHD) filter, and the Marginal Multi-target Multi-Bernoulli (m-MeMBer) filter.

This paper proposes a multi-target tracking strategy using a delta-Generalized Multi-Bernoulli Poisson (delta-GMBP) filter applied in a multi-sensor scenario. The delta-GMBP distribution is closed under the Chapman-Kolmogorov equation and Bayes rule, and

Activities developed under hazardous conditions, such as mining, require an active investment from the industry to avoid serious injuries and even loss of lives. One of the safety measures taken to address this problem is to implement a video surveillance system able to track vehicles and workers to enforce safety protocols preventing dangerous events that could lead to accidents. In this paper, we propose an intelligent video surveillance system able to detect and predict possible dangerous interactions between workers and vehicles operating on a monitored environment. Our system is able to analyze video information from one or several cameras, detects and tracks objects and emits an alarm that warns the operator on possible safety hazards. A multiclass object detection is trained to detect vehicles and people. The system object detection stage was implemented in CUDA for parallel processing to achieve real time operation. A multitracking system was implemented to track detected objects that move from one camera field of view to a neighbor camera field of view. Experiments on a monitored parking space show that our proposed system yields good performance in tracking people and vehicles, detecting close interaction and generating relevant alarms.

We introduce a method for extracting the social network structure for the persons appearing in a set of video clips. Individuals are unknown, and are not matched against known enrollments. An identity cluster representing an individual is formed by grouping similar-appearing faces from different videos. Each identity cluster is represented by a node in the social network. Two nodes are linked if the faces from their clusters appeared together in one or more video frames. Our approach incorporates a novel active clustering technique to create more accurate identity clusters based on feedback from the user about ambiguously matched faces. The final output consists of one or more network structures that represent the social group(s), and a list of persons who potentially connect multiple social groups. Our results demonstrate the efficacy of the proposed clustering algorithm and network analysis techniques.

Illumination compensation has proven to be crucial in many machine vision applications including face recognition. This is especially important in non-controlled scenarios where face illumination is not homogeneous. An extension of the local normalisation (LN) method using Kolmogorov-Nagumo-based statistics to improve face recognition is proposed. The proposed method is a more general framework for illumination normalisation and it is shown that LN is a particular case of this framework. The proposed method using two different classifiers, PCA and local matching Gabor, on the standard face databases Extended Yale B, AR Face and Gray FERET is assessed. The method reached significantly better results than those previously published on the same databases.

Illumination compensation has proven to be crucial in face detection and face recognition. Several methods for illumination compensation have been developed and tested on the face recognition task using international available face databases. Among the methods with best results are the Discrete Cosine Transform (DCT), Local Normalization (LN) and Self-Quotient Image (SQI). Most of these methods have been applied with great success in face recognition using a principal component classifier (PCA). In the past few years, Local Matching Gabor (LMG) classifiers have shown great success in face classification relative to other classifiers. In this work we optimize several illumination compensation methods using the LMG face classifier. We use ge netic algorithms as the optimization tool. We test our results on the FERET international face database. Results show that face recognition can be significantly improved by illumination compensation methods. The best results are obtained with the optimized LN method which yields a 31% reduction in the total number of errors in the FERET database.

Face detection and recognition depend strongly on illumination conditions. In this reported work, genetic algorithms are used to optimise parameters of the modified local normalisation and self-quotient image methods in cascade for illumination compensation to improve face recognition. The main novelty of the proposed method is that it applies to non-homogeneous as well as homogeneous illumination conditions. The results are compared to those of the best illumination compensation methods published in the literature, obtaining 100% recognition on faces with non-homogeneous illumination and significantly better results than other methods with homogeneous illumination.