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The spatial–temporal conflicts in the construction process of concrete arch dams are related to the construction quality and duration, especially for pouring blocks with a continuous high-strength and high-density construction process. Furthermore, the complicated construction technology and limited space resources aggravate the spatial–temporal conflicts in the process of space resource allocation and utilization, directly affecting the pouring quality and progress of concrete. To promote the high-strength, quality-preserving and rapid construction of dams and to clarify the explosion moment and influence degree of the spatial–temporal conflicts of construction machinery during the pouring process, a quantification method and algorithm for a “Conflict Bubble” (CB) between construction machines is proposed based on the “Time–Space Microelement” (TSM).

First, the concept of a CB is proposed, which is defined as the spatial overlap of different entities in the movement process. The subsidiary space of the entity is divided into three layered spaces: the physical space, safe space and efficiency space from the inside to the outside. Second, the processes of “creation,” “transition” and “disappearance” of the CB at different levels with the movement of the entity are defined as the evolution of the spatial–temporal state of the entity. The mapping relationship between the spatial variation and the running time of the layered space during the movement process is defined as “Time–Space” (TS), which is intended to be processed by a microelement.

The quantification method and algorithm of the CB between construction machinery are proposed based on the TSM, which realizes the quantification of the physical collision accident rate, security risk rate and efficiency loss rate of the construction machinery at any time point or time period. The risk rate of spatial–temporal conflicts in the construction process was calculated, and the outbreak condition of spatial–temporal conflict in the pouring process was simulated and rehearsed. The quantitative calculation results show that the physical collision accident rate, security risk rate and efficiency loss rate of construction machinery at any time point or time period can be quantified.

This study provides theoretical support for the quantitative evaluation and analysis of the spatial–temporal conflict risk in the pouring construction process. It also serves as a reference for the rational organization and scientific decision-making for pouring blocks and provides new ideas and methods for the safe and efficient construction and the scientific and refined management of dams.

Providing quality emergency supplies is crucial to mitigate and respond to emergencies. However, despite government and consumer oversight of emergency supplies’ quality, a troubling trend persists among some enterprises to sacrifice product quality for financial gain. This paper examines the influence of strategy selections among governments, enterprises, and consumers to enhance the quality of emergency supplies.

We develop a tripartite evolutionary game model consisting of three stakeholders: government, enterprises, and consumers, considering factors including subsidies and penalties. After analysing three stakeholders’ strategic choices to ascertain system stability, parametric analyses were conducted.

Excessive or insufficient subsidies are not conducive to encouraging enterprises to adopt an authentic production strategy; excessive subsidies may result in consumers enduring counterfeiting. Furthermore, the government’s supervision strategy can stabilise the system quickly, suggesting that consumer reporting cannot replace government supervision. Additionally, incentivising enterprises to adopt an authentic production strategy can be achieved by increasing penalties and enhancing compensation while reducing consumer reporting, government supervision, and raw materials costs.

We present a preliminary exploration of how to promote the production of qualified emergency supplies in the early stages of an emergency event. The model and findings proposed in this paper can be generalised and applied to various emergency events, including epidemics and earthquakes.

This study aims to systematically reveal the complex interaction between uncertainty and the international commodity market (CRB).

A composite uncertainty index and five categorical uncertainty indices, together with wavelet analysis and detrended cross-correlation analysis, were used. First, in the time-frequency domain, the coherency and lead-lag relationship between uncertainty and the commodity markets were investigated. Furthermore, the transmission direction of the cross-correlation over different lag periods and asymmetry in this cross-correlation under different trends were identified.

First, there is significant coherency between uncertainties and CRB mainly in the short and medium terms, with natural disaster and public health uncertainties tending to lead CRB. Second, uncertainty impacts CRB more markedly over shorter lag periods, whereas the impact of CRB on uncertainty gradually increases with longer lag periods. Third, the cross-correlation is asymmetric and multifractal under different trends. Finally, from the perspective of lag periods and trends, the interaction of uncertainty with the Chinese commodity market is significantly different from its interaction with CRB.

First, this study comprehensively constructs a composite uncertainty index based on five types of uncertainty. Second, this study provides a scientific perspective on examining the core and diverse interactions between uncertainty and CRB, as achieved by investigating the interactions of CRB with five categorical and composite uncertainties. Third, this study provides a new research framework to enable multiscale analysis of the complex interaction between uncertainty and the commodity markets.

This paper aims to develop a robust navigation enhancement framework to handle one of the most urgent needs for real applications of autonomous vehicles nowadays, as these corner cases act as the most commonly occurred risks in potential self-driving accidents.

In this paper, the main idea is to fully exploit the consistent features among spatio-temporal data and thus detect the anomalies and build residual channels to reconstruct the abnormal information. The authors first develop an anomaly detection algorithm, then followed by a corresponding disturbed information reconstruction network which has strong robustness to address both the nature disturbances and external attacks. Finally, the authors introduce a fully end-to-end resilient navigation performance enhancement framework to improve the driving performance of existing self-driving models under attacks and disturbances.

Comparison results on CARLA platform and real experiments demonstrate strong resilience of the authors’ approach which enhances the navigation performance under disturbances and attacks.

Reliable and resilient navigation performance under various nature disturbances and even external attacks is one of the most urgent needs for real applications of autonomous vehicles nowadays, as these corner cases act as the most commonly occurred risks in potential self-driving accidents. The information reconstruction approach provides a resilient navigation performance enhancement method for existing self-driving models.

The main purpose of this review was to synthesize pre-service teachers' digital self-efficacy in the context of education 5.0 and to identify the elements that affect pre-service teacher's digital self-efficacy and preparedness for future technologies. A systematic review approach was employed to analyze the 22 articles about the pre-service teachers' digital self-efficacy of in the paradigm of education 5.0. The review was conducted from 2012 to 2022 following the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines. In this study, PICO model was used in framing research questions. The findings of the review revealed that limited study has been carried out in the context of digital self-efficacy in the context of education 5.0. It was found that previous studies were only focusing general digital self-efficacy through traditional ways. Moreover, findings revealed there is lack of research on digital self-efficacy pre-service teachers in the realm of education 5.0 paradigm in the literature. More specifically, the results revealed that implementation of education 5.0 into teacher preparation programmes faces numerous challenges, including a lack of technological approach, poor digital infrastructure, the digital divide, a paucity of professional training opportunities for teachers and a lack of importance in policies. This synthesis of the literature review has practical implications for pre-service teachers along with policymakers. Pre-service teachers are required to increase their digital skills for the reason that they could teach the advanced generation. The policymakers can revisit and update the curriculum to incorporate education 5.0 principles in the teacher education programmes. This comprehensive review helps to better understand the pre-service teachers' digital self-efficacy towards education 5.0 and contributes to the body of existing knowledge on digital self-efficacy. Moreover, this study gives valuable insights on the incorporation of education 5.0.

This study aims to enhance an enterprise’s private cloud services by optimally determining the ownership of cloud computing resources and responsibility for maintenance and operations. The core objective is to identify the most cost-effective private cloud deployment model at the intersection of technology and business considerations.

This study evaluates three ownership and responsibility models, each encompassing decisions related to candidate data center locations, resource provisioning, and demand placements. Drawing from the cloud computing literature, these models are referred to as deployment models. The research formulates a private cloud deployment model selection problem and introduces an established Lagrangian-relaxation-based optimization approach, combined with a novel greedy relieving-pooling heuristic, to facilitate model selection.

This study identifies the optimal deployment model for a representative instance using real test-bed data from the US, demonstrating the private cloud deployment model selection problem. Various numerical examples are analyzed to explore the influence of environmental parameters. Generally, the virtual PC model is optimal for low demand arrival rates and resource requirements, while the on-premises PC model is preferable for higher values of these parameters. Additionally, the virtual PC model is found to be optimal when enroute latency coefficients are large.

This study contributes to the literature by formulating an optimization problem that integrates performance, financial, and assurance metrics for enterprises. The introduction of a solution approach enables enterprises to make informed decisions regarding ownership and responsibility design. The study effectively bridges the gap between academic research and industry demands from a business perspective.

To improve the speed and accuracy of turbine blade film cooling design process, the most advanced deep learning models were introduced into this study to investigate the most suitable define for prediction work. This paper aims to create a generative surrogate model that can be applied on multi-objective optimization problems.

The latest backbone in the field of computer vision (Swin-Transformer, 2021) was introduced and improved as the surrogate function for prediction of the multi-physics field distribution (film cooling effectiveness, pressure, density and velocity). The basic samples were generated by Latin hypercube sampling method and the numerical method adopt for the calculation was validated experimentally at first. The training and testing samples were calculated at experimental conditions. At last, the surrogate model predicted results were verified by experiment in a linear cascade.

The results indicated that comparing with the Multi-Scale Pix2Pix Model, the Swin-Transformer U-Net model presented higher accuracy and computing speed on the prediction of contour results. The computation time for each step of the Swin-Transformer U-Net model is one-third of the original model, especially in the case of multi-physics field prediction. The correlation index reached more than 99.2% and the first-order error was lower than 0.3% for multi-physics field. The predictions of the data-driven surrogate model are consistent with the predictions of the computational fluid dynamics results, and both are very close to the experimental results. The application of the Swin-Transformer model on enlarging the different structure samples will reduce the cost of numerical calculations as well as experiments.

The number of U-Net layers and sample scales has a proper relationship according to equation (8). Too many layers of U-Net will lead to unnecessary nonlinear variation, whereas too few layers will lead to insufficient feature extraction. In the case of Swin-Transformer U-Net model, incorrect number of U-Net layer will reduce the prediction accuracy. The multi-scale Pix2Pix model owns higher accuracy in predicting a single physical field, but the calculation speed is too slow. The Swin-Transformer model is fast in prediction and training (nearly three times faster than multi Pix2Pix model), but the predicted contours have more noise. The neural network predicted results and numerical calculations are consistent with the experimental distribution.

This paper creates a generative surrogate model that can be applied on multi-objective optimization problems. The generative adversarial networks using new backbone is chosen to adjust the output from single contour to multi-physics fields, which will generate more results simultaneously than traditional surrogate models and reduce the time-cost. And it is more applicable to multi-objective spatial optimization algorithms. The Swin-Transformer surrogate model is three times faster to computation speed than the Multi Pix2Pix model. In the prediction results of multi-physics fields, the prediction results of the Swin-Transformer model are more accurate.

The construction industry is widely recognized as one of the most hazardous sectors in the world. Despite extensive research on safety management, a critical issue remains that insufficient attention is devoted to safety practices in rural areas. Notably, accidents frequently occur during the construction of rural self-built houses (RSH) in China. Safety management tends to be overlooked due to the perceived simplicity of the construction process. Furthermore, it is essential to acknowledge that China currently lacks comprehensive laws and regulations governing safety management in RSH construction. This paper aims to analyze the behavior of key stakeholders (including households, workmen, rural village committee and the government) and propose recommendations to mitigate safety risks associated with RSH construction.

This paper applies evolutionary game theory to analyze the symbiotic evolution among households, workmen and rural village committee, in situations with or without government participation. Additionally, numerical simulation is utilized to examine the outcomes of various strategies implemented by the government.

Without government participation, households, workmen, and rural village committee tend to prioritize maximizing apparent benefits, often overlooking the potential safety risks. Numerical simulations reveal that while government involvement can guide these parties towards safer decisions, achieving the desired outcomes necessitates the adoption of reasonable and effective strategies. Thus, the government needs to offer targeted subsidies to these stakeholders.

Considering that during the construction phase, stakeholders are the main administrators accountable for safety management. However, there exists insufficient research examining the impact of stakeholder behavior on RSH construction safety. This study aims to analyze the behavior of stakeholders about how to reduce the safety risks in building RSH. Thus, the authors intend to contribute to knowledge in this area by establishing evolutionary game model. Firstly, this study carried out a theoretical by using tripartite evolutionary game to reveal the reasons for the high safety risk during building RSH. Practically, this research points out the important role of households, workmen and rural village committee in improving safety management in rural areas. Besides, some suggestions are proposed to the government about how to reduce construction safety risks in rural areas.

This study aims to investigate how source attractiveness (including platform credibility, product appeal and streamer attractiveness) and information quality impact viewers’ cognition and flow state. Additionally, it examines how these factors influence the development of consumer attitudes and behaviors toward livestreamed tourism shopping, as well as the moderating roles of perceived risk and personal characteristics.

A sequential mixed-methods research design was used. In addition to a literature review, 15 interviews were conducted to identify measurement items for the subsequent questionnaire survey. Sample 1 (n = 209) was used to refine these measurement items, whereas Sample 2 (n = 227) was used to evaluate the measurement model and test the proposed hypotheses.

Findings reveal that source attractiveness (streamer attractiveness, tourism product attractiveness and platform credibility) and information quality significantly influence customers’ behavior formation, with cognition and flow state acting as mediators. Findings further confirm the moderating effect of perceived risk in a livestreamed shopping environment.

The findings provide new insights into how consumers select livestreamed shopping platforms. As viewers assess the credibility and trustworthiness of each component to form their perception of the overall platform’s credibility, companies should market their products through credible platforms, studios and stores with extensive reach. It is also critical to collaborate with streamers who are sincere, trustworthy, knowledgeable and professional. Since perceived risk lowers consumer behavioral intentions, live streamers, platforms and merchants should offer convincing and credible arguments to alleviate viewers’ perceived risks.

This study advances the literature on livestreamed shopping by broadening the conceptualization of source attractiveness to include three dimensions: streamer attractiveness, product attractiveness and platform attractiveness. Furthermore, it differentiates itself from existing studies by integrating and adopting multidimensional measurements of cognition (perceived usefulness and ease of use) and flow state (control, enjoyment, curiosity and concentration).

本研究旨在探讨信息源吸引力(平台可信度、产品吸引力和主播吸引力)和信息质量如何影响旅游购物直播观众的认知和心流体验。本研究进一步探讨了这些因素如何影响旅游直播购物消费者的态度与行为。本研究还检验了感知风险和个人特征的调节作用。

采用顺序混合研究方法。基于文献和15次深度访谈确定了问卷调查中使用的测量项目。样本1 (n= 209)用于净化测量项目。样本2 (n= 227)用于评估测量模型并验证假设。

信息源吸引力(主播吸引力、旅游产品吸引力和平台可信度)和信息质量显著影响消费者行为, 其中认知和心流起中介作用。研究结果进一步证实了感知风险的调节作用。

研究结果为消费者如何选择直播购物平台提供了新的见解。由于观众对每个组成部分的可信度进行评估, 从而形成他们对整个平台可信度的看法, 因此公司应该通过可靠的平台、直播间和具有广泛影响力的店铺来营销他们的产品。与真诚、值得信赖、知识渊博、专业的主播合作也至关重要。由于感知风险会降低消费者的行为意愿, 因此主播、平台和商家应该提供令人信服和可信的论据, 以减轻观众的感知风险。

本研究将信息源吸引力的概念扩展到三个维度：主播吸引力、产品吸引力和平台可信度, 从而丰富了直播购物相关研究。通过整合并采用认知（感知有用性和感知易用性）和心流体验（控制、愉悦、好奇和沉浸）的多维测量, 本研究进一步区别于现有研究。

Este estudio investiga cómo el atractivo de la fuente (incluida la credibilidad de la plataforma, el atractivo del producto y el atractivo del emisor) y la calidad de la información influyen en la cognición y el estado de flujo de los espectadores. Además, examina cómo influyen estos factores en el desarrollo de las actitudes y los comportamientos de los consumidores hacia las compras turísticas retransmitidas en directo, así como los papeles moderadores del riesgo percibido y las características personales.

Se empleó un método de investigación mixto secuencial. Además de una revisión bibliográfica, se realizaron 15 entrevistas para identificar los ítems de medición para la posterior encuesta por cuestionario. La muestra 1 (n = 209) se utilizó para refinar estos ítems de medición, mientras que la muestra 2 (n = 227) se utilizó para evaluar el modelo de medición y probar las hipótesis propuestas.

Resultados revelan que el atractivo de la fuente (atractivo del emisor, atractivo del producto turístico y credibilidad de la plataforma) y la calidad de la información influyen significativamente en la formación del comportamiento de los clientes, con la cognición y el estado de flujo actuando como mediadores. Resultados confirman además el efecto moderador del riesgo percibido en el entorno de las compras en directo.

Los Resultados aportan nuevos conocimientos sobre cómo seleccionan los consumidores las plataformas de compras en directo. Dado que los espectadores evalúan la credibilidad y la fiabilidad de cada componente para formarse su percepción de la credibilidad global de la plataforma, las empresas deberían comercializar sus productos a través de plataformas, estudios y tiendas creíbles con un amplio alcance. También es fundamental colaborar con streamers que sean sinceros, dignos de confianza, informados y profesionales. Dado que el riesgo percibido disminuye las intenciones de comportamiento de los consumidores, los streamers en directo, las plataformas y los comerciantes deben ofrecer argumentos convincentes y creíbles para aliviar los riesgos percibidos por los espectadores.

Este estudio hace avanzar la literatura sobre las compras en directo ampliando la conceptualización del atractivo de la fuente para incluir tres dimensiones: el atractivo del streamer, el atractivo del producto y el atractivo de la plataforma. Además, se diferencia de los estudios existentes al integrar y adoptar mediciones multidimensionales de la cognición (utilidad percibida y facilidad de uso) y el estado de flujo (control, disfrute, curiosidad y concentración).

With the pressing need for environmental conservation, regulatory authorities are actively looking for measures to prevent global warming. In the proposed inventory model for deteriorating items, demand is dependent on the selling price and green technology investment (or carbon reduction investment) for the green product (GP), as well as an investment in price-based preservation technology to slow down the pace of deterioration. Furthermore, emission reduction measures are put in place to reduce carbon emissions (CEs).

The current study executed a thorough literature review to determine how to improve supply chain management performance. Furthermore, assumptions are made to fill research gaps, and a mathematical model is created to address the problem mentioned above. To collect the data, the available inventory literature was reviewed. Additionally, numerical illustrations and sensitivity analyses are presented to emphasize the model's robustness.

The research indicates that it is more prudent to invest in preservation technology based on its selling price in order to control the rate of deterioration. In addition, the proposed model facilitates the management of deteriorated waste through salvage trading and emission reduction investment. The findings validate sustainable practices with a 20.86% increase in profit and a 21.4% decrease in CEs, thereby signifying environmental and economic benefits.

The proposed model enhances understanding of the impact of investments in price-based preservation technology and carbon reduction efforts on consumer perceptions of their intention to purchase GPs. Moreover, the study provides valuable insights by identifying important recommendations for policymakers regarding areas that require further investigation. This guideline can help identify both current and unexplored gaps, enabling researchers to direct future research efforts toward producing new products.