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Human-to-Cattle Mycobacterium tb Complicated Transmitting in the usa.
Recently, wireless sensor networks (WSNs) have been extensively deployed to monitor environments. Sensor nodes are susceptible to fault generation due to hardware and software failures in harsh environments. Anomaly detection for the time-series streaming data of sensor nodes is a challenging but critical fault diagnosis task, particularly in large-scale WSNs. The data-driven approach is becoming essential for the goal of improving the reliability and stability of WSNs. We propose a data-driven anomaly detection approach in this paper, named median filter (MF)-stacked long short-term memory-exponentially weighted moving average (LSTM-EWMA), for time-series status data, including the operating voltage and panel temperature recorded by a sensor node deployed in the field. These status data can be used to diagnose device anomalies. First, a median filter (MF) is introduced as a preprocessor to preprocess obvious anomalies in input data. Then, stacked long short-term memory (LSTM) is employed for prediction. Finally, the exponentially weighted moving average (EWMA) control chart is employed as a detector for recognizing anomalies. We evaluate the proposed approach for the panel temperature and operating voltage of time-series streaming data recorded by wireless node devices deployed in harsh field conditions for environmental monitoring. Extensive experiments were conducted on real time-series status data. The results demonstrate that compared to other approaches, the MF-stacked LSTM-EWMA approach can significantly improve the detection rate (DR) and false rate (FR). The average DR and FR values with the proposed approach are 95.46% and 4.42%, respectively. MF-stacked LSTM-EWMA anomaly detection also achieves a better F2 score than that achieved by other methods. The proposed approach provides valuable insights for anomaly detection in WSNs by detecting anomalies in the time-series status data recorded by wireless sensor nodes.Anomaly detection in the performance of the huge number of elements that are part of cellular networks (base stations, core entities, and user equipment) is one of the most time consuming and key activities for supporting failure management procedures and ensuring the required performance of the telecommunication services. This activity originally relied on direct human inspection of cellular metrics (counters, key performance indicators, etc.). Currently, degradation detection procedures have experienced an evolution towards the use of automatic mechanisms of statistical analysis and machine learning. However, pre-existent solutions typically rely on the manual definition of the values to be considered abnormal or on large sets of labeled data, highly reducing their performance in the presence of long-term trends in the metrics or previously unknown patterns of degradation. In this field, the present work proposes a novel application of transform-based analysis, using wavelet transform, for the detection and study of network degradations. The proposed system is tested using cell-level metrics obtained from a real-world LTE cellular network, showing its capabilities to detect and characterize anomalies of different patterns and in the presence of varied temporal trends. This is performed without the need for manually establishing normality thresholds and taking advantage of wavelet transform capabilities to separate the metrics in multiple time-frequency components. Our results show how direct statistical analysis of these components allows for a successful detection of anomalies beyond the capabilities of detection of previous methods.Microfluidic separation technology has garnered significant attention over the past decade where particles are being separated at a micro/nanoscale in a rapid, low-cost, and simple manner. Amongst a myriad of separation technologies that have emerged thus far, acoustic microfluidic separation techniques are extremely apt to applications involving biological samples attributed to various advantages, including high controllability, biocompatibility, and non-invasive, label-free features. With that being said, downsides such as low throughput and dependence on external equipment still impede successful commercialization from laboratory-based prototypes. Here, we present a comprehensive review of recent advances in acoustic microfluidic separation techniques, along with exemplary applications. Specifically, an inclusive overview of fundamental theory and background is presented, then two sets of mechanisms underlying acoustic separation, bulk acoustic wave and surface acoustic wave, are introduced and discussed. Upon these summaries, we present a variety of applications based on acoustic separation. The primary focus is given to those associated with biological samples such as blood cells, cancer cells, proteins, bacteria, viruses, and DNA/RNA. Finally, we highlight the benefits and challenges behind burgeoning developments in the field and discuss the future perspectives and an outlook towards robust, integrated, and commercialized devices based on acoustic microfluidic separation.Background In recent years, there has been a growing international interest in family quality of life The objective of this systematic review is to understand and analyze the conceptualization of the quality of life of families with children with disabilities between 0 and 6 years of age, the instruments for their measurement and the most relevant research results. Method A bibliographic search was conducted in the Web of Science, Scopus and Eric databases of studies published in English and Spanish from 2000 to July 2019 focused on "family quality of life" or "quality of family life" in the disability field. A total of 63 studies were selected from a total of 1119 and analyzed for their theoretical and applied contributions to the field of early care. Results The functional conceptualization of family quality of life predominates in this area, and a nascent and enriching holistic conceptualization is appreciated. There are three instruments that measure family quality of life in early care, although none of them is based on unified theory of FQoL; none of them focus exclusively on the age range 0-6 nor do they cover all disabilities. Conclusions The need to deepen the dynamic interaction of family relationships and to understand the ethical requirement that the methods used to approach family quality of life respect the holistic nature of the research is noted.The paper presents a computational study of the magnetocaloric properties of the V12 polyoxovanadate molecular magnet. The description is restricted to low-temperature range (below approximately 100 K), where the magnetic properties of the system in question can be sufficiently modelled by considering a tetramer that consists of four vanadium ions with spins S=1/2. The discussion is focused on the magnetocaloric effect in the cryogenic range. The exact and numerical diagonalization of the corresponding Hamiltonian is used in order to construct the thermodynamic description within a version of the canonical ensemble. The thermodynamic quantities of interest, such as magnetic entropy, specific heat, entropy change under isothermal magnetization/demagnetization, temperature change under adiabatic magnetization/demagnetization, refrigerant capacity, and magnetic Grüneisen ratio, are calculated and discussed extensively. The importance of two quantum level crossings for the described properties is emphasized. The significant ranges of direct and inverse magnetocaloric effect are predicted. In particular, the maximized inverse magnetocaloric response is found for cryogenic temperatures.The world population is growing in an accelerated way urging the need for a more efficient and sustainable agricultural industry. Initially developed for smart cities which face the same challenges caused by an increasing population, Internet of Things (IoT) technologies have evolved rapidly over the last few years and are now moving successfully to agriculture. Wireless Sensor Networks (WSNs) have been reported to be used in the agri-food sector and could answer the call for a more optimized agricultural management. This paper investigates a PCB-made interdigited capacitive (IDC) soil humidity sensor as a low-price alternative to the existing ones on the market. An in-depth comparative study is performed on 30 design variations, part of them also manufactured for further investigations. By measurements and simulations, the influence of the aspect ratio and dielectric thickness on the sensitivity and capacitance of the sensor are studied. this website In the end, a Humidity and Temperature Measurement Wireless Equipment (HTMWE) for IoT agriculture applications is implemented with this type of sensor.As a growing number of exploration missions have successfully landed on the Moon in recent decades, ground infrastructures, such as radio beacons, have attracted a great deal of attention in the design of navigation systems. None of the available studies regarding integrating beacon measurements for pinpoint landing have considered uncertain initial beacon locations, which are quite common in practice. In this paper, we propose a radio beacon/inertial measurement unit (IMU)/altimeter localization scheme that is sufficiently robust regarding uncertain initial beacon locations. This scheme was designed based on the sparse extended information filter (SEIF) to locate the lander and update the beacon configuration at the same time. Then, an adaptive iterated sparse extended hybrid filter (AISEHF) was devised by modifying the prediction and update stage of SEIF with a hybrid-form propagation and a damping iteration algorithm, respectively. The simulation results indicated that the proposed method effectively reduced the error in the position estimations caused by uncertain beacon locations and made an effective trade-off between the estimation accuracy and the computational efficiency. Thus, this method is a potential candidate for future lunar exploration activities.This paper investigated the static behaviour of glass fibre reinforced polymer (GFRP) built-up hollow and concrete filled built-up beams tested under four-point bending with a span-to-depth ratio of 1.67, therefore focusing their shear performance. Two parameters considered for hollow sections were longitudinal web stiffener and strengthening at the web-flange junction. The experimental results indicated that the GFRP hollow beams failed by web crushing at supports; therefore, the longitudinal web stiffener has an insignificant effect on improving the maximum load. Strengthening web-flange junctions using rectangular hollow sections increased the maximum load by 47%. Concrete infill could effectively prevent the web crushing, and it demonstrated the highest load increment of 162%. The concrete filled GFRP composite beam failed by diagonal tension in the lightweight concrete core. The finite element models adopting Hashin damage criteria yielded are in good agreement with the experimental results in terms of maximum load and failure mode. Based on the numerical study, the longitudinal web stiffener could prevent the web buckling of the slender GFRP beam and improved the maximum load by 136%. The maximum load may be further improved by increasing the thickness of the GFRP section and the size of rectangular hollow sections used for strengthening. It was found that the bond-slip at the concrete-GFRP interface affected the shear resistance of concrete-GFRP composite beam.
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