How do the non-cognitive skills affect retirees’ reemployment? Evidence from China

IntroductionPromoting the reemployment of retirees is important to effectively recognize the capacity of older adults and to help governments cope with an aging global population. Existing research on the factors that impact reemployment has mainly focused on the role of traditional forms of human capital, like education and experience, while ignoring non-cognitive skills.MethodsBased on 3,693 samples, this study examines the impact of non-cognitive skills on the reemployment of Chinese retirees using the Logit model through the lens of human capital theory.ResultsThe results show that non-cognitive skills incentivize retirees to seek reemployment. The incentive effect is greater for retirees who are male, live in a rural household, and are of lower age and education level. Further, the mediation effect model reveals the mediating role of social capital between non-cognitive skills and the reemployment of retirees. Social capital is important to the promotion of retiree reemployment.DiscussionThis study ultimately sheds light on the relationship between non-cognitive skills and the reemployment of retirees. Findings will help improve governments’ understandings of non-cognitive skills so that they may develop better policies on retiree reemployment

Silk Fibroin Based Conductive Film for Multifunctional Sensing and Energy Harvesting

Development of biomaterial based flexible electronics has got intensive attention owing to the potential applications in the wearable and epidermal devices. Silk fibroin, as a natural textile material with excellent performance, has been widely concerned by industry and academy. However, the property of electrical insulation limits his development in the field of flexible electronics. In this paper, a regenerated silk fibroin/carbon nanotube (RSF/CNT) conductive film has been successfully fabricated and applied in flexible capacitive-type pressure sensor and wearable triboelectric nanogenerator by a facile method. The electrical conductivity and mechanical property of RSF/CNT film was optimized by investigating with different composite ratio from 10 to 90% (W-RSF/W-CNT). The RSF/CNT film has a good photothermal response and electric heating performance. We furtherly demonstrated that the RSF/CNT based sensor can be used as epidermal self-powered sensor for multifunction human motion monitoring and Morse code compilation. The observed research results have shown that the RSF/CNT film has a wide range of potential application prospects in the wearable electronics field

Probability density function based data augmentation for deep neural network automatic modulation classification with limited training data

Abstract Deep neural networks (DNN) based automatic modulation classification (AMC) has achieved high accuracy performance. However, DNNs are data‐hungry models, and training such a model requires a large volume of data. Insufficient training data will cause DNN models to experience overfitting and severe performance degradation. In practical AMC tasks, training the deep model with sufficient data is challenging due to the costly data collection. To this end, a novel probability density function (PDF) based data augmentation scheme and a method to determine the required minimum sampling size for data enlargement is proposed. Compared with the known image‐based augmentation scheme, the proposed waveform‐based PDF technique has low complexity and is easy to implement. Experimental results show that the required size of the training dataset is one order of magnitude smaller than the sufficient dataset in the additive white Gaussian noise channel, and effective recognition can be achieved using around 60% of the total examples under the Rayleigh channel. Moreover, the presented scheme can expand training data under frequency and phase offsets

Quantum Circuit Synthesis Using a New Quantum Logic Gate Library of NCV Quantum Gates

Since Controlled-Square-Root-of-NOT (CV, CV‡) gates are not permutative quantum gates, many existing methods cannot effectively synthesize optimal 3-qubit circuits directly using the NOT, CNOT, Controlled-Square-Root-of-NOT quantum gate library (NCV), and the key of effective methods is the mapping of NCV gates to four-valued quantum gates. Firstly, we use NCV gates to create the new quantum logic gate library, which can be directly used to get the solutions with smaller quantum costs efficiently. Further, we present a novel generic method which quickly and directly constructs this new optimal quantum logic gate library using CNOT and Controlled-Square-Root-of-NOT gates. Finally, we present several encouraging experiments using these new permutative gates, and give a careful analysis of the method, which introduces a new idea to quantum circuit synthesis

Comprehensive Analyses of Four PtoNF-YC Genes from Populus tomentosa and Impacts on Flowering Timing

Flowering is an important link in the life process of angiosperms, and it is also an important sign of the transformation of plants from vegetative to reproductive growth. Although the flowering regulation network of Arabidopsis is well-understood, there has been little research on the molecular mechanisms of perennial woody plant flower development regulation. Populus tomentosa is a unique Chinese poplar species with fast growth, strong ecological adaptability, and a long lifecycle. However, it has a long juvenile phase, which seriously affects its breeding process. Nuclear factor-Y (NF-Y) is an important type of transcription factor involved in the regulation of plant flowering. However, there are few reports on PtoNF-Y gene flowering regulation, and the members of the PtNF-YC subfamily are unknown. In this study, four key genes were cloned and analyzed for sequence characteristics, gene structure, genetic evolution, expression patterns, and subcellular localization. The plant expression vector was further constructed, and transgenic Arabidopsis and P. tomentosa plants were obtained through genetic transformation and a series of molecular tests. The flowering time and other growth characteristics were analyzed. Finally, the expression level of flowering genes was detected by quantitative PCR, the interaction between PtoNF-YC and PtoCOL proteins was measured using the yeast two-hybrid system to further explain the flowering regulation mechanism, and the molecular mechanisms by which PtNF-YC6 and PtNF-YC8 regulate poplar flowering were discussed. These results lay the foundation for elucidating the molecular regulation mechanism of PtoNF-YC in flowering and furthering the molecular design and breeding of poplar, while providing a reference for other flowering woody plants

A Review of the Research Progress of Social Network Structure

Social network theory is an important paradigm of social structure research, which has been widely used in various fields of research. This paper reviews the development process and the latest progress of social network theory research and analyzes the research application of social network. In order to reveal the deep social structure, this paper analyzes the structure of social networks from three levels: microlevel, mesolevel, and macrolevel and reveals the origin, development, perfection, and latest achievements of complex network models. The regular graph model, P1 model, P2 model, exponential random graph model, small-world network model, and scale-free network model are introduced. In the end, the research on the social network structure is reviewed, and social support network and social discussion network are introduced, which are two important contents of social network research. At present, the research on social networks has been widely used in coauthor networks, citation networks, mobile social networks, enterprise knowledge management, and individual happiness, but there are few research studies on multilevel structure, dynamic research, complex network research, whole network research, and discussion network research. This provides space for future research on social networks

Towards Industrial IoT-AR Systems using Deep Learning-Based Object Pose Estimation

Augmented Reality (AR) is known to enhance user experience, however, it remains under-adopted in industry. We present an AR interaction system improving human-machine coordination in Internet of Things (IoT) and Industry 4.0 applications including manufacturing and assembly, maintenance and safety, and other highly-interactive functions. A driver of slow adoption is the computational complexity and inaccuracy in localization and rendering digital content. AR systems may render digital content close to the associated physical objects, but traditional object recognition and localization modules perform poorly when tracking texture-less objects and complex shapes, presenting a need for robust and efficient digital content rendering techniques. We propose a method of improving IoT-AR by integrating Deep Learning with AR to increase accuracy and robustness of the target object localization module, taking both color and depth images as input and outputting the target's pose parameters. Quantitative and qualitative experiments prove this system's efficacy and show potential for fusing these emerging technologies in real-world applications

Applications of in vivo SPME based on mass spectrometry for environmental pollutants analysis and non-target metabolomics: A review

In vivo analysis of environmental organic pollutants and endogenous metabolites has great impacts on understanding their environmental behavior and exposure risks. This calls for the development of convenient and reliable analytical methods with either high specificity or wide coverage of target compounds in complex matrixes. Different from traditional solvent extraction, solid-phase microextraction (SPME) has emerged as an integrated sample preparation technique by combining sampling, extraction, and cleanup into one step, which exerts great potential on in vivo sampling due to its high sensitivity and low invasiveness. In this view, the design principles of SPME fibers for in vivo sampling, novel SPME fibers, and devices over recent years are summarized for the guidance of SPME fiber design. The applications of in vivo SPME on monitoring uptake, translocation, and degradation of environmental organic pollutants in organisms, as well as non-target metabolomics in environmental toxicology and clinical analysis based on mass spectrometry, are described. SPME fiber directly coupled with mass spectrometry for in vivo analysis has been introduced. The practical limitations and prospects of further research are also discussed. We conceive that in vivo SPME will promote the development of environmental analysis and toxicity assessment in a high-efficiency and high-throughput manner

IL-27 Signaling Promotes Th1 Response by Downregulating IL-10 Production in DCs during Chlamydial Respiratory Infection

Chlamydia trachomatis usually causes mucosal infections, bringing considerable morbidity and socioeconomic burden worldwide. We previously revealed that IL-27/IL-27R mediates protection against chlamydial invasion by promoting a protective Th1 response and suppressing neutrophilic inflammation. Here, we used the mouse model of Chlamydia muridarum (C. muridarum) respiratory infections to further investigate the impact of IL-27 signaling in the DCs-regulated immune response, since an elevated IL-27/IL-27R expression in DCs was identified following chlamydial infection. An adoptive transfer of Chlamydia muridarum-stimulated DCs to wild-type mice approach was subsequently used, and the donor-DCs-promoted resistance with a higher Th1 response against chlamydial infection was attenuated when DCs lacking IL-27R were used as donor cells. Flow cytometry analysis revealed the suppression of IL-27 signaling on DCs phenotypic maturation. A further functional maturation analysis of DCs revealed that IL-27 signaling restricted the protein and mRNA expression of IL-10 from DCs following infection. Thus, these findings suggest that IL-27 signaling could support the Th1 response via inhibiting IL-10 production in DCs, thus mediating the protective host defense against chlamydial respiratory infection

Responses of Soil Respiration and Organic Carbon to Straw Mulching and Ridge Tillage in Maize Field of a Triple Cropping System in the Hilly Region of Southwest China

Soil disturbance by tillage practices promotes soil respiration which is a main source of carbon dioxide emission into the atmosphere. The present study was conducted to investigate the effect of different tillage practices on soil respiration and the carbon source/sink characteristics of maize farmland ecosystems in the wheat−maize−soybean cropping system. Six tillage treatments, namely, traditional tillage (T), ridge tillage (R), traditional tillage + straw mulching (TS), ridge tillage + straw mulching (RS), traditional tillage + straw mulching + decomposing inoculants (TSD), and ridge tillage + straw mulching + decomposing inoculants (RSD), were used to measure the soil respiration and its hydrothermal factors. The results showed that the intensity of soil respiration increased initially and decreased afterwards throughout the growth period of maize ranging from 1.011 to 5.575 μmol (m2·s)−1. The soil respiration rate under different treatments varied remarkably presenting a trend of RSD > TSD > TS > RS > T > R. Ridge tillage reduced the soil respiration rate of maize farmland while straw mulching improved it. Meanwhile, ridge tillage and straw mulching increased the soil temperature sensitivity index of soil respiration, but the addition of decomposing inoculants reduced this trend. The soil moisture response threshold under ridge tillage was lower, while the straw mulching was found to increase it, compared with the control. Moreover, there was a positive correlation between trapped soil fauna and soil respiration. Compared with the control, ridge tillage and straw mulching were beneficial to the carbon sink of the farmland ecosystem as shown by the maize field for the entire growing season