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This paper investigates the problem of spacing control between adjacent trains in train formation and proposes a distributed train-formation speed-convergence cooperative-control algorithm based on barrier Lyapunov function. Considering practical limitations such as communication distance and bandwidth constraints during operation, not all trains can directly communicate with the leader and obtain the expected trajectory it sends, making it difficult to maintain formation consistency as per the predetermined ideal state. Furthermore, to address the challenge of unknown external disturbances encountered by trains during operation, this paper designs a distributed observer deployed on each train in the formation. This observer can estimate and dynamically compensate for unknown reference trajectories and disturbances solely based on the states of adjacent trains. Additionally, to ensure that the spacing between adjacent trains remains within a predefined range, a safety hard constraint, this paper encodes the spacing hard constraint using barrier Lyapunov function. By integrating nonlinear adaptive control theory to handle model parameter uncertainties, a barrier Lyapunov function-based adaptive control method is proposed, which enables all trains to track the reference trajectory while ensuring that the spacing between them remains within the preset interval, therefore guaranteeing the asymptotic stability of the closed-loop system. Finally, a practical example using data from the Guangzhou Metro Line 22, specifically the route from Shiguang Road Station to Chentougang Station over three stations and two sections, is utilized to validate the effectiveness and robustness of the proposed algorithm. [ABSTRACT FROM AUTHOR]

Historic timber structures face substantial fire loads and complex fire risks. Subsequent renovations and utilization may influence their fire safety performance. Therefore, accurately predicting indoor fire development in historic buildings and assessing their fire safety performance is crucial. Numerical fire simulation is currently at the forefront of analyzing and assessing fire risks in historic buildings. However, there is a shortage of globally accessible historic building fire data. This paper proposes a method to determine fire scenarios, peak heat release rates, and development curves of indoor fires in wooden historic buildings through a fire load investigation. Using the Guangzhou ancestral hall as an example, PyroSim fire dynamics simulation software is employed to calculate fire development and assess the available safe evacuation time. The simulation results are subsequently input into the Pathfinder evacuation simulation software to ascertain the required safe evacuation time for indoor occupants. A comparative assessment is conducted to evaluate the fire safety performance before and after the renovation of historic buildings. The research findings indicate that installing closed glass curtain walls in the courtyards of ancestral hall buildings in Guangzhou accelerates the infiltration of smoke during fires, leading to rapid fire spread and long-distance ignition, significantly reducing the time available for safe evacuation. Therefore, when renovating and utilizing the ancestral hall buildings in Guangzhou, the installation of ventilation and smoke extraction systems should be prioritized to slow down fire development. Additionally, controlling the number of indoor occupants is an effective management measure to mitigate fire damage in historic buildings. [ABSTRACT FROM AUTHOR]

The study of clan paintings reveals a shift in perspective from art aesthetics to cultural connotations to cultural identity, yet literature seldom discusses the relationship between art and kinship culture. Taking the murals of ancestral hall architecture in Guangzhou as an example, this paper utilizes text mining to identify factors influencing its decorative art. It reveals the traditional rites' artistic expression through dimensions of characters' demeanor and the transmission of content values, offering a fresh perspective for heritage value research. Findings: (1) themes and implications are mostly oriented towards positive value transmission, transitioning from idealistic layman life to the realism of lower-class existence, emphasizing humanization; (2) the extroverted portrayal of characters contrasts with the dignified, restrained etiquette of traditional rites, with some characters' portrayal and facial expressions exuding approachability; (3) murals conveying positive emotions are mostly related to longevity, auspiciousness, fortune, and heroic deeds, while those conveying negative emotions mainly involve elderly male figures, reflecting a content bias related to characters; (4) historical allusion murals with complex content reduce the emotional resonance and arousal efficiency of the viewer; (5) incomplete mural content increases negative emotions in perceivers, highlighting the impact of mural preservation on emotional resonance. To delve into the formation of clan painting art, it is essential not only to interpret the diversity of its patterns but also to demonstrate the representation of its social attributes in decorative art. The formation of clan painting decorative art exhibits kinship cultural attributes, epitomizing the essence of traditional ceremonial thought. [ABSTRACT FROM AUTHOR]

​​​​​​​As a crucial constituent of fine particulate matter (PM2.5), secondary organic aerosols (SOAs) influence public health, regional air quality, and global climate patterns. This paper highlights the use of hydrophilic interaction liquid chromatography (HILIC) which effectively retains strongly polar analytes that might exhibit incomplete or no retention in reverse chromatography, resulting in superior separation efficiency. A HILIC column was used to analyze six standards, environmental standards (1648a and 1649b), and samples collected in urban environments in Guangzhou in the Pearl River Delta region, which serve as valuable reference points for evaluating the organic composition of the atmospheric environment. The results indicate a high degree of accuracy in the analytical method. Sodium octyl- d17 sulfate serves as the internal standard, with a linear correlation coefficient of the six standards, boasting a linear correlation coefficient r ranging from 0.993–0.9991 and a slope, k , of the linear equation from 0.966–1.882. The instrument detection limits (IDLs) are established at 0.03–0.20 µgmL-1 , while the method detection limits (MDLs) fall within the range of 0.30–1.75 ngm-3 , demonstrating the method's exceptional sensitivity. Since isoprene-derived organosulfates (iOSs) are highly polar due to containing a hydrophilic bond to the hydroxyl group and a hydrophobic bond to the sulfate, and as such showed strong retention using this method, this technique employs sodium ethyl sulfate and sodium octyl sulfate standards for semi-quantitative compound analysis of iOSs. The error in sample analysis (EA) ranged from 12.25 %–95.26 %, and the two standards maintained a consistent recovery rate between 116 %–131 % and 86.4 %–127 %. These findings indicate a high level of precision when semi-quantifying compounds with similar structural characteristics, affirming the analysis method's minimal relative error and underscoring its repeatability, process stability, and the reliability of its results for iOSs. To enhance the method's reliability assessment, the study analyzed polar organic components of standard particulate matter samples (1648a and 1649b), providing precise determinations of several iOSs using this method. Methyltetrol sulfate (m/z 215, C5H11SO7-) is the highest concentration in the ambient samples, up to 67.3 ngm-3 in the daytime. These results serve as valuable reference points for assessing the organic composition of the atmospheric environment. [ABSTRACT FROM AUTHOR]

Accurate and fast traffic prediction is the data-based foundation for achieving traffic control and management, and the accuracy of prediction results will directly affect the effectiveness of traffic control and management. This paper proposes a new spatiotemporal periodic polynomial model for road traffic, which integrates the temporal, spatial, and periodic features of speed time series and can effectively handle the nonlinear mapping relationship from input to output. In terms of the model, we establish a road traffic speed prediction model based on polynomial regression. In terms of spatial feature extraction methods, we introduce a maximum mutual information coefficient spatial feature extraction method. In terms of periodic feature extraction methods, we introduce a periodic trend modeling method into the prediction of speed time series, and effective fusion is carried out. Four strategies are evaluated based on the Guangzhou road speed dataset: a univariate polynomial model, a spatiotemporal polynomial model, a periodic polynomial model, and a spatiotemporal periodic polynomial model. The test results show that the three methods proposed in this article can effectively improve prediction accuracy. Comparing the spatiotemporal periodic polynomial model with multiple machine learning models and deep learning models, the prediction accuracy is improved by 5.94% compared to the best feedforward neural network. The research in this article can effectively deal with the temporal, spatial, periodic, and nonlinear characteristics of speed prediction, and to a certain extent, improve the accuracy of speed prediction. [ABSTRACT FROM AUTHOR]

The park-level integrated energy system (PIES) can realize the gradient utilization of energy and improve the efficiency of energy utilization through the coupling between multiple types of energy sub-networks. However, energy analysis and exergy analysis cannot be used to evaluate the economics of PIES. In addition, conflicts of interest among integrated energy suppliers make the economic scheduling of the PIES more difficult. In this paper, we propose a multi-objective collaborative game-based optimization method based on exergy economics, in which the introduction of exergy economics realizes the economic assessment of any link within the PIES, and the optimization model constructed based on the potential game solves the problem of conflict of interest among multiple energy suppliers and improves the benefits of each supplier. Finally, taking a PIES in Guangzhou as an example, the rationality of the optimization scheme proposed in this paper is demonstrated by comparing it with the classical optimization scheme. [ABSTRACT FROM AUTHOR]

To provide diverse book lending channels and services to the public and to guide healthy competition in library industry, this paper discusses the coopetition relationship between online shared bookstores and libraries from the perspective of readers' choice behavior. According to the questionnaire survey from 187 readers in Guangzhou, MNL model is adopted to quantitatively analyze the effect of various factors such as personal attributes, borrowing requirements and habits on readers' choice. Research shows that online shared bookstore and library, mainly public library, have different competition in terms of multi-dimensional needs and preferences. Thus, this paper proposes the potential cooperation mode between online bookstore and library in collection, data and space, so as to promote the mutually beneficial coexistence between them. [ABSTRACT FROM AUTHOR]

Along with reducing traffic congestion, electric car-sharing (ECS) can also solve travel demand, utilize idle resources, and enhance forms of transportation. Accordingly, university students are identified in Guangzhou, China as a promising customer group for ECS companies. Although university students in Guangzhou, China are unique in the context of ECS, few researchers have studied the factors that influence their willingness to use such services. Hence, this paper investigates the key determinants that affect university students' behavioral intention (BI) to use ECS in Guangzhou, China. Based on an extension of the unified theory of acceptance and use of technology (UTAUT), this paper examines the effects of the constructs on BI to use ECS and the moderating role of trust in ECS. Based on results from a questionnaire of 486 university students in Guangzhou, China, conditional value (CV) was the most critical driver of BI, followed by personal attitude (PA) and sustainability (SUST). The moderating analysis showed that taking gender, education, and driving experience of university students as control variables, trust was found to have a significant positive moderating impact on the relationship between PA and BI to use ECS, and a significant negative moderating effect of trust on the relationship between CV and BI to use ECS. The implications of these findings are discussed. [ABSTRACT FROM AUTHOR]

The relationship between social factors and the formation of exported mug designs is blurred. This study addresses how they influence material design. Based on a quantitative typological analysis, this paper interprets the cultural relationships underlying the evolution of mug designs exported during the Ming and Qing dynasties. The study reveals: (1) the typology and handle styles of mugs can be categorized into six types, with a predominance of Cylindrical bodies and Ear-shaped handles. Notably, artistic emphasis is concentrated on Bulbous cup bodies and Tail outward curved handles; (2) the design of mugs in the eighteenth century exhibited diversity, morphological similarities, and feature continuity, evolving from representational (Ming Dynasty) to abstract and then to minimalistic styles (Qing Dynasty), particularly evident in the three-dimensional modeling of handles for ergonomic comfort (transitioning from a singular Outward curved form to Ear-shaped form, and then to Entwined branch form); (3) the body shape of mugs transformed from Arc-barrel bodies to Bulbous bodies (focusing on heat retention), and eventually to Cylindrical bodies (prioritizing heat retention, practicality, and cost-efficiency).; (4) the volume of mugs steadily increased from the early eighteenth century, generally classified into large volume (≥ 500 ml), medium volume (200-499 ml), and small volume (< 200 ml); (5) considering the extreme range of volume (11090 ml) and height (25 cm), it indicates that these two variables do not necessarily have a direct positive correlation; (6) the low center of gravity in handle design (average width of 3.4 cm against an average height of 9.9 cm for the body) reflects considerations for the distribution of liquid weight and operational convenience. The volume of sample-10 (11127 ml) notably exceeds the typical range for tea-drinking utensils, revealing the mechanism of wealth and status symbolism. Market demand orientation emerged as the primary driver for the evolution of export mug designs during the Ming and Qing Dynasties, with adjustments in the merchant system playing a secondary role. The transformation in mug design reflects the interplay of multiple factors, with the trend toward minimalist design being a response to market demands. [ABSTRACT FROM AUTHOR]

Recent advances in knowledge graphs show great promise to link various data together to provide a semantic network. Place is an important part in the big picture of the knowledge graph since it serves as a powerful glue to link any data to its georeference. A key technical challenge in constructing knowledge graphs with location nodes as geographical references is the matching of place entities. Traditional methods typically rely on rule-based matching or machine-learning techniques to determine if two place names refer to the same location. However, these approaches are often limited in the feature selection of places for matching criteria, resulting in imbalanced consideration of spatial and semantic features. Deep feature-based methods such as deep learning methods show great promise for improved place data conflation. This paper introduces a Semantic-Spatial Aware Representation Learning Model (SSARLM) for Place Matching. SSARLM liberates the tedious manual feature extraction step inherent in traditional methods, enabling an end-to-end place entity matching pipeline. Furthermore, we introduce an embedding fusion module designed for the unified encoding of semantic and spatial information. In the experiment, we evaluate the approach to named places from Guangzhou and Shanghai cities in GeoNames, OpenStreetMap (OSM), and Baidu Map. The SSARLM is compared with several classical and commonly used binary classification machine learning models, and the state-of-the-art large language model, GPT-4. The results demonstrate the benefit of pre-trained models in data conflation of named places. [ABSTRACT FROM AUTHOR]

Efficient monitoring of water quality parameters (WQPs) is crucial for environmental health. Drone hyperspectral images have offered the potential for the flexible and accurate retrieval of WQPs. However, a machine learning (ML)-based multi-process strategy for WQP inversion has yet to be established. Taking a typical urban river in Guangzhou city, China, as the study area, this paper proposes a machine learning-based strategy combining spectral preprocessing and ML regression models with ground truth WQP data. Fractional order derivation (FOD) and discrete wavelet transform (DWT) methods were used to explore potential spectral information. Then, multiple methods were applied to select sensitive features. Three modeling strategies were constructed for retrieving four WQPs, including the Secchi depth (SD), turbidity (TUB), total phosphorus (TP), and permanganate index (CODMn). The highest R2s were 0.68, 0.90, 0.70, and 0.96, respectively, with corresponding RMSEs of 13.73 cm, 6.50 NTU, 0.06 mg/L, and 0.20 mg/L. Decision tree regression (DTR) was found to have the potential with the best performance for the first three WQPs, and eXtreme Gradient Boosting Regression (XGBR) for the CODMn. Moreover, tailored feature selection methods emphasize the importance of fitting processing strategies for specific parameters. This study provides an effective framework for WQP inversion that combines spectra mining and extraction based on drone hyperspectral images, supporting water quality monitoring and management in urban rivers. [ABSTRACT FROM AUTHOR]

Predicting carbon prices is crucial for the growth of China's carbon trading industry. This paper proposes a residual correction model that considers multiple influencing factors. First, the best historical data and main external factors input by the model are determined by using the partial autocorrelation function and Spearman correlation analysis, and the carbon price forecasting index system is constructed. Second, the whale optimization algorithm (WOA) is utilized to determine the optimal parameters of the extreme gradient boosting (XGBoost), and the WOA‐XGBoost model is built to perform preliminary carbon price forecasts and obtain the residual series. Finally, the carbon price residual series undergoes decomposition into multiple components utilizing the complete ensemble empirical mode decomposition for subsequent forecasting and the aggregation of outcomes. Experiments are conducted to predict two carbon trading markets in Hubei and Guangzhou, and a feature importance analysis is performed. The results indicate that the proposed hybrid model consistently outperforms the comparative models in terms of prediction accuracy. Furthermore, it is revealed that historical carbon prices and European Union carbon prices are the key factors influencing the prediction of carbon market prices. [ABSTRACT FROM AUTHOR]

Busy, complex railway stations that serve as origin and termination points for a significant proportion of trains are essential to regional railway networks. Resolving conflicts between arrival–departure operations and shunting operations of cross-line trains and originating or terminating passenger trains in the throat area is important for safety in these multidirectional stations. The main task of this paper is to study the train platforming problem, and we consider the integration of track and route allocation with shunting route allocation on the basis of the traditional TTP problem, so as to formulate a strong anti-interference track allocation plan for busy, complex railway stations. Therefore, in view of the complex characteristics of train operation in busy, complex railway stations, we extensively examine the technical operational characteristics of various trains in multidirectional stations, which are the key constraints of the model, and establish a mixed-integer linear programming model. This model aims to balance the buffer time for track occupation and optimize the routing and scheduling of trains in stations. Furthermore, an improved genetic algorithm is proposed to effectively implement the developed model. In the case study of Guangzhou Station, the occupation analysis after the optimization of the method in this paper indicates that the shunting operations significantly interfere with arrival–departure operations in throat areas. The optimization of buffer times and track utilization times resulted in notable reductions of 30.55% and 77.82%, respectively, in quadratic differences. These outcomes provide empirical evidence supporting the feasibility of the proposed model and algorithm for addressing train platforming problems, particularly in complex, multidirectional, and heavily trafficked railway stations. [ABSTRACT FROM AUTHOR]

As a crucial constituent of fine particulate matter (PM2.5), secondary organic aerosols (SOA) influence public health, regional air quality, and global climate patterns. This paper highlights the use of Hydrophilic interaction liquid chromatography (HILIC) which effectively retains strongly polar analytes that might exhibit incomplete or no retention in reverse chromatography, resulting in superior separation efficiency. A HILIC column was used to analyze 7 standards, environmental standards (1648a and 1649b), and samples collected in urban environments in the Pearl River Delta region of Guangzhou. That serve as valuable reference points for evaluating the organic composition of the atmospheric environment. The results indicate a high degree of accuracy in the analytical method, sodium octyl-d17 sulfate serves as the internal standard, with a linear correlation coefficient of the 7 standards, boasting a linear correlation coefficient R ranging from 0.987-0.999 and a slope, k, of the linear equation from 0.9662-2.2927. The instrument detection limit (IDLs) is established at 0.0026-0.0300 µg mL-1, while the method detection limit (MDLs) falls within the range of 0.0077-0.2300 ng m-3, demonstrating the method's exceptional sensitivity. Since isoprene sulphates are highly polar due to containing a hydrophilic bond to the hydroxyl group and a hydrophobic bond to the sulphate, and as such showed strong retention using this method. This technique employs Sodium ethyl sulfate and Sodium octyl sulfate standards for semi-quantitative compound analysis isoprene-derived OSs, the error in sample analysis (EA) ranged from 12.25-95.26% and the two standards maintaining a consistent recovery rate between 116%-131% and 86%-127%. These findings indicate a high level of precision when semi-quantifying compounds with similar structural characteristics, affirming the analysis method's minimal relative error and underscoring its repeatability, process stability, and the reliability of its results for isoprene OSs. To enhance the method's reliability assessment, the study analyzed polar organic components of standard particulate matter samples (1648a and 1649b), providing precise determinations of several isoprene OSs using this method. Methyltetrol sulfate (m/z 215) is the highest concentration in the ambient samples, up to 67.33 ng m-3 at daytime. These results serve as valuable reference points for assessing the organic composition of the atmospheric environment. [ABSTRACT FROM AUTHOR]

Guangzhou is located in the southern part of China, near the South China Sea, belongs to Guangdong Province. It serves as China's southern gateway to the world and was historically called "The Southern Treasure of the Emperor", playing a crucial role in China's maritime trade history. Throughout Chinese history, Guangzhou underwent several cycles of "closure" and "opening", policies that severely impacted trade. However, foreign merchants never ceased to request permission from the Chinese government to engage in trade. In 1757, Emperor Qianlong decreed Guangzhou as the only one official port for foreign trade. This edict led to the establishment of the one port trading system in Guangzhou, as the Qing government aimed to counter foreign influences and local uprisings while maintaining a self-sufficient small-scale agricultural economy. Western nations and their traders sought opportunities within this system, aspiring to expand trade to other regions of China, even the inland areas. The exclusive status of Guangzhou as China's only one trading port persisted until 1842, following the defeat of the Qing government in the Opium War against Britain, the Nanjing Treaty was signed. This treaty opened Guangzhou, Shanghai, Ningbo, Fuzhou, and Xiamen as trading ports, bringing an end to the one port trading system. The reasons and impacts of the establishment of this system have not been comprehensively analyzed to date. Therefore, this paper based on historical archives, analyzes both the direct and underlying reasons behind Emperor Qianlong's decision and elucidates the system's diverse impacts on various aspects of Qing-era China. [ABSTRACT FROM AUTHOR]

Accurately predicting carbon price is crucial for risk avoidance in the carbon financial market. In light of the complex characteristics of the regional carbon price in China, this paper proposes a model to forecast carbon price based on the multi-factor hybrid kernel-based extreme learning machine (HKELM) by combining secondary decomposition and ensemble learning. Variational mode decomposition (VMD) is first used to decompose the carbon price into several modes, and range entropy is then used to reconstruct these modes. The multi-factor HKELM optimized by the sparrow search algorithm is used to forecast the reconstructed subsequences, where the main external factors innovatively selected by maximum information coefficient and historical time-series data on carbon prices are both considered as input variables to the forecasting model. Following this, the improved complete ensemble-based empirical mode decomposition with adaptive noise and range entropy are respectively used to decompose and reconstruct the residual term generated by VMD. Finally, the nonlinear ensemble learning method is introduced to determine the predictions of residual term and final carbon price. In the empirical analysis of Guangzhou market, the root mean square error(RMSE), mean absolute error (MAE) and mean absolute percentage error (MAPE) of the model are 0.1716, 0.1218 and 0.0026, respectively. The proposed model outperforms other comparative models in predicting accuracy. The work here extends the research on forecasting theory and methods of predicting the carbon price. [ABSTRACT FROM AUTHOR]

Out-of-hospital cardiac arrest (OHCA) causes a high mortality rate each year, which is a threat to human well-being and health. An automated external defibrillator (AED) is an effective device for heart attack-related diseases and is a panacea to save OHCA. Most relevant literature focuses on the spatial distribution, accessibility, and configuration optimization of AED devices, which all belong to the characteristics of the spatial distribution of AED devices. Still, there is a lack of discussion on related potential influencing factors. In addition, analysis of AED facilities involving multiple city comparisons is less considered. In this study, data on AED facilities in two major cities in China were obtained through the WeChat applet. Then, the AED equity at the city and block scales and its socioeconomic factors were analyzed using the Gini coefficient, Lorenz curve, and optimal parameters-based geo-graphical detector (OPGD) model. Results show that the number of AEDs in Shenzhen was about eight-times that of in Guangzhou. The distribution of AEDs in Shenzhen was more equitable with a global Gini of 0.347, higher than that in Guangzhou with a global Gini of 0.504. As for the determinants of AED equity, residential density was the most significant determinant in both Guangzhou and Shenzhen from the perspective of individual effects on AED equity. Differently, due to the aging population in Guangzhou, the proportion of the elderly in blocks was influential to local AED equity. The local economic development level was crucial to local AED equity in Shenzhen. The results of the interaction detector model illustrate that relatively equitable AED distributions were found in the high-density residential areas with a balance of employment and housing, high-aging residential areas, and high-mobility residential areas in Guangzhou. The area with a high level of local economic development, dense population, and large mobility was the area with a relatively equitable distribution of AEDs in Shenzhen. The results of this paper are conducive to understanding the equity of AEDs and its socio-economic determinants, providing scientific reference for the optimization and management of AEDs. [ABSTRACT FROM AUTHOR]

Accurate forecasting of bus travel time and its uncertainty is critical to service quality and operation of transit systems: it can help passengers make informed decisions on departure time, route choice, and even transport mode choice, and it also support transit operators on tasks such as crew/vehicle scheduling and timetabling. However, most existing approaches in bus travel time forecasting are based on deterministic models that provide only point estimation. To this end, we develop in this paper a Bayesian probabilistic model for forecasting bus travel time and estimated time of arrival (ETA). To characterize the strong dependencies/interactions between consecutive buses, we concatenate the link travel time vectors and the headway vector from a pair of two adjacent buses as a new augmented variable and model it with a mixture of constrained multivariate Gaussian distributions. This approach can naturally capture the interactions between adjacent buses (e.g., correlated speed and smooth variation of headway), handle missing values in data, and depict the multimodality in bus travel time distributions. Next, we assume different periods in a day share the same set of Gaussian components, and we use time-varying mixing coefficients to characterize the systematic temporal variations in bus operation. For model inference, we develop an efficient Markov chain Monte Carlo (MCMC) algorithm to obtain the posterior distributions of model parameters and make probabilistic forecasting. We test the proposed model using the data from two bus lines in Guangzhou, China. Results show that our approach significantly outperforms baseline models that overlook bus-to-bus interactions, in terms of both predictive means and distributions. Besides forecasting, the parameters of the proposed model contain rich information for understanding/improving the bus service, for example, analyzing link travel time and headway correlation using covariance matrices and understanding time-varying patterns of bus fleet operation from the mixing coefficients. Funding: This research is supported in part by the Fonds de Recherche du Quebec-Societe et Culture (FRQSC) under the NSFC-FRQSC Research Program on Smart Cities and Big Data, the Canadian Statistical Sciences Institute (CANSSI) Collaborative Research Teams grants, and the Natural Sciences and Engineering Research Council (NSERC) of Canada. X. Chen acknowledges funding support from the China Scholarship Council (CSC). Supplemental Material: The e-companion is available at https://doi.org/10.1287/trsc.2022.0214. [ABSTRACT FROM AUTHOR]

In 2019, triggered lightning was used to directly strike phase C of a 1.5 km, 10 kV double‐circuit distribution line in Guangzhou, China, with 2 flashes (13 return strokes) recorded. This paper presents the characteristics of induced voltage at the closest pole on the parallel line. The induced voltage is bipolar, which can be divided into 3 stages: negative increase stage, fast and positive transition stage and slowly returning to zero stage. The positive peak value of phase A voltage ranges from 111.4 to 228.9 kV and that of phase C voltage is from 90.1 to 200.9 kV, corresponding to lightning current from −8.9 to −29.9 kA. The positive peak value of the induced voltage is approximately 3 to 10 times the negative peak value. The negative peak, positive peak and positive 10%–10% duration time of induced voltage show a good linear relationship with the peak value of the return stroke current. There was no flashover occurred during the whole process. The result in this paper can provide data support for numerical simulation and line protection analysis. [ABSTRACT FROM AUTHOR]

The regional energy system (RES) is a system that consumes multiple forms of energy in the region and achieves coordinated and efficient utilization of energy resources. The RES is composed of multiple micro energy systems (MESs); however, due to the mismatch of energy resources and different energy consumption within each MES, a large amount of clean energy is wasted, and each MES has to acquire extra energy. This significantly increases operation costs and contributes to environmental pollution. One of the promising ways to solve this problem is to deploy an energy storage system in the RES, which can make use of its advantages to transfer energy in space-time and fulfill the demand for loads in different periods, and conduct unified energy management for each MES in the RES. Nevertheless, a large number of users are deterred by the high investment in energy storage devices. A shared energy storage system (SESS) can allow multi-MESs to share one energy storage system, and meet the energy storage needs of different systems, to reduce the capital investment of energy storage systems and realize efficient consumption of clean energy. Taking multiple MESs as the object, this paper proposes a model and collaborative optimal strategy of energy management for the RES to accomplish high utilization of clean energy, environmental friendliness, and economy. First, the paper analyzes the internal energy supply characteristics of the RES and develops a model of the RES with an SESS. Then, the paper poses the management concept of load integration and unified energy distribution by using the operational information of each subsystem. An optimal operation strategy is established to minimize daily operation costs and achieve economic, environmentally friendly, and efficient operation of the RES. Third, by setting up scenarios such as no energy storage system and an independent energy storage system (IESS) of each MES and SESS, a case of a science and education park in Guangzhou, China, is illustrated for experiments. Numerical experiment results show that with an SESS built by the investor in the RES and applying the mentioned energy management strategy, the utilization of clean energy can be 100%, the operation costs can be reduced by up to 9.78%, the pollutant emission can be reduced by 3.92%, and the peak-to-valley difference can be decreased by 20.03%. Finally, the influence of energy storage service fees and electricity tariffs on daily operation costs is discussed, and the operation suggestions of the SESS are proposed. It validates the effectiveness of the proposed strategy. [ABSTRACT FROM AUTHOR]

In the second half of the 19th century, Shamian was established and developed as a colonial island enclave in the Chinese city of Guangzhou. Simultaneously, literary and cultural imaginations, depictions, and narrations of the place produced a discourse of Shamian as a utopian island: geographically insular and bounded, environmentally beautiful and peaceful, socially exclusive and harmonious, and technologically progressive and advantageous. This paper examines contemporaneous (predominantly English) literary and cultural representations of Shamian as a colonial utopia and their interrelations with the island's spatial formation and evolution. These texts (primarily written and pictorial descriptive, nonfictional accounts) reflected the spatial reality but also promoted spatial practices that reinforced the physical utopian island. This process exemplifies the theories of performative geographies in island studies and intertextuality in geocriticism, showing how a place's spatial representations and reality are mutually constructed. Adopting a conceptual model of intertextual performative geographies, this paper investigates the dynamic interplay of these literary and cultural texts with the spatial reality, arguing that literary and cultural representations of Shamian (re)produced the colonial enclave as a utopian island, both conceptually and practically. [ABSTRACT FROM AUTHOR]

The accurate prediction of passenger flow is crucial in improving the quality of the service of intercity high-speed railways. At present, there are a few studies on such predictions for railway origin–destination (O-D) pairs, and usually only a single factor is considered, yielding a low prediction accuracy. In this paper, we propose a neural network model based on multi-source data (NN-MSD) to predict the O-D passenger flow of intercity high-speed railways at different times in one day in the short term, considering the factors of time, space, and weather. Firstly, the factors that influence time-varying passenger flow are analyzed based on multi-source data. The cyclical characteristics, spatial and temporal fusion characteristics, and weather characteristics are extracted. Secondly, a neural network model including three modules is designed based on the characteristics. A fully connected network (FCN) model is used in the first module to process the classification data. A bi-directional Long Short-Term Memory (Bi-LSTM) model is used in the second module to process the time series data. The results of the first module and the second module are spliced and fused in the third module using an FCN model. Finally, an experimental analysis is performed for the Guangzhou–Zhuhai intercity high-speed railway in China, in which three groups of comparison experiments are designed. The results show that the proposed NN-MSD model can predict many O-D pairs with a high and stable accuracy, which outperforms the baseline models, and multi-source data are very helpful in improving the prediction accuracy. [ABSTRACT FROM AUTHOR]

The rapid expansion of metro networks, e.g., in many cities of China, continuously introduces the operation of new stations every year. Due to the lack of historical data and complicate variations of short-term passenger flow in the early stage of operation, it is difficult to accurately predict inbound and outbound passenger flows of new metro stations in the short term, which would be the database for train scheduling for new stations before operation, dynamic capacity optimization for new stations under operation, short-term prediction of cycle sharing demands near new stations, and so on. Traditional methods usually failed to exactly reflect the complicate rules or were unusable without the new station's historical data. In order to solve the above problems, this paper proposes a short-term inbound and outbound passenger flow prediction model for new metro stations at the early stage of operation by combining the K-means clustering algorithm, an improved spatiotemporal long short-term memory model (Sp-LSTM), and a real-time feedback error model (mean absolute error, MAE), where passenger flows' spatial-temporal characteristics and land-use relevance are considered. The application in Guangzhou Metro, China, where Line 21 is regarded as a new line, shows that the proposed K-Sp-LSTM model has the best prediction accuracy compared with traditional methods. [ABSTRACT FROM AUTHOR]

The urban road network is a large and complex system characterized by significant heterogeneity arising from different spatial structures and traffic demands. To facilitate effective management and control, it is necessary to partition the road network into homogeneous sub-areas. In this regard, we aim to propose a hybrid method for partitioning sub-areas with intra-area homogeneity, inter-area heterogeneity, and similar sizes, called CSDRA. It is specifically designed for bidirectional road networks with segment weights that encompass traffic flow, speed, or roadside facility evaluation. Based on community detection and spectral clustering, this proposed method comprises four main modules: initial partition, partitioning of large sub-areas, reassignment of small sub-areas, and boundary adjustment. In the preliminary partitioning work, we also design a road network reconstruction method which further helps to enhance the intra-area homogeneity and inter-area heterogeneity of partitioning results. Furthermore, to align with the requirement for comparable work units in practical traffic management and control, we control the similarity in the size of sub-areas by enforcing upper and lower bound constraints on the size of the sub-areas. We verify the outperformance of the proposed method by an experiment on the partitioning of an urban road network in Guangzhou, China, where we employ sidewalk barrier-free score data as segment weights. The results demonstrate the effectiveness of both the road network reconstruction method and the CSDRA proposed in this paper, as they significantly improve the partitioning outcomes compared with other methods using different evaluation indicators corresponding to the partitioning objectives. Finally, we investigate the influence of constraint parameters on the evaluation indicator. Our findings indicate that appropriately configuring these constraint parameters can effectively minimize sub-region size variations while having minimal impact on other aspects. [ABSTRACT FROM AUTHOR]

This paper summarizes the results of an effort aimed at improving train operation schedules on Wuhan-Guangzhou high-speed railway (WG-HSR). The real-record train operation and passenger tickets-booking records of WG-HSR are used for statistical analysis on the train service quality and passenger distribution. More specifically, the train service frequency and interval time at each station are analyzed. Based on this, the temporal and spatial distribution of capacity utilization in each section are investigated. In order to get a holistic view of passenger flow characteristics, the passenger volume during different time periods and between several origin and destination (OD) pairs are investigated to characterize travellers' spatial-temporal preferences. The passenger distributions on some long-distance trains are shown to get the number and proportion of cross-line passengers travelling on the WG-HSR. Moreover, for a better understanding of the seat capacity utilization of trains, the load rates of trains in various sections and time periods are investigated. Specifically, the relationship between the average load rate of trains and trains' running distance is explored, finding that the longer the non-cross-line train travels is, the higher the average load rate is. This study provides insightful findings that help understanding HSR operation and conducting further research. [ABSTRACT FROM AUTHOR]

High-rise building areas (HRBs) provide significant social and environmental services and play a crucial role in modern urbanization. The large-scale and long-term spatial distribution of HRBs is of great interest to many fields, such as urban planning and local climate analysis. While previous studies have confirmed the value of Sentinel-2 images in extracting HRBs and their changes, current work is limited to relatively local areas and short-term analysis. One reason is due to the fact that the earliest Sentinel-2 image can only date back to 2015. To address this research gap, this paper proposes an efficient procedure to intelligently extract HRBs and their changes from multitemporal Landsat-7 and Sentinel-2 images, using a specifically designed fully convolutional network. To validate the proposed method, we selected four typical cities in China, namely, Beijing, Shanghai, Guangzhou, and Zhengzhou, as study areas. We utilized Landsat-7 images acquired in 2000 and 2010, along with Sentinel-2 images acquired in 2020, as experimental data. We extracted and analyzed three periods of HRBs and their changes in the four cities, along with urban rail terminal data and gross domestic product (GDP) data in the same period. The results show that the proposed method can efficiently extract HRBs and their changes in the four cities over the past 20 years, with an overall accuracy of more than 90%. HRBs changes are primarily driven by urban planning policies and geographical factors. There is a strong positive correlation between the increase in HRBs and the increase in rail terminals, both in terms of quantity and spatial distribution. Additionally, there is a positive correlation between HRBs increase and GDP increase in terms of quantity, but the trend varies in different cities due to their diverse developing modes. Overall, the results indicate that the proposed method can be a potential operational tool to extract large-scale and long-term HRBs and their changes in China. [ABSTRACT FROM AUTHOR]

Relying on the Hongshengsha-Yufengwei Section Tunnel Project of the second phase of Guangzhou Metro Line 7, this paper used the particle swarm optimization random forest method (PSO-RF) to establish a sealed cabin pressure prediction model in the dual-mode shield soil pressure boring mode. Through the correlation analysis of shield boring parameters, the tunneling parameters that had a great influence on the pressure of the sealing chamber were screened out, including screw speed, screw torque, cutterhead speed, propulsion speed, penetration degree, cutterhead torque, and total thrust, and the screened tunneling parameters were used as the input parameters of the prediction model, and the pressure of the sealing chamber was used as the output parameter of the model to predict the pressure of the sealing chamber. The results showed that the random forest PSO-RF prediction model improved by particle swarm algorithm could effectively predict the pressure of the dual-mode shield sealed cabin, and compared with the traditional neural network prediction model, the PSO-RF model had higher prediction accuracy, the average absolute error was within 10%, and the predicted value and the actual goodness-of-fit R2 were 0.901 4, which was significantly superior to the BP neural network in terms of prediction accuracy and generalization ability of the model. [ABSTRACT FROM AUTHOR]

Simple Summary: Adhesive tapes are as effective as baits and pitfall traps in monitoring Solenopsis invicta and Tapinoma melanocephalum in urban areas in southern China. However, adhesive tapes detected less ants and fewer species of non-target ants than baits and pitfall traps. Our study demonstrated that tape-paving behavior can potentially be used in developing more specific monitoring methods for S. invicta and T. melanocephalum in urban areas in southern China. Our previous study discovered that two urban pest ants, red imported fire ants, Solenopsis invicta Buren (Formicidae: Myrmicinae), and ghost ants, Tapinoma melanocephalum (Fabricius) (Formicidae: Dolichoderinae), can pave viscose surfaces with particles to facilitate food search and transport. We hypothesize that this paving behavior can be applied to monitor S. invicta and T. melanocephalum. In the present study, 3998 adhesive tapes, each with a food source (sausage), were placed in 20 locations around Guangzhou, China (181–224 tapes per location), and their efficiency to detect S. invicta and T. melanocephalum was compared with two traditional ant-monitoring methods, baiting and pitfall trapping. Overall, S. invicta was detected by 45.6% and 46.4% of baits and adhesive tapes, respectively. In each location, the percentage of adhesive tapes detecting S. invicta and T. melanocephalum was similar when compared to baits and pitfall traps. However, significantly more non-target ant species showed up on bait and pitfall traps. Seven non-target ant species—Pheidole parva Mayr (Formicidae: Myrmicinae), Pheidole nodus Smith (Formicidae: Myrmicinae), Pheidole sinica Wu & Wang (Formicidae: Myrmicinae), Pheidole yeensis Forel (Formicidae: Myrmicinae), Carebara affinis (Jerdon) (Formicidae: Myrmicinae), Camponotus nicobarensis Mayr (Formicidae: Formicinae), and Odontoponera transversa (Smith) (Formicidae: Ponerinae)—also showed tape paving behavior, but they can be easily distinguished morphologically from S. invicta and T. melanocephalum. Our study showed that the paving behavior occurs in different subfamilies of ants (i.e., myrmicinae, dolichoderinae, formicinae, and ponerinae). In addition, paving behavior can potentially be used to develop more specific monitoring methods for S. invicta and T. melanocephalum in urban areas in southern China. [ABSTRACT FROM AUTHOR]

Background: In the context of the "silver wave" and "technology wave", smart home care for older adults in the community provide new ways for China and other countries to support ageing in place. Yet, only very few studies have focused on developing a quality index system of smart care. This study attempted to draw on the SERVQUAL model to establish a quality evaluation index system for smart senior care for older adults in the community. Methods: On the basis of the service quality model, this paper has integrated qualitative and quantitative analyses using the Delphi and Analytic Hierarchy Process (AHP) methods to construct the index system of smart home care in the community and obtain the weights. These were based on literature research and field interviews in Guangzhou and Shenzhen pilot districts. Results: A quality evaluation indexes system of smart home care for older adults in the community was developed, with 5 primary indices and 33 secondary indices. The weights of the 5 stair indices from high to low were smart emergency assistance 0.332, smart meal assistance 0.272, smart medical assistance 0.229, smart cleaning assistance 0.110 and smart amusement assistance 0.057. Conclusion: The results from the weight allocation revealed smart emergency assistance, smart meal assistance, and smart medical care assistance were the most important and crucial aspects of community-based smart home care. The study also suggested that "timeliness", "reliability", and "ease of use" should be given more attention. It is recommended to use this index system as a regulatory benchmark to guide the government bodies, senior care enterprises and communities to take measures to enhance the quality. [ABSTRACT FROM AUTHOR]

This paper presents a case study of deep excavation adjacent to an existing bridge in karst region of Guangzhou city, China. The movements of retaining structures, settlements of surrounding ground and pipelines, and the responses of bridge piles were measured and evaluated. A sudden surge of groundwater was recorded at the north pit when excavated halfway. Soil-cement columns using the Metro Jet System (MJS) method was employed along the outer perimeters of the diaphragm wall where water inflow occurred, for the sake of blocking the flow channels. The measured maximum wall deflection dhm in this case ranged from 0.13 %H to 0.3 %H, with a mean value of 0.2 %H (H is the excavation depth), which agreed well with the empirical prediction in mixed ground. During the MJS treatment, the wall and surrounding soils experienced notable lateral deflection and settlement. The bridge piles experienced significant settlement since the excavation commenced, which might be attributed to the inherent deficiency in geological condition and pile length. The soil disturbance induced by the adjacent deep excavation accelerated bridge settlement. The finite element analysis revealed that the excessive settlement of the bridge piles and ground surface resulted from confined-water withdrawal in sand layers. [ABSTRACT FROM AUTHOR]

Flood represents a temporary cover of water that submerges land, usually not covered by water, which is caused by water overflowing the watercourses. The floods that occur in the area of Zenica-Doboj Canton in Bosnia and Herzegovina cause massive damage to agriculture, housing, equipment and civil engineering facilities and can be characterized as hazards. The hydrographic backbone of this Canton is the river Bosna, and the subject of research in this paper is the flood vulnerability in Zenica-Doboj Canton. The result of the work is the production of flood hazard and risk maps using GIS. Geographic Information System (GIS)-based spatial analysis and visual elements have been used frequently in recent years for detection of flood hazard areas and preparation of maps. GIS applications are based on a database and analysis tools which have logical and mathematical relationships between the layers. When creating the flood hazard map, in addition to GIS tools, Hec-RAS was also used as a program intended for the analysis of hydraulic calculations. The results of this paper are of great importance for spatial planning and environmental protection, starting with local communities, municipalities and the entire Canton. [ABSTRACT FROM AUTHOR]

Best subset of groups selection (BSGS) is the process of selecting a small part of nonoverlapping groups to achieve the best interpretability on the response variable. It has attracted increasing attention and has far-reaching applications in practice. However, due to the computational intractability of BSGS in high-dimensional settings, developing efficient algorithms for solving BSGS remains a research hotspot. In this paper, we propose a group-splicing algorithm that iteratively detects the relevant groups and excludes the irrelevant ones. Moreover, coupled with a novel group information criterion, we develop an adaptive algorithm to determine the optimal model size. Under certain conditions, it is certifiable that our algorithm can identify the optimal subset of groups in polynomial time with high probability. Finally, we demonstrate the efficiency and accuracy of our methods by comparing them with several state-of-the-art algorithms on both synthetic and real-world data sets. History: Accepted by Andrea Lodi, Area Editor for Design & Analysis of Algorithms-Discrete. Funding: This work was supported by National Natural Science Foundation of China [Grants 72171216, 71921001, and 71991474], the Key Research and Development Program of Guangdong [Grant 2019B020228001], the Science and Technology Program of Guangzhou, China [Grant 202002030129], The Fundamental Research Funds for the Central Universities and the Research Funds of Renmin University of China [Grant 22XNH161], and the Outstanding Graduate Student Innovation and Development Program of Sun Yat-Sen University [Grant 19lgyjs64]. Supplemental Material: The online appendix and video are available at https://doi.org/10.1287/ijoc.2022.1241. [ABSTRACT FROM AUTHOR]

Hydro-meteorological monitoring systems are essential sources of information for designing sustainable water management and developing equitable policies. In this paper we discuss the role of a regional monitoring network in Switzerland, Canton Ticino, and identify a list of challenges and actions to be considered to address future expected issues. The identification is based on a retrospective review of the monitoring network and analysis of the state of the art. Based on our experience, the most critical aspects relate to data density and quality, improvement of archiving and dissemination, modernization of the network for increased quality of service and better interoperability to foster the transition towards an innovative and interdisciplinary water resources management. [ABSTRACT FROM AUTHOR]

In the distribution network planning work, taking into account the constraints of funds and construction annual plan, there is always the prioritization problem of multiple MVDC distribution networks. Aiming at the problem, this paper proposes a method for the optimization analysis of MVDC distribution network planning objects. Firstly, a comprehensive evaluation index system of urban MVDC distribution network planning and optimization is established from three aspects of improving power supply reliability and the quality of voltage, MVDC distribution network scenario types and distribution network social benefits. Then, the qualitative indexes are quantified based on the cloud model, and the comprehensive evaluation values of each distribution network are calculated and sorted by the hierarchical additive weighting of Delphi entropy weight method. Taking the maximum comprehensive evaluation value of the scheme as objective function and the annual total investment as constraint, the construction priority of multiple MVDC distribution networks is determined. Finally, taking several distribution networks in Guangzhou, Guangdong Province, China as examples, the simulation results show that the optimal objects of distribution network based on the urban scenario demands are X4, X8 and X1, with the lowest total investment cost of 19,180 yuan, thus verifying the effectiveness of the proposed method. [ABSTRACT FROM AUTHOR]

With the process of increasing urbanization, there is great significance in obtaining urban change information by applying land cover change detection techniques. However, these existing methods still struggle to achieve convincing performances and are insufficient for practical applications. In this paper, we constructed a new data set, named Wenzhou data set, aiming to detect the land cover changes of Wenzhou City and thus update the urban expanding geographic data. Based on this data set, we provide a new self-attention and convolution fusion network (SCFNet) for the land cover change detection of the Wenzhou data set. The SCFNet is composed of three modules, including backbone (local–global pyramid feature extractor in SLGPNet), self-attention and convolution fusion module (SCFM), and residual refinement module (RRM). The SCFM combines the self-attention mechanism with convolutional layers to acquire a better feature representation. Furthermore, RRM exploits dilated convolutions with different dilation rates to refine more accurate and complete predictions over changed areas. In addition, to explore the performance of existing computational intelligence techniques in application scenarios, we selected six classical and advanced deep learning-based methods for systematic testing and comparison. The extensive experiments on the Wenzhou and Guangzhou data sets demonstrated that our SCFNet obviously outperforms other existing methods. On the Wenzhou data set, the precision, recall and F1-score of our SCFNet are all better than 85%. [ABSTRACT FROM AUTHOR]

Time-varying passenger flow is the input data in the optimization design of intercity high-speed railway transportation products, and it plays an important role. Therefore, it is necessary to predict the origin-destination (O-D) passenger flow at different times of the day in combination with the stable time-varying characteristics. In this paper, three neural network-based hybrid forecasting models are designed and compared, named Variational Mode Decomposition-Multilayer Perceptron (VMD-MLP), Variational Mode Decomposition-Gated Recurrent Unit Neural Network (VMD-GRU), and Variational Mode Decomposition-Bidirectional Long Short-Term Memory Neural Network (VMD-Bi-LSTM). First, the time-varying characteristics of passenger travel demand under different time granularities are analyzed and extracted by the VMD method. Second, three neural network prediction models are constructed to predict the passenger flow sequence after VMD decomposition and reconstruction. Experimental analysis is performed on the Guangzhou Zhuhai intercity high-speed railway in China, and the passenger flow at different time periods of the day under different time granularities is predicted. The following results were found: (i) The number of hidden neurons and the number of iterations of the hybrid forecasting model have a great impact on the prediction accuracy. The error of the VMD-MLP model fluctuates less and it performs more smoothly than both the VMD-GRU model and the VMD-Bi-LSTM model. (ii) The VMD-MLP, VMD-GRU, and VMD-Bi-LSTM models can basically reduce the MAPE error to less than 10%. With the increase of time granularity, RMSE and MAE errors tend to gradually increase, while the MAPE error tends to gradually decrease. (iii) For passenger flow under a smaller time granularity, the prediction accuracy of the VMD-MLP model is higher, while for passenger flow under a larger time granularity, the prediction accuracy of the VMD-GRU and VMD-Bi-LSTM models is higher. (iv) The proposed neural network-based hybrid models outperform the existing models and the hybrid models perform better than the single models. [ABSTRACT FROM AUTHOR]

Reasonable road design can not only improve traffic efficiency, ensure driving safety, reduce construction costs, but also reduce the energy consumption of road transportation. However, existing road design research mainly focuses on safety, efficiency, and economic factors, with few studies considering the actual construction difficulty as well as energy saving and emission reduction of transportation. To fill the research gap, this paper proposes a longitudinal slope design method for highway sections that considers both vehicle fuel consumption minimization and construction feasibility. First, the passenger car trajectory data and fuel consumption data from the Beihuan Highway in Guangzhou, Guangdong Province, China were collected, and the optimal combination features of the random forest algorithm were extracted using parameters such as vehicle operating conditions and road grades, thus establishing a fuel consumption estimation model for passenger cars on highways. Second, by combining the fuel consumption estimation model with dynamic programming algorithm, an ecological speed planning model for passenger cars on highways was developed. Finally, for the scenario of ecological driving of intelligent vehicles, a fuel-efficient slope design (FSD) method was proposed based on the Nested Monte Carlo Tree Search algorithm, and the best fuel-efficient slope design was selected considering the balance of cut and fill factors. The results show that FSD can reduce the fuel consumption of passenger cars on the section by about 4.50%–6.52%; the fuel-efficient slope design considering the balance of cut and fill not only can reduce the fuel consumption of passenger cars on the section by about 5.21%, but also has the advantage of lower engineering volume. The research results conduce to establish a theory and standards for fuel-efficient slope design of highways suitable for ecological driving scenarios of intelligent vehicles, which can reduce the trip fuel consumption of highway traffic flow in the long run while considering traffic efficiency, and have great significance for reducing carbon emissions, saving travel costs, and protecting non-renewable resources. • The study proposes a fuel-efficient longitudinal slope design method for highway. • The method can reduce the fuel consumption of a passenger car by about 4.50%–6.52%. • The concave curve is effective in reducing fuel consumption for passenger cars. • Considering the balance of cut and fill makes the slope scheme more fuel-efficient. [ABSTRACT FROM AUTHOR]

A precise air pollution forecast is the basis for targeted pollution control and sustained improvements in air quality. It is desirable and crucial to select the most suitable model for air pollution forecasting (APF). To achieve this goal, this paper provides a comprehensive evaluation of performances of different models in simulating the most common air pollutants (e.g., PM2.5, NO2, SO2, and CO) in Guangzhou (23.13° N, 113.26° E), China. To simulate temporal variations of the above-mentioned air pollutant concentrations in Guangzhou in September and October 2020, we use a numerical forecasting model (i.e., the Weather Research and Forecasting model with Chemistry (WRF-Chem)) and two artificial intelligence models (i.e., the back propagation neural network (BPNN) model and the long short-term memory (LSTM) model). WRF-Chem is also used to simulate the meteorological elements (e.g., the 2 m temperature (T2), 2 m relative humidity (RH), and 10 m wind speed and direction (WS, WD)). In order to investigate the simulation accuracies of classical APF models, we simultaneously compare the simulations of the WRF-Chem, BPNN, and LSTM models to ground truth observations. Comparative assessment results show that WRF-Chem simulated air pollutant (i.e., PM2.5, NO2, SO2, and CO) concentrations have the best correlations with ground measurements (i.e., Pearson correlation coefficient R = 0.88, 0.73, 0.61, and 0.61, respectively). Furthermore, to evaluate model performance in terms of accuracy and stability, the normalized mean bias (NMB, %) and mean fractional bias (MFB, %) are adopted as the standard performance metrics (SPMs) proposed by Boylan et al. The comparison results indicate that when simulating PM2.5, WRF-Chem was more effective than the BPNN but less effective than the LSTM. While simulating concentrations of NO2, SO2, and CO, the WRF-Chem model performed better than the BPNN and LSTM models. With regards to WRF-Chem, the NMBs and MFBs for the PM2.5 simulations are, respectively, 6.49% and 0.02%, –11.96% and –0.031% for NO2, 7.93% and 0.019% for CO, and 5.04% and 0.012% for SO2. Our results suggest that WRF-Chem has superior performance and better accuracy than the NN-based prediction models, making it a promising and useful tool to accurately predict and forecast regional air pollutant concentrations on a city scale. [ABSTRACT FROM AUTHOR]

Previous studies on the Nanhaishen Temple 南海神廟 (Temple of the South Sea God) in Guangzhou in the Song dynasty focus mainly on its state sacrificial ritual and local temple fairs, without fully discussing the differences of the sacrificial ritual between the Southern and Northern Song dynasties or the changes of the sacrificial ritual in Lingnan after the Song dynasty. This paper aims to illuminate the following five points. First, after the reunification of the Northern Song dynasty, the sacrificial ritual to the South Sea God in Guangzhou was advanced. Second, when the South Sea God and his temple were conferred with the holy titles for the fourth time, the god's role to bless local stability was further manifested, which means the imperial power gradually permeated into the Lingnan culture. Third, the blessing of the South Sea God was more prominent than ever before because of its geographical location in the southeast of the state during the Southern Song dynasty, and thus the Nanhaishen Temple Fair was the largest of its kind in Lingnan. Fourth, the stele inscription of Liuhou Zhi Ji 六侯之記 (Records of the Six Lords) shows that local people attempted to incorporate their folk beliefs into the canonized sacrifice to the South Sea God, and thus many religious spots were built in other places in Lingnan as detached palaces (ligong 離宮) of the god who was generally endorsed by the local officialdom. Fifth, the sacrifice to the South Sea God in Guangzhou in the Song dynasty had a far-reaching influence, as the god was worshipped by the later generations in the temples which also accommodated the worship of Buddhism and Daoism. In summary, the lengthy process for the South Sea God to evolve from a national god to a local patron is the result of the country's long-term implementation of the ritual system as far as the ritual culture is concerned. [ABSTRACT FROM AUTHOR]

In this paper, an experimental rig of a prefabricated temporary house (PTH) was first established. Then, predicted models for the thermal environment of the PTH with and without considering long-wave radiation were developed. Next, the exterior-surface, interior-surface and indoor temperatures of the PTH were calculated by using the predicted models. The calculated results were then compared with the experimental results to study the influence of long-wave radiation on the predicted characteristic temperature of the PTH. Finally, the predicted models were used to calculate the cumulative annual hours and the intensity of the greenhouse effect of four different climate cities (Harbin, Beijing, Chengdu, Guangzhou, China). The results showed that: (1) the predicted temperature values of the model considering long-wave radiation were closer to the experimental results; (2) the effect level of the long-wave radiation on the three characteristic temperatures of the PTH from big to small was: exterior-surface temperature, interior-surface temperature, and indoor temperature; (3) the long-wave radiation had the greatest impact on the predicted temperature value of the roof; (4) under different climate conditions, the cumulative annual hours and the intensity of the greenhouse effect considering long-wave radiation were smaller than those without considering long-wave radiation; (5) the duration of the greenhouse effect considering and ignoring long-wave radiation varied significantly with the climate region, and that in Guangzhou was the longest, followed by Beijing and Chengdu, and that in Harbin was the shortest. [ABSTRACT FROM AUTHOR]

The purpose of this paper is to deal with the perception variance problem in regret model by scaling perception variance and relaxing the distribution assumption of the random error term, which are corresponding to the attribute and alternative differences, respectively. Specifically, the power coefficient in regret function can capture individual perception variance of specific attributes and latent class structure is used to analyze individual heterogeneity of alternative differences by defining weight function. Accordingly, perceiving behaviors of individuals are analyzed in depth and generating absolute and relative behavior interpretation and asymmetric property. The proposed models are estimated and analyzed using GPS data of taxis in Guangzhou, and bicycle household surveys are collected in Tel Aviv metropolitan area. The results show both significant effects of attributes on drivers' and cyclists' route choice behavior and identification of different types (absolute or relative) of perceiving attribute and alternative regret differences of the two travelers by incorporating the ratio of length between chosen route and the shortest route. [ABSTRACT FROM AUTHOR]

In this paper, we measured the amount of urban green space (UGS), defined here as park greenery and street greenery, in the Guangzhou Beltway region using remote sensing image data and the green view index (GVI) based on human visual images. We also evaluated the benefits of UGS comprehensively considering park greenery and street greenery within the Guangzhou Beltway region. We then calculated the urban green space score (UGSS) by assessing the amount of street greenery and park greenery and then juxtaposing the score with the population distribution of the region. The results show inequities in the spatial distribution of UGSS values within the Guangzhou Beltway region. The benefit score of street greenery is low. The service area of parks can't cover the whole study area. The comprehensive benefit score of UGS is composed of two parts, the park greenery score and the street greenery score, but the spatial distribution of UGSS values remains uneven. The UGS benefits enjoyed by one-half of the population of the study area are low, and the UGSS values of the more densely populated areas are not high. [ABSTRACT FROM AUTHOR]

Historical and cultural blocks not only carry numerous cultural heritages but always undertake urban functions. As a major driver of urban context construction, historical and cultural blocks have never been abandoned by the times. Regardless of the purposes for which they were built in the first place, all historical and cultural blocks should be extended functionally to rejuvenate them in current and future developments. In particular, in single-functional residential-type historical and cultural blocks, the large numbers of traditional architectures left vacant by out-migrating residents can be redeveloped for other functions. The effectiveness of block conservation and development affects the cultural construction of a city. More importantly, it is closely related to all kinds of users in blocks, as users engage in in-depth interactions with environments and are best able to discover the merits of block conservation and development. Moreover, the conservation and optimal design of historical and cultural blocks must, ultimately, be based on the users' needs. By collecting users' subjective evaluation opinions about two typical residential-type historical and cultural blocks based on the existing evaluation index and putting them through quantitative analysis, this paper analyzes block status in many aspects. It identifies focuses on optimization, offering references and suggestions for future development. [ABSTRACT FROM AUTHOR]

With the development of society and the improvement of urban economic level, people are no longer satisfied with the simple material and functional requirements of the city; thus, the spiritual requirements of city beauty, environmental quality, place atmosphere, and so on need to be improved. Based on the above background, the purpose of this paper is to analyze the landscape planning of Guangzhou's digital city based on spatial information from the perspective of smart cities. Based on the relevant theoretical research, this paper combs the ideas of intelligent urban road landscape design. This paper analyzes the concept of urban road and smart road and puts forward the definition and characteristics of intelligent urban road landscape; according to the research on the development status of urban road and the connotation of smart city, combined with the concept and principle of urban road landscape design, it summarizes the design method of smart city road landscape. This paper, taking the innovative design of urban landscape as the research object and using the research methods of literature analysis and field investigation, innovatively combines the urban landscape design with digital information technology, changes the traditional landscape design ideas, constructs the urban landscape innovative design model, realizes the personalization of the urban landscape design, as well as the intelligent, digital, diversified, and humanized service and function. The experimental results show that nearly 60% of people are satisfied with the Guangzhou digital urban landscape planning based on spatial information in the smart city perspective. [ABSTRACT FROM AUTHOR]

Information on the 4th International Symposium on Heat Transfer and Energy Conservation (ISHTEC2012) held in Guangzhou, China on January 6-9, 2012 is presented. It says that the conference was organized by Key Laboratory of Heat Transfer and Energy Conservation at the Education Ministry of the South China University of Technology. Moreover, a warm response from researchers in the heat transfer was received by the conference.

The construction of the Guangdong-Hong Kong-Macao Greater Bay Area requires the establishment of a comprehensive port cooperation mechanism. Coordination is key to port integration. This paper establishes a multi-group Lotka-Volterra model based on the theory of competition and cooperation to study the evolution of the relationships among Hong Kong Port, Shenzhen Port, and Guangzhou Port. The results show that the three major ports have predator-prey roles and form a symbiotic competition and cooperation ecological pattern. The extended Hotelling model is established to study the central ports' spatial differences; the results show that whether it is the central port cooperation body or the entire central port group, the gains from Shenzhen Port-Guangzhou Port cooperation are greater than between either and Hong Kong Port for Guangdong-Hong Kong-Macao Greater Bay Area's container business. The biggest competitor to Hong Kong Port in the container business is Guangzhou Port. Hong Kong Port will gain more from cooperation with Shenzhen Port and should strengthen such cooperation. The study provides quantitative support and reference for positioning the strategic development of the high-end shipping business in Hong Kong Port, the pilot free-trade port in Shenzhen Port, and the exploration of the Shenzhen-Hong Kong composite port, Guangzhou Port's comprehensive shipping hub. [ABSTRACT FROM AUTHOR]

The deep mining of passengers' travel data can identify competitive segments and gain insights into passengers' characteristics and differentiated demands. This can not only effectively support precise marketing strategy adjustment of railway transport but also improve its competitiveness in the passenger transportation market. In this paper, hidden railway travel behaviour is introduced and integrated with railway travel behaviour to create a complete passenger travel chain, based on existing distance-based competitive segment recognition methods. The loyalty index values of passengers are calculated using this travel chain to identify competitive segments. Furthermore, passenger classification and grouping currently ignore social relationships as well as personal travel characteristics. Therefore, a novel passenger grouping method is proposed; it integrates individuals' travel characteristics and social relations. Individual travel labels are created for travellers based on their travel data. Social relation topologies, such as ticketing relation, the relation of travelling together, and benefit relation via point redemption, can be extracted using these labels. Social relation traits can be retrieved using graph attention networks and multigraph fusion. Finally, travellers are categorised based on their individual travel characteristics. As an example, and a case study, the grouping of Guangzhou–Shanghai passengers in 2020 is taken which shows that the suggested method has the potential to improve both the precision and the feasibility of grouping railway passengers. As a result, new ideas for passenger grouping in railway marketing might be offered. [ABSTRACT FROM AUTHOR]

This paper focuses on the dynamic characteristics of the metro train's bogie frames based on the field test data. The acceleration signals of both motor bogie frame and trailer bogie frame of a standard B-type metro train were measured. Running tests on the Metro line 21 of Guangzhou (China) were carried out. The acquired acceleration signals of bogie frames were analyzed through several methods to identify the dynamic characteristics of the motor and trailer bogies in the time-frequency domain. The spectral analysis and time-frequency representations show that noise components exist in the high-frequency domain of the original signal, especially for the acceleration signal of the motor bogie frame. Then, the soft thresholding process and discrete wavelet transform decomposition process are conducted to obtain a denoised version of the original signals in the time-frequency domain. The vibration frequency domain and energy distribution of bogie frames under different train speeds are analyzed. The track irregularity wavelength of the metro line is calculated and analyzed based on the measured bogie frames' acceleration signals. The dynamic characteristics of the metro train's bogie frames in this paper can be adopted as a reference in the track diagnosis of the elevated metro line. [ABSTRACT FROM AUTHOR]

This paper proposes an intelligent modeling and optimization method to set up the smart energy hub model for the multiple energy system and optimize the operation of its associated energy hub model automatically. The core idea of the proposed method is to divide the complex energy hub model into several simple energy hub models. In this paper, the proposed computerized algorithm, which is applied to the smart energy hub model division, includes an energy loop intelligent searching method, a real nodes arrangement method, and a virtual nodes insertion method. One case study for the multiple energy system of a residential district in Guangzhou city is performed to validate the effectiveness of the proposed method. Compared with the traditional calculation method, the proposed method guarantees global optimal operation decision for the whole multiple energy system and the computational burden is significantly reduced. [ABSTRACT FROM AUTHOR]