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research on Predicting Economic Indicators of Public-Private Partnership Projects through Case-Based Reasoning

IAENG International Journal of Applied Mathematics 2025年第3期55卷 565-574页

作者： Zhang, Xiaofu Feng, Nianqing School of Civil Engineering and Architecture Henan Engineering Technology Research Center of Digital Intelligent Building and Low Carbon Building Materials Anyang Normal University Anyang455000 China School of Civil Engineering and Architecture Anyang Normal University Anyang455000 China

This study aims to address the issues faced by social capital during the preliminary decision-making stage of tracking and bidding for Public-Private Partnership (PPP) projects, which are characterized by tight schedules, heavy workloads, low efficiency in financial estimation, and varying quality of the results. This study identifies and analyzes the main factors affecting the economic indicators of PPP projects and develops a model to predict these indicators through case-based reasoning. Furthermore, candidate cases that closely resemble the target project are selected from reliable and representative PPP projects previously implemented by the company. Key feature factors closely related to the economic indicators are used to predict the target case’s investment net profit margin. The results show that this model is simple and more practical compared to other methods. It is particularly suitable for helping social capital perform financial calculations rapidly and efficiently during the preliminary decision-making stage of PPP projects. © (2025), (International Association of Engineers). All rights reserved.

关键词： Decentralized finance

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Personalized intelligent Job Recommendation Based on Deep Learning in Elasticsearch Environment

Journal of Network Intelligence

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Journal of Network Intelligence 2025年第1期10卷 339-352页

作者： Cui, Xiu-Yan Hwang, David Hebei Software Institute Hebei Province University Intelligent Interconnection Equipment and Multimodal Big Data Application Technology Research and Development Center Baoding071000 China College of Engineering St. Paul University Philippines Tuguegarao3500 Philippines

Aiming at the issue that the jobs predicted by the existing job recommendation methods cannot be precisely matched with the job seekers, this paper designs a personalized intelligent job recommendation method based on deep learning in the Elas-ticsearch environment. Firstly, to deal with the data sparsity and cold start issue of traditional collaborative filtering algorithms, the similarity calculation formula of Pearson’s correlation in the algorithm is modified to enhance the accuracy of similarity calculation. Secondly, on the ground of the history of different job seekers, the keywords of job seekers are constructed in terms of various weights, and input into ElasticSearch to recall the set of jobs that are strongly related to the job seekers. Then adopting Convolutional Neural Network (CNN) to extract the text data features of job seekers and positions, and meanwhile, integrating the attention mechanism to get the auxiliary features of job seekers and positions, and finally, integrating the implicit feedback data with other at-tribute features of job seekers and positions as the input of the improved collaborative filtration algorithm, so that we can realize the intelligent recommendation of jobs for job seekers. Simulation outcome indicates that the Mean Absolute Error (MAE), Mean Absolute Percentage Error (MAPE), Root Mean Squared Error (RMSE) and Root Mean Square Percentage Error (RMSPE) of the suggested method are 0.0681, 0.0519, 0.0834 and 0.0628 respectively, which are lower than those of the comparison method, and the accuracy of the recommendation is effectively enhanced. © 2025, Taiwan Ubiquitous Information CO LTD. All rights reserved.

关键词： Convolutional neural networks

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Dynamic properties of low-density expandable polystyrene concrete materials

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Defence technology(防务技术) 2025年第1期43卷 94-108页

作者： Jue Han Hualin Fan Army Engineering University of PLA Nanjing 210007China Research Center of Lightweight Structures and Intelligent Manufacturing State Key Laboratory of Mechanics and Control of Aerospace StructuresNanjing University of Aeronautics and AstronauticsNanjing 210016China Henan International Joint Laboratory of Dynamics of Impact and Disaster of Engineering Structures Department of Civil EngineeringNanyang Institute of TechnologyNanyang 473004China

Expanded polystyrene(EPS)concrete,known for its environmental friendliness,energy absorption capacity,and low impedance,has significant potential application in the fields of wave absorption and vibration *** study designed and prepared EPS concrete materials with four levels of ***-static uniaxial compression and Split Hopkinson Pressure Bar(SHPB)impact tests were conducted to obtain stress-strain curves,elastic moduli,failure modes,energy absorptions,and strain rate effects of the EPS concrete under quasi-static and dynamic loading *** influences of density on various performance indicators were *** combining the Zhu-Wang-Tang(ZWT)constitutive model with a modified elastic-brittle model,a modified dynamic constitutive model was *** accuracy of the model was validated by the experimental *** results indicate that the addition of EPS particles enhances the ductility of the EPS *** EPS concrete has significant strain rate effect,which gets stronger as density *** modifiedconstitutive model accurately characterizes the dynamic stress-strain curves of the EPS concrete.

关键词： EPS concrete Impact behavior Constitutive model SHPB test

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research on agricultural residue detection and traceability system based on internet of things and blockchain 5

Research on agricultural residue detection and traceability ...

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5th International Conference on Signal Processing and Computer Science, SPCS 2024

作者： Wang, Meng Hu, Feng Chen, Dandan Zhao, Qing Qin, Yilang Yang, Zhangqing Li, Guoqiang Institute of Agricultural Information Technology Henan Academy of Agricultural Sciences Henan Zhengzhou450002 China Key Laboratory of Huang-Huai-Hai Smart Agricultural Technology Ministry of Agriculture and Rural Affairs Henan Zhengzhou450002 China Henan Engineering and Technology Research Center for Intelligent Agriculture Henan Zhengzhou450002 China

ISBN: (数字)9781510686731

ISBN: (纸本)9781510686724

In the face of serious centralization of the pesticide residue detection platform system structure for agricultural products, difficulty in ensuring the authenticity of detection results, and low confidence in the traceability information chain, a pesticide residue detection traceability system based on internet of things technology and blockchain is constructed to meet the changing needs. The pesticide residue detection and traceability system consists of two main parts: agricultural product pesticide residue embedded detection equipment and blockchain based traceability system. Based on the Internet of Things and blockchain technology, a one-time unique detection code is generated as the task authorization basis for single detection. The detection results adopt a dual storage design of storing traceability hash values on the traceability information chain off chain, and a microservice platform is built using PAAS architecture and HTML5 technology. In addition, the Apriori algorithm is used to construct a pesticide residue risk warning model. This system has achieved reliable collection, transmission, and traceability of pesticide residue detection data for agricultural products. Consumers can obtain agricultural product detection information through traceability source code, effectively solving the problem of low confidence and authenticity of pesticide residue detection data. It has high application value in the construction of agricultural product quality and safety. © 2025 SPIE.

关键词： Insecticides

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SPDGrNet: A Lightweight and Efficient Image Classification Network for Zea mays Diseases

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Journal of Crop Health 2025年第3期77卷 1-12页

作者： Xu, Laixiang Cao, Bingxu Su, Jing feng Zhang, Fengyue Song, Hongyun Zhao, Junmin School of Computer and Data Science Research Center of Smart City and Big Data Engineering of Henan Province Henan University of Urban Construction Pingdingshan 467036 China School of Information Engineering Luohe Vocational Technology College Henan Luohe China Institute of Technology University of Technology Luohe Luohe 462000 China School of Computer and Data Science Innovation Laboratory of Smart Traffic and Big Data Development of Hainan Province Henan University of Urban Construction Pingdingshan 467036 China School of Computer and Data Science Henan University of Urban Construction Pingdingshan 467036 China School of Computer and Data Science Research Center of Smart City and Big Data Engineering of Henan Province Research Center of Intelligent Campus Application Engineering of Henan Province Innovation Laboratory of Smart Traffic and Big Data Development of Hainan Province Henan University of Urban Construction Pingdingshan 467036 China

Controlling diseases on the foliage of cultivated plants has a major impact on crop yield and quality. Leaf symptoms and environmental information are the basis of crop disease and pest identification. Due to the complexity and diversity of disease leaf symptoms and environmental information, the accuracy of crop disease detection methods has decreased. Aiming at low disease identification efficiency and difficult feature modeling, we propose a new deep learning algorithm and obtain ideal results. We used improved convolutional neural networks (CNNs) SPDNet and GrNet (SPDGrNet) to perform Zea mays disease identification. The proposed research and analysis focus on four categories of Zea mays symptoms: healthy, leaf blight, southern leaf blight and grey spot. The experimental results and analysis confirm that the proposed approach outperforms other excellent plant disease recognition algorithms and achieves an overall classification accuracy of 98.96%. When compared to AlexNet, VGG-16, Inception-V4, ShuffleNet-V3, and Efficient-B7, the proposed model achieves accuracy improvements of 27.78%, 19.05%, 14.59%, 10.28%, and 7.06%, respectively, with corresponding accuracies of 71.18%, 79.91%, 84.37%, 88.68%, and 91.9%. The proposed model has outstanding generalization ability in open-source databases, such as the PlantifyDr, Plant Pathology, and New Plant Diseases Dataset. The suggested strategy can provide ideas for transplanting models into mobile disease detection equipment. This is very helpful in the intelligent development of agricultural systems and crop leaf disease analysis. © The author(s), exclusively licensed to Springer-Verlag GmbH Germany, a part of Springer Nature 2025.

关键词： Crop leaf disease control Deep learning Disease detection Generalization Zea mays

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Enhancing Reranking for Recommendation with LLMs through User Preference Retrieval 31

Enhancing Reranking for Recommendation with LLMs through Use...

引用

31st International Conference on Computational Linguistics, COLING 2025

作者： Zhang, Haobo Zhu, Qiannan Dou, Zhicheng Gaoling School of Artificial Intelligence Renmin University of China China School of Artificial Intelligence Beijing Normal University China Engineering Research Center of Intelligent Technology and Educational Application MOE China

ISBN: (纸本)9798891761964

Recently, large language models (LLMs) have shown the potential to enhance recommendations due to their sufficient knowledge and remarkable summarization ability. However, the existing LLM-powered recommendation may create redundant output, which generates irrelevant information about the user's preferences on candidate items from user behavior sequences. To address the issues, we propose a framework UR4Rec that enhances reranking for recommendation with large language models through user preference retrieval. Specifically, UR4Rec develops a small transformer-based user preference retriever towards candidate items to build the bridge between LLMs and recommendation, which focuses on producing the essential knowledge through LLMs from user behavior sequences to enhance reranking for recommendation. Our experimental results on three real-world public datasets demonstrate the superiority of UR4Rec over existing baseline models. © 2025 Association for Computational Linguistics.

关键词： Computational linguistics

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Federated Incremental Named Entity Recognition 31

Federated Incremental Named Entity Recognition

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31st International Conference on Computational Linguistics, COLING 2025

作者： Liu, Zesheng Zhu, Qiannan Li, Cuiping Chen, Hong School of Information Renmin University of China Beijing China Key Laboratory of Data Engineering and Knowledge Engineering MOE China Engineering Research Center of Database and Business Intelligence MOE China School of Artificial Intelligence Beijing Normal University Beijing China Engineering Research Center of Intelligent Technology and Educational Application MOE China

ISBN: (纸本)9798891761964

Federated learning-based Named Entity Recognition (FNER) has attracted widespread attention through decentralized training on local clients. However, most FNER models assume that entity types are pre-fixed, so in practical applications, local clients constantly receive new entity types without enough storage to access old entity types, resulting in severe forgetting on previously learned knowledge. In addition, new clients collecting only new entity types may join the global training of FNER irregularly, further exacerbating catastrophic forgetting. To overcome the above challenges, we propose a Forgetting-Subdued Learning (FSL) model which solves the forgetting problem on old entity types from both intra-client and inter-client two aspects. Specifically, for intra-client aspect, we propose a prototype-guided adaptive pseudo labeling and a prototypical relation distillation loss to surmount catastrophic forgetting of old entity types with semantic shift. Furthermore, for inter-client aspect, we propose a task transfer detector. It can identify the arrival of new entity types that are protected by privacy and store the latest old global model for relation distillation. Qualitative experiments have shown that our model has made significant improvements compared to several baseline methods. © 2025 Association for Computational Linguistics.

关键词： Federated learning

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Comparative analysis of data-driven models on detection and classification of electrical faults in transmission systems: Explainability, applicability and industrial implications

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Alexandria engineering Journal 2025年 127卷 75-91页

作者： Ukwuoma, Chibueze D. Cai, Dongsheng Ukwuoma, Chiagoziem C. Otuka, Chinedu I. Huang, Qi College of Nuclear Technology and Automation Engineering Chengdu University of Technology Sichuan 610059 China Sichuan Engineering Technology Research Center for Industrial Internet Intelligent Monitoring and Application Chengdu University of Technology Sichuan 610059 China OBU Sino-British Collaborative Education Chengdu University of Technology Sichuan 610059 China Information Engineering Department Southwest University of Science and Technology Sichuan Mianyang China

Most data-driven fault detection methods often face challenges in accuracy, adaptability, and real-time implementation, particularly in complex transmission networks. To address these issues, this study presents an in-depth comparative analysis of data-driven models, including machine learning, neural networks, and deep learning techniques, for detecting and classifying electrical faults in transmission lines. A few key gaps were identified to persist particularly in terms of the accuracy, computational complexity, explainability, applicability, and industrial implications of these models. Hence, a Quadratic Discriminant Analysis by Projection (QDA-P) model which effectively captures non-linear relationships between electrical fault features is proposed, enhancing classification accuracy, less computationally complex and explainable for both binary and multi-class scenarios. Using a publicly available simulated dataset generated through MATLAB Simulink to represent various fault types, the QDA-P model achieved binary classification scores of 0.988, 0.980, 0.982, 0.995, and 0.987 while recording a multi-class classification score of 0.982, 0.979, 0.982, 0.982, and 0.980 for accuracy, precision, specificity, recall, and F1-score, respectively. Feature importance analysis showed that voltage at the sending or receiving end of the transmission line for phase A was the most influential while the length of the transmission line in phase A had the lowest importance. In contrast, techniques such as SHAP, LIME, and PDP reveal that the length of the transmission line in Phases A & C significantly influenced Class 1 and Class 5 predictions, with the length of the transmission line in Phase C contributing positively and the length of the transmission line in phase A showing varied effects. An industrial applicability analysis using the Mahalanobis distance plot confirmed that the QDA-P model effectively captured underlying data patterns without significant outliers. These findi

关键词： Discriminant analysis

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Dual-head alternating attention-based adaptive convolutional framework for improving low-resource and imbalanced text classification

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Neural Computing and applications 2025年 1-21页

作者： Agbesi, Victor Kwaku Chen, Wenyu Hossin, Md Altab Ukwuoma, Chiagoziem C. School of Computer Science and Engineering University of Electronic Science and Technology of China No. 2006 Xiyuan Ave West Hi-Tech Zone Sichuan Chengdu China School of Innovation and Entrepreneurship Chengdu University No. 2025 Chengluo Avenue 610106 Sichuan Chengdu China College of Nuclear Technology and Automation Engineering Chengdu University of Technology Sichuan Chengdu610059 China Sichuan Engineering Technology Research Center for Industrial Internet Intelligent Monitoring and Application Chengdu University of Technology Sichuan Chengdu610059 China

Low-resource imbalance texts pose significant challenges due to class imbalance, adversely affecting the performance of deep learning approaches. To address this issue, we propose a dual-head alternating attention-based adaptive convolutional framework with label embedding (AltXLM-AdaCLE), a novel framework designed for imbalanced text classification in low-resourced datasets. Our framework features a new alternating attention model, which combines a dual-head attention mechanism with a bidirectional long short-term memory network, facilitating dynamic filter generation that adapts to the input data’s characteristics and enhances the capture of relevant features and long-range dependencies. A key innovation in this study is the dual-head attention mechanism, which utilizes an alternating reading sequence to extract information from multiple document segments, thereby minimizing information loss and improving the understanding of complex relationships. Furthermore, we introduce a method for embedding class labels in a shared vector space with the text, allowing the model to leverage label information throughout the classification process, which is particularly beneficial for imbalanced datasets. Our framework also incorporates an adaptive convolutional layer that processes text features and label embeddings, generating more representative features of the underlying data distribution and improving classification performance. Experimental results on six low-resourced imbalance text datasets validate the effectiveness of AltXLM-AdaCLE with an average AUC gain of 26.42%, demonstrating its superiority over existing methods. © The Author(s), under exclusive licence to Springer-Verlag London Ltd., part of Springer Nature 2025.

关键词： Vector spaces

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Investigation on working efficiency and performance of journal bearings using various microtextures

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International Journal of Computational Materials Science and Surface engineering 2025年第2期12卷 168-185页

作者： Li, Qian Mei, Yundong Tian, Xiaoguang Li, Ye Sun, Bei College of Mechanical and Electrical Engineering Huanghe Jiaotong University Henan Provincial Engineering Technology Research Center for Intelligent Manufacturing Technology and Equipment Henan Jiaozuo454950 China College of Mechanical and Electrical Engineering Jiaozuo University Henan454950 China

To research the working efficiency and performance of journal bearings (JBs) using microtextures, the various microtextures including the cylindrical-round-structure (CRS), hemispherical-structures (HS), square-cylinder-structures (SCS), and square-wedge-structures (SWS) were proposed and evaluated based on the lubrication model of JBs. Results indicated that by adding microtextures in JBs, the lubrication efficiency and friction of JBs were remarkably improved compared to ignoring the microtextures. Besides, both HS and SWS are better than CRS and SCS in improving the bearing capacity of JBs while both CRS and HS are better than SCS and SWS in reducing the friction of JBs. Especially, HS improves both the bearing capacity and the friction of JBs better than other structures. The research results not only found the optimal lubrication efficiency of JBs using HS but also enriched the reference literature in the specialised field. Copyright © 2025 Inderscience Enterprises Ltd.

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