Kernel Density Research Articles

Unraveling popping quality through insights on kernel physical, agro-morphological, and quality traits of diverse popcorn (Zea mays var. everta) inbreds from indigenous and exotic germplasm

Popcorn is a specialty corn with worldwide popularity as a snack. Despite having great market demand, genetic improvement in popping quality is limited, which is caused by the limited germplasm utilization and narrow genetic base. An assortment of diverse germplasm, their effective characterization, and integration into popcorn breeding pipeline is the foundation for an efficient breeding program. Here, kernel characteristics, popping quality traits, and agro-morphological traits were evaluated across three locations on a diverse panel of 48 popcorn inbreds derived from diverse landraces and populations of exotic and indigenous origin. The variations due to genotypes, locations, and genotype × location interaction were highly significant. The popping quality traits recorded wide variation with a high coefficient of genotypic determination. The kernel dimensions, kernel density, test weight, and grain yield were negatively correlated with popping quality traits. Genotypes with rice-type kernels exhibited better popping quality than pearl-type kernels. Analysis of genotype × location (G×L) interaction identified two target locations for the key popping quality trait, popping expansion volume. PMI-PC-175, PMI-PC-187, PMI-PC-188, and PMI-PC-189 were identified as superior genotypes over checks for desirable popping quality, agronomic performance, and high grain yield. The contrasting inbreds for popping quality and flake shape (mushroom vs. butterfly) can be utilized for developing mapping populations to enhance our understanding of molecular aspects of popping quality traits. Further, the promising inbreds can be utilized in the genetic improvement of popcorn and crossed to develop superior popcorn hybrids. The results suggest a potential opportunity to establish an efficient popcorn breeding program.

Condition monitoring of wind turbine based on a novel spatio-temporal feature aggregation network integrated with adaptive threshold interval

Condition monitoring (CM) technology based on supervisory control and data acquisition (SCADA) data is crucial for ensuring reliable operation and reducing maintenance costs of wind turbine (WT). However, SCADA data is highly dimensional and sophisticated, making it challenging to extract features and determine fault thresholds to improve CM accuracy. In this paper, we propose a new approach for effectively estimating the operating condition and diagnose the faults of WTs, which is founded on the integration of spatio-temporal feature aggregation network and adaptive threshold interval. Briefly, the Kalman filter is used to preprocess the SCADA data and the variables are selected by Pearson correlation coefficient to improve data quality. Then, a novel stacked sample convolution and interaction network (SCINet) embedded in a coordinate attention mechanism (CA) is designed to extract and merge the multi-dimensional and multi-resolution spatial and temporal features. Moreover, an adaptive threshold interval is proposed for early warning of fault occurrence based on Monte Carlo (MC) dropout and kernel density estimation (KDE). The effectiveness of the proposed method is demonstrated through two real cases from a wind farm, which show superior performance compared to other comparative methods.

Combination of Muller matrix imaging polarimetry and artificial intelligence for classification of mice skin cancer tissue in-vitro and in-vivo

Mueller matrix imaging polarimetry is a fast and non-invasive technique for discriminating between different types of biological samples based on the characteristics of polarized light interacting with them. Combining Mueller matrix imaging polarimetry with artificial intelligence provides further advantages in detecting different kinds of medical conditions in an automated manner. Accordingly, the present study proposes a method based on Mueller matrix polarimetry and machine learning algorithms for discriminating between (1) four different types of mice skin tissues (normal, acanthosis, papilloma, and squamous cell carcinoma); (2) two types of mice skin tissues which show histological similarities to human equivalents (normal and squamous cell carcinoma); and (3) 7,12-dimethylbenz[a]anthracene/estrogen-induced mice skin tissues. Five machine learning classifiers, namely Random Forest, Support Vector Machine, K-Nearest Neighbor, Gradient Boosting, and Gaussian Naïve Bayes, are considered in the first and second applications, while three models (Support Vector Machine, K-Nearest Neighbor, Gradient Boosting) are considered in the third application. For each application, the features which dominate the machine learning prediction performance are determined through multivariate correlation matrix analysis, kernel density estimation, and Analysis of Variance tests. The experimental results show that the Random Forest model achieves the highest classification accuracy (93.55 %) for the first application, while the Support Vector Machine model yields the highest accuracy for both the second and third applications (97.66 % and 100 %, respectively). Overall, the proposed framework consisting of Mueller matrix imaging polarimetry and machine learning provides a strong foundation for the on-going development of screening and diagnosis methods for human skin cancer.

Spatio-Temporal Characteristics,Regional Differences and Dynamic Evolution of Green Finance Development Level in China

As an emerging field that integrates finance and environmental protection, green finance aims to promote sustainable development and environmental protection through financial instruments and market mechanisms. The Chinese green finance market is rapidly emerging as a significant force in the global green finance arena. This paper applies methods such as entropy weight method, Dagum Gini coefficient, kernel density estimation, and spatial Markov chain to analyze the spatiotemporal characteristics, regional differences, and dynamic evolution of China’s green finance development level from 2010 to 2021. The results of the study indicate that the comprehensive index of China’s green finance development level has been increasing, with the mean comprehensive index of green finance development level showing a pattern of "East >National>Central>West". The overall level of green finance development in Chinese provinces has been increasing as a whole, and the level of some provinces has been moving upwards. The regional differences in green finance development level have been decreasing over the study period, with the central region having the smallest regional differences and the eastern region having the largest. From the perspective of distribution dynamics, it can be seen that the degree of imbalance in the green finance regional development in China is gradually increasing, with polarization phenomenon existing. The green finance development of Chinese provinces does not exist in isolation, and there is a certain degree of interdependence among them. When the green finance development level of neighboring provinces is better than that of the local area, it is conducive to the improvement of the local green finance development level, and there is the possibility of leapfrogging transfer.

ANALYSIS OF MOVEMENT DATA FROM GPS-MONITORED LIVESTOCK GUARDIAN DOGS (LGDS) AT TWO SHEEPFOLDS IN THE NORTH-WESTERN MARAMURES LAND, ROMANIA USING THE KERNEL DENSITY ESTIMATION METHOD. IMPLICATIONS FOR OUTDOOR TOURISM

The Carpathians and other mountainous regions around the world are renowned for their specific landscapes shaped by pastoralism, a millennia-old traditional and sustainable economic system. In Romania, this traditional occupation has an established place within the Romanian culture. In an environment where large predators are present, the livestock owners and shepherds have traditionally relied, and still do, on livestock guardian dogs (LGDs) to protect the flock against carnivores or theft, therefore, the dogs are perceived as an integral component of the traditional pastoral system. However, from late April until the end of September, many outdoor recreational activities like hiking, mountain trail running, or biking overlap with the pastoral calendar, creating a potential for conflict between two, very different categories of landscape users, with recurring incidents happening over the years. In this study, a winter GPS monitoring campaign was proposed, between November 2023 and January 2024 that used GPS professional collars to track the movements of two livestock guardian dogs stationed at two sheepfolds located at their winter bases in the hills at the foot of Ignis Mountains (part of the Romanian northern Carpathians) from northwestern Maramureș Land, Romania. The campaign generated point-based spatiotemporal data processed and analyzed in M. Excel and QGIS using Kernel density estimation as the main method to generate metrics and identify potential clusters of LGD activity in their usual environment. The results highlight high observational clusters near the winter folds but also lower observational clusters in areas situated hundreds of meters distance around the main compound, in certain locations. Although temporally limited, the results have the potential to help the understanding of the animal's preferred zone of habitation and substantiate future win-win cohabitation solutions that minimize conflictual encounters between the shepherds and their guardian dogs on one side, as primary land users, and outdoor recreationists as other landscape users.

Time series anomaly detection using generative adversarial network discriminators and density estimation for infrastructure systems

Efficient data-driven defect detection techniques are crucial for maintaining service quality and providing early warnings for infrastructure systems. To this end, we proposed an effective unsupervised anomaly detection framework (DEGAN) using Generative Adversarial Networks (GANs). The framework relies solely on normal time series data as input to train well-configured discriminators into standalone anomaly predictors by leveraging repeatedly collected data from an infrastructure system. Expected normal patterns in data are identified by generators, and well-configured discriminators are extracted to evaluate anomalies in unseen time series. Kernel density estimation (KDE) is coupled with discriminators for probabilistic anomaly detection. Through a Class I railroad track case study, we evaluated the performance of a convolutional DEGAN in detecting anomalies identified by operators, achieving recall and precision of 80% and 86%, respectively. We also investigated the influence of GAN architectures and parameters, model validation scheme (supervised vs. unsupervised), clustering, and the KDE parameters.

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.

Research on the Spatial Distribution Characteristics and Influencing Factors of Educational Facilities Based on POI Data: A Case Study of the Guangdong–Hong Kong–Macao Greater Bay Area

This study aims to provide a precise assessment of the distribution of educational facilities within the Guangdong–Hong Kong–Macao Greater Bay Area, serving as a crucial foundation for managing educational resource allocation and enhancing the quality of educational services. Utilizing a kernel density analysis, global autocorrelation analysis, and geographic detectors, this research systematically analyzes the spatial distribution characteristics and influencing factors of educational facilities in the area. The findings reveal significant geographical disparities in facility distribution with dense clusters in urban centers such as Guangzhou and Shenzhen, and less dense distributions in peripheral areas like Zhongshan and Macau. These facilities exhibit a multi-center cluster pattern with strong spatial autocorrelation, mainly influenced by the population size and economic and urban development levels. The results provide actionable insights for refining educational planning and resource allocation, contributing to the enhancement of educational quality across diverse urban landscapes.

Combined impact of dynamic air pollution and transportation noise on cardiometabolic disorders in San Diego County.

Air pollution and noise exposure may synergistically contribute to increased cardiometabolic disorders; however, few studies have examined this potential interaction nor considered exposures beyond residential location. This study investigates the combined impact of dynamic air pollution and transportation noise on cardiometabolic disorders in San Diego County. Using the Community of Mine Study (2014-2017), 602 ethnically diverse participants were assessed for obesity, dyslipidemia, hypertension, and metabolic syndrome (MetS) using anthropometric measurements and biomarkers from blood samples. Time-weighted measures of exposure to PM2.5, NO2, road and aircraft noise were calculated using global positioning system (GPS) mobility data and Kernel Density Estimation. Generalized estimating equation models were used to analyze associations. Interactions were assessed on the multiplicative and additive scales using relative excess risk due to interaction (RERI). We found that air pollution and noise interact to affect metabolic disorders on both multiplicative and additive scales. The effect of noise on obesity and MetS was higher when air pollution was higher. The RERI of aircraft noise and NO2 on obesity and MetS were 0.13 (95%CI 0.03, 0.22) and 0.13 (95%CI 0.02, 0.25), respectively. This finding suggests that aircraft noise and air pollution may have synergistic effects on obesity and MetS.

Evolution and zoning of the "production-living- ecological" spatial pattern in the coastal urban agglomeration of Guangdong, Fujian and Zhejiang

Clarifying the spatial differentiation patterns and evolutionary characteristics of urban territories is the basis and prerequisite for enhancing regional spatial governance capacity and realizing sustainable urban development patterns. In order to make up for the inadequacy of the existing coastal urban agglomerations of Guangdong, Fujian and Zhejiang in the exploration of regional evolutionary characteristics and zoning. Based on the theory of production-life-ecology space, the land use data from 2010 to 2020 are used to analyze the land space pattern and evolution characteristics of urban agglomeration by applying the methods of transfer matrix, kernel density analysis, and comparative advantage index. The results show that: (1) the ecological space continued to decrease and the area of production space and ecological space continued to increase. (2) The high-value areas of production space show multinuclear diffusion, the high-value areas of ecological space are mainly distributed in a faceted manner, and the living space are distributed in a band along the coastline. (3) The entire urban agglomeration was categorized into six types of land function zones based on the comparative advantage indices of the three spaces at the county scale.

Spatiotemporal Distribution of Inscription Sites in Henan Province

This paper takes 1929 inscription sites in Henan Province as the study object and uses methods such as kernel density and nearest neighbor index to analyze their spatiotemporal distribution patterns and influencing factors. It also studies those patterns of various levels of inscription cultural relics’ protection units and content. All of these will help our understanding of the development process and characteristics of Central Plains art and provide reference for the protection and development of inscriptions in Henan in the future. The study indicates the following: (1) The spatial distribution of inscription sites is relatively uneven and the clustering is obvious, being mainly concentrated in the northern and northwestern regions of Henan, showing the characteristics of “one belt and four clusters” as a whole. The density is high in the north and low in the south, gradually decreasing from north to south. (2) In terms of time, the number of these inscription sites shows a fluctuating trend of first a slight increase and then a decrease with a significant increase and then a decrease. The center of the sites migrates from southwest to northeast over time. (3) These inscriptions can be divided into five primary themes and further subdivided into 16 secondary themes in terms of content. The main type is chronicle. (4) Inscriptions in Henan are mainly influenced by five major factors: topography, climate, economy and transportation, politics and society, culture and religion.

GIS-based methodology for tracking the grazing cattle site use

Interest in tracking and monitoring animals in livestock farming using wearable sensors has been steadily increasing. The use of these devices is particularly crucial in extensive livestock systems where direct interaction between animals and farmers is infrequent, necessitating strenuous efforts in long-distance herd monitoring. Internet of Things (IoT) technologies offer a promising solution to address the challenges posed by vast distances, enabling real-time and remote animal monitoring. In this study, an experimental trial was conducted using a custom-designed device, located in a Polyvinyl Chloride (PVC) case, specifically tailored to fit onto a collar. This case incorporates an integrated SigFox communication system, i.e., a Low Power Global Positioning System (LP-GPS) omnidirectional system, and a power supply. The trial took place in two grazing areas located in different territorial zones, designated as Case Study I and II. A LP-GPS collar was provided for each selected animal, and the data were recorded at 20-min intervals for Case Study I and 10-min intervals for Case Study II. The acquired data were then imported and analysed using Geographical Information Systems (GIS) software. Information was collected through a purpose-built web application (AppWeb). The objective was to analyze those territorial areas mostly occupied by animals within the two considered grazing areas by developing a GIS-based methodology. Specifically, customized algorithms such as Heatmap and Kernel Density Estimation (KDE) plugins were employed to conduct spatial analyses. The maps obtained through Heatmap plugin, showed the temporal-spatial distribution of animals within their grazing areas. Additionally, the KDE tool was used to classify preferred territorial areas, generating tailored charts for each animal in the sample. The individual Core Areas, determined through KDE evaluation for each animal, were overlaid to provide a comprehensive analysis of the monitored animals.The results achieved applying the GIS-based methodology facilitated the identification of animal positions and could be adopted to provide insights into feeding behavior and soil erosion, thereby aiding in the prevention of environmental issues.

Enhancing semantic segmentation in chest X-ray images through image preprocessing: ps-KDE for pixel-wise substitution by kernel density estimation.

In medical imaging, the integration of deep-learning-based semantic segmentation algorithms with preprocessing techniques can reduce the need for human annotation and advance disease classification. Among established preprocessing techniques, Contrast Limited Adaptive Histogram Equalization (CLAHE) has demonstrated efficacy in improving segmentation algorithms across various modalities, such as X-rays and CT. However, there remains a demand for improved contrast enhancement methods considering the heterogeneity of datasets and the various contrasts across different anatomic structures. This study proposes a novel preprocessing technique, ps-KDE, to investigate its impact on deep learning algorithms to segment major organs in posterior-anterior chest X-rays. Ps-KDE augments image contrast by substituting pixel values based on their normalized frequency across all images. We evaluate our approach on a U-Net architecture with ResNet34 backbone pre-trained on ImageNet. Five separate models are trained to segment the heart, left lung, right lung, left clavicle, and right clavicle. The model trained to segment the left lung using ps-KDE achieved a Dice score of 0.780 (SD = 0.13), while that of trained on CLAHE achieved a Dice score of 0.717 (SD = 0.19), p<0.01. ps-KDE also appears to be more robust as CLAHE-based models misclassified right lungs in select test images for the left lung model. The algorithm for performing ps-KDE is available at https://github.com/wyc79/ps-KDE. Our results suggest that ps-KDE offers advantages over current preprocessing techniques when segmenting certain lung regions. This could be beneficial in subsequent analyses such as disease classification and risk stratification.

Growing Glycans in Rosetta: Accurate de novo glycan modeling, density fitting, and rational sequon design.

Carbohydrates and glycoproteins modulate key biological functions. However, experimental structure determination of sugar polymers is notoriously difficult. Computational approaches can aid in carbohydrate structure prediction, structure determination, and design. In this work, we developed a glycan-modeling algorithm, GlycanTreeModeler, that computationally builds glycans layer-by-layer, using adaptive kernel density estimates (KDE) of common glycan conformations derived from data in the Protein Data Bank (PDB) and from quantum mechanics (QM) calculations. GlycanTreeModeler was benchmarked on a test set of glycan structures of varying lengths, or "trees". Structures predicted by GlycanTreeModeler agreed with native structures at high accuracy for both de novo modeling and experimental density-guided building. We employed these tools to design de novo glycan trees into a protein nanoparticle vaccine to shield regions of the scaffold from antibody recognition, and experimentally verified shielding. This work will inform glycoprotein model prediction, glycan masking, and further aid computational methods in experimental structure determination and refinement.

The ecological utility study on carbon metabolism of cultivated land: A case study of Hubei Province, China

Exploring the ecological utility of cultivated land's carbon metabolism offers policy insights for ensuring its healthy operation and promote the dual carbon goals (carbon peak and carbon neutrality). We employed ecological network analysis (ENA) and kernel density estimation to conduct an empirical study, taking Hubei Province from 2000 to 2020 as an example. The results revealed apparent negative effects of carbon metabolic flow on regional carbon balance. Specifically, cultivated land conversion into transportation and industrial land contributed significantly to the harmful carbon flow. Ecological relationships showed fierce competition for carbon storage, leading to overall adverse ecological effects. The ecological utility indicated detrimental impacts on the orderly functioning of land-use carbon metabolism. Cultivated land's carbon metabolism will be essential in achieving land-use carbon neutrality. Therefore, territorial spatial low-carbon optimization should be implemented to realize its green and sustainable development.

Spatial–temporal evaluation of total-factor energy efficiency in Chinese construction industry based on three-stage super-efficiency SBM-DEA model

PurposeThe study aims to enhance energy efficiency within the high-energy consuming construction industry. It explores the spatial-temporal dynamics and distribution patterns of total factor energy efficiency (TFEE) across China’s construction industry, aiming to inform targeted emission reduction policies at provincial and city levels.Design/methodology/approachUtilizing a three-stage super-efficiency SBM-DEA model that integrates carbon emissions, the TFEE in 30 Chinese provinces and cities from 2004 to 2019 is assessed. Through kernel density estimation and exploratory spatial data analysis, the dynamic evolution and spatial patterns of TFEE are examined.FindingsAnalysis reveals that environmental investments positively impact TFEE, whereas Gross Regional Product (GRP) exerts a negative influence. R&amp;D expenditure intensity and marketization show mixed effects. Excluding environmental and random factors, TFEE averages declined, aligning more closely with actual development trends, showing a gradual decrease from east to west. TFEE exhibited fluctuating growth with a trend moving from inefficient clusters to a more even distribution. Spatially, TFEE demonstrated aggregation effects and characteristics of space-time transition.Originality/valueThis research employs the three-stage super-efficiency SBM-DEA model to measure the total factor energy efficiency of the construction industry, taking into account external environment, random disturbances, and multiple effective decision-making units. It also evaluates energy efficiency changes before and after removing disturbances and comprehensively examines regional and temporal differences from static and dynamic, overall and phased perspectives. Additionally, Moran scatter plots and LISA cluster maps are used to objectively analyze the spatial agglomeration and factors influencing energy efficiency.

Distribution and reference values of peripheral perfusion index in neonates from population-wide screening

BackgroundPeripheral perfusion index (PPI) is useful in a variety of neonatal settings. Currently, available reference values are from small numbers and highly variable.MethodsWe sought to generate reference values of PPI...

Spatial Distribution and Type Division of Traditional Villages in Zhejiang Province

Exploring the spatial distribution and type characteristics of traditional villages is crucial for promoting their protection and sustainable development. This article used the average nearest neighbor index, imbalance index, kernel density, standard deviation ellipse, and cluster analysis to analyze the overall distribution characteristics of traditional villages in Zhejiang Province and completed the classification of traditional village types and distribution areas in Zhejiang Province. The results indicate that traditional villages in Zhejiang Province exhibit a clustered and unbalanced distribution in space. They tend to be located below an altitude of 200 m, within 500 m of the river system, with terrain undulations of 20–200 m, 10–30 km away from county-level administrative centers, and 0–3 km away from main roads but with sparse road networks. Based on the comprehensive influence of geographical elements in the geographical environment of traditional villages, traditional villages in Zhejiang Province can be divided into five types. Based on the spatial distribution and type characteristics of traditional villages in Zhejiang Province, distribution areas of traditional villages in Zhejiang Province can be divided into six types. Finally, corresponding protection and development suggestions were proposed. These results can enrich the research content of traditional villages in Zhejiang Province and provide theoretical support for the protection and sustainable development of traditional villages.

Potential impacts of spatial restrictions on tobacco retail availability in China: a simulation study in Shanghai

ObjectivesRegulating tobacco retail availability provides promising new opportunities for effective tobacco control. This study simulates the potential impacts of introducing spatial restrictions on the availability of tobacco in Shanghai, the...

Integration of Manifold Learning and Density Estimation for Fine-Tuned Face Recognition

With the rapid advancements in data analysis and the increasing complexity of high-dimensional datasets, traditional dimensionality reduction techniques like Local Linear Embedding (LLE) often face challenges in maintaining accuracy and efficiency. This research aims to overcome the limitations of LLE, specifically its reliance on the nearest neighbor concept, its inability to distinguish differences among manifold points, and its underutilization of data discrimination information. To address these issues, we propose an advanced LLE algorithm that integrates decision tree-based neighbor recognition with Gaussian kernel density estimation. Decision trees accurately determine neighboring relationships, which are then optimized using Gaussian kernel density estimation to better reflect the distribution of sample points on the manifold. The algorithm also incorporates data discrimination information to enhance classification accuracy and efficiency. Evaluations in facial recognition tasks using SVM classifiers demonstrate significant improvements. Integrating decision trees (LLE-DT) yielded accuracy gains, with LFW at 98.75%, CFP 96.10%, and Olivetti 92.18%. Gaussian density estimation (LLE-GDE) achieved further enhancements, especially in LFW (99.13%), with CFP at 96.85%, and Olivetti at 91.82%. Combining both methods (LLE-DT-GDE) led to substantial improvements: LFW 99.61%, CFP 97.23%, and Olivetti 93.56%, highlighting the synergy between decision trees and Gaussian estimation. This advanced LLE algorithm effectively addresses the limitations of traditional approaches, showing promising results in complex data processing tasks such as facial recognition. These findings suggest its potential for broader applications in fields requiring robust data analysis and classification.