Temperature Dependent Piezoelectric Properties of Lead-Free (1-x)K0.6Na0.4NbO3–xBiFeO3 Ceramics

(1-x)K0.4Na0.6NbO3–xBiFeO3 lead-free piezoelectric ceramics were successfully prepared in a single perovskite phase using the conventional solid-state synthesis. Relative permittivity (εr) as a function of temperature indicated that small additions of BiFeO3 not only broadened and lowered the cubic to tetragonal phase transition (TC) but also shifted the tetragonal to orthorhombic phase transition (TO–T) toward room temperature (RT). Ceramics with x = 1 mol.% showed optimum properties with small and large signal piezoelectric coefficient, d33 = 182 pC/N and d∗33 = 250 pm/V, respectively, electromechanical coupling coefficient, kp = 50%, and TC = 355°C. kp varied by ∼5% from RT to 90°C, while d∗33 showed a variation of ∼15% from RT to 75°C, indicating that piezoelectric properties were stable with temperature in the orthorhombic phase field. However, above the onset of TO–T, the properties monotonically degraded in the tetragonal phase field as TC was approached

Do financial development and energy efficiency ensure green environment? Evidence from R.C.E.P. economies

The issue of climate change and environmental degradation has been prevailing for the last few decades. Yet economies are further expanding due to free trade agreement which accelerates the trade of energy and carbon intensive commodities across the regions. A prominent example of such free trade is the Regional Comprehensive Economic Partnership (R.C.E.P.), which mostly remains ignored. The current research study explores the influence of financial development (F.D.) and energy efficiency (E.N.E.F.) on carbon emissions in the R.C.E.P. economies. Also, this study analyses the role of economic growth and renewable energy on environmental quality during the period from 1990 to 2020. Panel data approaches such as slope heterogeneity, crosssection dependence, and the second-generation panel unit root test are used. The non-normally distributed variables are found cointegrated. Therefore, a novel method of moments quantile regression is used. The results demonstrate that F.D. and economic growth are positively associated with CO2 emissions. At the same time, E.N.E.F. and renewable energy consumption (R.E.C.) significantly reduce the emissions level and promote a green environment in all quantiles. The environmental Kuznets curve is found valid in the R.C.E.P. economies. These results are robust as validated by Fully-Modified Ordinary Least Square – a parametric approach. A two-way significant causal association exists between carbon-economic growth, carbon-F.D., carbon- R.E.C., and carbon-E.N.E.F.. The findings suggest an enhancement in R.E.C., improvement in the E.N.E.F. approaches, and implications for green F.D. in the region

Co-movement dynamics of US and Chinese stock market: evidence from COVID-19 crisis

This paper aims to examine the co-movement between the two economic powers, namely the USA and China. The authors are mainly interested in examining the dynamics of co-movements during, and in the pre-covid periods. Additionally, they have aimed to examine the volatility spillover between USA and China, during and in the pre-covid periods. In order to achieve the research-based objectives, advanced econometrics models have been applied to the data from July1, 2010, to April 30, 2021. The results show that the sample market is integrated in the long run. The results also indicate that the behaviour of the Chinese market is same as the US market, and offers negligible opportunities for investors for diversification during this time. The findings indicate that the Ganger Causality between the stock markets during crisis is significantly higher than the pre-crisis period. The results of EGARCH model confirm the presence of asymmetric volatility spillover effects between the US and Chinese markets, during the considered time periods. This study also examines the co-movement in China, grounded upon the robust approach that facilitates examining the dependence structure between the sample variables. The findings offer valuable understanding for investors who are looking for investment diversification opportunities worldwide

Contactless WiFi Sensing and Monitoring for Future Healthcare:Emerging Trends, Challenges and Opportunities

WiFi sensing has recently received significant interest from academics, industry, healthcare professionals and other caregivers (including family members) as a potential mechanism to monitor our aging population at distance, without deploying devices on users bodies. In particular, these methods have gained significant interest to efficiently detect critical events such as falls, sleep disturbances, wandering behavior, respiratory disorders, and abnormal cardiac activity experienced by vulnerable people. The interest in such WiFi-based sensing systems stems from its practical deployments in indoor settings and compliance from monitored persons, unlike other sensors such as wearables, camera-based, and acoustic-based solutions. This paper reviews state-of-the-art research on collecting and analysing channel state information, extracted using ubiquitous WiFi signals, describing a range of healthcare applications and identifying a series of open research challenges, untapped areas, and related trends.This work aims to provide an overarching view in understanding the technology and discusses its uses-cases from a perspective that considers hardware, advanced signal processing, and data acquisition

Energy-Aware Radio Resource Management in D2D-Enabled Multi-Tier HetNets

Hybrid networks consisting of both millimeter wave (mmWave) and microwave (μW) capabilities are strongly contested for next-generation cellular communications. A similar avenue of current research is device-to-device (D2D) communications, where users establish direct links with each other rather than using central base stations. However, a hybrid network, where D2D transmissions coexist, requires special attention in terms of efficient resource allocation. This paper investigates dynamic resource sharing between network entities in a downlink transmission scheme to maximize energy efficiency (EE) of the cellular users (CUs) served by either (μW) macrocells or mmWave small cells while maintaining a minimum quality-of-service (QoS) for the D2D users. To address this problem, first, a self-adaptive power control mechanism for the D2D pairs is formulated, subject to an interference threshold for the CUs while satisfying their minimum QoS level. Subsequently, an EE optimization problem, which is aimed at maximizing the EE for both CUs and D2D pairs, has been solved. Simulation results demonstrate the effectiveness of our proposed algorithm, which studies the inherent tradeoffs between system EE, system sum rate, and outage probability for various QoS levels and varying densities of D2D pairs and CUs

Evaluation of deep learning models in contactless human motion detection system for next generation healthcare

Recent decades have witnessed the growing importance of human motion detection systems based on artificial intelligence (AI). The growing interest in human motion detection systems is the advantages of automation in the monitoring of patients remotely and giving warnings to doctors promptly. Currently, wearable devices are frequently used for human motion detection systems. However, such devices have several limitations, such as the elderly not wearing devices due to lack of comfort or forgetfulness and/or battery limitations. To overcome the problems of wearable devices, we propose an AI-driven human motion detection system (deep learning-based system) using channel state information (CSI) extracted from Radio Frequency (RF) signals. The main contribution of this paper is to improve the performance of the deep learning models through techniques, including structure modification and dimension reduction of the original data. In this work, We firstly collected the CSI data with the center frequency 5.32 GHz and implemented the structure of the basic deep learning network in our previous work. After that, we changed the basic deep learning network by increasing the depth, increasing the width, adapting some advanced network structures, and reducing dimensions. After finishing those modifications, we observed the results and analyzed how to further improve the deep learning performance of this contactless AI-enabled human motion detection system. It can be found that reducing the dimension of the original data can work better than modifying the structure of the deep learning model

From barriers to novel strategies: smarter CAR T therapy hits hard to tumors

Chimeric antigen receptor (CAR) T cell therapy for solid tumors shows promise, but several hurdles remain. Strategies to overcome barriers such as CAR T therapy-related toxicities (CTT), immunosuppression, and immune checkpoints through research and technology are needed to put the last nail to the coffin and offer hope for previously incurable malignancies. Herein we review current literature and infer novel strategies for the mitigation of CTT while impeding immune suppression, stromal barriers, tumor heterogeneity, on-target/off-tumor toxicities, and better transfection strategies with an emphasis on clinical research and prospects