Comprehensive analysis and experimental validation of five-level converters for ev battery chargers framed in smart grids

The electric vehicle (EV) is the foremost element for spreading electric mobility in smart grids. Its integration into the electrical power grid involves the use of battery chargers with improved power quality aspects, and therefore, the topology of the front-end power converter represents a vital role in the EV battery charger (EVBC). Since multilevel topologies offer a set of advantages to accomplish with the power grid interface, a comprehensive investigation of two five-level topologies for EVBCs is presented in this paper. An accurate computational validation and a meticulous explanation of the hardware and software required for the five-level topologies under study is presented and explained considering realistic operating conditions. The obtained results show the pros and cons of each topology targeting EVBC applications for smart grids.This work has been supported by FCT – Fundação para a Ciência e Tecnologia within the Project Scope: UID/CEC/00319/2019. This work has been supported by FCT Project newERA4GRIDs – New Generation of Unified Power Conditioner with Advanced Control, Integrating Electric Mobility, Renewables, and Active Filtering Capabilities for the Power Grid Improvement: POCI-01-0145-FEDER-030283. Mr. Tiago Sousa is supported by the doctoral scholarship SFRH/BD/134353/2017 granted by the Portuguese FCT agency

Ectopic ossification presenting as osteoid metaplasia in a salivary mucocele in a Shih Tzu dog

<p>Abstract</p> <p>Background</p> <p>Salivary mucocele is an accumulation of saliva in a single or multiloculated cavity lined by connective tissue that is contiguous to a salivary gland-duct complex and is the most common condition affecting the salivary glands in dogs. Occasionally, different types of metaplastic lesions, such as squamous and osseous metaplasia - which are rare lesions in animals - can be observed in association with salivary mucocele.</p> <p>Case presentation</p> <p>A right facial enlargement was suddenly observed in a 4-year-old non-spayed female Shih-Tzu dog. The lesion presented itself as a soft and fluctuant mass located in the right side of the face near to the neck. Histologically, the mass consisted of a cavitary formation without an epithelial lining. Additionally, microscopic examination revealed the presence of osteoid-producing cells which gave rise to areas of bone formation, probably induced by irritation due to the presence sialoliths. Such cells and bone formations were also present in the cavity wall, consequently leading us to classify the condition as a salivary mucocele with osseous metaplasia.</p> <p>Conclusions</p> <p>In the present case, the pathogenesis was probably associated with the presence of sialoliths, which can behave as etiological agents for the metaplastic lesion. The occurrence of osteoid metaplasia is a rare peculiar condition in the canine salivar y gland, and due to the rarity and lack of information about this specific disease, no clinical data can yet be associated with the development of salivary mucocele with osseous metaplasia in dogs.</p

Periodontal regeneration after third molar extraction causing attachment loss in distal and furcation sites of the second molar: a case report with 12 months follow-up

Introduction: The periodontal support apparatus is extremely important due to guarantee insertion to the tooth. It helps with the maintenance of the structural resistance and reconstructive property, and its loss may contribute to root exposure and the possibility to obtain an augmentation of the sensitivity. Thereby, this paper aimed to describe a case report, using regenerative techniques in a bone loss involving distal and furcation regions of the mandibular second molar, which was associated with the extraction of the adjacent impacted third molar without bone interposing. Case presentation: The patient was referred to a private dental clinic reporting an unpleasant odor while using dental floss in the distal region of the mandibular left second molar, during the three years. In addition, reported a history of extraction of the wisdom tooth six years ago. Periapical examination and periodontal index were performed and an extensive radiolucent image involving distal and furcation regions, with a 12 mm probing depth involving the distal root at tooth (2nd molar). No other region showed probing depth greater than 3mm. Thus, after rigorous scaling and root planing, periodontal guided tissue regeneration was performed as an attempt to fill the bone defect. Conclusion: The regenerative process is a predictable way to treat vital mandibular second molar after extraction of the adjacent third molar. Clinicians must give special attention to extraction involving the mandibular third molars, mainly when there is no bone interposed between the second and third molars.info:eu-repo/semantics/publishedVersio

Comparison of major LiDAR data-driven feature extraction methods for autonomous vehicles

Object detection is one of the areas of computer vision that has matured very rapidly. Nowadays, developments in this research area have been playing special attention to the detection of objects in point clouds due to the emerging of high-resolution LiDAR sensors. However, data from a Light Detection and Ranging (LiDAR) sensor is not characterised by having consistency in relative pixel densities and introduces a third dimension, raising a set of drawbacks. The following paper presents a study on the requirements of 3D object detection for autonomous vehicles; presents an overview of the 3D object detection pipeline that generalises the operation principle of models based on point clouds; and categorises the recent works on methods to extract features and summarise their performance.FEDER - Federación Española de Enfermedades Raras(POCI-01-0247-FEDER-037902). European Structural and Investment Funds in the FEDER component, through the Operational Competitiveness and Internationalization Programme (COMPETE 2020) [Project no 037902; Funding Reference: POCI-01-0247-FEDER-03790

Point-cloud based 3D object detection and classification methods for self-driving applications: A survey and taxonomy

Autonomous vehicles are becoming central for the future of mobility, supported by advances in deep learning techniques. The performance of aself-driving system is highly dependent on the quality of the perception task. Developments in sensor technologies have led to an increased availability of 3D scanners such as LiDAR, allowing for a more accurate representation of the vehicle's surroundings, leading to safer systems. The rapid development and consequent rise of research studies around self-driving systems since early 2010, resulted in a tremendous increase in the number and novelty of object detection methods. After the first wave of works that essentially tried to expand known techniques from object detection in images, more recently there has been a notable development in newer and more adapted to LiDAR data works. This paper addresses the existing literature on object detection using LiDAR data within the scope of self-driving and brings a systematic way for analysing it. Unlike general object detection surveys, we will focus on point-cloud data, which presents specific challenges, notably its high-dimensional and sparse nature. This work introduces a common object detection pipeline and taxonomy to facilitate a thorough comparison between different techniques and, departing from it, this work will critically examine the representation of data (critical for complexity reduction), feature extraction and finally the object detection models. A comparison between performance results of the different models is included, alongside with some future research challenges.This work is supported by European Structural and Investment Funds in the FEDER component, through the Operational Competitiveness and Internationalization Programme (COMPETE 2020) [Project n. 037902; Funding Reference: POCI-01-0247-FEDER-037902]

Evaluation of stress distribution in different miniplate designs: a study for finite element analysis/ Avaliação da distribuição de tensão em diferentes projetos de miniplacas: um estudo para análise de elementos finitos

The aim of the study is to propose new designs of orthodontic miniplates, which were evaluated by finite element method, regarding to the distribution of stress after application of forces on them. Six three-dimensional models of miniplates were designed, in the forms of T, Y, and I with inverted hook ends and two buttons. Forces of 1N, 2N, and 3N were applied in vertical, horizontal and diagonal directions. Charging was performed. It was observed that the T-shaped miniplates were the ones that showed greater tension accumulation located in the region of the neck of the device. The "Y" shaped miniplates showed a region of tension accumulation in the neck region of the accessory only when 3N horizontal force was applied and the "I" miniplates showed good voltage distribution in all magnitudes and directions of force applied. A different distribution of forces was still observed when the activation end was changed. In general, the miniplates showed a good distribution of tensions, being a good option of skeletal anchorage in orthodontic treatments

Real-time 3D object detection and SLAM fusion in a low-cost LiDAR test vehicle setup

Recently released research about deep learning applications related to perception for autonomous driving focuses heavily on the usage of LiDAR point cloud data as input for the neural networks, highlighting the importance of LiDAR technology in the field of Autonomous Driving (AD). In this sense, a great percentage of the vehicle platforms used to create the datasets released for the development of these neural networks, as well as some AD commercial solutions available on the market, heavily invest in an array of sensors, including a large number of sensors as well as several sensor modalities. However, these costs create a barrier to entry for low-cost solutions for the performance of critical perception tasks such as Object Detection and SLAM. This paper explores current vehicle platforms and proposes a low-cost, LiDAR-based test vehicle platform capable of running critical perception tasks (Object Detection and SLAM) in real time. Additionally, we propose the creation of a deep learning-based inference model for Object Detection deployed in a resource-constrained device, as well as a graph-based SLAM implementation, providing important considerations, explored while taking into account the real-time processing requirement and presenting relevant results demonstrating the usability of the developed work in the context of the proposed low-cost platform

A framework for representing, building and reusing novel atate-of-the-art three-dimensional object detection models in point clouds targeting self-driving applications

The rapid development of deep learning has brought novel methodologies for 3D object detection using LiDAR sensing technology. These improvements in precision and inference speed performances lead to notable high performance and real-time inference, which is especially important for self-driving purposes. However, the developments carried by these approaches overwhelm the research process in this area since new methods, technologies and software versions lead to different project necessities, specifications and requirements. Moreover, the improvements brought by the new methods may be due to improvements in newer versions of deep learning frameworks and not just the novelty and innovation of the model architecture. Thus, it has become crucial to create a framework with the same software versions, specifications and requirements that accommodate all these methodologies and allow for the easy introduction of new methods and models. A framework is proposed that abstracts the implementation, reusing and building of novel methods and models. The main idea is to facilitate the representation of state-of-the-art (SoA) approaches and simultaneously encourage the implementation of new approaches by reusing, improving and innovating modules in the proposed framework, which has the same software specifications to allow for a fair comparison. This makes it possible to determine if the key innovation approach outperforms the current SoA by comparing models in a framework with the same software specifications and requirements.This work has been supported by FCT—Fundação para a Ciência e Tecnologia within the R&D Units Project Scope: UIDB/00319/2020 and the project “Integrated and Innovative Solutions for the well-being of people in complex urban centers” within the Project Scope NORTE-01-0145-FEDER 000086. The work of Pedro Oliveira was supported by the doctoral Grant PRT/BD/154311/2022 financed by the Portuguese Foundation for Science and Technology (FCT), and with funds from European Union, under MIT Portugal Program. The work of Paulo Novais and Dalila Durães is supported by National Funds through the Portuguese funding agency, FCT—Fundação para a Ciência e a Tecnologia within project 2022.06822.PTDC

How Does the Level of Physical Activity Influence Eating Behavior? A Self-Determination Theory Approach

Physical activity and diet are two predominant determinants of population health status that may influence each other. Physical activity has been identified as a behavior that may lead to a healthier diet and regulates eating behaviors. This research aimed to investigate how the level of physical activity is associated with the motivation related to eating behaviors and, consequently, the eating style individuals have on a daily basis. This was a cross-sectional study in which participants completed an online questionnaire that assessed the following variables: the level of physical activity, the motivation toward eating behavior, and the type of eating behavior. In total, 440 individuals (180 men and 260 women) who regularly exercised in gyms and fitness centers aged between 19 and 64 years (M = 33.84; SD = 10.09) took part in the study. The data were collected following the Declaration of Helsinki and with the approval of the Ethics Committee of the Polytechnic of Leiria. For the statistical analysis, mean and standard deviations were first calculated, as well as bivariate correlations between all the variables of interest. Then, structural equation model analyses were performed considering the levels of physical activity as the independent variable, motivations toward eating behavior as the mediators, and eating styles as the dependent variables. It was concluded that a greater level of physical activity leads to a more self-determined type of eating regulation, which in turn results in less constricted eating behaviors that are influenced by external factors and emotional factors.info:eu-repo/semantics/publishedVersio

Bone impairment in adolescent female rats chronically exposed to ethanol

258-262Ethanol consumption has increased among teenagers worldwide considerably,including females. Long-term ethanol consumption in women has been reported to cause bone metabolism imbalance. However, only few studies are available on the impact of long-term ethanol consumptionon bone morphology during adolescence. Here, we report the effects of chronic ethanol consumption on bone structure in adolescent female rats. Twenty female Wistar rats (35 days old) received, by gavage, distilled water (control) or ethanol (6.5 g/kg/day, 22.5% w/v) once daily for 55 days. After ethanol administration, animals were perfused, and the femora were collected. Morphometric evaluations were performed by electron microscopy scanning. Femora length, cortical bone thickness and medullar bone diameter was measured. The results demonstrated that ethanol exposure during adolescence reduced the length of femurs, with a decrease of the anterior thickness, posterior thickness, and mid-lateral diameter (P<0.05). Thus, long-term ethanol intake may lead to alterations on bone morphometry, reducing the thickness of compact bone and femur length in adolescent females