Outage performance analysis of non-orthogonal multiple access with time-switching energy harvesting

In recent years, although non-orthogonal multiple access (NOMA) has shown its potentials thanks to its ability to enhance the performance of future wireless communication networks, a number of issues emerge related to the improvement of NOMA systems. In this work, we consider a half-duplex (HD) relaying cooperative NOMA network using decode-and-forward (DF) transmission mode with energy harvesting (Ell) capacity, where we assume the NOMA destination (D) is able to receive two data symbols in two continuous time slots which leads to the higher transmission rate than traditional relaying networks. To analyse EH, we deploy time-switching (TS) architecture to comprehensively study the optimal transmission time and outage performance at D. In particular, we are going to obtain closed-form expressions for outage probability (OP) with optimal TS ratio for both data symbols with both exact and approximate forms. The given simulation results show that the placement of the relay (R) plays an important role in the system performance.Web of Science253918

Outage probability analysis for hybrid TSR-PSR based SWIPT systems over log-normal fading channels

Employing simultaneous information and power transfer (SWIPT) technology in cooperative relaying networks has drawn considerable attention from the research community. We can find several studies that focus on Rayleigh and Nakagami-m fading channels, which are used to model outdoor scenarios. Differing itself from several existing studies, this study is conducted in the context of indoor scenario modelled by log-normal fading channels. Specifically, we investigate a so-called hybrid time switching relaying (TSR)-power splitting relaying (PSR) protocol in an energy-constrained cooperative amplify-and-forward (AF) relaying network. We evaluate the system performance with outage probability (OP) by analytically expressing and simulating it with Monte Carlo method. The impact of power-splitting (PS), time-switching (TS) and signal-to-noise ratio (SNR) on the OP was as well investigated. Subsequently, the system performance of TSR, PSR and hybrid TSR-PSR schemes were compared. The simulation results are relatively accurate because they align well with the theory

An adaptive hierarchical sliding mode controller for autonomous underwater vehicles

The paper addresses a problem of efficiently controlling an autonomous underwater vehicle (AUV), where its typical underactuated model is considered. Due to critical uncertainties and nonlinearities in the system caused by unavoidable external disturbances such as ocean currents when it operates, it is paramount to robustly maintain motions of the vehicle over time as expected. Therefore, it is proposed to employ the hierarchical sliding mode control technique to design the closed-loop control scheme for the device. However, exactly determining parameters of the AUV control system is impractical since its nonlinearities and external disturbances can vary those parameters over time. Thus, it is proposed to exploit neural networks to develop an adaptive learning mechanism that allows the system to learn its parameters adaptively. More importantly, stability of the AUV system controlled by the proposed approach is theoretically proved to be guaranteed by the use of the Lyapunov theory. Effectiveness of the proposed control scheme was verified by the experiments implemented in a synthetic environment, where the obtained results are highly promising. © 2021 by the authors. Licensee MDPI, Basel, Switzerland. **Please note that there are multiple authors for this article therefore only the name of the first 5 including Federation University Australia affiliate “Linh Nguyen" is provided in this record*

Outage performance analysis of non-orthogonal multiple access systems with RF energy harvesting

Non-orthogonal multiple access (NOMA) has drawn enormous attention from the research community as a promising technology for future wireless communications with increasing demands of capacity and throughput. Especially, in the light of fifth-generation (5G) communication where multiple internet-of-things (IoT) devices are connected, the application of NOMA to indoor wireless networks has become more interesting to study. In view of this, we investigate the NOMA technique in energy harvesting (EH) half-duplex (HD) decode-and-forward (DF) power-splitting relaying (PSR) networks over indoor scenarios which are characterized by log-normal fading channels. The system performance of such networks is evaluated in terms of outage probability (OP) and total throughput for delay-limited transmission mode whose expressions are derived herein. In general, we can see in details how different system parameters affect such networks thanks to the results from Monte Carlo simulations. For illustrating the accuracy of our analytical results, we plot them along with the theoretical ones for comparison

Extraction of Polyphenols from Mentha aquatica Linn. var. crispa

Mentha aquatica Linn. var. crispa is commonly used as a spice in many Asian countries. Although its biological activities, such as its applications, antimicrobial properties, have been studied, its antioxidation properties have not been investigated. This study establishes the most suitable extraction conditions concerning the independent variables affecting the total polyphenol content (TPC) and antioxidant activity (AA) of M. aquatica extract (stem and leaf). Investigated factors include the type of solvent used; solvent concentration, the ratio of raw material to solvent, extraction time and extraction temperature. The efficiency of polyphenol extraction was evaluated by TPC and AA through the ability to neutralize the free radicals 1,1-diphenyl-2-picrylhydrazyl (DPPH) and 2,2\u27-azinobis (3-ethylbenzothiazoline-6- sulfonic acid) (ABTS), and the ferric reducing antioxidant power (FRAP) was used as the evaluation indicator. The results have shown that acetone at a concentration of 50%, at a ratio of 1:20 (w/v), extraction time of 2 h and a temperature of 40 °C give the highest values of TPC and AA, with values of 120.92 mg GAE g-1 dw for TPC, 169.36 μmol TE g-1 dw by DPPH assay, 264.03 μmol by ABTS assay, and 425.35 μmol Fe2+ g-1 dw by FRAP assay. This study demonstrates that extracts of M. aquatica can be used for research as food antioxidant

Exploring Value Co-Destruction Process in Customer Interactions with AI-Powered Mobile Applications

Background: Mobile applications have emerged as important touchpoints for addressing service requests and optimizing human resources. Within the service industry, the integration of artificial intelligence (AI) into these applications has enabled the inference of product demand, provision of personalized service offers, and enhancement of overall firm value. Customers now engage with these apps to stay informed, seek guidance, and make purchases. It is important to recognize that the interactive and human-like qualities of AI can either foster the co-creation of value with customers or potentially lead to the co-destruction of customer value. Although prior research has examined the process of value co-creation, the present study aims to investigate the underlying factors contributing to the value co-destruction process, specifically within AI-powered mobile applications. Method: Our research employs topic modelling and content analysis to examine the value co-destruction process that occurs when customers engage with AI apps. We analyze 7,608 negative reviews obtained from eleven AI apps available on Google Play and App Store AI apps. Results: Our findings reveal six distinct types of value - utilitarian, hedonic, symbolic, social, epistemic, and economic value - that can be co-destroyed during the process. System failure, self-threat and privacy violation are some contributing factors to this value co-destruction process. These values change over time and vary depending on the type of app. Conclusion: Theoretically, our findings extend the concept of value co-destruction in the context of AI apps. We also offer practical recommendations for designing an AI app in a more service-friendly way

Abietane diterpenoids and neolignans from the roots of Pinus kesiya

The phytochemical investigation of the ethyl acetate extract of Pinus kesiya Royle ex Gordon roots led to the isolation of two abietane diterpenes, 7-oxo-15-hydroxy-dehydroabietic acid (1) and dehydroabietic acid (2) as well as two neolignans, cedrusin (3) and cedrusin-4-O-β-D-glucopyranoside (4). Their structures were determined by combination of spectral analysis and comparison with reported data. Among them, compound 1 was isolated from the genus Pinus for the first time. Keywords. Pinus kesiya, abietane diterpenes, neolignans, dehydroabietic acid, cedrusin

Quality of life among urban hypertensive patients

Hypertension is a leading risk factor for major chronic illnesses. This study investigated the quality of life (QOL) of hypertensive patients in an urban setting and evaluate related factors. A cross-sectional study on 220 hypertensive patients was performed in Hanoi, Vietnam. Short-form 12 version 2 (SF12-v2) was used to assess QOL. Sociodemographic and clinical characteristics were also obtained. Multivariate regression was utilized to explore the related factors with patients’ QOL. The mean physical health (PCS-12) and mental health (MCS-12) scores were 43.3 (SD=7.9) and 56.3 (SD=6.5), respectively, Higher age was related to a lower PCS-12. People living in low-population-density settings have a higher MCS-12 score than those living in high-density settings. Increasing comorbidity and medication reduced both component scores. Patients participating in social activity had the MCS-12 score higher than those not participating. This study found a moderate level of health-related quality of life (HRQOL) in hypertensive patients regardless of treatment progress. Regular screening and controlling comorbidities, as well as motivating active employment and social activities involvement, are the potential to enhance the HRQOL of this population

A Machine Learning-based Approach to Vietnamese Handwritten Medical Record Recognition

Handwritten text recognition has been an active research topic within computer vision division. Existing deep-learning solutions are practical; however, recognizing Vietnamese handwriting has shown to be a challenge with the presence of extra six distinctive tonal symbols and extra vowels. Vietnam is a developing country with a population of approximately 100 million, but has only focused on digitalization transforms in recent years, and so Vietnam has a significant number of physical documents, that need to be digitized. This digitalization transform is urgent when considering the public health sector, in which medical records are mostly still in hand-written form and still are growing rapidly in number. Digitization would not only help current public health management but also allow preparation and management in future public health emergencies. Enabling the digitalization of old physical records will allow efficient and precise care, especially in emergency units. We proposed a solution to Vietnamese text recognition that is combined into an end-to-end document-digitalization system. We do so by performing segmentation to word-level and then leveraging an artificial neural network consisting of both convolutional neural network (CNN) and a long short-term memory recurrent neural network (LSTM) to propagate the sequence information. From the experiment with the records written by 12 doctors, we have obtained encouraging results of 6.47% and 19.14% of CER and WER respectively