Quantitative evaluation of science and technology financial policies based on the PMC-AE index model: A case study of China’s science and technology financial policies since the 13th five-year plan

PLoS ONE, Journal Year: 2024, Volume and Issue: 19(8), P. e0307529 - e0307529

Published: Aug. 1, 2024

The formulation of science and technology financial policies directly influences the direction national economic development. Quantitative evaluation these is an important method to reflect consistency strengths weaknesses policy interrelations. This paper analyzes 16 documents issued by Chinese central government from 2016 2022, using text analysis content extract keyword frequencies, constructs 9 primary variables 34 secondary variables. For first time, a PMC-AE index model for established, quantitative conducted on 5 significant out 16. results show that, overall analysis, Policy 1 4 are at good level, while other three excellent level. From individual indexes, rankings in descending order are: P2 > P5 P3 P4 P1. Overall, effectively meet needs China’s development, with P2, P3, being P1 acceptable mainly reflecting need improvement aspects such as synchronization current stage, targeted entities, guiding fields, content. It recommended that departments should focus five formulation: building talent system finance, improving quality services, coordinating local policies, protecting intellectual property rights strengthening supervision. will be conducive effective implementation policies.

Language: Английский

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Drivers and Multi-Scenario Projections of Life Cycle Carbon Emissions from China’s Construction Industry

Sustainability, Journal Year: 2025, Volume and Issue: 17(9), P. 3828 - 3828

Published: April 24, 2025

Life cycle carbon emissions from the construction industry (CE) have a profound impact on China’s “dual carbon” goals, with significant posing severe challenges to environment. In this paper, four prediction models were trained and compared, optimal model, Genetic Algorithm Optimized BP Neural Network (GA-BP), was finally selected for multi-scenario of CE. Firstly, study performs comprehensive accounting indicator analysis CE over its entire life cycle. addition, paper further conducts spatial differentiation Subsequently, parameter conducted using an improved STIRPAT followed by LMDI factor decomposition based model. Finally, model performance verified three evaluation metrics: coefficient determination (R2), mean absolute error (MAE), percentage (MAPE). The results indicate that (1) in emission assessment, reached peak 42.52 t per capita annually 8.90 CO2/m2 unit area; (2) year-end resident population has greatest influence CE, other related variables also contributing positively; (3) GA-BP outperforms models, R2 increasing 0.0435 0.0981, MAE reducing 63% 76%, MAPE decreasing 23% 68%.

Language: Английский

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Optimizing AI Performance at Scale: A FLOPs-Centric Framework for Efficient Deep Learning

European Journal of Computer Science and Information Technology, Journal Year: 2025, Volume and Issue: 13(31), P. 10 - 22

Published: April 15, 2025

This framework introduces a novel approach for designing, measuring, and optimizing AI models through FLOPs-centric methodology, enabling scalable deep learning with reduced computational energy overhead. By analyzing model architecture, hardware utilization, training efficiency, the supports both cloud-scale edge deployments. Through comprehensive profiling, dynamic scaling, computation-aware training, system addresses efficiency challenges across vision, NLP, multimodal without compromising accuracy. The environmental impact assessment component provides organizations tools to quantify reduce carbon footprint of workloads. Key innovations include FLOPs-first design philosophy, granular profiling capabilities, FLOPs-aware loss formulations, integrated benchmarking metrics that unify performance considerations, contributing greener, more sustainable development practices.

Language: Английский

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Small Conjugated Organic Ligand-Modified Polyoxometalate-Based Hybrid Materials for Boosting Photocatalytic CO₂ Reduction

Inorganic Chemistry, Journal Year: 2024, Volume and Issue: 63(36), P. 16791 - 16798

Published: Aug. 27, 2024

Photocatalytic carbon dioxide (CO

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