Alguns contributos da nanotecnologia para a sustentabilidade dos materiais de construção

Os avanços nanotecnológicos atingem inúmeras áreas da ciência, mas no âmbito da sustentabilidade dos materiais de construção os progressos embora importantes são escassos e acima de tudo objeto de reduzida divulgação. O presente artigo sintetiza uma avaliação do estado da arte relativa a alguns dos contributos da nanotecnologia para a sustentabilidade dos materiais de construção. No mesmo se aborda a compreensão dos compostos gerados durante a hidratação do cimento Portland, o aumento da resistência e da durabilidade de argamassas e betões pela adição de nanopartículas e nanotubos e de que forma a adição de nanopartículas pode contribuir para a autolimpeza, a purificação do ar e a capacidade bactericida em materiais construtivos por via do efeito fotocatalítico. O presente artigo aborda ainda os últimos desenvolvimentos da nanotecnologia com vista à eficiência energética, nomeadamente pela produção de isolamentos térmicos de elevado desempenho, janelas com baixa condutibilidade térmica, vidros com transmitância variável e materiais de mudança de fase mais eficientes

Contributos da nanotecnologia para a sustentabilidade dos materiais de construção

Dissertação de mestrado integrado em Engenharia CivilOs avanços nanotecnológicos atingem inúmeras áreas da ciência, mas no âmbito da sustentabilidade dos materiais de construção os progressos embora importantes são escassos e acima de tudo objeto de reduzida divulgação. O presente artigo sintetiza uma avaliação do estado da arte relativa a alguns dos contributos da nanotecnologia para a sustentabilidade dos materiais de construção. No mesmo, se aborda a compreensão dos compostos gerados durante a hidratação do cimento Portland, o aumento da resistência e da durabilidade de argamassas e betões pela adição de nanopartículas e nanotubos e de que forma a adição de nanopartículas pode contribuir para a autolimpeza, a purificação do ar e a capacidade bactericida em materiais construtivos por via do efeito fotocatalítico. A presente dissertação de mestrado aborda ainda os últimos desenvolvimentos da nanotecnologia com vista à eficiência energética, nomeadamente pela produção de isolamentos térmicos de elevado desempenho, janelas com baixa condutibilidade térmica, vidros com transmitância variável e materiais de mudança de fase mais eficientes. Os contributos da nanotecnologia para a ecoeficiência dos materiais de construção são muito amplos, mas quase nada foi, ainda, conquistado comparado com as verdadeiras potencialidades que a nanotecnologia oferece à engenharia civil. Os produtos nanotecnológicos são ainda muito caros e difíceis de produzir em massa e com a qualidade desejada, mas prevê-se que o constante investimento e novas descobertas sejam alcançadas para tornar a própria nanotecnologia mais sustentável.Advances in nanotechnology have impacts in many areas of science. However, within building materials and its sustainability, the advances are few and subject to reduced disclosure. This article summarizes an evaluation of the state of the art relating to some of the contributions of nanotechnology to the sustainability of building materials. In it, is approached the understanding of compounds generated during the hydration of Portland cement, increased strength and durability of mortar and concrete by adding nanoparticles and Nano carbon filaments and how the addition of nanoparticles may contribute to self-cleaning, air purification and bactericidal capacity in building materials by means of photo catalytic effect. This dissertation also discusses the latest developments in nanotechnology aimed at energy efficiency, including the production of high performance thermal insulation, windows with low thermal conductivity, glasses with variable transmittance and more efficient phase change materials. Contributions of nanotechnology to eco-efficiency of building materials are wide, but almost nothing has yet achieved compared with the real potential that nanotechnology offers for civil engineering. Nanotechnology products are still very expensive and hard to mass-produce with the desired quality, but it´s expected that the ongoing investment and new discoveries be achieved to make nanotechnology itself more sustainable

Development and characterization of a novel hybrid tissue engineering-based scaffold for spinal cord injury repair

Spinal cord injury (SCI) represents a significant health and social problem, and therefore it is vital to develop novel strategies that can specifically target it. In this context, the objective of the present work was to develop a new range of three-dimensional (3D) tubular structures aimed at inducing the regeneration within SCI sites. Up to six different 3D tubular structures were initially developed by rapid prototyping: 3D bioplotting–based on a biodegradable blend of starch. These structures were then further complemented by injecting Gellan Gum, a polysaccharide-based hydrogel, in the central area of structures. The mechanical properties of these structures were assessed using dynamic mechanical analysis, under both dry and wet conditions, and their morphologies= porosities were analyzed using micro-computed tomography and scanning electron microscopy. Biological evaluation was carried out to determine their cytotoxicity, using both minimum essential medium (MEM) extraction and MTS tests, as well as by encapsulation of an oligodendrocyte-like cell (M03-13 cell line) within the hydrogel phase. The histomorphometric analysis showed a fully interconnected network of pores with porosity ranging from 70% to 85%. Scaffolds presented compressive modulus ranging from 17.4 to 62.0MPa and 4.42 to 27.4 MPa under dry and wet conditions, respectively. Cytotoxicity assays revealed that the hybrid starch=poly-ecaprolactone= Gellan Gum scaffolds were noncytotoxic, as they did not cause major alterations on cell morphology, proliferation, and metabolic viability. Moreover, preliminary cell encapsulation assays showed that the hybrid scaffolds could support the in vitro culture of oligodendrocyte-like cells. Finally, preliminary in vivo studies conducted in a hemisection rat SCI model revealed that the above-referred structures were well integrated within the injury and did not trigger chronic inflammatory processes. The results herein presented indicate that these 3D systems might be of use in future SCI regeneration approaches.Portuguese Foundation for Science and Technology through funds from Programa Operacional Ciencia, Tecnologia, Inovacao (POCTI) and/or Fundo Europeu de Desenvolvimento Regional (FEDER) programs (funding to ICVS, 3B's Research Group, predoctoral and postdoctoral fellowships to N. A. Silva, J. T. Oliveira, A. J. Salgado, and R. A. Sousa-SFRH/BD/40684/2007; SFRH/BD/17135/2004; SFRH/BPD/17595/2004; SFRH/BPD/17151/2004)

Re-Identification in Urban Scenarios: A Review of Tools and Methods

With the widespread use of surveillance image cameras and enhanced awareness of public security, objects, and persons Re-Identification (ReID), the task of recognizing objects in non-overlapping camera networks has attracted particular attention in computer vision and pattern recognition communities. Given an image or video of an object-of-interest (query), object identification aims to identify the object from images or video feed taken from different cameras. After many years of great effort, object ReID remains a notably challenging task. The main reason is that an object's appearance may dramatically change across camera views due to significant variations in illumination, poses or viewpoints, or even cluttered backgrounds. With the advent of Deep Neural Networks (DNN), there have been many proposals for different network architectures achieving high-performance levels. With the aim of identifying the most promising methods for ReID for future robust implementations, a review study is presented, mainly focusing on the person and multi-object ReID and auxiliary methods for image enhancement. Such methods are crucial for robust object ReID, while highlighting limitations of the identified methods. This is a very active field, evidenced by the dates of the publications found. However, most works use data from very different datasets and genres, which presents an obstacle to wide generalized DNN model training and usage. Although the model's performance has achieved satisfactory results on particular datasets, a particular trend was observed in the use of 3D Convolutional Neural Networks (CNN), attention mechanisms to capture object-relevant features, and generative adversarial training to overcome data limitations. However, there is still room for improvement, namely in using images from urban scenarios among anonymized images to comply with public privacy legislation. The main challenges that remain in the ReID field, and prospects for future research directions towards ReID in dense urban scenarios, are also discussed

Assessment of potato peel and agro-forestry biochars supplementation on in vitro ruminal fermentation

UIDB/50006/2020 UIDB/04033/2020 grant ref. PDE/BDE/114434/2016 DL 57/2016 -Norma transitória.Background. The awareness of environmental and socio-economic impacts caused by greenhouse gas emissions from the livestock sector leverages the adoption of strategies to counteract it. Feed supplements can play an important role in the reduction of the main greenhouse gas produced by ruminants-methane (CH4). In this context, this study aims to assess the effect of two biochar sources and inclusion levels on rumen fermentation parameters in vitro. Methods. Two sources of biochar (agro-forestry residues, AFB, and potato peel, PPB) were added at two levels (5 and 10%, dry matter (DM) basis) to two basal substrates (haylage and corn silage) and incubated 24-h with rumen inocula to assess the effects on CH4 production and main rumen fermentation parameters in vitro. Results. AFB and PPB were obtained at different carbonization conditions resulting in different apparent surface areas, ash content, pH at the point of zero charge (pHpzc), and elemental analysis. Relative to control (0% biochar), biochar supplementation kept unaffected total gas production and yield (mL and mL/g DM, pD0.140 and pD0.240, respectively) and fermentation pH (p D 0.666), increased CH4 production and yield (mL and mL/g DM, respectively, pD0.001) and ammonia-N (NH3-N, pD0.040), and decreased total volatile fatty acids (VFA) production (p < 0.001) and H2 generated and consumed (p ≤ 0.001). Biochar sources and inclusion levels had no negative effect on most of the fermentation parameters and efficiency. Acetic.propionic acid ratio (pD0.048) and H2 consumed (pD0.019) were lower with AFB inclusion when compared to PPB. Biochar inclusion at 10% reduced H2 consumed (p < 0.001) and tended to reduce total gas production (pD0.055). Total VFA production (pD0.019), acetic acid proportion (pD0.011) and H2 generated (pD0.048) were the lowest with AFB supplemented at 10%, no differences being observed among the other treatments. The basal substrate affected most fermentation parameters independently of biochar source and level used. Discussion. Biochar supplementation increased NH3-N content, iso-butyric, iso-valeric and valeric acid proportions, and decreased VFA production suggesting a reduced energy supply for microbial growth, higher proteolysis and deamination of substrate N, and a decrease of NH3-N incorporation into microbial protein. No interaction was found between substrate and biochar source or level on any of the parameters measured. Although AFB and PPB had different textural and compositional characteristics, their effects on the rumen fermentation parameters were similar, the only observed effects being due to AFB included at 10%. Biochar supplementation promoted CH4 production regardless of the source and inclusion level, suggesting that there may be other effects beyond biomass and temperature of production of biochar, highlighting the need to consider other characteristics to better identify the mechanism by which biochar may influence CH4 production.publishersversionpublishe

Barley heads east: Genetic analyses reveal routes of spread through diverse Eurasian landscapes

One of the world’s most important crops, barley, was domesticated in the Near East around 11,000 years ago. Barley is a highly resilient crop, able to grown in varied and marginal environments, such as in regions of high altitude and latitude. Archaeobotanical evidence shows that barley had spread throughout Eurasia by 2,000 BC. To further elucidate the routes by which barley cultivation was spread through Eurasia, simple sequence repeat (SSR) analysis was used to determine genetic diversity and population structure in three extant barley taxa: domesticated barley (Hordeum vulgare L. subsp. vulgare), wild barley (H. vulgare subsp. spontaneum) and a six-rowed brittle rachis form (H. vulgare subsp. vulgare f. agriocrithon (Åberg) Bowd.). Analysis of data using the Bayesian clustering algorithm InStruct suggests a model with three ancestral genepools, which captures a major split in the data, with substantial additional resolution provided under a model with eight genepools. Our results indicate that H. vulgare subsp. vulgare f. agriocrithon accessions and Tibetan Plateau H. vulgare subsp. spontaneum are closely related to the H. vulgare subsp. vulgare in their vicinity, and are therefore likely to be feral derivatives of H. vulgare subsp. vulgare. Under the eight genepool model, cultivated barley is split into six ancestral genepools, each of which has a distinct distribution through Eurasia, along with distinct morphological features and flowering time phenotypes. The distribution of these genepools and their phenotypic characteristics is discussed together with archaeological evidence for the spread of barley eastwards across Eurasia

Urban Safety: An Image-Processing and Deep-Learning-Based Intelligent Traffic Management and Control System

With the rapid growth and development of cities, Intelligent Traffic Management and Control (ITMC) is becoming a fundamental component to address the challenges of modern urban traffic management, where a wide range of daily problems need to be addressed in a prompt and expedited manner. Issues such as unpredictable traffic dynamics, resource constraints, and abnormal events pose difficulties to city managers. ITMC aims to increase the efficiency of traffic management by minimizing the odds of traffic problems, by providing real-time traffic state forecasts to better schedule the intersection signal controls. Reliable implementations of ITMC improve the safety of inhabitants and the quality of life, leading to economic growth. In recent years, researchers have proposed different solutions to address specific problems concerning traffic management, ranging from image-processing and deep-learning techniques to forecasting the traffic state and deriving policies to control intersection signals. This review article studies the primary public datasets helpful in developing models to address the identified problems, complemented with a deep analysis of the works related to traffic state forecast and intersection-signal-control models. Our analysis found that deep-learning-based approaches for short-term traffic state forecast and multi-intersection signal control showed reasonable results, but lacked robustness for unusual scenarios, particularly during oversaturated situations, which can be resolved by explicitly addressing these cases, potentially leading to significant improvements of the systems overall. However, there is arguably a long path until these models can be used safely and effectively in real-world scenarios

Transformers for Urban Sound Classification - A Comprehensive Performance Evaluation

Many relevant sound events occur in urban scenarios, and robust classification models are required to identify abnormal and relevant events correctly. These models need to identify such events within valuable time, being effective and prompt. It is also essential to determine for how much time these events prevail. This article presents an extensive analysis developed to identify the best-performing model to successfully classify a broad set of sound events occurring in urban scenarios. Analysis and modelling of Transformer models were performed using available public datasets with different sets of sound classes. The Transformer models’ performance was compared to the one achieved by the baseline model and end-to-end convolutional models. Furthermore, the benefits of using pre-training from image and sound domains and data augmentation techniques were identified. Additionally, complementary methods that have been used to improve the models’ performance and good practices to obtain robust sound classification models were investigated. After an extensive evaluation, it was found that the most promising results were obtained by employing a Transformer model using a novel Adam optimizer with weight decay and transfer learning from the audio domain by reusing the weights from AudioSet, which led to an accuracy score of 89.8% for the UrbanSound8K dataset, 95.8% for the ESC-50 dataset, and 99% for the ESC-10 dataset, respectively

Sound Classification and Processing of Urban Environments: A Systematic Literature Review

Audio recognition can be used in smart cities for security, surveillance, manufacturing, autonomous vehicles, and noise mitigation, just to name a few. However, urban sounds are everyday audio events that occur daily, presenting unstructured characteristics containing different genres of noise and sounds unrelated to the sound event under study, making it a challenging problem. Therefore, the main objective of this literature review is to summarize the most recent works on this subject to understand the current approaches and identify their limitations. Based on the reviewed articles, it can be realized that Deep Learning (DL) architectures, attention mechanisms, data augmentation techniques, and pretraining are the most crucial factors to consider while creating an efficient sound classification model. The best-found results were obtained by Mushtaq and Su, in 2020, using a DenseNet-161 with pretrained weights from ImageNet, and NA-1 and NA-2 as augmentation techniques, which were of 97.98%, 98.52%, and 99.22% for UrbanSound8K, ESC-50, and ESC-10 datasets, respectively. Nonetheless, the use of these models in real-world scenarios has not been properly addressed, so their effectiveness is still questionable in such situations