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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]

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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]

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]

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]

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]

• 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]

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]

The low-frequency oscillation (LFO) has occurred in the train–network system due to the introduction of the power electronics of the trains. The modeling and analyzing method in current researches based on electrified railway unilateral power supply system are not suitable for the LFO analysis in a bilateral power supply system, where the trains are supplied by two traction substations. In this work, based on the single-input and single-output impedance model of China CRH5 trains, the node admittance matrices of the train–network system both in unilateral and bilateral power supply modes are established, including three-phase power grid, traction transformers and traction network. Then the modal analysis is used to study the oscillation modes and propagation characteristics of the unilateral and bilateral power supply systems. Moreover, the influence of the equivalent inductance of the power grid, the length of the transmission line, and the length of the traction network are analyzed on the critical oscillation mode of the bilateral power supply system. Finally, the theoretical analysis results are verified by the time-domain simulation model in MATLAB/Simulink. [ABSTRACT FROM AUTHOR]

Aiming at a large number of decentralized resources such as rural biomass, rooftop photovoltaic, and decentralized wind power, a novel rural virtual power plant (VPP) has been conceptualized. The VPP is integrated by biomass waste conversion system (BWC), carbon to energy (C2P), demand response aggregator (DRA), and distributed renewable energy (DRE), namely, BDCD-VPP. Then, this paper proposes a data-driven two-stage robust optimization dispatching model for BDCD-VPP considering carbon-green certificates equivalent conversion mechanism. This model aims to address the uncertainty of wind power plants (WPP) and photovoltaic (PV) power generation. Strong duality theorem (SDT) and C&CG algorithm are applied to construct this model. Thirdly, the Aumann-Shapley method (A-S) was enhanced by incorporating risk factor, cost factor, and carbon reduction factor. This refinement results in the development of a synergistic scheduling revenue allocation method for electricity‑carbon-green certificates. Finally, the Lankao rural energy revolution pilot program in China is selected as the case study, the results showed: (1) The BDCD-VPP aggregates distributed energy sources such as the rural WPP and the PV to realize electricity‑carbon-electricity cycle effect. The BDCD-VPP generates a green certificate revenue of 47.17¥/MWh and exhibited carbon emissions of 0.37 t/MWh. The proposed Carbon-Green Certificates Equivalent Conversion Mechanism increases BDCD-VPP benefit ratio by 3.52%. (2) The proposed two-stage robust optimization dispatching model enhances the ability of BDCD-VPP to adapt to uncertainty. Compared to the day-ahead stage, biomass power generation (BPG) and waste power generation (WPG) increase upward peaking power by 24.05% and 9.99% in intra-day stage. Flue gas treatment system (FG-TS), gas-power plant carbon capture (GPPCC) and power to gas (P2G) increase downward peaking power by 65.15%, 70.99% and 25.94%. (3) Utilizing the proposed benefit allocation methodology, BPG and WPG need to concede 38.25¥/MWh and 65.02¥/MWh for carbon emissions associated with electricity. WPP and PV need to concede 64.25¥/MWh and 33.71¥/MWh for power generation uncertainty. GPPCC, P2G, DRA, and small hydropower station (SHS) obtain revenues of 24.48 ¥/MWh, 55.07 ¥/MWh, 70.38 ¥/MWh, and 51.30 ¥/MWh. Compared to the A-S, the proposed benefit allocation methodology exhibits 6.92% improvement in satisfaction. Overall, the proposed data-driven two-stage robust optimization dispatching model and benefit allocation strategy facilitates the optimal aggregation and utilization of rural distributed energy resources, considering the interests of various stakeholders. This is conducive to achieving a clean and low-carbon transformation of the overall energy structure. • Proposed a novel rural virtual power plant considering biomass waste conversion and carbon to power. • Designed carbon-green certificate equivalence conversion mechanism. • Proposed a data-driven scenarios generation and reduction strategy for uncertainty factors. • Construct a two-stage robust optimization dispatching model and strong duality C&CG algorithm. • Construct an improved Aumann-Shapley-based benefit allocation strategy [ABSTRACT FROM AUTHOR]

As the integration of renewable energy sources (RES) progresses and energy technologies advance, integrated energy systems (IES) have emerged as a practical solution for leveraging RES and balancing local demand–supply dynamics. This paper investigates the planning and optimization of operations for an electricity-hydrogen integrated energy system (EH-IES), considering degradation and multi-timescale operations of storage devices to minimize the life cycle costs. To mitigate computational challenges, the planning model adopts a Representative Time Period (RTP) framework, and a novel intertemporal operation model is developed to coordinate the operations of battery and hydrogen storage. Furthermore, we propose a two-stage approach to derive representative weeks and hours from meteorological and load data using fewer data points, ensuring chronological coherence and preserving long-term patterns to validate storage device constraints and improve planning accuracy. Case studies in southeastern China's IES verify the effectiveness of our planning approach, which lowers equivalent annual costs by 4.5% and reduces battery capacity needs by 12.8%, thereby highlighting its significant economic benefits. Additionally, numerical analyses reveal that our two-stage method surpasses conventional RTP selection techniques, delivering at least 10% less average error with an equal number of operating points. • A novel planning and intertemporal model of EH-IES based on representative time periods is established. • The multi-timescale operations of the battery and hydrogen storage system is considered. • An efficient and accurate representative time-period selection method is developed. [ABSTRACT FROM AUTHOR]

As an effective approach in ranking decision making units (DMUs), cross-efficiency evaluation is only extended to two basic network systems: series and parallel, which is not applicable to the common complex network system in reality. By using the directional distance function (DDF), this paper extends the cross-efficiency evaluation to a complex system called the parallel two-stage system, which consists of several independent parallel processes and each of them is a sub-system with a general two-stage series structure. The proposed method is able to decompose the cross-efficiency measure of the system into the weighted average of those of processes, stages and overall stages, respectively, and as a result, successfully achieves the layer-by-layer decomposition of cross-efficiency measures under complex nested networks. An empirical study on 40 open-ended equity funds in China from 2018 to 2020 verifies that the newly proposed method not only has a stronger performance discrimination power than self-evaluation, but also can identify the relationships among efficiencies of the system, processes and stages, and meanwhile it can reflect the effects of processes and stages on the system. More importantly, our method has a better practical value for actual fund investment selection. [ABSTRACT FROM AUTHOR]

This paper is the second in a two-part series on the concavity-convexity of the distribution system security region (DSSR). In Part I, the concave-convex characteristics and mechanism of DSSR are revealed by observing many case grids. In Part II (this paper), the concave-convex principle of DSSR is revealed through mathematical deduction. Firstly, the relevant concepts of sub-regions are proposed, and a DSSR model based on the union of sub-regions is developed. Secondly, the concave-convex principle of DSSR is proposed. The proposed criteria show that the security region of a distribution system with a single load transfer path for each feeder is convex, while that with multiple load transfer paths for some feeders can be concave or convex. Thirdly, the proposed criteria are deduced mathematically. Fourthly, based on the criteria, a method is proposed to judge whether a DSSR is concave or convex. Fifthly, the proposed criteria and method are verified through several cases, and the DSSR concavity-convexity of real distribution systems in China is also analyzed. Finally, the applications of DSSR concavity-convexity are illustrated. This work lays a mathematical foundation for the research of the topological properties of DSSR and is also useful for the application of DSSR. • The DSSR mathematical model based on the union of sub-regions is developed. • Three criteria are proposed and proved, revealing the concave-convex principle of DSSR. • A judgment method for DSSR concavity-convexity is proposed based on these criteria. • The DSSR concavity-convexity of real distribution systems in China is analyzed. • The application values of DSSR concavity-convexity are demonstrated. [ABSTRACT FROM AUTHOR]

The intensification of global climate change has led to a widespread consensus on carbon reduction, with the power industry being the principal contributor to carbon emissions necessitating an inevitable transition of its energy structure. Concomitantly, the escalating frequency of natural disasters caused by extreme weather presents formidable challenges to both secure power supply and the low-carbon transition of the power system. To fulfill the developmental requirements of a low-carbon power system and address the carbon emission risks imposed by natural disasters, this paper proposes a bi-level model for generation expansion planning (GEP) that incorporates constraints on the carbon emission trajectory and the influence of natural disasters. The planning-level model optimizes investment costs of various generation technologies and energy storage (ES), as well as the overall operational expenses over the planning period, with an objective to minimize them. It incorporates carbon emission trajectory constraints and policy constraints, such as carbon peaking, carbon neutrality and renewable energy (RE) penetration rates, in order to optimize the planning installed capacity of power sources. The operational-level model aims to minimize typical daily operating costs while also simulating power unit outputs in routine and disaster scenarios. A case study is conducted in a disaster-prone province in southern China to analyze the power generation expansion planning and the trajectory of carbon emissions from 2020 to 2060 under different scenarios. The simulation results show that compared to thermal power, the planning scheme mainly focused on RE with ES is better suited to achieve the goal of a low-carbon transition of the power grid. Moreover, after considering natural disasters, the cost and carbon emissions of power system planning are higher, and the risk of carbon emissions increases with the severity of disasters. • A multi-time scale bi-level low-carbon GEP model is proposed. • Natural disaster scenarios and low-carbon policy constraints are considered in the GEP model. • Carbon emission trajectory of the planned power system is estimated and introduced as a constraint into the GEP model. • Evaluation indicators that comprehensively consider low-carbon performance and economic feasibility are proposed. [ABSTRACT FROM AUTHOR]

Urbanization and the release of carbon emission are two significant features of contemporary economic and social progress in China. However, there are few studies that consider how urbanization policy goals affect carbon emission. This paper aims to study the nonlinear effects of urbanization policy goals on carbon emission in 13 cities of the Beijing-Tianjin-Hebei region. In this paper, a multi-dimensional spatial disturbance grey model is proposed. Based on matrix perturbation theory, this paper explores the spatial perturbation bounds of the novel model and verifies that the spatial property of the model is consistent with the viewpoint of the First Law of Geography. Then, the novel model is used to predict the urban carbon emission under different urbanization policy goal scenarios. The results showed that the novel model has spatial regional correlation in spatial region prediction. With the development of urbanization level, carbon emissions show a downward trend in more than 50% of cities from 2022 to 2025. In addition, the improvement of urbanization policy goals will promote the carbon peak time of most cities. The carbon emission intensity will exhibit an inverted U-shaped trend distribution in space. The research results are conducive to providing scientific guidance for the formulation of new-type urbanization policies and helping to achieve the dual carbon goals. • A multi-dimensional spatial disturbance grey model is proposed. • The novel grey model is consistent with the viewpoint of the First Law of Geography. • The spatial distribution of carbon intensity showed an inverted U-shaped pattern. [ABSTRACT FROM AUTHOR]

• The model for multiple horizontal wells considers heterogeneous fracture network. • The number of frac hits is determined by pressure differentials and derivatives. • Higher conductivity promotes inter-well pressure equilibrium. • Lower conductivity is beneficial to extended interference testing. Frac hits can significantly impact the final development results. Therefore, accurately and quickly understanding the frac-hit interference between wells is crucial for efficient development. At present, traditional pressure/production data analysis methods and numerical simulation methods can characterize the frac-hit interference. However, challenges such as quantitatively assess interference, high uncertainty in modeling parameters and computational time still exist. A quantitative evaluation method for the interference between wells with complex fracture networks has not been reported yet. This paper takes shale oil reservoir with three production wells as an example and establishes a semi-analytical model (frac-hit semi-analytical model, FSM) based on the linear flow characteristics. The model accurately characterizes the non-uniform distribution of complex fracture networks in the stimulated reservoir volume by introducing fractal theory. A quantitative evaluation index, interference coefficient, is defined to assess the interference. The coupled flow model is applied to evaluate the effects of interference in three typical wells in a continental shale oil field in China. We can see from the actual data fitting of the FSM model that the interference coefficients between target well A and adjacent wells B and C are 0.1 and 0.05, respectively, which means there are about 8–10 and 3–5 frac hits, respectively. Comprehensive analysis for the effects of interference shows that the current inter-well interference is beneficial for production in low-pressure areas but detrimental to new well production. Based on this, sensitivity analysis is generated to illustrate the relationship between the conductivity of combined fractures arising from hits and the interference coefficient. As the interference increases, the propagation of inter-well pressure accelerates, significantly reducing the time required for inter-well pressure equilibrium. The research findings have important guiding implications for the quantitative evaluation of inter-well interference in field operations of horizontal shale oil and gas wells and optimization of fracturing operations. [ABSTRACT FROM AUTHOR]

To investigate the development and dynamics of green manure research, we collected 802 and 4588 papers on green manure research from China national knowledge infrastructure (CNKI) and Web of Science (WoS) between 1992 and 2021, respectively, and conducted quantitative and visual analyses based on bibliometric methods on the annual number of publications, authors and institutions, keywords, and emergent words. The main research hotspots in the field of green manure are discussed and summarized based on the results of the visualization analysis and the important publications explored by the results of the visualization analysis. The results showed that: the overall number of publications on green manure research exhibits an increasing trend, and research in this field is receiving more and more attention; the United States is the core country in this research field, with a great influence; although, the number of Chinese publications is large, the intermediary centrality value is small and the level of scientific research needs to be improved; the research characteristics in the field of green manure research mainly focus on green manure cultivation technology, the effects on soil and crops, and the effects on pests and diseases. The study was conducted based on CiteSpace, a visualization tool of bibliometrics, with comprehensive literature information and intuitive results, providing important references for relevant researchers to understand the extent of research in this field, academic communication, and grasp research dynamics. [ABSTRACT FROM AUTHOR]

• To schedule maintenance task with multiple priorities efficiently, a two-layer optimization strategy is proposed in this paper. • A lumped general task for each well cluster is considered in upper layer optimization, and a time length for general task is obtained. • In lower layer model, general tasks are further scheduled, taking the obtained time length in upper layer as the constraint. • The application results at a coalbed methane field in China are provided and demonstrated the effectiveness of the proposed method. Timely and effective maintenance scheduling is the key to the safe and stable operation in oil and gas fields. Large-scale maintenance tasks with multiple priorities are difficult to complete in an acceptable time. To accelerate computation, a two-layer strategy is proposed. At the upper layer, a lumped general task for each well cluster is generated. This lumping allows the upper layer model to concentrate on the urgent and important tasks, and thus a significantly reduced-scale mixed integer linear programming (MILP) model can be resulted to determine the dispatch and time length assigned for each technician. At the lower layer, a small-scale MILP model is established for each technician, utilizing the reserved time in the upper layer as the constraint. The results demonstrate that our approach significantly outperforms the complete MILP model in efficiency. The method is presently being implemented in real-world scenarios through our collaboration with the industry. [ABSTRACT FROM AUTHOR]

Tunnel structure are often subjected to continuous deformation caused by the creep effect of soft rock, leading to posing significant long-term operational risks. This paper focuses on ensuring the safe operation of a typical soft rock tunnel in the complex and dangerous mountainous area of western China. On the basis of studying the structural mechanic characteristics of the tunnel under the influence of soft rock creep, the VlseKriterijumska Optimizacija I Kompromisno Resenje (VIKOR) model based on Analytic Hierarchy Process (AHP) and Entropy Weight Method (EWM) to determine subjective and objective weights, and game theory (GT) to obtain comprehensive weights was established. Additionally, the differential evolution-grey wolf optimization (DE-GWO) improved extreme learning machine (ELM) model was also constructed. By comparing the safety evaluation results of the two models on typical monitoring sections of soft rock tunnel structure, it is demonstrated that the proposed knowledge-based decision-making and data-driven models effectively and accurately evaluated the long-term safety of typical monitoring sections. Ulteriorly, to address the issue of structure safety evaluation caused by the inability to comprehensively monitor the entire tunnel (missing monitoring data), the DE-GWO improved ELM model was further adopted, and the structure safety status of the entire tunnel was precisely deduced. [ABSTRACT FROM AUTHOR]

INTRODUCTION: After the 2008 Olympic Games, China has gradually become a prominent sports country, but there is still a certain distance from a sports power. China should improve the level of sports ability testing while continuously strengthening the construction of sports power. At present, the method of sports professional athletic ability tests in China can not be better combined with algorithms, so it is crucial to study the athletic ability test of edge computing. OBJECTIVES: To improve the ability of sports testing of sports majors in China, to improve the technical level of the construction of China's sports power, to solve the problem that China's sports ability testing cannot be better combined with algorithms, and to solve the problem that China's physical education disciplines cannot be well applied to computer technology. METHODS: Use the motor function theory and edge computing to establish the model needed, test the athletic ability of swimming sports according to the model, and analyze the advanced level and shortcomings of China's swimming sports with measurement according to the results of the athletic ability test. RESULTS: Firstly, edge computing and other algorithms are more accurate for professional athletic ability testing of swimming sports, and improving the iteration level of algorithms can improve the problem of the inconspicuous effect of sports testing; secondly, edge algorithms combined with traditional testing tools can calculate athletic ability more accurately in athletic ability testing. CONCLUSION: China should vigorously improve the level of edge computing and other algorithms to improve the problem of China's sports disciplines not being able to apply computer technology well and technically improve the level of sports training. [ABSTRACT FROM AUTHOR]

INTRODUCTION: With the development of digital technology and computer technology, the theory of digital economy has also had far-reaching development, but at present, the digital economy is still not able to clearly define whether the digital economy is good or bad for the high-quality development of China's economy. Therefore, it is necessary to research the spillover effect of the digital economy and the high-quality development of the economy with the help of embedded system technology. OBJECTIVES: To solve the issue of the poor coupling between the digital economy and high-quality development, to raise the degree of China's digital economic development, to improve the high energy consumption in the course of China's economic high-quality development, and to improve the application level of system technology in the study of China's economic development. METHODS: The digital economy model is established using the entropy weight method; its efficiency is evaluated using embedded system technology; the spillover effect analysis of high-quality development is then conducted using the digital economy index; and lastly, the overall assessment of the spillover effect is conducted using the comprehensive evaluation results of the entropy weight method. RESULTS: Embedded system technology has a noticeable promotion effect on the development of the digital economy; although the development of the digital economy and economic high-quality development are poorly coupled, their promotion effect is more significant; embedded system technology can indirectly promote the development of economic high-quality development through the digital economy. CONCLUSION: China should vigorously develop embedded system technology to promote the application of new technology in the economy and should further standardize the market economic order to maintain the orderly development of the digital economy. [ABSTRACT FROM AUTHOR]

Whether environmental regulation incentivizes green technology innovations is debated in the literature. The potential endogeneity of environmental regulation is the major empirical challenge for identifying the causal effect of environmental regulation on green technology innovations. To address this issue, we use the quasi-natural experiment of China's Total Emission Control Policy as exogenous shocks of environmental regulation. Specifically, we construct a new dataset of the city-level emission reduction mandates and employ the difference-in-difference-in-differences estimation to explore how stringency of environmental regulation affects green technology innovations. The empirical results show that environmental regulation promotes green innovations. Further mechanism analysis shows environmental regulation can attract "new entrants" to join the green technology market and launch more green innovations. This paper further explores the significance of environmental regulation in narrowing the gap between green and non-green technologies. [ABSTRACT FROM AUTHOR]

Understanding the relationship between land-use patterns and regional carbon storage, as well as predicting future land-use changes for regional carbon sink and emission management, are of immense significance. This study utilized land-use data from 1990, 2000, 2010, and 2020, as well as the InVEST model, to evaluate the spatiotemporal evolution of carbon storage in the Sanjiangyuan area over the past three decades. Furthermore, predictions for carbon storage in 2035 were presented using the PLUS model. The findings revealed the following key results: (1) the land types in the Sanjiangyuan area are mainly low cover grassland, medium cover grassland and unused land, among which unused land decreased significantly from 1990 to 2020, and the area of low cover grassland and wetland increased, which is the main reason for the increase of carbon storage. (2) Climatic-environmental and social-economic factors jointly influenced the land-use change in the Sanjiangyuan area. Except for the three types of grassland, the expansion of other land types was mainly influenced by climatic and environmental factors. (3) During 1990–2020, carbon storage in the source region of Sanjiangyuan area showed an overall upward trend, with a total increase of 39.97 × 107 t, and land-use change had a positive potential impact on carbon storage as a whole. (4) Under the natural change scenario, both carbon storage and regional carbon density increased in the simulation of 2035, and the potential impact of land-use change on carbon storage is positive. On this basis, the paper puts some suggestions forward to improve the carbon storage capacity of the Sanjiangyuan area in the future. This study provides valuable scientific insights for land-use management decision-making and promotes sustainable development of carbon storage functions in this region. • Total carbon storage increased between 1990 and 2020. • The simulation predicts higher carbon storage in 2035 than in 2020. • Ecological protection policies have a crucial contribution to improving carbon storage. • Regulating regional Socioeconomic activities is an effective measure. [ABSTRACT FROM AUTHOR]

This paper qualitatively and quantitatively examines the development of the sovereign debt market in Prewar China under different governments. During the Beijing Era (1912–26), accompanied by the establishment of necessary financial institutions, the sovereign debt market emerged to meet fiscal needs. Surprisingly, the Nationalist government, in power from 1927, successfully cultivated a robust market characterized by its expanding size and liquidity. Setting itself apart from its predecessors, the government established credibility as a borrower in two key ways. Firstly, it demonstrated unwavering commitment to debt service by settling previous debts and offering well‐structured new ones, even during challenging times. Furthermore, the government escrowed fiscal revenue, pledged for debt repayments, to a semi‐independent committee of private bankers on behalf of debtholders, enhancing public confidence. Secondly, the government showcased its ability to secure tax revenues for debt repayments. However, starting from 1931/2, the debt market experienced a decline due to the government's compromised ability to pay resulting from external wars and shifting political priorities that weakened its commitment to debt repayments. Empirical evidence confirms the market's responsiveness to regime shifts and policy changes. This paper sheds light on how a nascent autocratic government can successfully borrow from the public. [ABSTRACT FROM AUTHOR]

This paper studies the impact of collectivization on patricide in China during the Cultural Revolution. From 1955 to 1957, nearly 96 per cent of farmers were organized into communes. Consequently, fathers lost control over family wealth. We propose that this shift decreased fathers' bargaining power over their adult sons, which might increase family conflicts. On the basis of a novel dataset, we find that the speed of collectivization significantly increased patricide, and the result is robust by employing ruggedness to instrumenting for the speed of collectivization. Our study extends the literature on intra‐household bargaining from couples to intergenerational relationships. [ABSTRACT FROM AUTHOR]

The purpose of video process detection is to identify key processes of interest and associated semantic categories, which is an important computer vision task for localizing key processes in space science experiments. To detect experimental processes in space electrostatic suspension material experiment on China's Space Station, we propose a Slow-and-Fast Dual-Stream Network. It extracts multi-scale temporal semantic information by designing a Slow-and-Fast Temporal Stream structure and effectively detects the experimental processes with various temporal scales. In addition, the Two Path Temporal Pyramid Network (TP2N) enhances the bi-direction flow among multi-scale features on the temporal dimension to alleviate boundary sensitivity. The validation experiments on our established dataset demonstrate the effectiveness of our proposed method with 62.43% mAP, outperforming the related state-of-the-art temporal action localization methods with nearly 7% mAP gain. This paper is the first application of deep learning in space electrostatic suspension material experiments on China's Space Station and further provides important technical support for the identification of key processes in space science experiments. [ABSTRACT FROM AUTHOR]

The interfacial bonding performance between Fe-based shape memory alloys (Fe-SMA) and steel is of utmost importance for the reinforcement of steel structures using Fe-SMA. In order to better reveal the bonding performance and ensure the reliability of Fe-SMA-to-steel bonded interfaces, the single-lap-shear tests of 27 adhesive joints were conducted. The experimental results indicate that the bearing capacity increases with the bonding length until reaching the effective bonding length (L e), with SA, Sika, and LC adhesive joints possessing L e of around 90 mm, 90 mm, and 100 mm, respectively. Better bonding performance can be achieved with 1–1.5 mm thickness for the 3 types of adhesive joints, while the bearing capacity of LC adhesive joints is less affected by the adhesive thickness. The SA joints experienced interface debonding failure, the Sika joints experienced the mixed failure mode of interface debonding and cohesive failure, and the LC joints exhibited cohesive failure. For the bonded interfaces, cohesive failure is the preferred mode, evidenced by Fe-SMA plates in LC joints reaching 60% of their tensile strength at failure. Furthermore, utilizing the established constitutive model of Fe-SMA and the strain propagation from experiments, the shear stress evolution and interfacial bond-slip relationship of Fe-SMA-to-steel bonded interfaces were analyzed, demonstrating that the nonlinear behavior and high ductility of Fe-SMA significantly affect the load-bearing performance and failure evolution. This paper verifies the feasibility of reinforcing steel structures with Fe-SMA through adhesive bonding connection, providing an experimental basis for the theoretical analysis and practical application of Fe-SMA-to-steel bonded interfaces. • The reliability of reinforcing steel structures with Fe-SMA by adhesive bonding is verified. • The adhesive type, bonding length, and adhesive thickness are experimentally studied. • The elastic-plastic behavior of Fe-SMA significantly affects the load-bearing performance. • The constitutive relationship of China-made Fe-SMA plates is provided. [ABSTRACT FROM AUTHOR]

This paper looks at the relationship between enterprise size and pollution abatement costs using firm-level datasets in China, where size-dependent distortions may exist in the distribution of enterprises of different sizes. We find that the pollution abatement costs decrease with enterprise size. Compared to the smallest size, the magnitudes of marginal decreases in pollution abatement costs per 1000 CNY of industrial output in the largest size are 3.29 CNY, 0.54 CNY and 2.60 CNY for gas, SO2 and dust, respectively. Moreover, we further explore the potential mechanisms for the decrease of pollution abatement costs from three perspectives: environmental protection-related investment, regulation intensity, and technological capability. Our findings suggest that the decrease in the intensity of environmental protection-related investment for large enterprises may be directly reduce the intensity of pollution abatement costs. Although large enterprises have low intensity of pollution abatement costs, this does not mean that they are subject to weaker environmental regulation. More importantly, we find that large enterprises may have more adoption of abatement technologies. For SO2 and dust, the pollutant removal efficiency of the largest enterprises is 4.92% and 1.82% higher than that of the smallest enterprises, respectively. Our results are robust to different specifications. These findings help us enact appropriate policies for enterprises based on their size, thereby improving the efficacy of pollution abatement and achieving the optimal level of pollution abatement for the entire society. [ABSTRACT FROM AUTHOR]

Broflanilide is a newly-developed meta-diamide insecticide, proposed for the control of a wide variety of chewing pests on many crops. In view of the proposed use of broflanilide and its environmental fate, it may be exposed to consumers and non-target organisms, which adversely affect human and the environment. In this paper, a rapid, sensitive and valid UPLC-MS/MS method was established for simultaneous analysis of broflanilide and its two major metabolites, DM-8007 and S (PFP-OH)-8007, in cauliflower. Then, the dissipation behaviors and final residues of broflanilide and its two major metabolites in cauliflower from eight sites with different climatic conditions in China were studied via the described analytical method. In addition, the acute toxicity test of 9.5 % suspension concentrate of broflanilide, broflanilide standard, DM-8007 and S (PFP-OH)-8007 were conducted to non-target terrestrial organisms. Risk assessment for human and non-target terrestrial organisms in cauliflower production was evaluated based on the maximum annual application rates and intervals. The results showed that the highest residue of broflanilide detected in cauliflower samples was all lower than the corresponding MRLs (2 mg/kg) in Japan. Chronic food dietary risk estimates for broflanilide do not exceed 50 % for all the Chinese population groups. Moreover, broflanilide is of low acute toxicity to birds and earthworm, while broflanilide and its metabolites is classified as highly toxic to adult honeybees. Acute risks of broflanilide to birds and earthworms were deemed to be acceptable in a realistic worst-case scenario, while its risk to adult honeybees and ladybug was unacceptable. A protection statement for honeybees and ladybug is required to recognize the high toxicity of broflanilide on related product labels. The study will be conducive to provide guidance for the rational application of broflanilide in cauliflower production. [Display omitted] • Chronic food dietary risk does not exceed 50 % for all the Chinese population. • Broflanilide is of low acute toxicity to birds and earthworm. • Broflanilide and its metabolites is highly toxic to adult honeybees. • Acute risk to adult honeybees and ladybug was unacceptable. [ABSTRACT FROM AUTHOR]

Carbon peaking and carbon neutrality will be important policies to constrain China's economic and social development in the future. Based on the theory of computable general equilibrium, CGE model of carbon emission was constructed in this paper to simulate the influence of different scenarios of carbon emission reduction on economic and social development. The major contribution is to subdivide the energy sector according to primary energy and secondary energy, and construct the macro SAM table and micro SAM table of 21 energy sectors in 2017 to quantitatively simulate the impact of carbon emission reduction policies on China's economy and society, and put forward some policy suggestions based on this. [ABSTRACT FROM AUTHOR]

Purpose: This study aims to explore the spillover effects of supply chain corruption practices (SCCPs) on stock returns along the supply chain and within the industry. Specifically, it investigates how SCCPs affect the stock returns of corrupt firms' bystander supply chain partners and industry peers, both of which are not involved in the SCCPs. Design/methodology/approach: The authors employ the event study methodology to quantify SCCPs' spillover effects in terms of abnormal stock returns. The analysis is based on 117 SCCPs occurring in China between 2014 and 2021. Findings: The event study results show that SCCPs have negative effects on the stock returns of corrupt firms' bystander supply chain partners. Such negative effects are more pronounced for bystander buyers than bystander suppliers. However, SCCPs do not have a significant impact on the stock returns of corrupt firms' industry peers. Additional analysis further suggests that SCCPs are more likely to affect the stock returns of domestic rather than overseas bystander supply chain partners. Originality/value: This study is the first attempt to thoroughly examine the spillover effects of SCCPs along the supply chain and within the industry, advancing the understanding of the financial consequences of SCCPs and providing important implications for future research and practices related to supply chain corruption. [ABSTRACT FROM AUTHOR]

Purpose: Companies are concerned about the well-being of workers in their supply chains, but conventional audits fail to uncover critical problems. Yet, if the happy worker – productive worker thesis is correct, it would benefit factories in fast-developing countries, particularly China which is key to many global supply chains, to ensure the well-being of their workers. The authors set out to better understand the relationship between well-being and performance in four Chinese factories. Design/methodology/approach: Over 12-months the authors collected digital diaries from 466 workers in four factories, and monthly data about the performance of their factories. The authors used this data to gain insights into the well-being of workers in these factories; to design experimental interventions to improve this; and to consider any effects these had on factory performance. Findings: The experiments showed that training interventions to improve workers' well-being through their work relationships and individual skills improved not just a factory's general worker well-being, but also some aspects of its performance and worker retention. Thus, it brought benefits not only for the workers but also for the factory owners and their client companies. Originality/value: While there is a significant body of research investigating the happy worker – productive worker thesis, this was not conducted in Chinese factories. The authors' work demonstrates that in this and similar environments, workers' eudaimonic well-being is more important than might be assumed, and that in this context there is a relationship between well-being and performance which can be practically addressed. [ABSTRACT FROM AUTHOR]

Knowledge sharing (KS) and team creativity are crucial to achieving complex goals in a construction project. However, in the information age, there is a lack of research on exploring the influencing mechanisms among two dimensions of information technology (IT) innovations (i.e., intra- and interorganizational IT innovations) and two types of KS (i.e., tacit and explicit KS), along with team creativity in construction projects. Therefore, this paper builds a theoretical model of the relationships among the aforementioned variables. To validate the feasibility of this model, 289 valid questionnaires from the construction industry in China are analyzed. Results indicate that the influence of interorganizational IT innovation on team creativity is significant, and the influence is partially mediated by tacit and explicit KS. The influence of intraorganizational IT innovation on team creativity is significant, and the influence is partially mediated by explicit KS but not tacit KS. This study addresses the controversial issue of whether or how IT can promote tacit KS. This study also provides fresh insights into the influencing mechanisms of team creativity in construction projects owing to changes in the KS level affected by organizational IT innovations. There are also important practical implications relating to how project managers may rationally use IT to enhance KS and team creativity. [ABSTRACT FROM AUTHOR]

Peach shell, a distinctive agricultural residue in China, exhibits the potential to substitute concrete aggregates, facilitating the creation of eco-lightweight concrete. This study determines the optimal volume substitution rate based on strength and density for peach-shell concrete. It delves into microscopic features, hydration outcomes, mechanical traits, sound absorption, impermeability, and techniques to enhance the impermeability. The outcomes reveal that peach-shell concrete's compressive, flexural, and splitting tensile properties are comparatively lower than ordinary concrete due to reduced peach-shell aggregate, cement paste, and interfacial bonding strength. However, it excels in sound absorption owing to larger biological pores. While its impermeability trails ordinary concrete, polyvinyl alcohol coating proves effective for improvement. In essence, this research comprehensively examines peach-shell concrete's attributes, offering insights for practical applications of lightweight peach-shell concrete. As the planet warms up and its impact on our future becomes more apparent, many researchers have been working on finding ways to use building materials that help cut down on carbon emissions. One effective strategy is adding biochar to concrete, which is exactly what this article looks into. It is all about how replacing a significant portion of the concrete aggregate with peach shells—a type of agricultural waste in China—affects the concrete's physical, mechanical, and acoustic properties, and how well it resists water seepage. What the article discovered is that this peach-shell concrete is not as strong when it comes to bearing weight, pulling forces, or bending. Additionally, it is not as watertight. But here is the upside: it is lighter in weight and can soak up sound better. This means it could be a good option for making lightweight walls inside buildings that do not have to hold heavy loads. The whole point of this paper is to explore whether we can create useful lightweight concrete using peach shells. [ABSTRACT FROM AUTHOR]

Insufficient compaction of the subgrade can result in nonuniform deformation, leading to severe subgrade distress. To address this issue and find a new method for rapid detection and evaluation of subgrade compaction, we used the lightweight deflectometer (LWD) to analyze the dynamic deformation modulus (Evd) of subgrades filled with four types of soils—silt (ML), well-graded gravel (GW), lean clay (CL), and poorly graded sand (SP)—in different regions of Gansu province, China. Concurrently, we measured the degree of compaction (Doc) using the sand replacement method (SRM) to establish its correlation with dynamic deformation modulus (Evd). A strong correlation between the degree of compaction and dynamic deformation modulus was established for soils ML, GW, CL, and SP, and suitable formulas were selected based on curve variations. The developed formulas enabled back-calculation of the dynamic deformation modulus requirements corresponding to different degrees of compaction ranging from 90 to 100, facilitating direct queries and quick field checks. Results demonstrated that the LWD, as a reliable rapid detection method, effectively controlled subgrade compaction quality in field construction. Moreover, it extended the testing area and increased measurement frequency, thus providing a practical means for quickly evaluating the qualification rate and uniformity of subgrade compaction. When we build roads, most subgrade underneath the roads needs to be filled with soil. If we don't pack the soil properly in the filling process, the subgrade can crack and become uneven over time. It's hard to fix this problem once the road is open, so it's important to make sure the soil is packed tightly enough before the road is finished. There are two ways to check if the soil is packed properly: one is a traditional method called the sand replacement method, and the other is a machine called the lightweight deflectometer. These methods help identify insufficient compaction of the subgrade and prevent uneven deformation problems during the filling process. What's more, the lightweight deflectometer is a fast, convenient, and reliable tool that can assist or serve as an alternative to the traditional sand replacement method for controlling subgrade construction quality. This study is important because it helps us understand how to evaluate compaction of the filled subgrade in a different way. [ABSTRACT FROM AUTHOR]

This paper presents a massively parallel approach of the multilevel fast multipole algorithm (PMLFMA) on homegrown many-core SW26010 cluster of China, noted as (SW-PMLFMA), for 3-D electromagnetic scattering problems. In this approach, the multilevel fast multipole algorithm (MLFMA) octree is first partitioned among management processing elements (MPEs) of SW26010 processors following the ternary partitioning scheme using the message passing interface (MPI). Then, the computationally intensive parts of the PMLFMA on each MPI process, matrix filling, aggregation and disaggregation are accelerated by using all the 64 computing processing elements (CPEs) in the same core group of the MPE via the Athread parallel programming model. Different parallelization strategies are designed for many-core accelerators to ensure a high computational throughput. In coincidence with the special characteristic of local Lagrange interpolation, the compressed sparse row (CSR) and the compressed sparse column (CSC) sparse matrix storage format is used for storing interpolation and anterpolation matrices, respectively, together with a specially designed cache mechanism of hybrid dynamic and static buffers using the scratchpad memory (SPM) to improve data access efficiency. Numerical results are included to demonstrate the efficiency and versatility of the proposed method. The proposed parallel scheme is shown to have excellent speedup. [ABSTRACT FROM AUTHOR]

Background: As the largest group of healthcare professionals, nurses play an indispensable and crucial role in disaster response. The enhancement of nurses' disaster literacy is imperative for effective disaster emergency management. However, there is currently a lack of knowledge regarding nurses' disaster literacy. This study represents the first attempt to explore the key components and characteristics of disaster literacy among nurses. Methods: A qualitative descriptive design was employed, and the reporting followed the Consolidated Criteria for Reporting Qualitative Research (COREQ) guidelines (refer to File S1). The purposive sampling method was utilized. Thirty-one rescue nurses from 31 medical institutions across 25 provinces and regions in China were recruited to participate in the study. The respondents were requested to share their experiences and insights regarding disaster rescue operations. Inductive content analysis was employed for data examination. Results: The results indicated that rescue nurses universally recognized that there was a pressing need to enhance the level of disaster literacy among nurses. The disaster literacy of nurses encompasses nine dimensions: physical and mental quality, disaster rescue general knowledge, professional and technical competence, professional ethics, teamwork, emotional ability, information literacy, leadership, and knowledge transformation. Conclusions: To ensure national sustainability, government departments, healthcare organizations, and hospital administrators can accurately evaluate the disaster literacy of individual clinical nurses, groups, and the workforce as a whole through nine dimensions, which also can provide evidence to support the development of precision strategies to strengthen the disaster literacy of nurses. [ABSTRACT FROM AUTHOR]

Background: Ciliary body tumor is extremely rare and treatment is challenging. The aim of this study is to present our experience in treating this rare entity, especially large tumors with more than 5 clock hours of involvement, and to evaluate the surgical outcomes and complications of local resection via partial lamellar sclerouvectomy in four cases of ciliary body tumors in China. Methods: Four patients with ciliary body tumors underwent partial lamellar sclerouvectomy between October 2019 and April 2023 in Shanghai General Hospital, China. Tumor features, histopathologic findings, complications, visual acuity, and surgical outcomes were reviewed at a mean follow-up of 20.8 months. Results: Four patients with a mean age of 31.8 years were included in this study. The histopathological diagnosis was adenoma of non-pigmented ciliary epithelium (ANPCE), schwannoma, and multiple ciliary body pigment epithelial cysts. The mean largest tumor base diameter was 6.00 mm (range: 2.00–10.00) and the mean tumor thickness was 3.50 mm (range: 2.00–5.00). Preoperative complications included cataract in 3 (75%) eyes, lens dislocation in 2 (50%), and secondary glaucoma in 1 (25%). Temporary ocular hypotonia was observed in one case and no other postoperative complications were observed. At a mean follow-up of 20.8 months, the best corrected visual acuity increased in 3 eyes and was stable in 1 eye. Tumor recurrence was absent in all eyes. All patients were alive at the end of follow-up. Conclusions: Local tumor resection via PLSU is useful in the treatment of ciliary body tumors, including large tumors occupying more than five clock hours of pars plicata. Surgery-related complications were manageable with adequate preoperative assessment and careful operation during surgery. Key messages: What is known 1. Ciliary body tumor is a rare intraocular tumor that is difficult to detect and determine the malignancy, sometimes leading to overdiagnosis and overtreatment such as eyeball enucleation. 2. Partial lamellar sclerouvectomy (PLSU) is used as a local resection method for iridociliary tumors but surgical procedure is challenging and limited by tumor size. What this paper adds 1. PLSU can also be used in the treatment of large iridociliary tumors occupying more than five clock hours of pars plicata. 2. Surgery-related complications were manageable with adequate preoperative assessment and careful operation during surgery. [ABSTRACT FROM AUTHOR]

Background: Mental health problems among children at preschool age are a common issue across the world. As shown in literature, a caregiver's parenting style can play a critical role in child development. This study aims to examine the associations between a caregiver's parenting style and the mental health problems (or not) of their child when he/she is at preschool age in rural China. Methods: Participants were children, aged 49 to 65 months, and their primary caregivers. The primary caregivers of the sample children completed the Parenting Styles and Dimensions Questionnaire, Short Version, the Strengths and Difficulties Questionnaire, and a questionnaire that elicited their socio-demographic characteristics. The level of cognitive development of each sample child was assessed using the Wechsler Preschool and Primary Scale of Intelligence, Fourth Edition. Pearson correlation analysis, linear regression analysis, and multivariable regression analysis were used to analyze the data. Results: The prevalence of mental health problems among sample children at preschool age was high (31.6%). If a caregiver practices an authoritative parenting style, it was found to be negatively associated with the mental health problems of their child. In contrast, a caregiver's authoritarian parenting style was positively associated with the mental health problems of their child. Compared to those in a subgroup of primary caregivers that used a combination of low authoritative and low authoritarian parenting style, primary caregivers that used a combination of high authoritarian and low authoritative or a combination of high authoritative and high authoritarian were found to have positive association with child health problems. A number of demographic characteristics were found to be associated with the adoption of different parenting styles. Conclusion: Different parenting styles (including authoritative, authoritarian, and combination of authoritative and authoritarian) of the sample caregivers had different associations with the mental health problems of the sample children. Parenting programs that aim to improve the parenting styles (favoring authoritative parenting styles) should be promoted in an effort to improve the status of child mental health in rural China. [ABSTRACT FROM AUTHOR]