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During the construction process of the China Railway Track System (CRTS) I type filling layer, the nonwoven fabric bags have been used as grouting templates for cement asphalt (CA) emulsified mortar. The porous structure of nonwoven fabrics endowed the templates with breathability and water permeability. The standard requires that the volume expansion rate of CA mortar must be controlled within 1%–3%, which can generate expansion pressure to ensure that the cavities under track slabs are filled fully. However, the expansion pressure caused some of the water to seep out from the periphery of the filling bag, and it would affect the actual mix proportion of CA mortar. The differences in physical and mechanical properties between the CA mortar under track slabs and the CA mortar formed in the laboratory were studied in this paper. The relevant results could provide important methods for the research of filling layer materials for CRTS I type and other types of ballastless tracks in China.

During the inspection of filling layer, the samples of CA mortar from different working conditions and raw materials were taken by uncovering the track slabs and drilling cores. The physical and mechanical properties of CA mortar under the filling layer of the slab were systematically analyzed by testing the electrical flux, compressive strength and density of mortar in different parts of the filling layer.

In this paper, the electric flux, the physical properties and mechanical properties of different parts of CA mortar under the track slab were investigated. The results showed that the density, electric flux and compressive strength of CA mortar were affected by the composition of raw materials for dry powders and different parts of the filling layer. In addition, the electrical flux of CA mortar gradually decreased within 90 days’ age. The electrical flux of samples with the thickness of 54 mm was lower than 500 C. Therefore, the impermeability and durability of CA mortar could be improved by increasing the thickness of filling layer. Besides, the results showed that the compressive strength of CA mortar increased, while the density and electric flux decreased gradually, with the prolongation of hardening time.

During 90 days' age, the electrical flux of the CA mortar gradually decreased with the increase of specimen thickness and the electrical flux of the specimens with the thickness of 54 mm was lower than 500 C. The impermeability and durability of the CA mortar could be improved by increasing the thickness of filling layer. The proposed method can provide reference for the further development and improvement of CRTS I and CRTS II type ballastless track in China.

The climate change effects on agricultural output in different regions of the world and have been debated in the literature of emerging economies. Recently, the agriculture sector has influenced globally through climate change and also hurts all sectors of economies. This study aims to examine and explore the impact of global climate change on agricultural output in China over the period of 1982-2014.

Different unit root tests including augmented Dickey–Fuller, Phillips–Perron and Kwiatkowski, Phillips, Schmidt and Shin are used to check the order of integration among the study variables. The autoregressive distributed lag (ARDL) bounds testing approach to cointegration and the Johansen cointegration test are applied to assess the association among the study variables with the evidence of long-run and short-run analysis.

Unit root test estimations confirm that all variables are stationary at the combination of I(0) and I(1). The results show that CO₂ emissions have a significant effect on agricultural output in both long-run and short-run analyses, while temperature and rainfall have a negative effect on agricultural output in the long-run. Among other determinants, the land area under cereal crops, fertilizer consumption, and energy consumption have a positive and significant association with agricultural output in both long-run and short-run analysis. The estimated coefficient of the error correction term is also highly significant.

China’s population is multiplying, and in the coming decades, the country will face food safety and security challenges. Possible initiatives are needed to configure the Chinese Government to cope with the adverse effects of climate change on agriculture and ensure adequate food for the growing population. In concise, the analysis specifies that legislators and policy experts should spot that the climate change would transmute the total output factors, accordingly a county or regional specific and crop-specific total factor of production pattern adaptation is indorsed.

The present empirical study is the first, to the best of the authors’ knowledge, to investigate the impact of global climate change on agricultural output in China by using ARDL bounds testing approach to cointegration and Johansen cointegration test.

In an era marked by heightened geopolitical uncertainties, such as international conflicts and economic instability, the dynamics of energy markets assume paramount importance. Our study delves into this complex backdrop, focusing on the intricate interplay the between traditional and emerging energy sectors.

This study analyzes the interconnections among green financial assets, renewable energy markets, the geopolitical risk index and cryptocurrency carbon emissions from December 19, 2017 to February 15, 2023. We investigate these relationships using a novel time-frequency connectedness approach and machine learning methodology.

Our findings reveal that green energy stocks, except the PBW, exhibit the highest net transmission of volatility, followed by COAL. In contrast, CARBON emerges as the primary net recipient of volatility, followed by fuel energy assets. The frequency decomposition results also indicate that the long-term components serve as the primary source of directional volatility spillover, suggesting that volatility transmission among green stocks and energy assets tends to occur over a more extended period. The SHapley additive exPlanations (SHAP) results show that the green and fuel energy markets are negatively connected with geopolitical risks (GPRs). The results obtained through the SHAP analysis confirm the novel time-varying parameter vector autoregressive (TVP-VAR) frequency connectedness findings. The CARBON and PBW markets consistently experience spillover shocks from other markets in short and long-term horizons. The role of crude oil as a receiver or transmitter of shocks varies over time.

Green financial assets and clean energy play significant roles in the financial markets and reduce geopolitical risk. Our study employs a time-frequency connectedness approach to assess the interconnections among four markets' families: fuel, renewable energy, green stocks and carbon markets. We utilize the novel TVP-VAR approach, which allows for flexibility and enables us to measure net pairwise connectedness in both short and long-term horizons.

It would take billions of miles’ field road testing to demonstrate that the safety of automated vehicle is statistically significantly higher than the safety of human driving because that the accident of vehicle is rare event.

This paper proposes an accelerated testing method for automated vehicles safety evaluation based on improved importance sampling (IS) techniques. Taking the typical cut-in scenario as example, the proposed method extracts the critical variables of the scenario. Then, the distributions of critical variables are statistically fitted. The genetic algorithm is used to calculate the optimal IS parameters by solving an optimization problem. Considering the error of distribution fitting, the result is modified so that it can accurately reveal the safety benefits of automated vehicles in the real world.

Based on the naturalistic driving data in Shanghai, the proposed method is validated by simulation. The result shows that compared with the existing methods, the proposed method improves the test efficiency by 35 per cent, and the accuracy of accelerated test result is increased by 23 per cent.

This paper has three contributions. First, the genetic algorithm is used to calculate IS parameters, which improves the efficiency of test. Second, the result of test is modified by the error correction parameter, which improves the accuracy of test result. Third, typical high-risk cut-in scenarios in China are analyzed, and the proposed method is validated by simulation.

This study aims to provide a picture of the current state of art in the use of big data for decision-making processes for the management of health-care organizations.

A systematic literature review was carried out. The research uses two analyses: descriptive analysis, describing the evolution of citations; keywords; and the ten most influential papers, and bibliometric analysis, for content evaluation, for which a cluster analysis was performed.

A total of 48 articles were selected for bibliographic coupling out of an initial sample of more than 5,000 papers. Of the 48 articles, 29 are linked on the basis of their bibliography. Clustering the 29 articles on the basis of actual content, four research areas emerged: quality of care, quality of service, crisis management and data management.

Health-care organizations believe strongly that big data can become the most effective tool for correctly influencing the decision-making processes. Thus, more and more organizations continue to invest in big data analytics, and the literature on this topic has expanded rapidly. This study seeks to provide a comprehensive picture of the different streams of literature existing, together with gaps in research and future perspectives. The literature is mature enough for an analysis to be made and provide managers with useful insights on opportunities, criticisms and perspectives on the use of big data for health-care organizations. However, to date, there is no comprehensive literature review on the big data analysis in health care. Furthermore, as big data is a “sexy catchphrase,” more clarity on its usage may be needed. It represents an important tool to be investigated and its great potential is often yet to be discovered. This study thus sheds light on emerging issues and suggests further research that may be needed.

This study aims to evaluate the characteristics of climate change in Yunnan minority areas and identify an effective path to promote sustainable livelihoods based on climate change.

Taking Yunnan Province as an example, based on the expansion of the traditional sustainable livelihood framework, the authors constructed a system dynamics (SD) model of sustainable livelihood from the six subsystems of natural, physical, financial, social, human and cultural and tested the accuracy and effectiveness of the model with data from Cangyuan County. By adjusting these parameters, five development paths are designed to simulate the future situation of the livelihood system and determine the optimal path.

Climate change has exacerbated the vulnerability of people’s livelihoods. In future, each of the five development paths will be advantageous for promoting sustainable livelihoods. However, compared with Path I (maintaining the status quo), Path III (path of giving priority to culture) and Path IV (path of giving priority to economic development) have more obvious advantages. Path II (path of giving priority to people’s lives) gradually increases the development rate by promoting people’s endogenous motivation, and Path V (path of coordinated development) is better than the other paths because of its more balanced consideration.

The analytical framework of sustainable livelihoods based on the characteristics of minority areas is broadened. By constructing a SD model of the livelihood system, the limitations of traditional static analysis have been overcome and a development path for promoting sustainable livelihoods through simulation is proposed. This study offers a theoretical framework and reference method for livelihood research against the backdrop of climate change and a decision-making basis for enhancing climate adaptability and realizing sustainable livelihoods.

Healthcare organizations worldwide were badly hit by the “surprise” of the pandemic. Hospitals in particular are trying hard to manage problems it caused, searching for solutions to protect the health of citizens and reorienting operations. The implementation of resilience solutions in the coping phase and the ability to react promptly and redefine activities is essential. Integrating crisis management and resiliency literature, this paper discusses how health organizations were able to cope with adversity during the crisis.

The research is conducted through a case study of a large Italian hospital, the Gemelli Polyclinic Foundation, which was one of the leading hospitals in the Italian response to the pandemic.

The case reports actions taken in order to continue functioning and to maintain core activities despite severe adversity. The overall response of the Gemelli was the result of the three types of response: behavioral (effective leadership), cognitive (rapid resource reallocation) and the contextual reinforcement (multiagency network response). The authors highlight how an integrative framework of crisis management and resiliency could be applied to healthcare organizations in the coping phase of the pandemic. The experience of the Gemelli can thus be useful for other hospitals and organizations facing external crises and for overall improvement of crisis management and resilience. Responding to crisis brings the opportunity to make innovations introduced during emergencies structural, and embed them moving forward.

The paper focuses only on the coping phase of the response to the pandemic, whereas building long-term resilience requires understanding how organizations accumulate knowledge from crises and adapt to the “new normal.”

The paper responds to the call for empirical studies to advance knowledge of an integrative framework of crisis management and resiliency theories with reference to complex organizations such as healthcare.

This paper proposes a solution for recognizing human faces under mask-wearing. The lower part of human face is occluded and could not be used in the learning process of face recognition. So, the proposed solution is developed to recognize human faces on any available facial components which could be varied depending on wearing or not wearing a mask.

The proposed solution is developed based on the FaceNet framework, aiming to modify the existing facial recognition model to improve the performance of both scenarios of mask-wearing and without mask-wearing. Then, simulated masked-face images are computed on top of the original face images, to be used in the learning process of face recognition. In addition, feature heatmaps are also drawn out to visualize majority of parts of facial images that are significant in recognizing faces under mask-wearing.

The proposed method is validated using several scenarios of experiments. The result shows an outstanding accuracy of 99.2% on a scenario of mask-wearing faces. The feature heatmaps also show that non-occluded components including eyes and nose become more significant for recognizing human faces, when compared with the lower part of human faces which could be occluded under masks.

The convolutional neural network based solution is tuned up for recognizing human faces under a scenario of mask-wearing. The simulated masks on original face images are augmented for training the face recognition model. The heatmaps are then computed to prove that features generated from the top half of face images are correctly chosen for the face recognition.

The aim of this study was to investigate the role of immersive gaming on brand coolness and brand equity, with particular emphasis on fashion gaming collaborations. We used game theory as a theoretical framework to analyse immersive branding strategies and gain a deeper understanding of fashion consumers’ decision-making process in gaming environments. Gender, as a significant factor affecting gamers, has been studied as a moderator that impacts the overall proposed framework.

A quantitative method was used to assess the significance of relationships within the proposed model empirically. The partial least squares structural equation modelling technique was implemented to assess the relationships of the framework with a sample size of 160 active Malaysian gamers.

The findings indicate that brand equity is positively associated with perceived brand coolness. Furthermore, of the three core values of online games, perceived enjoyment is most strongly associated with perceived brand coolness, ahead of the values of self-expression and perceived emotional challenge. The results of the multigroup analysis further suggest that in the fashion industry, building brand equity through online games is strongly related to perceived brand coolness among female respondents, the role of perceived brand coolness being weaker among male respondents.

The study contributes to the existing literature by providing a deeper understanding of the impact of immersive gaming branding practices on the overall equity of the fashion brand. The results provide insight for fashion brand managers into the significant effect on consumer behaviour outcomes of fashion-gaming collaborations.

本研究擬探討身歷其境的遊戲體驗在品牌酷感和品牌資產上所扮演的角色; 研究特別強調裝扮遊戲的合作。研究人員以博弈論作為研究的理論框架,來分析沉浸品牌策略、和對時裝消費者在遊戲的環境中如何作出決策取得更深入的瞭解。研究人員探討了性別作為影響遊戲參與者的重要因素,這因素被視為為整個被提出的框架帶來調節的影響和作用。

研究人員採用定量方法進行研究,目的為於被提出的模型內之各種關聯的意義進行以經驗為依據的評估。研究人員採用了基於偏最小平方法的結構方程模型研究法,來對一個包含160名活躍的馬來西亞遊戲參與者的樣本進行框架的各個聯繫的評估研究。

研究結果顯示,品牌資產與品牌酷感成正相關; 而且,在網絡遊戲的三個核心價值中,感知享受與品牌酷感之間的聯繫最為強烈和密切,超過自我表現和感知情感挑戰兩者的價值; 多群組的分析結果更暗示了在時裝產業裡,透過網絡遊戲去建立品牌資產在女性回應者中是與品牌酷感有強烈密切的關係; 而在男性回應者中,品牌酷感所扮演的角色則較弱。

本研究使我們能更深入認識沉浸遊戲品牌的慣常做法如何影響時裝品牌的整體資產。研究結果為時裝品牌經理提供了啟示,使他們明瞭時裝遊戲的合作會給消費者行為產生重要的影響。

The purpose of this study is to analyze how university men and women in different disciplines of study in Mexico perceive social entrepreneurship competencies, using a multifactorial analysis to find possible areas of opportunity to reduce the gender gap in social-entrepreneurship-project proposals.

This is a quantitative study with a validated questionnaire that records the perception levels of five social entrepreneurship subcompetencies. The survey, which includes 28 indicators, was applied to 140 university students from different disciplines. Hypothesis testing was applied to identify significant differences between men and women in each subcompetency by disciplinary area.

In the global sample, significant differences by gender were observed only in the social value subcompetency. In the disciplinary analysis, significant differences were found in architecture and design, business, and engineering and science.

The questionnaire only gathered data about the students' perceptions. To the extent that perception is triangulated with other instruments, it is possible to increase knowledge regarding how to train in social entrepreneurship.

The results can be useful for university training and increasing the envisioning and formulating of government projects by young people who create new businesses.

This research contributes to the literature on the role of gender-specific perceptions of social entrepreneurship in Mexico.