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1,010 result(s) for "Li, Yanlong"
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Digital financial inclusion and welfare: Effect, mechanism and imbalance
This study utilizes provincial panel data from China spanning from 2013 to 2019 and considers indices such as residents’ income, employment, housing, education, and medical care to comprehensively measure the welfare index of each province using the coefficient of variation. It also examines the impact and mechanism of digital financial inclusion on welfare and evaluates its influence on welfare imbalance through panel data models and RIF regression. The estimation results of digital financial inclusion on welfare indicate that digital financial inclusion is an important driver elevating the income, employment, housing, education, medical treatment and other welfares of Chinese residents, and the conclusion remains robust after using instrumental variable estimation and replacing the measurement method of welfare. Regional differences reveal that digital financial inclusion has a greater contribution to the welfare in the Midwest. The effect mechanism test shows that digital financial inclusion improves the welfare by creating employment opportunities and plays a greater role in the Midwest, and will also improve the welfare by increasing the welfare expenditure. However, this mechanism will exacerbate the welfare imbalance between the Midwest and East. The estimation results of digital financial inclusion on the welfare imbalance show that digital financial inclusion reduces welfare imbalance between the central and western regions and the eastern regions, as well as within the central and western regions.
Recent Advances, Future Trends, Applications and Challenges of Internet of Underwater Things (IoUT): A Comprehensive Review
Oceans cover more than 70% of the Earth’s surface. For various reasons, almost 95% of these areas remain unexplored. Underwater wireless communication (UWC) has widespread applications, including real-time aquatic data collection, naval surveillance, natural disaster prevention, archaeological expeditions, oil and gas exploration, shipwreck exploration, maritime security, and the monitoring of aquatic species and water contamination. The promising concept of the Internet of Underwater Things (IoUT) is having a great influence in several areas, for example, in small research facilities and average-sized harbors, as well as in huge unexplored areas of ocean. The IoUT has emerged as an innovative technology with the potential to develop a smart ocean. The IoUT framework integrates different underwater communication techniques such as optical, magnetic induction, and acoustic signals. It is capable of revolutionizing industrial projects, scientific research, and business. The key enabler technology for the IoUT is the underwater wireless sensor network (UWSN); however, at present, this is characterized by limitations in reliability, long propagation delays, high energy consumption, a dynamic topology, and limited bandwidth. This study examines the literature to identify potential challenges and risks, as well as mitigating solutions, associated with the IoUT. Our findings reveal that the key contributing elements to the challenges facing the IoUT are underwater communications, energy storage, latency, mobility, a lack of standardization, transmission media, transmission range, and energy constraints. Furthermore, we discuss several IoUT applications while highlighting potential future research directions.
The Future of Graphene: Preparation from Biomass Waste and Sports Applications
At present, the main raw material for producing graphene is graphite ore. However, researchers actively seek alternative resources due to their high cost and environmental problems. Biomass waste has attracted much attention due to its carbon-rich structure and renewability, emerging as a potential raw material for graphene production to be used in sports equipment. However, further progress is required on the quality of graphene produced from waste biomass. This paper, therefore, summarizes the properties, structures, and production processes of graphene and its derivatives, as well as the inherent advantages of biomass waste-derived graphene. Finally, this paper reviews graphene’s importance and application prospects in sports since this wonder material has made sports equipment available with high-strength and lightweight quality. Moreover, its outstanding thermal and electrical conductivity is exploited to prepare wearable sensors to collect more accurate sports data, thus helping to improve athletes’ training levels and competitive performance. Although the large-scale production of biomass waste-derived graphene has yet to be realized, it is expected that its application will expand to various other fields due to the associated low cost and environmental friendliness of the preparation technique.
Association of ambient temperature on acute ischemic stroke in Yancheng, China
Background Acute ischemic stroke (AIS) is a major global public health issue. There is limited research on the relationship between ambient temperature and AIS hospital admissions, and the results are controversial. Our objective is to assess the short-term impact of ambient temperature on the risk of AIS hospital admissions in Yancheng, China. Methods We collected data on daily AIS hospital admissions, meteorological factors, and air quality in Yancheng from 2014 to 2019. We used Poisson regression to fit generalized linear models and distributed lag non-linear models to explore the association between ambient temperature and AIS hospital admissions. The effects of these associations were evaluated by stratified analysis by sex and age. Results From 2014 to 2019, we identified a total of 13,391 AIS hospital admissions. We observed that the influence of extreme cold and heat on admissions for AIS manifests immediately on the day of exposure and continues for a duration of 3–5 days. Compared to the optimal temperature (24.4 °C), the cumulative relative risk under extreme cold temperature (-1.3 °C) conditions with a lag of 0–5 days was 1.88 (95%CI: 1.28, 2.78), and under extreme heat temperature (30.5 °C) conditions with a lag of 0–5 days was 1.48 (95%CI: 1.26, 1.73). Conclusions There is a non-linear association between ambient temperature and AIS hospital admission risk in Yancheng, China. Women and older patients are more vulnerable to non-optimal temperatures. Our findings may reveal the potential impact of climate change on the risk of AIS hospital admissions.
NOMA-Based VLC Systems: A Comprehensive Review
The enhanced proliferation of connected entities needs a deployment of innovative technologies for the next generation wireless networks. One of the critical concerns, however, is the spectrum scarcity, due to the unprecedented broadcast penetration rate nowadays. Based on this, visible light communication (VLC) has recently emerged as a viable solution to secure high-speed communications. VLC, a high data rate communication technology, has proven its stature as a promising complementary to its radio frequency (RF) counterpart. VLC is a cost-effective, energy-efficient, and secure technology that exploits the current infrastructure, specifically within indoor and underwater environments. Yet, despite their appealing capabilities, VLC systems face several limitations which constraint their potentials such as LED’s limited bandwidth, dimming, flickering, line-of-sight (LOS) requirement, impact of harsh weather conditions, noise, interference, shadowing, transceiver alignment, signal decoding complexity, and mobility issue. Consequently, non-orthogonal multiple access (NOMA) has been considered an effective technique to circumvent these shortcomings. The NOMA scheme has emerged as a revolutionary paradigm to address the shortcomings of VLC systems. The potentials of NOMA are to increase the number of users, system’s capacity, massive connectivity, and enhance the spectrum and energy efficiency in future communication scenarios. Motivated by this, the presented study offers an overview of NOMA-based VLC systems. This article provides a broad scope of existing research activities of NOMA-based VLC systems. This article aims to provide firsthand knowledge of the prominence of NOMA and VLC and surveys several NOMA-enabled VLC systems. We briefly highlight the potential and capabilities of NOMA-based VLC systems. In addition, we outline the integration of such systems with several emerging technologies such as intelligent reflecting surfaces (IRS), orthogonal frequency division multiplexing (OFDM), multiple-input and multiple-output (MIMO) and unmanned aerial vehicles (UAVs). Furthermore, we focus on NOMA-based hybrid RF/VLC networks and discuss the role of machine learning (ML) tools and physical layer security (PLS) in this domain. In addition, this study also highlights diverse and significant technical hindrances prevailing in NOMA-based VLC systems. We highlight future research directions, along with provided insights that are envisioned to be helpful towards the effective practical deployment of such systems. In a nutshell, this review highlights the existing and ongoing research activities for NOMA-based VLC systems, which will provide sufficient guidelines for research communities working in this domain and it will pave the way for successful deployment of these systems.
Vitamin E intake is inversely associated with NAFLD measured by liver ultrasound transient elastography
Non-alcoholic fatty liver disease (NAFLD) is one of the most common chronic liver diseases, whose severe form is associated with oxidative stress. Vitamin E as an antioxidant has a protective potential in NAFLD. Whether dietary intake of vitamin E, supplementary vitamin E use, and total vitamin E have a preventive effect on NAFLD requires investigation. A cross-sectional study used data from the National Health and Nutrition Examination Survey (2017–2020) was conducted. Vitamin E intake, including dietary vitamin E, supplementary vitamin E use, and total vitamin E, was obtained from the average of two 24-h dietary recall interviews. The extent of hepatic steatosis was measured by liver ultrasound transient elastography and presented as controlled attenuated parameter (CAP) scores. Participants were diagnosed with NAFLD based on CAP threshold values of 288 dB/m and 263 dB/m. The statistical software R and survey-weighted statistical models were used to examine the association between vitamin E intake and hepatic steatosis and NAFLD. Overall, 6122 participants were included for NAFLD analysis. After adjusting for age, gender, race, poverty level index, alcohol consumption, smoking status, vigorous recreational activity, body mass index, abdominal circumference, hyperlipidemia, hypertension, diabetes, and supplementary vitamin E use, dietary vitamin E was inversely associated with NAFLD. The corresponding odds ratios (OR) and 95% confidence intervals (CI) of NAFLD for dietary vitamin E intake as continuous and the highest quartile were 0.9592 (0.9340–0.9851, P  = 0.0039) and 0.5983 (0.4136–0.8654, P  = 0.0091) ( P trend  = 0.0056). Supplementary vitamin E was significantly inversely associated with NAFLD (fully adjusted model: OR = 0.6565 95% CI 0.4569–0.9432, P  = 0.0249). A marginal improvement in total vitamin E for NAFLD was identified. The ORs (95% CIs, P ) for the total vitamin E intake as continuous and the highest quartile in the fully adjusted model were 0.9669 (0.9471–0.9871, P  = 0.0029) and 0.6743 (0.4515–1.0071, P  = 0.0538). Sensitivity analysis indicated these findings were robust. The protective effects of vitamin E significantly differed in the stratum of hyperlipidemia ( P interaction  < 0.05). However, no statistically significant results were identified when the threshold value was set as 263 dB/m. Vitamin E intake, encompassing both dietary and supplemental forms, as well as total vitamin E intake, demonstrated a protective association with NAFLD. Augmenting dietary intake of vitamin E proves advantageous in the prevention of NAFLD, particularly among individuals devoid of hyperlipidemia.
A Survey of Deep Learning Based NOMA: State of the Art, Key Aspects, Open Challenges and Future Trends
Non-Orthogonal Multiple Access (NOMA) has become a promising evolution with the emergence of fifth-generation (5G) and Beyond-5G (B5G) rollouts. The potentials of NOMA are to increase the number of users, the system’s capacity, massive connectivity, and enhance the spectrum and energy efficiency in future communication scenarios. However, the practical deployment of NOMA is hindered by the inflexibility caused by the offline design paradigm and non-unified signal processing approaches of different NOMA schemes. The recent innovations and breakthroughs in deep learning (DL) methods have paved the way to adequately address these challenges. The DL-based NOMA can break these fundamental limits of conventional NOMA in several aspects, including throughput, bit-error-rate (BER), low latency, task scheduling, resource allocation, user pairing and other better performance characteristics. This article aims to provide firsthand knowledge of the prominence of NOMA and DL and surveys several DL-enabled NOMA systems. This study emphasizes Successive Interference Cancellation (SIC), Channel State Information (CSI), impulse noise (IN), channel estimation, power allocation, resource allocation, user fairness and transceiver design, and a few other parameters as key performance indicators of NOMA systems. In addition, we outline the integration of DL-based NOMA with several emerging technologies such as intelligent reflecting surfaces (IRS), mobile edge computing (MEC), simultaneous wireless and information power transfer (SWIPT), Orthogonal Frequency Division Multiplexing (OFDM), and multiple-input and multiple-output (MIMO). This study also highlights diverse, significant technical hindrances in DL-based NOMA systems. Finally, we identify some future research directions to shed light on paramount developments needed in existing systems as a probable to invigorate further contributions for DL-based NOMA system.
Acoustic characterization for creep behaviors of marine sandy hydrate-bearing sediment
Marine natural gas hydrate (NGH) is a promising substitutive low-carbon energy resource, whereas NGH-production induced geoengineering concerns remain challenging. Advanced forecast of possible geoengineering risks is the fundamental for eco-friendly NGH exploitation. Reservoir creep deformation is an early symptom of the geoengineering risks. However, whether the creep deformation behaviors of the NGH-bearing strata is predictable remains controversial. In this study, a series of multi-step loading creep test are conducted for sandy gas hydrate bearing sediment (GHBS) samples, during which the ultrasonic responses are recorded simultaneously. The acoustic velocity, compression-to-shear velocity ratio, Poission’s ratio, main frequency, and main frequency amplitude are used to characterize creep failures of the GHBS for the first time. Combining analyses of the creep behaviors and acoustic responses yield the following conclusions. Firstly, the long-term strength derived from creeping test is 0.45–0.60 times of the shear strength derived from triaxial shearing. Ignoring the creep effect might underestimate the scale and intensity of possible geoengineering risks during long-term NGH exploitation. Secondly, the acoustic velocity increases gently and then decreases continuously during creeping. Once the accelerated creep appears, the acoustic velocity plummets significantly, together with a sudden decrease in the compression-to-shear velocity ratio, and fluctuations in the main frequency and its amplitude. Furthermore, the main frequency and its amplitude shall fluctuate abruptly prior to the emergence of the accelerated creep. Therefore, we anticipate that the combination of abnormal fluctuations of main frequency and its amplitude can be used as early-warning indicators for possible creep failure of the GHBS. The results might have great significance for in-situ detection and prediction of possible reservoir failure during long-term NGH exploitation.
The association between smoking and blood pressure in men: a cross-sectional study
Background Cigarette smoking is a known risk factor for cardiovascular disease (CVD), but the association between smoking and blood pressure is unclear. Thus, the current study examined the association between cigarette smoking and blood pressure in men. Methods Systolic blood pressure (SBP), diastolic blood pressure (DBP), mean arterial pressure (MAP) and pulse pressure (PP) were examined using digital blood pressure measuring device, and smoking status was determined with China National Health Survey. Results The ANCOVA showed that the adjusted DBP and MAP were lower in current smokers versus nonsmokers and the adjusted SBP was lower in current smokers versus former smokers ( P  < 0.05). Additionally, the adjusted PP tend to be decreased steadily as the pack·years increased in current smokers. In a fully adjusted logistic regression model, former smokers had increased ORs (95% CI) of 1.48 (1.01, 2.18) of hypertension and current smokers had not increased ORs (95% CI) of 0.83 (0.61, 1.12), compared with never smokers. Conclusions The findings revealed that the adjusted blood pressure were lower in current smokers versus nonsmokers and former smokers. No significant dose-dependent effect of current smoking on blood pressure indices except PP was observed. Smoking cessation was significantly associated with an increased risk of hypertension. However, current smoking was not a risk factor of hypertension.
An Online Hashing Algorithm for Image Retrieval Based on Optical-Sensor Network
Online hashing is a valid storage and online retrieval scheme, which is meeting the rapid increase in data in the optical-sensor network and the real-time processing needs of users in the era of big data. Existing online-hashing algorithms rely on data tags excessively to construct the hash function, and ignore the mining of the structural features of the data itself, resulting in a serious loss of the image-streaming features and the reduction in retrieval accuracy. In this paper, an online hashing model that fuses global and local dual semantics is proposed. First, to preserve the local features of the streaming data, an anchor hash model, which is based on the idea of manifold learning, is constructed. Second, a global similarity matrix, which is used to constrain hash codes is built by the balanced similarity between the newly arrived data and previous data, which makes hash codes retain global data features as much as possible. Then, under a unified framework, an online hash model that integrates global and local dual semantics is learned, and an effective discrete binary-optimization solution is proposed. A large number of experiments on three datasets, including CIFAR10, MNIST and Places205, show that our proposed algorithm improves the efficiency of image retrieval effectively, compared with several existing advanced online-hashing algorithms.