Extenics-based Study on Evaluation of Urban Community Home-care Service for the Elderly

AbstractThis paper tries to introduce extenics theory into the evaluation of the urban community home-care service for the elderly. The paper analyzes the feasibility of using extenics to evaluate the service, uses analytic hierarchy process to decide the weight of index and constructs a comprehensive evaluation model for the service on the basis of extenics. Based on the case study of communities in Ningbo, the paper has completed the evaluation of home-care service in operation and put forward countermeasures to the existing problems

The boundedness of commutators of sublinear operators on Herz Triebel-Lizorkin spaces with variable exponent

In this paper, the authors first discuss the characterization of Herz Triebel-Lizorkin spaces with variable exponent via two families of operators. By this characterization, the authors prove that the Lipschitz commutators of sublinear operators is bounded from Herz spaces with variable exponent to Herz Triebel-Lizorkin spaces with variable exponent. As an application, the corresponding boundedness estimates for the commutators of maximal operator, Riesz potential operator and Calder\'on-Zygmund operator are established.Comment: commutator; sublinear operator; Lipschitz spaces; Herz Triebel-Lizorkin spaces; variable exponen

An Efficient Synthesis and Photoelectric Properties of Green Carbon Quantum Dots with High Fluorescent Quantum Yield

© 2020 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (http://creativecommons.org/licenses/by/4.0/)To greatly improve the production quality and efficiency of carbon quantum dots (CQDs), and provide a new approach for the large-scale production of high-quality CQDs, green carbon quantum dots (g-CQDs) with high product yield (PY) and high fluorescent quantum yield (QY) were synthesized by an efficient one-step solvothermal method with 2,7-dihydroxynaphthalene as the carbon source and ethylenediamine as the nitrogen dopant in this study. The PY and QY of g-CQDs were optimised by adjusting reaction parameters such as an amount of added ethylenediamine, reaction temperature, and reaction duration. The results showed that the maximum PY and QY values of g-CQDs were achieved, which were 70.90% and 62.98%, respectively when the amount of added ethylenediamine, reaction temperature, and reaction duration were 4 mL, 180 °C, and 12 h, respectively. With the optimised QY value of g-CQDs, white light emitting diodes (white LEDs) were prepared by combining g-CQDs and blue chip. The colour rendering index of white LEDs reached 87, and the correlated colour temperature was 2520 K, which belongs to the warm white light area and is suitable for indoor lighting. These results indicate that g-CQDs have potential and wide application prospects in the field of white LEDs.Peer reviewedFinal Published versio

Gamified System Effectiveness on Social Trading Platforms

To motivate user engagement and generate desired engagement outcomes, some social trading platforms have introduced gamified systems with hierarchical badges and financial incentives. However, previous studies have not examined the effectiveness of such an application. Based on data collected from a popular social trading platform, eToro, we empirically examine the effectiveness of the gamified system and its mechanism. Results indicate that the gamified system under social trading is effective in inducing user-to-user and user-to-system interactions, thus leading to some desired engagement outcomes on social media. However, the gamified system does not contribute to the engagement outcomes on financial investment. Our paper contributes to the literature on both gamification and social trading, highlighting gamification’s important practical implications for platform managers, cautioning against the possible ineffectiveness of the dual-outcome gamified system and shedding light on the design of gamified systems

Focal Inverse Distance Transform Maps for Crowd Localization and Counting in Dense Crowd

In this paper, we propose a novel map for dense crowd localization and crowd counting. Most crowd counting methods utilize convolution neural networks (CNN) to regress a density map, achieving significant progress recently. However, these regression-based methods are often unable to provide a precise location for each person, attributed to two crucial reasons: 1) the density map consists of a series of blurry Gaussian blobs, 2) severe overlaps exist in the dense region of the density map. To tackle this issue, we propose a novel Focal Inverse Distance Transform (FIDT) map for crowd localization and counting. Compared with the density maps, the FIDT maps accurately describe the people's location, without overlap between nearby heads in dense regions. We simultaneously implement crowd localization and counting by regressing the FIDT map. Extensive experiments demonstrate that the proposed method outperforms state-of-the-art localization-based methods in crowd localization tasks, achieving very competitive performance compared with the regression-based methods in counting tasks. In addition, the proposed method presents strong robustness for the negative samples and extremely dense scenes, which further verifies the effectiveness of the FIDT map. The code and models are available at https://github.com/dk-liang/FIDTM.Comment: The code and models are available at https://github.com/dk-liang/FIDT

Research status and progress of intelligent wearable system for first aid based on body area network

With the rise of electronic health services, wireless body area network (WBAN) technology has attracted great international attention. The body area network can obtain human vital sign parameters in its natural state, and support applications in areas such as clinical diagnosis and treatment, emergency rescue and treatment, and health information services. This article introduces the concept of body area network and the electronic medical architecture of body area network, summarizes the advantages of body area network: in low data rate scenarios, the system power consumption of body area network is much lower than that of other wireless communication standards, providing more choices for special frequency bands for medical equipment (500 MHz to 5 GHZ), thereby reducing the interference problem between different communications; proposing bottlenecks and hot spots of body area network: ultra-low power consumption requirements of sensor nodes and hardware resource constraints with limited computing power, and data security protection problems in body area network sensor nodes; the application of body area network in emergency scenarios was analyzed, and the hot spots of body area network research in the field of emergency were summarized and predicted: the development of ultra-low-power chips, wearable wireless nodes, intelligent medical terminals, health and monitoring instruments and other devices and equipment

Integrative Statistical Models for Genomic Signal Detection

Although the cost of high-throughput technologies has decreased dramatically, it is still expensive to obtain a large number of biological replicates. On the other hand, with the wide adoption of high-throughput biology, multiple related genomic datasests are often available. The first two chapters tackle the challenging problem of borrowing information across multiple datasets, allowing context specificity, and overcoming the exponential growth of parameter space simultaneously to improve signal detection for noisy genomic data. Chapter 1 proposes a flexible Bayesian hierarchical mixture model to capture the latent correlation structures embedded in the data, named as "correlation motifs", and utilizes that piece of information to improve signal detection. The application is illustrated by differential gene expression detection when the expression datasets have only a small number of replicate samples. Chapter 2 demonstrates that a generalized version of the correlation motif approach can also help detect allele-specific protein-DNA binding from ChIP-seq data, which often suffers from low statistical power due to the limited number of sequence reads mapped to heterozygote SNPs. For both cases, the correlation motif approach substantially improves signal detection for low-signal-to-noise ratio data. Moreover, the current high-throughput technologies such as immunoprecipitation (ChIP) with high-throughput sequencing (ChIP-seq) or tiling array hybridization (ChIP-chip) for studying protein-DNA interactions are "high-throughput" in terms of mapping a given type of transcription factor (TF) genome-widely. Nevertheless, mapping genome-wide binding sites of all TFs in all biological contexts is a critical step toward understanding gene regulation. From this perspective, ChIP-seq and ChIP-chip are low-throughput with respect to surveying many TFs. Recent advances in genome-wide chromatin profiling, including development of technologies such as DNase-seq, FAIRE-seq and ChIP-seq for histone modifications, make it possible to predict in vivo TF binding sites by analyzing chromatin features at computationally determined DNA motif sites for many TFs simultaneously. Chapter 3 compares different models and discusses various issues arising from this new approach