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The geological conditions of Longyang Reservoir are complex and it is located in strong earthquake area. In order to determine its seismic characteristics, the seismic response and residual deformation of embankment dam should be studied to provide calculation basis for dam design and construction.

Based on the geological survey data of Longyang Reservoir, the dam filling materials are tested by dynamic deformation tests, and the equivalent linear constitutive model parameters of the filling materials are obtained. Based on Duncan-Chang E-B model, the stress state of embankment dam in Longyang Reservoir before earthquake is calculated, and the dynamic response and earthquake residual deformation of embankment dam in Longyang Reservoir under earthquake condition are calculated by using equivalent linear model and Shen Zhu-jiang’s residual deformation model.

The results show that with the increase of dynamic shear strain, the dynamic shear modulus decreases and the damping ratio increases. The scatter plot between dynamic shear modulus and dynamic shear strain, and the scatter plot between damping ratio and dynamic shear strain under different confining pressures show strips. The calculation results shows that the acceleration of embankment dam in Longyang Reservoir increases with the increase of dam height, and the acceleration distribution has obvious amplification effect. Combined with the maximum dynamic shear strain during the earthquake and the state before the earthquake, the maximum vertical residual deformation of embankment dam in Longyang Reservoir is 2.98 cm which occurs at the top of the dam, calculated by the Shen Zhu-jiang’s residual deformation model.

Finite element calculation model parameters of embankment dam are obtained by dynamic triaxial tests. Seismic dynamic responses and residual deformation of embankment dam are analyzed. With the increase of dam height, the acceleration distribution shows an obvious amplification effect. The vertical displacement of embankment dam is larger along the dam axis and decreases in the upstream and downstream direction. The maximum horizontal displacement of embankment dam occurs in the middle of upstream and downstream dam slopes.

• (1)

  Finite element calculation model parameters of embankment dam are obtained by dynamic triaxial tests.

• (2)

  Seismic dynamic responses and residual deformation of embankment dam are analyzed.

• (3)

  With the increase of dam height, the acceleration distribution shows an obvious amplification effect.

• (4)

  The vertical displacement of embankment dam is larger along the dam axis and decreases in the upstream and downstream direction.

• (5)

  The maximum horizontal displacement of embankment dam occurs in the middle of upstream and downstream dam slopes.

Finite element calculation model parameters of embankment dam are obtained by dynamic triaxial tests.

Seismic dynamic responses and residual deformation of embankment dam are analyzed.

With the increase of dam height, the acceleration distribution shows an obvious amplification effect.

The vertical displacement of embankment dam is larger along the dam axis and decreases in the upstream and downstream direction.

The maximum horizontal displacement of embankment dam occurs in the middle of upstream and downstream dam slopes.

In the field of steel defect detection, the existing detection algorithms struggle to achieve a satisfactory balance between detection accuracy, computational cost and inference speed due to the interference from complex background information, the variety of defect types and significant variations in defect morphology. To solve this problem, this paper aims to propose an efficient detector based on multi-scale information extraction (MSI-YOLO), which uses YOLOv8s as the baseline model.

First, the authors introduce an efficient multi-scale convolution with different-sized convolution kernels, which enables the feature extraction network to accommodate significant variations in defect morphology. Furthermore, the authors introduce the channel prior convolutional attention mechanism, which allows the network to focus on defect areas and ignore complex background interference. Considering the lightweight design and accuracy improvement, the authors introduce a more lightweight feature fusion network (Slim-neck) to improve the fusion effect of feature maps.

MSI-YOLO achieves 79.9% mean average precision on the public data set Northeastern University (NEU)-DET, with a model size of only 19.0 MB and an frames per second of 62.5. Compared with other state-of-the-art detectors, MSI-YOLO greatly improves the recognition accuracy and has significant advantages in computational cost and inference speed. Additionally, the strong generalization ability of MSI-YOLO is verified on the collected industrial site steel data set.

This paper proposes an efficient steel defect detector with high accuracy, low computational cost, excellent detection speed and strong generalization ability, which is more valuable for practical applications in resource-limited industrial production.

Mukbang is a live-streaming format where hosts, often referred to as broadcast jockeys (BJs) or streamers, eat copious amounts of food while engaging with their audience through commentary and reactions. This unique cultural phenomenon has sparked curiosity and diverse reactions globally. Mukbang’s popularity highlights the transformative power of digital platforms on traditional media and society, facilitating a shift toward more interactive and participatory forms of media consumption. This conceptual paper explores Mukbang’s widespread allure through frameworks such as the long tail theory, which illuminates the success of niche markets; parasocial relationship theory, which sheds light on viewers' bonds with streamers and social identity theory, which explains audience loyalty. We also forecast the implications of these streaming trends on the television, retail and sports industries, predicting a broad impact on traditional consumption and engagement models.

This study employs various theories, including the long tail concept, parasocial interactions and social identity theory, to describe and analyze the perplexing yet significant phenomenon of Mukbang.

Digital technology has transformed business models across various media industries, similar to how iTunes and the MP3 format revolutionized traditional music labels. This study suggests that streaming platforms, with their ability to cater to diverse needs and facilitate two-way communication, have the potential to disrupt traditional entertainment and retail industries.

This study addresses the gap in understanding how and why streaming platforms like AfreecaTV, Twitch and Huya can disrupt the traditional TV and entertainment industries. It highlights key elements that enable users to become prosumers, broadcasting content such as Mukbang. These platforms outperform traditional entertainment with numerous channels, ease of access, live chat sessions and digital rewards. The ability to build intimate relationships with viewers through two-way communication strengthens parasocial relationships, fostering emotional connections with streamers. Additionally, live chats with other viewers create a sense of belonging and social identity, helping viewers reduce uncertainty and enhance self-esteem.

This paper examines the role of the anchoring effect, including internal anchor formed by prior experience or external anchor produced by similar external practices of industrial competitors and investor networks in the decision-making of corporate social behaviors (CSBs).

This paper sets corporate donations and pollution as examples of CSBs, and conducts an empirical study through the data of A-share listed companies between 2010 and 2020 in China.

This paper found that both internal and external anchoring effects exist in CSBs. In addition, when internal and external anchors appear simultaneously, they will have the same intensity and promote each other.

This paper not only adds to the literature on the motives for CSBs and links cognitive and social psychology with strategic decisions but also has managerial implications for firms and managers.

For the purpose of saving nickel, this study aims to develop new duplex stainless steel cored wires suitable for wire arc additive manufacturing (WAAM) with the addition of nitrogen.

The effect of nitrogen content on the microstructure and mechanical properties of the thin-walled deposits is investigated in detail.

The microstructure of thin-walled deposits mainly consists of austenite, ferrite and secondary austenite. With increasing nitrogen content, the austenite in the deposited metals increases. The austenite proportion in the bottom region is more than that in the top region of the deposited metals. The χ phase is randomly distributed at the grain boundaries and within ferrite. The σ phase is mainly precipitated at ferrite and austenite grain boundaries. With increasing nitrogen content, the tensile strength of the deposited metals increases, but the impact toughness of the deposited metals deteriorates.

This study proposes new duplex stainless steel cored wires for WAAM, which realizes the objective of saving nickel.

The purpose of this paper is to develop an index for measuring urban house price affordability that integrates spatial considerations and to explore the drivers of housing affordability using the post-least absolute shrinkage and selection operator (LASSO) approach and the ordinary least squares method of regression analysis.

The study is based on time-series data collected from 2005 to 2021 for 256 prefectural-level city districts in China. The new urban spatial house-to-price ratio introduced in this study adds the consideration of commuting costs due to spatial endowment compared to the traditional house-to-price ratio. And compared with the use of ordinary economic modelling methods, this study adopts the post-LASSO variable selection approach combined with the k-fold cross-test model to identify the most important drivers of housing affordability, thus better solving the problems of multicollinearity and overfitting.

Urban macroeconomics environment and government regulations have varying degrees of influence on housing affordability in cities. Among them, gross domestic product is the most important influence.

The paper provides important implications for policymakers, real estate professionals and researchers. For example, policymakers will be able to design policies that target the most influential factors of housing affordability in their region.

This study introduces a new urban spatial house price-to-income ratio, and it examines how macroeconomic indicators, government regulation, real estate market supply and urban infrastructure level have a significant impact on housing affordability. The problem of having too many variables in the decision-making process is minimized through the post-LASSO methodology, which varies the parameters of the model to allow for the ranking of the importance of the variables. As a result, this approach allows policymakers and stakeholders in the real estate market more flexibility in determining policy interventions. In addition, through the k-fold cross-validation methodology, the study ensures a high degree of accuracy and credibility when using drivers to predict housing affordability.

This study aims to compare traffic sign (TS) and obstacle detection for autonomous vehicles using different methods. The review will be performed based on the various methods, and the analysis will be done based on the metrics and datasets.

In this study, different papers were analyzed about the issues of obstacle detection (OD) and sign detection. This survey reviewed the information from different journals, along with their advantages and disadvantages and challenges. The review lays the groundwork for future researchers to gain a deeper understanding of autonomous vehicles and is obliged to accurately identify various TS.

The review of different approaches based on deep learning (DL), machine learning (ML) and other hybrid models that are utilized in the modern era. Datasets in the review are described clearly, and cited references are detailed in the tabulation. For dataset and model analysis, the information search process utilized datasets, performance measures and achievements based on reviewed papers in this survey.

Various techniques, search procedures, used databases and achievement metrics are surveyed and characterized below for traffic signal detection and obstacle avoidance.

Financial market sustainability is gaining attention as investors and stakeholders become more aware of environmental, social and governance issues, pushing demand for responsible and ethical investment practices. Therefore, this study aims to investigate the impact of carbon (CO2) emissions from three sources, oil, gas and coal, on the stock market sustainability via effective government policies.

The eight countries belong to two different regions of world: Asian economies such as Pakistan, India, Malaysia and China, and OECD economies such as Germany, France, the UK and the USA are selected as a sample of the study. The 22-year data from 2000 to 2022 are collected from the DataStream and the World Bank data portal for the specified countries. The generalized methods of movement (GMM) and wavelet are used as the econometric tool for the analysis.

Our findings show that the CO2 emission from coal and gas significantly negatively impacts stock market sustainability, but CO2 emission from oil positively impacts stock market sustainability. Moreover, all the emerging Asian economies’ CO2 emissions from coal and gas have a much greater significant negative impact on the stock market sustainability than the OECD countries due to the critical situation. However, the government’s effective policies have a positive significant moderating impact between them, reducing the effect of CO2 emission on the stock market.

This study advocated strong implications for policymakers, governments and investors.

Effective government policies can protect the environment and make business operations suitable, leading to market financial stability. This study advocated strong implications for policymakers, governments and investors.

This study provides fresh evidence of the government’s effective role to control the carbon environment that provide the sustainability to the organizations with respect to OECD and emerging economy.

This study aims to identify the key factors that influence household pro-environmental behaviors (HPEBs) and explore the differences caused by the same influencing factors between household waste management behavior (HWM) and household energy-saving behavior (HES).

A meta-analysis was conducted on 90 articles about HPEBs published between 2009 and 2023 to find the key factors. HPEBs were further categorized into HWM and HES to investigate the difference influenced by the above factors on two behaviors. The correlation coefficient was used as the unified effect size, and the random-effect model was adopted to conduct both main effect and moderating effect tests.

The results showed that attitude, subjective norms, and perceived behavioral control all positively influenced intention and HPEBs, but their effects were stronger on intention than on HPEBs. Intention was found to be the strongest predictor of HPEBs. Subjective norms were found to have a more positive effect on HES compared to HWM, while habits had a more positive effect on HWM. Furthermore, household size was negatively correlated with HWM but positively correlated with HES.

The same variables have different influences on HWM and HES. These results can help develop targeted incentives to increase the adoption of HPEBs, ultimately reducing household energy consumption and greenhouse gas emissions and contributing to the mitigation of global warming.

High levels of mental health disorders in students can affect their academic activity. Students should think positively to control the mental health disturbances they experience. This study aims to address the mental health problems of students by increasing their gratitude because gratitude is the key to facing the problems that a person faces. Therefore, the study focuses on finding the influence of gratitude on student mental health disorders.

The subjects in this study were 420 students in Eastern Kalimantan using quantitative methods with gratitude scales GQ-6 and DASS-21. Data collection method with online questionnaires to be analyzed with regression analysis.

The findings of this study empirically show that gratitude has a significant and positive effect on mental health (depression, anxiety, and stress). This shows that gratitude has an important effect on mental health in students. If gratitude is higher, then anxiety and stress decreases significantly. This study aims can serve as a foundation for student mental health improvement programs.

This study was cross-sectional, which makes it impossible to conclude a causal relationship between the variables. Therefore, future research should use more advanced research methods, including longitudinal and experimental studies. Another potential limitation that could bias the results is the fact that the data were based solely on self-reports. Then, the study group included a larger proportion of female students than male students.

This study provides a deeper understanding of the importance of gratitude in maintaining mental health. Gratitude has been shown to have a positive correlation with increased psychological well-being, decreased levels of depression and increased resilience in the face of everyday stress and challenges in college students at private univesity and state university.

This research changes students' mindset to always be grateful by thinking positively during the teaching and learning process at university. Many students complain about the current conditions, especially related to study assignments, differences in the teaching and learning process at school. This study is useful for students to reduce mental health disorders at university.