Kernel Density Estimation Method Research Articles

Measurement and Spatial-Temporal Evolution of Industrial Carbon Emission Efficiency in Western China

As it is an important industrial base in China, it is of great significance to improve the industrial carbon emission efficiency in the western region to promote the low-carbon sustainable development of the region. This paper selects the input–output panel data of 11 provinces in western China from 2010 to 2021, and adopts the three-stage DEA model to measure the industrial carbon emission efficiency in western China under a non-traditional geographic division at the overall and regional levels and analyze its influencing factors. The Dagum Gini coefficient, its decomposition method, and the kernel density estimation method are used to analyze the regional differences and dynamic evolution process of industrial carbon emission efficiency in the western region. The results of the study show that (1) after removing environmental and random factors, the industrial carbon emission efficiency in western China has been improved, but there are inter-regional differences, characterized by “the third region > the second region > the first region”; (2) the levels of green development, shared development, innovative development, and coordinated development have a positive impact on the improvement of industrial carbon emission efficiency in western China, while the level of industrialization has a relatively smaller influence, and economic development, government support, open development level, and energy consumption structure have not yet played a significant role; (3) the spatial differences in the efficiency of industrial carbon emissions in western China have generally increased during the sample period, with inter-regional differences being the main source; and (4) the industrial carbon emission efficiency in western China is characterized by overall improvements in time and space but with stage differences and multi-polarization of regional differences. This study has a certain reference value for improving industrial carbon emission efficiency in western China.

Regional differences, dynamic evolution and trend prediction of green manufacturing development levels in China

Green manufacturing has become a necessary way to promote new industrialization and realize the high-quality development of China’s manufacturing industry. Based on the panel data of 30 provinces in China from 2012 to 2022, this paper constructs a comprehensive evaluation index system for the green manufacturing development level and introduces the TOPSIS- Gray correlation method to comprehensively measure the green manufacturing development level of China as a whole and the four major regions in the eastern, central, western, and northeastern parts of the country. The regional differences, distribution dynamics and evolutionary trends of China's green manufacturing development level are also explored with the help of the Gini coefficient, kernel density estimation and Markov chain methods. Research Findings: (1) The green manufacturing development level in China is on an upward trend, with an overall spatial distribution pattern of “East is superior and West is inferior”. (2) There are regional differences in the green manufacturing development level in China, and the differences are widening, with interregional differences being the main reason for this overall difference. (3) The country as a whole, the central region and the western region are polarized to varying degrees, with the rest of the country showing an improvement in polarization. (4) Without considering spatial factors, the development of green manufacturing in each province experiences “club convergence” in the short term, and it is difficult to realize rapid development. Considering spatial factors, China's green manufacturing development level is generally characterized by “elevated in proximity to high levels and suppressed in proximity to low levels”, and in the long run, it shows a distribution trend toward the concentration of high values. The findings of this study can provide new ideas for promoting synergistic efficient development of green manufacturing in China.

A Deep Learning Quantile Regression Photovoltaic Power-Forecasting Method under a Priori Knowledge Injection

Accurate and reliable PV power probabilistic-forecasting results can help grid operators and market participants better understand and cope with PV energy volatility and uncertainty and improve the efficiency of energy dispatch and operation, which plays an important role in application scenarios such as power market trading, risk management, and grid scheduling. In this paper, an innovative deep learning quantile regression ultra-short-term PV power-forecasting method is proposed. This method employs a two-branch deep learning architecture to forecast the conditional quantile of PV power; one branch is a QR-based stacked conventional convolutional neural network (QR_CNN), and the other is a QR-based temporal convolutional network (QR_TCN). The stacked CNN is used to focus on learning short-term local dependencies in PV power sequences, and the TCN is used to learn long-term temporal constraints between multi-feature data. These two branches extract different features from input data with different prior knowledge. By jointly training the two branches, the model is able to learn the probability distribution of PV power and obtain discrete conditional quantile forecasts of PV power in the ultra-short term. Then, based on these conditional quantile forecasts, a kernel density estimation method is used to estimate the PV power probability density function. The proposed method innovatively employs two ways of a priori knowledge injection: constructing a differential sequence of historical power as an input feature to provide more information about the ultrashort-term dynamics of the PV power and, at the same time, dividing it, together with all the other features, into two sets of inputs that contain different a priori features according to the demand of the forecasting task; and the dual-branching model architecture is designed to deeply match the data of the two sets of input features to the corresponding branching model computational mechanisms. The two a priori knowledge injection methods provide more effective features for the model and improve the forecasting performance and understandability of the model. The performance of the proposed model in point forecasting, interval forecasting, and probabilistic forecasting is comprehensively evaluated through the case of a real PV plant. The experimental results show that the proposed model performs well on the task of ultra-short-term PV power probabilistic forecasting and outperforms other state-of-the-art deep learning models in the field combined with QR. The proposed method in this paper can provide technical support for application scenarios such as energy scheduling, market trading, and risk management on the ultra-short-term time scale of the power system.

Research on the coupling measurement of medical education and health resource allocation under the background of healthy China

Objective To analyze the coupling and coordination level of medical education and health resource allocation in China, and to provide scientific basis for promoting the high-quality development of medical education and the efficient allocation of health resources.MethodsBased on the panel data from 2011 to 2021, the coupling coordination degree model was used to measure the coupling coordination index of medical education and health resources in China. The spatial auto-correlation model was used to analyze the development status and distribution characteristics of the coupling coordination degree of the two systems. The kernel density estimation method was used to analyze the dynamic evolution trend of the coupling coordination of the two systems. The QR quantile regression model was used to explore the key factors affecting the coupling coordination degree of the two systems.ResultsDuring the observation period, the coupling coordination degree of the two systems increased from 0.393 to 0.465, with a growth rate of 18.3%. The coupling coordination degree between regions gradually decreased in the eastern-central and eastern-western regions, and there were still large differences between the central and western regions. The coupling coordination degree of the two systems in the region was significantly different in the eastern and western regions, and the central region was relatively similar. There is a positive spatial correlation between the provinces, and 25.81% of the provinces have transitions. Finally, the number of points in the first and third quadrants is higher than that in the second and fourth quadrants. In the process of dynamic distribution, the degree of polarization of the coupling coordination degree curve of the two systems is gradually weakened. Per capita GDP, residents ' income difference and population size are the positive and significant factors driving the coupling and coordinated development of the two systems.ConclusionThe coupling and coordination degree of the two systems of medical education and health resource allocation showed a stable upward trend during the observation period, and the global spatial positive correlation also gradually increased, showing the spatial agglomeration characteristics of ' high-high agglomeration ' and ' low-low agglomeration '. The spatial difference of coupling coordination degree shows a shrinking trend and develops towards equalization. The coupling coordination degree of the two systems is affected by social, economic and demographic factors to varying degrees. Therefore, it is necessary to innovate the coordinated development mechanism of the two systems, promote the two-way flow of medical education and health resource allocation in talents, technology and other elements, and then promote the coupling and coordinated development of the two systems.

Reliability Assessment Method Based on Small Sample Accelerated Life Test Data

Aiming at the problems of small sample size, few test failure data and low reliability evaluation efficiency in accelerated life test of high-reliability and long-life products, an improved virtual augmentation Bootstrap-Bayes reliability evaluation method based on small sample accelerated life test data was proposed. Firstly, the test data under various stress conditions are augmented by virtual fusion. Secondly, the empirical distribution function and bootstrap sampling method are improved, and the kernel density estimation method is used to fit the density distribution of test data as prior information. Then, the parameter estimates of the test data are obtained by Gibbs sampling combined with Bayes formula. Finally, the reliability index under normal stress level is obtained by accelerating model extrapolation. The feasibility of the proposed method is verified by the accelerated life test data of a type of Ship communication equipment.

Assessment and Spatio-Temporal Evolution of Marine Fisheries’ Carbon Sink Capacity in China’s Three Marine Economic Circles

With the increasing pressure of resource decline and environmental pollution faced by the green transformation of marine fisheries, marine fishery carbon sinks are an increasingly close link to national strategic interests and economic lifelines. It is, therefore, necessary to explore paths for the enhancement and development of the carbon sink capacity of marine fisheries. Based on the economic data of marine fisheries from 2002 to 2021, this paper measures the capacity and characteristics of marine fishery carbon sinks in provincial areas, applies the kernel density estimation method to depict the dynamic evolution of the distribution of absolute differences in marine fishery carbon sinks, and at the same time, identifies the factors influencing the spatial imbalance in the development of marine fishery carbon sinks as well as the spatial spillover effects. The results show the following: (1) From the total amount, the fishery carbon sink capacity of China’s three marine economic circles has fluctuated and increased over the past 20 years, with obvious differences among different circles, and the marine fishery carbon sink capacity of the eastern, northern, and southern marine economic circles has risen sequentially. This trend corresponds to the economic disparities among the three circles, demonstrating a transition from economic to industrial driving effects. (2) From the spatial point of view, the fishery carbon sink capacity shows a trend of increasing year by year, concentrating in the circles, and overflowing between the circles. (3) The results of a partial derivative test further show that among the main influencing factors of marine fishery carbon sinks, capital, labor, and market openness have direct positive effects on marine fishery carbon sinks, and the direct effect of innovation is the strongest, and the significance of the indirect effect of the three circles is weaker compared to the direct effect and total effect. As a result, the carbon sink of China’s marine fisheries has a large room for improvement, and in order to promote the high-quality development of China’s marine fisheries, it is still necessary to accelerate the circulation of resources within the marine economic circle, accelerate the research of fishery technology, actively develop the carbon sink fishery, and enhance the specialization of aquatic product processing.

From super-Earths to sub-Neptunes: Observational constraints and connections to theoretical models

The growing number of well-characterized exoplanets smaller than Neptune enables us to conduct more detailed population studies. We have updated the PlanetS catalog of transiting planets with precise and robust mass and radius measurements and use this comprehensive catalog to explore mass-radius (M–R) diagrams. On the one hand, we propose new M–R relationships to separate exoplanets into three populations: rocky planets, volatile-rich planets, and giant planets. On the other hand, we explore the transition in radius and density between super-Earths and sub-Neptunes around M-dwarfs and compare them with those orbiting K- and FG-dwarfs. Using Kernel density estimation method with a re-sampling technique, we estimated the normalized density and radius distributions, revealing connections between observations and theories on composition, internal structure, formation, and evolution of these exo-planets orbiting different spectral types. First, the substantial 30% increase in the number of well-characterized exoplanets orbiting M-dwarfs compared with previous studies shows us that there is no clear gap in either composition or radius between super-Earths and sub-Neptunes. The “water-worlds” around M-dwarfs cannot correspond to a distinct population, their bulk density and equilibrium temperature can be interpreted by several different internal structures and compositions. The continuity in the fraction of volatiles in these planets suggests a formation scenario involving planetesimal or hybrid pebble-planetesimal accretion. Moreover, we find that the transition between super-Earths and sub-Neptunes appears to happen at different masses (and radii) depending on the spectral type of the star. The maximum mass of super-Earths seems to be close to 10 M⊕ for all spectral types, but the minimum mass of sub-Neptunes increases with the star’s mass, and is around 1.9 M⊕, 3.4 M⊕, and 4.3 M⊕, for M-dwarfs, K-dwarfs, and FG-dwarfs, respectively. The precise value of this minimum mass may be affected by observational bias, but the trend appears to be reliable. This effect, attributed to planet migration, also contributes to the fading of the radius valley for M-planets compared to FGK-planets. While sub-Neptunes are less common around M-dwarfs, smaller ones (1.8 Re < Rp < 2.8 R⊕) exhibit lower density than their equivalents around FGK-dwarfs. Nonetheless, the sample of well-characterized small exoplanets remains limited, and each new discovery has the potential to reshape our understanding and interpretations of this population in the context of internal structure, composition, formation, and evolution models. Broader consensus is also needed for internal structure models and atmospheric compositions to enhance density interpretation and observable predictions for the atmospheres of these exoplanets.

An Optimal Spatio-Temporal Hybrid Model Based on Wavelet Transform for Early Fault Detection.

An optimal spatio-temporal hybrid model (STHM) based on wavelet transform (WT) is proposed to improve the sensitivity and accuracy of detecting slowly evolving faults that occur in the early stage and easily submerge with noise in complex industrial production systems. Specifically, a WT is performed to denoise the original data, thus reducing the influence of background noise. Then, a principal component analysis (PCA) and the sliding window algorithm are used to acquire the nearest neighbors in both spatial and time dimensions. Subsequently, the cumulative sum (CUSUM) and the mahalanobis distance (MD) are used to reconstruct the hybrid statistic with spatial and temporal sequences. It helps to enhance the correlation between high-frequency temporal dynamics and space and improves fault detection precision. Moreover, the kernel density estimation (KDE) method is used to estimate the upper threshold of the hybrid statistic so as to optimize the fault detection process. Finally, simulations are conducted by applying the WT-based optimal STHM in the early fault detection of the Tennessee Eastman (TE) process, with the aim of proving that the fault detection method proposed has a high fault detection rate (FDR) and a low false alarm rate (FAR), and it can improve both production safety and product quality.

Rural Settlement Optimization for Ecologically Sensitive Area Evaluations Based on Geo-Proximity and the Soil‒Water Conservation Capacity

Rural communities in ecologically sensitive areas are confronted with environmental challenges and land waste. The hollowing out of villages because of the steady loss of young people has brought these problems to the fore. Research on the remediation of rural settlements in ecologically sensitive regions is scarce. This paper focuses on Tong Yu County, a region located in the agricultural and pastoral intertwined zones of western Jilin, which is known for its ecological sensitivity. On the basis of the results of the geographical assessment of the area via hierarchical analysis (AHP method)and soil and water conservation capacity evaluation via the revised universal soil loss equation model(RUSLE) and the net primary production quantitative index model(NPP model), we propose a rural development program that divides the study area into three different zones with different development orientations, namely, potential enhancement, agglomeration development, and expansion restrictions. Moreover, we conceived a rural settlement remediation scheme using the kernel density estimation method and provided remediation strategies for rural settlements of various density levels. The rural settlement development program and the rural settlement remediation scheme led to the optimization of rural settlements. Notably, with increasing kernel density, the area of rural settlements tends to increase and then decrease. Our research helps save 5.059 km2 of land resources in the study area and offers guidance for improving the rural settlement layout in Tong Yu County.

Geospacialization and statistical analysis of the evolution of cases of sporotrichosis: Timbaúba and Grande Recife - Brasil

With a predominance in countries with tropical and subtropical climates, dermatozoonosis such as sporotrichosis occurs in several nations. Sporotrichosis is caused by species of the genus Sporothrix, whose dissemination between animals and humans is due to the traumatic inoculation of the fungus. The objective of the present study was to propose a management model based on geospatialization, with statistical methods applied to preventive and corrective care in order to control the evolution of cases of sporotrichosis in care at animal health establishments in Timbaúba and Grande Recife/PE - Brazil. Computational tools and statistical techniques were used, verifying the emphasis on places of occurrence of the disease by geospatialization with the Kernel density estimation method and descriptive statistical analysis. The research was approved by the CEP (CAAE: 44330921.2.0000.9547) in the period from 2011 to 2020. The results demonstrated the prevalence of the disease in adult felines and, lack of records in the units. It is concluded that the lack of information and knowledge contributes to dispersion and abandonment of treatment. In this sense, a management plan is proposed with the implementation of a computer program.

Dynamic production capacity assessment of aircraft overhaul shop based on digital twin

Aircraft overhaul is of the highest level in aircraft MRO (Maintenance, Repair, Overhaul), which is an important and necessary activity for ensuring safety and reliability of aircraft throughout its whole lifecycle. Production capacity of aircraft overhaul shop has huge strategic value and determines the cycle time and throughput of the overhauled aircraft. However, due to the inherent complexity and stochasticity, aircraft overhaul tasks are generally executed manually by skilled workers, whose stochastic task execution time (TET) makes it challenging to assess the production capacity of aircraft overhaul shop. Digital twin provides an up-to-date technical measure to simulate, evaluate, and predict the whole lifecycle process of its physical entity, which makes it possible to tackle the complexity and stochasticity of aircraft overhaul process. This paper proposes to apply digital twin to assess the production capacity of aircraft overhaul shop dynamically. Firstly, the aircraft overhaul process and its dynamic production capacity assessment are analyzed. A modeling framework of aircraft overhaul shop digital twin is proposed with a novel twin fusion mechanism. Then, an improved kernel density estimation (KDE) method and a quota hour density proportion (QHDP) method are proposed and verified to process stochastic TET for production capacity assessment. Finally, the digital twin of a typical aircraft avionics repair shop is constructed to assess its production capacity. Four assessment results are compared with real observations from the aircraft avionics repair shop, which verifies the feasibility and advantage of the proposed methodology. A bottleneck analysis is also conducted to optimize the production capacity, which provides managerial insights.

Strength analysis method of CMCs tip shroud structure considering random distribution of preform properties

This paper considers the impact of the random distribution of yarn properties on the mechanical and failure behavior of ceramic matrix composites (CMCs) structures and proposes an integrated analysis method based on preform-structure. Tensile tests of CMCs tip shroud specimens were carried out, and the digital image correlation technique was used to record the deformation of the specimens in real-time during loading. Based on the actual structure, a preform model of the CMCs tip shroud was established, and the kernel density estimation method was used to obtain the distribution of the yarn cross-sectional area. The random distribution of the cross-sectional area is equivalent to material properties, simulating the dispersion of the performance of CMCs. The progressive damage method simulates the failure mode during the specimen's loading process. The peak load error is 1.5 %, and the failure modes such as transverse shear damage, stitching yarn fracture, and interlaminar separation were successfully predicted.

Exploring the Spatiotemporal Dynamics and Simulating Heritage Corridors for Sustainable Development of Industrial Heritage in Foshan City, China

Industrial heritage serves as a testament to the historical and cultural legacy of industrialization, and its preservation and adaptive reuse are crucial for promoting sustainable urban development. This study explores the spatiotemporal dynamics of industrial heritage in Foshan City, China, and simulates potential heritage corridors to inform effective conservation and revitalization strategies. By employing Kernel Density Estimation (KDE) and Standard Deviational Ellipse (SDE) methods, the research investigates the spatial and temporal distribution patterns of industrial heritage across different historical periods and industrial types. An Analytic Hierarchical Process (AHP) is used to construct a hierarchical model of resistance factors, which serves as the basis for simulating potential heritage corridors using the Minimum Cumulative Resistance (MCR) model. The results unveil distinct spatiotemporal distribution patterns, with concentrations of industrial heritage in the central Chancheng District and southeastern Shunde District. Two primary potential heritage corridors are identified, and prioritized strategies for their adaptive reuse are proposed. The findings contribute to a comprehensive understanding of industrial heritage distribution in Foshan City and provide valuable insights for the conservation, planning, and sustainable development of these significant sites. The study highlights the importance of integrating spatiotemporal analysis and heritage corridor modeling in the decision-making process for industrial heritage revitalization, ensuring the preservation of invaluable industrial history and culture while fostering sustainable urban growth.

Estimation of complete mutual information exploiting nonlinear magnitude-phase dependence: Application to spatial FNC for complex-valued fMRI data

BackgroundReal-valued mutual information (MI) has been used in spatial functional network connectivity (FNC) to measure high-order and nonlinear dependence between spatial maps extracted from magnitude-only functional magnetic resonance imaging (fMRI). However, real-valued MI cannot fully capture the group differences in spatial FNC from complex-valued fMRI data with magnitude and phase dependence. MethodsWe propose a complete complex-valued MI method according to the chain rule of MI. We fully exploit the dependence among magnitudes and phases of two complex-valued signals using second and fourth-order joint entropies, and propose to use a Gaussian copula transformation with a lower bound property to avoid inaccurate estimation of joint probability density function when computing the joint entropies. ResultsThe proposed method achieves more accurate MI estimates than the two histogram-based (normal and symbolic approaches) and kernel density estimation methods for simulated signals, and enhances group differences in spatial functional network connectivity for experimental complex-valued fMRI data. Comparison with existing methodsCompared with the simplified complex-valued MI and real-valued MI, the proposed method yields higher MI estimation accuracy, leading to 17.4 % and 145.5 % wider MI ranges, and more significant connectivity differences between healthy controls and schizophrenia patients. A unique connection between executive control network (EC) and right frontal parietal areas, and three additional connections mainly related to EC are detected than the simplified complex-valued MI. ConclusionsWith capability in quantifying MI fully and accurately, the proposed complex-valued MI is promising in providing qualified FNC biomarkers for identifying mental disorders such as schizophrenia.

From global to national: The role of urban agglomerations in China's new development paradigm.

Urban agglomerations (UAs), which serve as pivotal hubs for economic and innovative convergence, play a crucial role in enhancing internal circulation and strengthening external linkages. This study utilizes the China city-level multi-regional input-output tables, incorporating the Dagum Gini coefficient and kernel density estimation methods, to perform a thorough quantitative analysis. Disparities within the national and global value chains ("dual value chains") of Chinese UAs from 2012 to 2017 were assessed. Additionally, the logarithmic mean Divisia index (LMDI) method was applied to disaggregate the drivers of both national and global intermediate inputs (NII and GII). The study's key findings include the following: (1) The national value chain (NVC) within UAs exhibits robust growth, contrasting with the decline in the global value chain (GVC). (2) The inter-UA disparity contribution rate significantly surpasses the combined rates of intra-UA contribution and super-variation density. (3) Distinct evolutionary peak trends are discerned among various UAs within the "dual value chains", highlighting diverse spatial polarization characteristics and expansiveness. (4) The growth of the NVC has transitioned from a negative to a positive impact on NII, while the decline in GVC has substantially counteracted GII growth. Economic and demographic factors notably drive positive improvements in both NII and GII, whereas the efficiency of outflows presents a negative driving effect. Based on these findings, this study offers strategic recommendations to facilitate the effective integration of UAs into the new development paradigm, thereby providing a scientific basis for related decision-making processes.

Exploring Differences and Evolution of Coordination Level of the Industrial Structure, Economy and Ecological Environment Complex System in Beijing–Tianjin–Hebei Urban Agglomeration

Industrial structure (IS), economy (EC), and ecological environment (EE) could influence each other and constitute a complex system (IS-EC-EE). This study is to explore the differences and evolution of the coordination level of the IS-EC-EE complex system of 13 cities in the Beijing–Tianjin–Hebei (BTH) urban agglomeration by coupling coordination degree model, Markov chain, GM (1,1) model, Dagum Gini coefficient, and Kernel density estimation method to provide a reference for regional sustainable development. The results show that the comprehensive evaluation index of IS, EC, and EE had significant differences among the 13 cities in the BTH region. The IS-EC-EE system of the whole BTH region was in a highly coupled and low-coordinated development state. And the coordination degrees of the 13 cities in the BTH region had spatial differences, which were mainly due to inter-regional differences, and the differences became larger. Furthermore, the coordinated development of the 13 cities had the probability of keeping high, moderate, and low coordination. It was predicted that the coordination degree of the IS-EC-EE system in the whole BTH region in 2020–2030 was roughly similar to the trend in 2009–2019. The coordination levels of Beijing and Tianjin were higher than in Hebei Province, so optimizations need to be considered for regional overall sustainable development.

Research on Territorial Spatial Development Non-Equilibrium and Temporal–Spatial Patterns from a Conjugate Perspective: Evidence from Chinese Provincial Panel Data

Clarifying the intrinsic nature and formation mechanisms of the territorial spatial development non-equilibrium, optimizing the allocation of territorial resources, promoting regional balanced development, and alleviating regional development disparities have become common endeavors of all countries seeking to enhance development quality. This study, based on the land use and socio-economic data of 31 provinces and cities in China from 2006 to 2020, utilized the kernel density estimation method and ArcGIS spatial analysis to explore the spatiotemporal evolution characteristics of China’s territorial spatial development non-equilibrium. The research findings are as follows: (1) From 2006 to 2020, both the land development intensity and land supply capacity showed an increasing trend, with increases of 21.4% and 8.03%, respectively. However, their spatiotemporal evolutions significantly differed. (2) The state of the territorial spatial development non-equilibrium in China significantly improved, with a rising trend in the territorial spatial equilibrium degree. Regional disparities in the territorial spatial equilibrium degree decreased. The number of provinces and cities in a state of underdevelopment decreased from 23 to 8, while the number in an equilibrium state increased from 7 to 21. The central and western regions showed more significant improvements in the territorial spatial equilibrium degree and a greater reduction in regional disparity when compared to the eastern and northeastern regions. (3) China exhibited distinct regional disparities in the territorial spatial equilibrium degree. The overdeveloped areas were Beijing and Shanghai. The underdeveloped regions were primarily concentrated in western areas, including Sichuan, Yunnan, and Xinjiang. The high-level equilibrium regions were predominantly located in the southeastern coastal provinces and the central plains region, while the low-level equilibrium regions included Inner Mongolia, Ningxia, Qinghai, and the three northeastern provinces. (4) From the perspective of the number of agglomeration types, the order is HH (high–high) > LH (low–high) > LL (low–low) > HL (high–low), and China’s TSED exhibits a spatial proximity peer effect. The innovation of this study consists of the following aspects: (1) The clarification of the fact that the essence of equilibrium does not require an exact 1:1 match, as with “two horses”; rather, it entails the maintenance of a slight surplus of land supply capacity over land development intensity. This approach allows the possibility of future sustainable development by leaving room for expansion. (2) The differentiation between high-intensity conjugation (in terms of both quantity and quality) and low-intensity conjugation (quantity only) from a conjugate perspective. This categorization aids in a more comprehensive and in-depth understanding of the territorial spatial development non-equilibrium and its spatiotemporal patterns. It provides crucial information for the formulation of sustainable national territorial development plans and the promotion of a regional development equilibrium.

A data-driven robust optimization method based on scenario clustering for PVC production scheduling under uncertainty

In this paper, a data-driven robust optimization method based on scenario clustering is proposed for addressing energy consumption uncertainty characterized by correlation and multi-peaked distribution within the PVC production process. Firstly, principal component analysis (PCA) and kernel density estimation (KDE) methods are used to capture the correlation and distribution information effectively across multidimensional uncertain parameters; then a modified K-means clustering method based on density peaks is applied to cluster energy consumption scenarios and the flexible uncertainty subsets is established. A two-stage robust optimization model for the vinyl chloride production section is then proposed, and the column and constraint generation algorithm is applied to solved. Finally, the effectiveness of the proposed method is validated through a PVC production case study. Comparative results demonstrate that the proposed model reduces energy consumption under uncertainty and improves the robustness of PVC production scheduling.

Optimizing asphalt mix design through predicting volumetric properties using interpretable machine learning

Traditional asphalt mix design methods primarily rely on experience, consuming immense time and resources. Currently, advanced machine learning (ML) method can accelerate the asphalt mix design procedure. However, historical data on asphalt mix design exhibit high variability, and the outputs of ML models are often challenging to interpret. To address these issues, this study selectively utilized outlier detection algorithms for data preprocessing and employed interpretable ML to predict and explain the volumetric properties of the mixtures, achieving an automated and rapid mix design method. A database (1525 sets of data) of volumetric properties was established based on experimental data of the engineering site. Z-score, KDE (kernel density estimation) and LOF (local outlier factor) methods were employed to detect outliers of the database. Three ML models were trained to predict the volumetric properties of mixture (air void and voids in mineral aggregate), based on material proportion, gradation morphological and material performance features. The SHAP method was used to interpret the extreme gradient boosting (XGB) model, quantifying feature importance, and aiding in decision-making. Finally, an asphalt mix design framework was proposed. The results indicate that the combination of KDE and LOF methods for outlier detection enabled the machine learning model to achieve optimal performance. The XGB model had the best prediction effect on volumetric properties. According to the SHAP method results, the influence of the critical features on the results was explained by the physical laws. The method proposed effectively reducing the design time and costs. The feasibility of the proposed design method was proved by the case study.

Spatial and Temporal Distribution and Flow Characteristics of High-Quality Development of Modern Service Industry in Chinese Sub-provincial Cities

Based on the new development concept of the service industry, this paper constructs a comprehensive evaluation index system for the high-quality development of modern service industry from a total of 22 indicators in four dimensions, namely, development efficiency, structural optimization, innovation agglomeration and industrial scale. The composite index of 15 sub-provincial cities in China from 2013 to 2022 was measured by entropy weight method, describing the current development status and development differences of each city, and with the help of Kernel density estimation method and Dagum Gini coefficient method, the law of inter-city evolution was inscribed, which revealed the spatial differences of the four major geographic regions and their sources and contributions. The study shows that the overall high-quality development of the service industry in the 15 cities is at a relatively low level, with obvious differences, but all of them are on a slowly rising trend. According to the division of the four major geographic regions, the northeast region has the largest intra-regional differences, and the eastern region has the smallest intra-regional differences. Meanwhile, the spatial imbalance between regions is still aggravating, and there is a clear differentiation between the development level of the eastern and western regions.