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Revealing the synergy between carbon reduction and pollution control in the process of new-type urbanization: Evidence from China's five major urban agglomerations

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SUSTAINABLE CITIES AND SOCIETY 2025年 120卷

作者： Wang, Kewen Fang, Chuanglin Chinese Acad Sci Inst Geog Sci & Nat Resources Res Key Lab Reg Sustainable Dev Modeling Beijing 100101 Peoples R China Univ Chinese Acad Sci Beijing 100049 Peoples R China

As a sustainable transformation of urbanization, it is unclear whether China's new-type urbanization (NTU) can promote the collaborative governance of carbon reduction (CR) and pollution control (PC). Based on the data of China's five major urban agglomerations, this study constructed a systematic framework consisting of synergy measurement, relationship exploration and driver analysis to reveal the synergy between CR and PC in the process of NTU. The results indicated that with the steady growth of CR and PC, the coupling coordination degree of the two increased from 0.6457 to 0.9030 in 2014-2022, upgrading from primary synergy to excellent synergy. From the perspective of decoupling, all urban agglomerations improved the relationship between NTU and CR/ PC with 89 and 92 cities exhibiting the state of strong decoupling, respectively, but there were differences in the decoupling types of cities within each subsystem of NTU. Overall, NTU and its subsystems significantly drove the synergy between CR and PC, while the driving effects presented a spatial heterogeneity. The Yangtze River Delta was the only urban agglomeration that fulfilled the driving effects of all subsystems of NTU. These findings provide theoretical and empirical values for taking advantage of NTU to mitigate climate change and optimize air quality.

关键词： Carbon reduction Pollution control New-type urbanization Synergy Decoupling relationship China's five urban agglomerations

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Intelligent control system based on BIM visualization for indoor thermal energy regulation: A dynamic architectural design scheme

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THERMAL SCIENCE AND ENGINEERING PROGRESS 2025年 59卷

作者： Zou, Zinan Wang, Sijia Xian Univ Architecture Sch Architecture Xian 710043 Shanxi Peoples R China Technol Huaqing Coll Xian 710043 Shanxi Peoples R China Zhejiang Univ Technol Sch Design & Architecture Hangzhou 310023 Zhejiang Peoples R China

With the impact of global climate change and the continuous increase in energy consumption, the thermal energy regulation of indoor environments has become an important issue in the field of architectural design. This article aims to explore the application of intelligent control systems based on BIM (Building Information modeling) visualization in indoor thermal energy regulation, and propose a dynamic building design scheme to optimize indoor comfort and energy efficiency. The study pointed out the shortcomings of existing indoor environmental conditioning systems in terms of flexibility and energy efficiency. On this basis, an intelligent control system based on BIM visualization was designed, whose framework and functions include real-time data collection, analysis, and feedback to enhance the system's control capabilities. Real time monitoring and optimization adjustment of indoor environment have been achieved through BIM visualization design method. The system test results show that the intelligent control system significantly improves efficiency in thermal energy regulation and reduces energy consumption. In order to further promote the energy-saving design of dynamic buildings, this project studied the process of dynamic building design, including the integration of environmental analysis and user needs. At the same time, a method for dynamic building environment optimization control was proposed, which achieved comprehensive management of building energy consumption through joint simulation of daylighting energy consumption. This study indicates that dynamic building design can effectively respond to environmental changes and improve the overall energy efficiency of buildings.

关键词： BIM visualization Intelligent control system Indoor thermal energy regulation Dynamic Architectural Design

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Online analysis of filling power of cut tobacco by dynamic modeling on near-infrared spectral data

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JOURNAL OF FOOD COMPOSITION AND analysis 2025年 142卷

作者： Du, Guorong Xu, Tongguang Zhao, Wei Zhu, Yuchen Zhang, Zhenzhen Wang, Yi Huang, Yue China Agr Univ Coll Food Sci & Nutr Engn Beijing 100083 Peoples R China Shanghai Tobacco Grp Res & Dev Dept Beijing Stn Beijing 101121 Peoples R China China Univ Min & Technol Sch Emergency Management & Safety Engn Beijing 100083 Peoples R China

The filling power of cut tobacco is a crucial indicator for evaluating the overall quality of cigarette products. Rapid, effective, and accurate online analysis of tobacco filling power is quite essential for product quality control. In this study, a total of 1023 real online cut tobacco samples, including the flue-cured and sun-cured types, were collected and analyzed in a specific intermediate process of cigarette production. Subsequently, model analysis of tobacco filling power was conducted using near-infrared diffuse reflectance spectroscopy combined with the proposed dynamic modeling (DM) strategy. Results showed the determination coefficient (R2) of the optimal dynamic model exceeded 0.90, the ratio of prediction to deviation (RPD) was over 3, and the prediction error of 95 % testing samples ranged from- 0.179-0.168 cm3/g. Overall results demonstrated that the dynamic model based on NIR diffuse reflectance data can accurately analyze the filling power of cut tobacco online. The proposed method has proven to pose a significant potential for monitoring important indicators of tobacco products.

关键词： Cut tobacco Filling power Dynamic model Online analysis Near-infrared spectroscopy

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Expensive deviation-correction drilling trajectory planning: A constrained multi-objective Bayesian optimization with feasibility-oriented bi-objective acquisition function

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control ENGINEERING PRACTICE 2025年 156卷

作者： Xu, Jiafeng Chen, Xin Zhou, Yang Zhang, Menglin Cao, Weihua Wu, Min China Univ Geosci Sch Automat Wuhan 430074 Peoples R China Hubei Key Lab Adv Control & Intelligent Automat Co Wuhan 430074 Peoples R China Minist Educ Engn Res Ctr Intelligent Technol Geoexplorat Wuhan 430074 Peoples R China

While conducting large-depth vertical drilling, correcting well trajectory deviations is a critical and challenging task. Designing a feasible deviation-correction trajectory becomes an expensive constrained multi-objective optimization problem due to the need for refined modeling of large-depth wellbore stability analysis. There is a pressing need for advanced drilling trajectory planning methods designed to handle robust constraints and to consider refined geological formation modeling, as current surrogate model-assisted optimization algorithms lack efficiency and balance among feasibility, convergence, and diversity. A Gaussian process-assisted Bayesian Multi-Objective Evolutionary Algorithm (MOEA) based on the reference point-based Non-dominated Sorting Genetic Algorithm (NSGA-III) is developed to manage the expensive wellbore stability objective. While surrogate models can effectively mitigate the computational expense, they may not adequately satisfy the stringent trajectory planning constraints. To enhance the constraint handling ability, an intricately devised infill criterion, Feasibility-oriented Bi-objective Acquisition Function (FBAF), tends to select promising feasible solutions to infill into the next generation. The deviation-correction trajectory planning simulation experiment was carried out under limited evaluations with real vertical well data. The results of empirical attainment function analysis demonstrate that the proposed FB-NSGA-III reduces the number of evaluations and exhibits superior performance compared to 11 other traditional surrogate-assisted MOEAs, particularly in terms of feasibility. FB-NSGA-III successfully prevents the back-hook by avoiding constraint violations and maintaining curvature within the specified safety and directional drilling tool build-up range.

关键词： Multi-objective optimization Expensive optimization Surrogate modeling Drilling trajectory planning Empirical attainment function Acquisition function

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ConcVAE: Conceptual Representation Learning

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IEEE TRANSACTIONS ON NEURAL NETWORKS AND LEARNING SYSTEMS 2025年第4期36卷 7529-7541页

作者： Togo, Ren Nakagawa, Nao Ogawa, Takahiro Haseyama, Miki Hokkaido Univ Fac Informat Sci & Technol Sapporo 0600814 Japan Hokkaido Univ Grad Sch Informat Sci & Technol Sapporo 0600814 Japan

Disentangled representation learning aims at obtaining an independent latent representation without supervisory signals. However, the independence of a representation does not guarantee interpretability to match human intuition in the unsupervised settings. In this article, we introduce conceptual representation learning, an unsupervised strategy to learn a representation and its concepts. An antonym pair forms a concept, which determines the semantically meaningful axes in the latent space. Since the connection between signifying words and signified notions is arbitrary in natural languages, the verbalization of data features makes the representation make sense to humans. We thus construct Conceptual VAE (ConcVAE), a variational autoencoder (VAE)-based generative model with an explicit process in which the semantic representation of data is generated via trainable concepts. In visual data, ConcVAE utilizes natural language arbitrariness as an inductive bias of unsupervised learning by using a vision-language pretraining, which can tell an unsupervised model what makes sense to humans. Qualitative and quantitative evaluations show that the conceptual inductive bias in ConcVAE effectively disentangles the latent representation in a sense-making manner without supervision. Code is available at https://***/ganmodokix/concvae.

关键词： Representation learning Task analysis Natural languages Feature extraction Information science data models Computational modeling Deep learning disentanglement interpretable machine learning representation learning vision and language

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A data-driven brain network modeling for epileptogenic spread analysis

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BIOMEDICAL SIGNAL processING AND control 2025年 105卷

作者： Liu, Xiaotong Yu, Ying Han, Fang Zhou, Jian Liu, Zhao Luan, Guoming Wang, Qingyun Beihang Univ Dept Dynam & Control Beijing 100191 Peoples R China Donghua Univ Coll Informat Sci & Technol Shanghai 201620 Peoples R China Capital Med Univ Sanbo Brain Hosp Beijing Key Lab Epilepsy Beijing 100093 Peoples R China

Epileptic seizure propagation is a dynamic process that can be triggered by local abnormal discharges, leading to widespread network abnormalities in the brain. Understanding the causal relationship between the changes in brain network characteristics and the diverse propagation dynamics of epileptic seizures is crucial. We gather stereo-EEG data from 17 patients with temporal lobe epilepsy and utilize cross-channel phase amplitude coupling to extract the dynamic functional networks. Further, the patterns of brain network changes during seizure in patients with different surgeries are assessed using Hidden Markov Model. And characteristics of state transitions under different seizure periods are explored. Results show that the frequency of state transitions increases with seizures, and all epilepsy patients have a main state network with weakly connected network structure centered on the epileptogenic zone. The occupancy ratio of main state is inversely proportional to state transition frequency, where the emergence of strongly connected networks facilitates the seizure propagation. Variability in state characteristics is observed cross patients with different surgeries. The heterogeneous epileptor network model driven by the state transition is developed to simulate seizure propagation. Results show that state transition frequency and relationships affect seizure onset time and spread range. Under the main state network, seizures occur only in the epileptogenic zone and do not propagate to surrounding regions. Additionally, increasing the proportion of the main state network delays the onset of seizures. This suggests that the characteristics of the state network and its transitions may play a role in controlling the propagation of epileptic seizures.

关键词： Phase-amplitude coupling (PAC) Brain state transition Hidden Markov Model (HMM) Coupled-epileptor model

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modeling Information Influence and control in Social Networks: Integrating Opinions, Trust, Reputation, and Agent Dynamics 12th

Modeling Information Influence and Control in Social Netwo...

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12th International conference on analysis of Images, Social Networks and Texts, AIST 2024

作者： Gubanov, Dmitry V.A. Trapeznikov Institute of Control Sciences of Russian Academy of Sciences Moscow Russia

ISBN: (纸本)9783031880353

This paper presents a comprehensive analytical review of contemporary mathematical models of information influence and control in social networks, emphasizing the integration of agent-level factors such as trust, reputation, decision-making, and action execution. We introduce extensions to classical models, including the DeGroot model, by incorporating these critical components to more accurately reflect social interactions. Special attention is given to control strategies and the application of game-theoretic methods for analyzing interactions among controlling entities. We apply these models and methods to real-world social network data, including analyses of ideological preferences and public opinions on health measures during the COVID-19 pandemic. Our approach provides new perspectives for future research in social process modeling and offers insights for designing effective strategies for information influence and control. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： Game theory

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Adaptive Robust Stochastic Configuration Networks for Near-Infrared Multivariate analysis

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IEEE TRANSACTIONS ON NEURAL NETWORKS AND LEARNING SYSTEMS 2025年 PP卷 PP页

作者： Li, Yuqiang Du, Wenli Wang, Xinjie Yang, Minglei Zhao, Yunmeng East China Univ Sci & Technol Minist Educ Key Lab Smart Mfg Energy Chem Proc Shanghai 200237 Peoples R China Qingyuan Innovat Lab Quanzhou 362801 Peoples R China Hangzhou Normal Univ Sch Informat Sci & Technol Hangzhou 311121 Peoples R China

Near-infrared (NIR) technology has gained wide acceptance in practical processes and is now the measurement of choice in many sectors. However, with increasing spectral dimensionality, it is challenging to establish a prediction model with satisfactory stability and generalization. Stochastic configuration networks (SCNs) based on supervisory learning mechanism have demonstrated significant advantages in developing nonlinear learners. However, existing incremental learning strategies make it difficult to achieve fast convergence while obtaining a suitable-scale network in high-dimensional spectra modeling. In addition, the linear or regularization weight estimation methods are vulnerable to outliers and noise in NIR analysis. To accelerate model construction and improve model performance in high-dimensional spectra analysis, the adaptive robust SCN (AR-SCN) algorithm is proposed in this work, which can perform adaptive incremental learning according to the prediction residual and robustly estimate the output weights by the global-local shrinkage strategy. Comparison results on three benchmark NIR datasets and real-world gasoline blending process verify the effectiveness of the proposed method. Compared with the state-of-the-art SCNs, the AR-SCN method can simultaneously improve the construction efficiency and robustness of SCNs.

关键词： Adaptation models Incremental learning Stochastic processes Robustness Predictive models Bayes methods Analytical models Learning systems Convergence Vectors Adaptive incremental learning multivariate modeling near-infrared (NIR) spectroscopy sparse Bayesian learning (SBL) stochastic configuration networks (SCNs)

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Why multilayer networks are needed for complex data analysis 2nd

Why multilayer networks are needed for complex data analysis

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2nd International conference on Next-Generation Computing and Information Systems, ICNGCIS 2023

作者： Chakravarthy, Upendranatha Sharma The University of Texas at Arlington United States

ISBN: (纸本)9781032738659

We are on the cusp of holistically analyzing a variety of data being collected in every walk of life in diverse ways. For this, current analytics and science are being extended (Big data Analytics/Science) along with new approaches to benefit humanity at large in the best possible way. These warrants developing and/or using new approaches, technological, scientific, and systems, in addition to building upon and integrating with the ones that have been developed so far. In the first part of the presentation, we introduce multilayer networks (or MLNs) and illustrate their elegance for modeling complex data sets from well-known applications (IMDb and DBLP). Flexibility of analysis comes from this approach to modeling. We will show a dashboard that we have developed for MLN analysis. In second part of this talk, we discuss situation analysis on videos using a novel approach. We extract contents and apply stream processing techniques. This approach has the potential to process videos in real-time and raise alerts for situations, such as Nancy Pelosi’s house break-in and Las Vegas shooting. © 2025 The Author(s).

关键词： data Analytics

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GAN Training With Kernel Discriminators: What Parameters control Convergence Rates?

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IEEE TRANSACTIONS ON SIGNAL processING 2025年 73卷 433-445页

作者： Becker, Evan Pandit, Parthe Rangan, Sundeep Fletcher, Alyson K. UCLA Comp Sci Dept Los Angeles 90095 CA USA Indian Inst Technol Ctr Machine Intelligence & Data Sci Mumbai 400076 Maharashtra India NYU Dept Elect & Comp Engn Brooklyn NY 11201 USA UCLA Dept Stat Los Angeles 90095 CA USA

Generative Adversarial Networks (GANs) are widely used for modeling complex data. However, the dynamics of the gradient descent-ascent (GDA) algorithms, often used for training GANs, have been notoriously difficult to analyze. We study these dynamics in the case where the discriminator is kernel-based and the true distribution consists of discrete points in Euclidean space. Prior works have analyzed the GAN dynamics in such scenarios via simple linearization close to the equilibrium. In this work, we show that linearized analysis can be grossly inaccurate, even at moderate distances from the equilibrium. We then propose an alternative non-linear yet tractable second moment model. The proposed model predicts the convergence behavior well and reveals new insights about the role of the kernel width on convergence rate, not apparent in the linearized analysis. These insights suggest certain shapes of the kernel offer both fast local convergence and improved global convergence. We corroborate our theoretical results through simulations.

关键词： Kernel Convergence Generators Training Generative adversarial networks Stability analysis Optimization Neural networks Mathematical models Standards Convergence rates GANs kernel machines stability

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