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With the rapid development of the world city network, the traditional location theory has gradually been disproven, and the advantages of the flow space over the traditional vertical organizational structure are gradually being revealed. Therefore, from corporate branch networks and corporate investment networks, 21 cities in urban agglomerations of Guangdong are taken as case studies for this paper. Furthermore, in this paper, 5 representative types of corporate contact data (catering service, financial service, life service, sports and leisure and accommodation service) are selected, the social network analysis (SNA) method is used to quantitatively analyze the network structure characteristics of urban agglomerations, and a spatial interaction model is constructed to explore the factors influencing. The results indicate that secondary networks have developed in Guangdong. The financial service network is the most complex, followed by the life services, sports and leisure and catering networks. The accommodation service network structure is the simplest. Among all kinds of networks, Guangzhou and Shenzhen have the highest status. The catering and accommodation corporations in Yangjiang in the west have a relatively major external development. Shantou in the east has many branches of various types, while most of the capital exchange in the region is concentrated in Heyuan and Qingyuan in the north. The coefficients of geographical proximity and the urban development level play a significant role in promoting the development of networks. However, administrative capacity limits the attractiveness of origin cities to a certain extent. [ABSTRACT FROM AUTHOR]

Scientific and technological innovation and ecological progress are important forces for China's high-quality development. Nevertheless, the current body of research seldom explores the mutual dependence and evolutionary patterns of the two regional systems when considering both historical and future perspectives. This paper aims to enhance the current theoretical research framework by focusing on the coastal region of China. It analyzes and predicts the co-evolution and space–time distribution process of two systems by constructing a coupled model and a gray prediction model. The results show the following: (1) the coupling and coordinated development level of the two coastal systems has basically reached the stage of good coupling and coordinated development, and the level has increased, but the gap has gradually increased, and Jiangsu and Guangdong have reached the leading level; (2) the spatial distribution in coastal areas is uneven, showing a convex spatial pattern of "high in the middle and low at both ends"; (3) during the forecast period, the overall coupling and coordination level of the two systems will continue to improve, but individual cities still cannot jump out of the development state of slow development processes. With the aim of fostering the harmonious growth of both systems at both regional and national levels, this paper proposes practical recommendations concerning the establishment of a sustainable interaction mechanism, the evolution of the industrial structure towards ecological sustainability, and the economic contribution to scientific and technological innovation. Furthermore, this article serves as a valuable reference for advancing the sustainable development of other nations. [ABSTRACT FROM AUTHOR]

Due to the imbalance in the supply and demand of agricultural land, there is an increasing trend of land resources being remoted utilized across regions. Within the context of regional trade, changes in the agroecosystem service value (ESV) can be used to assess consumer responsibility. However, the embodied flow of ESV and its driving factors have been largely unexplored. This paper, utilizing the latest Chinese multi-regional input-output tables and the equivalence factor method, explores the spatiotemporal characteristics of embodied agricultural land and ESV flow between Guangdong and 30 other provinces, and further seeks to construct three types of embodied ESV models to reveal the driving factors. The findings reveal that during the process of domestic trade in goods and services, Guangdong Province has an inflow of 2.10 × 107 hm2 of land from other provinces, with arable land, forestland, and grassland accounting for 25, 61, and 14%, respectively. Guangdong's utilization of ESV is mainly dependent on external inflows, with minimal local consumption and outflows. The embodied ESV between Guangdong and other provinces is 1626.10 billion yuan, with an outflow of 325.32 billion yuan. The "Y"-shaped region consisting of the northwest, northeast, southwest, and Hainan bears significant potential ESV losses for Guangdong Province. Population growth and the intensity of ESV loss will promote the flow of cropland and forestland ESV, while economic development has a certain inhibitory effect on ESV transfer. This paper provides a new analytical perspective on issues such as the spatial distribution mismatch of land resources and ecologically unequal exchange. These insights are pivotal for promoting sustainable utilization of land resources and regional equity. [ABSTRACT FROM AUTHOR]

To ensure the safe operation and dispatching control of a low-voltage distributed photovoltaic (PV) power distribution network (PDN), the load forecasting problem of the PDN is studied in this study. Based on deep learning technology, this paper proposes a robot-assisted load forecasting method for low-voltage distributed photovoltaic power distribution networks using enhanced long short-term memory (LSTM). This method employs the frequency domain decomposition (FDD) to obtain boundary points and incorporates a dense layer following the LSTM layer to better extract data features. The LSTM is used to predict low-frequency and high-frequency components separately, enabling the model to precisely capture the voltage variation patterns across different frequency components, thereby achieving high-precision voltage prediction. By verifying the historical operation data set of a low-voltage distributed PV-PDN in Guangdong Province, experimental results demonstrate that the proposed “FDD+LSTM” model outperforms both recurrent neural network and support vector machine models in terms of prediction accuracy on both time scales of 1 h and 4 h. Precisely forecast the voltage in different seasons and time scales, which has a certain value in promoting the development of the PDN and related technology industry chain. [ABSTRACT FROM AUTHOR]

Based on the best-track dataset from the Shanghai Typhoon Institute/China Meteorological Administration, the paper provides a comprehensive summary and analysis of tropical cyclone (TC) activities in the Western North Pacific (WNP) and the South China Sea (SCS) for 2022. Using the historical climatology from 1951 to 2020, the anomalous conditions during 2022 in TC frequency, origin locations, tracks, intensity, and duration for the entire ocean basin as well as landfall events in China are examined. Results show that the overall TC frequency is slightly lower than normal, but the multiple TC events have a very high frequency of occurrence. Origin locations of TCs, which mark the starting points of their paths, show a large westward and northward deviation from climatology. Around 40% of the named TCs exhibit a shift in their direction of movement from westerly to easterly. Additionally, comparisons of the means, medians, upper and lower quartiles all indicate that the intensity of TCs in 2022 is generally lower than the climatology, with the duration of TCs at tropical storm intensity or above being shorter than usual. A notable observation is the fewer incidence of TC landfalls in China, but with a geographical concentration in Guangdong Province. These anomalous annual TC activities are influenced by related atmospheric and oceanic environmental conditions modulated by multi-scale climate variability. The findings provide useful information for enhancing disaster mitigation strategies in the Asia-Pacific region. [ABSTRACT FROM AUTHOR]

Air-booster vacuum preloading (AVP) without sand blanket and surcharge vacuum preloading (SVP) methods were used to treat two adjacent areas in a foundation reinforcement project on Donghai Island, Zhanjiang City, Guangdong Province, China. In this study, engineering characteristics of the AVP foundation were obtained through analyzing monitoring data. Results showed that the AVP method obtained superior foundation consolidation results compared to SVP method. This study proposed a new simplified mechanical model for AVP, with a single PVD (prefabricated vertical drain) centered and the air-booster pipes symmetrically distributed around the PVD, applied equivalent air-booster pressure to the drainage boundary. Furthermore, this study established a mathematical analytical model for predicting consolidation settlement based on the proposed simplified model. By comparing the theoretical settlement value with the measured value, this paper verifies the rationality of the analytical solution. Finally, this study analyzed the effects of well resistance, smearing effect, compression index, and permeability index ratio on consolidation characteristics. Land reclamation projects will generate a large amount of dredged ultrasoft soil foundation. The AVP method can achieve superior foundation treatment effects by injecting gas into soil mass through air-booster pipes. The results of this case study demonstrate that the AVP method has substantial advantages compared to the conventional vacuum preloading method. The AVP method can increase foundation settlement, shorten the time for foundation treatment construction, and improve the foundation reinforcement effect. In addition, the AVP method can alleviate PVD clogging and maintain soil permeability, while simultaneously avoiding the excessive use of dredging sand. Therefore, the AVP method can lead to potential economic benefits and protect the natural environment, and it is a method worth considering. Moreover, the mathematical model for predicting consolidation settlement of the AVP method was proposed based on new mechanical assumptions, which can be applied as a reference for settlement prediction in relevant engineering, and the rationality has been verified through comparison with measured settlement results. [ABSTRACT FROM AUTHOR]

Multiple datasets related to pond and marine aquaculture have been published using diverse remote sensing technologies, yet a comprehensive dataset detailing spatial distribution on both land and sea sides is lacking. Firstly, a meticulous comparison of datasets which we selected related to aquaculture ponds and marine, ensuring consistency in trends. Subsequently, the datasets published by our team were edited and integrated to illustrate aquaculture activities on both sides of China's coastal zone. Finally, a spatial differentiation of coastal aquaculture in major provinces was analyzed. This analysis also utilizes the types of coastline and statistical data, guiding coordinated resource management efforts. The results unveil a distinctive spatial distribution pattern, concentrating aquaculture in the northern regions—Bohai Sea, Jiangsu, Fujian, and Pearl River coasts in Guangdong. The provinces rich in aquaculture resources, such as Shandong, Guangdong, and Liaoning, exhibit extensive coastlines. However, remote sensing monitoring suggests an underestimation of Liaoning's marine aquaculture compared to statistical yearbook data. Furthermore, southern provinces like Guangdong and Fujian exhibit significantly higher aquaculture output than Liaoning. Zhejiang leads in fishing output. The paper outlines the future development direction of coastal aquaculture, emphasizing a strategic, integrated land–sea approach for sustainable development. [ABSTRACT FROM AUTHOR]

As one of the four major bay areas in the world, the Guangdong–Hong Kong–Macao Greater Bay Area (GBA) is a highly integrated mega urban agglomeration and its unparalleled urbanization has induced prominent land contradictions between humans and nature, which hinders its sustainability and has become the primary concern in this region. In this paper, we probed the historical characteristics of land use and land cover change (LUCC) in the GBA from 2005 to 2015, and forecasted its future land use pattern for 2030, 2050, and 2070, using a cellular automata–Markov (CA–Markov) model, under three typical tailored scenarios, i.e., urban development (UD), cropland protection (CP), and ecology security (ES), for land use optimization. The major findings are as follows: (1) The encroachments of build-up land on the other land uses under rapid urbanization accounted for the leading forces of LUCCs in the past decade. Accordingly, the urban sprawl was up to 1441.73 km2 (23.47%), with cropland, forest land, and water areas reduced by 570.77 km2 (4.38%), 526.05 km2 (1.76%), and 429.89 km2 (10.88%), respectively. (2) Based on the validated CA–Markov model, significant differences are found in future land use patterns under multiple scenarios, with the discrepancy magnified over time and driven by different orientations. (3) Through comprehensive comparisons and tradeoffs, the ES scenario mode seems optimal for the GBA in the next decades, which optimizes the balance between socio-economic development and ecological protection. These results serve as an early warning for future land problems and can be applied to land use management and policy formulation to promote the sustainable development of the GBA. [ABSTRACT FROM AUTHOR]

The traditional medium-voltage distribution network fault location method uses mainly the voltage and current measurements of the medium-voltage side, which results in problems such as high installation costs at the measuring points and complicated postoperation and maintenance work. Therefore, a fault location idea based on the distributed measurement of low-voltage side voltage is proposed in this paper. First, the characteristic voltage is adaptively selected according to the fault type. Second, the suspected fault section is determined by comparing the characteristic voltage amplitude of each measuring point. Third, the fault section is located using the section unit characteristic voltage drop defined for each suspected fault section. Finally, fault distance estimation is achieved based on the voltage difference matrix and characteristic voltage analysis. This method achieves accurate fault distance identification based on the distribution difference of the characteristic voltage of the low-voltage side under the fault state. This work provides a new economical and practical idea for determining the fault locations of distribution networks. The effectiveness of this method is evaluated by considering a 10 kV distribution network in Guangdong Province built in PSCAD/EMTDC. [ABSTRACT FROM AUTHOR]

In this paper, we focus on Guangdong province, which is a frontier area of Reform and Opening up, and analyze the variation of urban and rural temperature to detect the urbanization warming effect from 1951 to 2018, based on the daily temperature data of 85 weather stations. We classified the metrological stations into urban and rural stations according to the urbanization population size, population density and urban built-up area data. The combined methodology of mathematical statistics, trend analysis, urban/rural station comparison and spatial analysis was adopted to reveal the urbanization warming and its spatial–temporal heterogeneity. The results show that the warming process in Guangdong Province is very complex and has high temporal and spatial heterogeneity. From 1951 to 2018, the average temperature in the coldest month in Guangdong Province increased with the fastest (0.27 °C/10a) rate, and the annual average temperature increased slightly faster than the average temperature in the warmest month (0.15 °C/10a vs. 0.12 °C/10a). The comparative study on the temperature of urban and rural stations shows that the temperature increase rate of urban stations is faster than that of rural stations (0.30 °C/10a vs. 0.23 °C/10a), especially the average temperature in the coldest month, which is most significantly driven by urbanization, and the contribution rate of urbanization is about 24.09% in general. Affected by the background climate and the stages of urbanization, the warming effect of urbanization fluctuates obviously in different periods. Stronger urbanization effect can be witnessed during 1980–2000, when the urbanization developed into mid-level with highest rate. The spatial differentiation of urbanization warming effect is prominent. The temperature of urban stations in Eastern Guangdong increases most rapidly. The urbanization contribution rates to the coldest month average, annual average and the warmest month average temperature reach 33.86%, 25.70% and 17.64%, respectively, which are around 1.69, 1.50 and 1.19 times that of the Pearl River Delta. The average temperature of the warmest month in Western Guangdong shows a weak cooling effect. Multi-faceted, multi-level regulation of sustainable urbanization is the key to mitigating the warming caused by urbanization. [ABSTRACT FROM AUTHOR]

Recently, the economy in Guangdong province has ranked first in the country, maintaining a good growth momentum. The prediction of Gross Domestic Product (GDP) for Guangdong province is an important issue. Through predicting the GDP, it is possible to analyze whether the economy in Guangdong province can maintain high-quality growth. Hence, to accurately forecast the economy in Guangdong, this paper proposed an Elman neural network combining with wavelet function. The wavelet function not only stimulates the forecast ability of Elman neural network, but also improves the convergence speed of Elman neural network. Experimental results indicate that our model has good forecast ability of regional economy, and the forecast accuracy reach 0.971. In terms of forecast precision and errors, our model defeats the competitors. Moreover, our model gains advanced forecast results to both individual economic indicator and multiple economic indicators. This means that our model is independently of specific scenarios in regional economic forecast. We also find that the investment in education has a major positive impact on regional economic development in Guangdong province, and the both surges positive correlation. Experimental results also show that our model does not exhibit exponential training time with the augmenting of data volume. Consequently, we propose that our model is suitable for the prediction of large-scale datasets. Additionally, we demonstrate that using wavelet function gains more profits than using complex network architectures in forecast accuracy and training cost. Moreover, using wavelet function can simplify the designs of complexity network architectures, reducing the training parameter of neural networks. [ABSTRACT FROM AUTHOR]

Severe Typhoon Koinu passed south of Hong Kong on 8 and 9 October 2023, triggering the issuance of the Increasing Gale or Storm Signal No. 9, the second highest tropical cyclone (TC) warning signal in Hong Kong. Koinu was a difficult case for TC warning service due to its compact size and rather erratic movement over the coastal waters of Guangdong. To monitor Koinu's movement and wind structure, the Hong Kong Observatory utilized various observational platforms, including meteorological aircraft, ocean radar, and synthetic aperture radar on polar orbiting satellites. The paper presents major observations derived from these measurements. The aircraft probe and dropsonde data suggested boundary layer inflow, warm core structure, eyewall updraft, and high turbulence in the eyewall of the typhoon. The weather radar observations indicated the occurrence of a waterspout in the vicinity of the typhoon. Additionally, the study highlights the forecasting performance of the AI‐based Pangu‐Weather model, which could outperform the conventional global numerical weather prediction models in forecasting TC track in the region. The documentation of these observations aims to provide valuable references for weather forecasters and stimulate further research on forecasting this type of tropical cyclone. Key Points: Observations from meteorological aircraft, ocean radar, and synthetic aperture radar show the structure of Severe Typhoon KoinuWeather radar observations suggest the occurrence of a waterspout in the vicinity of KoinuAI‐based Pangu‐Weather model could outperform the conventional global numerical weather prediction models in forecasting Koinu's track [ABSTRACT FROM AUTHOR]

In the field of world heritage conservation, there has been broad consensus on carrying out heritage conservation research on the basis of spatial integration and interregional and international cooperation. However, there are still many deficiencies in the integration of culture with the environment, regional economic and social development, and the regional, holistic and multimodal conservation and utilization of cultural heritage sites. In China, the Dongjiang-Hanjiang River Basin is a representative area of substantial cultural and ecological value for both Guangdong Province and the whole country. This paper uses the morphological spatial pattern analysis and the minimum cumulative resistance model to integrate cultural ecology sources and establish a cross-regional and large-scale cultural ecology network system that includes 1 main corridor, 22 important corridors and 17 secondary corridors. In addition, based on identified cultural landscape nodes and cultural ecology services, the economy of the cultural ecology corridor could be developed with large-scale co-construction, co-governance and shared working mechanisms to overcome administrative limits and realize the conservation and utilization of multimodal and large-scale heritage sites. This approach has strong theoretical and practical significance for innovative methods in cultural ecology research, as well as for new content in the research of Lingnan culture, ecosystem restoration, and the economic and social development of towns and villages. This article supplements unilateral studies of regional culture and ecology and demonstrates an in-depth application of cultural ecology theory. [ABSTRACT FROM AUTHOR]

The spatial-temporal mismatch of water resources and socio-economic development in rapidly urbanized regions has been the focus of water resource management, and is one of the main limitations to sustainable development goals (SDGs). Guangdong Province is represented by a highly developed economy and society, and has been undergoing disproportionately rapid population growth during the past two decades. The uneven distribution and spatial mismatch of water resources have aggravated the contradictions between water supply demand. In this paper, we evaluate the matching degree of water resources and social economic elements, detect the spatial-temporal changing pattern of the matching degree, and reveal the changing mechanism using the combined methods of the Thiel index, the space–time Gini coefficient, and the Lorenz curve. The results show that (1) the temporal matching is relatively balanced and reasonable, while the spatial mismatch is prominent and deteriorating constantly, especially the connection between the amount of water and GDP; (2) the water volume pressure is mainly concentrated in the Peral River Delta and eastern and western Guangdong, while water consumption stress is relatively lower in northeastern Guangdong; and (3) the water volume inequality is dominated by an intercity difference and is primarily caused by regional differences. Based on the results, policy implications, such as the implementation of integrated water resource management plans, investment in the development of alternative water sources, as well as enhanced public education and the establishment of a water saving society, have been provided to alleviate the mismatch issue between water elements and socio-economic parameters, and to push the realization of water-related SDGs. [ABSTRACT FROM AUTHOR]

As a unit hydrograph method widely used in engineering practice, the synthetic unit hydrograph of Guangdong is obtained from the analysis and synthesis of rain and flood data before 1983, which reflects the production-confluence relationship before urbanization in Guangdong Province. However, there are few studies on the evaluation and modification of its applicability in urbanized areas. Taking Jing-tian Watershed of Shenzhen City, Guangdong Province as an example, based on the rainstorm and flood records of Jingtian Hydrology Station from 2011 to 2019, this paper evaluates the applicability of the comprehensive unit line in Guangdong Province by using evaluation indexes such as Nash efficiency coefficient, identification index and correlation coefficient, and conducts a sensitivity analysis of important parameters by Spearman rank correlation coefficient method. Based on Latin hypercube sampling method and constrained least square method, the comprehensive unit line method of Guangdong Province is modified. The results show that the Jingtian Basin has experienced rapid urban expansion since the 1980s, and the proportion of impervious surface has exceeded 90%. The comprehensive unit line method in Guangdong Province has obvious shortcomings in describing the storm-flood process in this basin. The two schemes proposed in this paper can improve the simulation effect of the model by determining important parameters and modifying the unit line. Among them, the cross-validation parameter optimization method based on grid search and the unit line correction method based on constrained least square method proposed for small samples show a good simulation performance, and the indexes such as Nash efficiency coefficient, identification index and correlation coefficient are significantly improved, which better reflects the characteristics of production and convergence in Jingtian Basin. Compared with the recommended unit line of the integrated unit line method in Guangdong Province, the modified unit hydrograph method shows the characteristics of larger peak discharge, earlier peak time and smaller total duration, which is more in line with the law of production and confluence of urbanization. [ABSTRACT FROM AUTHOR]

As China's economy transitions from a stage of high-speed growth to a stage of high-quality development, the concept of low-carbon and green economic development has gained increasing popularity. Mastering the regional differences and changing patterns of low-carbon economy and economic growth quality is an important prerequisite for further promoting low-carbon economic development and improving the quality of economic growth. Taking the data of 21 prefecture-level cities in Guangdong Province from 2008 to 2019 as examples, we calculated the low-carbon economy and the quality index of economic growth, and analyzed the convergences between them through coefficient of variation analysis and a panel data convergence model with fixed effects. The results showed that: First, the convergence of low-carbon economy was better than the convergence of economic growth quality. Second, the low-carbon economy of Guangdong Province had σ convergence, and the imbalance between regions of low-carbon economy was alleviated, but the quality of economic growth of Guangdong Province did not have σ convergence. Third, there was absolute and conditional β convergence in the quality of low-carbon economy and economic growth in Guangdong Province. Fourth, the convergence rate of low-carbon economy in Guangdong Province showed "club difference"; the same was true of σ convergence, absolute β convergence, conditional β convergence, and dimensional convergence of economic growth quality in various regions of Guangdong Province. The exploration conducted in this article was conducive to better grasping the changing patterns of low-carbon economy and economic growth quality, enriching relevant research. The conclusions of this paper can provide decision-making basis for China to formulate urban and regional economic policies, achieve high-quality economic development, and "double carbon goal". [ABSTRACT FROM AUTHOR]

Macao's academic book publishing industry has a rich history and has grown significantly over the past two decades. This study employs a semi‐structured in‐depth interview to evaluate the current and prospective state of Macao's academic book publishing industry. The SWOT (Strengths, Weaknesses, Opportunities, and Threats) analysis and the PEST (Political, Economic, Social, and Technological) analysis are used in this study. The strengths of Macao's academic book publishing industry include that requesting an ISBN in Macao is easy and quick under the principle of 'One Country, Two Systems' and financial support from the Macao SAR government. However, the industry faces several weaknesses, including an underdeveloped cultural industry, a small seller market and a talent shortage, and less copyright output. Opportunities for the industry exist through constructing a Culture‐based Bay Area in the Guangdong–Hong Kong–Macao Greater Bay Area and developing an international hub for digital trade in the Guangdong–Macao In‐Depth Cooperation Zone–Hengqin. However, Macao's academic book publishing industry is threatened by emerging Internet technologies, such as augmented reality, the metaverse, and ChatGPT. This paper also proposes strategies for future practice, such as taking the initiative to encourage a group of top‐tier publishers to establish branches in Macao and improve publications' cultural elements. The findings of this study provide valuable insights into the strategic management of Macao's academic book publishing industry and will be of interest to publishers, academic researchers, and policymakers. [ABSTRACT FROM AUTHOR]

This paper studies when and under what conditions the actions undertaken by the power plants involved in China's emission trading scheme (ETS) pilot became cost effective. Based on unique plant-level panel data and the difference-in-differences strategy, we identify that an insignificant initial reduction in cost efficiency occurred at the announcement stage for power plants in the pilot provinces; however, the cost efficiency of the pilot plants increased significantly following formal policy implementation. Additionally, the by-stage treatment effects differed across the pilot provinces due to localized market and non-market variations. Localized conditions of higher marketization, stricter policy enforcement, and lower carbon dependence enhanced this positive effect. The synthetic control results confirmed this variation in the policy effects. The carbon trading pilots resulted in improved efficiency in power plants in Shanghai, Guangdong, and Tianjin during the period 2013--2017, with an associated total cost saving of approximately 29.75 million RMB. To enhance the efficacy of the ETS policy, our findings suggest that the design of the policy should consider localized external factors. [ABSTRACT FROM AUTHOR]

The magnetotelluric (MT) method is a widely used geophysical technique for deep structure exploration. However, rapid industrial development has resulted in significant anthropogenic interference for practical applications of this method in recent years. To carry out MT survey in regions with strong electromagnetic (EM) noise, we develop a strategy, in which part of MT sites are set for night time period and a newly proposed automatic clustering method based on MT signal patterns is applied to process measured data from Yingde area in Guangdong. The MT sites are employed in night time to avoid human activity and measure the quiet time MT signal to make sure that we can capture portion of MT signal. Based on cosine similarity, we can use clustering algorithms to separate the signals from the noise. The application of this strategy for geothermal exploration in Yingde area in Guangdong, indicates the validity of our proposed strategy for real geothermal detection. [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]

Bioenergy is a significant renewable energy source with great potential for addressing global environmental challenges. This study aims to construct and analyze the patent collaboration network of bioenergy in Chinese provinces along the Belt and Road (BR) initiative. By collecting and analyzing co-patents from the China National Intellectual Property Administration (CNIPA) between 1993 and 2017, this research investigates the network's structure, evolution, and the provincial differences in bioenergy technology development. The results reveal an increasing number of co-patents since 2006. I-I, E-E, E-R, and E-U are common types of collaboration, and this paper believes that E-E will be the most important type in the future. The network is relatively sparse and in its early development stage. However, the cohesion of the network is gradually increasing, and it has good development potential. From the perspective of IPC, the scope of patents for bioenergy is relatively narrow, and the breadth and depth of research need to be improved. Moreover, the study highlights Guangdong as a promising province for bioenergy and emphasizes the close collaboration between provinces along the Belt and Road and those not along it, such as Guangdong and Jiangsu. [ABSTRACT FROM AUTHOR]

Digital technology innovation is the core driving force for the high-quality development of the digital economy, and in-depth exploration of the regional distribution pattern and formation mechanism of digital technology innovation in China is conducive to the rational layout and coordinated development of the inter-provincial digital economy. Based on the Reference Relationship Table of the Classification of Core Industries of Digital Economy and the International Patent Classification (2023), the patent authorization data of digital technology from 2012 to 2022 were obtained, and the spatiotemporal situation of China's digital technology innovation was analyzed by using ArcGIS software, Dagum's Gini coefficient, and Moran's I index, and the spatial Dubin panel model was used to explore the influencing factors of digital technology innovation. It is found that: (1) the scale and vitality of China's digital technology innovation have increased significantly, and there are obvious spatial differentiation characteristics, and the innovation level of "eastern coastal—central and western interior" is decreasing, forming a cluster distribution pattern in the Yangtze River Delta region, Beijing, Guangdong, and other places, and the degree of agglomeration is decreasing. (2) The overall regional differences in China's digital technology innovation are large, the differences between the East and the West dominate the interregional differences, and the net differences between regions are the main factors leading to regional differences. (3) There is a significant positive spatial correlation between the scale and vitality of digital technology innovation, which has a significant spatial spillover effect. (4) The results confirm that the level of economic development, digital access, financial scientific and technological support, technology market development level, and R&D intensity have a significant positive impact on the scale and vitality of digital technology innovation; The investment in scientific and technological talents has a significant positive impact on the scale of digital technology innovation, but has no significant impact on the vitality of digital technology innovation. [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]

• 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]

Currently, fiber optic communication has become the primary means of communication within China's telecommunications network. Both domestic and international scholars have conducted extensive research on various types of optical cables, achieving significant results. However, to date, there has been no research, either domestically or internationally, addressing the discharge issues associated with the insulated optical-unit phase conductor (IOPPC) in 110-kV transmission lines. In the course of promoting the use of 110-kV lines, there was an incident in Guangdong Province, China, involving the fracture of an IOPPC downlead cable. This paper proposes a modified structure for the IOPPC downlead cable to address the aforementioned fracture incident. First, the finite element method is utilized to calculate the electric field intensity of both the traditional and modified IOPPC downlead cable structures under a voltage of 110-kV. Preliminary verification of the effectiveness of the modified structure is then conducted. Subsequently, the proposed modified structure is verified and further optimized through power frequency withstand voltage tests. Simulation results indicate that the traditional structure exhibits a high electric field intensity in the air region near the PVC cube. In contrast, the addition of a semiconductor shielding layer in the modified structure effectively suppresses the increase in electric field intensity in this area. Power frequency withstand voltage test results further confirm the effectiveness of the semiconductor shielding layer in suppressing the electric field intensity. However, it was observed that discharge phenomena still occurred at the cutoff point of the semiconductor shielding layer. Finally, the modified structure is optimized to effectively eliminate the discharge phenomena at the cutoff point of the semiconductor shielding layer. [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]

Cultivated land resources are crucial for food security and economic and social development. However, with the acceleration of urbanization and shifts in land use, cultivated land fragmentation (CLF) has emerged as a significant factor constraining the sustainable development of agriculture in China. As the most urbanized region, optimizing cultivated land resources and coordinating urban and rural development has become an urgent issue for rural sustainable development in Guangdong Province. This study analyzes the spatiotemporal characteristics of CLF in Guangdong Province from 2000 to 2020 using landscape pattern indices, CRITIC empowerment, and a multiscale geographically weighted regression (MGWR) model. The cultivated land fragmentation index (CLFI) for Guangdong Province reveals a fluctuating trend from 2000 to 2012, increasing from 0.453 in 2012 to 0.641 in 2020. The CLFI is notably high in the Pearl River Delta region, as well as in Meizhou and Maoming. The results show the dynamic changes of the driving factors of CLF at the county scale in 2000, 2010, and 2020. Slope and grain output consistently emerge as key driving factors of CLF. Furthermore, agricultural benefits played a significant role in 2000 and 2020, whereas the coefficient for social economic development was more pronounced in 2010. By identifying the heterogeneity of the driving factors, this study suggests that strategies to address CLF should comprehensively consider aspects such as the optimization of cultivated land resources, farmers' interests, industrial restructuring, and the multifunctional development of farmland. The study findings can assist government policy-making for rural sustainable development, addressing CLF and food insecurity, and alleviating the regional development imbalance and urban–rural income gap, with the ultimate aim of achieving common prosperity. [ABSTRACT FROM AUTHOR]

Background: Pebrine, caused by microsporidium Nosema bombycis, is a devastating disease that causes serious economic damages to the sericulture industry. Studies on development of therapeutic and diagnostic options for managing pebrine in silkworms are very limited. Methionine aminopeptidase type 2 (MetAP2) of microsporidia is an essential gene for their survival and has been exploited as the cellular target of drugs such as fumagillin and its analogues in several microsporidia spp., including Nosema of honeybees. Methods: In the present study, using molecular and bioinformatics tools, we performed in-depth characterization and phylogenetic analyses of MetAP2 of Nosema bombycis isolated from Guangdong province of China. Results: The full length of MetAP2 gene sequence of Nosema bombycis (Guangdong isolate) was found to be 1278 base pairs (bp), including an open reading frame of 1,077 bp, encoding a total of 358 amino acids. The bioinformatics analyses predicted the presence of typical alpha-helix structural elements, and absence of transmembrane domains and signal peptides. Additionally, other characteristics of a stable protein were also predicted. The homology-based 3D models of MetAP2 of Nosema bombycis (Guangdong isolate) with high accuracy and reliability were developed. The MetAP2 protein was expressed and purified. The observed molecular weight of MetAP2 protein was found to be ~43-45 kDa. The phylogenetic analyses showed that MetAP2 gene and amino acids sequences of Nosema bombycis (Guangdong isolate) shared a close evolutionary relationship with Nosema spp. of wild silkworms, but it was divergent from microsporidian spp. of other insects, Aspergillus spp., Saccharomyces cerevisiae, and higher animals including humans. These analyses indicated that the conservation and evolutionary relationships of MetAP2 are closely linked to the species relationships. Conclusion: This study provides solid foundational information that could be helpful in optimization and development of diagnostic and treatment options for managing the threat of Nosema bombycis infection in sericulture industry of China. [ABSTRACT FROM AUTHOR]

Simple Summary: As a zoonotic pathogen, the Hepatitis E virus (HEV) is extensively distributed worldwide. Animal infections are typically asymptomatic, while human infections can result in both acute and chronic viral hepatitis. The transmission pathway is typically a fecal–oral one. Pigs are the primary host of HEV genotype 4 (HEV-4), which primarily causes sporadic infections in China. The aim of this study was to evaluate HEV infection in certain farms in Guangdong, China. Twenty-five pig farms provided 1568 blood samples, of which 902 (902/1568, 57.53%) were positive for anti-HEV IgG. Such a high serum positive rate indicates that pigs in Guangdong are widely exposed to HEV, which is a considerable public health and safety concern. The Hepatitis E virus (HEV) causes acute and chronic Hepatitis E and is a global public health concern. HEV genotypes 3 (HEV-3) and 4 (HEV-4) are common to humans and animals, and domestic pigs and wild boars have been identified as the main reservoirs. However, limited information is available on the status of HEV infection in pigs, particularly in the Guangdong Province, China. This study aimed to investigate the seroprevalence of HEV in pig farms within the Guangdong Province. A total of 1568 serum samples were collected from 25 farms and tested for anti-HEV IgG antibodies. Enzyme-linked immunosorbent assay (ELISA) results revealed that 57.53% (902/1568) of serum samples from 24 farms (24/25, 96%) were positive for anti-HEV IgG antibodies. Year, season, region, and age were all linked risk factors for HEV in Guangdong, with season and region showing more significant impacts. The results showing a high seroprevalence of HEV confirmed its circulation among domestic pigs in the Guangdong Province, China. The presence of this antibody indicates that HEV infection was or is present on farms, posing a risk of zoonotic transmission of HEV from pigs to exposed workers and from pork or organs to consumption. [ABSTRACT FROM AUTHOR]

This study investigates the dynamic relationship between cross-border e-commerce and logistics co-development strategies within the context of small and medium-sized enterprises (SMEs) in China. The primary objective is to formulate an empirical model capable of estimating the utility and flexibility of cross-border e-commerce logistics, specifically focusing on its role in achieving competitive advantage for SMEs. The research employs a comprehensive approach, considering various factors that influence the formulation and implementation of cross-border e-commerce logistics strategies. Factors such as scale, quality, potential, and infrastructure are scrutinized to provide a nuanced understanding of the dynamic interplay. Real-world data is analyzed using advanced statistical techniques to derive meaningful insights. The data of CBEC and logistics planning in Guangdong from 2013 to 2018 was used as a case example to demonstrate its and flexibility and usefulness of empirically proposed model estimation and develop a holistic indicator system for the evaluation of the synergistic development of CBEC and logistics. This research focus on the modelling of the CBEC for the synergistic effect analysis and evaluate the usefulness and flexibility of CBEC to achieve competitive advantage. The study aims to uncover insights into the most effective approaches for Chinese SMEs in navigating the landscape of cross-border e-commerce. By examining the results derived from the empirical model, the research sheds light on the impact of cross-border e-commerce logistics on competitive advantage. The findings contribute to a deeper understanding of how SMEs can strategically position themselves in the cross-border e-commerce arena. Through the analysis of real-world data and the application of advanced statistical methods, this research offers valuable insights for Chinese SMEs. The insights generated from the study not only illuminate the intricacies of cross-border e-commerce logistics but also provide practical recommendations to reduce associated costs. Ultimately, the study aims to equip SMEs with the knowledge necessary to thrive in the evolving landscape of cross-border e-commerce. [ABSTRACT FROM AUTHOR]

When considering the economic growth of the Greater Bay Area, digital transformation stands out as a crucial catalyst. Given its significance, it is imperative to delve into both the theoretical and empirical aspects of how digital transformation affects the region's position in the global value chain. Theoretical hypotheses are put forward regarding the impact and mechanisms of digital transformation on the global value chain position of the Greater Bay Area based on a global value chain perspective. A three-tier nested input–output table that incorporates the Greater Bay Area is constructed, and trade decomposition and global value chain position measurement models specific to the area are developed for analyzing its current state in terms of global value chain position. Empirical testing was conducted to examine how digital transformation impacts this position. Digital transformation will significantly enhance the position of the Greater Bay Area in the division of global value chains, mainly through enhancing technological innovation transformation capabilities and value added by exports from the Greater Bay Area. Digital transformation within the Greater Bay Area has had a more substantial positive impact than the digital transformation in other provinces in China and digital transformation from foreign sources. In terms of city heterogeneity, it is evident that the global value chain from Hong Kong and Macao has experienced a more significant impact from digital transformation compared to cities in the Pearl River Delta. Therefore, the Greater Bay Area should increase the use of both digital transformation and differentiated use of digital transformation. PRD cities should actively learn from Hong Kong and Macao's forms of digital construction and promote the Greater Bay Area's global value chain status. [ABSTRACT FROM AUTHOR]

Robert Morrison 馬禮遜, the first Protestant missionary to China, came to Guangdong as an employee of the East India Company and with the support of the London Missionary Society in 1807. Amongst his path-breaking translation work, he published the first Chinese Bible (Shen Tian Shengshu 神天聖書) in 1823. As many foreigners in Guangdong could not speak Cantonese, Morrison compiled a three-volume Cantonese learning aid, A Vocabulary of the Canton Dialect (1828), using specifically Cantonese Chinese characters and his Cantonese romanization system. In consequence, missionaries translated Christian literature and the Bible into Cantonese, for they realized that proficiency in Cantonese was essential for proselytization among ordinary people. Over the past twenty years, we have collected and identified around 260 Cantonese works written and translated by Western Protestant missionaries, and these Cantonese writings can be categorized as follows: 1. dictionaries; 2. textbooks; 3. Christian literature; 4. Bibles; and 5. miscellanea. In the study of the Western Protestant missions, their linguistic contribution is relatively under-represented. Through analyzing the phonological, lexical, and grammatical features of early Cantonese expressions in these selected missionary works, we strive to highlight the missionaries' contributions to the diachronic study of the Cantonese language in modern southern China. [ABSTRACT FROM AUTHOR]

The development of precision agriculture requires unmanned aerial vehicles (UAVs) to collect diverse data, such as RGB images, 3D point clouds, and hyperspectral images. Recently, convolutional networks have made remarkable progress in downstream visual tasks, while often disregarding the trade-off between accuracy and speed in UAV-based segmentation tasks. The study aims to provide further valuable insights using an efficient model named Taoism-Net. The findings include the following: (1) Prescription maps in agricultural UAVs requires pixel-level precise segmentation, with many focusing solely on accuracy at the expense of real-time processing capabilities, being incapable of satisfying the expectations of practical tasks. (2) Taoism-Net is a refreshingly segmented model, overcoming the challenges of complexity in deep learning, based on minimalist design, which is used to generate prescription maps through pixel level classification mapping of geodetic coordinates (the lychee tree aerial dataset in Guangdong is used for experiments). (3) Compared with mainstream lightweight models or mature segmentation algorithms, Taoism-Net achieves significant improvements, including an improvement of at least 4.8% in mIoU, and manifested a superior performance in the accuracy–latency curve. (4) "The greatest truths are concise" is a saying widely spread by ancient Taoism, indicating that the most fundamental approach is reflected through the utmost minimalism; moreover, Taoism-Net expects to a build bridge between academic research and industrial deployment, for example, UAVs in precision agriculture. [ABSTRACT FROM AUTHOR]

The Guangdong historical trail comprises two ancient trails, known as Yile (宜乐) and Xijing (西京); can be found within the borders of Lechang (乐昌); and is an integral part of the northern section of the Guangdong historical trail, connecting Hunan and Guangdong. Along these routes, there are various types of ancestral halls. The objectives of this study include analyzing various levels of information about ancestral hall architecture, determining their respective importance through AHP hierarchical analysis, classifying architectural features via cluster analysis using SPSS, and exploring the geographical distribution trajectories of ancestral halls along the ancient paths using GIS technology. Ultimately, this study seeks to shed light on the influence of different cultures brought by the ancient road on the distribution of architectural types and identify three distinct types of ancestral halls. [ABSTRACT FROM AUTHOR]

Energy consumption is an important indicator for measuring economic development and is closely related to the atmospheric environment. As a demonstration zone for China's high-quality development, the Guangdong–Hong Kong–Macao Greater Bay Area imposes higher requirements on ecological environment and sustainable development. Therefore, accurate data on energy consumption is crucial for high-quality green development. However, the statistical data on local energy consumption in China is insufficient, and the lack of data is severe, which hinders the analysis of energy consumption at the metropolitan level and the precise implementation of energy policies. Nighttime light data have been widely used in the inversion of energy consumption, but they can only reflect socio-economic activities at night with certain limitations. In this study, a random forest model was developed to estimate metropolitan-level energy consumption in the Guangdong–Hong Kong–Macao Greater Bay Area from 2000 to 2020 based on nighttime light data, population data, and urban impervious surface data. The estimation results show that our model shows good performance with an R2 greater than 0.9783 and MAPE less than 9%. A long time series dataset from 2000 to 2020 on energy consumption distribution at a resolution of 500 m in the Guangdong–Hong Kong–Macao Greater Bay Area was built using our model with a top-down weight allocation method. The spatial and temporal dynamics of energy consumption in the Greater Bay Area were assessed at both the metropolitan and grid levels. The results show a significant increase in energy consumption in the Greater Bay Area with a clear clustering, and approximately 90% of energy consumption is concentrated in 22% of the area. This study established an energy consumption estimation model that comprehensively considers population, urban distribution, and nighttime light data, which effectively solves the problem of missing statistical data and accurately reflects the spatial distribution of energy consumption of the whole Bay Area. This study provides a reference for spatial pattern analysis and refined urban management and energy allocation for regions lacking statistical data on energy consumption. [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]

Female entrepreneurs have irreplaceable status and essential significance in entrepreneurship research. Improving females' entrepreneurial intentions is an important topic in this area. Accordingly, this study, based on the theory of planned behavior, investigates the factors that affect female students' entrepreneurial intention at China's vocational colleges and whether household income moderates the relationship between entrepreneurial education, attitude, competence, self-efficacy and entrepreneurial intention. 2149 females from vocational colleges in Guangdong Province, Zhejiang Province, and Jiangxi Province were randomly chosen to participate in the study. They had taken part in entrepreneurial courses throughout 2021–2022. In addition, data were analyzed by structural equation modeling partial least squares. The results demonstrate that entrepreneurial education did not directly affect female students' intentions. Entrepreneurial competence, self-efficacy, and attitude positively affect entrepreneurial intention. It is further concluded that household income significantly moderates the relationship between entrepreneurial education, attitude, competence, and intention. However, there is no significant difference in the relationship between self-efficacy and entrepreneurial intention between high and low-household-income students. While females continue to confront sexism in the workplace, it is crucial that we conduct empirical research into the factors influencing female entrepreneurial intention to boost economic growth and gender parity. This research helps bridge a gap in the prior literature and adds substantial value to encouraging female entrepreneurs. [ABSTRACT FROM AUTHOR]

Background: This study aimed to develop a nomogram for predicting temporary acute agitated delirium after surgery in patients with chronic subdural hematoma (CSH) without neurological compromise and hospitalized in the neurosurgery. Methods: We included 289 patients with chronic subdural hematoma (CSH) from the medical information system of Yuebei People's Hospital of Shaoguan City, Guangdong Province, and collected 16 clinical indicators within 24 h of admission. We used the least absolute shrinkage and selection operator (LASSO) regression to identify risk factors. We established a multivariate logistic regression model and constructed a nomogram. We performed internal validation by 1,000 bootstrap samples; we plotted a receiver operating curve (ROC) and calculated the area under the curve (AUC), sensitivity, and specificity. We also evaluated the calibration of our model by the calibration curve and the Hosmer-Lemeshow goodness-of-fit test (HL test). We performed a decision curve analysis (DCA) and a clinical impact curve (CIC) to assess the net clinical benefit of our model. Results: The nomogram included alcoholism history, hepatic insufficiency, verbal rating scale for postoperative pain (VRS), pre-hospital modified Rankin Scale (mRS), and preoperative hematoma thickness as predictors. Our model showed satisfactory diagnostic performance with an AUC value of 0.8474 in the validation set. The calibration curve and the HL test showed good agreement between predicted and observed outcomes (p = 0.9288). The DCA and CIC showed that our model had a high predictive ability for the occurrence of postoperative delirium in patients with CSDH. Conclusion: We identified alcoholism, liver dysfunction, pre-hospital mRS, preoperative hematoma thickness, and postoperative VRS pain as predictors of postoperative delirium in chronic subdural hematoma patients. We developed and validated a multivariate logistic regression model and a nomogram. [ABSTRACT FROM AUTHOR]

In the context of the digital information era, the impact of "The Internet Plus," "Big Data," and other technologies on urban social development has been far beyond any preceding era, under the influence of information technology, urban agglomeration space exhibits a new layout. Based on the search engine data of eleven cities in the Guangdong-Hong Kong-Macao Greater Bay Area from 2012 to 2021, this research constructs the inter-city information network strength linkage matrix to examine the evolution characteristics of city network structure and its driving causes. The results reveal that (1) the overall information linkage strength exhibits a pattern of steadily growing the radiating effect from the leading cities of Guangdong, Shenzhen, and Hong Kong to the surrounding cities, and a closer and more balanced information linkage network is gradually built. (2) Guangzhou-Shenzhen-Hong Kong-Guangdong-Hong Kong-Macao Greater Bay Area information linkage absolute control advantage, four cities Foshan, Dongguan, Zhuhai, Macao regional hub position steadily highlighted. The entire information connection network of the urban agglomerations tends to be flat and polycentric at the same time. (3) The regional core-edge hierarchy is well established, with the four cities of Guangzhou, Dongguan, Shenzhen, and Hong Kong creating a northwest-southeast orientation. The core metropolis regions of Guangdong, Hong Kong, and Macao in the Greater Bay Area increasingly exert a radiation spreading effect to the northeast and southwest. (4) The urban economy, transportation distance, and information infrastructure have substantial effects on the information connection intensity network of urban clusters. [ABSTRACT FROM AUTHOR]

Introduction: The characteristic of human immunodeficiency virus type 1 (HIV-1) is its susceptibility to erroneous replication and recombination, which plays a crucial role in the diverse and dynamic variation of HIV-1. The spread of different subtypes in the same population often leads to the emergence of circulating recombination forms (CRFs). At present, the main recombinant subtypes of HIV-1 in China are CRF07_BC, CRF01_AE, CRF08_BC and B' subtypes, while CRF55_01B has become the fifth major epidemic strain in China after rapid growth in recent years since it was first reported in 2013. In this study, we obtained five nearly full-length genomes (NFLGs) and one half-length genome from five different cities in Guangdong. Here, we focused on analyzing their characteristics, parental origin and drug resistance. Methods: Plasma samples were collected from six HIV-1 infected patients in Guangdong Province who had no epidemiological association with each other. The NFLGs of HIV-1 were amplified in two overlapping segments by the near-terminal dilution method. The positive products were sequenced directly to obtain genomic sequences. The recombinant patterns and breakpoints of the NFLGs were determined using the Simplot software and confirmed by the maximum likelihood trees for segments using the IQ-TREE and BEAST software. The genotypic resistance profiles of the protease reverse transcriptase and integrase were resolved by the Stanford HIV drug resistance database. Results: The six genomes shared highly similar recombinant pattern, with the CRF55_01B backbone substituted by CRF07_BC segments, therefore assigned as CRF156_0755. The evolutionary analysis of the segments showed that CRF07_ BC segments were not clustered with the Chinese MSM variants in the CRF07_ BC lineage. All the five NFLGs were identified with the non-nucleoside reversetranscription inhibitors (NNRTIs) resistance mutation V179E. Discussion: With the accumulation and evolution of recombination between CRF55_01B and CRF 07_BC, the prevalence of more recombinant strains of CRF55_01B and CRF 07_BC may occur. Therefore, it is necessary to strengthen the identification and monitoring of the recombination of CRF55_01B and CRF 07_BC. [ABSTRACT FROM AUTHOR]

The concentration of antimicrobial agents in environments like water and food has increased rapidly, which led to a rapid increase in antimicrobial resistance levels in the environment. Monitoring of bacterial resistance levels is considered as a necessary means to control the bacterial resistance. Reference standards are critical for antimicrobial susceptibility testing. CLSI M45 A3 standard defines pathogenic microorganisms that cause infections less frequently than those covered by CLSI M02, M07, and M100 as Infrequently Isolated or Fastidious Bacteria and specifies antimicrobial susceptibility testing methods. Our study investigated the epidemiology and antimicrobial susceptibility testing data of Infrequently Isolated or Fastidious Bacteria strains isolated from blood specimens in 70 hospitals in Guangdong Province between 2017 and 2021. We defined testing methods other than those specified in CLSI M45 A3 as "Non-Standardized." The proportion of standardized antimicrobial susceptibility testing for penicillin increased significantly (Corynebacterium spp. 17.4% vs. 50.0% p < 0.05; Micrococcus spp. 50.0% vs. 77.8% p < 0.05; Abiotrophia spp. and Granulicatella spp. 21.4% vs. 90.9% p < 0.001), while for cefotaxime (Corynebacterium spp. 0.0% vs. 45.2% p < 0.05; Abiotrophia spp. and Granulicatella spp. 0.0% vs. 14.3% p = 0.515) and vancomycin increased finitely. Non-standardized methods were used for all other antimicrobials. Due to limitations in the economic and medical environment, some clinical laboratories are unable to fully comply with CLSI M45 A3 standard. We recommend that CLSI should add breakpoints for disk diffusion method to improve the standardization of antimicrobial susceptibility testing. [ABSTRACT FROM AUTHOR]

Storm surges, a significant coastal hazard, cause substantial damage to both property and lives. Precise and efficient storm surge models are crucial for long-term risk assessment and guiding emergency management decisions. While high-fidelity dynamic models offer accurate predictions, their computational costs are substantial. Hence, recent efforts focus on developing data-driven storm surge surrogate models. This study focuses on the Pearl River Estuary in Guangdong Province. Initially, the dynamic ADvanced CIRCulation (ADCIRC) model was utilized to construct storm surge data for 16 historical typhoons, serving as training, validation, and testing data for data-driven models. Subsequently, Long Short-Term Memory (LSTM), Convolutional Neural Networks (CNN), and Informer deep learning (DL) models were employed for forecasting of storm surge over the next 1h, 3h, 6h, 12h, and 18h. Finally, Shapley Additive exPlanations (SHAP) values were used for interpretability analysis of the input factors across different models. Results indicated that the proposed DL storm surge prediction model can effectively replicate the dynamic model's simulation results in short-term forecasts, significantly reducing computational costs. This model offers valuable scientific assistance for future coastal storm surge forecasts in the Greater Bay Area. [ABSTRACT FROM AUTHOR]

In the granite regions of southern China, benggang poses a substantial threat to the ecological environment due to significant soil erosion. This phenomenon also imposes constraints on economic development, necessitating substantial investments in restoration efforts in recent decades. Despite these efforts, there remains a notable gap in comprehensive risk assessment that integrates both the erosion risk and disaster risk associated with benggang. This study focuses on a representative benggang area in Wuhua County, Guangdong province, employing transformer methods and high-resolution imagery to map the spatial pattern of the benggang. The integrated risk of benggang was assessed by combining soil-erosion risk and disaster risk, and cultivated land, residential land, and water bodies were identified as key disaster-affected entities. The machine-learning Segformer model demonstrated high precision, achieving an Intersection over Union (IoU) of 93.17% and an accuracy (Acc) of 96.73%. While the number of large benggang is relatively small, it constitutes the largest area proportion (65.10%); the number of small benggang is more significant (62.40%) despite a smaller area proportion. Prioritization for benggang management is categorized into high, medium, and low priority, accounting for 17.98%, 48.34%, and 33.69%, respectively. These priorities cover areas of 30.27%, 42.40%, and 27.33%, respectively. The findings of this study, which offer benggang management priorities, align with the nature-based solutions approach. Emphasizing the importance of considering costs and benefits comprehensively when formulating treatment plans, this approach contributes to sustainable solutions for addressing the challenges posed by benggang. [ABSTRACT FROM AUTHOR]

Terraces, farmlands built along hillside contours, are common anthropogenically designed landscapes. Terraces control soil and water loss and improve land productivity; therefore, obtaining their spatial distribution is necessary for soil and water conservation and agricultural production. Spatial information of large-scale terraces can be obtained using satellite images and through deep learning. However, when extracting terraces, accurately segmenting the boundaries of terraces and identifying small terraces in diverse scenarios continues to be challenging. To solve this problem, we combined two deep learning modules, ANB-LN and DFB, to produce a new deep learning framework (NLDF-Net) for terrace extraction using remote sensing images. The model first extracted the features of the terraces through the coding area to obtain abstract semantic features, and then gradually recovered the original size through the decoding area using feature fusion. In addition, we constructed a terrace dataset (the HRT-set) for Guangdong Province and conducted a series of comparative experiments on this dataset using the new framework. The experimental results show that our framework had the best extraction effect compared to those of other deep learning methods. This framework provides a method and reference for extracting ground objects using remote sensing images. [ABSTRACT FROM AUTHOR]

In the context of uncertain economic environments urban agglomerations play a crucial role in economic development, reshaping industrial chains and fostering inter-city cooperation. This study employs the Global Production Network (GPN) theory to enhance our understanding of how cities integrate into regions, emphasizing the often-overlooked governmental influence in strategic coupling processes. In examining the evolution of China's smartphone industry within the Guangdong-Hong Kong-Macao Greater Bay Area (GBA) this research categorizes 19,599 smartphone companies into five distinct groups. Through analyzing their spatial distribution and geographical linkage the study identifies four strategic coupling modes based on the localization of assets, considering spatial influence and technological complexity along horizontal and vertical dimensions. Structural and institutional elements within these modes are also explored. The research uncovers unique integration patterns among nine cities in the GBA's mobile industry, revealing distinct spatial clusters rooted in technological, resource and innovation factors. Crucially, local policies play a pivotal role. Cities such as Shenzhen and Dongguan emerge as technology hubs, contrasting with Foshan and Zhongshan, which leverage resource advantages. The spatial impact, contingent on specific assets, underscores the necessity for nuanced top-down coupling methods in regional development. Moreover, the study emphasizes the significance of nurturing innovation links, not only between leading companies but also among midstream and downstream enterprises, enhancing cities' strategic coupling capabilities. [ABSTRACT FROM AUTHOR]

This study delves into the efficiency and structure of the social organization network involved in flood disaster management in China's Guangdong-Hong Kong-Macao Greater Bay Area (GBA). Leveraging the global knowledge graph, we analyzed social media data and news reports to identify the pivotal roles and interactions of various social organization types in flood disaster governance. The empirical findings underscore the central role of national public social organizations, evidenced by their high frequency of news coverage and mentions. Meanwhile, International Social Organizations and Local Public Social Organizations exhibit growing influence and participation. As China shifts toward a comprehensive disaster reduction strategy, the study observes increased diversity and decentralization among these organizations, with nongovernmental organizations and volunteer groups playing vital roles in resource mobilization, information dissemination, and disaster coordination. Network analysis unveils evolving interaction patterns and co-occurrence frequencies among social organizations, reflecting the evolving roles of different organization types in disaster relief. This research aims to provide actionable insights to enhance the effectiveness of social organizations in disaster management, contributing to the establishment of a more resilient and efficient disaster management framework in the GBA. [ABSTRACT FROM AUTHOR]

Tracking long-term tidal flat dynamics is crucial for coastal restoration decision making. Accurately capturing the loss and gain of tidal flats due to human-induced disturbances is challenging in the micro-tidal areas. In this study, we developed an automated method for mapping the annual tidal flat changes in the micro-tidal areas under intense human activities, by integrating spectral harmonization, time series segmentation from dual spectral indices, and the tide-independent hierarchical classification strategy. Our method has two key novelties. First, we adopt flexible temporal segments for each pixel based on the dual full-time series spectral indices, instead of solely using a fixed period window, to help obtain more reliable inundation frequency features. Second, a tide-independent hierarchical classification strategy based on the inundation features and the Otsu algorithm capture the tidal flat changes well. Our method performed well in Guangdong, Hong Kong, and Macao (GHKM), a typical area with micro-tidal range and intense human activities, with overall accuracies of 89% and 92% for conversion types and turning years, respectively. The tidal flats in GHKM decreased by 24% from 1986 to 2021, resulting from the loss of 504.45 km2, partially offset by an accretion of 179.88 km2. Further, 70.9% of the total loss was in the Great Bay Area, concentrated in 1991–1998 and 2001–2016. The historical trajectories of tidal flat loss were driven by various policies implemented by the national, provincial, and local governments. Our method is promising for extension to other micro-tidal areas to provide more scientific support for coastal resource management and restoration. [ABSTRACT FROM AUTHOR]