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Older people with hearing impairment are more likely to develop depressive symptoms due to physical disability and loss of social communication. This study investigated the effects of social media on social relations, subjective aging, and depressive symptoms in these older adults based on the stimulus-organism-response (S-O-R) framework. It provides new empirical evidence to support improving the mental health and rebuilding the social relations of older people. A formal questionnaire was designed using the Wenjuanxing platform and distributed online through WeChat; 643 valid questionnaires were received from older people with self-reported hearing impairments, and SmartPLS 3.28 was used to analyze the data. The results show that (1) social media significantly impacts the social relations of older people with hearing impairment (social networks, β = 0.132, T = 3.444; social support, β = 0.129, T = 2.95; social isolation, β = 0.107, T = 2.505). (2) For these older people, social isolation has the biggest impact on their psychosocial loss (β = 0.456, T = 10.458), followed by the impact of social support (β = 0.103, T = 2.014); a hypothesis about social network size was not confirmed (β = 0.007, T = 0.182). Both social media (β = 0.096, T = 2.249) and social support (β = 0.174, T = 4.434) significantly affect the self-efficacy of hearing-impaired older people. (3) Both subjective aging (psychosocial loss, β = 0.260, T = 6.036; self-efficacy, β = 0.106, T = 3.15) and social isolation (β = 0.268, T = 6.307) significantly affect depressive symptoms in older people with hearing impairment. This study expands the theories of social media aging cognition, social support, and social networks and can provide practical contributions to the social media use and mental health of special persons 60 years and older.

The outbreak of COVID-19 in China at the beginning of 2020 has made the problems that the aged care agency face with large mobility and high turnover of aged nursing staff become more serious. Aiming at this problem, this paper incorporates psychological capital and social panic into the model from the perspective of the organizational safety climate and constructs a theoretical model of the mechanism of the effect on nursing staff’s willingness to stay in the context of the outbreak. Through a questionnaire survey in an aged care agency in Anhui Province, a total of 321 valid questionnaires were collected for empirical analysis. The results show that: (1) the safety climate of the organization has a significant positive impact on the transactional psychological capital and interpersonal psychological capital of nursing staff in the aged care industry and their willingness to stay; (2) transactional psychological capital and social panic have a significant positive impact on the willingness to stay of nursing staff, while interpersonal psychological capital has no significant impact on the willingness to stay; (3) the mediating role of transactional psychological capital and interpersonal psychological capital between the safety climate and the willingness to stay is established, and the moderating role of social panic between psychological capital and willingness to stay is also established. Finally, based on the research conclusions, corresponding countermeasures and suggestions are put forward to deal with the problems that occur in special periods.

Population aging has become an important factor restricting China′s social and economic development. The smart health and elderly care industry has developed rapidly in the past five years. However, the service resources among various elderly service providers are relatively isolated and scattered. In other words, the core management problem in the components of the smart elderly care service ecosystem is how to deal with the relationships of interest among multiple resource agents. Thus, the main contribution of this study is to employ symbiosis theory and the logistic growth model to construct a model of the evolution of the symbiosis of multiple resource agents in the smart elderly care service ecosystem. Then, we carry out a stability analysis, and analyze the evolutionary model of two resource agents′ symbiosis under different values of interdependence coefficients. Finally, we use computer simulations to dynamically simulate the model and comparatively analyze the population density of the hospital–nursing home symbiotic relationship using real cases in China. According to the study, we find that the enterprise goal in the smart elderly care service ecosystem should be to maximize the overall value of the multiple resource agents, and the result of the symbiotic evolution between different resource agents depends on the symbiotic interdependence coefficient, while the resource agent uses different strategies under different symbiosis models. Therefore, regulation is needed to ensure the relative fairness of the distribution of value co-creation in the smart elderly care service ecosystem when the resource agent takes actions that benefit itself. Of course, when the ecosystem is in a reciprocal symbiosis model, each resource agent benefits from the activities of the other resource agents, which is ideal in reality; in other words, the best symbiosis model between the two resource agents should be the similar reciprocal symbiosis model.

The accurate prediction of tourist flow is essential to appropriately prepare tourist attractions and inform the decisions of tourism companies. However, tourist flow in scenic spots is a dynamic trend with daily changes, and specialized methods are necessary to measure it accurately. For this purpose, a tourist flow forecasting method is proposed in this research based on seasonal clustering. The experiment employs the K-means algorithm considering seasonal variations and the particle swarm optimization-least squares support vector machine (PSO-LSSVM) algorithm to forecast the tourist flow in scenic spots. The LSSVM is also used to compare the performance of the proposed model with that of the existing ones. Experiments based on a dataset comprising the daily tourist data for Mountain Huangshan during the period between 2014 and 2017 are conducted. Our results show that seasonal clustering is an effective method to improve tourist flow prediction, besides, the accuracy of daily tourist flow prediction is significantly improved by nearly 3 percent based on the hybrid optimized model combining seasonal clustering. Compared with other algorithms which provide predictions at monthly intervals, the method proposed in this research can provide more timely analysis and guide professionals in the tourism industry towards better daily management.

In the current era, the rapid spread of Internet technology has combined with various traditional industries; this provides new research perspectives and solutions for current problems, such as those in the elderly care industry. Elderly health is an important social problem in various countries, and governments have turned to the internet for new methods and better solutions. However, internet-use behavior has a certain influence on the elderly’s health status. This study investigates the effects of internet use on the elderly’s physical health, mental health, and self-rated health, along with the moderating role of individual cognitive ability in the above relationship. This study uses data from the Chinese General Social Survey (CGSS) in 2012 and 2015 as samples for analysis via the hierarchical regression method. The sample is from China and had 2821 and 3185 valid respondents in 2012 and 2015, respectively. Results show that internet use significantly affects the physical and mental health of the elderly and does not significantly affect self-rated health. In addition, individual cognitive ability plays a negative moderating role between internet use and physical and mental health. Finally, on the basis of results analysis and discussion, this study provides new recommendations to achieve targeted health improvements.

This study investigated the effects of hot isostatic pressing (HIP) on the microstructures and mechanical properties of Ti6Al4V fabricated by electron beam melting (EBM). The differences of surface morphologies, internal defects, relative density, microstructures, textures, mechanical properties and tensile fracture between the as-built and HIPed samples were observed using various characterization methods including optical metallography microscopy, scanning electron microscopy (SEM), transmission electron microscopy (TEM) and electron backscattered diffraction (EBSD) and tensile tests. It was found that the main effects of HIP on microstructures include—the increase of average grain size from 7.96 ± 1.21 μm to 11.34 ± 1.89 μm, the increase of α lamellar thickness from 0.71 ± 0.15 μm to 2.49 ± 1.29 μm and the increase of β phase ratio from 4.7% to 10.5% in terms of area fraction on the transversal section. The combinatorial effects including densification, increase of grain size, α lamellar thickness, β phase ratio, reduction of dislocation density and transformation of dislocation patterns contributed to the improvement of elongation and ductility of EBM-fabricated Ti6Al4V. Meanwhile, these effects also resulted in a slight reduction of the yield strength and UTS mainly due to the coarsening effect of HIP.

Focusing on ill-structured multiple attribute decision-making (MADM) problems, including decision hesitancy and attribute prioritization relationships, this paper investigates appropriate approaches for decision making. Firstly, we introduce the probabilistic hybrid linguistic term set (P-HLTS) for capturing probabilistic preferences about possible linguistic labels belonging to a wide range of hesitant linguistic term sets. Entropy and distance measurements for P-HLTS are developed without arbitrary complementing operations. To facilitate decision making with attribute prioritization relationships, we present a probabilistic uncertain balanced linguistic-prioritized weighted average (PUBL-PWA) operator and the probabilistic uncertain balanced linguistic-induced prioritized ordered weighted average (PUBL-IPOWA) operator. In terms of the strength of the above tools, we further construct two multiple attribute group decision-making (MAGDM) approaches under P-HLTS environments, namely, an approach for decision-making situations where attribute prioritization relationships are known in advance and the relative importance of decision makers (DMs) or decision-making units (DMUs) is not required for consideration, and second approach for decision-making situations where both attribute prioritization relationships and the weighted vectors of DMs or DMUs are explicitly unknown. In general, our proposed approaches are more flexible and practical when considering heterogeneous opinions, avoiding information distortion brought about by complementing operation-based distance measures. Furthermore, illustrative application studies are conducted to verify our developed approaches.

Accurate tourist flow prediction is key to ensuring the normal operation of popular scenic spots. However, one single model cannot effectively grasp the characteristics of the data and make accurate predictions because of the strong nonlinear characteristics of daily tourist flow data. Accordingly, this study predicts daily tourist flow in Huangshan Scenic Spot in China. A prediction method (GA-CNN-LSTM) which combines convolutional neural network (CNN) and long-short-term memory network (LSTM) and optimized by genetic algorithm (GA) is established. First, network search data, meteorological data, and other data are constructed into continuous feature maps. Then, feature vectors are extracted by convolutional neural network (CNN). Finally, the feature vectors are input into long-short-term memory network (LSTM) in time series for prediction. Moreover, GA is used to scientifically select the number of neurons in the CNN-LSTM model. Data is preprocessed and normalized before prediction. The accuracy of GA-CNN-LSTM is evaluated using mean absolute percentage error (MAPE), mean absolute error (MAE), Pearson correlation coefficient and index of agreement (IA). For a fair comparison, GA-CNN-LSTM model is compared with CNN-LSTM, LSTM, CNN and the back propagation neural network (BP). The experimental results show that GA-CNN-LSTM model is approximately 8.22% higher than CNN-LSTM on the performance of MAPE.

Continuous environmental concerns regarding construction industry have been driving general constructors of mega infrastructure projects to incorporate green contractors. Although conventional multiple attributes decision-making (MADM) methodologies have provided feasible ways to select contractor, high complexity in scenarios of megaprojects still challenges existing MADM methods in concurrently accommodating three key issues of decision hesitancy, attributes interdependency, and group attitudinal character. To elicit decision-makers’ hesitant fuzzy assessments more objectively and comprehensively, we define an expression tool called interval-valued dual hesitant fuzzy uncertain unbalanced linguistic set (IVDHF_UUBLS) and develop aggregation operators through its operations. To exploit attributes interdependency, we establish a synthesized attributes’ weighting model to fuse an attributes interdependency-based weighting vector and an argument-dependent weighting vector, which are, respectively, derived through Decision-Making and Trial Evaluation Laboratory (DEMATEL) technique and maximizing deviation method. To effectively utilize decision-makers’ group attitudinal characters, we also develop a TOPSIS-based method to rationally transform group ideal attitudes into order-inducing vectors. On the strength of the above methods, an integrated MADM approach is then constructed. Finally, illustrative case study and experiments are conducted to validate our approach.

Aiming at multiple attributes group decision-making (MAGDM) problems that characterize uncertainty nature and decision hesitancy, firstly, we propose the interval-valued dual hesitant fuzzy unbalanced linguistic set (IVDHFUBLS) in which two sets of interval-valued hesitant fuzzy membership degrees and nonmembership degrees are employed to supplement the most preferred unbalanced linguistic term, as an effective hybrid expression tool to elicit complicate preferences of decision-makers more comprehensively and flexibly than existing tools based on classic linguistic term set. Basic operations for IVDHFUBLS are further defined; also a novel distance measure is developed to avoid potential information distortion that could be brought about by traditional complementing methodology for hesitant fuzzy set and its derivatives. In view of the fundamental role of aggregation operators in MAGDM modelling, we next develop some extended power aggregation operators for IVDHFUBLS, including power aggregation operator, weighted power aggregation operator, and induced power ordered weighted aggregation operator; their desirable properties and special cases are also analyzed theoretically. Subsequently, with support of the above methods, we develop two effective approaches for our targeted complex decision-making problems and verify their effectiveness and practicality by numerical studies.

Web 2.0 enables a two-way interaction between servers and clients. GPS receivers become available to more citizens and are commonly found in vehicles and smart phones, enabling individuals to record and share their trajectory data on the Internet and edit them online. OpenStreetMap (OSM) makes it possible for citizens to contribute to the acquisition of geographic information. This paper studies the use of OSM data to find newly mapped or built roads that do not exist in a reference road map and create its updated version. For this purpose, we propose a progressive buffering method for determining an optimal buffer radius to detect the new roads in the OSM data. In the next step, the detected new roads are merged into the reference road maps geometrically, topologically, and semantically. Experiments with OSM data and reference road maps over an area of 8494 km2 in the city of Wuhan, China and five of its 5 km × 5 km areas are conducted to demonstrate the feasibility and effectiveness of the method. It is shown that the OSM data can add 11.96% or a total of 2008.6 km of new roads to the reference road maps with an average precision of 96.49% and an average recall of 97.63%.

Extrusion container is the most important tooling for steel hot extrusion process. Conventional design using large castings and forgings is very difficult to execute due to high cost and risk. Steel wire wound containers have many advantages over conventional designs. However, conventional wire wound containers are developed for use at room temperature which are not applicable to steel hot extrusion process. In this article, the impacts of preheating on the design of steel wire wound containers are discussed in detail. A finite element model was established to examine the preheating temperature distribution, and a 1:10 scaled extrusion container was manufactured to verify the effectiveness of the finite element model. Based on the finite element model–computed temperature field, thermal stress analysis was performed. The thermal impacts on the stress of extrusion container and steel wire were obtained. Results showed that insufficient stability of internal cylinder and greatly enhanced steel wire stress may lead to the failure of extrusion container. To solve the problems, an improved design was put forward by increasing the stability factor of internal cylinder, reducing the prestress factor and lowering the allowable stress of steel wire. Results showed that the improved design can meet the requirements and counteract the thermal impacts.

The baculovirus nucleocapsid is formed through a rod-like capsid encapsulating a genomic DNA molecule of 80~180 kbp. The viral capsid is a large oligomer composed of many copies of various protein subunits. The assembly of viral capsids is a complex oligomerization process. The timing of expression of nucleocapsid-related proteins, transport pathways, and their interactions can affect the assembly process of preformed capsids. In addition, the selection of viral DNA and the injection of the viral genome into empty capsids are the critical steps in nucleocapsid assembly. This paper reviews the replication and recombination of baculovirus DNA, expression and transport of capsid proteins, formation of preformed capsids, DNA encapsulation, and nucleocapsid formation. This review will provide a basis for further study of the nucleocapsid assembly mechanism of baculovirus.

The Chinese tourism industry has been developing rapidly for the past several years, and the number of people traveling has been increasing year by year. However, many problems still beset current tourism management. Lack of effective management has caused numerous problems, such as tourists stranded during tourist season and the declining service quality of scenic spots, which have become the focus of tourists’ attention. Network search data can intuitively reflect the attention of most users through the combination of the network search index and the back propagation (BP) neural network model. This study predicts the daily tourism demand in the Huangshan scenic spot in China. The filtered keyword in the Baidu index is added to the hybrid neural network, and a BP neural network model optimized by a fruit fly optimization algorithm (FOA) based on the web search data is established in this study. Different forecasting methods are compared in this paper; the results prove that compared with other prediction models, higher accuracy can be obtained when it comes to the peak season using the FOA-BP method that includes web search data, which is a sustainable means of practically solving the tourism management problem by a more accurate prediction of tourism demand of scenic spots.

The power Heronian aggregation (PHA) operator can use the advantages of power average and the Heronian mean operator, which together could take into account the interrelationship of the aggregated arguments, and therefore alleviate the effects caused by unreasonable data through considering the support degree between input arguments. However, PHA operators cannot be used to process single-valued neutrosophic numbers (SVNNs), which is significant for extending it to SVNNs. We propose some new PHA operators for SVNNs and introduce a novel MAGDM method on the basis of the proposed operators. Firstly, the definition, properties, comparison method, and operational rules of SVNNs are introduced briefly. Then, some PHA operators are proposed, such as the single-valued neutrosophic power Heronian aggregation (SVNPHA) operator, the single-valued neutrosophic weighted power Heronian aggregation (SVNWPHA) operator, single-valued neutrosophic geometric power Heronian aggregation (SVNGPHA) operator, single-valued neutrosophic weighted geometric power Heronian aggregation (SVNWGPHA) operator. Furthermore, we discuss some properties of these new aggregation operators and several special cases. Moreover, the method to solve the MAGDM problems with SVNNs is proposed, based on the SVNWPHA and SVNWGPHA operators. Lastly, we verified the application and effectiveness of the proposed method by using an example for the MAGDM problem.

In this work, a novel strategy was developed to fabricate prevascularized cell-layer blood vessels in thick tissues and small-diameter blood vessel substitutes using three-dimensional (3D) bioprinting technology. These thick vascularized tissues were comprised of cells, a decellularized extracellular matrix (dECM), and a vasculature of multilevel sizes and multibranch architectures. Pluronic F127 (PF 127) was used as a sacrificial material for the formation of the vasculature through a multi-nozzle 3D bioprinting system. After printing, Pluronic F127 was removed to obtain multilevel hollow channels for the attachment of human umbilical vein endothelial cells (HUVECs). To reconstruct functional small-diameter blood vessel substitutes, a supporting scaffold (SE1700) with a double-layer circular structure was first bioprinted. Human aortic vascular smooth muscle cells (HA-VSMCs), HUVECs, and human dermal fibroblasts–neonatal (HDF-n) were separately used to form the media, intima, and adventitia through perfusion into the corresponding location of the supporting scaffold. In particular, the dECM was used as the matrix of the small-diameter blood vessel substitutes. After culture in vitro for 48 h, fluorescent images revealed that cells maintained their viability and that the samples maintained structural integrity. In addition, we analyzed the mechanical properties of the printed scaffold and found that its elastic modulus approximated that of the natural aorta. These findings demonstrate the feasibility of fabricating different kinds of vessels to imitate the structure and function of the human vascular system using 3D bioprinting technology.

3D printing has attracted a lot of attention in recent years. Over the past three decades, various 3D printing technologies have been developed including photopolymerization-based, materials extrusion-based, sheet lamination-based, binder jetting-based, power bed fusion-based and direct energy deposition-based processes. 3D printing offers unparalleled flexibility and simplicity in the fabrication of highly complex 3D objects. Tactile sensors that emulate human tactile perceptions are used to translate mechanical signals such as force, pressure, strain, shear, torsion, bend, vibration, etc. into electrical signals and play a crucial role toward the realization of wearable electronics and electronic skin. To date, many types of 3D printing technologies have been applied in the manufacturing of various types of tactile sensors including piezoresistive, capacitive and piezoelectric sensors. This review attempts to summarize the current state-of-the-art 3D printing technologies and their applications in tactile sensors for wearable electronics and electronic skin. The applications are categorized into five aspects: 3D-printed molds for microstructuring substrate, electrodes and sensing element; 3D-printed flexible sensor substrate and sensor body for tactile sensors; 3D-printed sensing element; 3D-printed flexible and stretchable electrodes for tactile sensors; and fully 3D-printed tactile sensors. Latest advances in the fabrication of tactile sensors by 3D printing are reviewed and the advantages and limitations of various 3D printing technologies and printable materials are discussed. Finally, future development of 3D-printed tactile sensors is discussed.

LiFePO4 (LFP) is a promising cathode material for lithium-ion batteries. In this study, low temperature direct writing (LTDW)-based 3D printing was used to fabricate three-dimensional (3D) LFP electrodes for the first time. LFP inks were deposited into a low temperature chamber and solidified to maintain the shape and mechanical integrity of the printed features. The printed LFP electrodes were then freeze-dried to remove the solvents so that highly-porous architectures in the electrodes were obtained. LFP inks capable of freezing at low temperature was developed by adding 1,4 dioxane as a freezing agent. The rheological behavior of the prepared LFP inks was measured and appropriate compositions and ratios were selected. A LTDW machine was developed to print the electrodes. The printing parameters were optimized and the printing accuracy was characterized. Results showed that LTDW can effectively maintain the shape and mechanical integrity during the printing process. The microstructure, pore size and distribution of the printed LFP electrodes was characterized. In comparison with conventional room temperature direct ink writing process, improved pore volume and porosity can be obtained using the LTDW process. The electrochemical performance of LTDW-fabricated LFP electrodes and conventional roller-coated electrodes were conducted and compared. Results showed that the porous structure that existed in the printed electrodes can greatly improve the rate performance of LFP electrodes.

The nitrogen-containing austenitic stainless steel 316LN has been chosen as the material for nuclear main-pipe, which is one of the key parts in 3rd generation nuclear power plants. In this research, a constitutive model of nitrogen-containing austenitic stainless steel is developed. The true stress-true strain curves obtained from isothermal hot compression tests over a wide range of temperatures (900-1250°C) and strain rates (10(-3)-10 s(-1)), were employed to study the dynamic deformational behavior of and recrystallization in 316LN steels. The constitutive model is developed through multiple linear regressions performed on the experimental data and based on an Arrhenius-type equation and Zener-Hollomon theory. The influence of strain was incorporated in the developed constitutive equation by considering the effect of strain on the various material constants. The reliability and accuracy of the model is verified through the comparison of predicted flow stress curves and experimental curves. Possible reasons for deviation are also discussed based on the characteristics of modeling process.

Ni was effectively recovered from spent electroless nickel (EN) plating baths by forming a nano-nickel coated activated carbon composite. With the aid of ultrasonication, melamine-formaldehyde-tetraoxalyl-ethylenediamine chelating resins were grafted on activated carbon (MFT/AC). PdCl2 sol was adsorbed on MFT/AC, which was then immersed in spent electroless nickel plating bath; then nano-nickel could be reduced by ascorbic acid to form a nano-nickel coating on the activated carbon composite (Ni/AC) in situ. The materials present were carefully examined by Fourier transform infrared spectroscopy, X-ray diffraction, field emission scanning electron microscopy, X-ray photoelectron spectroscopy and electrochemistry techniques. The resins were well distributed on the inside and outside surfaces of activated carbon with a size of 120 ± 30 nm in MFT/AC, and a great deal of nano-nickel particles were evenly deposited with a size of 3.8 ± 1.1 nm in Ni/MFT. Moreover, Ni/AC was successfully used as a catalyst for ultrasonic degradation of 2,6-dichlorophenol. [ABSTRACT FROM AUTHOR]