检索结果-内蒙古大学图书馆

SSRN 2024年

作者： Lin, Ziqi Xu, Chenfei Sun, Huaiwei Xiong, Ming Chen, Lu Liu, Yi Wang, Jia Li, Haichen He, Qianyu School of Civil and Hydraulic Engineering Huazhong University of Science and Technology Wuhan430074 China Hubei Key Laboratory of Digital River Basin Science and Technology Huazhong University of Science and Technology Wuhan430074 China Institute of Water Resources and Hydropower Huazhong University of Science and Technology Wuhan430074 China College of Water Conservancy & Architectural Engineering Shihezi University China Changjiang Institute of Survey Planning Design and Research Wuhan430010 China Bureau of Hydrology Changjiang Water Resources Commission Hubei Wuhan430010 China State Key Laboratory of Simulation and Regulation of Water Cycle in River Basin China Institute of Water Resources and Hydropower Research Beijing100038 China

As numerous mountain settlements in China are intertwined with poverty and mountain disasters, information for estimating the occurrence of external shocks is lacked for residents in remote mountain villages. At the same time, studies on public perception of flash flood disasters are rarely mentioned, and different types of disasters lead to different public cognitive characteristics and coping strategies for disasters. Based on the survey data (N = 420) from Yiling District, Yichang City in China, the partial least squares structural equation model (PLS-SEM) is employed in this study to investigate the interaction between different dimensions of local residents' perception of flash flood disaster to verify the correctness of the hypothesis. The disaster perception was divided into four dimensions: subjective perception, disaster prevention knowledge and skills, disaster reduction policy perception and disaster reduction information attention. Four possible relationships among the dimensions were hypothesized. The results showed that: (1) subjective perception had a positive but insignificant effect on disaster prevention knowledge and skills;(2) risk perception affected each other in different dimensions. Individuals who pay more attention to disaster information have better cognition of policies, knowledge and skills of disaster prevention;(3) the impact of disaster reduction policy awareness on disaster prevention knowledge and skills was 0.454, indicating that external objective factors had a greater impact on disaster awareness than individual subjective factors. Overall, it is suggested that relevant departments increase the frequency of disaster reduction training to improve the public's disaster awareness level. © 2024, The Authors. All rights reserved.

关键词： Risk perception

来源：评论

学校读者我要写书评

暂无评论

Deep learning for assessment of environmental satisfaction using BIM big data in energy efficient building digital twins

引用

SUSTAINABLE ENERGY TECHNOLOGIES AND ASSESSMENTS 2022年第0期50卷 101897-101897页

作者： Wang, Weixi Guo, Han Li, Xiaoming Tang, Shengjun Xia, Jizhe Lv, Zhihan [a]Research Institute for Smart Cities School of Architecture and Urban Planning Shenzhen University & Shenzhen Key Laboratory of Spatial Smart Sensing and Services & MNR Technology Innovation Center of Territorial & Spatial Big Data & Key Laboratory of Urban Land Resources Monitoring and Simulation Ministry of Natural Resources Shenzhen 518060 China [b]Shenzhen Research Center of Digital City Engineering Shenzhen 518034 China [c]Department of Game Design Faculty of Arts Uppsala University Sweden

Energy efficient Building digital Twins (BDTs) are researched using Building Information Model (BIM) to explore the key techniques of digital Twins (DTs). DTs in buildings can be regarded as an expression of "BIM+, " born to digital descriptions. Comprehensive perception of physical systems is the preconditions for DTs implementation. BIM's energy-saving design includes the selection of building orientation and building shape. BIM energy consumption analysis can compare different materials, examine the performance of various materials, and select the most suitable and most energy-efficient materials for building structure maintenance. Data Fusion Algorithm (DFA) in Wireless Sensor Networks (WSNs) is improved. A novel DFA is constructed by combining Back propagation Neural Network (BPNN) with Dynamic Host Configuration Protocol (DCHP), recorded as BP-DCHP. simulation experiment proves that BP-DCHP can prolong sensor nodes' survival time and provide the highest data fusion quality. BP-DCHP runs for about 310 s, 500 s, and 705 s in WSNs consisting of 20, 50, and 100 WSNs, respectively. Moreover, BP-DCHP can provide higher quality given insufficient data fusion degree. Once the WSNs consume 50% of the total initial energy, BP-DCHP presents a shorter network delay, only 0.6 s on average in the 100-sensor-node-WSN. To validate BDTs' effectiveness, the environmental satisfaction of residents from two Beijing intelligent communities is assessed using Deep Learning (DL) approach. Taking the data as the clue, the study establishes DTs serving the application of urban scene, which plays a certain role in promoting the technological innovation of BDTs, better optimizing the city and managing the city.

关键词： Building digital twins BIM big data Deep learning Assessment of environmental satisfaction Energy efficient building

来源：评论

学校读者我要写书评

暂无评论

The Joint Model of Multi-Intent Detection and Slot Filling Based on Bidirectional Interaction Structure

引用

Wuhan University Journal of Natural Sciences 2025年第1期30卷 21-31页

作者： WANG Changjing ZENG Xianghui WANG Yuxin SUN Yuxin ZUO Zhengkang School of Digital Industry Jiangxi Normal UniversityShangrao 334000JiangxiChina School of Computer Information Engineering Jiangxi Normal UniversityNanchang 330022JiangxiChina Jiangxi Provincial Key Laboratory for High Performance Computing Jiangxi Normal UniversityNanchang 330022JiangxiChina State International Science and Technology Cooperation Base of Networked Supporting Software Jiangxi Normal UniversityNanchang 330022JiangxiChina

Intent detection and slot filling are two important components of natural language *** their relevance,joint training is often performed to improve *** studies mostly use a joint model of multi-intent detection and slot-filling with unidirectional interaction,which improves the overall performance of the model by fusing the intent information in the slot-filling *** this basis,in order to further improve the overall performance of the model by exploiting the correlation between the two,this paper proposes a joint multi-intent detection and slot-filling model based on a bidirectional interaction structure,which fuses the intent encoding information in the encoding part of slot filling and fuses the slot decoding information in the decoding part of intent *** results on two public multi-intent joint training datasets,MixATIS and MixSNIPS,show that the bidirectional interaction structure proposed in this paper can effectively improve the performance of the joint *** addition,in order to verify the generalization of the bidirectional interaction structure between intent and slot,a joint model for single-intent scenarios is proposed on the basis of the model in this *** model also achieves excellent performance on two public single-intent joint training datasets,CAIS and SNIPS.

关键词： natural language understanding multi-intent detection slot filling bidirectional interaction joint training

来源：评论

学校读者我要写书评

暂无评论

The Effects of Top Management Team R&D Functional Background on Corporate Social Responsibility: Detrimental or Beneficial in Family Firms?

引用

International Journal of Finance and Economics 2025年第0期

作者： Ren, Yi-Shuai Elommal, Najoua Kong, Xiaolin Liu, Xukang School of Public Administration Hunan University Changsha China Key Laboratory of High-Performance Distributed Ledger Technology and Digital Finance (Hunan University) Ministry of Education Hunan China Research Institute of Digital Society and Blockchain Hunan University Hunan China Center for Resource and Environmental Management Hunan University Hunan China The Energy Centre University of Auckland Auckland New Zealand ISTEC Business School Paris France School of Digital Economics Jiangxi University of Finance and Economics Nanchang China Business School Hunan University Changsha China

This study investigates the influence of TMT R&D functional background on corporate social responsibility (CSR), using data from Chinese-listed family firms from 2010 to 2020. The results demonstrate that the CSR performance of family firms is negatively influenced by the R&D functional background of TMT. These conclusions continuously demonstrate robustness following endogeneity and robustness tests. Furthermore, the primary channels via which the TMT R&D functional background influences CSR performance are digital transformation and family control channels. The TMT R&D functional background exerts a more significant adverse impact on CSR in family firms with multiple shareholding or actual controllers not serving as the chairman or general manager. © 2025 John Wiley & Sons Ltd.

关键词： corporate governance corporate social responsibility digital transformation family firms R&D functional background top management team

来源：评论

学校读者我要写书评

暂无评论

Hierarchical Attention Feature Fusion and Refinement Network for Point Cloud Upsampling

Hierarchical Attention Feature Fusion and Refinement Network...

引用

IEEE International Conference on Multimedia and Expo (ICME)

作者： Yaori Zhang Shujin Lin Fan Zhou Ruomei Wang National Engineering Research Center of Digital Life School of Computer Science and Engineering Guangdong Provincial Science and Technology Collaborative Innovation Center for Culture and Tourism Sun Yat-sen University Guangzhou China School of Communication and Design Guangdong Key Laboratory for Big Data Analysis and Simulation of Public Opinion Guangdong Provincial Science and Technology Collaborative Innovation Center for Culture and Tourism Sun Yat-sen University Guangzhou China Guangdong Provincial Science and Technology Collaborative Innovation Center for Culture and Tourism National Engineering Research Center of Digital Life School of Computer Science and Engineering Sun Yat-sen University Guangzhou China National Engineering Research Center of Digital Life School of Computer Science and Engineering Sun Yat-sen University Guangzhou China

ISBN: (数字)9798350390155

ISBN: (纸本)9798350390162

This paper presents a novel hierarchical attention feature fusion and refinement network designed to address challenges in existing deep learning based point cloud upsampling methods. The network combines self-attention layers with a multi-level feature extraction architecture, effectively integrating local and global features, thereby enhancing the robustness and uniformity of the point cloud. Furthermore, a spatial refinement module is employed to predict the offset between the generated coarse dense point clouds and real point clouds, thereby enhancing consistency with the ground truth. Concurrently, a filter function is applied in the loss function to handle outliers of generated point clouds. Extensive experimental results across multiple datasets indicate that our method outperforms existing approaches.

关键词： Point cloud compression Deep learning Feature extraction Robustness Encoding

来源：评论

学校读者我要写书评

暂无评论

On-Road GHG Emissions Characteristics and Durability of Typical China-Vi Hddts in Intercity Transportation

SSRN

引用

SSRN 2023年

作者： Lyu, Liqun Zhang, Youyuan Qin, Zhaoxiang Wang, Junfang Yin, Hang Feng, Jianyong Tan, Jianwei Hao, Lijun Ge, Yunshan School of Mechanical Engineering Beijing Institute of Technology Beijing100081 China Dongfeng Liuzhou Motor Co. Ltd Liuzhou545005 China State Environmental Protection Key Laboratory of Vehicle Emission Control and Simulation Chinese Research Academy of Environmental Sciences Beijing100012 China TRADING CO. LTD Beijing Branch Beijing100080 China National Laboratory of Automotive Performance &amp Emission Test China

China confronts rising greenhouse gas (GHG) emissions from transportation sector, particularly under-addressed emissions from heavy-duty diesel vehicles (HDDVs), challenging its carbon neutrality goal by 2060. Using a portable emission measurement system (PEMS), the on-road CO2, CH4, and N2O emissions characteristics and durability of seven typical China-VI HDDVs applied for intercity transportation were evaluated. A direct correlation was observed between increased instantaneous CO2 emissions and vehicle specific power (VSP), affirming the positive dependency of CO2 emissions on engine power. However, CH4 emissions remained negligible across various speeds, displaying no obvious correlation with VSP, suggesting that power fluctuations were not the primary factor impacting the CH4. N2O emissions, on the other hand, were independent of speed but increased with increasing VSP. The main explanation is that power fluctuations change the pathway of selective reduction processes in selective catalytic reduction (SCR), affecting N2O selectivity. On-road emission factors indicated declining CO2 and CH4 with increased speed, in contrast to N2O, which presented minimal speed correlation. CH4 and N2O collectively constituted 2.78 – 6.41% of total CO2-equivalent emissions, with negligible CH4 contributions. Consequently, enhancing energy efficiency and optimizing N2O selectivity in SCR are pivotal strategies for controlling GHG emissions of China-VI HDDVs. Regarding durability, engine wear will not significantly increase CO2 emissions, and the CH4 emission durability does not require attention. However, the N2O emission durability necessitates particular attention, given that SCR aging markedly increases N2O selectivity. Notably, the N2O emissions from China-VI HDDVs appear to deteriorate linearly with the increased driving mileages, with R2 ranging from 0.85 to 0.94, and the emission factors are significantly higher than those of HDDVs equipped with other after-treatment techn

关键词： Selective catalytic reduction

来源：评论

学校读者我要写书评

暂无评论

An integrated framework for accelerating reactive flow simulation using GPU and machine learning models

arXiv

引用

arXiv 2023年

作者： Mao, Runze Wang, Yingrui Zhang, Min Li, Han Xu, Jiayang Dong, Xinyu Zhang, Yan Chen, Zhi X. State Key Laboratory of Turbulence and Complex Systems Aeronautics and Astronautics College of Engineering Peking University Beijing100871 China Beijing100080 China Shanghai SenseTime Intelligent Technology Co. LTD Shanghai200233 China CAEP Software Center for High Performance Numerical Simulation Beijing100088 China Institute of Applied Physics and Computational Mathematics Beijing100088 China

Recent progress in artificial intelligence (AI) and high-performance computing (HPC) have brought potentially game-changing opportunities in accelerating reactive flow simulations. In this study, we introduce an open-source computational fluid dynamics (CFD) framework that integrates the strengths of machine learning (ML) and graphics processing unit (GPU) to demonstrate their combined capability. Within this framework, all computational operations are solely executed on GPU, including ML-accelerated chemistry integration, fully-implicit solving of PDEs, and computation of thermal and transport properties, thereby eliminating the CPU-GPU memory copy overhead. Optimisations both within the kernel functions and during the kernel launch process are conducted to enhance computational performance. Strategies such as static data reorganisation and dynamic data allocation are adopted to reduce the GPU memory footprint. The computational performance is evaluated in two turbulent flame benchmarks using quasi-DNS and LES modelling, respectively. Remarkably, while maintaining a similar level of accuracy to the conventional CPU/CVODE-based solver, the GPU/ML-accelerated approach shows an overall speedup of over two orders of magnitude for both cases. This result highlights that high-fidelity turbulent combustion simulation with finite-rate chemistry that requires normally hundreds of CPUs can now be performed on portable devices such as laptops with a medium-end GPU. © 2023, CC BY.

关键词： Graphics processing unit

来源：评论

学校读者我要写书评

暂无评论

可持续发展情景下丝绸之路沿线国家土地利用及其对陆地碳库影响

引用

Science Bulletin 2023年第7期68卷 740-749页

作者：曹敏田雅吴凯陈旻陈玉胡雪孙中昶左丽君林键骆磊朱瑞许振赐 Temenoujka Bandrova Milan Konecny 袁文平郭华东林珲闾国年 Key Laboratory of Virtual Geographic Environment Ministry of EducationNanjing Normal UniversityNanjing 210023China International Research Center of Big Data for Sustainable Development Goals Beijing 100094China Jiangsu Center for Collaborative Innovation in Geographical Information Resource Development and Application Nanjing 210023China School of Geography and Environment Jiangxi Normal UniversityNanchang 330022China Aerospace Information Research Institute Chinese Academy of SciencesBeijing 100094China The Key Laboratory of Urban Land Resources Monitoring and Simulation Ministry of Natural ResourcesShenzhen 518034China Sierra Nevada Research Institute University of CaliforniaMerced CA 95348USA Institute of High Performance Computing Agency for ScienceTechnology and ResearchSingapore 138632Singapore Department of Geography the University of Hong KongHong Kong 999077China Laboratory on Cartography University of ArchitectureCivil Engineering and GeodesySofia 1164Bulgaria Laboratory on Geoinformatics and Cartography Institute of GeographyMasaryk UniversityBrno 60177Czech Republic School of Atmospheric Sciences Southern Marine Science and Engineering Guangdong Laboratory(Zhuhai)Sun Yat-sen UniversityZhuhai 519082China Key Laboratory of Poyang Lake Wetland and Watershed Research Ministry of EducationJiangxi Normal UniversityNanchang 330022China

可持续发展目标(SDGs)呼吁全世界共同采取行动,促进繁荣并保护地球.本研究面向SDGs构建了环境、粮食和经济等可持续发展情景,预测2020~2030年丝绸之路沿线国家的土地利用变化,并分析其对陆地碳库的影响。研究表明:未来土地利用变化及其... 详细信息

可持续发展目标(SDGs)呼吁全世界共同采取行动,促进繁荣并保护地球.本研究面向SDGs构建了环境、粮食和经济等可持续发展情景,预测2020~2030年丝绸之路沿线国家的土地利用变化,并分析其对陆地碳库的影响。研究表明:未来土地利用变化及其对陆地碳库的影响在不同情景下存在显著差异。在可持续环境情景下,林地得到了有效保护,相较于2020年中国林地碳储量增加了约0.60%;在可持续粮食情景下,耕地流失得到了缓解,尤其在南亚及东南亚地区的耕地面积呈现上升趋势:在可持续经济情景下,城市扩张速度加快,引起的碳损失量最多这项研究有助于推进丝绸之路沿线甚至全球可持续发展进程,为探寻可持续发展路径提供了科学依据。

关键词： Sustainable development goals(SDGs) Land-use projection Carbon pool Scenarios System dynamics model Cellular automata

来源：评论

学校读者我要写书评

暂无评论

Forest or grassland? A quantitative analysis of urban residents' green exposure preference by using multi-temporal mobile signal data

引用

Urban Forestry and Urban Greening 2025年 108卷

作者： Geng, Hongkai Lin, Tao van Bodegom, P.M. Hu, Mingming Zheng, Yicheng Jia, Zixu Zhang, Junmao Guo, Xiangzhong Chen, Yuan Lin, Meixia Cai, Jiayu Lin, Jing Key Laboratory of Urban Environment and Health Institute of Urban Environment Chinese Academy of Sciences Xiamen 361021 China University of Chinese Academy of Sciences Beijing 100049 China Fujian Key Laboratory of Digital Technology for Territorial Space Analysis and Simulation Fuzhou 350108 China State Key Laboratory of Regional and Urban Ecology Institute of Urban Environment Chinese Academy of Sciences Xiamen 361021 China Institute of Environmental Sciences (CML) Leiden University Leiden Netherlands School of Geographical Sciences Faculty of Science and Engineering University of Nottingham Ningbo 315100 China School of Architecture and Urban-Rural Planning Fuzhou University Fuzhou 350108 China

Urban forests and grasslands provide diverse services from their unique characteristics. Optimizing green spaces by understanding urban residents’ preferences is a critical challenge for sustainable city development amid limited land resources. However, the mechanism influencing exposure across various types of green remains unclear. This study utilized multi-temporal mobile signal data from Shanghai to quantify the exposure intensity (EI) and density (ED) for forests and grasslands. These metrics addressed the gap by revealing spatiotemporal variations in exposure preference (EP) and related socioeconomic influences. Specifically, the study addressed two key questions: (1) Do urban residents exhibit preferences between forests and grasslands in terms of EI and ED? (2) How do socioeconomic features influence these preferences? Results showed: (1) Forests had almost double the annual EI (542.86 p/h) and ED (2.69 p/m²/h) of grasslands (P < 0.001). However, grasslands in central regions exhibited significantly higher ED (13.60 vs. 11.83 p/m²/h;P < 0.001). (2) Commercial House (34.4 % importance) and Sports & Recreation (15.7 %) maximized green exposure, while Road Furniture reduced it. (3) Evening exposure peaks in central regions extended by 1 hour due to commercial-cultural synergies. Forest ED, highly driven by Commercial House, clustered in central cores and specific non-central communities, whereas Road Furniture most negatively impacted central periphery communities. These findings directly inform differentiated urban planning strategies: forests should prioritize improving accessibility to sustain prolonged exposures, while grasslands need spatial optimization to accommodate peak social demand. By aligning green space planning with socioeconomic drivers, cities can enhance the effectiveness of their service under land constraints. © 2025 Elsevier GmbH

关键词： Exposure preference Green exposure Machine learning Multi-scale geographically weighted regression Urban green space

来源：评论

学校读者我要写书评

暂无评论

Multi-action deep reinforcement learning-based collaborative production dynamic scheduling approach for networked multi-factory under mass customization

引用

Engineering Applications of Artificial Intelligence 2025年 157卷

作者： Mingzhou Liu Zhengfu Ding Xi Zhang Lin Ling Maogen Ge School of Mechanical Engineering Hefei University of Technology Hefei 230009 China National Virtual Simulation Laboratory Teaching Center Hefei University of Technology Hefei 230009 China Anhui Key Laboratory of Digital Design and Manufacturing Hefei University of Technology Hefei 230009 China

Increasing customization demand is accelerating the decentralization of manufacturing systems, elevating the distributed scheduling problem (DSP) to a prominent research focus. However, the dynamic characteristics of mass customization (MC) and the practically adaptable networked multi-factory systems remain underexplored. To address this gap, this study formulates a networked multi-factory collaborative production scheduling problem in the context of MC (MC-NMF-CPSP), and proposes a solution framework based on multi-action deep reinforcement learning (MA-DRL). The framework formulates a multiple Markov Decision Process (MMDP) incorporating a multi-dimensional state space, a two-stage action space, and a composite reward, providing a unified modeling paradigm for high-dimensional complex scheduling problems. To accurately characterize the dynamic demand, a consistent measurement model is proposed and embedded within the state space for efficient state representation. To enhance learning performance, an improved Proximal Policy Optimization (PPO) is developed, and a Multi-PPO architecture is integrated to construct a networked multi-factory collaborative production scheduling model (MaDRL-NMF-CPSM), which demonstrates exceptional coordination and adaptability under dynamic demand environments. Furthermore, a retraining mechanism is introduced to enable continual model evolution, thereby improving robustness and generalization. Extensive experiments show that the proposed method significantly outperforms several benchmark algorithms and heuristic rules in terms of convergence efficiency, scheduling performance, and dynamic adaptability, offering a novel theoretical foundation and practical solution for collaborative scheduling in networked manufacturing systems under dynamic demand.

关键词：

来源：评论

学校读者我要写书评

暂无评论

建议与咨询留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

时间限定

文献类型

馆藏选择

核心期刊

语言

文献类型

帮助

文字说明：

检索规则说明：

检索范例：

分类表

所选分类

限定检索结果

文献类型

馆藏范围

日期分布

学科分类号

主题

机构

作者

语言

请选择保存的检索档案：

请选择收藏分类：

通借通还

建议与咨询 留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

时间限定

文献类型

馆藏选择

核心期刊

语言

文献类型

帮助

文字说明：

检索规则说明：

检索范例：

分类表

所选分类

限定检索结果

文献类型

馆藏范围

日期分布

学科分类号

主题

机构

作者

语言

请选择保存的检索档案： 新增检索档案 确定 取消

请选择收藏分类： 新增自定义分类 确定 取消

通借通还

建议与咨询留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

请选择保存的检索档案：

请选择收藏分类：