Activity

For selected systems, the characteristics of air voids in hardened concrete were also defined. The beneficial effect of ground waste glass added as a filler to the concrete mixture on the strength and durability of concrete was confirmed by the obtained test results.Grape pomace powder (GPP), a by-product from the winemaking process, was used to substitute flour for wheat bread fortification within 0, 5, and 10 g/100 g. Rheological properties of control and fortified doughs, along with physicochemical and nutritional characteristics, antioxidant activity, and the sensory analysis of the obtained bread were considered. The GPP addition influenced the doughs’ rheological properties by generating more tenacious and less extensible products. Concerning bread, pH values and volume of fortified products decreased as the GPP inclusion level increased in the recipe. Total phenolic compounds and the antioxidant capacity of bread samples, evaluated by FRAP (ferric reducing ability of plasma) and ABTS (2,2′-azino-bis (3-ethylbenzothiazoline-6-sulfonic acid)) assays, increased with GPP addition. Moreover, the GPP inclusion level raised the total dietary fiber content of bread. Regarding sensory evaluation, GPP fortification had a major impact on the acidity, the global flavor, the astringency, and the wine smell of bread samples without affecting the overall bread acceptability. The current results suggest that GPP could be an attractive ingredient used to obtain fortified bread, as it is a source of fiber and polyphenols with potentially positive effects on human health.Age-related health issues have been increasing with the rise of life expectancy all over the world. One of these problems is cognitive impairment, which causes elderly people to have problems performing their daily activities. Detection of cognitive impairment at an early stage would enable medical doctors to deepen diagnosis and follow-up on patient status. Recent studies show that daily activities can be used to assess the cognitive status of elderly people. Additionally, the intrinsic structure of activities and the relationships between their sub-activities are important clues for capturing the cognitive abilities of seniors. Existing methods perceive each activity as a stand-alone unit while ignoring their inner structural relationships. This study investigates such relationships by modelling activities hierarchically from their sub-activities, with the overall goal of detecting abnormal activities linked to cognitive impairment. For this purpose, recursive auto-encoders (RAE) and their linear vs. greedy and supervised vs. semi-supervised variants are adopted to model the activities. Then, abnormal activities are systematically detected using RAE’s reconstruction error. Moreover, to apply RAEs for this problem, we introduce a new sensor representation called raw sensor measurement (RSM) that captures the intrinsic structure of activities, such as the frequency and the order of sensor activations. As real-world data are not accessible, we generated data by simulating abnormal behaviour, which reflects on cognitive impairment. learn more Extensive experiments show that RAEs can be used as a decision-supporting tool, especially when the training set is not labelled to detect early indicators of dementia.After an extensive voting period, we are proud to present the winner of the Micromachines Young Investigator Award to […].The impairment of skeletal muscle function is one of the most debilitating least understood co-morbidity that accompanies acromegaly (ACRO). Despite being one of the major determinants of these patients’ poor quality of life, there is limited evidence related to the underlying mechanisms and treatment options. Although growth hormone (GH) and insulin-like growth factor-1 (IGF-1) levels are associated, albeit not indisputable, with the presence and severity of ACRO myopathies the precise effects attributed to increased GH or IGF-1 levels are still unclear. Yet, cell lines and animal models can help us bridge these gaps. This review aims to describe the evidence regarding the role of GH and IGF-1 in muscle anabolism, from the basic to the clinical setting with special emphasis on ACRO. We also pinpoint future perspectives and research lines that should be considered for improving our knowledge in the field.Among various localization methods, a localization method that uses a radio frequency signal-based wireless sensor network has been widely applied due to its robustness against noise factors and few limits on installation location. In this paper, we focus on an iterative localization scheme for a mobile with a limited number of time difference of arrival (TDOA) and angle of arrival (AOA) data measured from base stations. To acquire the optimal location of a mobile, we propose a recursive solution for localization using an iteratively reweighted-recursive least squares (IR-RLS) algorithm. The proposed IR-RLS scheme can obtain the optimal solution with a fast computational speed when additional TDOA and/or AOA data is measured from base stations. Moreover, while the number of measured TDOA/AOA data was limited, the proposed IR-RLS scheme could obtain the precise location of a mobile. The performance of the proposed IR-RLS method is confirmed through some simulation results.In this study, we proposed a model combing parallel imaging (PI) with generative adversarial network (GAN) architecture (PIC-GAN) for accelerated multi-channel magnetic resonance imaging (MRI) reconstruction. This model integrated data fidelity and regularization terms into the generator to benefit from multi-coils information and provide an “end-to-end” reconstruction. Besides, to better preserve image details during reconstruction, we combined the adversarial loss with pixel-wise loss in both image and frequency domains. The proposed PIC-GAN framework was evaluated on abdominal and knee MRI images using 2, 4 and 6-fold accelerations with different undersampling patterns. The performance of the PIC-GAN was compared to the sparsity-based parallel imaging (L1-ESPIRiT), the variational network (VN), and conventional GAN with single-channel images as input (zero-filled (ZF)-GAN). Experimental results show that our PIC-GAN can effectively reconstruct multi-channel MR images at a low noise level and improved structure similarity of the reconstructed images.