Staalmosley5926

With the response of the mFBAR and FID obtained in one injection, an injection mass-independent parameter can be calculated and used to identify the analyte of interest.Landslides are one of the most destructive natural hazards worldwide, affecting greatly built-up areas and critical infrastructure, causing loss of human lives, injuries, destruction of properties, and disturbance in everyday commute. Traditionally, landslides are monitored through time consuming and costly in situ geotechnical investigations and a wide range of conventional means, such as inclinometers and boreholes. Earth Observation and the exploitation of the freely available Copernicus datasets, and especially Sentinel-1 Synthetic Aperture Radar (SAR) images, can assist in the systematic monitoring of landslides, irrespective of weather conditions and time of day, overcoming the restrictions arising from in situ measurements. In the present study, a comprehensive statistical analysis of coherence obtained through processing of a time-series of Sentinel-1 SAR imagery was carried out to investigate and detect early indications of a landslide that took place in Cyprus on 15 February 2019. The application of the proposed methodology led to the detection of a sudden coherence loss prior to the landslide occurrence that can be used as input to Early Warning Systems, giving valuable on-time information about an upcoming landslide to emergency response authorities and the public, saving numerous lives. The statistical significance of the results was tested using Analysis of Variance (ANOVA) tests and two-tailed t-tests.Peak-to-peak intervals in Photoplethysmography (PPG) can be used for heart rate variability (HRV) estimation if the PPG is collected from a healthy person at rest. Many factors, such as a person's movements or hardware issues, can affect the signal quality and make some parts of the PPG signal unsuitable for reliable peak detection. Therefore, a robust HRV estimation algorithm should not only detect peaks, but also identify corrupted signal parts. We introduce such an algorithm in this paper. It uses continuous wavelet transform (CWT) for peak detection and a combination of features derived from CWT and metrics based on PPG signals' self-similarity to identify corrupted parts. We tested the algorithm on three different datasets a newly introduced Welltory-PPG-dataset containing PPG signals collected with smartphones using the Welltory app, and two publicly available PPG datasets TROIKAand PPG-DaLiA. The algorithm demonstrated good accuracy in peak-to-peak intervals detection and HRV metric estimation.Airport pavements should ensure regular and safe movements during their service life; the management body has to monitor the functional and structural characteristics, and schedule maintenance work, balancing the often conflicting goals of safety, economic and technical issues. This paper presents a remote monitoring system to evaluate the structural performance of a runway composed of concrete thresholds and a flexible central runway. Thermometers, strain gauges, and pressure cells will be embedded at different depths to continuously monitor the pavement's response to traffic and environmental loads. An innovative system allows data acquisition and processing with specific calculation models, in order to inform the infrastructure manager, in real time, about the actual conditions of the pavement. In this way, the authors aim to develop a system that provides useful information for the correct implementation of an airport pavement management system (APMS) based on real-life data. Indeed, it permits comprehensive monitoring functions to be performed, based on the embedded sensing network.This paper investigates damage identification metrics and their performance using a cantilever beam with a piezoelectric harvester for Structural Health Monitoring. In order to do this, the vibrations of three different beam structures are monitored in a controlled manner via two piezoelectric energy harvesters (PEH) located in two different positions. CP690550 One of the beams is an undamaged structure recognized as reference structure, while the other two are beam structures with simulated damage in form of drilling holes. Subsequently, five different damage identification metrics for detecting damage localization and extent are investigated in this paper. Overall, each computational model has been designed on the basis of the modified First Order Shear Theory (FOST), considering an MFC element consisting homogenized materials in the piezoelectric fiber layer. Frequency response functions are established and five damage metrics are assessed, three of which are relevant for damage localization and the other two for damage extent. Experiments carried out on the lab stand for damage structure with control damage by using a modal hammer allowed to verify numerical results and values of particular damage metrics. In the effect, it is expected that the proposed method will be relevant for a wide range of application sectors, as well as useful for the evolving composite industry.If damage to a building caused by an earthquake is not detected immediately, the opportunity to decide on quick action, such as evacuating the building, is lost. link2 For this reason, it is necessary to develop modern technologies that can quickly obtain the structural safety condition of buildings after an earthquake in order to resume economic and social activities and mitigate future damage by aftershocks. A methodology for the prediction of damage identification is proposed in this study. Using the wavelet spectrum of the absolute acceleration record measured by a single accelerometer located on the upper floor of a building as input data, a CNN model is trained to predict the damage information of the building. The maximum ductility factor, inter-story drift ratio, and maximum response acceleration of each floor are predicted as the damage information, and their accuracy is verified by comparing with the results of seismic response analysis using actual earthquakes. Finally, when an earthquake occurs, the proposed methodology enables immediate action by revealing the damage status of the building from the accelerometer observation records.Closed-form evaluation of key performance indicators (KPIs) of telecommunication networks help perform mathematical analysis under several network configurations. This paper deals with a recent mathematical approach of indefinite quadratic forms to propose simple albeit exact closed-form expressions of the expectation of two significant logarithmic functions. These functions formulate KPIs which include the ergodic capacity and leakage rate of multi-user multiple-input multiple-output (MU-MIMO) systems in Rayleigh fading channels. Our closed-form expressions are generic in nature and they characterize several network configurations under statistical channel state information availability. As a demonstrative example of the proposed characterization, the derived expressions are used in the statistical transmit beamformer design in a broadcast MU-MIMO system to portray promising diversity gains using standalone or joint maximization techniques of the ergodic capacity and leakage rate. The results presented are validated by Monte Carlo simulations.Recent developments in massive machine-type communication (mMTC) scenarios have given rise to never-seen requirements, which triggered the Industry 4.0 revolution. The new scenarios bring even more pressure to comply with the reliability and communication security and enable flawless functionality of the critical infrastructure, e.g., smart grid infrastructure. We discuss typical network grid architecture, communication strategies, and methods for building scalable and high-speed data processing and storage platform. This paper focuses on the data transmissions using the sets of standards IEC 60870-6 (ICCP/TASE.2). The main goal is to introduce the TASE.2 traffic generator and the data collection back-end with the implemented load balancing functionality to understand the limits of current protocols used in the smart grids. To this end, the assessment framework enabling generating and collecting TASE.2 communication with long-term data storage providing high availability and load balancing capabilities was developed. The designed proof-of-concept supports complete cryptographic security and allows users to perform the complex testing and verification of the TASE.2 network nodes configuration. Implemented components were tested in a cloud-based Microsoft Azure environment in four geographically separated locations. The findings from the testing indicate the high performance and scalability of the proposed platform, allowing the proposed generator to be also used for high-speed load testing purposes. The load-balancing performance shows the CPU usage of the load-balancer below 15% while processing 5000 messages per second. This makes it possible to achieve up to a 7-fold improvement of performance resulting in processing up to 35,000 messages per second.The purpose of this work was to describe the leg-muscle-generated push force characteristics in sprint kayak paddlers for females and males on water. Additionally, the relationship between leg pushing force characteristics and velocity was investigated. Twenty-eight paddlers participated in the study. The participants had five minutes of self-chosen warm-up and were asked to paddle at three different velocities, including maximal effort. Left- and right-side leg extension force were collected together with velocity. Linear regression analyses were performed with leg extension force characteristics as independent variables and velocity as the dependent variable. A second linear regression analysis investigated the effect of paddling velocity on different leg extension force characteristics with an explanatory model. The results showed that the leg pushing force elicits a sinus-like pattern, increasing and decreasing throughout the stroke cycle. Impulse over 10 s showed the highest correlation to maximum velocity (r = 0.827, p less then 0.01), while a strong co-correlation was observed between the impulse per stroke cycle and mean force (r = 0.910, p less then 0.01). The explanatory model results revealed that an increase in paddling velocity is, among other factors, driven by increased leg force. Maximal velocity could predict 68% of the paddlers' velocity within 1 km/h with peak leg force, impulse over 10 s, and stroke rate (p-value less then 0.001, adjusted R-squared = 0.8). Sprint kayak paddlers elicit a strong positive relationship between leg pushing forces and velocity. link3 The results confirm that sprint kayakers' cyclic leg movement is a key part of the kayaking technique.Virtual training systems are in an increasing demand because of real-world training, which requires a high cost or accompanying risk, and can be conducted safely through virtual environments. For virtual training to be effective for users, it is important to provide realistic training situations; however, virtual reality (VR) content using VR controllers for experiential learning differ significantly from real content in terms of tangible interactions. In this paper, we propose a method for enhancing the presence and immersion during virtual training by applying various sensors to tangible virtual training as a way to track the movement of real tools used during training and virtualizing the entire body of the actual user for transfer to a virtual environment. The proposed training system connects virtual and real-world spaces through an actual object (e.g., an automobile) to provide the feeling of actual touch during virtual training. Furthermore, the system measures the posture of the tools (steam gun and mop) and the degree of touch and applies them during training (e.