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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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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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development and validation of a nomogram model for predicting overall survival in patients with gastric carcinoma

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World Journal of Gastrointestinal Oncology 2025年第2期17卷 132-143页

作者： Guan-Zhong Liang Xiao-Sheng Li Zu-Hai Hu Qian-Jie Xu Fang Wu Xiang-Lin Wu Hai-Ke Lei Chongqing Key Laboratory of Translational Research for Cancer Metastasis and Individualized Treatment Chongqing University Cancer HospitalChongqing 400030China Chongqing Cancer Multi-omics Big Data Application Engineering Research Center Chongqing University Cancer HospitalChongqing 400030China Department of Health Statistics School of Public HealthChongqing Medical UniversityChongqing 400016China Research Center for Medicine and Social Development School of Public HealthChongqing Medical UniversityChongqing 400016China The Research Center of Big Data Chongqing University Cancer HospitalChongqing 400030China

BACKGROUND The prevalence and mortality rates of gastric carcinoma are disproportionately elevated in China,with the disease's intricate and varied characteristics further amplifying its health *** forecasting of overall survival(OS)is of paramount importance for the clinical management of individuals afflicted with this *** To develop and validate a nomogram model that provides precise gastric cancer prevention and treatment guidance and more accurate survival outcome prediction for patients with gastric *** data analysis was conducted on samples collected from hospitalized gastric cancer patients between 2018 and *** absolute shrinkage and selection operator,univariate,and multivariate Cox regression analyses were employed to identify independent prognostic factors.A nomogram model was developed to predict gastric cancer patient *** model's predictability and discriminative ability were evaluated via receiver operating characteristic *** evaluate the clinical utility of the model,Kaplan-Meier and decision curve analyses were *** A total of ten independent prognostic factors were identified,including body mass index,tumor-node-metastasis(TNM)stage,radiation,chemotherapy,surgery,albumin,globulin,neutrophil count,lactate dehydrogenase,and platelet-to-lymphocyte *** area under the curve(AUC)values for the 1-,3-,and 5-year survival prediction in the training set were 0.843,0.850,and 0.821,*** AUC values were 0.864,0.820,and 0.786 for the 1-,3-,and 5-year survival prediction in the validation set,*** model exhibited strong discriminative ability,with both the time AUC and time C-index exceeding *** with TNM staging,the model demonstrated superior clinical ***,a nomogram was developed via a web-based *** This study established and validated a novel nomogram model for predicting the OS of gastric cancer patients,which demonstrat

关键词： Gastric carcinoma Prediction Overall survival Nomogram Prospective

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Hydrogen Energy Demand Management in China:A Department Scenario Analysis Method

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Energy Engineering 2025年第3期122卷 971-983页

作者： Zhongxun Li Bing Wang Xiaolin Liu Center for Sustainable Development and Energy Policy Research School of Energy and Mining EngineeringChina University of Mining and Technology-BeijingBeijing100083China Research Institute of Decision-Making Science and Big Data School of ManagementChina University of Mining and Technology-BeijingBeijing100083China

The proposal of carbon neutrality target makes decarbonization and hydrogenation typical features of future energy development in *** a wide range of application scenarios,hydrogen energy will experience rapid growth in production and *** formulate an effective hydrogen energy development strategy for the future of China,this study employs the departmental scenario analysis method to calculate and evaluate the future consumption of hydrogen energy in China’s heavy industry,transportation,electricity,and other related *** technical parameters are selected and predicted accurately and reliably in combination with different development *** findings indicate that the period from 2030 to 2050 will enjoy rapid development of hydrogen energy,having an average annual growth rate of 2%to 4%.The technological progress and breakthroughs scenario has the greatest potential for hydrogen demand scale among the four development *** this scenario,the total demand for hydrogen energy is expected to reach 446.37Mt in *** sector will be the sector with the greatest potential for hydrogen deployment growth from 2023 to 2060,which is expected to rise from 0.038Mt to about 163.18Mt,with the ambitious growth in the ***,hydrogen energy has a considerable development potential in the steel sector,and the trend of de-refueling coke by hydrogenation in this sector will be imperative for this energy-intensive industries.

关键词： Hydrogen demand management department scenario analysis carbon neutrality

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Geological big data utilization in public health

Geological big data utilization in public health

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2024 International Conference on Remote Sensing and Digital Earth, RSDE 2024

作者： Wang, Liang Jia, Liqiong Yang, Shuqi Li, Sixia Yan, Hongwei Natural Resources Integrated Survey Command Center China Geological Survey Beijing100055 China Applied Geology Data Research Center Laboratory for Big Data and Decision Beijing100055 China Development and Research Center China Geological Survey Beijing100037 China Research Center of Applied Geology China Geological Survey Sichuan Chengdu610036 China

ISBN: (数字)9781510688223

ISBN: (纸本)9781510688216

This research delves into the uses of big data, in public health and emphasizes its crucial role in assessing health risks,data control of disease transmission and allocation of public health *** big data,serving as an resource could shed light on the complex connection between environmental factors and public health introducing new predictive models for health risks evidence based strategies, for mitigating diseases and sensible distribution of public health resources. Facing challenges related to data accuracy and privacy protection as technical processing issues doesn't diminish the undeniable value of using this technology for making well informed decisions, in public health matters. Looking ahead to the synergy of advancements and regulatory improvements in disciplines and anticipating the integration of extensive geological data, into public health initiatives promises to enhance the precision and effectiveness of managing healthcare systems in the future. © 2025 SPIE.

关键词： Electronic health record

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Decoding maize meristems maintenance and differentiation:integrating single-cell and spatial omics

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Journal of Genetics and Genomics 2025年第3期52卷 319-333页

作者： Bin Li Wenhao Liu Jie Xu Xuxu Huang Long Yang Fang Xu The Key Laboratory of Plant Development and Environmental Adaptation Biology Ministry of EducationShandong Key Laboratory of Precision Molecular Crop Design and Breeding School of Life SciencesShandong UniversityQingdaoShandong 266237China Housing and Urban Rural Development Bureau of Jimo District QingdaoShandong 266200China Agricultural Big-Data Research Center and College of Plant Protection Shandong Agricultural UniversityTai’anShandong 271018China

All plant organs are derived from stem cell-containing *** maize,the shoot apical meristem(SAM)is responsible for generating all above-ground structures,including the male and female inflorescence meristems(IMs),which give rise to tassel and ear,*** and reverse genetic studies on maize meristem mutants have driven forward our fundamental understanding of meristem maintenance and differentiation ***,the high genetic redundancy of the maize genome has impeded progress in functional *** review comprehensively summarizes recent advancements in understanding maize meristem development,with a focus on the integration of single-cell and spatial *** discuss the mechanisms governing stem cell maintenance and differentiation in SAM and IM,emphasizing the roles of gene regulatory networks,hormonal pathways,and cellular omics insights into stress responses and *** directions include cross-species comparisons,multi-omics integration,and the application of these technologies to precision breeding and stress adaptation research,with the ultimate goal of translating our understanding of meristem into the development of higher yield varieties.

关键词： Maize meristem Stem cell Single-cell and spatial technologies Meristem maintenance Regulatory mechanism

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Hybrid forecasting of demand flexibility: A top-down approach for thermostatically controlled loads

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Energy and AI 2025年 20卷

作者： Massidda, Luca Marrocu, Marino CRS4 Center for Advanced Studies Research and Development in Sardinia Italy National Research Center in High Performance Computing Big Data and Quantum Computing Italy

Demand-side flexibility is crucial to balancing supply and demand, as renewable energy sources are increasingly integrated into the energy mix, and heating and transport systems are becoming more and more electrified. Historically, this balancing has been managed from the supply side. However, the shift towards renewable energy sources limits the controllability of traditional fossil fuel plants, increasing the importance of demand response (DR) techniques to achieve the required flexibility. Aggregators participating in flexibility markets need to accurately forecast the adaptability they can offer, a task complicated by numerous influencing variables. Based on a top-down approach, this study addresses the problem of forecasting electricity demand in the presence of flexibility from thermostatically controlled loads. We propose a hybrid model that combines data-driven techniques for probabilistic estimation of electricity consumption with a disaggregation of electricity consumption to identify the fraction of thermal loads, subject to flexibility, which is simulated by a virtual battery model. The technique is applied to a synthetic dataset that simulates the response of a European neighborhood to demand response interventions. The results demonstrate the model's ability to accurately predict both the reduction in electricity demand during DR events and the subsequent rebound in consumption. The model achieves a mean absolute percentage error (MAPE) lower than 17.0%, comparable to the accuracy without flexibility. The results obtained are compared with a direct data-driven approach, demonstrating the validity and effectiveness of our model. © 2025

关键词： Demand response

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PACDNet:A landslide extraction network based on parallel attention 3

PACDNet:A landslide extraction network based on parallel att...

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3rd International Conference on Environmental Remote Sensing and Geographic Information Technology, ERSGIT 2024

作者： Zhu, Meijiang Wang, Lei Chen, Fang Yu, Bo Wang, Ning School of Computer Science and Information Security Guilin University of Electronic Technology Guilin China International Research Center of Big Data for Sustainable Development Goals Beijing 100094 China

ISBN: (纸本)9781510690219

Landslides pose a serious threat to the economy and human safety. Rapid and efficient landslide extraction is essential for post-disaster relief and reconstruction efforts. Current landslide extraction techniques still face challenges in accurately identifying landslides boundaries. To address this issue, we propose the Parallel Attention Change Detection Network (PACDNet), a network designed to extract landslides by calculating changes between pre-disaster and post-disaster images. Our method is an encoder-decoder network that utilizes a Siamese network structure with StarBlock from StarNet in the encoding stage, allowing for higher feature extraction capability with lower computational costs. In the decoding phase, the Parallel Attention Module (PAM) enhances the model's landslide boundaries extraction capability by simultaneously computing spatial attention and channel attention. To validate the effectiveness of our proposed PACDNet, we used the Global Very-High-Resolution Landslide Mapping (GVLM) dataset. This dataset includes pre-disaster and post-disaster images of 17 different landslide events. After comparing our model with four recently proposed models, PACDNet demonstrated improvements of 2.31% and 3.41% in Intersection over Union (IoU) on the Kaikoura and Tbilisi datasets, respectively. This indicates that PACDNet has a significant advantage in landslide extraction across different landslide events. © 2025 SPIE.

关键词： Change Detection Deep Learning Landslide detection Remote sensing

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Monitoring crop growth at Redstar Farm based on Sentinel-2 6

Monitoring crop growth at Redstar Farm based on Sentinel-2

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6th International Conference on Geoscience and Remote Sensing Mapping, GRSM 2024

作者： Hua, Zhiming Meng, Jihua Jing, Juanli Lin, Zhenxin You, Xinyan He, Rongpeng College of Geomatics and Geoinformation Guilin University of Technology Guangxi Guilin541004 China Key Laboratory of Digital Earth Science Aerospace Information Research Institute Chinese Academy of Sciences Beijing100094 China International Research Center of Big Data for Sustainable Development Goals Beijing100094 China

ISBN: (纸本)9781510688018

Using remote sensing technology to monitor crop growth is one of the very important research topics in precision agricultural remote sensing. Crop growth monitoring refers to monitoring crop growth status and predicting its development trend through satellite remote sensing. Real-time knowledge of crop growth status can provide timely and reliable information for field management, lay the foundation for estimating crop yields, and provide the National Agricultural Department with a basis for food management and decision-making. This paper takes the Redstar Farm of the Beian Branch of the Heilongjiang General Administration of Agricultural Reclamation as the research area, selects the Sentinel-2 image data and vector data files in the area in 2023, and uses basic software such as ENVI5.3 and ArcMap10.4 to obtain the crop phenological phase growth information map of the study area. By comparing and analyzing monthly growth charts, we can obtain the characteristics of changes in time and space of crop growth in Redstar Farm in 2023 and analyze the main reasons for these changes. © 2025 SPIE.

关键词： Crop rotation

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Oesophageal cancer-specific mortality risk and public health insurance: Prospective cohort study from China

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World Journal of Gastrointestinal Oncology 2025年第4期17卷 274-285页

作者： Xiang-Lin Wu Xiao-Sheng Li Jing-Han Cheng Lin-Xin Deng Zu-Hai Hu Jun Qi Hai-Ke Lei Chongqing Key Laboratory of Translational Research for Cancer Metastasis and Individualized Treatment Chongqing University Cancer HospitalChongqing 400030China Chongqing Cancer Multi-omics Big Data Application Engineering Research Center Chongqing University Cancer HospitalChongqing 400030China Research Center for Medicine and Social Development Chongqing Medical UniversityChongqing 400016China Department of Health Statistics School of Public HealthChongqing Medical UniversityChongqing 400016China Department of Thoracic Surgery The People’s Hospital of Changshou ChongqingChongqing 401220China

BACKGROUND Oesophageal cancer is a significant health concern worldwide,with high inci-dence and mortality *** China,the disease burden is particularly high,accounting for a substantial proportion of oesophageal cancer cases and related deaths *** To explore the relationship between the mortality rate of oesophageal cancer patients and insurance type,out-of-pocket ratio,and the joint effects of insurance type and out-of-pocket *** The χ^(2) test was used to analyze patients’demographic and clinical *** logistic regression,the Cox proportional hazard model,and the competitive risk model were used to calculate the cumulative hazard ratios(HRs)of all-cause death and oesophageal cancer-specific death among patients with different types of insurance and out-of-pocket *** Compared with patients covered by basic medical insurance for urban and rural residents,patients covered by urban employee basic medical insurance for urban workers(UEBMI)had a 23.30%increased risk of oesophageal cancer-specific death[HR=1.233,95%confidence interval(CI):1.093-1.391,P<0.005].Compared with patients in the low out-of-pocket ratio group,patients in the high out-of-pocket ratio group had a 25.80%reduction in the risk of oesophageal cancer-specific death(HR=0.742,95%CI:0.6555-0.84,P<0.005).With each 10%increase in the out-of-pocket ratio,the risk of oesophageal cancer-specific death decreased by 10.10%in patients covered by ***,the risk of oesophageal cancer-specific death increased by 26.90%in patients in the high out-of-pocket ratio *** This study reveals the relationships of the specific mortality rate of patients with oesophageal cancer with the out-of-pocket ratio and medical insurance types as well as their combined *** study provides practical suggestions and guidance for the formulation of relevant policies in this area.

关键词： Oesophageal cancer Public health Insurance Prognosis Mortality risk

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