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The aim of this study is to establish the nonlinear dynamics equations of roller bearings with surface faults on outer raceway, inner raceway and rolling elements to analyze the dynamic characteristics of the double row self-aligning roller bearings, and provided theoretical basis for bearing fault diagnosis and life prediction.

First, based on the momentum theorem, the formulas for quantitative calculation of impact load were established, when roller was in contact with the fault of the inner or outer raceway. Then, the fault position piecewise functions and the load-carrying zone piecewise functions were established. Based on these, the nonlinear dynamic equations of double row self-aligning roller bearings are established, and Matlab is used to simulate the faulty bearings at different positions, sizes and rotational speeds. Finally, the vibration test of the fault bearings are completed, and the correctness of the nonlinear dynamic equations of the rolling bearing are verified.

The simulation and test results show that: the impact load increased with the increasing rotate speed and fault size, and the larger the fault size, the longer the impact load existed and the shorter vice versa.

The nonlinear dynamic equation of double row self-aligning roller bearings is established, which provides a theoretical basis for bearing faults diagnosis and fatigue life prediction.

This paper aims to develop an innovative 3D printer based on material extrusion to expand applied material field and shorten the production cycle. The developed 3D printer can fabricate products directly using various powders, including polymers and fillers. In addition, the influence of extrusion on the orientation of thermal conductive filler is also investigated.

To ensure the plasticizing effect and the mixing ability, the printing head is a conical twin-screw extruder, which have a smaller volume. PA12 and h-BN powders were selected for printing as matrix and filler, respectively. The properties of printing products were characterized.

The results show that the new printer can fabricate products directly using polymer powders because of the mixing ability of the twin-screw. The h-BN filler orient in the PA12 matrix and form thermal conduction paths due to the extrusion process, which make the printed samples have an anisotropic thermal conductivity.

The innovative 3D printer provides a method of printing products directly using powders, which can expand material field and shorten the production cycle. For composites, the extrusion process can make fillers orient in the matrix to fabricate products with anisotropic characteristics.

In the practice of live streaming e-commerce, the consumer demand is usually uncertain, and the inventory and prices can be decided by brand owners or streamers. To this end, this study examines the inventory and pricing decisions of the brand owner and streamer in a live streaming e-commerce supply chain under demand uncertainty.

In this study, four scenarios are considered, i.e. the brand owner determines the inventory and price (Scenario BB), the brand owner determines the inventory and the streamer determines the price (Scenario BS), the streamer determines the inventory and the brand owner determines the price (Scenario SB), and the streamer determines the inventory and price (Scenario SS).

The results show that the inventory and prices, as well as the profits of the brand owner and streamer increase with the consumer sensitivity to streamer’s sales effort level under the four scenarios. The inventory (price) is the highest under Scenario SS (SB), while that is the lowest under Scenario BB (BS). In addition, when the sensitivity is low, the brand owner’s profit is the highest under Scenario BB, otherwise, the profit is the highest under Scenario SS. Regardless of the sensitivity, the streamer’s profit is always the highest under Scenario SS.

Few studies focused on the inventory and pricing decisions of brand owners and streamers in live streaming e-commerce supply chains under demand uncertainty, while this work bridges the research gap. This study can provide theoretical basis and decision support for brand owners and streamers.

This chapter documents how the early request for citizens to participate in health-protective behaviors to quell the spread of the disease became politicized. Health-protective behaviors, such as social distancing and mask wearing, were found to reduce the spread of COVID-19. Yet, despite the evidence that compliance helped control the pandemic’s spread, mask wearing became a politicized symbol during the early stages of the pandemic. Particularly in the United States, bipartisan stances for and against mask wearing developed quickly as conspiracy theories, supported by President Trump, downplayed the seriousness of the pandemic. As vaccines appeared by late 2020, this polarization continued, again with President Trump aiming blame that the release of the vaccine was timed with 2020 election and raising questions with its safety. In comparison, Prime Minister Marin took a pro-science, global approach to Finland’s mandate and vaccine response. Using regression analysis, I examine the growing political divide that occurred between April 2020 and November 2020, highlighting the growth of politicization for both mask wearing and vaccine intention in both the United States and Finland. While analyses from April 2020 show support for the party in power (Republicans for the United States and left-leaning parties for Finland) was not a significant predictor of mask wearing in either country, by November 2020, political party significantly predicted both mask wearing and vaccine intention in both countries. Additionally, other important predictive factors, particularly state/citizen collaborative dimensions, are reviewed and discussed.

To the best of the authors’ knowledge, this study is the first to investigate the intricate relationship between crude oil spot and futures prices, focusing on both cointegration and market efficiency during the COVID-19 pandemic, and the beginning of the Russia–Ukraine conflict. Using daily West Texas Intermediate data from January 2020 to March 2024, like Cunado and Pérez de Gracia (2003), the authors use advanced statistical methods to identify structural breaks and assess cointegration levels. Linear and nonlinear Granger causality tests are used to reveal underlying dynamics.

This paper uses the Lagrange Multiplier test by Arai and Kurozumi (2007) to check for cointegration with various shifts in crude oil spot and futures markets. The two-step procedure by Kejriwal and Perron (2010) and Kejriwal et al. (2022) is then applied to assess partial parameter stability in cointegration models. Efficiency is examined using both bivariate and trivariate models based on non-arbitrage and expectations hypotheses. Finally, causality is analyzed with the vector error correction model for linear Granger causality, and the tests by Bai et al. (2018) and Diks and Panchenko (2006) for nonlinear causality.

The analysis reveals that futures prices generally lead spot prices through both linear and nonlinear causality during certain periods, while only linear causality is present in others. This inconsistency suggests fluctuating market efficiency and potential arbitrage opportunities. Structural breaks indicate that the equilibrium between spot and futures prices adjusts in response to significant events like the COVID-19 pandemic and the Russia–Ukraine war. The study identifies specific periods, particularly between January 2020 and March 2024, where both linear and nonlinear forecasting between futures and spot oil prices are effective, highlighting the dynamic nature of their relationship.

Despite extensive efforts, pinpointing the exact break date for COVID-19 remains challenging due to limitations in the data set and methodology. Additionally, the analysis of the Russia–Ukraine conflict is still ongoing. These challenges highlight the complexity of addressing structural breaks linked to unprecedented events.

The findings offer valuable insights for both academia and industry practitioners. The study reveals potential arbitrage opportunities stemming from inconsistent market efficiency and fluctuating causality between futures and spot prices, allowing traders to optimize their trades and timing. It also enhances risk management by identifying when linear and nonlinear causality is most effective. Policymakers can use these insights to evaluate market stability, especially during major disruptions such as the COVID-19 pandemic and geopolitical conflicts, guiding regulatory decisions. Furthermore, the study highlights the importance for investors to adjust their strategies in response to structural breaks and evolving market conditions.

This study’s social implications are diverse, extending beyond finance and academia. It influences economic stability by revealing inefficiencies and arbitrage opportunities in crude oil markets, aiding better resource allocation. Enhanced transparency benefits stakeholders, promoting fair market practices and consumer protection. Policymakers can refine regulations based on identified structural breaks, ensuring market stability. The study indirectly impacts environmental discussions by examining crude oil’s link to global energy consumption. Financially, it guides investment strategies, influencing resource distribution and the broader economy. Additionally, its educational contribution stimulates academic discourse, fostering growth in energy economics and financial market knowledge, shaping future research.

The originality and value of this paper lie in its comprehensive examination of the dynamic relationship between futures and spot oil prices, particularly through both linear and nonlinear causality across different periods. By identifying and analyzing periods of both linear and nonlinear causality, the study uncovers fluctuating market efficiency and potential arbitrage opportunities that are not typically addressed in conventional analyses. Additionally, the paper’s focus on the impact of significant global events, such as the COVID-19 pandemic and the Russia–Ukraine war, on the equilibrium between spot and futures prices offers a novel perspective on how structural breaks influence market dynamics. This nuanced understanding enhances both theoretical and practical knowledge, offering valuable insights for traders, investors and policymakers to navigate and respond to evolving market conditions.

Polyethylene terephthalate (PET) as a fiber molding polymer is widely used in aerospace, electrical and electronic, clothing and other fields. The purpose of this study is to improve the thermal insulation performance of polyethylene terephthalate (PET), the SiO₂ aerogel/PET composites slices and fibers were prepared, and the effects of the SiO₂ aerogel on the morphology, structure, crystallization property and thermal conductivity of the SiO₂ aerogel/PET composites slices and their fibers were systematically investigated.

The mass ratio of purified terephthalic acid and ethylene glycol was selected as 1:1.5, which was premixed with Sb₂O₃ and the corresponding mass of SiO₂ aerogel, and SiO₂ aerogel/PET composites were prepared by direct esterification and in-situ polymerization. The SiO₂ aerogel/PET composite fibers were prepared by melt-spinning method.

The results showed that the SiO₂ aerogel was uniformly dispersed in the PET matrix. The thermal insulation coefficient of PET was significantly reduced by the addition of SiO₂ aerogel, and the thermal conductivity of the 1.0 Wt.% SiO₂ aerogel/PET composites was reduced by 75.74 mW/(m · K) compared to the pure PET. The thermal conductivity of the 0.8 Wt.% SiO₂ aerogel/PET composite fiber was reduced by 46.06% compared to the pure PET fiber. The crystallinity and flame-retardant coefficient of the SiO₂ aerogel/PET composite fibers showed an increasing trend with the addition of SiO₂ aerogel.

The SiO₂ aerogel/PET composite slices and their fibers have good thermal insulation properties and exhibit good potential for application in the field of thermal insulation, such as warm clothes. In today’s society where the energy crisis is becoming increasingly serious, improving the thermal insulation performance of PET to reduce energy loss will be of great significance to alleviate the energy crisis.

In this study, SiO₂ aerogel/PET composite slices and their fibers were prepared by an in situ polymerization process, which solved the problem of difficult dispersion of nanoparticles in the matrix and the thermal conductivity of PET significantly reduced.

In order to improve the rationality of the design of carbon fiber composite riveting structures in engineering products and reduce physical tests, the stress changes of various parts during the carbon fiber-reinforced polymer (CFRP) riveting process, the influence area of hole edge stress and the damage of CFRP plates were studied from the perspective of numerical simulation. The reasonable arrangement requirements of composite riveted structures, installation speed and damage characteristics of CFRP plates in engineering applications are obtained. The research results provide technical references for the design and installation of composite riveting structures in engineering products.

Taking the forming process of the riveted structure between countersunk blind rivets and CFRP plates as the research object, the forming principle of countersunk blind rivets and the damage characteristics of CFRP plates were analyzed. Using contact nonlinearity theory, the anisotropic material modeling method and the Hashin failure criterion, the stress changes in various parts during the riveted joint forming process, the influence area of stress at the hole edge of the rivet holes and the damage of the CFRP plates were analyzed.

Reasonable rivet layout spacing was obtained; the peak stress of the rivet assembly increases with the increase of the rivet installation speed, and the influence area of the hole edge on the CFRP plate is a circular area with an outward extension radius of 0∼6 mm. Therefore, the arrangement distance between rivets is greater than the ring area, the damage law and installation speed of CFRP plates are given, the damage extends inward from the first layer of the laminates and the installation speed of 15 mm/s can satisfy the requirement of riveting installation better.

The riveting and forming process under different speeds is analyzed from the perspective of numerical simulation, and the stress variation rule of each part of the rivet and the damage of the CFRP plate under each speed are obtained. Reasonable rivet arrangement requirements and installation speed were given. This study provides technical support for the rivet arrangement method and mechanical property analysis of CFRP riveted structures in complex engineering products.

Because the food service industry is more dependent on customer contact and human resources than other industries, it is crucial to understand the factors influencing employee job satisfaction to ensure that employees provide satisfactory service to customers. However, few studies have incorporated employee reviews of job portals into their research. Many job seekers tend to trust company reviews posted by employees on job portals based on the information provided by the company itself. Thus, this study utilized company reviews and job satisfaction ratings from employees in the food service industry on a job portal site, Job Planet, to conduct mixed-method research.

For qualitative research, we applied the Latent Dirichlet Allocation (LDA) model to food service industry company reviews to identify 10 job satisfaction factors considered important by employees. For quantitative research, four algorithms were used to predict job satisfaction ratings: regression tree, multilayer perceptron (MLP), random forest and XGBoost. Thus, we generated predictor variables for six cases using the probability values of topics and job satisfaction ratings on a five-point scale through LDA and used them to build prediction algorithms.

The analysis showed that algorithm accuracy performed differently in each of the six cases, and overall, factors such as work-life balance and work environment have a significant impact on predicting job satisfaction ratings.

This study is significant because its methodology and results suggest a new approach based on data analysis in the field of human resources, which can contribute to the operation and planning of corporate human resources management in the future.

The online healthcare platform (OHP) has become an essential element of the healthcare system, representing a technological shift in the job responsibilities of medical professionals. Drawing on a technology-based job demands–resources (JD-R) model, this study aims to examine how the technological characteristics of OHP affect doctors’ OHP use psychology and behavior.

This empirical study was based on a survey conducted among 423 doctors with OHP use experience. The proposed model underwent assessment through partial least squares structural equation modeling (PLS-SEM) to reveal the effects of technology-based job demands (i.e. technology-based work overload and technology-based work monitoring) and resources (i.e. perceived usefulness, facilitating conditions and IT mindfulness) on doctors’ OHP fatigue and continuance use intention.

Results suggest that technology-based work monitoring, perceived usefulness and facilitation conditions have significant impacts on doctors’ psychological and behavioral responses to using OHP, whereas technology-based work overload and IT mindfulness have a single impact on continuance use intention and fatigue of OHP.

It assists doctors, healthcare administrators, policymakers and technology developers in understanding OHPs’ technological characteristics, enabling them to harness its benefits and mitigate potential challenges. Additionally, given the self-reported cross-sectional data from China, future studies can improve generalizability and adopt experimental methods or longitudinal designs with objective data.

It extends the research on OHP by employing a technology-based JD-R model to explore work attributes and dual effects associated with OHP’s technological characteristics. It also enriches existing research by examining the role of OHP’s technological characteristics in doctors’ psychological and behavioral responses.

To propose a new investment-income valuation model by real options approach (ROA) for old community renewal (OCR) projects, which could help the government attract private capital's participation.

The new model is proposed by identifying the types of options private capital has in the OCR project, selecting the option model most suitable for private capital investment decisions, improving the valuation model through the triangular fuzzy numbers to take into account the uncertainty and flexibility, and demonstrating the feasibility of the calculation model through an actual OCR project case.

The new model can valuate OCR projects more accurately based on considering uncertainty and flexibility, compared with conventional methods that often underestimate the value of OCR projects.

The investment-income of OCR projects shall be re-valuated from the lens of real options, which could help reveal more real benefits beyond the capital growth of OCR projects, enable the government to attract private capital's investment in OCR, and alleviate government fiscal pressure.

The proposed OCR-oriented investment-income valuation model systematically analyzes the applicability of real option value (ROV) to OCR projects, innovatively integrates the ROV and the net present value (NPV) as expanded net present value (ENPV), and accurately evaluate real benefits in comparison with existing models. Furthermore, the newly proposed model holds the potential to be transferred to various social welfare projects as a tool to attract private capital's participation.