The influence of low-frequency noise pollution on the quality of life and place in sustainable cities: a case study from northern Portugal

Discussing urban planning requires rethinking sustainability in cities and building healthy environments. Historically, some aspects of advancing the urban way of life have not been considered important in city planning. This is particularly the case where technological advances have led to conflicting land use, as with the installation of power poles and building electrical substations near residential areas. This research aims to discuss and rethink sustainability in cities, focusing on the environmental impact of low-frequency noise and electromagnetic radiation on human health. It presents data from a case study in an urban space in northern Portugal, and focuses on four guiding questions: Can power poles and power lines cause noise? Do power poles and power lines cause discomfort? Do power poles and power lines cause discomfort due to noise? Can power poles and power lines affect human health? To answer these questions, we undertook research between 2014 and 2015 that was comprised of two approaches. The first approach consisted of evaluating the noise of nine points divided into two groups â near the sourceâ (e.g., up to 50 m from power poles) and â away from the sourceâ (e.g., more than 250 m away from the source). In the second approach, noise levels were measured for 72 h in houses located up to 20 m from the source. The groups consist of residents living within the distance range specified for each group. The measurement values were compared with the proposed criteria for assessing low-frequency noise using the DEFRA Guidance (University of Salford). In the first approach, the noise caused discomfort, regardless of the group. In the second approach, the noise had fluctuating characteristics, which led us to conclude that the noise caused discomfort.

Hand gesture recognition for human computer interaction : a comparative study of different image features

Hand gesture recognition for human computer interaction, being a natural way of human computer interaction, is an area of active research in computer vision and machine learning. This is an area with many different possible applications, giving users a simpler and more natural way to communicate with robots/systems interfaces, without the need for extra devices. So, the primary goal of gesture recognition research is to create systems, which can identify specific human gestures and use them to convey information or for device control. For that, vision-based hand gesture interfaces require fast and extremely robust hand detection, and gesture recognition in real time. In this study we try to identify hand features that, isolated, respond better in various situations in human-computer interaction. The extracted features are used to train a set of classifiers with the help of RapidMiner in order to find the best learner. A dataset with our own gesture vocabulary consisted of 10 gestures, recorded from 20 users was created for later processing. Experimental results show that the radial signature and the centroid distance are the features that when used separately obtain better results, with an accuracy of 91% and 90,1% respectively obtained with a Neural Network classifier. These to methods have also the advantage of being simple in terms of computational complexity, which make them good candidates for real-time hand gesture recognition.

Osteogenic differentiation of human mesenchymal stem cells in the absence of osteogenic supplements: a surface-roughness gradient study

The use of biomaterials to direct osteogenic differentiation of human mesenchymal stem cells (hMSCs) in the absence of osteogenic supplements is thought to be part of the next generation of orthopedic implants. We previously engineered surface-roughness gradients of average roughness (Ra) varying from the sub-micron to the micrometer range ( 0.5–4.7 lm), and mean distance between peaks (RSm) gradually varying from 214 lm to 33 lm. Here we have screened the ability of such surface-gradients of polycaprolactone to influence the expression of alkaline phosphatase (ALP), collagen type 1 (COL1) and mineralization by hMSCs cultured in dexamethasone (Dex)-deprived osteogenic induction medium (OIM) and in basal growth medium (BGM). Ra 1.53 lm/RSm 79 lm in Dex-deprived OI medium, and Ra 0.93 lm/RSm 135 lm in BGM consistently showed higher effectiveness at supporting the expression of the osteogenic markers ALP, COL1 and mineralization, compared to the tissue culture polystyrene (TCP) control in complete OIM. The superior effectiveness of specific surface-roughness revealed that this strategy may be used as a compelling alternative to soluble osteogenic inducers in orthopedic applications featuring the clinically relevant biodegradable polymer polycaprolactone.