Herbertcates5240

3 times and reached 13.5%, which is more than 3 orders of magnitude higher than that of the device without the engineered antenna. The peak responsivity was further enhanced by 19.9 times and responsivity reached 1500 V/W at 1 THz. The resonant frequency can be tuned by changing the size of the antenna. Over the frequency range of 0.5 THz to 1.5 THz, the peak responsivity was further enhanced by 8.1 to 19.9 times, and the polarization extinction ratio was enhanced by 2.7 to 22.3 times. The highest polarization extinction ratio reached 3.04 × 105 at 0.5 THz. The results are based on the numerical simulations of the light and the thermal fields.This study sought to evaluate the training load in different age category soccer players associated with distinct pitch size small-sided games (SSGs). Twenty-four soccer players (eight in each age category U-12, U-15, and U-23) performed three consecutive 4 vs. 4 ball possession SSGs (SSG1 16 × 24 m; SSG2 20 × 30 m; and SSG3 24 × 36 m) all with 3 min duration and 3 min rest. Subjects carried ultra-wideband-based position-tracking system devices (WIMU PRO, RealTrack System). Total distance covered increased from SSG1 to SSG3 in all age categories and predominantly in running speeds below 12 km·h-1. Moreover, distance covered in 12-18 km·h-1 running speed was different in all performed SSGs and age categories. Residual or null values were observed at 18-21 km·h-1 or above running speed, namely in U-12, the only age category where metabolic power and high metabolic load distance differences occurred throughout the performed SSGs. Edwards' TRIMP differences between age categories was only observed in SSG2 (U-12 less then U-15). The design of SSGs must consider that the training load of the players differs according to their age category and metabolic assessment should be considered in parallel to external load evaluation in SSGs. Wearable technology represents a fundamental support in soccer.A pervasive assessment of air quality in an urban or mobile scenario is paramount for personal or city-wide exposure reduction action design and implementation. The capability to deploy a high-resolution hybrid network of regulatory grade and low-cost fixed and mobile devices is a primary enabler for the development of such knowledge, both as a primary source of information and for validating high-resolution air quality predictive models. The capability of real-time and cumulative personal exposure monitoring is also considered a primary driver for exposome monitoring and future predictive medicine approaches. Leveraging on chemical sensing, machine learning, and Internet of Things (IoT) expertise, we developed an integrated architecture capable of meeting the demanding requirements of this challenging problem. A detailed account of the design, development, and validation procedures is reported here, along with the results of a two-year field validation effort.The penetration of wearable devices in our daily lives is unstoppable. Although they are very popular, so far, these elements provide a limited range of services that are mostly focused on monitoring tasks such as fitness, activity, or health tracking. Besides, given their hardware and power constraints, wearable units are dependent on a master device, e.g., a smartphone, to make decisions or send the collected data to the cloud. However, a new wave of both communication and artificial intelligence (AI)-based technologies fuels the evolution of wearables to an upper level. Concretely, they are the low-power wide-area network (LPWAN) and tiny machine-learning (TinyML) technologies. This paper reviews and discusses these solutions, and explores the major implications and challenges of this technological transformation. Finally, the results of an experimental study are presented, analyzing (i) the long-range connectivity gained by a wearable device in a university campus scenario, thanks to the integration of LPWAN communications, and (ii) how complex the intelligence embedded in this wearable unit can be. This study shows the interesting characteristics brought by these state-of-the-art paradigms, concluding that a wide variety of novel services and applications will be supported by the next generation of wearables.The switch and crossing (S&C) is one of the most important parts of the railway infrastructure network due to its significant influence on traffic delays and maintenance costs. Two central questions were investigated in this paper (I) the first question is related to the feasibility of exploring the vibration data for wear size estimation of railway S&C and (II) the second one is how to take advantage of the Artificial Intelligence (AI)-based framework to design an effective early-warning system at early stage of S&C wear development. The aim of the study was to predict the amount of wear in the entire S&C, using medium-range accelerometer sensors. Vibration data were collected, processed, and used for developing accurate data-driven models. Within this study, AI-based methods and signal-processing techniques were applied and tested in a full-scale S&C test rig at Lulea University of Technology to investigate the effectiveness of the proposed method. A real-scale railway wagon bogie was used to study different relevant types of wear on the switchblades, support rail, middle rail, and crossing part. All the sensors were housed inside the point machine as an optimal location for protection of the data acquisition system from harsh weather conditions such as ice and snow and from the ballast. The vibration data resulting from the measurements were used to feed two different deep-learning architectures, to make it possible to achieve an acceptable correlation between the measured vibration data and the actual amount of wear. The first model is based on the ResNet architecture where the input data are converted to spectrograms. The second model was based on a long short-term memory (LSTM) architecture. The proposed model was tested in terms of its accuracy in wear severity classification. The results show that this machine learning method accurately estimates the amount of wear in different locations in the S&C.

The effectiveness of transcranial direct current stimulation (tDCS) in the upper limb (UL) motor rehabilitation of stroke has been widely studied. However, the long-term maintenance of its improvements has not yet been proven.

A systematic search was conducted in MEDLINE/Pubmed, Web of Science, PEDRo, and Scopus databases from inception to April 2021. Randomized controlled trials were included if they performed a tDCS intervention combined with UL rehabilitation in stroke patients, performed several sessions (five or more), and assessed long-term results (at least three-month follow-up). Risk of bias and methodological quality were evaluated with the Cochrane RoB-2 and the Oxford quality scoring system.

Nine studies were included, showing a high methodological quality. Findings regarding UL were categorized into (1) functionality, (2) strength, (3) spasticity. GSK-3 activity All the studies that showed significant improvements retained them in the long term. Baseline functionality may be a limiting factor in achieving motor improvements, but not in sustaining them over the long term.

It seems that the improvements achieved during the application of tDCS combined with UL motor rehabilitation in stroke were preserved until the follow-up time (from 3 months to 1 year). Further studies are needed to clarify the long-term effects of tDCS.

It seems that the improvements achieved during the application of tDCS combined with UL motor rehabilitation in stroke were preserved until the follow-up time (from 3 months to 1 year). Further studies are needed to clarify the long-term effects of tDCS.Identifying cracks in the incipient state is essential to prevent the failure of engineering structures. Detection methods relying on the analysis of the changes in modal parameters are widely used because of the advantages they present. In our previous research, we found that eigenfrequencies were capable of indicating the position and depth of damage when sufficient vibration modes were considered. The damage indicator we developed was based on the relative frequency shifts (RFS). To calculate the RFSs for various positions and depths of a crack, we established a mathematical relation that involved the squared modal curvatures in the healthy state and the deflection of the healthy and damaged beam under dead mass, respectively. In this study, we propose to calculate the RFS for beams with several cracks by applying the superposition principle. We demonstrate that this is possible if the cracks are far enough from each other. In fact, if the cracks are close to each other, the superposition method does not work and we distinguish two cases (i) when the cracks affect the same beam face, the frequency drop is less than the sum of the individual frequency drops, and (ii) on the contrary, cracks on opposite sides cause a decrease in frequency, which is greater than the sum of the frequency drop due to individual damage. When the RFS curves are known, crack assessment becomes an optimization problem, the cost function being the distance between the measured RFSs and all possible RFSs for several vibration modes. Thus, the RFS constitutes a benchmark that characterizes damage using only the eigenfrequencies. We can accurately locate multiple cracks and estimate their severity through experiments and thus prove the reliability of the proposed method.Root zone soil moisture (RZSM) is an essential variable for weather and hydrological prediction models. Satellite-based microwave observations have been frequently utilized for the estimation of surface soil moisture (SSM) at various spatio-temporal resolutions. Moreover, previous studies have shown that satellite-based SSM products, coupled with the soil moisture analytical relationship (SMAR) can estimate RZSM variations. However, satellite-based SSM products are of low-resolution, rendering the application of the above-mentioned approach for local and pointwise applications problematic. This study initially attempted to estimate SSM at a finer resolution (1 km) using a downscaling technique based on a linear equation between AMSR2 SM data (25 km) with three MODIS parameters (NDVI, LST, and Albedo); then used the downscaled SSM in the SMAR model to monitor the RZSM for Rafsanjan Plain (RP), Iran. The performance of the proposed method was evaluated by measuring the soil moisture profile at ten stations in RP. The results of this study revealed that the downscaled AMSR2 SM data had a higher accuracy in relation to the ground-based SSM data in terms of MAE (↓0.021), RMSE (↓0.02), and R (↑0.199) metrics. Moreover, the SMAR model was run using three different SSM input data with different spatial resolution (a) ground-based SSM, (b) conventional AMSR2, and (c) downscaled AMSR2 products. The results showed that while the SMAR model itself was capable of estimating RZSM from the variation of ground-based SSM data, its performance increased when using downscaled SSM data suggesting the potential benefits of proposed method in different hydrological applications.