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Introduction: Although rural industrial integration is a crucial pathway for advancing the revitalization of rural economies, it continues to grapple with financial challenges. This paper delves into the theoretical underpinnings of how capital marketization influences rural industrial integration. Methods: Using panel data from China's provinces spanning the years 2010 to 2020, a comprehensive index of rural industrial integration is constructed from the vantage point of a new development paradigm. The paper employs the system GMM method to empirically investigate the impact of capital marketization on rural industrial integration and to dissect its transmission mechanisms. Additionally, a threshold regression model is applied to explore the specific patterns of the nonlinear relationship between the two variables. Results and discussion: The study's findings reveal that the degree of rural industrial integration is significantly and positively influenced by its previous level, demonstrating an accumulative effect wherein the prior level of integration lays the groundwork for future advancements. The influence of capital marketization on the degree of rural industrial integration is characterized by a non-linear relationship, adhering to a "U-shaped" curve. Below the inflection point, the development of capital marketization is detrimental to rural industrial integration, whereas above this point, it exerts a positive influence. Currently, China's overall level of capital marketization is positioned beyond the inflection point, indicating substantial potential for enhancing industry integration in rural China. In addition, the study indicates that at very low levels of economic development, capital marketization does not affect the development of rural industries. As the economic development level rises, so does the impact of capital marketization on rural industrial integration. [ABSTRACT FROM AUTHOR]

Purpose: This paper aims to explore whether crowdfunding creators can learn from previous experiences to have a better financing performance of future crowdfunding projects. Design/methodology/approach: This paper uses Python to capture the data of 6,267 crowdfunding projects from one of the largest crowdfunding platforms in China (JingDong Crowdfunding) and the author use the negative binomial regression model and the OLS model in this empirical study. Findings: The empirical results show that both the early-stage experience of creating a crowdfunding project and the early-stage experience of supporting projects of other crowdfunding creators can improve the financing performance of their newly launched projects. The social network of the previous projects and the "Blockbuster" projects initiated before can also make the newly initiated projects obtain better financing performance. Originality/value: Current research on entrepreneurial experience shows that serial entrepreneurs have significantly different success rates than novice or inexperienced entrepreneurs but there is limited literature on the learning effect of crowdfunding creators. This study adds to the literature on entrepreneurial learning and provides suggestions to crowdfunding creators. [ABSTRACT FROM AUTHOR]

Small mammal species richness and relative abundance vary along elevational gradients, but there are different patterns that exist. This study reports the patterns of distribution and abundance of small mammals along the broader elevational gradient of Mt. Qilian range. The study was conducted in the Mt. Qilian range, north-western China, from June to August 2001. Removal trapping was conducted using a standardized technique at 7 sites ranging between 1600 and 3900 m elevation within three transects. Correlation, regression and graphical analyses were used to evaluate the diversity patterns along this elevational gradient. In total, 586 individuals representing 18 nonvolant small mammal species were collected during 20 160 trap nights. Species composition was different among the three transects with 6 (33%) of the species found only within one transect. Elevational distribution and relative abundance of small rodents showed substantial spatial variation, with only 2 species showing nonsignificant capture frequencies across elevations. Despite these variations, some general patterns of elevational distribution emerged: humped-shape relationships between species diversity and elevation were noted in all three transects with diversity peaks at middle elevations. In addition, relative abundance was negatively correlated with elevation. Results indicate that maximum richness and diversity of nonvolant small mammals occurred at mid-elevations where several types of plants reached their maximum diversity and primary productivity, and where rainfall and humidity reached a maximum. It is demonstrated that the mid-elevation bulge is a general feature of at least a large portion of the biota on the Mt. Qilian range. [ABSTRACT FROM AUTHOR]

Short-term power load forecasting is of great significance for the reliable and safe operation of power systems. In order to improve the accuracy of short-term load forecasting, for the problems of random fluctuation in load and the complexity of load-influencing factors, this paper proposes a two-stage short-term load forecasting method, SSA–VMD-LSTM-MLR-FE (SVLM–FE) based on sparrow search algorithm (SSA), to optimize variational mode decomposition (VMD) and feature engineering (FE). Firstly, an evaluation criterion on the loss of VMD decomposition is proposed, and SSA is used to find the optimal combination of parameters for VMD under this criterion. Secondly, the first stage of forecasting is carried out, and the different components obtained from SSA–VMD are predicted separately, with the high-frequency components input to a long short-term memory network (LSTM) for forecasting and the low-frequency components input to a multiple linear regression model (MLR) for forecasting. Finally, the forecasting values of the components obtained in the first stage are input to the second stage for error correction; factors with a high degree of influence on the load are selected using the Pearson correlation coefficient (PCC) and maximal information coefficient (MIC), and the load value at the moment that has a great influence on the load value at the time to be predicted is selected using autocorrelation function (ACF). The forecasting values of the components are fused with the selected feature values to construct a vector, which is fed into the fully connected layer for forecasting. In this paper, the performance of SVLM–FE is evaluated experimentally on two datasets from two places in China. In Place 1, the RMSE, MAE, and MAPE are 128.169 MW, 102.525 MW, and 1.562%, respectively; in Place 2, the RMSE, MAE, and MAPE are 111.636 MW, 92.291 MW, and 1.426%, respectively. The experimental results show that SVLM–FE has high accuracy and stability. [ABSTRACT FROM AUTHOR]

Aims: The purpose of this study was to investigate the status of self‐management behaviour and illness perceptions and to examine illness perceptions in relation to self‐management behaviour in elderly patients with chronic obstructive pulmonary disease (COPD). Methods: A cross‐sectional study was conducted, and 152 elderly COPD patients were recruited via the convenience sampling method. The COPD Self‐Management Scale and the Revised Illness Perception Questionnaire for COPD patients were used to examine self‐management behaviour and illness perceptions. Pearson correlation analysis, univariate analysis and hierarchical linear regression analysis were used to explore illness perceptions in relation to self‐management behaviour. Results: The mean overall score for self‐management behaviour was 2.90 ± 0.39. Among the subscales of self‐management behaviour, information management had the lowest score of 2.20 ± 0.76. Patients' demographic and clinical characteristics, including educational level, smoking status, type of primary caregiver, home oxygen therapy and COPD duration, were found to be significant determinants of self‐management behaviour. After controlling for these variables, several illness perception subscales, including treatment control, personal control, coherence, timeline cyclical and identity, were significantly correlated with self‐management behaviour. Conclusions: This study confirmed that elderly COPD patients' self‐management behaviour was unsatisfactory and that illness perceptions were significant determinants of self‐management behaviour. The findings may contribute to the development of self‐management interventions for elderly COPD patients. Summary statement: What is already known about this topic? Self‐management behaviour plays an essential role in managing COPD.Illness perceptions are predictors of self‐management behaviours in COPD patients, but little is known about how illness perceptions influence self‐management behaviour in elderly patients with COPD. What this paper adds? Several illness perception subscales, including treatment control, personal control, coherence, timeline cyclical and identity, were significantly correlated with self‐management behaviour.Patients' demographic and clinical characteristics, including educational level, smoking status, type of primary caregiver, home oxygen therapy and COPD duration, were found to be determinant of self‐management behaviour. The implications of this paper: The findings may contribute to developing tailored interventions that help patients establish beneficial illness perceptions to improve their self‐management behaviour. [ABSTRACT FROM AUTHOR]

Aims: This study aimed to explore the effects of compassion satisfaction and compassion fatigue on the posttraumatic growth of psychiatric nurses. Background: Psychiatric nurses' prolonged and repeated exposure to trauma and complex situations in the work environment can lead to negative emotions such as compassion fatigue, but they also experience positive consequences such as posttraumatic growth, and the relationship between compassion fatigue and posttraumatic growth is not unclear. Methods: A total of 336 nurses from psychiatry departments of seven tertiary hospitals in Sichuan Province, China, were administered an online questionnaire. Participants were recruited through convenience sampling. The posttraumatic growth level was measured with the Simplified Chinese version of the Posttraumatic Growth Inventory. The Professional Quality of Life Scale‐Chinese version was used to assess compassion satisfaction, burnout and secondary traumatic stress. T‐test and one‐way variance analysis, Pearson correlation analysis and hierarchical linear regression were used for data analysis. A STROBE checklist was used to guide the reporting of the research. Results: Psychiatric nurses' posttraumatic growth is at a low level, influenced by demographic and occupational factors, whereas compassion satisfaction, burnout and secondary traumatic stress are at a moderate level. Hierarchical regression analysis suggested that compassion satisfaction and secondary traumatic stress had a positive impact on posttraumatic growth, whereas burnout had a negative impact on posttraumatic growth. Conclusions: Findings suggest that nursing managers should attach importance to the mental health of psychiatric nurses, improve compassion satisfaction, alleviate burnout and promote posttraumatic growth. Summary statement: What is already known about this topic? Psychiatric patients generally have complex and special conditions, which makes psychiatric nursing work high‐risk and stressful, which often poses significant challenges for psychiatric nurses, such as compassion fatigue.There is a correlation between compassion satisfaction, compassion fatigue and posttraumatic growth among nurses. What this paper adds? Psychiatric nurses have low levels of posttraumatic growth and moderate levels of compassionate satisfaction, burnout and secondary traumatic stress, and posttraumatic growth is influenced by sleep hours per day, marital status, children and job satisfaction.Compassion satisfaction and secondary traumatic stress have a positive impact on posttraumatic growth of psychiatric nurses, whereas burnout has a negative impact on posttraumatic growth of psychiatric nurses. The implications of this paper: This study emphasizes that policy makers should clarify the relationship between posttraumatic growth, compassion satisfaction and compassion fatigue as soon as possible to support the rapid growth of psychiatric nurses in nursing work.This study provides a basis for nursing managers to explore and implement interventions to improve compassion satisfaction among psychiatric nurses, reduce burnout and promote personal psychological growth. [ABSTRACT FROM AUTHOR]

Low fertility willingness has become a major practical reality in China. From the perspective of family gender equality, this paper analyzes the impact of division of housework on women's fertility willingness by using the data of China Family Panel Studies (CFPS). The results of propensity score matching method and instrumental variable method show that husbands' participation in housework can effectively improve wives' fertility willingness. Mechanism analysis shows that husbands' active participation in housework can effectively reduce wives' work–family conflict, increase wives' satisfaction with husbands' housework contribution, and help improve wives' happiness and fertility willingness. Further discussion on whether families have had children or not shows that women who have had children are more sensitive to their husbands' participation in housework. This study provides empirical evidence for the applicability of family gender equality theory in China and the factors affecting women's fertility willingness. [ABSTRACT FROM AUTHOR]

The process of trade liberalization is also a process of deepening and improving China's institutions, which will certainly have a profound impact on social trust, but literature on how trade liberalization affects trust is scarce. This paper examines the impact of import trade liberalization on Chinese residents' social trust using a fixed-order regression model based on the Chinese General Social Survey (CGSS) data from 2010 to 2017. The study finds that import trade liberalization significantly contributes to the enhancement of social trust among Chinese residents. The mechanism analysis confirms that import trade liberalization enhances the social trust of Chinese residents mainly through the channel of improving institutional quality. Heterogeneity analysis shows that different sensitivities to institutions of heterogeneous individuals lead to a more significant contribution of import trade liberalization to residents' social trust levels in highly institutionalized regions, party and government organs, the military, non-agricultural households, and higher educated groups of residents. The findings of this paper imply that building an all-around opening pattern in the new era can contribute to the enhancement of mutual trust in Chinese society. [ABSTRACT FROM AUTHOR]

Introduction: The global trend toward consuming green food is on the rise. College students, as the future consumer base, have sparked significant interest in their inclination toward consuming environmentally-friendly foods. This paper integrates the Theory of Planned Behavior with the Norm Activation Theory to investigate the factors influencing college students' propensity to choose green foods, establishing a comprehensive model. Methods: This study utilized online and paper questionnaires for data collection, distributing them among college students and receiving 350 valid responses. In the data analysis phase, SPSS was employed for statistical data analysis, including descriptive statistics and regression analysis, while AMOS was used for structural equation modeling. Results and discussion: The data analysis results indicate that subjective norms, behavioral attitudes, and individual norms have a significant positive influence on college students' inclination to purchase green food. Purchase intention is indirectly influenced by personal norms and behavioral attitudes through perceptions of consequences. Responsibility attribution indirectly impacts purchase intention of green food through personal norms. Personal norms are indirectly influenced by responsibility attribution through perceptions of consequences. [ABSTRACT FROM AUTHOR]

Purpose: Accurate and timely cost prediction is critical to the success of construction projects which is still facing challenges especially at the early stage. In the context of rapid development of machine learning technology and the massive cost data from historical projects, this paper aims to propose a novel cost prediction model based on historical data with improved performance when only limited information about the new project is available. Design/methodology/approach: The proposed approach combines regression analysis (RA) and artificial neural network (ANN) to build a novel hybrid cost prediction model with the former as front-end prediction and the latter as back-end correction. Firstly, the main factors influencing the cost of building projects are identified through literature research and subsequently screened by principal component analysis (PCA). Secondly the optimal RA model is determined through multi-model comparison and used for front-end prediction. Finally, ANN is applied to construct the error correction model. The hybrid RA-ANN model was trained and tested with cost data from 128 completed construction projects in China. Findings: The results show that the hybrid cost prediction model has the advantages of both RA and ANN whose prediction accuracy is higher than that of RA and ANN only with the information such as total floor area, height and number of floors. Originality/value: (1) The most critical influencing factors of the buildings' cost are found out by means of PCA on the historical data. (2) A novel hybrid RA-ANN model is proposed which proved to have the advantages of both RA and ANN with higher accuracy. (3) The comparison among different models has been carried out which is helpful to future model selection. [ABSTRACT FROM AUTHOR]

Background: The condition and correlation of fatigue, sleep and physical activity in postoperative patients with pituitary adenomas remain unclear. This survey aimed to evaluate the current status and influencing factors of fatigue, sleep and physical activity in postoperative patients with pituitary adenomas. Methods: Patients undergoing pituitary adenoma resection in two tertiary hospitals from November 2019 to November 2021 were included. The general data questionnaire, Multidimensional Fatigue Inventory (MFI‐20), Pittsburgh Sleep Quality Index (PSQI) and international physical activity questionnaire were used for data analysis. Results: In total, 184 patients with pituitary adenomas were included. The postoperative patients with pituitary adenomas had a high level of fatigue. In total, 34 (18.5%) patients had low level of physical activity, 76(41.3%) patients had medium level of physical activity and 74 (40.2%) had high level of physical activity. Postoperative time, PSQI, physical activity level and gender were the influencing factors of fatigue in patients with pituitary adenomas (all P < 0.05). Conclusions: Postoperative patients with pituitary adenomas have a higher level of fatigue, and it is related to reduced sleep quality and activity. Relevant nursing measures should be taken according to the influencing factors of fatigue to reduce the fatigue of postoperative patients with pituitary adenomas. Summary statement: What is already known about this topic? The postoperative nursing care of patients is very important to the quality of life in patients with pituitary adenomas.Fatigue, sleep and physical activity in postoperative patients with pituitary adenomas remain unclear. What this paper adds? There is a high level of fatigue in patients with pituitary adenomas within 1 year after operation.The degree of fatigue is negatively correlated with the course of disease and the level of physical activity. The implications of this paper: Interventions and nursing care strategies targeted on the factors affecting postoperative fatigue of patients with pituitary adenomas are needed. [ABSTRACT FROM AUTHOR]

Improving the innovation quality of manufacturing companies has currently received increasing attention in the transformation from "Made in China" to "Created in China". Equity restriction is now one of the most talked about issues in China's corporate governance structures, but we have a limited understanding of the impact of equity restriction on innovation quality. This paper empirically analyzes the relationship and intrinsic mechanism between equity restriction and innovation quality using all A-share listed manufacturing companies in China from 2007 to 2021 as the research sample. First, the Tobit regression model is used to analyze the impact of equity restriction on innovation quality, and the Heckman two-stage model is used for the endogenous test. Then, the three-step regression model with mediating effects is used to validate the intrinsic mechanism of equity restriction to promote innovation quality from two paths, namely equity incentives and R&D investment. The research results show that equity restriction has a significant positive impact on innovation quality; equity incentives and R&D investment play a mediating role between equity restriction and innovation quality. This paper enriches the research on the influencing factors of innovation quality and provides a theoretical basis based on equity restriction for the transformation of manufacturing towards high-quality innovation, and explores the intrinsic mechanisms by which equity restriction affects innovation quality. [ABSTRACT FROM AUTHOR]

Natural disasters have enormous impacts on human society, especially on the development of the economy. To support decision making in mitigation and adaption to natural disasters, assessment of economic impacts is fundamental and of great significance. Based on a review of the literature of economic impact evaluation, this paper proposes a new assessment model of economic impact from drought by using the sugar industry in China as a case study, which focuses on the generation and transfer of economic impacts along a simple value chain involving only sugarcane growers and a sugar producing company. A perspective of profit loss rate is applied to scale eco nomic impact with a model based on cost-and-benefit analysis. By using analysis of "with-and-without", profit loss is defined as the difference in profits between disaster-hit and disaster-free scenarios. To calculate profit, analysis on a time series of sugar price is applied. With the support of a linear regression model, an endogenous trend in sugar price is identified, and the time series of sugar price "without" disaster is obtained using an autoregressive error model to separate impact by disasters from the internal trend in sugar price. Unlike the settings in other assessment models, representative sugar prices, which represent value level in disaster-free condition and disaster-hit condition, are integrated from a long time series that covers the whole period of drought. As a result, it is found that in a rigid farming contract, sugarcane growers suffer far more than the sugar company when impacted by severe drought, which may promote the reflections on economic equality among various economic bodies at the occurrence of natural disasters. [ABSTRACT FROM AUTHOR]

As a leading industry in the national economy, circulation industry can not only guide production and consumption, but also play a vital role in absorbing employment. With the progress of science and technology, technical change has penetrated into the circulation industry of China, which has not only improved its development, but also affected its employment. This paper uses the standardized supply-side system approach to measure the biased technical progress of circulation industry in China and investigates the influence of the biased technical progress index on the employment scale of circulation industry in China with panel regression model. We find that the overall technical progress in China's circulation industry during 2004–2018 is biased toward capital, and the elasticity of substitution between capital and labor is less than 1. We also find capital-biased technical progress in China's circulation industry is negatively related to the overall employment scale of circulation industry. The heterogeneity analysis indicates that the employment of non-state-owned units in circulation industry is significantly negatively affected by capital-biased technical progress, while state-owned units doesn't. [ABSTRACT FROM AUTHOR]

Currently, most university campuses in China are plagued by issues such as high food demand, imbalanced diets, serious food waste and poor environmental quality. Research has shown that the multiple benefits of an edible campus, which may also be referred to as a school garden or farm catering to various educational levels, can help alleviate the aforementioned problems. However, there is limited research on how to promote the benefits of an edible campus through design. Therefore, taking the South Campus of Hebei University of Technology in China as an example, this paper aims to explore the correlation between design and benefits of an edible campus. The design factors and benefit factors related to an edible campus are extracted through literature search. The design preferences and benefit predictions are obtained through 261 questionnaire surveys and interviews with 30 participants. During the statistical analysis phase, principal component analysis and multiple regression analysis are applied to analyze the correlation between design factors and benefit factors. The results indicate: (1) The design factors of an edible campus can be categorized into seven categories: spatial location, spatial carrier, size, space function, facility configuration, planting and crop varieties, and technology application. (2) The benefits of an edible campus include environmental education, physical and mental health, social interaction, ecological protection, and economic output. (3) There are significant differences in design factors that positively or negatively correlate with different benefits. Among them, the strongest positive correlation exists between planting function and the five benefits, followed by central landscape and container planting. Furthermore, the causes behind the correlation between design factors and benefit factors are analyzed, and design strategies for an edible campus under different benefit orientations are proposed. The findings of this study can contribute to the sustainable development of university campuses in China. [ABSTRACT FROM AUTHOR]

Aim s : The objective of this study was to examine the relationship between caregiving burden and pre‐death grief, with a specific focus on adult‐child caregivers in mainland China. Additionally, the study explored whether coping strategies played a mediating role in this correlation. Methods: A convenience sample of 320 adult‐child caregivers of older parents with dementia from Kunming, mainland China, was recruited for the study. Data were collected using the Chinese version of the Marwit‐Meuser Caregiver Grief Inventory‐Short Form, the Burden Scale for Family Caregivers, and the Ways of Coping Checklist‐Revised. Results: Results using linear regression and multiple mediation analysis with Hayes' process model indicated that caregiving burden was positively related to pre‐death grief and that active coping mediated the relationship between them. In the female group, active coping partially mediated the association between caregiving burden and pre‐death grief, but in the male group, this mediating effect did not exist. Conclusion: The study found evidence supporting the link between caregiving burden and pre‐death grief among adult‐child caregivers of older parents with dementia in mainland China. Furthermore, the caregiving burden prevented the use of active coping, and this decrease in coping increased the perception of pre‐death grief. These associations only existed in the female group. Implications: This paper presented the importance of active coping skills in the dementia caregiving process. Impact: The findings of this study emphasize the necessity for health practitioners to provide targeted interventions regarding pre‐death grief among dementia caregivers and strengthen caregivers' active coping strategies to reduce their pre‐death grief. Community‐based and personal care support services should be promoted to alleviate their caregiving burden. Reporting Method: We have adhered to the transparent reporting of evaluations with quantitative design statements and the corresponding checklist was followed. Patient or Public Contribution: 'No patient or public involvement'. [ABSTRACT FROM AUTHOR]

Aims: Glioma patients are at high risk for postoperative delirium (POD), yet studies focusing on this population in general neurosurgical ward settings are limited. This paper investigates the incidence of POD and related risk factors in glioma patients hospitalized in general wards. Design: Prospective observational study. Methods: This prospective study included 133 adult glioma patients hospitalized in the general neurosurgery ward. In addition to collecting routine perioperative general clinical data, patients' psychological status was assessed preoperatively using the Hospital Anxiety and Depression Scale (HADS). POD was assessed within 3 days postoperatively using the Confusion of Consciousness Assessment method, twice daily. The incidence of POD was calculated, and risk factors were identified using logistic regression analysis. Results: The incidence of POD in glioma patients admitted to the general ward was 31.6% (40/133). Multivariate regression revealed advanced age (age > 50 years), frontal lobe tumour, presence of preoperative anxiety or depression, retention of a luminal drain, postoperative pain, indwelling catheter these six factors were independent risk factors for the development of delirium in patients after surgery. Conclusion: In general ward settings, supratentorial glioma patients exhibit a high risk of POD. Critical risk factors include preoperative psychological conditions, as well as postoperative pain, drainage and catheterization. Rigorous preoperative evaluations, effective pain management strategies and the integration of humanistic care principles are essential in mitigating the risk of POD for glioma patients. Relevance to Clinical Practice: In general ward settings, this study reveals the high occurrence of POD in glioma patients and identifies preoperative psychological states, age, tumour location and several postoperative factors as significant risk factors for POD, which provides a framework for targeted interventions. By integrating these insights into clinical practice, healthcare teams can better identify glioma patients at risk for POD and implement preventive measures, thereby enhancing recovery and overall care quality for glioma patients in general neurosurgical wards. Reporting Method: This study adheres to the STROBE guidelines, ensuring a transparent and comprehensive reporting of the observational research methodology and results. Patient or Public Contribution: Patients involvement was limited to the provision of data through their participation in the study's assessments and the collection of clinical information. The study did not involve a direct patient or public contribution in the design, conduct, analysis, or interpretation of the data, nor in the preparation of the manuscript. [ABSTRACT FROM AUTHOR]

The bridgehead settlement problem continues to be one of the most chronic issues affecting long-term bridge performance. In addition, the magnitude of non-staggered-step settlement across the bridge approach transition has not been quantified. Non-contact measurement is considered an alternative to manual inspection, enabling automated damage evaluation for structural maintenance. This paper proposes an inexpensive automatic system using an inertial navigation sensor and a line scanning camera to evaluate the non-staggered-step bridgehead settlement with acceptable accuracy. By analyzing road longitudinal slope data, driving distance, and pavement images, this paper established a calculation model and algorithm of non-staggered-step bridgehead settlement, in which case, a new calculation index named the settlement point ratio (SPR) was proposed. Moreover, the effect of the vehicular detection system and the distance gradient tested at three speeds were measured. The results illustrate that the system has a good performance in longitudinal slope data with an absolute error of less than 1.5%. In addition, 31 bridges in China, Ningbo city, were selected. Combined with the test data, 50 groups of SPR were output using the established model and algorithm. By validating the system's output with the standard measurement method, correlation, and regression analysis were carried out in order to verify the SPR model's reliability. The correlation coefficient is 0.934, and the determination coefficient of the regression model is 0.872, which confirms its capability for accurate data collection and settlement measurement. Therefore, the proposed method is scientific and reasonable for detecting and quantifying non-staggered-step bridgehead settlement, effectively completing the research blank of bridgehead settlement detection. [ABSTRACT FROM AUTHOR]

Purpose: This paper aims to assess the overall social housing development, point out factors affecting it and propose some policy implications for social housing development. Design/methodology/approach: The research investigated investors, credit institutions and officials involved in social housing development. Bac Ninh province currently has 51 social housing projects that have been and are being implemented. The hypothetical regression model has seven latent variables and is tested by the criteria through the SPSS25.0 software. Findings: There are 29 factors belonging to seven groups affecting housing development. Their impact rates range from 3.47% to 30.25%. Research limitations/implications: The study has only identified the factors affecting social housing development but has not undertaken an in-depth assessment of its development status and forecast for the future. Therefore, this gap needs to be further studied. The proposed research method could also be applied when researching social housing developments in other countries around the world. Practical implications: To develop social housing to meet the needs of the real estate market, it is necessary to improve the policies that have the strongest impact first. Then, it is necessary to improve the factors with a smaller impact. Social implications: The study proposes policy implications for faster housing development for low-income people that improve their living standards. Originality/value: To the best of the authors' knowledge, the paper has studied for the first time social housing development and the factors affecting it. The paper also shows the level of their impact so that priority policies can be applied to each factor. [ABSTRACT FROM AUTHOR]

The water eutrophication restricts the development of economy and society in China, which attracts increasing attention. It also affects the health and ecological environment. The evaluation of water eutrophication is very complicated due to the dynamic variability of the water quality data. This paper adopts the hesitant fuzzy set (HFS) to depict the massive data of samples and uncertain preference information of experts, which reduces the complexity of calculation and avoids the loss of information. After that, we construct the projection index function based on the main factors of water eutrophication. The particle swarm optimization (PSO) algorithm is applied to determine the global optimal projection direction by optimizing the projection index function. Therefore, we construct an improved projection pursuit regression (PPR) model. Finally, the water eutrophication evaluation of several lakes in China is used to demonstrate the improved PPR model. Also, the comparative analysis and contribution rate analysis are conducted to validate its rationality and advantages. [ABSTRACT FROM AUTHOR]

Since 2012, China's economic growth rate has been much higher than the world average; its economic aggregate ranks second in the world, people's living standards have risen significantly, and absolute poverty has been historically eliminated, laying a solid foundation for solid promotion of common prosperity. However, China is still in the primary stage of socialism, and the problem of unbalanced and inadequate development has not been fundamentally solved. It is urgent to monitor the status of common prosperity construction in all regions. By constructing an evaluation index system of common prosperity, this paper uses spatial correlation analysis model and geographically weighted regression model to assess the comprehensive development level of common prosperity quantitatively, reveal the spatiotemporal evolution characteristics and analyze the main influencing factors. The results show that the comprehensive development level of China's common prosperity has been continuously improved. It gradually presents a spatial distribution pattern with Beijing-Shanghai as the development pole and the eastern coastal area as the highland, and there is a significant positive spatial correlation, but the local spatial heterogeneity has increased. The development level of common prosperity is affected by multiple factors, and the increase in per capita GDP is the leading factor in promoting common prosperity. Therefore, we should focus on promoting high-quality economic development, coordinated regional development and expanding middle-income groups, so that all people can enjoy the fruits of reform and development and ultimately achieve common prosperity. [ABSTRACT FROM AUTHOR]

Accurate delineation of debris-flow-endangered areas (e.g., the maximum runout distance) is a necessary prerequisite for the debris-flow risk assessment and countermeasures design. Recently, machine-learning models have been proved to be an effective tool in predicting debris-flow parameters. However, existing machine-learning models are generally developed based on a very limited number of observation data, which may result in the predictive model overfitting or underfitting. How to develop a robust model for accurate forecasting of debris-flow-endangered areas still remains a difficult task. This paper proposes a hybrid method for predicting debris-flow hazard zone by integrating machine-learning algorithms and an empirical regression model. The proposed method takes the calculated maximum runout distance obtained from the empirical model as supplementary inputs to increase the amount of training data to construct hybrid machine-learning models. Three commonly used machine-learning models (i.e., multivariate adaptive regression splines (MARS), random forest (RF), and support vector machine (SVM)) are developed based on the training datasets of a specific debris basin. Then, these three machine-learning models are combined with an empirical relationship developed using the same training datasets to generate corresponding hybrid models. Finally, the performance metrics (i.e., coefficient of determination (R2), root-mean-square error (RMSE), and mean absolute error (MAE)) of the proposed hybrid models are comprehensively investigated and compared with the single predictive model (i.e., MARS, RF, SVM, and the empirical model) under fivefold cross-validation. The proposed method is illustrated using 134 channelized debris-flow events in Sichuan province, China. Results show that compared with the three individual machine-learning models, hybridization of machine-learning algorithms and the empirical model results in R2, RMSE, and MAE improved by 70.5%, 32.9%, and 41.1%, respectively. In contrast to the empirical model, the R2, RMSE, and MAE value of the proposed hybrid models are improved by 29.6%, 22.3%, and 32.5%, respectively. The proposed hybrid models generally perform better than the single machine-learning and the empirical model, providing a promising tool for accurate forecasting of a debris-flow-endangered area. [ABSTRACT FROM AUTHOR]

In order to defraud state subsidies, some unscrupulous users use improper means to steal photovoltaic (PV) power. This behavior brings potential safety hazards to photovoltaic grid-connected operations. In this paper, a photovoltaic power-stealing identification method based on similar-day clustering and interval prediction of the quantile regression model for long short-term memory neural network (QRLSTM) is proposed. First, photovoltaic data are clustered into three similar days by the similar-day clustering according to weather conditions. Second, compared with the quantile regression neural network (QRNN) prediction method, the good prediction performance of the QRLSTM method is illustrated. Third, using the prediction intervals with different confidence levels on three similar days, according to the time scale (short-term, medium-term and long-term) combined with different electricity-stealing judgment indicators, a three-layer photovoltaic power-stealing screening framework is constructed, and the degree of user power stealing is qualitatively analyzed. Last, the power generation data of eight photovoltaic users in a certain region of northwest China and the data of four groups of artificially constructed power-stealing users are used as an example for simulation. The simulation results prove the feasibility of the proposed method in this paper. [ABSTRACT FROM AUTHOR]

The spread of the COVID-19 pandemic in 2020 had a huge impact on the life course of all of us. This rapid spread has also caused an increase in the research production in topics related to different aspects of COVID-19. Italy has been one of the first countries to be massively involved in the outbreak of the disease. In this paper, we present an extensive scientometric analysis of the research production both at global (entire literature produced in the first 2 years after the beginning of the pandemic) and local level (COVID-19 literature produced by authors with an Italian affiliation). Our results showed that US and China are the most active countries in terms of number of publications and that the number of collaborations between institutions varies depending on geographical distance. Moreover, we identified the medical-biological as the field with the greatest growth in terms of literature production. As regards the analysis focused on Italy, we have shown that most of the collaborations follow a geographical pattern, both externally (with a preference for European countries) and internally (two clusters of institutions, north versus center-south). Furthermore, we explored the relationship between the number of citations and variables obtained from the data set (e.g. number of authors). Using multiple correspondence analysis and quantile regression we shed light on the role of journal topics and impact factor, the type of article, the field of study and how these elements affect citations. [ABSTRACT FROM AUTHOR]

Innovative startups can bring many benefits to society. Drawing on the social network theory (SNT) and resource-based view (RBV), with mix methods approach, this paper argues that as the primary path for startups acquiring external resources, social networks are beneficial to improving innovative startups' innovation performance. Using a large amount of data from GEM (Global Entrepreneurship Monitor), this paper first runs correlation analysis and regression analysis to empirically analyze entrepreneurs' social networks' impact on China's innovative startups' innovation performance. The results show that both formal and informal social networks are positively correlated with innovative startups' innovation performance. Then we consider the moderating effect of entrepreneurial competence and motivation. And the results show both entrepreneurial competence and motivation positively moderate the correlation above. Second, to explore the above correlation's internal mechanism, we conduct semi-structured interviews with 14 entrepreneurs. Drawing on the resource management theory (RMT) and the process of cross-border knowledge search and assimilation, the mechanism model of entrepreneurs' social networks on startups' innovation performance is proposed through content analysis. [ABSTRACT FROM AUTHOR]

The mixed ownership reform of China is a kind of further partial privatization for the listed state-owned enterprises (SOEs), and an opportunity for non-state-owned enterprises (non-SOEs) to expand their commerce boundaries. The purpose of this paper is to investigate the effect of heterogeneous blockholders on corporate innovation. Spanning the analysis with listed companies in China from 2007 to 2017, we find that heterogeneous blockholders have a significant positive effect on enterprise innovation. Lowering agency costs and improving corporate innovation efficiency are the two plausible mechanisms. From further research, we find that compared with non-SOEs, the positive effect of heterogeneous blockholders on enterprise innovation is more pronounced for SOEs, and the effect is more positive with the improvement of relative power balance between heterogeneous blockholders. The paper sheds light on the innovation effects of mixed-ownership reform in emerging and transitioning countries. JEL Classification : G32, M13, D23, G34 [ABSTRACT FROM AUTHOR]

The quick access recorder (QAR), as an airborne device used to monitor and record flight parameters, has been widely installed on various types of aircraft. Based on QAR data, research on runway overrun, a typical flight safety incident, has attracted widespread attention in recent years. However, existing runway overrun risk models generally suffer from oversimplified risk metrics or insufficient consideration of risk dynamics. In this paper, we propose a new dynamic runway overrun risk assessment model based on QAR data. We first consider the noise of aircraft trajectory data in the QAR parameters and present a landing trajectory correction method combining ground speed and runway position information. Second, to improve the accuracy of the risk assessment model, we design an algorithm to automatically recognize the aircraft autobrake level during the landing phase, based on which a new dynamic risk assessment model is developed. Finally, feature engineering is performed to extract the relevant contributing factors of runway overrun risk, based on which classification and regression models are applied to identify risky flights and predict the risk values. The proposed risk assessment model was evaluated using QAR data from an airline in China. The results show that the automatic deceleration rate, the way that the aircraft approaches the runway, the touchdown distance, and the kinetic energy at 50 ft are key factors in the risk of runway overrun during the landing phase. [ABSTRACT FROM AUTHOR]

One of the secrets for China's economic growth miracle is continuous investment in infrastructure, and the role of power infrastructure is irreplaceable. However, previous studies mostly focused on generation-side indicators rather than grid-side indicators, ignoring the importance of the mega-grid established in China. As a result, this paper is committed to exploring the economic growth pulling effect of power grids with different voltage levels in the nonlinear framework. On the premise of determining the robustness of estimation parameters by instrumental variable method, the panel smooth transition regression (PSTR) model is used to capture panel heterogeneity. To be exact, the regional economic growth driven by extra-high voltage (EHV) and ultra-high voltage (UHV) power grids presents a regime transition accompanied by the growth of regional power generation and electricity consumption per capita. This means that the mega-grid is more conducive to the economic growth of energy bases and load centers. To further build and improve the state-leveled mega-grid into a nationwide basic platform for trans-regional renewable energy transmission and distribution is a key measure to enhance China's sustainable economic growth under the constraints of carbon peak and carbon neutrality targets. [ABSTRACT FROM AUTHOR]

Copyright of Psychological Science is the property of Psychological Science Editorial Office and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

With the rapid development of China's economy, power demand has been closely linked with economic development in order to analyze and predict the future power situation in China. Based on the historical data of China's electricity consumption, this paper analyzes the data characteristics of China's electricity consumption by using Eviews software. The long-term trend of power consumption sequence is eliminated by fitting the regression curve, and then the residual sequence is analyzed and identified according to the relevant theory of time series. Using the data of China's power demand from 2004 to 2019, the ARMA time series model is used to analyze China's future power situation finally according to the test statistics of the model. Finally, the exponential regression ARMA model is selected to predict China's electricity consumption. The index regression analysis method extracts the long-term characteristic information of the sequence, which is conducive to the stability processing and empirical analysis of long-term prediction. The result predicts China's power demand in 2020 more accurately, which can provide a reference for the future productivity layout of China's power. [ABSTRACT FROM AUTHOR]

Telecommunication network fraud crimes frequently occur in China. Predicting the number and trend of telecommunication network fraud will be of great significance to combating crimes and protecting the legal property of citizens. This paper proposes a combined model of predicting telecommunication network fraud crimes based on the Regression-LSTM model. First, we find that there is a strong correlation between privacy data illegally sold on the dark web and telecommunication network fraud data. Hence, this paper constructs a Linear Regression model using the privacy data illegally sold on the dark web to predict the number of telecommunication network fraud crimes. Second, an LSTM prediction model is constructed using the data of telecommunication network fraud cases on China Judgments Online based on the time-series feature of telecommunication network fraud crimes. Third, this paper uses the error reciprocal method to combine the two models for prediction. In addition, this paper selects the monthly data set of telecommunication network fraud occurring in 2021 for experimental evaluation. The experimental results show that the accuracy of the Regression-LSTM model constructed in this paper is 86.80%, and the RMSE is 0.149. Compared with the ARIMA, Linear Regression, LSTM, Additive-ARIMA-LSTM, and Multiplicative-ARIMA-LSTM models, the Regression-LSTM model proposed has the highest prediction accuracy. [ABSTRACT FROM AUTHOR]

As a type of coronavirus, COVID-19 has quickly spread around the majority of countries worldwide, and seriously threatens human health and security. This paper aims to depict cumulative numbers of COVID-19 infections in China using the growth model chosen by cross validation. The residual plot does not look like a null plot, so we can not find a distribution function for the disturbance term that is close enough to the true frequency. Therefore, the disturbance term can not be characterized as random variables, and stochastic regression analysis is invalid in this case. To better describe this pandemic automatically, this paper first employs uncertain growth models with the help of uncertain hypothesis tests to detect and modify outliers in data. The forecast value and confidence interval for the cumulative number of COVID-19 infections in China are provided. [ABSTRACT FROM AUTHOR]

Precipitation is the basic component of the Earth's water cycle. Obtaining high-resolution and high-precision precipitation data is of great significance. This paper establishes a precipitation retrieval model based on a random forest classification and regression model during the day and at night with FY-4B/AGRI Level1 data on China from July to August 2022. To evaluate the retrieval effect of the model, the GPM IMERG product is used as a reference, and the retrieval results are compared against those of the FY-4B/AGRI operational precipitation product. In addition, the retrieval results are analyzed according to different underlying surfaces. The results showed that compared with the FY-4B/AGRI operational precipitation product, the retrieval model can better identify precipitation and capture precipitation areas of light rain, moderate rain, heavy rain and torrential rain. Among them, the probability of detection (POD) of the day model increased from 0.328 to 0.680, and the equitable threat score (ETS) increased from 0.252 to 0.432. The POD of the night model increased from 0.337 to 0.639, and the ETS score increased from 0.239 to 0.369. Meanwhile, the precipitation estimation accuracy of the day model increased by 38.98% and that of the night model increased by 40.85%. Our results also showed that due to the surface uniformity of the ocean, the model can identify precipitation better on the ocean than on the land. Our findings also indicated that for the different underlying surfaces of the land, there is no significant difference in each evaluation index of the model. This is a strong argument for the universal applicability of the model. Notably, the results showed that, especially for more vegetated areas and areas covered by water, the model is capable of estimating precipitation. In conclusion, the precipitation retrieval model that is proposed herein can better determine precipitation regions and estimate precipitation intensities compared with the FY-4B/AGRI operational precipitation product. It can provide some reference value for future precipitation retrieval research on FY-4B/AGRI. [ABSTRACT FROM AUTHOR]

The fourth Industrial Revolution is in full swing, the whole world is pondering the same question: Where is the core breakthrough? With the continuous breakthrough and extensive application of cloud computing, big data, artificial intelligence and other next-generation information technologies, One sign has emerged: digital economy is becoming a new driving force for global economic growth. The impact of digital transformation on enterprises is to improve the quality and efficiency、Improve information quality, Or spread out limited resources and reduce productivity, There is no consensus among academics. To fill that gap, this paper aims to sort out the internal mechanism of how digital transformation improves the output quality of the technological innovation of enterprises from a theoretical perspective, and to establish a digital transformation index at the micro level. On this basis, this study empirically examines how digital transformation affects the output quality of technological innovation.we adopts panel data. It uses data collected from 159 manufacturing enterprises in China. By adopting the methods of PLS structural equation and fuzzy set qualitative comparative analysis (fsQCA), it empirically tests the research model and conducts a mediation analysis through regression analysis. The findings indicated that, Digital transformation can help manufacturing enterprises to improve the output quality of their technological innovation; digital infrastructure plays a significant mediating role between digital transformation and the output quality of technological innovation; the investment of manufacturing enterprises in software and hardware digital infrastructure have a positive impact on the output quality of technological innovation. There is a complementary relationship between the two. The paper was conducted in the context of Chinese culture and can be generalized to other Asian countries. We believe that the research conclusions of this study have important practical significance for how manufacturing enterprises can effectively carry out digital transformation and improve the output quality of technological innovation. [ABSTRACT FROM AUTHOR]

How to reduce environmental pollution is fundamental for current civilization. Increasing in R&D investment may reduce the environmental pollution, yet whether and how R&D investment influence the environmental pollution needs further discussion and verification. Considering that the R&D investment can directly and also indirectly influence the environmental pollution by affecting the economic growth, and the fact that there is an obvious discontinuity for economic growth during the observed period, this paper firstly proposes an integrated approach based on panel moderated mediation analysis and regression discontinuity. It examines the R&D impact on environmental pollution on the basis of province-level data in China and uses the integrated approach to test its direct and indirect effect. Finally conclusion is made according to the findings. [ABSTRACT FROM AUTHOR]

China's rapid road infrastructure construction (RIC) occupies farmland and promotes the flow of rural development factors, which causes farmland use transition (FUT). Although prior research on RIC and their impact on FUT has attracted considerable attention, few studies have investigated the threshold effect of RIC on FUT. Threshold effect is the phenomenon that when the level of RIC reaches a certain critical value, the level of FUT changes abruptly. This paper uses China's provincial panel data from 2004 to 2018 to evaluate FUT. We also consider the spatial spillover effect of RIC and construct a panel threshold regression model to assess the impact of RIC on FUT. The results demonstrate that, considering the spatial spillover effect, RIC significantly promotes FUT, and the threshold effect first rises and then falls. Also, the threshold effect of RIC on FUT also has significant regional heterogeneity. There is a significant single threshold effect for RIC in central and western China, with threshold values of 0.90 and 0.84, respectively, while the spatial lag of RIC in eastern China has a single threshold with a value of 1.10. Our research indicates that the Chinese central government should promote the synergistic increase in RIC and sustainable farmland use by improving the accessibility of road infrastructure and basing RIC planning on the actual development needs of the region. [ABSTRACT FROM AUTHOR]

The carbon emission reduction potential of photothermal utilization of solar energy is outstanding, but the breakthrough of technology is closely related to the management of innovation and research in higher educational institutions. What factors affect such relationship is discussed in this paper. Based on the photothermal technology project data released by the National Natural Science Foundation of China (NSFC) and China education yearbook, this paper analyzes the variable relationship between the output of solar thermal technology innovation and industrial demand. Specifically, the negative binomial regression was constructed based on the technological project approval data. The results show that the input of scientific research resources has a key influence on the development of solar thermal technology, the establishment of technological projects or the subsequent patent application derived from technological projects. The results also show that the demand for photothermal technology and green innovation complement each other. [ABSTRACT FROM AUTHOR]

The development of local government online questioning is a practical exploration to promote the digital governance of government. Exploring the influencing factors of local government online questioning response can provide guidance for the governance practice of online questioning. Based on the TOE framework, this paper constructs an analysis model of the influencing factors of local government online questioning response. By using SPSS tools to conduct regression analysis on data from 230 questionnaire samples, the results show five factors: technical competence, high-level support, perceived benefits, public readiness, and public satisfaction to the responsiveness of local government were 0.019, 0.332, 0.265, 0.156, and 0.048, respectively. Therefore, the research conclusion that technical ability, high-level support, perceived benefits, public readiness, and public satisfaction, have a positive impact on local government's response ability. The analysis of the influencing factors of government's online in this paper improves the existing research to a certain extent and provides a new path and perspective for the development of network politics in China. [ABSTRACT FROM AUTHOR]

High-quality vocational education is a vital basis for China's efforts to develop high-quality teachers and technical and skilled workers. The increasing development of contemporary vocational education is given higher importance in the new era. We proposed the stick to moral education while carefully integrating it with the requirements of changes in technology and modernization, continuous improvements in the quality of college vocational education, and the development of more conceptual and skilled talented individuals with both political integrity and the capacity to build modernization. Using artificial intelligence technology to challenge these difficulties in the context of social informatization can play important role in enhancing the teaching quality of high vocational education. With the advent of the Internet era, students and parents have an increasingly strong demand for online independent vocational English reading materials, although there is some English learning software that provides online vocational English reading materials in the education market. However, most of this software only changes the reading form of paper vocational English reading materials but fails to completely meet the real demand of students for vocational English reading materials, so how to carry out personalized recommendation reading according to the real reading situation and reading preferences of students is a vital problem to be resolved. In this background, this paper studies the personalized recommendation technology, in the use of recommendation algorithms on the based association rules to obtain higher vocational English reading content recommended the strongest association rules, on the based combination of user-based collaborative clarifying recommendation algorithm. The linear regression model combined with the users reading interest and personalized recommendation of higher vocational English reading process is optimized, improving the accuracy of English reading recommendation in vocational colleges. [ABSTRACT FROM AUTHOR]

Shrinking cities are a global issue with regional characteristics. This paper focuses on the county-level administrative units in the Three Northeastern Provinces in China to identify and classify shrinking cities using a two-step identification method and explores their spatial-temporal evolution. The paper utilizes the panel threshold regression model for empirical testing. The results indicate the following: (1) The number of shrinking cities in the region is large and deep. Quantitatively, the shrinking cities account for about 50% of the whole; spatially, there are six major shrinking city "groups", showing the distribution trend around the "Ha-Da" urban corridor. (2) The threshold effect test reveals that GDP is a critical threshold variable influencing the formation of shrinking cities. Moreover, cities are classified into three types based on the threshold values: Type I (GDP > 2,270,731 yuan), Type II (434,832 < GDP ≤ 2,270,731), and Type III (GDP < 434,832). (3) The results of the dual-threshold and grouped regression models show significant variations in the dominant factors of shrinking cities of different scales. Variables such as impervious area, fiscal revenue, and grass area demonstrate relatively stable promoting effects. [ABSTRACT FROM AUTHOR]

China is now undergoing the largest and fastest urbanization process in the world, developing various reinforced concrete high-rise buildings during this process. Most high-rise structures are constructed in densely populated and economically active areas and earthquakes pose a potential possibility of serious economic loss and casualties, which cannot be ignored. This paper conducts on-site dynamic tests on 129 high-rise RC buildings in eastern China by utilizing ambient vibration measurement. Fundamental vibration periods, second vibration periods, and third vibration periods are statistically analyzed in translational directions based on two high-rise building types: shear wall slab building and shear wall tower building (including framed shear wall structures and framed tube structures). After fully considering building usage conditions and comparing the fitting formulas in existing codes, fitting empirical formulas of the vibration periods in two translational directions based on the above structural types are given. The ratio distribution ranges of vibration periods are calculated by utilizing the vertex displacement method, concentrated mass method, and Rayleigh-Ritz energy method. In addition, by analyzing the vibration period ratio in horizontal directions of 543 high-rise RC buildings, different ranges are distinguished as well as suggested boundaries are given. The conclusion of this paper provides a reference and convenient method for future on-site dynamic testing and a new proposal is provided to the empirical formula of vibration period in the current building seismic design code. [ABSTRACT FROM AUTHOR]

Serial entrepreneurial performance is affected by serial entrepreneurs' risk preference, but the way prior experience affects the relationship between the risk preference and performance remains unclear. Through regression analysis of 588 listed serial entrepreneurial companies in China, this paper shows that serial entrepreneurs who make more use of risk have higher serial entrepreneurial performance. For serial entrepreneurs with relevant industry experience, the degree of influence of their risk preference on serial entrepreneurial performance will be strengthened. For serial entrepreneurs with rich entrepreneurial experience, the degree of influence of their risk preference on serial entrepreneurial performance will be weakened. The results are conducive to the effective use of prior experience and reasonable adjustment of risk preference for serial entrepreneurial enterprises, thereby improving the performance of serial entrepreneurship. [ABSTRACT FROM AUTHOR]

Water, energy, and food security are global concerning issues especially in China. To promote regional environmental management cooperation as well as find resource security influencing factor differences among regions, this paper calculates the water-energy-food (W-E–F) pressure, find W-E–F pressure's regional differences, and the influencing factors by Dagum Gini coefficient decomposition and geographically and temporally weighted regression model for panel data (PGTWR). First, the temporal trend of W-E–F pressure is decreasing and then increasing during 2003–2019; pressure in the eastern provinces is significantly higher than in other provinces and structurally energy pressure is the dominant resource pressure in W-E–F in most provinces. Besides, inter-regional differences are the main source of regional differences in China's W-E–F pressure, particularly for the inter-regional differences between eastern regions and other regions. In addition, there are obvious spatio-temporal heterogeneity effects of population density, per capita GDP, urbanization, energy intensity, effective irrigated area, and forest cover on W-E–F pressure. Balancing regional development gaps and developing differentiated resource pressure mitigation strategies based on the characteristics of different regional drivers are of great importance. [ABSTRACT FROM AUTHOR]

Flight demand forecasting is a particularly critical component for airline revenue management because of the direct influence on the booking limits that determine airline profits. The traditional flight demand forecasting models generally only take day of the week (DOW) and the current data collection point (DCP) adds up bookings as the model input and uses linear regression, exponential smoothing, pick-up as well as other models to predict the final bookings of flights. These models can be regarded as time series flight demand forecasting models based on the interval between the current date and departure date. They fail to consider the early bookings change features in the specific flight pre-sale period, and have weak generalization ability, at last, they will lead to poor adaptability to the random changes of flight bookings. The support vector regression (SVR) model, which is derived from machine learning, has strong adaptability to nonlinear random changes of data and can adaptively learn the random disturbances of flight bookings. In this paper, flight bookings are automatically divided into peak, medium, and off (PMO) according to the season attribute. The SVR model is trained by using the vector composed of historical flight bookings and adding up bookings of DCP in the early stage of the flight pre-sale period. Compared with the traditional models, the priori information of flight is increased. We collect 2 years of domestic route bookings data of an airline in China before COVID-19 as the training and testing datasets, and divide these data into three categories: tourism, business, and general, the numerical results show that the SVR model significantly improves the forecasting accuracy and reduces RMSE compared with the traditional models. Therefore, this study provides a better choice for flight demand forecasting. [ABSTRACT FROM AUTHOR]

Copyright of Tourism Review is the property of Emerald Publishing Limited and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

This paper discusses the transmission mechanism and effects that trade liberalization influences energy intensity through energy‐saving technological change (ESTC). We construct panel data from manufacturing industries in China over the period of 1994–2017, thereby conducting empirical tests through mediation models and discontinuity regression models. The results find that trade liberalization significantly promotes the lowering of energy intensity through ESTC. Heterogeneity test results show that the trade liberalization effect is higher in industries with low barriers to entry and industries with high pollution. Fuzzy regression discontinuity (FRD) results show that energy intensity decreasing as China's accession to the World Trade Organization (WTO). [ABSTRACT FROM AUTHOR]

This paper aims to demonstrate the impact of coal enterprises’ vertical integration on the performance of innovation corporate social responsibility (ICSR) and to elaborate its specific transmission path through financing structure and market power. This paper takes the data of A-share listed coal company from 2008 to 2017 as samples, uses input-output table method to measure the degree of vertical integration, and explores the relationship between the four factors through multiple linear regression and Bootstrap method. We found that: (1) the vertical integration of coal enterprises has positive incentives for the promotion of ICSR. (2) Financing structure and market power play a chain intermediary role in this process of incentive. (3) In areas with high marketization process, vertical integration has more significant incentive effect on ICSR. The paper extends the research on the relationship between vertical integration and innovation, which provides a reference for the improvement of China’s supply-side reform and corporate social responsibility (CSR) fulfillment policy. It is helpful to promote the sustainable development of the coal industry, stimulate the innovation vitality of enterprises, and improve the fulfillment of CSR. [ABSTRACT FROM AUTHOR]

As economy is growing, many problems occur, such as environmental problems. Scholars and government officials begin to focus on the corporate social responsibility (CSR). This paper is to study the relationship between corporate social responsibility (CSR) and Chinese economic development (ED), and this paper wants to provide a reference for the government officials in China and the investors in the capital market. This paper takes two separate research steps to achieve the research objective. Firstly, in order to test the relationship between the level of CSR and the economic development, this paper uses independent-samples T test. Secondly, this paper builds multiple variables linear regression models, and uses the SPSS 22 software to analyze the correlation between CSR and Chinese economic development. As a result, this paper finds that a relatively high level of CSR results in a more objective economic development, on the contrary, the economic development of the time when companies are not willing to take on the corporate social responsibility may be very general. [ABSTRACT FROM AUTHOR]

In this paper, we theoretically discuss and empirically show how IT capability spurs companies towards greener strategies. Based on the data of listed Chinese manufacturing companies from 2008 to 2018, using a panel regression model, the results show that: (1) corporate IT capability can promote green technological innovation. (2) Compared with state-owned enterprises (SOEs), the IT effect is more significant for non-state-owned enterprises (non-SOEs); compared with regions with weak environmental regulation, the IT effect is more significant in regions with strong environmental regulation. (3) Additionally, we found that the promotion of IT software capability is stronger than IT hardware capability, and they have a synergistic effect on green technological innovation. Overall, our findings offer a new point view for a deeper understanding of green technological innovation, and provide microscopic evidence for the objective evaluation of corporate IT capability. [ABSTRACT FROM AUTHOR]

Background: Inquiry‐based learning is a salient instructional approach to cultivate students' higher order thinking skills (HOTS). With the presence and advancement of new technologies, their usage for inquiry learning in university context is increasingly ubiquitous. However, in most circumstances, college students cannot integrate technologies to their inquiry learning in efficient ways or their abilities to use technologies are not good enough. Previous studies found that intrinsic motivation (IM), peer interaction (PI) and classroom connectedness (CC) were key factors to influence students' approaches to using technologies from the personal, behavioural and environmental perspectives. Objectives: This study investigated the influence of these three essential learning factors (IM, PI and CC) on students' HOTS and the mediate influence of deep approach to using technologies (DAUT) between these learning factors and HOTS in the technology‐enhanced open inquiry‐based learning context. Methods: A questionnaire was administered to 80 university students who had studied with the technology‐enhanced open inquiry‐based learning approach. The partial least squares method was employed to investigate the relationships among learning factors, DAUT and HOTS. Results and Conclusions: Results showed that CC and PI were positively directly related to HOTS. DAUT was a significant mediator between two (i.e., IM and PI) of the three learning factors and HOTS. The findings of the study have practical implications for instructors, indicating that they should pay attention to students' IM and PI in deepening their approach to using technologies as well as to improve students' HOTS in technology‐enhanced open inquiry‐based learning. Lay Description: What Is Already Known About This Topic: Inquiry‐based learning is considered to be a salient instructional approach to cultivate students' higher order thinking skills (HOTS).Technologies usage in inquiry learning is ubiquitous in university context.In most cases, college students are not able to integrate technologies in their inquiry learning in effective ways. What This Paper Adds: This paper proposed a research model that explains and predicts the mediating influence of deep approach to using technologies on the association between learning factors and HOTS in the technology‐enhanced open inquiry‐based learning context.The mediating effect of approaches to using technologies between learning factors and HOTS was identified.Two of the three learning factors (peer interaction and classroom connectedness) had direct effect on HOTS.Deep approaches to using technologies variable significantly mediates two of the three learning factors (peer interaction and intrinsic motivation) and HOTS. Implications for Practice or Policy: Instructors should pay attention to students' intrinsic motivation (IM) and peer interaction in order to deepen their approach to using technologies.Instructors should pay particular attention to peer interaction and classroom connectedness when conducting technology‐enhanced open inquiry‐based learning activities to improve college students' HOTS.Students' interest improvement and IM in using technologies, the integration of technologies and open inquiry‐based learning activities could be discussed and decided by both the instructor and students. [ABSTRACT FROM AUTHOR]