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Background: Ameloblastoma, a common benign tumor found in the jaw bone, necessitates accurate localization and segmentation for effective diagnosis and treatment. However, the traditional manual segmentation method is plagued with inefficiencies and drawbacks. Hence, the implementation of an AI-based automatic segmentation approach is crucial to enhance clinical diagnosis and treatment procedures. Methods: We collected CT images from 79 patients diagnosed with ameloblastoma and employed a deep learning neural network model for training and testing purposes. Specifically, we utilized the Mask R-CNN neural network structure and implemented image preprocessing and enhancement techniques. During the testing phase, cross-validation methods were employed for evaluation, and the experimental results were verified using an external validation set. Finally, we obtained an additional dataset comprising 200 CT images of ameloblastoma from a different dental center to evaluate the model's generalization performance. Results: During extensive testing and evaluation, our model successfully demonstrated the capability to automatically segment ameloblastoma. The DICE index achieved an impressive value of 0.874. Moreover, when the IoU threshold ranged from 0.5 to 0.95, the model's AP was 0.741. For a specific IoU threshold of 0.5, the model achieved an AP of 0.914, and for another IoU threshold of 0.75, the AP was 0.826. Our validation using external data confirms the model's strong generalization performance. Conclusion: In this study, we successfully applied a neural network model based on deep learning that effectively performs automatic segmentation of ameloblastoma. The proposed method offers notable advantages in terms of efficiency, accuracy, and speed, rendering it a promising tool for clinical diagnosis and treatment. [ABSTRACT FROM AUTHOR]

The discrete grey modelling technique is a novel methodology of grey prediction models, which is effective to improve the effectiveness and applicability of grey models. In order to build a more general and effective univariate grey prediction model, the discrete grey modelling technique is utilised in this paper to build a Quadratic Polynomial Discrete Grey Model (QPDGM). The properties of the QPDGM have been discussed, which indicate that the new model can be regarded as an extension of the conventional discrete grey model and nonhomogeneous grey model, and it is also coincidence with three classes of exponential sequences. The QPDGM is finally applied to predict the energy consumption of China, including the electric power, crude oil and natural gas consumptions. The results have been compared to some commonly used univariate grey prediction models, which indicates the QPDGM is generally more accurate than other models. [ABSTRACT FROM AUTHOR]

Copyright of Trans/Form/Ação is the property of Trans/Form/Acao 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.)

Copyright of Trans/Form/Ação is the property of Trans/Form/Acao 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.)

Copyright of Trans/Form/Ação is the property of Trans/Form/Acao 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.)

Copyright of Trans/Form/Ação is the property of Trans/Form/Acao 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.)

Copyright of Trans/Form/Ação is the property of Trans/Form/Acao 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.)

Using multi-criteria decision making (MCDM) technique to rank alternatives is a well-known area of study in which aggregation operators, such as ordered weighted averaging (OWA) and induced OWA (IOWA) operators, play an important role in merging information and producing an overall ranking. The distance measures from ideal argument values in aggregation operators have gained attention in recent literature. Distance measures are traditionally used as argument variables, which leads to the depiction that the attribute cannot be aggregated directly. In this paper, a generalized form of distance-induced OWA (DIOWA) operators is proposed with distance measures used as order-inducing variables. A distinctive benefit of DIOWA operators is that they permit us to consider ideal argument values while simultaneously also taking the attribute values as argument variables. Three variants of DIOWA operators are proposed and investigated, namely a) the Hamming distance-induced OWA (HDIOWA) operator, b) the normalized Hamming distance-induced OWA (NHDIOWA) operator, and c) the weighted Hamming distance-induced OWA (WHDIOWA) operator. We highlight their important properties and provide proofs to necessary theorems, and also suggest the determination methods for calculating their associated weights. We discuss further extensions of the proposed DIOWA operators with the help of generalized and quasi-arithmetic means. We discuss the use of our proposed family of operators for two different decision making situations, and demonstrate their validity by an illustrative numerical example. Finally, we apply the proposed operators to a real-life problem of ranking Chinese provinces for their science and technology (S&T) development levels. The proposed operators are shown to be a useful addition to the aggregation toolbox for decision analysts. [ABSTRACT FROM AUTHOR]

• Explain the economic value of the quadratic cost of MECM in terms of marginal cost and elasticity. • Adopt an opinion modification mechanism based on social network. • The MECMs with uncertain adjustment costs are developed under three uncertain scenarios. • The proposed models are applied to the agricultural insurance premiums subsidy policymaking in China. In the realm of group decision making (GDM), the maximum expert consensus model (MECM) emerges as a potent tool for consensus optimization. The complexity of decision-making environment leads to the uncertainty of adjustment costs and the intricate social relationships between decision makers (DMs). Therefore, this paper aims to develop the MECM that integrates both social relationships and uncertain adjustment costs to support social network group decision making (SNGDM) problems. Specifically, we propose a MECM with quadratic cost that can more accurately reflect DMs' sensitivity for opinion adjustment. Additionally, we adopt an opinion modification mechanism based on the information obtained from the social network. The paper also develops the robust MECM (RMECM) to handle the uncertainty of the unit adjustment cost under three uncertain scenarios. Finally, the efficiency of the proposed models is demonstrated by applying them to the agricultural insurance premiums subsidy policymaking in China, further substantiated by sensitivity analysis and comparative analysis showcasing their robust performance. [ABSTRACT FROM AUTHOR]

Heritage conservation, often referred to as historic preservation or heritage preservation, is the process of safeguarding and maintaining objects, buildings, landscapes, and other artifacts that hold cultural, historical, or architectural importance. This encompasses a diverse array of both physical and non-physical cultural assets, including but not limited to, ancient structures, archaeological locations, monuments, artifacts, customs, and cultural environments. A bibliometric analysis was conducted to ascertain research tendencies and contributions in the field of Heritage Conservation. The study primarily investigated themes, authors, publications, and organizations. The Scopus database was employed to retrieve the data, resulting in the identification of 357 papers after applying filters. The five leading journals in the field of heritage conservation from 2003 to 2023 were "Sustainability Switzerland" (with 69 published articles), "Geoheritage" (with 55 published articles), "Journal of Sustainable Tourism" (with 13 published articles), "Geojournal of Tourism and Geosites" (with 10 published articles), and "Journal of Heritage Tourism" (with nine published articles). The most commonly used keywords in the analyzed papers were "Tourism," "Geotourism," "Conservation," "Heritage tourism," "Heritage," "Geoheritage," "Heritage Conservation," "Cultural Heritage," "sustainable development," "Sustainability," "Geoconservation," "Heritage Management," "Geopark," "Sustainable Tourism," "China," "Geosites," and "Tourism Development." [ABSTRACT FROM AUTHOR]

• First estimation of nitrosamine intake via tobacco by smokers in 30 provinces, China. • First estimation of cancer risk (CR) of nitrosamines in tobacco to smokers in China. • CRs of NNK and NDMA are comparable in tobacco smoke (1.99 × 10−4 vs. 1.66 × 10 -4). • CR by nitrosamine intake: tobacco (3.80 × 10−4) > food (1.74 × 10−4) > water (1.38 × 10−5). • Tobacco smoking brings 202% extra cancer risk of nitrosamines to Chinese smokers. Nitrosamines are a class of carcinogens which have been detected widely in food, water, some pharmaceuticals as well as tobacco. The objectives of this paper include reviewing the basic information on tobacco consumption and nitrosamine contents, and assessing the health risks of tobacco nitrosamines exposure to Chinese smokers. We searched the publications in English from "Web of Science" and those in Chinese from the "China National Knowledge Infrastructure" in 2022 and collected 151 literatures with valid information. The content of main nitrosamines in tobacco, including 4-(methylnitrosoamino)-1-(3-pyridyl)-1-butanone (NNK), N -nitrosonornicotine (NNN), N -nitrosoanatabine (NAT), N -nitrosoanabasine (NAB), total tobacco-specific nitrosamines (TSNA), and N -nitrosodimethylamine (NDMA) were summarized. The information of daily tobacco consumption of smokers in 30 provinces of China was also collected. Then, the intakes of NNN, NNK, NAT, NAB, TSNAs, and NDMA via tobacco smoke were estimated as 1534 ng/day, 591 ng/day, 685 ng/day, 81 ng/day, 2543 ng/day, and 484 ng/day by adult smokers in 30 provinces, respectively. The cancer risk (CR) values for NNN and NNK inhalation intake were further calculated as 1.44 × 10−5 and 1.95 × 10−4. The CR value for NDMA intake via tobacco smoke (inhalation: 1.66 × 10−4) indicates that NDMA is similarly dangerous in tobacco smoke when compared with the TSNAs. In China, the CR values caused by average nitrosamines intake via various exposures and their order can be estimated as the following: smoke (3.75 × 10−4) > food (1.74 × 10−4) > drinking water (1.38 × 10−5). Smokers in China averagely suffer 200% of extra cancer risk caused by nitrosamines in tobacco when compared with non-smokers. [Display omitted] [ABSTRACT FROM AUTHOR]

In this paper, we report on how authors of academic writing in Chinese (AWC) refer to themselves as single authors in the area of language studies. We find that AWC writers rely on the 1st-person plural 我们 women "we," 3rd-person NPs such as 作者/笔者 zuozhe/bizhe "this author," and inanimate NPs such as 本文 benwen "this article/paper" for self-reference. Based on these findings and subsequent surveys of journal style guides and interviews of authors, we propose that (1) these self-referring expressions are a set of conventions; (2) the motivation for these conventions is modesty, a deep-routed value of Chinese society; and (3) these expressions serve as indexicals to the writers' identity of a modest scholar in the particular discursive context: the genre of academic writing. By so doing, our work links language use to social values, to identity studies, as well as to genre analysis, thus contributing to the literature in all these fields of investigation. [ABSTRACT FROM AUTHOR]

• In 2013-2021, rising count of shrinking Chinese cities due to uneven regional growth. • China's carbon emissions slowed, more cities with negative or reduced growth. • Shrinking cities often show discord between urban development and carbon emissions. • China's four major urban agglomerations displayed varied developmental traits. Chinese cities face challenges of uneven development, high carbon emission intensity, and low efficiency in urbanization processes. Achieving harmonization and equilibrium between urban development and carbon emissions is crucial for sustainable development. This paper analyzes the development trajectory from 2013 to 2021 of four urban agglomerations in China: Northeast China (NEC), Beijing-Tianjin-Hebei (BTH), Yangtze River Delta (YRD), and Pearl River Delta (PRD). It identifies and calculates the urban spatial development patterns and carbon emissions, comparing the spatial-temporal variations in carbon emissions under different urban development patterns. The study also explores the coupling coordination relationship between these two aspects at various stages. The findings suggest that urban spatial development and carbon emissions are often uncoordinated in shrinking cities. In terms of coordination within urban agglomerations, BTH and NEC show suboptimal development, characterized by overall lag and polarization; YRD exhibits the best development, being consistently stable and balanced; PRD is generally improving, though with some cities lagging. Planning departments should revise urban and environmental policies to optimize spatial structures and infrastructure, aligning with regional development and promoting synergy in urban agglomerations. This research provides theoretical and empirical support for China to formulate relevant policies aimed at green, coordinated, and sustainable urban development. [ABSTRACT FROM AUTHOR]

• Based on MATLAB/Simulink simulation platform, a simulation model of nuclear power unit is built and verified. • According to two typical faults in power system, the influence of grid faults on the operation characteristics of nuclear power units is studied. • According to the fault condition of power system, the influence of the difference coefficient of primary frequency modulation and frequency modulation dead zone on the operation characteristics of nuclear power units is studied. • The influence of overspeed protection controller on nuclear power units is analyzed and an optimized overspeed protection scheme is established in view of the load step drop condition caused by failure of fault cutting. With the development of nuclear power industry in China, the proportion of nuclear power generation in the total power generation is constantly rising. As the power system is always faced with the threat of faults of different degrees, these faults will not only affect the power quality, but also are potential risks for nuclear power units with high safety requirements. Therefore, it is necessary to study the influence of typical power system faults on nuclear power units. The corresponding control strategies for typical power system fault conditions are studied. The primary frequency regulation (PFR) parameter optimization scheme and overspeed protection optimization scheme proposed in this paper can effectively solve the frequency disturbance and steam turbine overspeed problems caused by power system faults. The research results can provide reference for large PWR nuclear power units to deal with typical power system faults, and have certain engineering application value. [ABSTRACT FROM AUTHOR]

The lightning clustering algorithm for spaceborne lightning observations clusters events into flash products and plays a crucial role in lightning-related research. This paper modified the lightning clustering algorithm based on the routine algorithm of Lightning Mapping Imager (LMI) and applied it to lightning events recorded by the LMI in 2019 to study the flash duration and flash area and specially to reveal the extreme lightning. To reveal the temporal and spatial features of lightning, the MCA (modified cluster algorithm) flash products defined flash duration and flash area. The results showed that continental regions have higher lightning frequencies but smaller events per flash, flash durations and flash areas than the ocean. The Tibetan Plateau has a low lightning frequency and smaller flash duration and area than eastern China. The flash count, events per flash, flash duration and flash area are higher during nighttime than during daytime. The relationship between flash duration and flash area showed distinct regional differences. In the MCA flash product, about 0.6% (0.1%) flashes have duration (area) that exceeds 1.0 s (5,000 km2). Finally, we analyzed two extreme lightning cases in eastern China, where the longest flash duration was 4.748 s and the largest flash area was 17,100 km2. This research contributes to advancing our understanding in the features of space-based lightning in the Eastern Hemisphere and applications to convection study. • Clustering algorithm was modified to study lightning with extreme duration and area. • Lightning duration and area both implied regional differences. • Quantitative relationship of duration and area was established in different regions. • The longest flash duration/largest flash area was 4.75 s/17100 km2 in eastern China. [ABSTRACT FROM AUTHOR]

By purchasing emission reduction equipment or reducing production scale, it is possible to effectively decrease CO 2. Therefore, understanding the marginal abatement cost (MAC) associated with these methods is crucial for making decisions. However, previous studies using the directional distance function (DDF) approach often mis-specified production functions and neglected data noise. They also assumed decision-making units (DMUs) to be on the production frontier and used proportional changes in inputs and outputs as abatement paths. This paper addresses these limitations by developing the convex quantile non-radial directional distance function (CQR-NDDF) method, which estimates the MAC of CO 2 and determines optimal abatement paths for DMUs without assuming a specific production function, employing linear programming techniques. Applying this method to 30 provinces in mainland China from 2011 to 2019, the study finds that China's CO 2 MAC increased from 182 to 247 yuan/ton. The lowest-cost abatement path varies by province and time. The club convergence and ordered probit model are employed to conclude that the second industry and urbanization increase the MAC of CO 2 , while factors such as foreign direct investment, openness level, and human capital decrease the MAC. Moreover, the CQR-NDDF method yields significantly lower MAC estimates than the NDDF method. In conclusion, this paper provides new insights into China's CO 2 MAC, emphasizing the importance of considering inefficiency and data noise in MAC estimation. We anticipate that utilizing CO 2 MAC as a benchmark for carbon trading market prices could lead to an increase in prices within China's carbon trading market. • A novel method for estimating CO 2 MAC has been developed. • Ignoring inefficiency will result in an overestimation of CO 2 MAC. • Three different emission reduction pathways are considered in the estimation. • The heterogeneity of the lowest cost abatement pathways has been identified. [ABSTRACT FROM AUTHOR]

Prefabricated buildings (PB) are assembled using prefabricated components, known for their cost-effectiveness and high efficiency. They are seen as an effective means to achieving sustainable development goals and mitigating climate change. This paper examines the impact of prefabricated buildings on urban green total factor energy efficiency (GTFEE) using panel data from 278 cities in China. The analysis is conducted through the application of a super-efficiency slack-based measure (SBM) model and a difference-in-differences model. The results of the study indicate that the promotion of PB can significantly improve urban GTFEE; there is heterogeneity in the improvement of urban GTFEE by PB; the innovation effect, industrial effect and marketization effect of PB are the main ways to improve urban GTFEE. Specifically, PB exerts an innovation effect by promoting technological innovation. It influences consumer and investor demand, leading to increased activity in the secondary industry, thereby creating an industrial effect. Moreover, PB facilitates the marketization effect by phasing out outdated industries, encouraging autonomous enterprise innovation, and fostering the growth of new advanced enterprises. This study reveals the critical role of PB in urban GTFEE improvement. It clarifies its mechanism of action, which provides a reference for urban sustainable development strategies and energy efficiency. • Prefabricated buildings can improve green total-factor energy efficiency. • Urban green total-factor energy efficiency has been measured. • PB improves GTFEE through innovation, industry and marketization effect. [ABSTRACT FROM AUTHOR]

The focus of this paper is on achieving a win-win situation regarding the economic, environmental, and social impacts of the cold chain logistics terminal distribution system. This paper proposes three multi-objective models to investigate the above effects by incorporating soft time windows, heterogeneous fleets, and path flexibility, with defining the objectives of examining logistics costs, fuel consumption, carbon emissions, quality damage to perishable commodities, and customer satisfaction using six evaluation functions. To solve the proposed models, an efficient optimization framework is developed by combining domain operators with versatile multi-objective evolutionary algorithms (MOEA) to obtain Pareto solutions. Extensive experiments are conducted to test the validity of the concerned model and algorithms. The results demonstrate that: (1) the proposed algorithm is effective in solving the proposed model; (2) the proposed multi-path strategy can effectively improve the performance of cold-chain logistics systems compared to single-path strategies; (3) evaluation functions that assess customer satisfaction greatly affect the performance of cold-chain logistics systems; and (4) the trade-off relationship between the objectives should be investigated to define the model. The paper also provides valuable managerial insights for improving the efficiency and sustainability of cold-chain logistics operations. [ABSTRACT FROM AUTHOR]

With global climate change posing a major threat to human society, a growing number of countries have taken "carbon-neutral" as a national strategy and proposed a vision of carbon-free future. As an important supplement to traditional fossil energy, renewable energy is the main force to reduce the use of high-carbon energy and carbon dioxide emissions, which will become the trend of social development in the future. Finding the optimal renewable energy source is of particular significance for achieving the net zero emissions. However, the existing evaluation methods of renewable energy sources have obvious shortcomings. In terms of weight calculation methods, such as the randomness of the subjective method is strong and the index weights do not reflect the small changes of the evaluation matrix, which affect the reliability and accuracy of the evaluation result. The existing ranking methods can only achieve the complete ranking of the different objects, but cannot classify the renewable energy technical alternatives into different grades. Given this background, this paper proposes a novel evaluation method for renewable energy plans based on improved sparrow search algorithm and projection pursuit model. Firstly, this paper improves the traditional sparrow search algorithm from three aspects: population initialization, population update and population variation. Then, the projection pursuit model is constructed, and the improved sparrow search algorithm is applied to optimize the projection target to find the optimal projection direction, so as to determine the weight values of each evaluation index. Finally, the weighted rank-sum ratio method is used to select the best renewable energy technical plan, which can not only realize the complete ranking of different plans, but also classify the technical plans into different levels. Based on the actual renewable energy development data from a province in China, experiments were carried out to investigate the effectiveness of the proposed method. Experimental results show that the proposed method performs better than some existing evaluation methods of renewable energy technical plans. [ABSTRACT FROM AUTHOR]

Over recent decades, China's economy has continued to develop steadily and rapidly, contributing to a large environmental cost. Therefore, many researchers propose various methods to evaluate environmental efficiency to deal with these serious issues accurately. This paper is the first to evaluate environmental efficiency using a model featuring not only a parallel structure with three component processes (primary, secondary, and tertiary industries) in the first stage but also a sewage treatment process in the second stage. Simultaneously, we propose radial but also nonradial versions of the Environmental Multiple Variable Return to Scale (EMVRS) model. These two models both consider shared inputs as well as shared outputs, and the proportion of the shared inputs and shared outputs utilized in each component process is assumed to be unknown. Additionally, we further analyze the factors influencing environmental efficiency by utilizing the Tobit model. The results when EMVRS is applied to Chinese provinces can be divided into 3 parts: (1) Most provinces do not purify wastewater appropriately contributing to the bad environmental efficiency. (2) The efficiency values of the three industry types are similar or identical to each other in most provinces. (3) The population of people employed negatively impacts environmental efficiency. [ABSTRACT FROM AUTHOR]

• The distribution characteristics of CaS in the radial direction of the hearth were analyzed. • The formation mechanism and migration process of CaS in the hearth were analyzed. • The interface reaction between slag and coke is the main source of CaS formation in the furnace hearth. As the main occurrence form of sulfur element in blast furnace hearth, high content of CaS within the blast furnace hearth have a certain impact on low-carbon smelting of blast furnace. In this paper, the micro-morphology and distribution of CaS in the hearth area are studied by scanning electron microscopy and industrial CT, and the formation process of CaS in the deadman is clarified by drilling core samples of residual iron in the hearth of 3200 m3 blast furnace in China. The research results indicate that CaS in the hearth is mainly formed by slag-coke interface reaction and slag-iron interface reaction. Based on an analysis of the relative difficulty of these two pathways, it was observed that CaS is more readily generated through the slag-coke interface reaction. Based on the analysis of the difficulty of the two generation paths, it is found that CaS in the blast furnace hearth is more easily generated through slag coke interface reaction. The CaS particles generated in the hearth take the slag as the growth matrix, and with the flow of slag and iron and the renewal of the deadman coke, they are finally enriched and precipitated in the edge area of the deadman in the form of agglomerates. Based on the formation mechanism of CaS in the hearth, it is analyzed that the formation of CaS aggregates in the hearth is mainly affected by three factors: sulfur load, slag basicity and hearth temperature. This study provides a certain theoretical reference and guidance for steel enterprises aiming to reduce fuel consumption and achieve green production practices. [ABSTRACT FROM AUTHOR]

China shale oil can be classified as interlayer shale oil (in sandstone, carbonate, and diamictite) and genuine shale oil. Considering the characteristics and strong heterogeneity of continental shales, it is recommended to evaluate the interlayer shale oil resources using the elemental volume method based on pore volume, and the genuine shale oil resources using the elemental weight method based on oil content. Four key parameters for genuine shale oil resource evaluation (i.e., S1, TOC, Ro, and shale thickness) and three key parameters for interlayer shale oil resource evaluation (i.e., interlayer thickness, porosity, and oil saturation) are determined, and the lower limits of parameters are defined accordingly. The evaluation results show that the geological resources of genuine shale oil in Qing-1 member of Songliao Basin are 52.23 × 108 t; the geological resources of shale oil in Chang-73 sub-member of Ordos Basin are 66.80 × 108 t, including 27.73 × 108 t interlayer shale oil and 39.07 × 108 t genuine shale oil; the geological resources of shale oil in Lucaogou Formation of Junggar Basin are 15.62 × 108 t, including 11.99 × 108 t interlayer shale oil and 3.63 × 108 t genuine shale oil. This study provides a reference for exploration decision of shale oil in China. [ABSTRACT FROM AUTHOR]

This paper examines the causal effect of regional market integration on firm productivity through the use of 2001–2008 firm-level microdata in China. The research design relies on a regression discontinuity design based on China's Pan-Pearl River Delta (PPRD) regional cooperation policy. The results indicate that the PPRD regional cooperation policy increases the total factor productivity (TFP) of firms by 6% on average. The positive impact of the PPRD regional cooperation policy on TFP is greater for private firms, large firms, and labour-intensive firms. Exploring the mechanism through which regional cooperation increases firm TFP, we find that the PPRD regional cooperation policy improves firms' productivity primarily by expanding the scale of firms, promoting technological innovation, and optimizing the allocation of factors. The study findings deepen our understanding of productivity improvement from the perspective of the market environment. [ABSTRACT FROM AUTHOR]

Energy plays a vital part in stimulating economic progress, and the shift towards a cleaner energy system is highly significant for ensuring the sustainable development of the economy. China's energy structure urgently needs to be transitioned. The fast advancement and implementation of artificial intelligence (AI) has provided a new and important tool for promoting the transition of energy structure. So, what is the relationship between the application of artificial intelligence and the transition of the energy structure? This research introduces artificial intelligence into the energy sector, focusing on the relationship between artificial intelligence and energy transition. Since nonlinear models are better able to study the complex effects and phase differences of artificial intelligence. Using China's provincial panel data spanning from 2006 to 2019, this study employs nonlinear modeling to explore the stage differences in the process of AI in facilitating energy structure transformation. This paper derives the following findings based on empirical research. First, there is a U-shaped relationship between artificial intelligence and the transition of energy structure. Specifically, before the inflection point, the initial application of artificial intelligence, artificial intelligence may adversely impact energy transition. When the inflection point is passed, AI will help facilitate the energy transition. Second, the U-shaped relationship between AI and energy transition is more pronounced in coastal and non-resource-based regions. Third, energy intensity, government investment in science and technology, and informatization will moderate the U-shaped relationship between artificial intelligence and energy transition, changing the steepness of the original U-shaped relationship and even reversing it. Hence, it is imperative to effectively utilize the technological benefits of artificial intelligence through the development patterns and distinctive features of different regions, thereby facilitating the smooth transition of the energy structure. [Display omitted] • The role of artificial intelligence in the energy transition process is studied. • Stage differences in the role of AI are analyzed. • The nonlinear relationship between AI and energy transition is investigated. • Provide insights into the factors affecting the relationship between AI and energy transition. • This research helps the government to advance the energy structure transition. [ABSTRACT FROM AUTHOR]

As the penetration of renewable energy sources (RES) significantly increases, their inherent uncertainty has brought about a paradigm shift in distribution system planning and operation. The existing planning methodologies usually consider the uncertainty during the investment and day-ahead stages while neglecting the interactive power fluctuations during the intraday stage, resulting in planning schemes with a risk of inadequate regional stability. In light of this, this paper proposes a novel adaptive robust distribution system planning approach that integrates local uncertainty management (LUM) to provide a comprehensive and refined consideration of uncertainty. First, we introduce the concept of LUM that can effectively address the deviations between predicted and actual power of RES and load. Second, we propose the adaptive robust planning model for the distribution system, in which the investment and day-ahead operation are considered in the first stage, while LUM is considered in the second stage to maintain the local stability of interactive power. The proposed model can actively manage uncertainties of RES and load demand, reaching the optimal balance between the investment cost, operational cost, and LUM cost. Finally, by employing the column-and-constraint generation (C&CG), the adaptive robust planning model is decoupled into a master min problem and a slaver max-min problem, after which the max-min problem is converted into a single mixed-integer linear programming (MILP) problem through the strong duality theory and big-M method. The effectiveness of the proposed model is demonstrated using a real-world distribution system located in Fujian Province, China. The simulation results indicate that the proposed model achieves a quantitative decrease of approximately 10% in total cost compared to deterministic planning. Sensitivity analysis is also conducted to reveal the trade-off between economic cost and robustness level. • Local uncertainty management (LUM) is incorporated into distribution system planning. • A three-stage framework is developed for the planning problem incorporating LUM. • An adaptive robust distribution system planning model incorporating LUM is proposed. [ABSTRACT FROM AUTHOR]

With large volume of passengers boarding and alighting through subway platforms, the stations are getting crowded, resulting in drops in the level of service and safety concerns, especially for subway systems operating at capacity during peak hours. Thus, it is crucial for subway agencies to sense changes in travel demand and adjust their management schemes accordingly. In this paper we propose a statistical approach to estimate dynamic passenger flows with automated data. First, we develop a dynamic logistic model for calculating passenger tap-out times, which can be employed to infer passenger flow characteristics at the aggregate level. In addition, a new passenger-to-train assignment model for any subway route is derived based on the dynamic model. Subsequently, we apply an expectation-maximization algorithm to estimate the model parameters with automated fare collection and automated vehicle location data. Finally, a cross-validation method is employed to validate our approach with data obtained from several routes in Beijing subway system in China. Results of 95% prediction intervals indicate the effectiveness of the models and the proposed estimation methods. • Propose a dynamic logistic model for inferring and predicting subway passenger tap-out times. • Apply an expectation-maximization algorithm for model parameters estimation. • Develop a novel passenger-to-train assignment strategy. • No prior distributions of parameters or additional information required in the proposed methods. • Results show good effectiveness of the models and acceptable accuracy of the prediction methods. [ABSTRACT FROM AUTHOR]

COVID-19 can be considered as the largest public health incident since 2019, posing disturbances to the medical, economic, and social systems. Understanding different levels of city resilience to the impact of COVID-19 on sustainable urban development is therefore essential. In this paper, we analyzed the evolution of the pandemic, the recovery of urban activities and major drivers of urban resilience to COVID-19 based on urban activity patterns derived from NO 2 monitoring stations from 217 cities in China. It is observed that nearly all urban activities have been affected by the epidemic with a reduced NO 2 emission, indicating a significant decline in the intensity of urban activity. The recovery patterns of human activity among different cities show that: (1) northern cities where low-resilience cities agglomerated have been severely affected. As of April 31, 2020, 12 northern cities have not recovered to pre-epidemic levels; (2) about three-quarters of the cities went through multiple stages to return to the pre-pandemic level, which related to both pandemic stress and prevention measures; (3) about 46.04% of the cities experienced increasing activity patterns that exceeded the pre-epidemic activity level. It is also observed that urban resilience can behave quite differently driven by variations in green coverage, economic aggregate, employment security, and medical system. Our study highlights the potential of an elevated urban resilience by optimizing the urban layout, improving the quantity and quality of green space, and enhancing the medical system. [Display omitted] • Most of the southern cities of China show higher urban activity than pre-pandemic. • Low/high-resilient urban agglomerations located in the north-northeast/south China. • Most cities undergo two stages of recovery at the beginning and end of March 2020. • Urban greenery is positively correlated to urban resilience. • Hospital capacity, instead of number, is more crucial to urban resilience. [ABSTRACT FROM AUTHOR]

In recent years, urban shrinkage, as one of the phenomena revealed in the process of new urbanisation development in China, has gradually attracted the attention of many scholars. To address the question of whether urban shrinkage has an impact on the level of local basic public services supply, this paper uses panel data from 298 prefecture-level cities in China from 2010 to 2018 to empirically analyse the impact of urban shrinkage on the level of basic public services supply by dividing shrinking cities into different regions and dividing basic public services into different dimensions. The results show that the impact of urban shrinkage on the supply level of basic public services is an 'inverted U-shaped' non-linear process that first promotes and then inhibits the level of basic public services supply; the impact of urban shrinkage on various dimensions of the level of basic public services supply varies, and generally shows a positive and then negative trend. Regarding regional heterogeneity, urban shrinkage has a more significant negative impact on the central region's basic public services supply level, followed by the western region and, to a lesser extent, the northeastern region. Therefore, local governments should make Smart Shrinkage according to local conditions, improve the basic public services supply system to enhance residents' happiness and satisfaction, introduce corresponding policies for the introduction of talents and focus on employee training and education activities to achieve the goal of equalising of basic public services supply in China. This research could also provide identification methods and improvement measures for other countries and regions just beginning to face the shrinking problem, especially those with high population densities and tight urban connectivity, such as China's cities. • Urban shrinkage, as one of the phenomena revealed in the process of new urbanisation development in China, has gradually attracted the attention of many scholars. To address the question of whether urban shrinkage has an impact on the level of local basic public services supply, this paper uses panel data from 298 prefecture-level cities in China from 2010 to 2018 to empirically analyse the impact of urban shrinkage on the level of basic public services supply by dividing shrinking cities into different regions and dividing basic public services into different dimensions. • This research could also provide identification methods and improvement measures for other countries and regions just beginning to face the shrinking problem, especially those with high population densities and tight urban connectivity, such as China's cities. [ABSTRACT FROM AUTHOR]

Given its significant implications for the economy and society, this paper investigates the impact of housing supply regulation on household wealth inequality. Utilizing data from the China Household Finance Survey and land transaction records spanning from 2013 to 2019, we employ recentered influence function regression and two-way fixed effects models to analyze the effect of unexpected housing supply shocks on the wealth gap among households. Our findings reveal that positive unexpected housing supply shocks are crucial in reducing household wealth inequality. This reduction is attributed to the heterogeneous impacts of housing supply regulation on different wealth groups. By deregulating the housing supply, the upward trend in housing prices has slowed, which in turn makes homeownership more accessible and facilitates wealth accumulation for households that fall within the 20 % and 80 % wealth quantiles. Conversely, deregulation has a negative impact on property values for households in the 80–100 % wealth quantile, thus mitigating their overall wealth growth. Overall, this study emphasizes the significant role of housing supply regulation in shaping wealth inequality in China. • This paper examines the housing supply regulation's impact on wealth inequality in China. • Positive housing supply shocks play a vital role in decreasing household wealth inequality. • Deregulation boosts homeownership for 20 %–80 % wealth quantile households. • Wealthiest households face property value drops with deregulation. [ABSTRACT FROM AUTHOR]

During the urbanization process in China, issues such as the widening wealth gap, class stratification, and social exclusion influenced residents' decisions to pursue entrepreneurship. Inclusiveness is an inevitable trend in new urbanization, and it is also a key factor in promoting residents' entrepreneurship and stimulating the potential for social innovation. Based on three key principles, "fairness, openness, and sharing, " this paper assesses the relationship, and the internal mechanism driving the relationship, between urban social inclusiveness and resident entrepreneurship. This includes constructing an urban social inclusiveness index, and analyzing data from the 2018 China General Social Survey (CGSS). The key results indicate that urban social inclusiveness has a significant positive effect on resident entrepreneurship. The mechanism analysis shows that urban social inclusiveness can improve the probability of residents being entrepreneurial, by improving risk attitude, promoting class mobility, and expanding social networks. Heterogeneity analysis shows that urban social inclusiveness has a significant impact on the entrepreneurial of local residents, and low-class and high-class residents; urban social inclusiveness has no significant impact on the entrepreneurial choice of transient residents and middle-class residents. Besides, urban social inclusiveness plays a more significant role in promoting residents' choice of survival entrepreneurship, compared with opportunistic entrepreneurship. • Explore the relationship between urban social inclusiveness and resident entrepreneurship in China. • A new method for assessing the social inclusion of cities, which considers "fairness, openness, and sharing." • Examine the mediating role of risk attitudes, class mobility, and social networks. • Understand the heterogeneity of social classes, household registrations, and entrepreneurial types. [ABSTRACT FROM AUTHOR]

As the take-off of China's macro economy, as well as the rapid development of infrastructure construction, real estate industry, and highway logistics transportation industry, the demand for heavy vehicles is increasing rapidly, the competition is becoming increasingly fierce, and the digital transformation of the production line is imminent. As one of themost important components of heavy vehicles, the transmission front andmiddle case assembly lines have a high degree of automation, which can be used as a pilot for the digital transformation of production. To ensure the visualization of digital twins (DT), consistent control logic, and real-time data interaction, this paper proposes an experimental digital twin modeling method for the transmission front and middle case assembly line. Firstly, theDT-based system architecture is designed, and theDTmodel is created by constructing the visualization model, logic model, and data model of the assembly line. Then, a simulation experiment is carried out in a virtual space to analyze the existing problems in the current assembly line. Eventually, some improvement strategies are proposed and the effectiveness is verified by a new simulation experiment. [ABSTRACT FROM AUTHOR]

Climate change leads to the frequent occurrence of natural disasters. It has had a severe impact on agricultural economic growth in China. Based on data in China from 2004 to 2020, this paper uses a fixed-effect model to discuss the effects of natural disasters on agricultural economic growth. The empirical results are as follows. (1) Natural disasters negatively affect agricultural economic growth. The regression coefficient of natural disasters on agricultural economic growth is −0.1183. (2) The inhibition of agricultural economic growth by natural disasters is more prominent in areas with low government attention and average agricultural production conditions. (3) The mechanism analysis shows that natural disasters inhibit agricultural economic growth through channels that cause the spread of diseases and pests. (4) Expanding the scale of agricultural insurance can prevent the impact of natural disasters on agricultural economic growth. This research provides policy recommendations for preventing the negative impacts of natural disasters. Agricultural producers should raise awareness of disaster management and improve the accuracy of disaster warning mechanisms. • This paper measures the level of natural disasters in China. • This paper expands the application field of functional data analysis methods. • This paper examines the mechanism of natural disasters affecting agricultural economic growth. • This paper provides policy suggestions for ensuring agricultural economic growth. [ABSTRACT FROM AUTHOR]

Government auditing plays a crucial role in enhancing energy efficiency and advancing green and low-carbon economic development. Given that audit oversight's quality and efficiency are influenced by the independence of auditing institutions, this study utilizes the vertical management reform of government auditing institutions as an exogenous shock, and empirically examines its impact on urban energy efficiency (UEE) using a Difference-in-Differences model (DID). We find that the audit reform improved UEE by 0.023 which is both economically and statistically significant. Concurrently, the audit reform and the development of the digital economy play substitutive roles in enhancing UEE. Finally, the results of the mediation analysis using the stepwise regression method indicate that the improvements in audit efficiency, green technology innovation, and energy structure optimization have mediated the promotion of UEE in the audit reform process, accounting for respective mediation effects of 4.35%, 4.35%, and 8.69%. • We investigate the audit independence from the level of audit management systems. • This paper finds a substitution effect of digital economic development. • The study discusses the heterogeneity in audit oversight from a global perspective. [ABSTRACT FROM AUTHOR]

Carbon emission flow is an effective tool to obtain carbon emission distribution in power systems, which can guide the active carbon reductions of consumers through proper incentive schemes. With the increasing penetration of renewable energy, the existing single-valued carbon emission flow model based on deterministic forecasted power outputs cannot describe the impacts of renewable energy uncertainties on carbon flows. Therefore, this paper mainly focuses on the assessment of the probabilistic carbon emission flow through the power systems considering the impacts of load and renewable energy uncertainties. In this paper, the probabilistic carbon emission flow (PCEF) is innovatively proposed. With the PCEF, consumers can make better energy use plans considering their own risk appetites for carbon emission payment. Firstly, the impact factors of the PCEF are modeled, including load and renewable energy uncertainties and integrated carbon intensity of thermal plants. The uncertainties of load, wind power and photovoltaics are modeled based on a p-box method while the uncertainty of hydropower is modeled based on the multi-state model. Besides, the integrated carbon intensity model of thermal plants are proposed considering the carbon intensity variations during the operating, start-up, and shut-down stages. Furthermore, the probabilistic carbon emission evaluation framework is proposed to assess the probabilistic carbon distribution. In this framework, novel probabilistic carbon indices are defined to precisely characterize the carbon emission situation with probabilistic representations. A solution method based on an adaptive interval point estimation (AIPEM) algorithm is proposed to efficiently solve the developed evaluation problem. Tests on an IEEE-30 node system, and a real provincial power system in China validate the effectiveness and application of the proposed model. The results showed that the PCEF can be a useful tool for obtaining the probabilistic representation of carbon emission flow and guiding the consumers' active carbon reduction in power systems. • Probabilistic carbon emission flow is proposed considering multiple uncertainties. • Integrated carbon intensity model for thermal plants is developed. • A p-box model is used to describe the uncertainties of load and renewable energy. • PCEF can help consumers balance the cost and carbon risk when making decisions. [ABSTRACT FROM AUTHOR]

To identify the influence of digital transformation on energy consumption, this paper introduces digitalization and energy elements into the multi-sector energy efficiency analysis model. Furthermore, this paper empirically tests the influence of digital transformation on energy efficiency from the micro level. The results show that the digital transformation indeed improves the enterprises energy efficiency, in which enterprises with higher energy efficiency are more affected. The improvement effect of digital transformation on enterprise energy efficiency continues to increase over time. In comparison, digital transformation shows more effective improvement effect on energy efficiency in enterprises that substantive transformation priority, with same group and maturity stage. Mechanism analysis shows that digital transformation can improve the energy efficiency of enterprises by reducing costs, increasing income, improving production efficiency and promoting technological innovation. In addition, the digital inclusive finance, government subsidies, intellectual property protection and service industry opening show positive regulatory effects. This paper proposes that, with the goal of peak carbon dioxide emissions and carbon neutrality, enterprises should actively increase digital investment and strengthen digital management of energy utilization. [Display omitted] • Digital transformation indeed improves the enterprises energy efficiency. • The promotion effect of digital transformation increases over time. • Digital transformation drives efficiency improvement and technological innovation. • Digital transformation contributes to cost reduction and benefit increase. [ABSTRACT FROM AUTHOR]

Industrial steam heating is a significant aspect of energy consumption in China, playing a crucial role in industrial energy efficiency. Historically, demand response research has predominantly focused on the electricity sector, with limited emphasis on steam supply. This study explores a novel Time-of-Use pricing approach for steam heating systems, anchored in the demand response theory of price incentives. It develops a peak-valley segmentation method by considering both the load differences between supply and demand and the thermal storage capacity of the pipeline system. Additionally, the paper delves into user response characteristics to formulate an optimized Time-of-Use pricing model for steam heating systems. Implemented in a Jiangsu-based case study, the model demonstrates significant efficiency improvements: it reduces the peak-valley load difference by up to 68.3% and cuts steam purchasing costs by ¥135,000. Furthermore, the alignment between the users' total steam load curve and the heat source's steam supply is enhanced by 18.8%, illustrating the model's effectiveness in balancing demand and supply. • Cluster analysis to extract steam user load characteristics. • Divide peak/valley times based on network storage and user traits. • Create a multi-objective Time-of-Use model for steam pricing optimization. • Carry out a case validation in an actual industrial park in Jiangsu. [ABSTRACT FROM AUTHOR]

• Integration of blockchain characteristics into the collaborative carbon emission reduction game model of both the supply and demand sides of electricity is proposed to encompass source control and terminal suppression. • Blockchain is utilized to trace electricity types within the electricity market and to quantify the carbon emissions generated by the demand side of electricity. • By incorporating blockchain, users can actively engage in individual-level carbon trading to control and reduce carbon emissions. • Blockchain facilitates enhanced carbon emission reduction efforts by the power supply side. The task of carbon emission reduction is severe in the power industry in China under the national goal of "carbon peaking and carbon neutrality". The current carbon reduction is mainly based on supply side, but the effect is limited. To solve this problem, this paper proposed a coordinated supply–demand carbon emission reduction strategy based on blockchain technology. Based on the carbon emission reduction of complementary thermal power and renewable energy under the carbon trading mechanism of power supply side, users are made to participate in the individual level carbon trading mechanism with the help of blockchain technology, and the carbon emission reduction in power demand side is guided through the market mechanism, thus forming a supply–demand collaborative carbon emission reduction strategy of source side control and terminal inhibition. By analyzing the decision changes of both the supply and demand sides of electricity before and after the introduction of blockchain, quantifying the influence of blockchain on electricity quantity, electricity price and users' utility in turn, and establishing the personal carbon trading mechanism supported by blockchain, a game model of two-side interaction between supply and demand was constructed. The simulation results show that the collaborative carbon emission reduction strategy based on blockchain gives full play to the potential of carbon reduction in power demand side, and the personal carbon trading mechanism can better inhibit terminal carbon emissions, which is conducive to the deep carbon emission reduction of the power industry. [ABSTRACT FROM AUTHOR]

To solve the problem where the prediction formula for vibration velocity by dynamic compaction cannot fully reflect the elevation amplification effect, the elevation amplification effect of vibration velocity by dynamic compaction was studied theoretically, and the factors that influence the elevation amplification effect of vibration velocity by dynamic compaction were systematically analyzed in this paper. The elevation amplification effect of a loess high slope under dynamic compaction is coformed by the refraction and reflection superposition effect of dynamic compaction vibration waves at the soil layer interface and the diffraction effect of dynamic compaction vibration waves. The propagation path of dynamic compaction vibration waves in soil was analyzed, and the elevation amplification effect was verified theoretically. The related physical quantities that affected the dynamic compaction vibration velocity of a loess high slope were analyzed based on the dimensional analysis method, and the calculation and improved calculation formulas that considered the elevation amplification effect of dynamic compaction vibration velocity were obtained. The multiple regression analysis of the calculation and improved calculation formula that considered the elevation amplification effect of the dynamic compaction vibration velocity was carried out using MATLAB with engineering examples. The results show that the relative error of the two formulas is small when the elevation difference is small; when the elevation difference is large, the calculation accuracy of the improved calculation formula that considered the elevation difference is higher, and it is more in line with the actual situation. Finally, according to the selected maximum allowable value of the slope particle velocity (e.g., 1.5, 2.0, and 2.5 cm/s), the allowable safe distance for dynamic compaction on a loess high slope is proposed area of northern Shaanxi Province, China. The results of this paper could provide a reliable basis for predicting vibration velocity by dynamic compaction in the Loess Plateau area of northern Shaanxi Province, China. They might have a reference value for dynamic compaction vibration control under similar terrain environments with large elevation differences and provide a safe guide for a dynamic compaction construction site in the Loess Plateau area of northern Shaanxi Province, China. [ABSTRACT FROM AUTHOR]

China's numerous resource-based cities have confronted severe environmental and economic problems, which were substantial impediments to the regional sustainable development. In 2013, China started to implement its 'National Sustainable Development Plan of Resource-Based Cities', in order to transform the previous development patterns. This study employs the difference-in-differences (DID) method to evaluate the policy's impact on the city-level carbon dioxide (CO2) emission intensity. CO2 intensity is defined as the scale of CO2 emitted per unit of GDP. The research sample covers 326 Chinese cities during 2008—2017. Research results indicate that the policy significantly increased the CO2 intensity in resource-based cities. Moreover, it is found that the policy dampened resource-based cities' innovation levels and these cities did not strengthen their environmental regulations, which can be provided as the explanations of why the policy effect on carbon intensity was positive. Furthermore, this study reports the discrepancies in policy effects when cities have different development stages, spatial locations and marketization levels. Overall, this research discovers an undesirable policy effect. More actions should be taken to reduce the carbon intensity during the structural transformation in resource-based cities. [ABSTRACT FROM AUTHOR]

Carbon mitigation and net-zero targets have an important place on the political agenda for all countries. Countries have been seeking to decouple economic growth and carbon emissions to achieve decarbonization. However, there is a cost of the decarbonization of economies, which can be called as Green Sacrifice Ratio (GSR), and this is critical for developing countries because they have been in the growing stage. By considering the critical role of GSR in achieving net-zero emissions by decarbonizing economies, the study comparatively examines how much environmental quality should be sacrificed for per capita economic growth in China and India. In line with this purpose, the study uses the Fourier-Shin cointegration test and various time series estimators with Fourier approximations for the period from 1989/Q1 to 2022/Q4. Estimation results are as follows: (i) all variables follow the non-stationary process, (ii) the long-run relationship exists between variables, and (iii) Estimation results show that GSR is 0.65% for China and 0.78% for India. Therefore, China and India sacrifice a significant amount of environmental quality for a 1% increase in per capita income level. Also, the ecological cost of increasing economic welfare in India is higher with regard to China. Besides, the results are robust according to the alternative method. Thus, the study recommends that both Chinese and Indian policymakers should revise the economic expansion structure with green growth strategies to prevent irreversible environmental degradation due to higher economic growth. [ABSTRACT FROM AUTHOR]

Aims and Objectives: To assess the effectiveness of different nonpharmacological treatments for severe radiation‐induced oral mucositis in patients with head and neck cancer. Background: Radiation‐induced oral mucositis is highly prevalent in patients with head and neck cancer. Current medications for radiation‐induced oral mucositis are limited in effectiveness and susceptible to side effects, and while there is an increasing adoption of nonpharmacological interventions, the optimal one remains unclear. Design: Systematic review and network meta‐analysis based on the PRISMA‐NMA guidelines. Methods: Six databases were searched. Two authors independently performed the literature screening, data extraction and methodological quality assessment of the included studies. Traditional pairwise meta‐analysis was performed by R Studio. A network meta‐analysis was then conducted to assess the effects of nonpharmacological interventions for severe radiation‐induced oral mucositis in patients with head and neck cancer. Results: Fifty‐two studies involving seven types of nonpharmacological interventions were enrolled. The network meta‐analysis indicated that natural plant‐based therapies might be the most effective, health education interventions might be the second most effective, and honey might be the third most effective interventions for reducing the incidence of severe radiation‐induced oral mucositis. For reducing the incidence of severe oral mucositis‐related pain, the pairwise meta‐analysis showed that only natural plant‐based therapies and health education interventions were effective. Conclusions: Nonpharmacological interventions are effective in the management of severe radiation‐induced oral mucositis among patients with head and neck cancer. Relevance to clinical practice: Nonpharmacological interventions are a category of safe and effective adjunctive therapies that should be encouraged in clinical practice. Trial registration details: CRD42023400745. [ABSTRACT FROM AUTHOR]

Aim: To summarize existing studies that focused on improving hospitalized patient safety through patient and family engagement interventions to identify priorities and gaps. Design: A scoping review. Methods: Eight databases and citations of important reviews were searched on 30 September 2022. Two researchers independently screened the records. Then, two researchers extracted the data and cross‐checked. The results were synthesized narratively, and a comparison was performed for studies from China and those from other countries. Results: Ninety‐eight studies were included. The results indicated that patient and family engagement interventions were applied to decrease the incidence of patient safety incidents, and to improve the healthcare providers' and patients' knowledge, attitude or practice of patient safety. Most studies only engaged patients and families at the direct care level, and the engagement strategies at the organization and health system levels were insufficient. For stakeholders, many studies failed to consider patients' perspectives in intervention design and report taking staff training as a supportive strategy. Healthcare providers, especially nurses, were the main implementers of current interventions. Certain differences were observed between studies from China and those from other countries in the above aspects. Conclusions: International interest in engaging patient and family for patient safety is growing. Future studies should enhance the patient and family engagement as a partner in various patient safety at the direct care level, and further explore the engagement at the organization and health system levels. Relevance to Clinical Practice: Nurses, as the main formal caregivers for patients, should promote patient and family engagement in patient safety, especially at direct care level. Nurse should also incorporate the perspectives of patients in the design and implementation of interventions. Reporting Method: PRISMA‐ScR Checklist. [ABSTRACT FROM AUTHOR]

Aims: To comprehensively examine the prevailing condition of care dependence among middle‐aged individuals who have experienced hemiplegia subsequent to a stroke and were currently undergoing post‐acute rehabilitation. Additionally, the study sought to analyse the determinants that impacted this phenomenon. Design: A single‐centre, cross‐sectional study design. Methods: During the period from January 2020 to October 2022, a cohort of 196 hemiplegic stroke patients, aged between 40 and 65, and within 6 months of their stroke onset, was selected from the cerebrovascular outpatient clinic at a tertiary hospital in Hangzhou. The demographic and disease‐related data, care dependence level, mental state, nutrition and depression status were collected. Furthermore, all collected data were analysed by descriptive and correlative statistical methods. Results: The care dependence level was 51.04 ± 9.42, with an incidence of care dependence of 78.1%. Multivariate regression analysis showed that age, history of falls, physical dysfunction, chronic comorbidities, depression, nutritional status and cognitive dysfunction were influencing factors for care dependence in the participants after a stroke. Conclusion: The incidence of care dependence among hemiplegic patients aged from 40 to 65 years old in the early stage after a stroke was high. Nursing staff should focus on these patients with a history of falling, physical dysfunction, comorbidity, depression status, nutritional status and cognitive dysfunction in clinical practice. Relevance to Clinical Practice: The incidence of care dependence in middle‐aged hemiplegic patients following a stroke is significantly increased. Some risk factors should be assessed, monitored, and controlled by nursing staff as early as possible in order to reduce the dependence levels in post‐acute rehabilitation period and improve the quality of life of hemiplegia patients. Reporting Method: Our study complies with the Strengthening the Reporting of Observational Studies in Epidemiology (STROBE) Checklist: cross‐sectional studies (see Table S1). Patient or Public Contribution: No patient or public contribution. [ABSTRACT FROM AUTHOR]

Complex risk factors make metro construction safety accidents prone to occur, and there are various types of accidents. Accident reports record detailed information about different types of accidents in text form. However, effectively utilizing such unstructured data presents a significant challenge. Text mining (TM) provides a viable foundation for addressing this challenge, but related studies have limitations in risk feature extraction and lack of in-depth analysis capability. To address the deficiencies of existing studies and provide a feasible strategy for identifying key risk factors in the metro construction domain, this paper proposes an integrated model combining TM and machine learning–based Bayesian networks. Firstly, the term frequency-inverse document frequency (TF-IDF) algorithm in TM was used to separately extract the direct and indirect cause factors from the accident reports, with the missing factors supplemented using the TextRank algorithm. Then, depending on the assumption of whether to consider the conditional independence between factors, an improved naive Bayesian network (NBN) and a tree-augmented naive Bayesian network (TAN) were built based on the extracted factors and the corresponding accident types, respectively, for further in-depth analysis. Finally, the training set was divided to train the two network models, and sensitivity analysis was used to identify the key risk factors. Using 162 accident reports from China as an application example, the results showed that TAN exhibited a higher average accuracy (79.62%) in the test set compared with the improved NBN (71.75%), and the importance of risk factors for different accident types was successfully ranked from multiple perspectives using TAN. Meanwhile, some new insights into metro accidents in China were obtained, which can support decision-making for accident prevention and control. In conclusion, this paper effectively addresses the relevant limitations of accident text utilization and presents a novel approach for metro construction safety management. Analyzing accident texts can help gain insights from objective historical data to support safety management efforts. However, accident texts are often unstructured and contain a lot of irrelevant content. How to quickly extract valid information from accident text and use it to analyze accidents in depth is of continuous interest to safety managers. In particular, those models that have real-time decision support capabilities in addition to theoretical insights. This paper proposes an integrated model that combines text mining and machine-learning Bayesian networks. This model achieves comprehensive textual feature extraction, multifaceted accident causation analysis, and allows safety managers to input current accident information into the model to obtain real-time decision support for accident prevention and control. Although the proposed model is developed for metro construction, it can be slightly adapted by incorporating the characteristics of accident texts from similar domains to obtain an integrated model suitable for these domains, so as to effectively control the occurrence of safety accidents. [ABSTRACT FROM AUTHOR]

Carbon emission reduction (CER) in the construction industry can aid in achieving the international community's carbon neutrality target. However, the low motivation of construction enterprises to reduce carbon emissions and the unsatisfactory effect of CER are still unresolved issues. This study aims to address these issues by constructing a building supply chain (BSC) consisting of a developer and a contractor using government subsidies and cost-sharing contracts. The optimal government subsidy and feedback equilibrium strategies of the BSC are examined using a combination of differential game theory and numerical simulations. The findings demonstrate that (1) cost-sharing contracts may enhance developer and contractor profits and building CER, goodwill, and demand without relying on government subsidies; (2) when subsidized by the government, each equilibrium strategy meets or exceeds the performance of the centralized model, with the cost-sharing contract having a negligible effect on the enhancement of each equilibrium strategy; and (3) both the game structure and the positioning of construction enterprises in the BSC are connected to the number of government subsidies. The city of Shenzhen, one of China's first low-carbon pilot cities, provides a realistic environment for the simulation analysis, with the Block K residential building in Nanshan District serving as a case study. Furthermore, this work contributes to the body of knowledge by proposing a novel CER model for the BSC using differential game theory. The research provides new insights into the role of government subsidies in shaping profit distribution, game structure, and enterprise positioning. Findings demonstrate the value of cost-sharing contracts in improving CER, goodwill, demand, and profits when implemented collaboratively. This advances the theoretical understanding of incentives and strategies for promoting CER in construction. This research highlights the importance of collaborative efforts between the government and the building supply chain (BSC) to promote sustainable construction and carbon emission reduction (CER). The study found that government subsidies can be an effective tool to encourage developers and contractors to adopt CER practices. However, relying solely on subsidies is insufficient—collaboration through mechanisms such as cost-sharing contracts can further enhance sustainability outcomes when enterprises work together. To facilitate low-carbon construction, the government could provide subsidies for developers' promotional and marketing activities as well as funding for contractors' research and development of sustainable materials and technologies. Financial incentives such as tax breaks and preferential lending for potential homebuyers can also accelerate consumer demand. Ultimately, creating a supportive environment where enterprises proactively pursue CER creates a win–win situation where the BSC becomes more sustainable while firms improve their branding and profitability. The insights from this research highlight the need for a multipronged approach. Governments play a key role through policy and financial support. Realizing the full potential of sustainable construction also requires active participation from developers, contractors, and consumers. By outlining optimal strategies and incentives, this study provides valuable guidance to construction industry stakeholders seeking to implement CER collaboratively. [ABSTRACT FROM AUTHOR]

Double-story high-speed railway stations have become increasingly popular due to their smaller footprint and improved operational efficiency. However, ensuring passenger comfort in such stations requires careful consideration of train-induced vibrations. This study investigates the train-induced vibrations in Beijing Fengtai Railway Station, which is Asia's largest and China's first double-story high-speed railway station. To carry out this work, a vibration source load model and the finite-element model of the station were first established, where the model was verified using field-measured data. Following these, the vibration characteristics of the station under various train passing conditions, including different track positions and train speeds, were investigated numerically in both time and frequency domains. The findings of this study provide valuable insights for the vibration control of the railway stations of the similar modes and scales. [ABSTRACT FROM AUTHOR]

Accessible Summary: What is already known?: SDM improves clinical outcomes by increasing attendance and treatment adherence in adolescents diagnosed with depression.SDM could reduce treatment disagreements and enhance consumers' and their families' satisfaction with mental healthcare services.Healthcare professionals are a critical part of SDM. However, MHPs' practices of SDM in the daily management of adolescents diagnosed with depression need to be clarified. What the paper adds to existing knowledge?: From the viewpoints of MHPs, SDM was not extensively applied in the daily management of adolescents diagnosed with depression.MHPs who trust their consumers and have received training related to SDM are more likely to practice SDM in the daily management of adolescents diagnosed with depression.The positive preferences for providing information and family involvement in treatment decision‐making are facilitators; working in closed inpatient mental health wards and open inpatient mental health wards are hindering factors for MHPs' practices of SDM. What are the implications for practice?: MHPs should encourage information sharing with consumers and their family members to help them participate in treatment decision‐making actively.A trusting and friendly therapeutic relationship with consumers should be maintained in the daily management of adolescents diagnosed with depression.SDM‐related training should be encouraged for MHPs to promote widespread SDM. Introduction: Shared decision‐making (SDM) is an ideal model for a therapeutic relationship that can improve health outcomes. Healthcare professionals are a critical part of SDM, and they play an important role in the practices of SDM in the clinical setting. Evidence suggests that adolescents diagnosed with depression can benefit substantially from SDM. However, mental health professionals' (MHPs) practices of SDM for adolescents diagnosed with depression in China are not well‐documented. Aim: This study aimed to investigate the practices of SDM for adolescents diagnosed with depression from the viewpoints of MHPs in China. Method: In this cross‐sectional study, we recruited a total of 581 MHPs by convenience sampling. The Shared Decision‐Making Questionnaire—Physician Version (SDM‐Q‐Doc) was used to evaluate the MHPs' practices of SDM for adolescents diagnosed with depression. Results: The mean SDM‐Q‐Doc was 80.47 (±16.31). Within the six specific decision‐making situations, most MHPs selected non‐SDM (52.7%–71.6%). Substantial numbers of respondents believed that MHPs made the final decision, especially with regard to the development (37%) and adjustment of medication regimens (42%). The practice of SDM was predicted by MHPs' preference for providing information, their trust in consumers, preference for family involvement in treatment decision‐making, working in an outpatient clinic and receiving SDM training (F = 23.582; p =.000; R2 =.198; adjusted R2 =.189). Discussion: Although the MHPs' self‐rated score of SDM‐Q‐Doc was high, SDM was not extensively applied in the daily management of adolescents diagnosed with depression. Thus, SDM needs to be further promoted by enhancing SDM‐related training for MHPs, thereby actively promoting the involvement of families, facilitating the information sharing for consumers and families, and building an active, trusting consumer‐practitioner relationship. Implications for Practice: MHPs should prioritise information sharing with consumers and families, as well as build trusting and friendly therapeutic relationships. Family involvement in treatment decisions should be encouraged when adolescents diagnosed with depression are in need. Actively participating in training related to SDM is also important. Future high‐quality evidence is still needed to explore the facilitators and barriers to SDM practices from a tripartite perspective of MHPs, adolescents diagnosed with depression and their families. [ABSTRACT FROM AUTHOR]

Redevelopment of industrial land is an essential component of urban regeneration in China, which has profound implications for promoting urban sustainable development. Therefore, quantitative evaluations of the spatial variation of industrial land transformation and its influencing factors are required. This study explores the spatial patterns of industrial land transformation using overlay analysis. Then, it establishes a multinomial logistic regression model to explore their influential factors by collecting empirical data, including of the industrial renewal projects from 2010 to 2018 in Shenzhen, China. The findings show that urban redevelopment practices have profoundly transformed industrial land use in the recent urban renewal process. Market actors implement industrial land redevelopment projects with policy guidance. These actors with diversified redevelopment goals have directly shaped the spatial outcomes of industrial land transformation. A small portion of industrial sites has been transformed into public facilities and open space as required by redevelopment policies. At the same time, the majority have been converted into residential and commercial spaces, as well as new industrial spaces for innovative industries under market orientation. Significant spatial differences in regeneration outcomes exist at the urban level. The regression results indicate that policy and planning regulations, and land ownership, are the critical factors affecting the transformation of urban industrial land into different types of land uses. A favorable plot location tends to promote the transition of industrial parcels for commercial functions, with less impact on other options. The impact of public demand is almost negligible. [ABSTRACT FROM AUTHOR]

A smart city is regarded as a new form of urban development proposed by humans to cope with urban problems. Smart cities are of great interest to theoretical circles and are favored by city governments. However, in China, research on common problems and solution strategies in the process of smart city construction is still insufficient. To address this insufficiency, we utilized Python language and the Selenium library to realize a web crawler and construct a smart city corpus background library. Then, the word2vec model was used for word vectorization to divide policy texts into phrases, and 132 policies from 85 cities above the prefecture level in China were analyzed using the Gaussian mixture model (GMM). The results showed many problems in the smart city construction process in China, including the homogenization of content, the wide topic coverage, the influence of higher-level governments, the mismatch with the local situation, and the neglect of relationship governance. Moreover, an efficiency-governance model was designed, and it held certain theoretical and practical guiding significance for further promoting the healthy and sustainable development of smart city construction in China. [ABSTRACT FROM AUTHOR]

The business environment is a critical element in promoting high-quality urban economic development. To evaluate the quality of the business environment in Chinese cities, we first build a business environment evaluation system in this paper. Then using the entropy weight, Dagum Gini coefficient, β-convergence model, and Markov chain method, we evaluate and analyze the development level, spatial differences, and dynamic development patterns of the business environments in 278 cities in China from 2003 to 2019. Our findings reveal that the overall business environment level in Chinese cities is not high. There are significant differences in business environment levels between megacities, large cities, and small–medium-sized cities, and within-group differences and between-group differences are the main reasons for the differences in business environment levels. However, the contribution rate of within-group differences shows a downward trend, while the contribution rate of between-group differences shows an upward trend. In addition, the business environment levels in Chinese cities have spatial correlation, and the national and three major city groups' business environment levels exhibit strong absolute and conditional β-convergence processes. Finally, the business environment levels in Chinese cities demonstrate path dependence, which is typified by low mobility and makes achieving upward mobility challenging. [ABSTRACT FROM AUTHOR]