Features Of Model Research Articles

Improving aerosol optical depth retrievals from Himawari-8 with ensemble learning enhancement: Validation over Asia

The Advanced Himawari Imager (AHI) onboard the Himawari-8 spacecraft from the Japan Meteorological Agency (JMA) enables retrieval of aerosol optical depth (AOD) with high spatio-temporal resolution. This work demonstrates that the latest version of Level-3 AHI AOD product is affected by an overall underestimation against ground measurements from 98 sunphotometer sites over Asia, with site-average R2 and mean bias error (MBE) of 0.54 and − 0.10, respectively. The MBE varies from −0.23 to 0.03 over land, depending on site, and averages 0.06 over water. To correct these errors, an XGBoost model has been developed based on the AHI AOD, as well as meteorological quantities and geographic information from the ERA5 ECMWF reanalysis, a digital elevation model (DEM), and ground-based AOD measurements obtained in 2018. After application of this correction algorithm, the testing dataset indicates that R2 and the fraction of data included within the expected error (EE) envelope (±15%) are 0.86 and 74.7%, respectively, thus representing substantial improvement compared to the corresponding uncorrected results of 0.59 and 42.2%. The optimal XGBoost also demonstrates a good generalization ability because 69.5% of the corrected AOD falls within the EE envelope when applying the model to AHI data from 2019. The model's Feature Importance analysis shows that, besides the raw AHI AOD, geographic coordinates, surface albedo, and atmospheric water vapor contribute significantly to the correction model. The AOD difference map (corrected AOD minus raw AHI AOD) reveals that the XGBoost model improves AOD values considerably for land areas by 0.14, 0.10, 0.09, and 0.11 for MAM, JJA, SON and DJF, respectively.

Improved YOLOv7-based steel surface defect detection algorithm.

In response to the limited detection ability and low model generalization ability of the YOLOv7 algorithm for small targets, this paper proposes a detection algorithm based on the improved YOLOv7 algorithm for steel surface defect detection. First, the Transformer-InceptionDWConvolution (TI) module is designed, which combines the Transformer module and InceptionDWConvolution to increase the network's ability to detect small objects. Second, the spatial pyramid pooling fast cross-stage partial channel (SPPFCSPC) structure is introduced to enhance the network training performance. Third, a global attention mechanism (GAM) attention mechanism is designed to optimize the network structure, weaken the irrelevant information in the defect image, and increase the algorithm's ability to detect small defects. Meanwhile, the Mish function is used as the activation function of the feature extraction network to improve the model's generalization ability and feature extraction ability. Finally, a minimum partial distance intersection over union (MPDIoU) loss function is designed to locate the loss and solve the mismatch problem between the complete intersection over union (CIoU) prediction box and the real box directions. The experimental results show that on the Northeastern University Defect Detection (NEU-DET) dataset, the improved YOLOv7 network model improves the mean Average precision (mAP) performance by 6% when compared to the original algorithm, while on the VOC2012 dataset, the mAP performance improves by 2.6%. These results indicate that the proposed algorithm can effectively improve the small defect detection performance on steel surface defects.

Complementary Calibration: Boosting General Continual Learning with Collaborative Distillation and Self-Supervision.

General Continual Learning (GCL) aims at learning from non independent and identically distributed stream data without catastrophic forgetting of the old tasks that don't rely on task boundaries during both training and testing stages. We reveal that the relation and feature deviations are crucial problems for catastrophic forgetting, in which relation deviation refers to the deficiency of the relationship among all classes in knowledge distillation, and feature deviation refers to indiscriminative feature representations. To this end, we propose a Complementary Calibration (CoCa) framework by mining the complementary model's outputs and features to alleviate the two deviations in the process of GCL. Specifically, we propose a new collaborative distillation approach for addressing the relation deviation. It distills model's outputs by utilizing ensemble dark knowledge of new model's outputs and reserved outputs, which maintains the performance of old tasks as well as balancing the relationship among all classes. Furthermore, we explore a collaborative self-supervision idea to leverage pretext tasks and supervised contrastive learning for addressing the feature deviation problem by learning complete and discriminative features for all classes. Extensive experiments on six popular datasets show that our CoCa framework achieves superior performance against state-of-the-art methods. Code is available at https://github.com/lijincm/CoCa.

CJS-YOLOv5n: A high-performance detection model for cigarette appearance defects.

In tobacco production, cigarettes with appearance defects are inevitable and dramatically impact the quality of tobacco products. Currently, available methods do not balance the tension between detection accuracy and speed. To achieve accurate detection on a cigarette production line with the rate of 200 cigarettes per second, we propose a defect detection model for cigarette appearance based on YOLOv5n (You Only Look Once Version 5 Nano), called CJS-YOLOv5n (YOLOv5n with C2F (Cross Stage Partial (CSP) Bottleneck with 2 convolutions-fast), Jump Concat, and SCYLLA-IoU (SIoU)). This model incorporates the C2F module proposed in the state-of-the-art object detection network YOLOv8 (You Only Look Once Version 8). This module optimizes the network by parallelizing additional gradient flow branches, enhancing the model's feature extraction capability and obtaining richer gradient information. Furthermore, this model uses Jump Concat to preserve minor defect feature information during the fusion process in the feature fusion pyramid's P4 layer. Additionally, this model integrates the SIoU localization loss function to improve localization accuracy and detection precision. Experimental results demonstrate that our proposed CJS-YOLOv5n model achieves superior overall performance. It maintains a detection speed of over 500 FPS (frames per second) while increasing the recall rate by 2.3% and mAP (mean average precision)@0.5 by 1.7%. The proposed model is suitable for application in high-speed cigarette production lines.

View-Aware Salient Object Detection for $360^{\circ }$ Omnidirectional Image

Image-based salient object detection (ISOD) in <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$360^{\circ }$</tex-math></inline-formula> scenarios is significant for understanding and applying panoramic information. However, research on <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$360^{\circ }$</tex-math></inline-formula> ISOD has not been widely explored due to the lack of large, complex, high-resolution, and well-labeled datasets. Towards this end, we construct a large scale <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$360^{\circ }$</tex-math></inline-formula> ISOD dataset with object-level pixel-wise annotation on equirectangular projection (ERP), which contains rich panoramic scenes with not less than 2K resolution and is the largest dataset for <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$360^{\circ }$</tex-math></inline-formula> ISOD by far to our best knowledge. By observing the data, we find current methods face three significant challenges in panoramic scenarios: diverse distortion degrees, discontinuous edge effects and changeable object scales. Inspired by humans' observing process, we propose a view-aware salient object detection method based on a Sample Adaptive View Transformer (SAVT) module with two sub-modules to mitigate these issues. Specifically, the sub-module View Transformer (VT) contains three transform branches based on different kinds of transformations to learn various features under different views and heighten the model's feature toleration of distortion, edge effects and object scales. Moreover, the sub-module Sample Adaptive Fusion (SAF) is to adjust the weights of different transform branches based on various sample features and make transformed enhanced features fuse more appropriately. The benchmark results of 20 state-of-the-art ISOD methods reveal the constructed dataset is very challenging. Moreover, exhaustive experiments verify the proposed approach is practical and outperforms the state-of-the-art methods.

Diversifying Design of Nucleic Acid Aptamers Using Unsupervised Machine Learning.

Inverse design of short single-stranded RNA and DNA sequences (aptamers) is the task of finding sequences that satisfy a set of desired criteria. Relevant criteria may be, for example, the presence of specific folding motifs, binding to molecular ligands, sensing properties, and so on. Most practical approaches to aptamer design identify a small set of promising candidate sequences using high-throughput experiments (e.g., SELEX) and then optimize performance by introducing only minor modifications to the empirically found candidates. Sequences that possess the desired properties but differ drastically in chemical composition will add diversity to the search space and facilitate the discovery of useful nucleic acid aptamers. Systematic diversification protocols are needed. Here we propose to use an unsupervised machine learning model known as the Potts model to discover new, useful sequences with controllable sequence diversity. We start by training a Potts model using the maximum entropy principle on a small set of empirically identified sequences unified by a common feature. To generate new candidate sequences with a controllable degree of diversity, we take advantage of the model's spectral feature: an "energy" bandgap separating sequences that are similar to the training set from those that are distinct. By controlling the Potts energy range that is sampled, we generate sequences that are distinct from the training set yet still likely to have the encoded features. To demonstrate performance, we apply our approach to design diverse pools of sequences with specified secondary structure motifs in 30-mer RNA and DNA aptamers.

Identification of plant leaf diseases by deep learning based on channel attention and channel pruning.

Plant diseases cause significant economic losses and food security in agriculture each year, with the critical path to reducing losses being accurate identification and timely diagnosis of plant diseases. Currently, deep neural networks have been extensively applied in plant disease identification, but such approaches still suffer from low identification accuracy and numerous parameters. Hence, this paper proposes a model combining channel attention and channel pruning called CACPNET, suitable for disease identification of common species. The channel attention mechanism adopts a local cross-channel strategy without dimensionality reduction, which is inserted into a ResNet-18-based model that combines global average pooling with global max pooling to effectively improve the features' extracting ability of plant leaf diseases. Based on the model's optimum feature extraction condition, unimportant channels are removed to reduce the model's parameters and complexity via the L1-norm channel weight and local compression ratio. The accuracy of CACPNET on the public dataset PlantVillage reaches 99.7% and achieves 97.7% on the local peanut leaf disease dataset. Compared with the base ResNet-18 model, the floating point operations (FLOPs) decreased by 30.35%, the parameters by 57.97%, the model size by 57.85%, and the GPU RAM requirements by 8.3%. Additionally, CACPNET outperforms current models considering inference time and throughput, reaching 22.8 ms/frame and 75.5 frames/s, respectively. The results outline that CACPNET is appealing for deployment on edge devices to improve the efficiency of precision agriculture in plant disease detection.

Attention-based Feature Fusion Generative Adversarial Network for yarn-dyed fabric defect detection

Defects on the surface of yarn-dyed fabrics are one of the important factors affecting the quality of fabrics. Defect detection is the core link of quality control. Due to the diversity of yarn-dyed fabric patterns and the scarcity of defect samples, reconstruction-based unsupervised deep learning algorithms have received extensive attention in the field of fabric defect detection. However, most existing deep learning algorithms cannot fully extract shallow, high-frequency and high-level information, which limits their ability to reconstruct yarn-dyed fabric images. In this article, we propose an Attention-based Feature Fusion Generative Adversarial Network framework for unsupervised defect detection of yarn-dyed fabrics. The framework utilizes a modified Feature Pyramid Network to fuse multi-level information and utilizes an attention mechanism to enhance the model's feature representation capabilities. The Attention-based Feature Fusion Generative Adversarial Network consists of an attention fusion generator and a patch-level discriminator. In the attention fusion generator, the Feature Pyramid Network with EfficientNetV2 as the backbone is used as the core building block, and different feature fusion methods are used to avoid the loss of information in the process of network deepening. The attention mechanism is used to enhance the channel and spatial-wise correlation of features, which helps the model to focus on more meaningful information by recalibrating the feature maps. In the discriminator, the patch-level discriminator is used to calculate the similarity between the reconstructed image and the original image from a local perspective, thereby improving the model's attention to texture details. Experimental results on public datasets demonstrate the effectiveness of the proposed method compared to other methods.

Research of asteroid mining mechanism and global fairness based on TOPSIS and AHP

With the development of human society, earth resources are increasingly exhausted, and the importance of asteroid mining for human survival is becoming more and more prominent. In this paper, we use TOPSIS, EWM, AHP, and Correlation analysis to describe the definition and model of global equity. We also integrate and divide using the multi-group fairness model's feature selection algorithm, converting the feature selection problem of asteroid resources for N into a multi-group M individual resource allocation problem. The behavior is analyzed and processed by the multi-population fairness algorithm to provide a better allocation solution (a better subset of features), which naturally merges random search and heuristic search to decrease computing effort while achieving improved allocation accuracy. Finally, we verify the practical significance of the model, which can provide policy reference suggestions for decision-makers and is of great significance for the development of mankind in the future.

Fluorescence Emission Wavelength QSPR Application with Linear Blending Method in Machine Learning Algorithms

Machine learning tools have been developed to analyze quantitative structure-activity/property relationship (QSAR/QSPR) modeling research. Better feature selection algorithms in the ensemble methods have been used to advance QSPR/QSAR modeling, helping to understand the relation between features and target variables and reducing the computational requirements. Implementing feature importance allows for a more effective and clearer view into features' relative importance and interpret the predictions. However, the main struggle of ensemble learning methods is that each model leads to different feature selections for interpretation. Therefore, it is necessary to summarize each model and its corresponding features for better performance, resulting in high prediction accuracy. In this article, we use a blending method for prediction and interpretability in terms of the experimental values of fluorescence wavelengths. The blender requires two levels. The first level uses multiple classifiers: Random Forest, ExtraTrees, Adaptive Boosting, and Gradient Boosting. The second level requires a linear blending method that summarizes information from the classifiers. Even though the ensemble learning models accurately predict properties and activities, the algorithms are often susceptible so that even small changes can drastically impact their efficiency and accuracy. Thus, the main idea to overcome the difficulty is to implement multiple times feature selections in each model to manipulate the sensitivity. Furthermore, it accurately predicts the fluorescence data set from a regression task of the Decision Tree based (DT-based) QSAR/QSPR model. This paper provides the best-optimized features when considering specific experimental chemical or biological values. Furthermore, the tables and figures representing each model's feature selections and accuracy demonstrate the result. It shows that even though the number of features for predicting the Fluorescence Emission Wavelength reduces, the accuracy of training and test sets is maintained, and the computational effectiveness is increased.

Affine body dynamics

Simulating stiff materials in applications where deformations are either not significant or else can safely be ignored is a fundamental task across fields. Rigid body modeling has thus long remained a critical tool and is, by far, the most popular simulation strategy currently employed for modeling stiff solids. At the same time, rigid body methods continue to pose a number of well known challenges and trade-offs including intersections, instabilities, inaccuracies, and/or slow performances that grow with contact-problem complexity. In this paper we revisit the stiff body problem and present ABD, a simple and highly effective affine body dynamics framework, which significantly improves state-of-the-art for simulating stiff-body dynamics. We trace the challenges in rigid-body methods to the necessity of linearizing piecewise-rigid trajectories and subsequent constraints. ABD instead relaxes the unnecessary (and unrealistic) constraint that each body's motion be exactly rigid with a stiff orthogonality potential, while preserving the rigid body model's key feature of a small coordinate representation. In doing so ABD replaces piecewise linearization with piecewise linear trajectories. This, in turn, combines the best of both worlds: compact coordinates ensure small, sparse system solves, while piecewise-linear trajectories enable efficient and accurate constraint (contact and joint) evaluations. Beginning with this simple foundation, ABD preserves all guarantees of the underlying IPC model we build it upon, e.g., solution convergence, guaranteed non-intersection, and accurate frictional contact. Over a wide range and scale of simulation problems we demonstrate that ABD brings orders of magnitude performance gains (two- to three-orders on the CPU and an order more when utilizing the GPU, obtaining 10, 000× speedups) over prior IPC-based methods, while maintaining simulation quality and nonintersection of trajectories. At the same time ABD has comparable or faster timings when compared to state-of-the-art rigid body libraries optimized for performance without guarantees, and successfully and efficiently solves challenging simulation problems where both classes of prior rigid body simulation methods fail altogether.

Research on Virtual Interactive Animation Design System Based on Deep Learning.

With the rapid development of computer network technology, the advantages of virtual reality technology in the field of instant messaging are becoming more and more significant. Virtual reality technology plays an important role in communication networks, including enhanced resource utilization, device redundancy, immersion, interactivity, conceptualization, and holography. In this paper, we use the basic theory of Restricted Boltzmann Machine to establish a semisupervised spatio-temporal feature model through the animation capture data style recognition problem. The bottom layer can be pretrained with a large amount of unlabeled data to enhance the model's feature perception capability of animation data, and then train the high-level supervised model with the labeled data to finally obtain the model parameters that can be used for the recognition task. The layer-by-layer training method makes the model have good parallelism, that is, when the layer-by-layer training method makes the model well parallelized, that is, when the bottom features cannot effectively represent the animation features, such as overfitting or underfitting, only the bottom model needs to be retrained, while the top model parameters can be kept unchanged. Simulation experiments show that the design assistance time of this paper's scheme for animation is reduced by 10 minutes compared to baseline.

The Comprehensive Automobile Research System (CARS) – a Python-based automobile emissions inventory model

Abstract. The Comprehensive Automobile Research System (CARS) is an open-source Python-based automobile emissions inventory model designed to efficiently estimate high-quality emissions from motor vehicle emission sources. It can estimate air pollutant, greenhouse gas, and air toxin criteria at any spatial resolution based on the spatiotemporal resolutions of input datasets. The CARS is designed to utilize local vehicle activity data, such as vehicle travel distance, road-link-level network geographic information system (GIS) information, and vehicle-specific average speed by road type, to generate an automobile emissions inventory for policymakers, stakeholders, and the air quality modeling community. The CARS model adopted the European Environment Agency's on-road automobile emissions calculation methodologies to estimate the hot exhaust, cold start, and evaporative emissions from on-road automobile sources. It can optionally utilize average speed distribution (ASD) of all road types to reflect more realistic vehicle speed variations. In addition, through utilizing high-resolution road GIS data, the CARS can estimate the road-link-level emissions to improve the inventory's spatial resolution. When we compared the official 2015 national mobile emissions from Korea's Clean Air Policy Support System (CAPSS) against the ones estimated by the CARS, there is a significant increase in volatile organic compounds (VOCs) (33 %) and carbon monoxide (CO) (52 %) measured, with a slight increase in fine particulate matter (PM2.5) (15 %) emissions. Nitrogen oxide (NOx) and sulfur oxide (SOx) measurements are reduced by 24 % and 17 %, respectively, in the CARS estimates. The main differences are driven by different vehicle activities and the incorporation of road-specific ASD, which plays a critical role in hot exhaust emission estimates but was not implemented in Korea's CAPSS mobile emissions inventory. While 52 % of vehicles use gasoline fuel and 35 % use diesel, gasoline vehicles only contribute 7.7 % of total NOx emissions, whereas diesel vehicles contribute 85.3 %. However, for VOC emissions, gasoline vehicles contribute 52.1 %, whereas diesel vehicles are limited to 23 %. Diesel buses comprise only 0.3 % of vehicles and have the largest contribution to NOx emissions (8.51 % of NOx total) per vehicle due to having longest daily vehicle kilometer travel (VKT). For VOC emissions, compressed natural gas (CNG) buses are the largest contributor at 19.5 % of total VOC emissions. For primary PM2.5, more than 98.5 % is from diesel vehicles. The CARS model's in-depth analysis feature can assist government policymakers and stakeholders in developing the best emission abatement strategies.

On Properties of Length-Biased Exponential Model

The type II half logistic length-biased exponential distribution, which extends the length-biased exponential model, is introduced and examined. This model's statistical features, such as moments, conditional moments, mean order statistics, and entropy, are derived. To estimate distribution parameters, statistical inference employs three estimation methods: maximum likelihood, Cramer–von Mises, and Anderson–Darling. To evaluate the performance of the aforementioned estimators, we run simulation experiments based on the graphical results. The TIIHLLBE model's adaptability has been demonstrated through applications to real-world datasets.

Predictive values of systemic inflammatory responses index in early neurological deterioration in patients with acute ischemic stroke.

Acute ischemic stroke (AIS) is the main cause of worldwide death and disability. Early neurological deterioration (END) can further increase the probability of death and disability in patients with ischemic stroke. Therefore, it is essential to find biomarkers to predict END early. Inflammatory response plays a crucial role in determining the course, outcome, and prognosis of END. Earlier studies focused on the relationship between routine hematological inflammatory markers and END, which limited the results. At present, relatively new and comprehensive markers of inflammatory response are relatively scarce. In this study, we investigate the predictive value of inflammatory markers in acute ischemic stroke cases for END which include systemic inflammatory response index (SIRI), platelet/lymphocyte ratio (PLR), lymphocyte/monocyte ratio (LMR), neutrophil/lymphocyte ratio (NLR), and then to establish a nomogram model. A total of 375 patients with AIS were analyzed who were admitted to the Second Affiliated Hospital of Harbin Medical University from September 2019 to June 2021. The associations between END and inflammatory markers were studied by employing the analysis of univariate. Following that, through regression models of the least absolute shrinkage and selection operator, the END risk model's feature selection was optimized. The development of the model of prediction was carried out by applying the multivariable logistic regression analysis. The calibration, discrimination, and clinical efficacy of the prediction model were studied via calibration plot, C-index, and decision curve analysis (DCA). The bootstrapping validation method was used for the evaluation of internal validation. We constructed a nomogram consisting of CRP, monocytes, NIHSS and SIRI. This model had desirable calibration and discrimination, with a C-index of 0.757 (95% confidence interval: 0.702-0.805). Interval validation could still achieve the higher C-index value of 0.747. When the risk threshold for END was greater than 13% but less than 84%, DCA proved to be clinically useful. Our research shows that SIRI can be used as a new predictor of END, as well as a monitor of treatment response. Compared with the traditional single inflammatory indicator, the integration of SIRI nomogram can predict the occurrence of END more objectively and reliably.

Modeling the spread of Leishmaniasis disease via delayed analysis

Leishmaniasis is a group of contagious diseases having high surveillance among dogs. According to World Health Organization (WHO), ninety-two territories were infected around the world in 2018. One billion dogs live in areas endemic for Leishmaniasis and are at risk of infection. Almost one million cases are reported annually. The dog's population is categorized into five classes like susceptible (S), latent (L), infectious (I), uninfected (R), and infected (Q). The Leishmaniasis delayed model is developed to analyze and control its spread. Equilibria, positivity, boundedness, reproduction number, and parameter sensitivity are the model's essential features. The stability of the model is investigated in a local and global sense with the help of the Routh Hurwitz criterion and Lyapunov theory. In the end, simulations are presented to verify the theoretical analysis of the delayed model.

On a moving boundary problem associated with the swelling drug release platforms

This paper studies a parabolic-type system of time-dependent partial differential equations coupled with Robin boundary conditions to model the solvent penetration and subsequently a controlled drug release from a swellable polymeric spherical platform. The model's key feature is that the solvent penetration and the polymer swelling result in moving boundaries. From an analytical perspective, some properties of the solutions, such as the positivity and the uniqueness of the solutions, are established. A second-order iterative procedure based on the backward finite difference method is carried out to solve the problem numerically. Finally, the numerical results and the asymptotic solutions are compared, intending to illustrate the validity and applicability of the numerical scheme.

Categorization of Common Pigmented Skin Lesions (CPSL) using Multi-Deep Features and Support Vector Machine.

The skin is the main organ. It is approximately 8 pounds for the average adult. Our skin is a truly wonderful organ. It isolates us and shields our bodies from hazards. However, the skin is also vulnerable to damage and distracted from its original appearance: brown, black, or blue, or combinations of those colors, known as pigmented skin lesions. These common pigmented skin lesions (CPSL) are the leading factor of skin cancer, or can say these are the primary causes of skin cancer. In the healthcare sector, the categorization of CPSL is the main problem because of inaccurate outputs, overfitting, and higher computational costs. Hence, we proposed a classification model based on multi-deep feature and support vector machine (SVM) for the classification of CPSL. The proposed system comprises two phases: First, evaluate the 11 CNN model's performance in the deep feature extraction approach with SVM, and then, concatenate the top performed three CNN model's deep features and with the help of SVM to categorize the CPSL. In the second step, 8192 and 12,288 features are obtained by combining binary and triple networks of 4096 features from the top performed CNN model. These features are also given to the SVM classifiers. The SVM results are also evaluated with principal component analysis (PCA) algorithm to the combined feature of 8192 and 12,288. The highest results are obtained with 12,288 features. The experimentation results, the combination of the deep feature of Alexnet, VGG16 and VGG19, achieved the highest accuracy of 91.7% using SVM classifier. As a result, the results show that the proposed methods are a useful tool for CPSL classification.

Modified quasispecies model: the analysis of a periodic therapy.

We propose a modified mathematical model of the quasispecies type to analyze an unstable tumor progression evolution. In our study, we consider a heterogeneous population with different individuals, generated by the accumulation of successive mutations. Our model's main feature is that it allows for variable growth rates for each subpopulation and takes into account mutations from nonconsecutive types of mutants. Bifurcations and linear stability of the steady states are analyzed. We focus on two equilibria; one of them implies the coexistence of anomalous growth and genetically unstable cells. The other one yields the dominance of the anomalous growth population and the extinction of the malignant cells. However, linear stability analysis of the second equilibrium is inconclusive and suggests a suitable environment for the study of periodic therapy. This is carried out by introducing a small perturbation modeling the effect of a periodic medical treatment. As a result, a Zero-Hopf periodic orbit is identified, showing a cyclic behavior among the populations, with a strong dominance of the parental anomalous growth cell population.

3D building model simplification method considering both model mesh and building structure

AbstractThe simplification of three‐dimensional (3D) building models to improve rendering efficiency has gained widespread attention. To maintain the model's overall appearance features while increasing the simplification rate, we propose a novel 3D building simplification method that considers both the model mesh and building structure. The method divides a 3D building into a primary structure and subsidiary structures. It then organizes these structures using StructureTree, a multi‐way tree. The structures are organized according to the dependency relationships between building structures. When simplifying a building, the decision whether to simplify the mesh or remove the subsidiary structure in the leaf node of the StructureTree depends on the volume change caused by the edge collapse and the visual saliency of the removed structure. The experimental results show that our method exhibits a better simplification effect than the traditional simplification method, and the proposed method can achieve a high simplification rate while maintaining the simplification quality. Furthermore, the results of some spatial analyses based on the highly simplified building model are consistent with those of the original model.