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An empirical study investigated the antecedents to perceived usefulness (PU) and its consequences in the context of smoking cessation online health communities (OHCs).

To validate a research model for perceived informational support, perceived emotional support and perceived esteem support, the authors conducted a partial-least-squares analysis of empirical data from an online survey (N = 173) of users of two smoking cessation OHCs. The proposed model articulates these as antecedents to PU from a social support perspective, and knowledge sharing and continuance intention are expressed as consequences of PU.

The empirical study identified that the PU of smoking cessation OHCs is influenced by perceived emotional support and perceived esteem support, and perceived informational support indirectly affects PU via these factors. In turn, PU exerts a positive influence on both knowledge sharing and continuance intention. Also, knowledge sharing positively affects continuance intention.

The study contributes to scholarship on users' postadoption behavior in the context of smoking cessation OHCs by disentangling the antecedents to PU from a social support perspective and pinpointing some important consequences of PU. The research also has practical implications for managing smoking cessation OHCs.

The purpose of this review is to comprehensively consider the material properties and processing of additive titanium alloy and provide a new perspective for the robotic grinding and polishing of additive titanium alloy blades to ensure the surface integrity and machining accuracy of the blades.

At present, robot grinding and polishing are mainstream processing methods in blade automatic processing. This review systematically summarizes the processing characteristics and processing methods of additive manufacturing (AM) titanium alloy blades. On the one hand, the unique manufacturing process and thermal effect of AM have created the unique processing characteristics of additive titanium alloy blades. On the other hand, the robot grinding and polishing process needs to incorporate the material removal model into the traditional processing flow according to the processing characteristics of the additive titanium alloy.

Robot belt grinding can solve the processing problem of additive titanium alloy blades. The complex surface of the blade generates a robot grinding trajectory through trajectory planning. The trajectory planning of the robot profoundly affects the machining accuracy and surface quality of the blade. Subsequent research is needed to solve the problems of high machining accuracy of blade profiles, complex surface material removal models and uneven distribution of blade machining allowance. In the process parameters of the robot, the grinding parameters, trajectory planning and error compensation affect the surface quality of the blade through the material removal method, grinding force and grinding temperature. The machining accuracy of the blade surface is affected by robot vibration and stiffness.

This review systematically summarizes the processing characteristics and processing methods of aviation titanium alloy blades manufactured by AM. Combined with the material properties of additive titanium alloy, it provides a new idea for robot grinding and polishing of aviation titanium alloy blades manufactured by AM.

Chronic kidney disease (CKD) is a worldwide public health problem which imposes a significant financial burden not only on patients but also on the healthcare systems, especially under the pressure of the rapid growth of the elderly population in China. The purpose of this paper is to examine the hospitalization costs of patients with CKD between two urban health insurance schemes and investigate the factors that were associated with their inpatient costs in Guangzhou, China.

This was a prevalence-based, observational study using data derived from two insurance claims databases during the period from January 2010 to December 2012 in the largest city, Guangzhou in Southern China. The authors identified 5,803 hospitalizations under two urban health insurance schemes. An extension of generalized linear model – the extended estimating equations approach – was performed to identify the main drivers of total inpatient costs.

Among 5,803 inpatients with CKD, the mean age was 60.6. The average length of stay (LOS) was 14.4 days. The average hospitalization costs per inpatient were CNY15,517.7. The mean inpatient costs for patients with Urban Employee-based Basic Medical Insurance (UEBMI) scheme (CNY15,582.0) were higher than those under Urban Resident-based Basic Medical Insurance (URBMI) scheme (CNY14,917.0). However, the percentage of out-of-pocket expenses for the UEBMI patients (19.8 percent) was only half of that for the URBMI patients (44.5 percent). Insurance type, age, comorbidities, dialysis therapies, severity of disease, LOS and hospital levels were significantly associated with hospitalization costs.

The costs of hospitalization for CKD were high and differed by types of insurance schemes. This was the first study to compare the differences in hospitalization costs of patients with CKD under two different urban insurance schemes in China. The findings of this study could provide economic evidence for understanding the burden of CKD and evaluating different treatment of CKD (dialysis therapy) in China. Such useful information could also be used by policy makers in health insurance program evaluation and health resources allocation.

In time and accurate detection of cancer can save the life of the person affected. According to the World Health Organization (WHO), breast cancer occupies the most frequent incidence among all the cancers whereas breast cancer takes fifth place in the case of mortality numbers. Out of many image processing techniques, certain works have focused on convolutional neural networks (CNNs) for processing these images. However, deep learning models are to be explored well.

In this work, multivariate statistics-based kernel principal component analysis (KPCA) is used for essential features. KPCA is simultaneously helpful for denoising the data. These features are processed through a heterogeneous ensemble model that consists of three base models. The base models comprise recurrent neural network (RNN), long short-term memory (LSTM) and gated recurrent unit (GRU). The outcomes of these base learners are fed to fuzzy adaptive resonance theory mapping (ARTMAP) model for decision making as the nodes are added to the F_2ˆa layer if the winning criteria are fulfilled that makes the ARTMAP model more robust.

The proposed model is verified using breast histopathology image dataset publicly available at Kaggle. The model provides 99.36% training accuracy and 98.72% validation accuracy. The proposed model utilizes data processing in all aspects, i.e. image denoising to reduce the data redundancy, training by ensemble learning to provide higher results than that of single models. The final classification by a fuzzy ARTMAP model that controls the number of nodes depending upon the performance makes robust accurate classification.

Research in the field of medical applications is an ongoing method. More advanced algorithms are being developed for better classification. Still, the scope is there to design the models in terms of better performance, practicability and cost efficiency in the future. Also, the ensemble models may be chosen with different combinations and characteristics. Only signal instead of images may be verified for this proposed model. Experimental analysis shows the improved performance of the proposed model. This method needs to be verified using practical models. Also, the practical implementation will be carried out for its real-time performance and cost efficiency.

The proposed model is utilized for denoising and to reduce the data redundancy so that the feature selection is done using KPCA. Training and classification are performed using heterogeneous ensemble model designed using RNN, LSTM and GRU as base classifiers to provide higher results than that of single models. Use of adaptive fuzzy mapping model makes the final classification accurate. The effectiveness of combining these methods to a single model is analyzed in this work.

This study investigates antecedents determining the inclination to engage in future environmental entrepreneurial activities. Building on passion research and social cognitive theory, the authors explore the role of environmental passion for environmental entrepreneurial intention, drawing attention to the mediating role of environmental self-efficacy.

A regression-based path analysis for mediation to test the developed hypotheses on a sample of 139 young individuals is applied.

The results demonstrate a significant positive effect of environmental passion on environmental entrepreneurial intention. The mediation analysis shows a positive direct and indirect effect of passion on intention, concluding that self-efficacy is a partial mediator. The results further suggest that environmental entrepreneurial intention is related to gender. In contrast, covariates like age, entrepreneurial exposure and entrepreneurship education have no significant effect.

The results have implications for practitioners and policymakers who aim to further entrepreneurship for environmental sustainability. It underlines the need to take emotional antecedents seriously, suggests policy for creative and interdisciplinary education with respect to its challenges and emphasizes the roles of teachers in fostering passion.

The results provide a deeper contextualized understanding of passion, self-efficacy and intention in environmental entrepreneurship. These results offer an original perspective of entrepreneurship as a conduit to channel energy, concerns and passionate interests in the natural environment. The study presents theoretical implications for passion theory by extending sources of passion and clarifying the direction of self-efficacy in entrepreneurship.

This study was inspired by research of strategists on strategic innovation (SI), aiming to provide a unique model to enhance the digitization of small and medium-sized enterprises (SMEs) in Bangladesh to fill the gap toward a digital economy.

A survey was used to collect data from 180 SMEs in the manufacturing industry for this research. The results indicate that strategic innovativeness (SI), human capital (HC), infrastructure and technology and resistance to change significantly influence the digitalization in Bangladesh SMEs.

The link between SI and SMEs' digitalization in Bangladesh is mediated by HC. The results show that HC plays a big role in the connection between SI and the digitalization of SMEs. This study may be valuable for SMEs managers, researchers and policymakers in Bangladesh and other developing nations, who want to learn more about SI in adopting digitalization.

The specialized knowledge and abilities of strategists allow them to establish parallels between the past and present, enabling them to make a sustained forecast about the digital economy. This study encourages small and medium-sized businesses to develop their SI and advance their HC, which could further deject resistance to change toward enhancing and adopting digitalization in SMEs sectors.