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The purpose of this paper is to propose a DC-port voltage balance strategy realizing it by logic combination modulation (LCM). This voltage balance strategy is brief and high efficient, which can be used in many power electronic devices adopting the cascaded H-bridge rectifier (CHBR) such as power electronic transformer (PET).

The CHBR is typically as a core component in the power electronic devices to implement the voltage or current conversion. The modulation method presented here is aiming to solve the voltage imbalance problem occurred in the CHBR with more stable work station and higher reliability in ordinary operating conditions. In particular, by changing the switch states smoothly and quickly, the DC-port voltage can be controlled as the ideal value even one of the modules in CHBR is facing the load-removed problem.

By using the voltage balance strategy of LCM, the problem of voltage imbalance occurring in three-phase cascaded rectifiers has been solved properly. With the lower modulation depth, the efficiency of the strategy is shown to be better and stronger. The strategy can work reliably and quickly no matter facing the problem as load-removed change or the ordinary operating conditions.

The limitation of the proposed DC-port voltage balance strategy is calculated and proved, in a three-module CHBR, the LCM could balance the DC-port voltage while one module facing the load-removed situation under 0.83 modulation depth.

This paper provides a useful and particular voltage balance strategy which can be used in the topology of three-phase cascaded rectifier. The value of the strategy is that a brief and reliable voltage balance method in the power electronic devices can be achieved. What is more, facing the problem, such as load-removed, in outport, the strategy can response quickly with no switch jump and switch frequency rising.

Problem-solving” is the most crucial key insight of scientific research. This study focuses on constructing the “problem-solving” knowledge graph of scientific domains by extracting four entity relation types: problem-solving, problem hierarchy, solution hierarchy and association.

This paper presents a low-cost method for identifying these relationships in scientific papers based on word analogy. The problem-solving and hierarchical relations are represented as offset vectors of the head and tail entities and then classified by referencing a small set of predefined entity relations.

This paper presents an experiment with artificial intelligence papers from the Web of Science and achieved good performance. The F1 scores of entity relation types problem hierarchy, problem-solving and solution hierarchy, which were 0.823, 0.815 and 0.748, respectively. This paper used computer vision as an example to demonstrate the application of the extracted relations in constructing domain knowledge graphs and revealing historical research trends.

This paper uses an approach that is highly efficient and has a good generalization ability. Instead of relying on a large-scale manually annotated corpus, it only requires a small set of entity relations that can be easily extracted from external knowledge resources.

Human resource (HR) process research emerged as a response to questions about how (bundles of) HR practices related to organizational outcomes. The goal of HR process research is to explain variability in employee and organization outcomes by focusing on how HR practices are intended (adopted) by senior managers, the way that these HR practices are implemented and communicated by line managers, and how employees perceive, understand, and attribute these HR practices. In the first part of this chapter, we present a review of 20 years of HR process research from the start, to how it developed, and is now maturing. Within the body of HR process research, several different research theoretical streams have emerged, which are largely studied in isolation without benefiting from each other. Therefore, in the second part of this chapter, we draw on previous work to propose a staged process model in which we integrate the different research streams of HR process research, recognizing contingencies in the model. This leads us to an agenda for future research and practical implications in the final part of the chapter.

The purpose of this study is to design a wearable medical device as a human care platform and to introduce the design details, key technologies and practical implementation methods of the system.

A multi-channel data acquisition scheme based on PCI-E (rapid interconnection of peripheral components) was proposed. The flexible biosensor is integrated with the flexible data acquisition card with monitoring capability, and the embedded (device that can operate independently) chip STM32F103VET6 is used to realize the simultaneous processing of multi-channel human health parameters. The human health parameters were transferred to the upper computer LabVIEW by intelligent clothing through USB or wireless Bluetooth to complete the transmission and processing of clinical data, which facilitates the analysis of medical data.

The smart clothing provides a mobile medical cloud platform for wearable medical through cloud computing, which can continuously monitor the body's wrist movement, body temperature and perspiration for 24 h. The result shows that each channel is completely accurate to the top computer display, which can meet the expected requirements, and the wearable instant care system can be applied to healthcare.

The smart clothing in this study is based on the monitoring and diagnosis of textiles, and the electronic communication devices can cooperate and interact to form a wearable textile system that provides medical monitoring and prevention services to individuals in the fastest and most accurate way. Each channel of the system is precisely matched to the display screen of the host computer and meets the expected requirements. As a real-time human health protection platform technology, continuous monitoring of human vital signs can complete the application of human motion detection, medical health monitoring and human–computer interaction. Ultimately, such an intelligent garment will become an integral part of our everyday clothing.

Surface roughness has a serious impact on the fatigue strength, wear resistance and life of mechanical products. Realizing the evolution of surface quality through theoretical modeling takes a lot of effort. To predict the surface roughness of milling processing, this paper aims to construct a neural network based on deep learning and data augmentation.

This study proposes a method consisting of three steps. Firstly, the machine tool multisource data acquisition platform is established, which combines sensor monitoring with machine tool communication to collect processing signals. Secondly, the feature parameters are extracted to reduce the interference and improve the model generalization ability. Thirdly, for different expectations, the parameters of the deep belief network (DBN) model are optimized by the tent-SSA algorithm to achieve more accurate roughness classification and regression prediction.

The adaptive synthetic sampling (ADASYN) algorithm can improve the classification prediction accuracy of DBN from 80.67% to 94.23%. After the DBN parameters were optimized by Tent-SSA, the roughness prediction accuracy was significantly improved. For the classification model, the prediction accuracy is improved by 5.77% based on ADASYN optimization. For regression models, different objective functions can be set according to production requirements, such as root-mean-square error (RMSE) or MaxAE, and the error is reduced by more than 40% compared to the original model.

A roughness prediction model based on multiple monitoring signals is proposed, which reduces the dependence on the acquisition of environmental variables and enhances the model's applicability. Furthermore, with the ADASYN algorithm, the Tent-SSA intelligent optimization algorithm is introduced to optimize the hyperparameters of the DBN model and improve the optimization performance.

Hazard warning schemes provide efficient hazard recognition and promote project safety. Nevertheless, these schemes perform poorly because the warning information is calibrated for individual characters and is not prioritized for the entire system. This study proposes a hazard warning scheme that prioritizes hazard characters from the inspection process based on the inspectors' experience.

First, hazard descriptions were decomposed into their characters, forming a double-layer network. Second, warning schemes based on cascading effects were proposed. Third, character-based warning schemes were simulated for various experiences.

The results show that when a specific hazard is detected, the degree centrality is the most effective parameter for prioritization, and hazard characters should be prioritized based on betweenness centrality for experienced inspectors, whereas degree centrality is preferred for novice inspectors.

The warning scheme theoretically supplements the information-processing theory in construction hazard warnings and provides a practical warning scheme with priority for the development of automated hazard navigation systems.

The occupational health risk associated with the production of prefabricated concrete components is often overlooked. This paper will use a damage assessment and cyclic mitigation (DACM) model to provide individualized exposure risk assessment and corresponding mitigation management measures for workers who are being exposed.

The DACM model is proposed based on the concept of life cycle assessment (LCA). The model uses Monte-Carlo simulation for uncertainty risk assessment, followed by quantitative damage assessment using disability-adjusted life year (DALY). Lastly, sensitivity analysis is used to identify the parameters with the greatest impact on health risks.

The results show that the dust concentration is centered around the mean, and the fitting results are close to normal distribution, so the mean value can be used to carry out the calculation of risk. However, calculations using the DACM model revealed that there are still some work areas at risk. DALY damage is most severe in concrete production area. Meanwhile, the inhalation rate (IR), exposure duration (ED), exposure frequency (EF) and average exposure time (AT) showed greater impacts based on the sensitivity analysis.

Based on the comparison, the DACM model can determine that the potential occupational health risk of prefabricated concrete component (PC) factory and the risk is less than that of on-site construction. It synthesizes field research and simulation to form the entire assessment process into a case-base system with the depth of the cycle, which allows the model to be continuously adjusted to reduce the occupational health damage caused by production pollution exposure.

The study examines the role of social media technologies (SMTs) as a driver of organization cultural intelligence (OCI) and new product development (NPD) capabilities, and how the complementary effects of these capabilities contribute to multinational corporations (MNCs)’ performance. Further, the study investigates the capability–performance relationship under conditions of high and low market and technological turbulence.

A quantitative survey method was implemented, with the data provided by senior marketing managers employed in MNC regional offices. The proposed model was tested using structural equation modeling and multi-group moderation analysis, and fuzzy-set qualitative comparative analysis (fsQCA).

The results indicate that SMTs support the development of OCI and NPD capabilities, which in turn contribute to MNC regional performance. A high level of technological turbulence only weakens the relationship between OCI and performance.

The results suggest that OCI contributes to MNCs’ performance, by deploying social media information and complementing the organization’s NPD capability under a specific environmental context.

The paper offers practical recommendations to MNCs on social media use when developing and launching new products in different regional markets. MNCs need to recruit culturally intelligent managers, who consider the level of market and technological turbulence when combining several types of capabilities.

Within the dynamic marketing capabilities literature, this is the first study to incorporate and reliably measure cultural intelligence capability. The research offers empirical evidence that OCI and NPD capabilities are necessary to achieve superior MNC performance and depend on the level of market and technological turbulence.

The color painting of ancient buildings has high historical and artistic value but is prone to aging due to long-term outdoor exposure. The purpose of this study is to develop a new type of sealing coating to mitigate the impact of ultraviolet (UV) light on color painting.

The new coating was subjected to a 500-h UV-aging test. Compared with the existing acrylic resin Primal AC33, the UV aging behavior of the new coating, such as color difference and gloss, was studied with aging time. The Fourier infrared spectra of the coatings were analyzed after the UV-aging test.

Compared with AC33, the antiaging performance of SF8 was substantially improved. SF8 has a lower color difference value and better light retention and hydrophobicity. The Fourier transform infrared spectroscopy results showed that the C-F bond and Si-O bonds in the resin of the optimized sealing coating protected the main chain C-C structure from degradation during the aging process; thus, the resin maintained good stability. The hindered amine light stabilizer TN292 added to the coating inhibited the antiaging process by trapping active free radicals.

To address the problem of UV aging of oil-decorated colored paintings, a new type of sealing coating with excellent antiaging properties was developed, laying the foundation for its demonstration application on the surface of ancient buildings.

The goal of this study is to investigate the mediating effect of referral rewards on consumer willingness to recommend poverty-alleviating products and to identify the most effective referral rewards for incentivizing consumers to recommend poverty-alleviating products.

Tournament rewards and piece-rate rewards are designed based on the theory of indebtedness, the related literature and the actual background. SPSS 26.0 and AMOS 17.0 are used to analyze the structural equation model.

According to the structural equation analysis, the following findings were found: under the tournament reward condition, social image, feelings of indebtedness and perceived reward value negatively affect consumer willingness to recommend. Under the piece-rate reward condition, social image and feelings of indebtedness significantly negatively affect consumer recommendation willingness, while perceived reward value significantly positively affects consumer recommendation willingness. The mean recommendation willingness of the tournament reward group is significantly lower than that of the control group. In contrast, the mean recommendation willingness of the piece-rate rewards group is significantly higher than that of the control group.

Based on the study findings, the authors propose that enterprises apply piece-rate rewards to incentivize consumers to recommend poverty-alleviating products when designing such rewards. In this way, the sale of poverty-alleviating products can be improved.