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Generally, the central air conditioners are centralized in a refrigeration room in public buildings. The proportion of energy consumption of the refrigeration room is about 40% of entire building. This paper aims to propose an energy plus and self-designed simulator to reduce the energy consumption of a large electronic factory in Zhuhai City, Guangdong province, China. In this paper, the real data collected from the electronic factory in February and the group control method used in the refrigeration room based on the kernel ridge regression algorithm. The correctness of the kernel ridge regression algorithm was verified with the experimental results. Compared to traditional PID control method, the kernel ridge regression algorithm could dynamically adjust those parameters, such as temperature and frequency to match the high efficiency zone of each device. The overall average COP of the system is increased from 4.17 to 7.04. The electricity of the refrigeration room could save 64 MWh with the cooling load forecast error being below 7% in February. The power consumption is expected to reduce by 8.2% and 562.3 MWh in 2022. [ABSTRACT FROM AUTHOR]

• This study summarizes all existing and unpublished data on the Late Triassic plant fossils in Guangdong. • Four regional assemblages have been distinguished in the Late Triassic by marine fossils such as ammonites and bivalves. • The Late Triassic climate in the Guangdong region was mainly warm and humid, either tropical or subtropical. • During the Late Triassic, central and northern Guangdong were located at similar latitude, along the southern coastline of the South China Block. Triassic-Jurassic deposits are well developed in Guangdong Province, South China. In particular, the Upper Triassic coal-bearing strata which alternate between marine and terrestrial deposits yield diverse and abundant fossil plants, ammonites and bivalves. These strata provide a significant reference for the study of palaeoenvironmental variations in the lower latitude regions. In this paper, we review the major progress on Late Triassic floral studies in this area. Specifically, we compare the regional plant assemblages from different fossil sites in central and northern Guangdong which have been dated with marine fauna. 155 species belonging to 56 genera of fossil plants have been reported so far in northern and central Guangdong. The Late Triassic floras in Guangdong are mainly composed of Bennettitales and ferns, followed by horsetails, seed ferns, cycads, ginkgos and conifers. In ascending order, four regional plant fossil assemblages can be recognized in the Late Triassic deposits, namely the Pachypteris - Lindleycladus Assemblage (Julian), the Pterophyllum - Baiera Assemblage (late Julian), the Clathropteris - Otozamites Assemblage (Tuvalian), and the Danaeopsis - Anomozamites Assemblage (Rhaetian). In general, the Late Triassic climate in the Guangdong region was mainly humid and warm and either tropical or subtropical. The fossil plants corroborate palaeomagnetic evidence that the central and northern Guangdong region was located at approximately the same latitude as it is today and formed the southern coastline of the South China Block during the Late Triassic. Palaeogeographically, the transgression started at the end of the Julian and the south coastal terrane consisted of a western bay, a peninsula and an eastern bay. During the regression period, post-Rhaetian, the bays evolved into a gulf coastal plain. [ABSTRACT FROM AUTHOR]

In recent years, extreme rainfall events have frequently occurred frequently, and heavy rainfall can cause drastic changes in the troposphere. Therefore, achieving to achieve real-time high-precision numerical prediction of key tropospheric parameters during heavy rainfall has become a major problem in global navigation satellite system (GNSS) meteorology. In this paper, two extreme rainfall events in southern China (Guangdong region) and northern China (Shandong region) in 2022 are used as case studies. Twenty-four-hour real-time numerical forecasts of key tropospheric parameters (atmospheric weighted mean temperature (Tm), precipitable water vapor (PWV), and GNSS zenith tropospheric delay (ZTD)) are obtained using three models, namely, the HGPT2, GPT3, and WRF models. Two optimization models, i.e., WRFDA (am) and WRFDA (pre), are then constructed by assimilating two types of data (global upper air and surface weather observations and daily advanced microwave sounding unit A (AMSU-A) brightness temperature) based on the WRF model. The experimental results for heavy rainfall show that (1) the WRF model predicts the key tropospheric parameters with better accuracy than the HGPT2 and GPT3 models, and the WRFDA (pre) model predicts PWV and ZTD with the highest accuracy; (2) the WRFDA (pre) model achieves a higher accuracy than the WRF model in predicting PWV and ZTD, where the PWV prediction accuracy is improved relative to the WRF model (in the south: MAE: 32.7 %; RMSE: 33.9 %; MAPE: 36.8 %; in the north: MAE: 27.3 %; RMSE: 24.2 %; MAPE: 28.0 %); this model achieves an MAE of 2.17 cm and an RMSE of 2.70 cm in 24-h ZTD prediction in the south, while the MAE reaches 2.48 cm, and the RMSE is 3.18 cm in the north; (3) the models provide a higher forecast accuracy in the southern region than in the northern region for heavy rainfall. The WRFDA (pre) model provides a favourable ZTD accuracy at GNSS stations near the ocean, while the WRFDA (am) model provides a satisfactory ZTD accuracy at inland GNSS stations, and the WRFDA (am) model provides the highest ZTD prediction accuracy at GNSS stations above 100 m. [ABSTRACT FROM AUTHOR]

Hydrogen is a type of clean secondary energy, and the hydrogen energy supply is vital to regional energy development. Guangdong is at the forefront of hydrogen energy deployment in China, and aims to achieve peak carbon by 2030. However, hydrogen production research is challenging due to complex technical routes, technical maturity levels, life-cycle carbon emissions, and costs. Multi-time-scale research is necessary to explore optimal production paths and their contributions to CO 2 emission reduction. This study discussed key technologies and the technical maturity of five hydrogen production routes: production from industrial byproducts and coal, wind, hydro-, and photovoltaic power. Policy scenarios were designed to investigate the potential of these routes and technology deployment intensity under medium- and long-term scenarios. This paper proposes a roadmap for hydrogen development and provides a reference for hydrogen production planning in Guangdong. This methodology can be applied in other regions for hydrogen energy development research. • Key technology maturity of the hydrogen production routes was evaluated. • Roadmap of hydrogen production in Guangdong Province was designed. • A method for research of hydrogen supply scenarios was proposed. [ABSTRACT FROM AUTHOR]

The urgency of addressing climate change and reducing carbon emissions necessitates the establishment of effective carbon markets. Our paper focuses on assessing and interpreting the efficiency of carbon markets, which is a crucial aspect of their successful implementation. We selected Hubei, Shenzhen, and Guangdong carbon pilots as examples to measure their dynamic information efficiency by combining the rolling time window and wild-bootstrap variance ratio test. Furthermore, we combined SHapley Additive exPlanations (SHAP) and Extra Trees (ET) regressor to reveal the nonlinear relationship between market performance and efficiency. Our findings highlight the time-varying nature of carbon market efficiency and reveal that China's carbon market has not yet achieved stable weak-form efficiency. Additionally, we identified excessive trading volume volatility as a significant constraint on market efficiency, while the trading volume itself exhibits weak explanatory power. Our proposed hybrid model demonstrates superior predictive and explanatory capabilities, offering regulators a valuable tool for continuously monitoring and interpreting carbon market efficiency. Finally, we suggested that reducing trading volume volatility should be a major concern for regulators to optimize market operations at present. [ABSTRACT FROM AUTHOR]

A good risk warning model is an effective way to guarantee the stable operation of the carbon market and exert its emission reduction function. This paper takes the China's pilot carbon market as the research object and measures the carbon market risk by ARMA-GARCH-VaR. Then, influential factors are selected as non-market characteristics from the perspective of causality and incorporated into the risk warning model, and SMOTE and SMOTEENN are introduced to deal with the unbalanced samples. Results show that (1) The risk fluctuations of Beijing and Shenzhen carbon market are large, and the risk fluctuations of Guangdong carbon market are relatively smooth in the early period and larger in the recent period. (2) Air quality, average daily temperature, CSI 300, power coal, natural gas, European carbon market, exchange rate, and Baidu index are all important factors in the risk volatility of carbon market. (3) The Classification Decision Tree (CDT) has the best performance. CDT has the best performance in Guangdong carbon market, SMOTE-CDT has the best performance in Hubei carbon market, and SMOTEENN-CDT has the best performance in Shanghai carbon market, in which SMOTEENN-CDT has a 76% Recall. The non-equilibrium treatment improves the overall Recall, and the risk warning model after non-equilibrium treatment can avoid missing important risk points. This paper can provide a certain degree of reference for the improvement of carbon trading market and risk management. [Display omitted] • A carbon market warning model is constructed. • The average VaR of the carbon trading markets in Shenzhen and Beijing is larger. • CDT performs better in market risk warning. • The model improves 34.8% in the avoidance of risk compared with other models. [ABSTRACT FROM AUTHOR]

This paper addresses the significant challenges faced by distribution system operators (DSOs) in competitive electricity markets due to uncertainties arising from electricity markets, electric vehicles (EVs), and conventional loads. To tackle these challenges, this paper proposes a pseudo-local reserve market between the DSO and EVs, which enables the DSO to integrate EV flexibility into the overall market framework while compensating for deviations in the conventional load across the wholesale electricity market. Furthermore, a two-stage framework is developed to facilitate the participation of EVs in electricity markets. In the first stage, the reserve capacity of EVs is integrated into the decision-making process for day-ahead bidding, taking into account uncertainties of the load and electricity. In the second stage, the day-ahead results are coordinated to enable the efficient dispatch of EVs for real-time market participation. To assess the efficacy of the proposed framework, a case study is conducted within a 31-node distribution system situated on a university campus located in Guangdong, China. This paper investigates the economic impact of load deviation constraints on EV reserves, revealing a penalty reduction of 3100 CNY for a daily reservation of 1000 EVs and a net profit increase of 1300 CNY for DSOs utilizing reserves. It further introduces a method for quantifying reserve capacity, enhancing DSO decision-making accuracy. • A pseudo-local reserve market is established considering EV's reserve potential. • Optimal bidding strategy for DSO in China's electricity markets is constructed. • A framework for EV coordinated V2G operation is developed. • The proposed method can limit load deviation cost for more profits. [ABSTRACT FROM AUTHOR]

Accurate photovoltaic power prediction is crucial for optimizing the performance of photovoltaic power plants and ensuring the stability of the power grid. However, existing forecasting methods are challenged when predicting the power output of multiple plants with different geographical locations and environmental conditions simultaneously, due to the complexity of the underlying factors that affect power generation. To address this issue, this paper proposes a novel LightGBM-XGBoost photovoltaic power prediction model based on a pre-training and fine-tuning paradigm. The model leverages a large dataset to pre-train and learn rich feature representations within meteorological elements, which are then fine-tuned on specific downstream tasks to improve prediction accuracy and generalization ability. Experimental verification is conducted using data from three photovoltaic generators operating in Guangdong, China. Results demonstrate that the proposed model outperforms other methods when predicting the power output of multiple plants with different conditions simultaneously, highlighting the effectiveness of the proposed approach. Overall, this work contributes to advancing the state-of-the-art in photovoltaic power prediction and has practical implications for improving the control and scheduling of photovoltaic power plants, as well as ensuring the stability and security of the power grid. [ABSTRACT FROM AUTHOR]

This paper proposes a novel management scheme, namely, the distribution system operator (DSO) as a key player participating in China's electricity markets owing to its high value-added capability, high customer loyalty and robust risk resistance. In light of this, this paper discusses the maximal marginal revenue for DSO in uncertain electricity markets with vehicle-to-grid operation incorporated. Initially, considering different benefit expectations of electric vehicles (EVs) owners, DSO could provide preferential contracts, including the discount price contract (DPC) and revenue-sharing contract (RSC), in exchange for the management rights of EVs. Then, the stochastic optimization model considering the spatiotemporal uncertainties of EVs and electricity markets is built up, and the optimal bidding and coordinating strategy are proposed to maximize the marginal revenue of DSO. Comprehensive case studies are conducted in a 31-node distribution system of a university campus under the electricity markets in Guangdong, China. Results reveal that different distribution of EVs parking lots has a significant impact on the profit of DSO, and the revenue for DSO is 13,477 yuan more than that for EVA by scheduling 1400 EVs' charging/discharging behavior properly. Additionally, the DSO with RSCs is exposed to 25% less market volatility risk than that with DPCs. • Comparing EV aggregator(EVA) and distribution system operator(DSO) characteristics. • Discount price contracts and revenue-sharing contracts are compared. • The advantages of DSO over EVA in market participation are analyzed. [ABSTRACT FROM AUTHOR]

Reasonable alignment control can simplify the bridge construction process, reduce the complexity and cost of construction, and improve construction efficiency. The prefabricated assembling construction method brings challenges to alignment control. This study proposes a method for predicting the alignment of concrete cable-stayed bridge assemblies in unstressed conditions to accelerate the construction process. The method uses the target paper to identify the 3D coordinates of control points and a coarse-to-accurate alignment method to achieve the virtual preassembly of precast concrete sections. The effectiveness of the method is demonstrated by comparing the errors in key parameters between virtual and physical preassembly and the predicted alignment results obtained through 3D coordinate conversion with the designed precast alignment. The method was applied to the construction of a large-span concrete cable-stayed bridge for a high-speed railroad in Guangdong, China, and the construction period was shortened by about 42 days. • A method to predict the alignment of a cable-stayed bridge constructed is proposed. • A method for identifying the 3D coordinates of geometric control points based on target paper. • The proposed method helps shorten the construction process at the construction site. • A point cloud alignment method from coarse to fine is proposed. • Monitoring of installation deviations of the girder segments relative to design status. [ABSTRACT FROM AUTHOR]

While urban redevelopment has been a global phenomenon for many decades, the redevelopment of urban villages (villages-in-the-city or c hengzhongcun) in China has attracted increasing scholarly attention in recent years. However, few studies focus on urban planning forces and their impacts on the redevelopment of urban villages, especially the transformation of open spaces. This paper therefore examines how urban planning transforms the space of urban villages through a case study of Yangji Village in Guangzhou City, Guangdong Province. The large increase in public spaces related to the transformation of Yangji results from collaborative governance driven by trends in urbanization and the work of urban planners. The latter are empowered by their strategic use of state support, professional knowledge and public interest incentives. However, in the Yangji redevelopment, as a state-hegemonic project, such planner-led collaborative governance remains limited in scope amid the exclusion of migrant tenants from decision-making. Although rarely regarded in the literature as playing a key role in redeveloping urban villages, this paper highlights urban planners as a salient force in the morphology of urban villages. Without challenging the hegemonic substance of urban village redevelopment, planners can coordinate with and occasionally manipulate other stakeholders to legitimate and facilitate negotiation during physical planning. Hence, redeveloped urban forms, rather than stakeholders' economic profits in short-term redevelopment, may influence urban lives in the long term due to planners' efforts. • Multiple stakeholders form collaborative governance in the physical planning of Yangji Village redevelopment to increase much open space. • The collaborative dynamics in the physical planning are built upon stakeholders' trust, fostered through informal meetings, led by planners, and fundamentally driven by urbanization. • Such collaborative governance is limited, largely due to the exclusion of migrant tenants. • Planners define the field of urban form beyond economic profit to improve redevelopment without challenging neoliberal and authoritarian hegemony. • State support, professional knowledge, and public interest ensure planners' leading role in collaboration. [ABSTRACT FROM AUTHOR]

• A measurement campaign on the wheel profile evolution of a metro train was conducted. • This paper converts the wheel wear model calibrating problem into an optimization problem. • The RBF-PSO method is proposed to calibrate the Archard-local model. • The calibrated wear model can roughly estimate the wheel wear of similar metro vehicles. The accuracy of physics-based wheel wear prediction approaches depends not only on the vehicle-track dynamics model but more importantly on the wear model. The currently available wear models, however, were developed for specific wheel/rail materials and under fixed operational conditions, and they need to be calibrated when used in new operational conditions. In this paper we introduce a data-driven approach into the physics-based approach to automatically calibrate the existing wear model so that it can be extended to a new operational scenario. Specifically, based on a one-year follow-up measurement data on the wheel profile evolution of a metro train commuting on a Guangdong metro line, we introduce radial basis function (RBF) and particle swarm optimization (PSO) to determine the boundary of each wear regime and the wear coefficient for each wear regime in the Archard-local model. Finally, we present a calibrated Archard-local model and then apply it to two cases. One application case shows that the calibrated model can be used for long-term wear prediction and another case shows that the calibrated model can be used to roughly estimate the wear evolution over the tread region of vehicles operating on other China metro lines. [Display omitted] [ABSTRACT FROM AUTHOR]

• From the perspective of spatial cross-sectional data, this study explores causal inference in the ecological environment. • Two primary causal pathways are identified. • The study unveils the intricate mechanisms among diverse factors within the ecological environment system. The ecological environment is closely related to natural conditions, climate change, and human activities. However, existing research predominantly concentrates on evaluating the direct impacts of various factors on the ecological environment, yet lacks an in-depth exploration of their causal relationships and pathways. This paper introduces a new perspective to research in this field by incorporating spatial cross-sectional data analysis methods. The study utilized the 2020 China's High-resolution Eco-Environmental Quality (CHEQ) dataset to evaluate the spatial differentiation characteristics of Ecological Environmental Quality (EEQ) within the western urban agglomeration of Guangdong Province. A framework of "terrain-urbanization-climate" influencing factors was constructed, and the Geographical convergent cross mapping (GCCM) method was employed to infer the causal relationship between EEQ and influencing factors. Additionally, the spatial distribution characteristics of influencing factors on ecological environment quality were studied using the Multiscale Geographically Weighted Regression Model (MGWR). The results indicate that seven influencing factors including Elevation, Population, Precipitation, TEMP, TMMX, TMMN, and Slope have clear causal relationships with EEQ and exhibit bidirectional coupling relationships. There exist complex causal relationships among influencing factors, revealing the interaction mechanisms and causal pathways among multiple factors in the ecological environment system. These analyses enrich our understanding of the intricate and complex relationships between the ecological environment and influencing factors, providing a valuable reference for ecological protection and sustainable development. [ABSTRACT FROM AUTHOR]

Carbon price prediction serves as a market indicator and economic driver, facilitating the adoption of more environmentally friendly production methods to achieve the emission reduction targets outlined in the Paris Agreement. In recent years, time series analysis and decomposition techniques have been widely applied to carbon price forecasting. However, few researchers have considered the issue of data feature drift caused by real-time decomposition. Specifically, as the sample size increases, data features undergo changes, rendering the trained models unable to fit the new data. This paper explains the underlying reasons for this phenomenon from new perspectives and proposes a novel paradigm that replaces the intrinsic mode function with a single-step fuzzy particle. This new paradigm corrects the issue of data feature drift and concentrates noise into a completely new sequence during the preprocessing stage. In the subsequent processing steps, the loss of correlation between the sequence and time lag, as well as the sequence and carbon prices, is addressed through multi-information association. This paradigm hybrid model can be applied to deterministic and interval multi-step predictions. Experimental results on the datasets from Guangdong and Hubei demonstrate that the proposed model outperforms other comparative models, achieving better predictive results than existing decomposition-based forecasting models. In the case of Guangdong, the normalized root mean square error (NRMSE%) for one-step, three-step, and five-step deterministic predictions are 2.18%, 2.51%, and 2.89%, respectively. The average interval score (AIS) for interval predictions are −0.357 and − 0.2375, respectively. • Provide new perspectives on the data feature drift caused by real-time decomposition. • Propose a novel paradigm to address the practical challenges of real-time decomposition. • Multi-information association solves the problem of correlation loss in traditional decomposition techniques. • This paradigm can be combined with secondary decomposition and applied to deterministic and interval multi-step prediction. [ABSTRACT FROM AUTHOR]

This paper studies the economic and financial feasibility analysis of hydrogen energy projects in China to identify appropriate green financing solutions for them. Cost-benefit and sensitivity analysis approaches were carried out for the three hydrogen projects in Guangdong province (two cases) and Jiang Xi province (one case). The hydrogen projects are more sensitive to borrowing interest rates and income tax rates, amongst other financing costs. The main reasons are the capital-intensive nature of green energy projects and the role of tax in the rate of return of green projects in the long term. The optimal weight of bank loans for the studied hydrogen projects in China was calculated at nearly 56%, meaning the weight of green bonds is approximately 44%. In other words, diversifying financing channels instead of just relying on bank loans is recommended to reduce the financing risk and capital cost. As a major policy implication, we recommended various de-risking tools, such as the green credit guarantee corporation, to attract private investments in hydrogen projects. • Green Financing solutions can enhance the feasibility of hydrogen projects. • Hydrogen projects' feasibility is highly sensitive to borrowing interest rates and income tax rates. • We calculated the optimal financing of 3 hydrogen projects in China using bank loans and green bonds. • Diversifying channels of financing instead of just relying on bank loans is recommended. • De-risking tools, such as the green credit guarantee corporation, attract private investments in hydrogen projects. [ABSTRACT FROM AUTHOR]

• Applying InVEST model to determine ecological sources. • Building ecological resistance surfaces for highly urbanized region. • Proposing a local evaluation model for ecological networks. • Summarizing the ecological network development suggestions. Metropolitan regions usually encounter sustainability difficulties such as overexploitation of resources, severe land scarcity, and ecological degradation, resulting in the progressive collapse of ecological networks (ENs). It is therefore essential to strengthen the construction and protection of ENs by integrating various resources, improving the quality of the urban ecological environment and creating a favourable living environment. Using the Guangdong-Hong Kong-Macao Greater Bay Area as a case study, in this paper, the ecological resistance indicator system that balances natural condition and human disturbance is rebuilt. In doing so, a construction scheme (applicable to highly urbanised areas) is established to simulate ENs at a more precise scale. Moreover, a local evaluation model of ENs is proposed and the spatial patterns of multiple indicators are fully summarised. The findings show that the ENs of the Guangdong-Hong Kong-Macao Greater Bay Area exhibit apparent spatial disparity. Disturbance through human activities seriously affects the spatial pattern of ENs, leading to structural imbalance and quality impairment of ecological corridors. Based on our assessment, the construction of complementary ecological corridors in the "two green belts" planning of the Guangdong-Hong Kong-Macao Greater Bay Area will improve structural problems. The findings of this paper can provide a reference to the harmonisation of urban development and ecological protection, by guiding the scientific management of ecological spaces in highly urbanised regions. [ABSTRACT FROM AUTHOR]

The construction of an ecological security pattern (ESP) is an important undertaking in ensuring the stability of the ecosystem and the long-term growth of humanity. Existing research, However, existing research often overlooks the quantification of policy implications in ESP development. The objective of this paper was to bridge this gap by integrating policy quantification analysis (PQA) with nature, society and into ESP construction and providing targeted ecological management recommendations. The policy texts were quantified into reusable research data, which served as a foundation for the selection of indicators and the optimization of ecological security patterns, by using machine learning techniques. The methodology "Patch-matrix-corridor" was used to create a spatially correlated ecological security pattern. Through factor analysis and Random Forest (RF), the impact of each indicator on ecological security was thoroughly examined, offering substantial support for policy development and ESP construction planning. The Guangdong-Hong Kong-Macao Greater Bay Area(GBA) was selected as the study area, the study found that: (1) The proposed framework PQA based-ESP could provide a useful eco-management tools. (2) Policy adaptability had a substantial impact on environmental security in the GBA, but the ecosystem service index has the highest impact. (3) Applying the framework, we identified a total of 1,993 km2 of ecological sources, 23 key ecological corridors, 58 general ecological corridors, and 31 ecological nodes. An improved ESP, characterized as "one circle, two bays, three rivers, multiple areas, corridors, and nodes," was constructed in the GBA. (4) Given the multi-scale analysis, tailored ecological management policy recommendations were proposed for each city in the GBA. The study's findings offer actionable solutions for the international community facing growing ecological challenges. [ABSTRACT FROM AUTHOR]

Nasopharyngeal carcinoma is one of the most prevalent malignant tumors in Guangdong, China. In the field of medicine, predicting the recurrence of early-stage nasopharyngeal carcinoma after radiotherapy holds significant importance. Our objective was to develop a novel classification model aimed at predicting recurrence following radiotherapy for early nasopharyngeal carcinoma. This paper introduces an innovative approach by combining unsupervised diagnostic feature learning (biclustering algorithm) with Adaboost ensemble learning to create a novel classification model. Notably, the nasopharyngeal carcinoma dataset underwent biclustering algorithm application for the first time. Initially, the Borderline-SMOTE oversampling algorithm was employed to address the dataset's imbalance issue. Subsequently, the biclustering algorithm, which is based on an improved multi-objective genetic algorithm (NSGA-II), was utilized to seek bicluster outcomes exhibiting consistent representation patterns. The attributes of these bicluster outcomes were assessed and employed as diagnostic rules for constructing weak classifiers. Ultimately, the Adaboost ensemble learning technique was employed to amalgamate the weak classifiers into a robust classifier. Following the application of 10-fold cross-validation, the model exhibited an accuracy of 80.33%, sensitivity of 79.55%, specificity of 80.43%, and GMean of 79.99%, accompanied by an AUC value of 0.904. The presented classification model outperformed alternative classification models in terms of classification accuracy and generalization when applied to the nasopharyngeal carcinoma dataset. Consequently, it serves as a valuable tool to aid medical professionals in predicting the likelihood of recurrence in the cervical lymph nodes following radiotherapy for early-stage nasopharyngeal carcinoma. • Developed a novel classification model for nasopharyngeal carcinoma MR feature data. • Using the Borderline-SMOTE algorithm to address the imbalance issue. • Innovatively combining a biclustering algorithm with FSDND. • Experimental results demonstrate the effectiveness of this classification model. [ABSTRACT FROM AUTHOR]

Along with habitat destruction and the intensification of climate change, economic and social development is threatening the living of species. In the context of China's domestic economic circulation, the spatial structure of biodiversity loss is complicated by the decentralization of production, with many regions affecting the survival of species in other regions. Based on the environmentally extended MRIO model, this paper calculated the subnational biodiversity loss footprint through structural path analysis and social network analysis, identified the regional linkages and spatial networks of species loss, and traced the conservation responsibility to specific sectors. Our findings indicate that Yunnan, Tibet, Guangdong, and Sichuan possess the most significant biodiversity footprints, comprising over 40% of the total footprint. In terms of threat patterns, Yunnan and Tibet have the largest intra-regional footprints, indicating their important impact on species within the region; Guangdong and Zhejiang have the largest extra-regional footprints, indicating their role in threatening other species through cross-regional trades. The primary industry, tap water and food and tobacco sectors are major biodiversity footprint producers. Overall, some eastern and coastal areas are the final drivers of biodiversity loss in China, driven by increasingly complex trade links. Notably, the directionality of species loss in biodiversity hotspots is evident, with Shandong and Guangdong significantly influencing the biodiversity of Yunnan and Guangxi, respectively. This study systematically analyzed the complete network of biodiversity loss and divided four biodiversity protection communities to promote the realization of trans-regional protection and compensation. Examining the biodiversity loss through the lens of networks, and connecting economic activities to species, serves as a warning to decision-making bodies that a future of increasingly intricate domestic trade could pose a critical threat to biodiversity, necessitating collaborative conservation efforts. [Display omitted] • 1.Inter-provincial trade contributes to the complex structure of biodiversity loss. • The characteristics of biodiversity footprints vary widely among provinces. • Some eastern and coastal provinces are the final drivers of biodiversity loss. • Four protection communities are established through social network analysis. [ABSTRACT FROM AUTHOR]

• A method based on Neural Network is proposed for provincial-level CO 2 emissions estimation. • An autoencoder is proposed to downscale the provincial-level CO 2 emissions to city-level. • A complete city-level CO 2 emissions inventory of GBA and surrounding cities is complied. • Spatial dependence and agglomeration characteristics of CO 2 emissions are analyzed. • Spatio-temporal heterogeneity of factors affecting CO 2 emissions is investigated. Significant attention has been given to the issue of CO 2 emissions worldwide, especially for China as the largest emitter. Cities, as the main carriers of China's economic development, account for up to 85 % of CO 2 emissions from energy use in the country. The Guangdong-Hong Kong-Macao Greater Bay Area (GBA) and surrounding cities play important leading roles in low-carbon development. However, compiling city-level CO 2 emissions is challenging due to the limited availability of data. In this paper, a new idea is proposed by using SSA-BP for estimating provincial-level CO 2 emissions and downscaling provincial-level CO 2 emissions to city-level CO 2 emissions by reconstructing missing values of city-level CO 2 emissions with an autoencoder. This study compiles a city-level CO 2 emissions inventory and conducts a spatio-temporal analysis of CO 2 emissions in the GBA and surrounding cities. The results show that CO 2 emissions exhibit a spatial distribution pattern with spatial dependence. Furthermore, according to the analysis of spatio-temporal heterogeneity, GDP influenced CO 2 emissions the most within the five influencing factors, and the influences of GDP, population, and energy intensity were mainly positive, while the influence of patents was mainly negative, and that of trade was both positive and negative. [ABSTRACT FROM AUTHOR]

The citizenization of peasant migrants is key to understanding China's long-term urban-rural imbalance and ongoing rural transformations. The majority of existing literature focused on structural/institutional or agentive/subjective factors, while an integrated perspective examining both structural and agentive factors related to the citizenization process is rarely seen. Borrowing theories from the strategic-relational approach, this paper unfolds the matching and mismatching mechanisms between the policy design and peasant migrants' intentions of citizenization. Drawing on a case study in China's Guangdong Province, this study finds that: (1) citizenization policies prefer to offer citizenship to the new generation of peasant migrants, which matches the age structure of peasant migrants; (2) although municipal governments are prone to attract young peasant migrants with high educational level and strong competitiveness, peasant migrants generally have a relatively low educational level and weak professional training; (3) the high threshold of citizenization in mega-metropolitan and metropolitan cities mismatches peasant migrants' preference towards the first-tier cities. This paper describes the matching and mismatching mechanisms between structure and agency associated with the citizenization of peasant migrants. The policy reflections proposed by this study point towards enhancing nearby urbanization and in situ urbanization, so as to increase the attraction of young peasant migrants by ordinary cities. Besides, this paper calls for the promotion of rural-urban co-governance, making rural revitalization an alternative and supplement to the urbanization driven by rural-urban migration. • The strategic-relational approach is a critical perspective for understanding citizenization. • As a progressive and dynamic process, the citizenization of peasant migrants intersects with local strategies of urbanization. • Governments should realize that ordinary cities are critical places for most peasant migrants to realize citizenization. [ABSTRACT FROM AUTHOR]

As the primary source sector of carbon emissions, the power industry's green innovation is an indispensable component of China's low-carbon transformation. This paper took China's Carbon Emission Trading Scheme as a quasi-natural experience and adopted the synthetic control method to evaluate the impact of Carbon Emission Trading Scheme pilot policy on power industry's green innovation based on the provincial industrial level data. Furthermore, this paper examined the impact mechanisms of Carbon Emission Trading Scheme on the power industry's green innovation through policy comparison, moderating and mediating effect analyses. The research findings show that: 1) there is heterogeneity in Carbon Emission Trading Scheme's effectiveness in inducing green innovation in China, the power industry only in Beijing and Guangdong has been significantly promoted, while other pilots have not; 2) the success of Beijing and Guangdong can be attributed to distinctive features of their carbon market and power industry, including the refined quota accounting mechanism, the application of paid quota allocation, the larger enterprise scale and more research and development investment; 3) enterprise scale and research and development investment have positive moderating effect and mediating effect in the promotional effect of the Carbon Emission Trading Scheme on green innovation respectively. Finally, some policy recommendations are put forward to promote the power industry's green innovation and carbon market construction. • Evaluate the impact of China's CETS on the power industry's green innovation in each pilot. • The CETS effects among pilots are heterogeneous, only significant in Beijing and Guangdong. • Rational CETS mechanism design could improve the power industry's green innovation. • Enterprise scale and R&D investment positively moderate and mediate the CETS effect. [ABSTRACT FROM AUTHOR]

Entities in the power supply chain usually tend to cooperate with each other for higher profits. The inter mechanism between the entities and power supply chain is an urgent research subject. To consider the power supply chain and entities better, a extended model of bilateral contract mechanism is proposed in this paper. Utilizing a Stackelberg game, this paper studies dynamic coordination and strategic interactions between power plants, power retailers, and consumers in the electricity trading process under demand uncertainty. An improved coordination model is proposed to help entities get potential profits as well as improve the stability of the power supply. The model is applied to power trading data from Guangdong, China which imposes penalties for inaccuracies in demand variation. Simulations suggest the model's solution exhibits higher reliability and minimizes fines. [ABSTRACT FROM AUTHOR]

Evaluating the rural population hollowing (RPH) caused by the massive out-migration of rural laborers is essential for China's rural land use policy formulation and rural revitalization. Existing studies mainly assessed the RPH based on survey data or statistical data. The former is mainly based on selected local villages and towns, and cannot be replicated on a large scale, while the latter is usually at the county or city scale and cannot reveal the cyclical migration of rural labors at fine spatial resolutions such as townships. To fill this gap, this paper proposed a social media data based-rural population hollowing (SM-RPH) index, which was calculated by Tencent User Density big data on holidays and non-holidays and was used to characterize the "migratory bird" process of rural laborers. Taking Guangdong Province, China as the case, this study employed the proposed novel index to evaluate the RPH at the county and township scales. This was integrated with a GWR model to explore how the important contributing factors of RPH vary across the province. The results showed a trend of transition from the cold spot areas in the core regions of PRD to the hot spot areas around the PRD. Additionally, this study revealed that distance to the nearest prefecture-level city, total nighttime lighting per capita, fiscal expenditure per unit of GDP and road network density were dominant factors in affecting rural population hollowing, and the influence of these four variables vary considerably throughout the province. Further, k-means cluster was used to group the hollowing towns according to the local coefficients of the influencing factors, and policy zoning was suggested to provide differentiated countermeasures and suggestions for rural revitalization. This methodological framework can be easily applied to other regions, and is expected to be used to evaluate the return characteristics of migrant workers in real time to help monitor the progress of rural revitalization in China and provide decision support for policy makers. • This study proposed a social media data-based index for assess rural population hollowing (RPH). • The index is effective in evaluating the RPH at fine spatial resolutions such as townships. • We revealed the spatially non-stationary relationship between RPH and the associated factors. • We further put forwards the policy zoning and planning implications for rural revitalization. [ABSTRACT FROM AUTHOR]

Understanding the patterns of energy usage through simulation and prediction from multiple perspectives is crucial for alleviating energy-environmental conflicts and achieving sustainable energy system development. Despite their importance, multidimensional interactions among the production, demand, and supply sides have not been systematically characterized, thus representing a significant research gap. This paper aims to address this gap by developing a a multidimensional factorial enviro-economic (MFEE) model, which offers an in-depth investigation of Guangdong's energy system. This model, designed for retrospective decomposition (1997–2017) and prospective projections (2035), uniquely links historical and future analysis from systematic and sectoral perspectives through structural decomposition analysis, sectoral evolution analysis, and the Biproportional Scaling RAS method. Our findings reveal that the final demand level and Research & development (R & D) are the principal drivers of increased energy usage. We also found that the wholesale and retail sector have the most significant impact on demand and input sides, accounting for over 70 %. Furthermore, the interaction of key sectors from the production-based perspective notably influences system symbiosis. To effectively manage these dynamics, our study recommends regulating and controlling measures for the circulation sector that consider the benefits of both the energy sector and the accommodation and catering sector. This comprehensive analysis, therefore, offers scientifically-supported guidance for sustainable energy system development. • A multidimensional factorial enviro-economic (MFEE) model from Guangdong perspective is developed. • Approaches of retrospective decomposition and prospective projection for energy systems are conducted. • Interactive effects of various socioeconomic activities on economy and environment are characterized. • The main effects of wholesale and retail sector is the strongest, accounting for more than 70 %. [ABSTRACT FROM AUTHOR]

To address the deficiencies in traditional communication towers in mobile communication engineering, such as heavy weight, easy corrosion and poor durability, this study proposes replacing traditional communication towers with GFRP communication lattice towers with lightweight, high-strength and good durability. Firstly, GFRP material test and GFRP circle tube bending capacity test are conducted. The maximum bending moment that can be subjected to the anchorage end of GFRP circular tube is 18.20 kN·m. Finite element analysis of GFRP circular tubes verifies the accuracy of the bending test results. Secondly, a GFRP communication lattice tower with a height of 25 m is designed adopting GFRP circular tubes as tower columns. Through the bending test of the GFRP tower, it is verified that the connection between the columns and web members of the GFRP tower is reliable and the force pattern of the GFRP tower is reasonable. The large deflection theory can be adopted to the displacement of GFRP towers accurately. Thirdly, according to the actual engineering requirements, the finite element analysis of 25 m-high GFRP tower under the basic wind pressure of 0.45 kN/m2 is carried out by using 3D3S (a spatial structure design software). It can be obtained that the vibration mode of the GFRP communication tower shows two forms of lateral bending and axial torsion. 0° wind load direction has the greatest influence on the displacement of the GFRP tower. The stress ratio of each component in the GFRP tower is less than 1, indicating that no damage occurs in the GFRP tower. Finally, GFRP towers have good economic benefits, good environmental performance and wide range of application after comparing the GFRP tower and traditional steel tower. The demonstration project of the GFRP communication lattice tower is established in Qingyuan City, Guangdong Province, China. When subjected to a typhoon with a wind speed of 17.26 m/s, The horizontal displacement of the GFRP tower top is 10 m, which is about 2/5 of the height of GFRP tower. Although the displacement is large, the GFRP tower is still not destroyed. It can be concluded from the research in this paper that GFRP towers have low cost, good mechanical properties, good durability, and wide application range, which can promote the rapid construction of 5 G communication base stations. [Display omitted] • The bending moment of the GFRP tube can reach 18.20 kN·m. The finite element analysis match well with bending test. • A 25-m-high GFRP lattice tower is designed. The deformation of GFRP tower can be calculated by large deflection theory. • The vibration mode of the GFRP tower shows two deformation forms: lateral bending and axial torsion around the z-axis. • The GFRP tower has a large displacement but no damage, when subjected wind with the speed of 17.26 m/s. • Applying GFRP tower can effectively improve the construction efficiency of 5 G communication base station. [ABSTRACT FROM AUTHOR]

[Display omitted] • Groundwater vulnerability zonation investigation is conducted for GHM Greater Bay Area. • Three different perspectives are comprehensively considered in this study. • Groundwater risk, function value and contamination status are superposed using the AHP method. • Quantification and visualization of groundwater contamination prevention regionalization. • The AHP-DRASTICH model is an effective method to evaluate the groundwater risk in shallow aquifers. The groundwater contamination assessment and prevention regionalization are an important part of groundwater investigation, which is of great significance to groundwater environmental management and contamination control. Although the Guangdong-Hong Kong-Macao Greater Bay Area (GHM Greater Bay Area) has faced significant groundwater contamination since 2020, there's been no investigation into its vulnerability zonation. In this paper, groundwater in a typical area of south China was taken as the research object. We analyzed groundwater contamination risk by considering both the groundwater vulnerability and the load from contamination sources. Using the analytic hierarchy process (AHP) method, we superposed the groundwater risk, function value and contamination status, and realized the quantification and visualization of groundwater contamination prevention regionalization. Based on the DRASTIC model, the zones of low, moderate, high, and very high vulnerability are respectively 14.2 %, 24.28 %, 37.67 %, and 20.45 % of the study area. Results show that the groundwater vulnerability grade in the area is III-IV, which means it is easy to be polluted. The functional value of groundwater in the south-central part of the region is relatively high. Most of the areas with high prevention and control levels are located in plain areas and the Piedmont plain area with an area of 460 km2. The secondary control levels (key protection area) are mainly located in the middle, southwest, and northeast of the plain, with an area of 6944 square kilometers. Consequently, the ideas and contamination prevention zone maps might provide a scientific foundation for long-term planning and management and groundwater conservation in the Guangdong-Hong Kong-Macao Greater Bay Area and peripheral zones. [ABSTRACT FROM AUTHOR]

In this paper, the typical 10-lane highway in the reconstruction and expansion project is taken as an example to clarify the noise propagation and distribution characteristics of ultra-wide cross section. With the aid of the highway noise prediction model based on the acoustic environment guidelines and field measurements, the noise propagation and distribution characteristics under ultra-wide cross section were summarized through visualized noise maps and quantitative data analysis. The results show that for the highway with ultra-wide cross section, reducing the speed of small vehicles can improve noise reduction in the horizontal direction by about 1 dB(A) than reducing the speed of large and medium-sized vehicles. In addition, the noise reduction effect of all floors can be improved by 0.2–0.9 dB(A) in the vertical direction, which proves that the noise reduction effect is more obvious by reducing the speed of small vehicles. Besides, the noise sound pressure level variation range on low-rise floors increases more significantly with increased traffic volume than high-rise floors. The variation between different building floors is also similar to the increase in traffic volume in the vertical direction. The result can provide theoretical guidance for the prevention of highway noise under ultra-wide cross section. • The noise propagation of highway under ultra-wide cross section was investigated. • 2D and 3D spatiotemporal noise maps were presented to analyze traffic noise. • Noise reduction effect of reducing small vehicles' speed was more obvious. • Provide reference for the prevention of highway noise of extension engineering. [ABSTRACT FROM AUTHOR]

As the population and industries continue to gather in cities, such cities face acute shocks caused by various natural disasters and high pressure caused by human interference. The means of accurately assessing and improving the level of urban resilience (UR) to cope with disturbances and shocks, enhance regional resilience, and improve sustainable urban development has become a hot topic. In this study, we constructed an UR assessment framework using "social-economic-institutional-ecological-engineering" to explore the dynamic evolution of spatial and temporal patterns in the Guangdong-Hong Kong-Macao Greater Bay Area (GBA) of China, to reveal the main factors influencing the evolution of this spatial pattern and the strength and interaction mechanisms among such influencing factors and to dynamically simulate the spatial and temporal evolution characteristics in the next 16 years. The results show that from 2000 to 2019, the difference in UR level in the GBA is obvious. Hong Kong and Macao are obviously ahead of other cities, and the comprehensive resilience level of Guangzhou and Shenzhen cities in the mainland is developing rapidly. The difference in per capita consumption capacity, industrial structure optimization, foreign trade vitality, infrastructure support and other factors are the main causes of the spatial differentiation of UR in the GBA. In the future, the UR level of the GBA urban agglomeration will show an overall upwards trend. The three development poles of Guangzhou-Foshan, Shenzhen-Hong Kong, and Zhuhai-Macao have obvious advantages in spatial distribution. The results of the study can enrich regional UR research, and also provide theoretical references to facilitate the high-quality development of the area. [Display omitted] • We constructed a "social-economic-institutional-ecological-engineering" urban resilience assessment framework. • The paper developed an evaluation system for the urban resilience to reveal its spatial and temporal evolution patterns. • The main factors influencing the evolution of the spatial patterns of urban resilience is investigated. • Our results provide a basis for the development of comprehensive management framework for urban resilient. [ABSTRACT FROM AUTHOR]

In the context of China's dual‑carbon goals and electricity market reforms, the high penetration of renewable energy sources (RES) in a single microgrid (MG) imposes challenges to its flexible operation. This paper addresses the cooperation within a virtual power plant (VPP) aggregated by multiple MGs. The VPP, managed by the VPP operator, acts as an intermediate entity to facilitate MGs' economic and low-carbon operation. To ensure the interests of various stakeholders in the VPP, this work proposes a low carbon oriented tri-stage collaborative energy management framework. In the 1st stage, the day-ahead optimal scheduling of individual MGs and the bidding strategy of the VPP operator are modeled, where an internal pricing method is incorporated. The 2nd stage addresses the real-time redispatch of the VPP operator using a receding horizon-based model predictive control (MPC) solution. This aims to satisfy the real-time operational requirements of MGs and reduce adjustment costs. Furthermore, in the 3rd stage, a penalty allocation mechanism is established for MGs based on their forecasting errors, to protect the VPP operator's interests. The effectiveness of the proposed solution is evaluated through comparative simulations in the context of the Guangdong electricity market. • The virtual power plant (VPP) aggregated by multiple microgrids (MG) is studied. • The quantification of carbon emission is involved in the whole scheduling process. • An peer-to-peer (P2P) electricity trading pricing method is improved and adopted. • A penalty allocation mechanism is proposed to guarantee interests of each entity. • The Guangdong electricity market is considered as the market mechanism. [ABSTRACT FROM AUTHOR]

• The paper proposes a data processing method based on data cleaning and multi-sensor fusion, which can greatly reduce the impact of background noise. • The intelligent feature recognition of vibration acceleration time-domain signals based on an improved 1DCNN algorithm can improve the monitoring accuracy and speed significantly. • An online monitoring technology for transmission towers bolt looseness has been developed and successfully applied to the Guangdong power grid. Minor damages such as tower bolt looseness are difficult to detect through manual inspections. Operational modal analysis plays an important role in the online monitoring of transmission tower structure safety. However, the traditional analysis methods select feature parameters manually, and the deviation generated will directly affect the estimation accuracy of structural modal parameters. This article proposes a method for diagnosing tower bolt looseness without human intervention. This method can reduce the influence of background noise greatly through data cleaning and data fusion. Besides, the intelligent feature recognition of vibration acceleration time-domain signals based on an improved 1DCNN algorithm can improve the monitoring accuracy and speed significantly. The effectiveness of the method has been validated by conducting dynamic response tests on a 110KV transmission tower under different bolt loosening conditions. In the end, an online monitoring technology for transmission towers bolt looseness has been developed and successfully applied to the Guangdong power grid. The results show that this method has high identification accuracy and provides a new approach for online monitoring of tower bolt looseness. [ABSTRACT FROM AUTHOR]

In theory, replacing workers with robots in dangerous working environments can protect workers' safety. Therefore, a higher frequency of occupational injuries (OIs) may force more industrial robot applications (IRAs). Based on panel data from cities in Guangdong Province from 2003 to 2015, this paper uses a fixed effects panel Tobit model to test the impact of OIs on IRAs. Considering the possibility of a reverse causal relationship between IRAs and OIs, we also use the number of urban cultural academies as an instrumental variable to overcome the endogeneity problem. This study shows that more frequent OIs force more IRAs. The government's industrial policies promote industrial intellectualization but also weaken the promotion effect of OIs on IRAs to a certain extent. The mechanism analysis shows that the heavy burden of public medical expenditure is an important mechanism of OIs forcing IRAs. This paper confirms the driving mechanism of OIs on IRAs, reveals the moderating effect of industrial policy on the impact of OIs on IRAs, and clarifies the mechanism of OIs on IRAs from the perspective of public medical expenditure. • Frequent occupational injuries force more robot applications. • The industrial policy weakens this effect. • The heavy burden of public medical expenditure is an important mechanism. • Urban cultural atmosphere is used as an instrumental variable. [ABSTRACT FROM AUTHOR]

• A flexible column-comb harvesting and synchronous collection of litchi harvester was proposed. • The litchi virtual simulation harvesting experiment was carried out in EDEM and the numerical analysis was carried out. • The experimental results proved that the proposed column-comb litchi harvester had improved working performance. Litchi is one of the most important commercial subtropical fruit with high nutritional and medicinal value widely planted in South China.With the problems of time-consuming, laborious and low mechanization in litchi harvesting continues to increase, growers need highly effective way to harvest litchi. To solve the problem mentioned above, this paper presented a flexible harvesting column-comb litchi harvester, a flexible column-comb device with an adjustable column-comb angle harvesting method and a collection device using a lifting and lowering adjustable folding collection method was proposed, which can complete the harvesting and collection of litchi fruits in the orchard at one time. A litchi harvester model was established, and a virtual simulation test was carried out in EDEM software by taking the distance of the column-comb rods d (A), the column-comb rod length l (B) and the column-comb axle angle amplitude a (C) as the test factors and the maximum compression force F of the litchi as the evaluation index. Through optimization analysis of the response surface, the optimal combination of parameters affecting the working performance of the litchi harvester was obtained. The simulation results showed that the factors d, l , and a affected the evaluation index F during the harvesting process; the optimal parameter combination after optimization was d = 66.69 mm, l = 673.80 mm, and a = 69.84, yielding F = 64.77 N, which was within the limit load of litchi damage. Under the optimal parameter combination conditions, the litchi harvesting test was conducted in a litchi orchard in Guangdong, China, the results of the orchard test showed that the breakage rate of the optimized column-comb harvester was 6.10%, the efficiency of mechanical harvesting was 2.2 times that of manual harvesting, which was basically meeting mechanized litchi harvesting requirements. To a certain extent, it was confirmed that the established EDEM model and the virtual test analysis were accurate and effective, providing a reference for the design and research of mechanized litchi harvesting equipment. [ABSTRACT FROM AUTHOR]

For offshore wind development in Southern China, typhoon represents the extreme environmental loading condition to be considered in the design of the turbine structures. For the geotechnical design of the foundations, it is specified by the design code that the effect of cyclic loading on the soil strength and stiffness must be considered. This necessitates the complete time history of loads on the foundation experienced during a design typhoon. Idealised storm profiles with staged mean wind speeds and significant wave heights are commonly used as input to time-domain simulations to generate the foundation load history. However, no such design typhoon profile is provided in the Chinese design standard which creates considerable difficulty and uncertainty in the design process. In this paper, the methodology and the rationale that were applied to derive the design typhoon profiles for an offshore wind project in Guangdong province are presented. The wind and wave characteristics generated by typhoon are described in detail and the implications for the foundation design are discussed. To demonstrate application of the proposed typhoon profile, a case study for the ULS design of a suction bucket jacket is presented. • Design typhoon profiles are developed for an offshore wind farm in Southern China. • The typhoon wind and wave characteristics are described and implications for geotechnical foundation design are discussed. • The methodology and the rationale for developing the design typhoon profiles are presented. [ABSTRACT FROM AUTHOR]

The regional integrated energy system (IES) is considered a promising paradigm for economic multi-energy provision while promoting the accommodation of renewable distributed energy resources (RDER). However, various uncertainties faced by the IES pose challenges to fulfilling its functionalities and economic promises. This paper proposes a day-ahead optimal bidding strategy of IES in electricity markets with multiple time scales of electricity settlement and energy dispatch for minimizing its expected operational cost based on the hybrid scenario-based stochastic and chance-constrained programming. It enables the IES to determine the optimal bidding electricity quantities in the day-ahead market in a multi-energy complementary manner by incorporating the potential real-time clearing price scenarios, with the uncertainties introduced by RDER generation and multi-energy loads efficiently addressed using chance constraints. Herein, a gradient boosted regression tree (GBRT) based quantile forecasting method is developed to equivalently reformulate the chance constraints to tractable deterministic constraints without any prior knowledge or probability distribution assumptions. The proposed day-ahead bidding strategy is extensively assessed through simulation experiments in the PJM and Guangdong electricity markets and the numerical results confirm the effectiveness of the proposed solution. • A day-ahead bidding strategy with multiple time scales of IES is presented. • The uncertainties of the electricity market, RDER and multi-energy loads are handled. • A data-driven quantile forecast method is developed for solving chance constraints. • The proposed strategy is validated in the PJM and Guangdong electricity markets. [ABSTRACT FROM AUTHOR]

Social support can lower perceived barriers to medical treatment, but this relationship may vary in groups of different socioeconomic statuses (SES). This study examined whether different types of social support predicted different types of perceived barriers to tuberculosis (TB) treatment and whether these relationships varied across different levels of SES. A paper-pencil survey covering 12 cities in Guangdong, China (N = 1386) was conducted in December 2020, which measured demographics, three types of perceived social support (informational, instrumental, and emotional) and barriers to TB treatment (cognitive, instrumental, and psychological). Informational support and instrumental support were negatively related to cognitive barriers and instrumental barriers. These relationships were stronger among more educated individuals and urban residents. However, emotional support predicted psychological barrier positively, and this relationship was stronger among less educated individuals and rural residents. High SES groups benefit more from individual-level support. Thus, there is a gap of social support, which reveals the power nature of social support exchanges. TB campaigns need provide support for low SES groups to compensate for their insufficient support. Campaigns need provide information about disease management and the legal and financial support for TB patients, and change tuberculosis-related norms. • Informational support predicted cognitive barrier to TB treatment negatively. • Instrumental support predicted instrumental barrier to TB treatment negatively. • These effects were stronger among highly educated individuals and urban residents. • Emotional support predicted psychological barrier to TB treatment positively. • This effect was stronger among less educated individuals and rural residents. [ABSTRACT FROM AUTHOR]

Institute of Modern Physics (IMP), Chinese Academy of Sciences is the first domestic institute to widely apply solid-state power amplifier (SSPA) to linear accelerator project from China Accelerator Driven Sub-critical System (C-ADS) project started since 2011. Until now, SSPA design has already been considered for two large-scale scientific facilities in Guangdong Province of southeast China: China initiative Accelerator Driven System (CiADS) and High Intensity Heavy-ion Accelerator Facility (HIAF). The SSPA was developed rapidly due to the reliable technologies of LDMOS-based (laterally diffused metal oxide semiconductor) power amplifiers. With the maturation of GaN (Gallium Nitride) technology, GaN-based amplifiers have been studied in the field of accelerators since 2020. Last year, IMP made new progress with GaN SSPA, measuring power modules at three fundamental frequency points of 162.5 MHz, 325 MHz, and 650 MHz. The first 50 kW rack with GaN SSPA was measured at 650 MHz in IMP, and its modules offer huge advantages of 40% output power and 8% efficiency compared to the newest LDMOS at the same frequency, which holds a fascination for our project. Until the beginning of 2023, the prototype power modules on three frequency points were completed and tested. This paper will detail the design development of GaN technology and its technical characteristics. [ABSTRACT FROM AUTHOR]

• An incentive mechanism is introduced to improve the responsive load model. • A real-time price mechanism is proposed based on the balance of supply and demand in flexible islanded microgrid (FIMG). • The definitions of FIMG flexibility and flexibility insufficiency rate (FIR) are given, and the importance of FIR in the hybrid energy storage system (HESS) capacity optimization is verified by a case study. • The NSGA-II and the improved TOPSIS are used to obtain the optimal solution. The islanded microgrid (IMG) is universally accepted as an important method to solve the island power supply problem. The optimal capacity of the hybrid energy storage system (HESS) is necessary to improve safety, reliability, and economic efficiency in an IMG. To improve the IMG ability to deal with uncertainty, this paper proposed a flexible islanded microgrid (FIMG) model with real-time price (RTP)-based demand response (DR). Through RTP to guide users' electricity consumption behaviors more in line with renewable energy sources (RES) output law, the capacity allocation of the HESS was optimized and the system had sufficient flexibility in each scheduling period. In this paper, two decision variables of the HESS, the number of lead-acid batteries and supercapacitors, were determined based on the objective of comprehensive operating cost (COC) and flexibility insufficiency rate (FIR) minimization. Firstly, the non-dominated sorting genetic algorithm II (NSGA-II) was devoted to obtaining the Pareto set that satisfies the FIMG day-ahead scheduling. Then, both the loss of produced power probability (LPPP) and the loss of power supply probability (LPSP) corresponding to each Pareto solution were gotten based on the real-time dispatch strategy. Finally, taking COC, FIR, LPPP, and LPSP as evaluation indicators, the improved Technique for Order Preference by Similarity to an Ideal Solution (TOPSIS) was applied to select the optimal solution from the Pareto set. In the case study, a FIMG on an island in Guangdong province was analyzed and discussed. By studying typical days in winter and summer of the FIMG, the superiority of the proposed model and methods was verified. [ABSTRACT FROM AUTHOR]

The land integration and titling policy (LITP) is a grassroots innovation for implementing a top-down land titling policy (LTP) that has critical institutional economic implications. Although the LITP can increase land economies of scale, institutional costs and practical constraints also exist. Specifically, land heterogeneity and fragmentation directly restrict the LITP. This paper uses survey data from 1507 farmers in Yangshan, Guangdong, in 2017 and a logistic semiparametric model to conduct an empirical analysis. The results show that the heterogeneity of geological fertility, irrigation facilities, and farmland rent have a significant negative effect on implementation of the LITP, with geological fertility heterogeneity having the most significant effect. Villages with hilly and mountainous terrain have more difficulty implementing the LITP. The degree of land fragmentation within the land heterogeneity factor significantly inhibits the LITP. Roads for cultivation machines and irrigation facilities significantly promote the implementation of the LITP, and moreover, cultivation-machine roads effectively eliminate the inhibiting effect of land fragmentation on the LITP. The LITP has its own system space, and a good institution always needs an institutional device for smooth promotion. Heterogeneity and the fragmentation of land are hard constraints for integration and titling, but they can be resolved through agricultural production infrastructure. Findings suggest that the joint promotion of a core and a supplementary institution can have great significance for the smooth progress of mandatory institutional changes, e.g., land titling. • The land integration and titling policy (LITP) is a grassroots innovation for implementing a top-down land titling policy. • The LITP can increase land economies of scale, institutional costs and practical constraints also exist. • This paper uses data from Yangshan, Guangdong and a logistic semiparametric model to conduct an empirical analysis. • Heterogeneity and fragmentation of land are hard constraints for LITP. • The constraints of LITP can be resolved through roads for cultivation machines and irrigation facilities. [ABSTRACT FROM AUTHOR]

The paper presents a systematic description about the observation scheme analysis and practical observation of artificially triggered lightning with a ground-based microwave radiometer together with other discharging observation instruments at Guangzhou Field Experiment Site for Lightning research and testing in Conghua, Guangdong in the thunderstorm season of the 4 consecutive years from 2016 to 2019. The results from statistical analysis of the brightness temperature data observed from 2017 to 2019 show that: (1) The frequencies such as 28, 30, 51.25 and 51.76 GHz in atmospheric window are more likely to respond with outstanding brightness temperature pulses than other observation frequencies of the radiometer; (2) When the elevation angle is 30°, the observation distance is relatively short (99 m in this study), and the "lightning observation mode" is adopted, the efficiency for a ground-based microwave radiometer to capture the lightning heating effect caused by artificially triggered lightning can be improved; (3) The observation efficiency of the 3 years as a whole is 71.4% but gradually increases year by year; (4) The radiometer observation data can be used to reveal the characteristics of lightning heating effects such as duration and lightning current integral; (5) Combined with the lightning current data measured by a coaxial shunt during the experiment, it was found that the estimated lightning heating effect durations based on 25 artificially triggered lightning observations are well consistent with the lightning current durations, as shown by the correlation coefficient of 0.73. This also implies that the radiometer working on the lightning observation mode presented by this paper can observe the lightning heating effect and estimate its duration. • Triggered lightning is observed with a ground-based microwave radiometer in 2016–2019. • Observation scheme is formed based on the simulation calculations and data analysis. • Lightning heating duration averaged 0.5 s, variations are the same as current duration. • The observation efficiency is 71.4% and keeps increasing every year from 2017 to 2019. [ABSTRACT FROM AUTHOR]

Since 2008, the urban redevelopment in China has entered a new era mainly characterised by the new redevelopment model proposed and practised in Guangdong province. In the new wave of urban redevelopment towards a more inclusive type of governance, the roles of various agents or stakeholders in the decision-making processes have changed correspondingly. Building upon Healey and Barrett's structure and agency approach in the land and property development process, this paper develops an analytical framework specifically to explore the decision-making processes and mechanisms in this urban redevelopment experiment conducted in China. The empirical basis of this paper is a pioneer city in urban redevelopment within Guangdong province, Shenzhen. Through exploring the redevelopment trajectory of a local collective-owned factory, the roles played by different stakeholders at each stage of the redevelopment process are clarified. The conflicts and compromises between land owners and the developer on the distribution proportion of potential profits, and between the district government and the developer on the inclusion and provision of public facilities, have revealed the key decision-making mechanisms of collective-owned industrial land redevelopment in China, which offer valuable insights and experiences for industrial land redevelopment in other cities. • An analytical framework is developed to explore the decision-making processes. • The case study reveals the roles of actors and their interaction patterns. • Various stakeholders have their own rights to participate in the negotiation arena. • The developer has become a key coordinator to ensure the decision-making process. • The roles of the municipal and district governments have changed fundamentally. [ABSTRACT FROM AUTHOR]

• A source–sink matching LCA–MILP method is proposed. • The unit cost of net CO 2 reduction ranges from 237.81 CNY/t to 253.51 CNY/t. • In Guangdong Province, China, the distribution of the CCUS supply chain shows a pattern of spreading from coastal to inland regions as the scale increases. • The implementation of carbon trading policies effectively reduces the costs of CO 2 emissions reduction initiatives. • Monte Carlo simulation is performed to evaluate the uncertainty in the supply chain. Carbon capture utilization and storage technology has been identified as a key solution for limiting the global temperature increase to 2 °C from preindustrial levels. In this study, we develop a source–sink matching mixed-integer linear programming model to obtain an optimal cost for the Carbon capture utilization and storage supply chain and evaluate the environmental benefits using life cycle assessment. Guangdong Province, China, with annual CO 2 emissions of 585 Mt, is chosen as the case study for this paper. The results show that the deployment of the carbon capture utilization and storage supply chain follows a pattern of spreading from coastal to inland regions as the CO 2 net reduction rate increases. Moreover, the unit cost of CO 2 reduction increases with the CO 2 reduction rate, specifically, from 237.81 Yuan per ton to 253.51 Yuan per ton as the CO 2 reduction rate increases from 10% to 60%. The CO 2 -derived methanol is not economically viable, while having low implied carbon emissions, it has approximately 2.5 times the unit cost of traditional methanol. We recommend promoting carbon trading due to its effectiveness in reducing the unit abatement cost. Additionally, the carbon price level of 220 Yuan/t can achieve a profitable supply chain at various reduction rates. However, carbon labeling subsidies have little effect on reducing methanol production costs. The 95% confidence intervals for unit abatement costs obtained from Monte Carlo simulations can provide robust support for policymakers. [ABSTRACT FROM AUTHOR]

A large number of aquaculture facilities produced during the farming process are made of plastics. These plastics can be a distinct habitat for bacteria due to their unique materials. Therefore, this paper focuses on plastic aquaculture facilities and investigates the impact of bacterial accumulation on plastic surfaces. In this study, the high-throughput sequencing of 16S rRNA was conducted to investigate bacterial community profiling associated with the pearl culture facilities (cultured net cages and foam buoys) and surrounding water of Liusha Bay. Alpha diversity analysis showed that the richness and diversity indexes of bacterial communities in pearl culture facilities were higher than those in the aquatic environment. The richness and diversity indexes of bacterial communities were different between cultured net cages and foam buoys. Spatially influenced bacterial communities attached to pearl culture facilities varied between aquaculture areas. Thus, plastic has become a habitat for bacteria, floating in the marine environment and providing a favorable living environment for marine microorganisms and specific preferences for different substrate types. The relative abundance of certain functions on the attached bacterial community of the culture facility was high, which suggested that plastics did not only alter community structure but also influenced bacterial function. In addition, we detected small amounts of pathogenic bacteria, such as Vibrio and Bruegeria , in pearl culture facilities and surrounding seawater, suggesting that plastics can act as vectors for potentially pathogenic bacteria that may have an impact on the development of aquaculture. Our understanding of plastic ecology has been enriched by the discovery of the various microbial assemblages that can occur in aquaculture facilities. • Bacterial community may have a specific preference for different plastic materials. • Different bacterial communities in colonized plastics may exhibit various microbial functions. • Plastics produced in aquaculture can act as carriers of potentially pathogenic bacteria. [ABSTRACT FROM AUTHOR]

This paper studies the structure and dynamics of urban agglomeration between 1992 and 2018 in one of most developed regions in China – the Guangdong-Hong Kong-Macao Greater Bay Area (GBA) – based on the harmonized night-time light (NTL) data from the Defense Meteorological Satellite Program (DMSP) and Visible Infrared Imaging Radiometer Suite (VIIRS) imagery. We adopted Zipf's law as the rule to control the spatial clustering of adjacent lit pixels. The clustered pixels as patches are concentration of human activities and can then be used for regional urbanization analysis. Unlike conventional spatial units that are rarely changed, such as administrative boundaries, these patches change subtly each year and, more importantly, can help us to examine precisely the urbanization pattern in both development mode and intensity. We investigated the urban growth pattern during the 27-year timespan and conducted the analysis at both region and city levels. Interestingly, the results suggest that Zipf's law of derived patch sizes can hold nearly every year, showcasing the consistent spatial coherency of the urban agglomeration process in the entire region. The correlation between urbanization metrics for each city at three stages (1992–2000, 2000–2010, and 2010–2018) can further lead to deep insights towards the synergetic course of the coordinated and multi-center development. [Display omitted] • Identify the urbanized area using spatial clustering of time-series NTL data guided by Zipf's law related statistics. • Explore the urban expansion dynamics by conducting a correlation analysis between development mode and intensity. • Confirm the integrated, connected, and polycentric urbanization development process evidenced by the findings. [ABSTRACT FROM AUTHOR]

• A novel carbon emission reduction(CER) methodology is proposed. • The Black-Scholes mode is introduced to measure carbon emission option pricing. • Case studies of campus grid CER assessment are conducted in Shenzhen. • Methods and policy suggestions of CER are given for grid integration of EVs. China, as the world's largest emitter of carbon emissions, has implemented carbon markets to reduce carbon emissions, but the economic feasibility of integrating electric vehicles (EVs) into these markets remains uncertain. To address this challenge, we propose a novel methodology for grid integration of EVs based on the Shenzhen carbon inclusive management measures. Our proposed methodology can optimize EV charging schedules based on carbon emission prices and improve the economic feasibility of low-carbon transitions through EVs in China. We incorporate the Black-Scholes model to measure carbon emission option pricing and develop an optimal bidding strategy that considers spatiotemporal uncertainties of EVs and electricity markets. We use numerical simulations with real-world data from Shenzhen carbon emission market and Guangdong electricity market to demonstrate the effectiveness of our proposed methodology. Results reveal that scheduling 100 EVs' charging/discharging behavior properly can generate a revenue of 2379.4 yuan for the distribution system operator, while the revenue for carbon emission reduction (CER) is only 9.35 yuan. This paper demonstrates that the potential revenue of CER may be substantially based on the future scenario for the carbon emission market. These findings provide valuable insights for policymakers and stakeholders seeking to reduce carbon emissions in China's electricity sector. [ABSTRACT FROM AUTHOR]

Due to the shortage of fossil energy, pyrolysis has become a promising way for the thermal utilization of biomass resources, so as to partly replace fossil energy as well as achieve efficient resource usage. The site selection of the biomass pyrolysis (BP) project is an important task in the preliminary planning stage of the project. Moreover, since low-carbon development has become a common global goal at present, this paper proposes a decision-making framework from the low-carbon point of view for BP project site selection. Firstly, a complete site selection decision-making criteria system embracing the novel concept of low-carbon development is established for the first time, and the subjective and objective weighting method is integrated into the comprehensive integrated weighting method based on game theory. More importantly, a brand-new distance formula for the probabilistic linguistic term set is defined to measure the deviation between information, which uses expectation, variance and scale function to make the measurement more reasonable. Based on this, an improved TODIM (an acronym in Portuguese of interactive and multi-criteria decision making) method is proposed to more accurately determine the best option. Finally, taking Guangdong Province as an example, the best choice for site selection is obtained. This work can provide insightful theoretical basis for the site selection of BP projects. • A criteria system is established for site selection from the low-carbon perspective. • Subjective and objective weighting models are used to determine the weights. • A new formula based on expectation, variance and scale functions is defined. • A site selection framework is proposed based on a mixed method. [ABSTRACT FROM AUTHOR]

This paper uses panel data from 2017 to 2019 in Yangshan County, Guangdong Province, to assess the impact of land titling on farmers' political trust. The results of a generalized difference-in-differences model suggest that land titling increases farmers' subjective trust in the local village council and reduces the probability of conflict with the local village council. We found that the mechanism by which land titling affects farmers' political trust is the stability of land rights. Furthermore, regulated procedural implementation can strengthen the impact of land titling on political trust. Additionally, we unexpectedly found that land titling positively impacts the relationship between villagers and cadres, and collective action participation, in addition to influencing farmers' political trust. ● The impact of land titling on farmers' political trust has a striking time-lag feature. ● Land titling affects farmers' political trust through property rights and interests security ● A standardized implementation procedure is a condition for land titling to increase farmers' political trust. ● Land titling also has a positive impact on cadre relations and collective action. [ABSTRACT FROM AUTHOR]

• This proposes a mixed multinomial multiplicative (MMNM) model for crash severity, considering the features of crash data. • The non-identical, heavy-tailed and asymmetric properties of the model are discussed, which may be desirable for crash severity analysis. • Empirical studies show that the MMNM model outperformed the classical additive error term structure model. • More advanced multiplicative models can be developed based on the basic structure proposed in this paper. Crash severity model is a classical topic in road safety research. The multinomial logit (MNL) model, as a basic discrete outcome method, is widely applied to measure the association between crash severity and possible risk factors. However, the MNL model has several assumptions and properties that are possibly not consistent with the actual crash mechanism, and therefore with the association measure for crash severity. One significant attribute is the variation in drivers' safety perception. Risk-taking drivers tends to drive at a higher speed, which increases the likelihood of severe crashes. However, the variations in speed and other driving performance lead to the error in the utility function more profound. This violates the assumption of identical error distributions between different crash severity outcomes. In this paper, we propose a multinomial multiplicative (MNM) model, as an alternative for crash severity model. There are two possible formulations for the proposed MNM model: (1) Weibull and (2) Fréchet, according to the distributions of random propensities and subject to the signs of the systematic parts of the regression equation. The two heavy-tailed distributions can capture the effect of unobserved contributory factors on crash injury severity. Additionally, the MNM model can incorporate the effects of the non-identical, heavy-tailed, and asymmetric properties of the distribution, whereas the conventional MNL model cannot. Several operational considerations are also attempted in this study, including the specifications of the systematic parts and the interpretations of the parameters. The MNM model is further extended to the mixed MNM (MMNM) model by considering unobserved heterogeneities using random coefficients, while the mixed MNL (MMNL) model is used as the benchmark model. The proposed MMNM model is calibrated using the crash dataset obtained from the Guangdong Province, China. Results indicated that the proposed MMNM model outperformed the MMNL model in this case. Also, the results of parameter estimates are indicative to impact factors on crash severity as well as the design and implementation of policies. This justified the use of MMNM model as an alternative for crash severity model in practice. This is the first application of MMNM model in the traffic safety literature, it is worth exploring the application of other advanced multiplicative models for safety analysis in the future. [ABSTRACT FROM AUTHOR]

• We compared the structural characteristics of 18 urban agglomerations in China. • Our framework contained analysis of 3 different scales based on 4 elements' flows. • Guangdong-Hong Kong-Macao urban Agglomeration had the greatest average flow (5.15). • Shandong Peninsula Urban Agglomeration had the best performance in overall network. • Cities with high centrality were in the bidirectional or main spillover subgroups. Flows of different elements among cities can affect urban organizational structures, leading to specific links between and roles for cities within urban agglomerations. Previous studies have mainly focused on flows produced by specific elements in a single urban agglomeration, and they cannot reveal differences in organizational structures affected by multiple elements among different urban agglomerations. Therefore we selected 18 typical urban agglomerations in China and explored their structural characteristics under comprehensive effects of four elements: economy, resources, population and technology, with gravity model and social network analysis. Guangdong-Hong Kong-Macao Urban Agglomeration had a maximum flow intensity of 5.15, this concentration range was the largest from 0.65 to 3.75, indicating that more elements flowed internally. Shandong Peninsula Urban Agglomeration had the largest network density at 0.412, the lowest efficiency at 0.567, and the lowest hierarchy at 0.000, showing that intercity relationships within SP were the tightest among the18 urban agglomerations. Geographical proximity and topological structure of subgroups proved that connections among subgroups were not complex, and cities of different provinces lacked close interactions. Furthermore, most of the key cities with high centrality indexes were municipalities directly under the Central Government or provincial capitals; these cities played important roles in networks and were distributed into "main spillover" and "bidirectional spillover" subgroups. This paper provides a unified analytical framework aimed at urban agglomerations that accounts for flows of multiple types of elements and explores urban organizational structures, and our results could provide scientific guidance for promoting the coordinated and sustainable development of urban agglomerations. [ABSTRACT FROM AUTHOR]

• The day-ahead bidding strategy of cloud energy storage (CES) is developed. • Two energy service modes are provided by the CES for microgrids (MGs). • The CES is considered an intermediate entity responsible for electricity storage and trading. • An improved peer-to-peer (P2P) electricity trading pricing method is adopted. • The Guangdong electricity market is considered as the market mechanism. Cloud energy storage (CES) receives increasing attention as an efficient and viable paradigm for the provision of distributed energy storage services. This paper exploits CES's service modes to both energy storage and electricity trading for its users, e.g., microgrid (MG). The optimal day-ahead bidding strategy is investigated for CES as an independent entity in the electricity market. Besides, two energy service modes are introduced considering MG's requirements and preferences. Each mode consists of a set of schemes for energy storage system (ESS) rent (power-based and capacity-based) and electricity trading (internal price with an improved pricing method and market clearing price). Finally, a stochastic programming (SP)-based optimization model is formulated to maximize the CES's expected profits fully considering the electricity market's settlement mechanism and uncertainty as well as the participation interest of MG. The proposed solution is extensively assessed through a case study of CES's energy services to five heterogeneous MGs with distinct electricity generation and consumption characteristics and two energy service modes are assessed through comparative experiments. The numerical results confirm the effectiveness and benefits of the proposed optimal day-ahead bidding solution. [ABSTRACT FROM AUTHOR]