Poly(4-styrenesulfonic acid) doped polypyrrole/tungsten oxide/reduced graphene oxide nanocomposite films based surface acoustic wave sensors for NO sensing behavior

Poly(4-styrenesulfonic acid) (PSSA) doped polypyrrole (PPy)/tungsten oxide (WO₃)/reduced graphene oxide (rGO) hybrid nanocomposite have been successfully synthesized using appropriate amounts of PSSA, pyrrole monomer, WO₃, and rGO dispersed in aqueous solution through in situ chemical oxidation polymerization. Here, a simple spin coating method was used to fabricate a nitric oxide (NO) gas sensor composed of the aforementioned nanocomposite on a surface acoustic wave (SAW) resonator. This sensor can detect NO gas at concentrations of 1–110 parts per billion (ppb) at room temperature in dry air, with a sensitivity of 12 Hz/ppb and response and recovery times of <2 min. Moreover, its limit of detection (LOD) is 0.31 ppb for a signal to noise ratio of 3. It demonstrates repeatability, fast response, and recovery at room temperature. Moreover, its sensory performance remains highly stable over 30 days with only a 6.3% decrease in sensitivity. In addition, the sensor is highly selective for NO, even when nitrogen dioxide, ammonia, and carbon dioxide are applied as interfering gases. The inclusion of rGO (with large specific surface area) and the synergic effect of n-type WO₃ nanoparticles in the p-type PPy matrix (leading to p-n heterojunction region formation) possibly underlie the efficient sensing performance of our sensor.     

A novel adaptive sampling based methodology for feasible region identification of compute intensive models using artificial neural network

Identification of feasible region of operations in multivariate processes is a problem of interest in several fields. This is particularly challenging when the process model is black-box in nature and/or is computationally expensive, as analytical solutions are not available and the number of possible model evaluations is limited. An efficient methodology is required to identify samples where the model is evaluated for developing a computationally efficient surrogate model. In this work, an artificial neural network based surrogate model is proposed which is integrated with a statistical-based approach (Jack-knifing) to estimate the variance of the surrogate model prediction. This allows implementation of an adaptive sampling approach where new samples are identified close to the feasible region boundary or in regions of high prediction uncertainty. The proposed approach performs better than a previously published kriging based method for different dimensionality case studies.     

Research and development of liquid hydrogen (LH2) temperature monitoring system for marine applications

Monitoring the temperature in liquid hydrogen (LH₂) storage tanks on ships is important for the safety of maritime navigation. In addition, accurate temperature measurement is also required for commercial transactions. Temperature and pressure define the density of liquid hydrogen, which is directly linked to trading interests. In this study, we developed and tested a liquid hydrogen temperature monitoring system that uses platinum resistance sensors with a nominal electrical resistance of approximately 1000 Ω at room temperature, PT-1000, for marine applications. The temperature measurements were carried out using a newly developed temperature monitoring system under different pressure conditions. The measured values are compared with a calibrated reference PT-1000 resistance thermometer. We confirm a measurement accuracy of ±50 mK in a pressure range of 0.1 MPa–0.5 MPa.     

Distributed two‐stage state estimation with event‐triggered strategy for multirate sensor networks

This paper investigates the state estimation issue for a class of wireless sensor networks (WSNs) with the consideration of limited energy resources. First, a multirate estimation model is established, and then, a new event‐triggered two‐stage information fusion algorithm is developed based on the optimal fusion criterion weighted by matrices. Compared with the existing methods, the presented fusion algorithm can significantly reduce the communication cost in WSNs and save energy resources of sensors efficiently. Furthermore, by presetting a desired containment probability over the interval [0,1] with the developed event‐triggered mechanism, one can obtain a suitable compromise between the communication cost and the estimation accuracy. Finally, a numerical simulation for the WSN tracking system is given to demonstrate the effectiveness of the proposed method.     

Correlation among hydrogenation,magnetoelastic coupling,magnetic anisotropy,and magnetoresistance in magnetostrictive,hydrogen-absorbing palladium-cobalt alloy films for hydrogen sensing

Hydrogen-absorbing magnetic alloy films, such as palladium-cobalt (PdCo) alloy films, are expected to play a significant role in the next generation of hydrogen sensors. However, effects of hydrogenation on such films are very complex, since these alloys show strong spin-orbit interaction, i.e., strong magnetoelastic coupling. Accordingly, we conducted integrated research on the hydrogenation, magnetoelastic coupling, magnetism, and galvanomagnetic effect of PdCo alloy films having different magnetic anisotropies of longitudinal and perpendicular magnetic anisotropies. As a result, it was revealed that the stress in the film determines its magnetic anisotropy. The magnetoresistance curves of films, consisting of ordinal and anisotropic magnetoresistance effects, correspond well to the magnetization-magnetic field curves. Hydrogenation results in the compressive stress and decreased magnetostriction, which both have a negative influence on the perpendicular magnetic anisotropy energy of the films. Moreover, the influence is observed also in ordinal and anisotropic magnetoresistances. In addition, the increases in coercivity and electronic resistivity due to the incorporated hydrogen atoms (and related defects) are detected. The results are summarized in a correlation diagram, which shows that hydrogen-absorbing magnetic alloy films are very suitable for use in hydrogen sensors—the films can detect hydrogen via various methods such as magnetic anisotropy, galvanomagnetic effect, coercivity, and resistivity.     

腔磁力系统中的诱导透明和快慢光传播

为了在腔磁力系统中实现可控的磁子诱导透明、磁力诱导透明以及快慢光传播,建立了一个混合腔磁力系统.该系统由一个含有YIG球的微波腔和在z方向对球施加一个均匀的偏置磁场组成,并用强泵浦场驱动磁子和弱探测场驱动微波腔.研究表明,通过调节腔与磁子之间的相互作用强度和微波腔与磁子的耗散比,可以增加磁子诱导透明(MIT)、磁力诱导透明(MMIT)的效果和提高快慢光传播的速度.该研究结果可为磁力诱导放大、量子光学操纵和量子信息存储以及灵敏光开关的研究提供参考.     

Multifunctional cementitious composites with integrated self-sensing and self-healing capacities using carbon black and slaked lime

This study aims to develop multifunctionality of cementitious composites with the integrated self-sensing and self-healing capacities by incorporating conductive carbon black (CB) with CB-encapsulated slaked lime (SL). The microsized SL particles were premixed with a half of designed content of nanosized CB particles. When CB agglomerations coat around the SL surfaces, SL does not hydrate until the CB coating is removed. Another half of designed weight of CB is uniformly dispersed using ultrasonication with superplasticizer and added to obtain piezoresistivity. The results show that the stress sensing capacity of CB-SL-cementitious composite performs well with the compressive stress. Autogenous healing performances presented significantly can improve the self-healing capacity with the increase of SL. Furthermore, the healing efficiency is affected by the crack width and dispersion of SL, and the smaller cracks with SL are more easily healed. The size of CB agglomerations decreases with the added SL, and the main product of self-healing is calcium carbonate.     

A precise sensor fault detection technique using statistical techniques for wireless body area networks

One of the major challenges in wireless body area networks (WBANs) is sensor fault detection. This paper reports a method for the precise identification of faulty sensors, which should help users identify true medical conditions and reduce the rate of false alarms, thereby improving the quality of services offered by WBANs. The proposed sensor fault detection (SFD) algorithm is based on Pearson correlation coefficients and simple statistical methods. The proposed method identifies strongly correlated parameters using Pearson correlation coefficients, and the proposed SFD algorithm detects faulty sensors. We validated the proposed SFD algorithm using two datasets from the Multiparameter Intelligent Monitoring in Intensive Care database and compared the results to those of existing methods. The time complexity of the proposed algorithm was also compared to that of existing methods. The proposed algorithm achieved high detection rates and low false alarm rates with accuracies of 97.23% and 93.99% for Dataset 1 and Dataset 2, respectively.     

基于QoE感知的星地一体化网络公平资源分配策略

星地一体化网络是卫星网络和地面网络互联，实现卫星网络和地面网络优势互补的一体化网络。随着无线通信的快速发展，卫星网络和地面网络的同频复用技术可有效提升资源利用效率，满足日益增长的无线数据业务需求。但由于无线数据业务类型的多样化和频谱资源的稀缺性，如何实现用户数据服务的服务体验质量（Quality of experience， QoE）和公平性提升也成为星地一体化网络亟待解决的关键问题。本文综合考虑信道时变性、服务体验多样性和服务公平性，提出了一种基于QoE感知的星地一体化网络资源分配策略。为此，将目标问题描述为基于时间平均的服务体验质量的网络公平效用最大化，并利用李雅普诺夫优化理论，将复杂的动态原始优化问题进一步转化并分解为3个瞬时优化子问题，每个子问题可以在每个时隙独立求解。同时，由于无线资源分配为典型的非凹混合组合优化问题，推导了一个低复杂度的两步算法来求解第三个资源分配子问题。仿真结果揭示了在不同的公平性参数下用户的长时QoE与公平性之间的折衷情况。     

Strategies to Modulate Specialized Metabolism in Mediterranean Crops: From Molecular Aspects to Field

Plant specialized metabolites (SMs) play an important role in the interaction with the environment and are part of the plant defense response. These natural products are volatile, semi-volatile and non-volatile compounds produced from common building blocks deriving from primary metabolic pathways and rapidly evolved to allow a better adaptation of plants to environmental cues. Specialized metabolites include terpenes, flavonoids, alkaloids, glucosinolates, tannins, resins, etc. that can be used as phytochemicals, food additives, flavoring agents and pharmaceutical compounds. This review will be focused on Mediterranean crop plants as a source of SMs, with a special attention on the strategies that can be used to modulate their production, including abiotic stresses, interaction with beneficial soil microorganisms and novel genetic approaches.