Kernel Density Research Articles

Temporal Variations in Land Surface Temperature within an Urban Ecosystem: A Comprehensive Assessment of Land Use and Land Cover Change in Kharkiv, Ukraine

Remote sensing technologies are critical for analyzing the escalating impacts of global climate change and increasing urbanization, providing vital insights into land surface temperature (LST), land use and cover (LULC) changes, and the identification of urban heat island (UHI) and surface urban heat island (SUHI) phenomena. This research focuses on the nexus between LULC alterations and variations in LST and air temperature (Tair), with a specific emphasis on the intensified SUHI effect in Kharkiv, Ukraine. Employing an integrated approach, this study analyzes time-series data from Landsat and MODIS satellites, alongside Tair climate records, utilizing machine learning techniques and linear regression analysis. Key findings indicate a statistically significant upward trend in Tair and LST during the summer months from 1984 to 2023, with a notable positive correlation between Tair and LST across both datasets. MODIS data exhibit a stronger correlation (R2 = 0.879) compared to Landsat (R2 = 0.663). The application of a supervised classification through Random Forest algorithms and vegetation indices on LULC data reveals significant alterations: a 70.3% increase in urban land and a decrement in vegetative cover comprising a 15.5% reduction in dense vegetation and a 62.9% decrease in sparse vegetation. Change detection analysis elucidates a 24.6% conversion of sparse vegetation into urban land, underscoring a pronounced trajectory towards urbanization. Temporal and seasonal LST variations across different LULC classes were analyzed using kernel density estimation (KDE) and boxplot analysis. Urban areas and sparse vegetation had the smallest average LST fluctuations, at 2.09 °C and 2.16 °C, respectively, but recorded the most extreme LST values. Water and dense vegetation classes exhibited slightly larger fluctuations of 2.30 °C and 2.24 °C, with the bare land class showing the highest fluctuation 2.46 °C, but fewer extremes. Quantitative analysis with the application of Kolmogorov-Smirnov tests across various LULC classes substantiated the normality of LST distributions p > 0.05 for both monthly and annual datasets. Conversely, the Shapiro-Wilk test validated the normal distribution hypothesis exclusively for monthly data, indicating deviations from normality in the annual data. Thresholded LST classifies urban and bare lands as the warmest classes at 39.51 °C and 38.20 °C, respectively, and classifies water at 35.96 °C, dense vegetation at 35.52 °C, and sparse vegetation 37.71 °C as the coldest, which is a trend that is consistent annually and monthly. The analysis of SUHI effects demonstrates an increasing trend in UHI intensity, with statistical trends indicating a growth in average SUHI values over time. This comprehensive study underscores the critical role of remote sensing in understanding and addressing the impacts of climate change and urbanization on local and global climates, emphasizing the need for sustainable urban planning and green infrastructure to mitigate UHI effects.

A study on the spatial and temporal evolution patterns of water resource–ecology–social economy composite system resilience in the Huaihe River Basin

ABSTRACT Water resources are important supporting elements in the ecological environment and socio-economic system of sustainable development. The purpose of this study is to evaluate the comprehensive resilience level of the water resources–ecology–socioeconomic system against external disturbances in the Anhui and Shandong sections of the Huaihe River Basin. The comprehensive index system is constructed with the characteristic dimensions of resistance, resilience, and adaptability. Use the game theory combination weighting-VIKOR method to quantify the resilience level, and use the kernel density analysis and Markov chain to explore the spatial and temporal evolution characteristics and transition probability. The results show that (1) the resilience index of the composite system in the Huaihe River Basin shows an increasing trend, and the resilience level of the Anhui section is generally higher than that of the Shandong section. (2) The resilience level gradually evolved into the spatial distribution characteristics of high in the west and low in the east, high in the south and low in the north during the study period. (3) The resilience grade has strong robustness, and the probability of transferring to the higher grade is greater than that of transferring to the lower grade.

Enhancing Yarn Quality Wavelength Spectrogram Analysis: A Semi-Supervised Anomaly Detection Approach with Convolutional Autoencoder

Abnormal detection plays a pivotal role in the routine maintenance of industrial equipment. Malfunctions or breakdowns in the drafting components of spinning equipment can lead to yarn defects, thereby compromising the overall quality of the production line. Fault diagnosis of spinning equipment entails the examination of component defects through Wavelet Spectrogram Analysis (WSA). Conventional detection techniques heavily rely on manual experience and lack generality. To address this limitation, this current study leverages machine learning technology to formulate a semi-supervised anomaly detection approach employing a convolutional autoencoder. This method trains deep neural networks with normal data and employs the reconstruction mode of a convolutional autoencoder in conjunction with Kernel Density Estimation (KDE) to determine the optimal threshold for anomaly detection. This facilitates the differentiation between normal and abnormal operational modes without the necessity for extensive labeled fault data. Experimental results from two sets of industrial data validate the robustness of the proposed methodology. In comparison to conventional Autoencoder and prevalent machine learning techniques, the proposed approach demonstrates superior performance across evaluation metrics such as Accuracy, Recall, Area Under the Curve (AUC), and F1-score, thereby affirming the feasibility of the suggested model.

A Risk Warning Method for Steady-state Power Quality Based on VMD-LSTM and Fuzzy Model

The risk warning for steady-state power quality in the power grid is essential for its prevention and management. However, current risk warning methods fall short in predicting the power quality trend while accounting for potential risks. Consequently, this study introduces a novel steady-state power quality risk warning method utilizing VMD-LSTM and a fuzzy model. Firstly, a power quality index prediction method based on variational mode decomposition (VMD) and long short-term memory (LSTM) is proposed. This approach significantly enhances prediction accuracy. Secondly, a power quality risk warning method incorporating kernel density estimation (KDE) and a fuzzy model is proposed, which systematically addresses the uncertainty associated with power quality risks. To validate the effectiveness and practicality of the proposed method, experiments are conducted using field monitoring data from a residential load in southern China. The results affirm the reliability and applicability of the proposed method. The simulation results show that the median error of prediction of power quality indexes by the proposed method is 5.03% during the evaluated time period, and the prediction accuracy is mostly maintained above 90%.

Accelerated reduction in China's cropland fires against the background of policy enhancement

Cropland fires contribute significantly to the emission of air pollutants, with implications for the atmospheric environment, public health, and climate change. However, the development of cropland fires in response to straw burning policy interventions has not been fully explored. With the Moderate Resolution Imaging Spectroradiometer Collection 6 (MODIS C6) active fire products and China land-cover data (CLCD), counts and fire radiative power (FRP) of cropland fire spots were extracted in China from 2003 to 2022, followed by analyses of their spatiotemporal patterns and evolutionary characteristics at the provincial, regional, national, and grid (0.25° × 0.25°) levels and at monthly to annual scales via trend analysis, kernel density analysis, and geographic distribution analysis. The key findings include: (1) National policies banning straw burning reversed the changes in the quantity and intensity of China's cropland fires in 2013, with the frequency and FRP in 2022 declining by 58% and 62% compared with the 2014 peak. (2) Cropland fires were highly prevalent in Northeast and North China (NEC and NC), with significant regional differences, particularly in spring, summer and autumn. (3) A notable lag effect of burning ban policies was seen in Northeast China (NEC), where the increasing trends of the frequency and intensity of cropland fires were not reversed until 2017, followed by declines in frequency and FRP of 67% and 62%, respectively during 2017–2022. (4) Cropland fires showed an increasing concentration trend but with enormous regional differences, with an increasing trend of a “high-high” concentration pattern in NEC and a decreasing trend in NC. This study contributes to understanding the effectiveness of national policies that ban straw burning and support cropland fire management and policy-making.

Spatial Analysis of Internally Displaced Persons’ Camps in Borno State, Nigeria

The situation in Borno State, Nigeria has been exacerbated by the ongoing insurgency led by Boko Haram in northeast Nigeria, leading to widespread displacement and the establishment of Internally Displaced Persons (IDP) camps. This study aims to identify the locations of IDP camps in Borno State and analyze their spatial distribution, density, and clustering patterns. Furthermore, it seeks to investigate the geospatial implications of these patterns. To achieve this, the study employs geospatial analysis techniques such as Kernel Density Estimation, Cluster and Outlier Analysis, Nearest Neighbour Analysis, and Spatial Autocorrelation Analysis. The data collected for this study includes location coordinates, satellite imagery, demographic information, and administrative maps. The findings reveal that the distribution of IDP camps in Borno State is non-random and demonstrates varying camp densities across senatorial districts, with predominant high-high clusters and a deliberate, non-random arrangement. These results align with spatial analysis theories, emphasizing the importance of recognizing hidden inequalities in IDP camps and informing targeted interventions and resource planning for a more effective and equitable humanitarian response in Borno State. The implications extend to policy planning and enhanced vulnerability assessment for effective humanitarian responses, stressing the need for tailored interventions based on identified hotspots that address root causes and implement both immediate and long-term solutions. Future research directions could involve more in-depth investigations into the influencing factors, socio-economic impacts, and temporal dynamics of spatial patterns in displacement.

CSR from different perspectives: The global ESG indexes updated

AbstractThe prevailing environmental, social and governance (ESG) framework is currently based on micro‐ESG indicators. Research on national ESG is often limited to theory building and policy analysis. Based on previous scholars, this paper constructs a national ESG index framework consisting of 39 indices and updates the national ESG indices for 121 countries worldwide from 1990 to 2021 using the entropy weight method, aiming to provide a set of instrumental indices that capture the status and evolution of national ESG performance. The research findings are as follows: First, the Gini coefficient shows that the gap between national ESG performance has gradually widened over time. Second, the kernel density distribution suggests that global ESG performance is on the rise. High‐income countries are placing greater emphasis on ESG growth. Third, the results of the Markov transformation matrix suggest that there is a “club convergence” in ESG performance across countries.

Global aerosol-type classification using a new hybrid algorithm and Aerosol Robotic Network data

Abstract. The properties of aerosols are highly uncertain owing to the complex changes in their composition in different regions. The radiative properties of different aerosol types differ considerably and are vital for studying aerosol regional and/or global climate effects. Traditional aerosol-type identification algorithms, generally based on cluster or empirical analysis methods, are often inaccurate and time-consuming. In response, our study aimed to develop a new aerosol-type classification model using an innovative hybrid algorithm to improve the precision and efficiency of aerosol-type identification. This novel algorithm incorporates an optical database, constructed using the Mie scattering model, and employs a random forest algorithm to classify different aerosol types based on the optical data from the database. The complex refractive index was used as a baseline to assess the performance of our hybrid algorithm against the traditional Gaussian kernel density clustering method for aerosol-type identification. The hybrid algorithm demonstrated impressive consistency rates of 90 %, 85 %, 84 %, 84 %, and 100 % for dust, mixed-coarse (mixed, course-mode aerosol), mixed-fine (mixed, fine-mode aerosol), urban/industrial, and biomass burning aerosols, respectively. Moreover, it achieved remarkable precision, with evaluation metric indexes for micro-precision, micro-recall, micro-F1-score, and accuracy of 95 %, 89 %, 91 %, and 89 %, respectively. Lastly, a global map of aerosol types was generated using the new hybrid algorithm to characterize aerosol types across the five continents. This study, utilizing a novel approach for the classification of aerosol, will help improve the accuracy of aerosol inversion and determine the sources of aerosol pollution.

Data-Driven-Based Intelligent Alarm Method of Ultra-Supercritical Thermal Power Units

In order to ensure the safe operation of the ultra-supercritical thermal power units (USCTPUs), this paper proposes an intelligent alarm method to enhance the performance of the alarm system. Firstly, addressing the issues of slow response and high missed alarm rate (MAR) in traditional alarm systems, a threshold optimization method is proposed by integrating kernel density estimation (KDE) and convolution optimization algorithm (COA). Based on the traditional approach, the expected detection delay (EDD) indicator is introduced to better evaluate the response speed of the alarm system. By considering the false alarm rate (FAR), and EDD, a threshold optimization objective function is constructed, and the COA is employed to obtain the optimal alarm threshold. Secondly, to address the problem of excessive nuisance alarms, this paper reduces the number of nuisance alarms by introducing an adaptive delay factor into the existing system. Finally, simulation results demonstrate that the proposed method significantly reduces the MAR and EDD, improves the response speed and performance of the alarm system, and effectively reduces the number of nuisance alarms, thereby enhancing the quality of the alarms.

An efficient analysis method for system reliability of semi-rigid base asphalt pavement structure based on direct probability integral method

The structural design parameters of asphalt pavement inherently possess randomness. Reliability theory aids in considering the stochastic nature of these parameters in asphalt pavement design. However, as the number of considered random factors increases, the complexity of reliability calculation models and the consumption of computational resources also significantly escalate. To analyze the structural reliability of semi-rigid base asphalt pavements more efficiently and accurately, a method combining Weighted Kernel Density Estimation (Weighted-KDE) with Direct Probability Integration Method (DPIM) using sampling strategies was employed. Furthermore, integrating the equivalent extreme value principle, an asphalt pavement structural system reliability analysis method was proposed, considering multiple failure mode correlations. The numerical solution steps of this method are also outlined. Based on the typical semi-rigid asphalt pavement structure in China, this study utilized the elastic layer system method (the program Apbi) and a computational program developed in MATLAB to compare the efficiency and accuracy of calculating reliability between DPIM and the Monte Carlo Simulations (MCS). The computational outcomes indicate that, under the premise of selecting 18 random parameters, DPIM can achieve the required accuracy in assessing the reliability of asphalt pavement structure and system reliability. Analyzing how the number of samples affects the computational results reveals that using weighted-KDE and DPIM only needs a few hundred sample points to limit the relative error within 4 %, compared to the results obtained from 105 samples in MCS. The research findings demonstrate that DPIM incorporating the principle of equivalent extreme value events can serve as an efficient analytical approach for assessing the system reliability of asphalt pavement structures characterized by more complex failure modes.

An Improved Artificial Potential Field Method for Ship Path Planning Based on Artificial Potential Field—Mined Customary Navigation Routes

In recent years, the artificial potential field has garnered significant attention in ship route planning and traffic flow simulation. However, the traditional artificial potential field method faces challenges in accurately simulating a ship’s customary route and navigating experience, leading to significant deviations in prediction results. To address these issues, in this study, we propose an innovative method for simulating and predicting ship traffic flow, building upon the artificial potential field approach. We introduce an AIS track heat map based on the kernel density function and enhance the artificial potential field model by incorporating factors, such as ship navigation habits and ship size. Through a comparison of traffic flow changes before and after the construction of a wind farm, the optimized model demonstrates its effectiveness in improving the accuracy of prediction results.

Spatial-Temporal Evolution and Influencing Factors of Animal Husbandry Carbon Emissions: A Case Study of Shandong Province, China

To further study the spatial distribution and dynamic evolution of carbon emissions from animal husbandry in Shandong Province, the panel data of 16 prefecture-level cities in Shandong Province from 2001 to 2022 were used to measure the carbon emissions of animal husbandry and the carbon emission intensity of animal husbandry. Based on the combination of space, kernel density estimation, and LMDI decomposition model, the spatial and temporal evolution of carbon emissions from animal husbandry in Shandong Province and its driving factors were investigated. The results show that: (1) The total amount of animal husbandry carbon emissions in Shandong Province showed a fluctuating downward trend, with a decrease of 10.10% during the investigation period, showing a peripheral-agglomeration distribution pattern. The carbon emission intensity showed a gradual downward trend, with an average annual decline of 7.47%, showing stepped distribution characteristics of high in the west and low in the east. (2) The difference in carbon emissions of animal husbandry among cities in Shandong Province increased first and then decreased, and the growth distribution was basically in the form of “bimodal”, showing a polarization pattern. (3) The intensity effect has the most obvious inhibitory effect on the carbon emission of animal husbandry; the effect of agricultural structure changes from a promoting effect to an inhibiting effect. The inhibitory effect of the industrial structure effect is second only to the intensity effect; the economic effect has the greatest promoting effect; and the promotion effect of the population size effect is small.

Correlation Among Physical Properties of Parboiled Milled Rice During Hydrothermal Pretreatment Processing

The rice cultivar MR297 has a high harvesting yield but a low milling and head rice yield. Parboiling is one of the methods to increase the head yield of rice varieties. The colour of parboiled rice is intense due to husk colour absorption during soaking. This study researched the influence of hydrothermal pretreatment (soaking time and temperature) on the physical properties such as dimension, thousand kernel weight (TKW), density, volume expansion (Ve), colour, and hardness of parboiled milled rice. MR297 raw rice cultivars were soaked at 1:1 (w/w). Paddy was soaked at 50, 60, and 70°C for 1, 2, 3, 4, and 5 hr and steamed at 100°C for 20 min prior to drying for 24 hr at 38°C. It was discovered that the effects of soaking time and temperature varied with the physical properties of rice. Soaking time and temperatures were found to be significant (p<0.05) in terms of dimensional properties (length [L], width [W], and thickness [T]), TKW, density, Ve, colour (L*, a*, b*) and hardness of parboiled rice. The L, W, T, Ve, TKW, a*, and b* increase proportionally as time and temperature increased, while the density and L* were negatively correlated. The hardness was negatively correlated with the L and colour b* (yellowness) rice measurements. The principal component analysis (PCA) results revealed that the soaking time was associated with L, W, T, density, Ve, and colour properties based on the loading analysis in the PC1. In contrast, the temperature, TKW and hardness were associated with PC2.

Spatiotemporal Evolution and Causal Analysis of Rural Tourism Popularity in Jilin Province Based on Multiple Data

The digital age provides greater possibilities for quantitative research on rural tourism. This article examines Jilin Province as a case and analyzes the interannual, monthly, and holiday characteristics of rural tourism heat using big data. Using A-level rural tourism operating units as research samples, a mathematical model is constructed to evaluate the rural tourism heat from 2016 to 2021. Through a trend surface analysis and kernel density analysis, the spatiotemporal differentiation characteristics are explored; additionally, the spatial evolution law of the rural tourism hot and cold pattern is analyzed using counties as units. The research results show the following: (1) As an important component of the Jilin tourism industry, temporally, rural tourism has an overall trend of increasing popularity, with clear seasonal and holiday distribution patterns. Simultaneously, the periodic sporadic occurrence of the COVID-19 epidemic caused an obvious vulnerability in rural tourism fever. (2) Spatially, the structure of rural tourism shows an evolutionary process of “single core → multi core” and a diffusion trend of “central → eastern” and “central → western”. (3) Regarding the influencing factors, transportation conditions and resource endowments are dominant, and the impact of the economic development level is gradually weakening, while the impact of the ecological environment and industrial foundation is gradually increasing.

Decoupling government spending from carbon emissions: A spatio-temporal analysis of 283 Chinese cities

A comprehensive evaluation of government spending efficiency and its connection to emissions reduction is vital for sustainable green economic progress. However, the precise nature of the decoupling relationship between carbon emissions and government spending at the city level remains uncertain. Here, using the panel data from 283 cities covering the period from 2007 to 2020, we employ diverse methodologies, such as Kernel density estimation and spatiotemporal autocorrelation analysis, to examine the spatiotemporal distribution of decoupling and identify key contributors through LMDI decomposition analysis. We find that initially, most cities demonstrated weak decoupling, but after 2010, a subset of cities achieved strong decoupling. The spatiotemporal distribution analysis highlights an expansion of inter-city differences in decoupling values and a reduction in the number of cities forming clusters after 2015. Furthermore, an increasing number of cities experienced positive emission intensity effects, while the positive effects of government spending on labor input consistently decreased. Initially, the positive effect of government spending on economic development declined but later showed a slight increase. The distribution of cities with a positive impact of government spending scale shifted from dispersed to concentrated in northern regions, resulting in a decrease in the number of cities with a positive impact.

Refined offshore wind speed prediction: Leveraging a two-layer decomposition technique, gated recurrent unit, and kernel density estimation for precise point and interval forecasts

Precise forecasting of offshore wind speeds is paramount for various applications, including offshore wind energy production, disaster prevention and mitigation, and maritime navigation. This study introduces a novel model for point and interval prediction of offshore wind speed, incorporating an innovative two-layer decomposition technique, gated recurrent unit (GRU), and kernel density estimation (KDE). Initially, the improved complete ensemble empirical mode decomposition with adaptive noise (ICEEMDAN) is utilized to decompose the raw series into multiple subsequences, thus alleviating the inherent data nonstationarity. Subsequently, we employ the refined composite multiscale fuzzy entropy (RCMFE) to reconstruct the subsequences into multiple components, thereby reducing the computational complexity. Variational mode decomposition (VMD) is utilized to enhance the modeling accuracy to decompose the high-frequency component, thereby generating a set of submodels. The GRU, a specialized recurrent neural network architecture, is leveraged to forecast the remaining components and submodels, producing a series of subresults. The subresults are linearly summed to obtain the final point prediction result. Finally, based on point prediction, employing KDE to gauge the probability density distribution of prediction errors culminates in the derivation of the offshore wind speed prediction interval. The experimental results substantiate the superior predictive performance of the proposed prediction model.

Examining Urban-Rural Public Service Equalization and Common Prosperity in China: A Data-Driven Study on Regional Disparity

Achieving equal access to basic public services between urban and rural areas in China has become an important pathway to eliminating inequality, improving national welfare, and ultimately achieving common prosperity.  Through data analysis and a data-driven approach, it is possible to better guide future policy formulation and implementation to help China achieve its goal of inclusive growth. This research endeavors to examine the interrelations between the discrepancies in basic public services provision in urban and rural areas, establishing a comprehensive evaluation indicator framework, while delving into the intricate dynamics between such service equalization and the attainment of common prosperity. Moreover, it aims to uncover the varying degrees of regional disparities witnessed during the journey to common prosperity in different parts of China, thereby providing valuable insights and references for nations and regions striving to enhance social equality. This study focused on the 31 provincial-level administrative regions of China(excluding the regions of Hong Kong, Macau, and Taiwan) The research methodology entails employing the entropy weight method to determine the weights of evaluation indicators, supplemented by a coupling coordination model to analyze the level of harmonization between urban and rural areas. Furthermore, the Dagum Gini coefficient method and kernel density estimation were utilized for an in-depth exploration of the distribution and disparities observed in regional coordination. The findings of this study reveal that between 2012 and 2021, the disparities in the equalization of basic public services between China’s urban and rural areas witnessed a progressive widening, despite modest improvements in the overall level of common prosperity. The prevailing pattern observed can be summarized as “high coupling, low coordination”. Notably, these disparities are particularly conspicuous in the western regions of the country. Additionally, this research identifies the imbalanced development of basic public service equalization between urban and rural areas as the primary cause for the observed low level of coordination. This study emphasized the intrinsic nexus and coordinated interplay existing between the equalization of urban and rural public services and the achievement of common prosperity. To foster enhanced coordination between urban and rural areas, it is imperative to prioritize the implementation of policies that promote the amalgamation of urban and rural realms, while concurrently striking a harmonious balance in the pursuit of multi-dimensional objectives. Furthermore, region-specific policies need to be formulated, with particular attention to addressing the disparities in coordination and the polarization issues prevalent in the western regions, ensuring the effectiveness and precision of these measures.

Robust Fault Detection in Monitoring Chemical Processes Using Multi-Scale PCA with KD Approach

Effective fault detection in chemical processes is of utmost importance to ensure operational safety, minimize environmental impact, and optimize production efficiency. To enhance the monitoring of chemical processes under noisy conditions, an innovative statistical approach has been introduced in this study. The proposed approach, called Multiscale Principal Component Analysis (PCA), combines the dimensionality reduction capabilities of PCA with the noise reduction capabilities of wavelet-based filtering. The integrated approach focuses on extracting features from the multiscale representation, balancing the need to retain important process information while minimizing the impact of noise. For fault detection, the Kantorovich distance (KD)-driven monitoring scheme is employed based on features extracted from Multiscale PCA to efficiently detect anomalies in multivariate data. Moreover, a nonparametric decision threshold is employed through kernel density estimation to enhance the flexibility of the proposed approach. The detection performance of the proposed approach is investigated using data collected from distillation columns and continuously stirred tank reactors (CSTRs) under various noisy conditions. Different types of faults, including bias, intermittent, and drift faults, are considered. The results reveal the superior performance of the proposed multiscale PCA-KD based approach compared to conventional PCA and multiscale PCA-based monitoring methods.

Taylor Approximation of Inventory Policies for One-Warehouse, Multi-Retailer Systems with Demand Feature Information

We consider a distribution system in which retailers replenish perishable goods from a warehouse, which, in turn, replenishes from an outside source. Demand at each retailer depends on exogenous features and a random shock, and unfulfilled demand is lost. The objective is to obtain a data-driven replenishment and allocation policy that minimizes the average inventory cost per time period. The extant data-driven methods either cannot guarantee a feasible solution for out-of-sample feature observations or generate one with excessive computational time. We propose a policy that resolves these issues in two steps. In the first step, we assume that the distributions of features and random shocks are known. We develop an effective heuristic policy by using Taylor expansion to approximate the retailer’s inventory cost. The resulting solution is closed-form, referred to as Taylor Approximation (TA) policy. We show that the TA policy is asymptotically optimal in the number of retailers. In the second step, we apply the linear quantile regression and kernel density estimation to the TA solution to obtain the data-driven policy called Data-Driven Taylor Approximation (DDTA) policy. We prove that the DDTA policy is consistent with the TA policy. A numerical study shows that the DDTA policy is very effective. Using a real data set provided by Fresh Hema, we show that the DDTA policy reduces the average cost by 11.0% compared with Hema’s policy. Finally, we show that the main results still hold in the cases of correlated demand features, positive lead times, and censored demand. This paper was accepted by J. George Shanthikumar, data science. Funding: Y. Yang acknowledges financial support from the NSFC [Grants 72125004, 71821002]. W. Zhou acknowledges financial support from the NSFC [Grants 72192823, 71821002]. Supplemental Material: The online appendix and data files are available at https://doi.org/10.1287/mnsc.2021.04241 .

A Review on the 3D Cartographic and Spatiotemporal GIS Models for Safety of Accidents in Deep Underground Coal Mines

AbstractThe aim of this study was the safety risk assessment of accidents that occur because of production dynamics in deep coal mining, one of the highly dangerous business class, with spatiotemporal GIS. In this study, accidents that occur at certain times during production in underground mining workplaces are discussed based on evaluations of spatial and temporal dimensions. Accordingly, various analyses have been handled using criminology science tactics and strategies such as 80–20, kernel density, space–time cube, and hot spot, which have been adapted to GIS tools. Implementations have been realized on 3D underground cartographic models covering accident data from deep coal mine workplaces in the Zonguldak-Kozlu basin in Turkey between 2019 and 2021, at an average elevation of – 400 m. Space–time cube, kernel density, and time series analyses suggest that the Annual Bonus Incentives, which are repeated every year and given before the new year, increase the psychosocial risks for employees. It has been determined that this situation can lead to problems such as poor concentration among miners and loss of focus in some shifts. Additionally, significant relationships were found between the number of accidents and monthly production progress rates as a temporal parameter. The results demonstrate that applying a reactive approach with spatiotemporal GIS to accidents in deep coal mines is becoming increasingly important.