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An indicator-based framework of circular cities focused on sustainability dimensions and sustainable development goal 11 obtained using machine learning and text analytics

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SUSTAINABLE CITIES AND SOCIETY 2025年 121卷

作者： Falah, Nadia Falah, Navid Solis-Guzman, Jaime Marrero, Madelyn Univ Seville US Sch Bldg Engn Dept Architectural Construct 2 Seville Spain AmirKabir Univ Tehran AUT Fac Comp Sci & Math Tehran Iran

The circular economy (CE) serves a practical pathway to facilitate sustainable development (SD) and achieve the Sustainable Development Goals (SDGs). Current frameworks for assessing city sustainability and circularity often lack comprehensibility and multi-dimensional indicator-based approaches, also fail to include city-level CE indicators. This study defines an innovative structure for defining the circular city indicators (CCIs) addressing critical gaps in existing methodologies and CCIs coverage of sustainability and SDGs, especially SDG11. The methodology encompasses an extensive literature review, integrating CE principles, macro level of CE parameters and current CCIs, resulting in a comprehensive list of 241 indicators. Using advanced machine-learning techniques-semi-supervised learning, text analysis, and clustering algorithms-enhances the accuracy, comprehensiveness of the indicator classification. The indicators are categorized into 3D space across environmental, economic, and social dimensions of sustainability. This multi-dimensional approach also reveals the relationships between CCIs and 16 SDG11 classes. The analysis shows 75% of CCIs are multi-dimensional, but, five SDG11 classes show the lowest coverage in the heatmap of CCIs probability distribution across SDG11 classes, indicating a need to revise SDG11 classes and the social indicators of CCIs. The findings offer urban planners and stakeholders a practical list of CCIs to evaluate sustainability and CE level in cities.

关键词： Circular city indicator Circular economy Macro level of circular economy Sustainable development goals (SDGs) Machine learning (ML) Semi-supervised learning clustering algorithms

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A Joint Edge Server and Service Deployment Method in C-RAN With Multilayer MEC for Multicommunities

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IEEE INTERNET OF THINGS JOURNAL 2025年第1期12卷 904-918页

作者： Pan, Jeng-Shyang Yang, Qingyong Chu, Shu-Chuan Chao, Han-Chieh Shandong Univ Sci & Technol Coll Comp Sci & Engn Qingdao 266590 Peoples R China Nanjing Univ Informat Sci & Technol Sch Artificial Intelligence Nanjing 211544 Peoples R China Chaoyang Univ Technol Dept Informat Management Taichung 413 Taiwan Tamkang Univ Dept Artificial Intelligence New Taipei 251 Taiwan Natl Dong Hwa Univ Dept Elect Engn Hualien 974 Taiwan UCSI Univ Inst Comp Sci & Innovat Kuala Lumpur 56000 Malaysia

Combining cloud radio access network (C-RAN) and mobile edge computing (MEC) can effectively reduce network service latency and improve network reliability. The locations of edge servers (ESs) in the architecture and the services deployed affect the network quality and the operator's revenue. However, few studies have focused on ES or service deployment in this architecture, and almost no research has addressed both joint deployments. Furthermore, previous studies rarely consider the collaborative deployment across multiple communities (regions) and the cooperation among ESs simultaneously. To fill this gap, this study first constructs a C-RAN network system model with multilayer MEC involving multicommunities and establishes a multiobjective mixed-integer programming (MOMIP) model aimed at maximizing the profit of service providers and the comprehensive average resource utilization of the ESs. Then, for the established model, this study proposes a three-step heuristic algorithm to solve. The algorithm has been used to determine the number of ESs to be deployed in each community, the deployment locations of ESs at each layer, and the services to be deployed on each ES. Finally, this study conducts a series of simulation experiments. Experimental results show that the proposed algorithm outperforms the baseline algorithms, and the obtained deployment scheme is more reasonable.

关键词： Heuristic algorithms Computer architecture Optimization Servers Mathematical models Resource management Microservice architectures Computational modeling clustering algorithms Artificial intelligence Cloud radio access network (C-RAN) heuristic algorithm mobile edge computing (MEC) server deployment service deployment

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Subset-Selection Weight Post-Training Quantization Method for Learned Image Compression Task

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IEEE ACCESS 2025年 13卷 5145-5153页

作者： Yang, Jinru Wang, Xiaoqin Li, Qiang Qiao, Shushan Zhou, Yumei Chinese Acad Sci Inst Microelect Beijing 100029 Peoples R China Univ Chinese Acad Sci Sch Integrated Circuits Beijing 101408 Peoples R China

Post-training quantization(PTQ) has been widely studied in recent years because it does not require retraining the network or the entire training dataset. However, naively applying the PTQ method to quantize low-level image tasks such as learned image compression(LIC) usually incurs significant accuracy degradation. Existing solutions often aim to optimize the calibration process or minimize quantization loss in the context of uniform quantization, which makes it difficult to further reduce the quantization loss. This work achieves accurate weight quantization using a non-uniform quantization method called subset-selection within the PTQ method. Subset-selection method uses a clustering algorithm to select a proper batch of quantization points from a large uniformly constructed quantization points pool that matches the current layer's weight distribution correctly. Compared to the state-of-the-art LIC PTQ method, experimental results show that our proposed method achieves an average 0.8% better BD-rate on several LIC models. Meanwhile, we extended our method to image classification models and achieved an average 0.64% better accuracy, further proving the generalization of our method.

关键词： Quantization (signal) Image coding Calibration clustering algorithms Decoding Accuracy Linear programming Image reconstruction Computational modeling Adaptation models Convolutional neural network post-training quantization non-uniform quantization learned image compression

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Truth Discovery for Multiple Judgments With Crowdsourced Sparse Data

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IEEE INTERNET OF THINGS JOURNAL 2025年第5期12卷 5122-5136页

作者： Wang, Pengfei Jiao, Dian Su, Zhan Zhou, Changjun Li, Minglu Dalian Univ Technol Sch Comp Sci & Technol Dalian 116024 Peoples R China Dalian Univ Technol Key Lab Social Comp & Cognit Intelligence Minist Educ Dalian 116024 Peoples R China Zhejiang Normal Univ Sch Comp Sci & Technol Jinhua 321000 Peoples R China

Crowdsourced data refers to the information contributed by a large number of individuals, which may originate from various sources, including social media, online surveys, crowdsourcing tasks, etc. It is utilized for analysis and research across diverse scenarios. However, due to various subjective and objective factors, including the participants and sensing devices, the quality of the data collected for crowdsourcing tasks could be inconsistent. Therefore, how to filter out reliable information from the inconsistent data is crucial and difficult. Additionally, since participants consider their time and monetary costs, the crowdsourced datasets obtained are typically based on partial event observations, indicating a pronounced sparsity in the data. Current truth discovery methods struggle to adapt to datasets with varying levels of sparsity and lack effectiveness in evaluating and predicting sparse datasets that contain multiple judgments. In this article, we propose an adaptive hypergraph-based expectation-maximization (EM) truth discovery method for crowdsourced datasets with multiple judgments, named MHGEM (short for multidimensional-hypergraph EM). MHGEM leverages hypergraph topological metrics to model sparse datasets, enhancing the assessment of participant reliability and the prediction of truth for observed events. Experiments in both simulated and real-world scenarios demonstrate that MHGEM achieves higher predictive accuracy.

关键词： Reliability Crowdsensing Sensors Internet of Things Accuracy Costs clustering algorithms Predictive models Micromechanical devices Measurement Crowdsourced sparse data hypergraph multiple judgments truth discovery

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Graph-Based Approach for Industrial Market Segmentation Using Financial Transaction Network Structure and Firm's Attribute

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IEEE ACCESS 2025年 13卷 16394-16412页

作者： Bose, Aparajita Kim, Byunghoon Hanyang Univ Dept Ind & Management Engn Ansan 15588 South Korea

Market segmentation is a challenging and necessary task for improving marketing strategies and allocating resources effectively in a complex scenario of industrial or business-to-business (B2B) markets. Most of the traditional methods have not provided comprehensive and detailed guidelines for processing massive, complex, and complicated data from financial transactions in industrial markets. Furthermore, they often overlook the overlapping nature of segments, where a firm might belong to multiple market segments. Financial transaction networks (FTNs) can be depicted as graphs, where nodes and edges represent firms and transactions, respectively. A limited number of network analysis studies have been employed for industrial market analysis, but those efforts failed to capture the important features of financial transactions, such as the number of transactions, transaction volume, direction of transactions, and firm-specific attributes. In this study, we propose a novel method for industrial market segmentation that utilizes both financial multiple transactions and an attribute of firms. By integrating a graph-based generative probabilistic model, we reveal and understand the complex and overlapping relationships within financial transaction data. Subsequently, we apply the proposed method to a real-world dataset. The results show that the proposed method outperforms established methods in network analysis, such as clustering and community detection in transactional industrial market (B2B) networks.

关键词： Network analyzers Probabilistic logic Optimization Industries Codes clustering algorithms Systematic literature review Standards Resource management Couplings Industrial market segmentation financial transaction network analysis graph transaction network and firms' attribute graph-based market analysis interfirm relationship

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Indoor-outdoor interactive thermal response in public building:onsite data collection and classification through cluster algorithm

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ENERGY AND BUILDINGS 2025年 328卷

作者： Jin, Zhineng Zhang, Yin Sun, Hongli Han, Meng Zheng, Yanhong Zhao, Ying Han, Wenyang Zhang, Menglong Xu, Bin Zhang, Zequn Lin, Borong Sichuan Univ Coll Architecture & Environm Chengdu 610065 Peoples R China Southwest Minzu Univ Sch Architecture Chengdu 610225 Peoples R China Sichuan Univ State Key Lab Hydraul & Mt River Engn Chengdu Peoples R China China Architecture Design & Res Grp Land Based Rationalism DRC Beijing 100044 Peoples R China Tsinghua Univ Coll Architecture Beijing 100083 Peoples R China

Efficient thermal environment control in large public buildings with atria is critical for reducing energy consumption and carbon emissions. This study investigates the indoor thermal environments of two distinct atria in Suining, China, within a Hot Summer and Cold Winter (HSCW) climate zone. A novel methodology combining Kmeans clustering algorithms and multiple linear regression (CAMLR) was employed to analyze extensive thermal data from a transitional atrium and a traditional atrium. CAMLR significantly outperformed Traditional Building Thermal environment analysis Methods (TBTM), achieving a 33.8 % higher R2 and reducing the residual sum of squares by 62.11 %. Key findings include: 1) A strong correlation (R2 = 0.9555) between outdoor temperature and indoor thermal conditions in the transitional atrium, highlighting a critical outdoor temperature threshold of 30 degrees C that leads to prolonged indoor heat retention. 2) Solar radiation plays a dominant role in the traditional atrium (R2 = 0.5584), influencing rapid temperature fluctuations. These insights underscore the critical role of atrium design in optimizing thermal performance and energy efficiency. Compared to TBTM, CAMLR provides faster, more accurate, and highly visualized data analysis, making it particularly suitable for complex thermal environment studies. This study establishes CAMLR as a robust tool for advancing thermal management strategies in large public buildings, offering practical guidance for energy-efficient architectural designs in similar climate zones.

关键词： clustering algorithms Public buildings Thermal environment Energy efficiency Indoor-outdoor interaction

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Commonality Feature Representation Learning for Unsupervised Multimodal Change Detection

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IEEE TRANSACTIONS ON IMAGE PROCESSING 2025年 34卷 1219-1233页

作者： Liu, Tongfei Zhang, Mingyang Gong, Maoguo Zhang, Qingfu Jiang, Fenlong Zheng, Hanhong Lu, Di Shaanxi Univ Sci & Technol Shaanxi Joint Lab Artificial Intelligence Xian 710021 Peoples R China Xidian Univ Key Lab Collaborat Intelligent Syst Minist Educ Xian 710071 Peoples R China City Univ Hong Kong Dept Comp Sci Hong Kong Peoples R China

The main challenge of multimodal change detection (MCD) is that multimodal bitemporal images (MBIs) cannot be compared directly to identify changes. To overcome this problem, this paper proposes a novel commonality feature representation learning (CFRL) and constructs a CFRL-based unsupervised MCD framework. The CFRL is composed of a Siamese-based encoder and two decoders. First, the Siamese-based encoder can map original MBIs in the same feature space for extracting the representative features of each modality. Then, the two decoders are used to reconstruct the original MBIs by regressing themselves, respectively. Meanwhile, we swap the decoders to reconstruct the pseudo-MBIs to conduct modality alignment. Subsequently, all reconstructed images are input to the Siamese-based encoder again to map them in a same feature space, by which representative features are obtained. On this basis, latent commonality features between MBIs can be extracted by minimizing the distance between these representative features. These latent commonality features are comparable and can be used to identify changes. Notably, the proposed CFRL can be performed simultaneously in two modalities corresponding to MBIs. Therefore, two change magnitude images (CMIs) can be generated simultaneously by measuring the difference between the commonality features of MBIs. Finally, a simple threshold algorithm or a clustering algorithm can be employed to divide CMIs into binary change maps. Extensive experiments on six publicly available MCD datasets show that the proposed CFRL-based framework can achieve superior performance compared with other state-of-the-art approaches.

关键词： Feature extraction Image reconstruction Training Data mining Autoencoders Representation learning Image sensors Electronic mail Decoding clustering algorithms Multimodal change detection unsupervised change detection heterogeneous images representation learning commonality feature

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Two-Round Optimization Algorithm Based on Quadric Error Metrics

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IEEE ACCESS 2025年 13卷 30021-30035页

作者： Chang, Han Zhang, Di Dong, Yanan Yang, Yijun Shafiq Ur Rehman Khalil, Hafiz Wang, Xueting Xi An Jiao Tong Univ Sch Human Settlement & Civil Engn Dept Architecture Xian 710049 Peoples R China Xi An Jiao Tong Univ Sch Comp Sci & Technol Xian 710049 Peoples R China

Triangular mesh models are widely used for 3D model displays, but large models can cause slow loading and transmission, especially on mobile or web platforms. The quadric error metrics (QEM) algorithm is known for its efficient simplification and effectiveness. Nevertheless, it lacks clear prioritization for folding flat areas within the model, leaving room for improvement. This paper reproduces the work of Zhang Yun et al., finding that while their method reduces simplification errors, it increases the folding time of flat areas. The study improves the QEM algorithm by combining the angle and area of the first-order neighboring triangles of the edge, resulting in faster simplification but higher errors, making it less versatile. To address these issues, a two-round simplification method is proposed. The first round simplifies planar areas, and the second focuses on the entire model. Compared to the recursive algorithm, the two-round method showcases shorter simplification times, particularly evident when the *** exhibits a lower simplification ratio. For simplification rates below 40%, it reduces the average simplification time by 22.77% and yields better model accuracy than the QEM algorithm. On bunny_***, the simplification error is smaller than with the QEM algorithm, showcasing an average error reduction of 21.82%. Visually, the two-round proposed algorithm retains more detailed features and has better visual effects. However, because of the lower computational efficiency of the two-round simplification, future research directions may involve parallel computing and GPU utilization for acceleration.

关键词： Approximation algorithms Solid modeling Three-dimensional displays Vectors Measurement uncertainty Costs Faces Load modeling Computational modeling clustering algorithms Quadric error metrics two-round proposed algorithm first-order neighborhood triangle area approximate curvature

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Joint Augmentation and Part Learning for Unsupervised Clothing Change Person Re-Identification

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IEEE TRANSACTIONS ON INFORMATION FORENSICS AND SECURITY 2025年 20卷 2944-2956页

作者： Pang, Zhiqi Zhao, Lingling Liu, Yang Sharma, Gaurav Wang, Chunyu Harbin Inst Technol Fac Comp Harbin 150001 Peoples R China Univ Rochester Dept Elect & Comp Engn Rochester NY 14627 USA

Clothing change person re-identification (CC-ReID) is a crucial task in intelligent surveillance, aiming to match images of the same person wearing different clothing. Promising performance in existing CC-ReID methods is achieved at the cost of labor-intensive manual annotation of identity labels. While some researchers have explored unsupervised CC-ReID, these methods still depend on additional deep learning models for preprocessing. To eliminate the need for additional models and improve performance, we propose a joint augmentation and part learning (JAPL) framework that obtains clothing change positive pairs in an unsupervised fashion by synergistically combining augmentation-based invariant learning (AugIL) and part-based invariant learning (ParIL). AugIL first constructs clothing change pseudo-positive pairs and then encourages the model to focus on clothing-invariant information by enhancing feature consistency between the pseudo-positive pairs. ParIL beneficially encourages high similarity between inter-cluster clothing change positive pair using part images and a prediction sharpening loss. PartIL also introduces a soft consistency loss that promotes clothing-invariant feature learning by encouraging consistency of class vectors between the real features actually used for CC-ReID and the part features. Experimental results on multiple ReID datasets demonstrate that the proposed JAPL not only surpasses existing unsupervised methods but also achieves competitive performance compared to some supervised CC-ReID methods.

关键词： Clothing Shape Optimization Identification of persons Costs Feature extraction clustering algorithms Cameras Surveillance Deep learning Unsupervised learning contrastive learning clustering algorithm clothing change person re-identification

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RAPO: An Automated Performance Optimization Tool for Redis Clusters in Distributed Storage Metadata Management

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IEEE ACCESS 2025年 13卷 58060-58074页

作者： Zhu, Yunkai Xia, Tian Zhu, Te Zhao, Zhoujie Li, Kexin Hu, Xingbo East China Normal Univ Sch Commun & Elect Engn Shanghai 200241 Peoples R China Chinese Univ Hong Kong Sch Data Sci Shenzhen 518172 Guangdong Peoples R China

Distributed storage systems are well-equipped to address the demands of massive data storage and rapid access in the era of big data. However, as the scale of these systems continues to expand, challenges related to data localization, performance optimization, and secure access have become increasingly prominent, making effective metadata management critically important. Redis, a memory-based NoSQL (Not only SQL) database, has emerged as an ideal choice for metadata management in distributed storage systems due to its high performance, flexible data structures, excellent scalability, and robust data persistence mechanisms. This study employs Redis as the caching layer for metadata services in distributed storage systems and develops the Redis-cluster Automatic Performance Optimizer (RAPO) to enhance performance. By implementing load balancing among main nodes and read-write separation between main and secondary nodes, the tool aims to improve the metadata service performance of Redis clusters during system maintenance and in high-concurrency scenarios. To address load balancing among main nodes, the research defines the Load Balance Index (LBI) as a metric for evaluating the degree of load distribution within the cluster. Greedy and random iterative search algorithms are designed to migrate hash slots from selected nodes, thereby balancing the node loads and enhancing overall cluster performance. By optimizing with the greedy and random iterative search algorithms, the cluster's LBI was reduced by an average of 29.36% and 25.20% respectively during random read operations and by 24.98% and 24.03% respectively during imbalanced write operations. A load threshold is defined to achieve read-write separation between main and secondary nodes. Random allocation, round-robin, weighted round-robin, and smooth weighted round-robin are employed to optimize the read-write separation. Experimental results demonstrate that the weighted round-robin and smooth weighted round-robin

关键词： Metadata Load management Main-secondary Optimization Distributed databases clustering algorithms Synchronization Resource management Indexes Cloud computing Distributed storage metadata management Redis cache load balancing read-write separation

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