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学者姓名：唐振鹏
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Evaluating the restoration quality of university outdoor spaces is often constrained by subjective surveys and manual assessment, limiting scalability and objectivity. This study addresses this gap by applying explainable machine learning to predict restorative quality from campus imagery, enabling large-scale, data-driven evaluation and capturing complex nonlinear relationships that traditional methods may overlook. Using Fujian Agriculture and Forestry University as a case study, this study extracted road network data, generated 297 coordinates at 50-m intervals, and collected 1197 images. Surveys were conducted to obtain restorative quality scores. The Mask2Former model was used to extract landscape features, and decision tree algorithms (RF, XGBoost, GBR) were selected based on MAE, MSE, and EVS metrics. The combination of optimal algorithms and SHAP was employed to predict restoration quality and identify key features. This research also used a multivariate linear regression model to identify features with significant statistical impact but lower features importance ranking. Finally, the study also analyzed heterogeneity in scores for three restoration indicators and five campus zones using k-means clustering. Empirical results show that natural elements like vegetation and water positively affect psychological perception, while structural components like walls and fences have negative or nonlinear effects. On this basis, this study proposes spatial optimization strategies for different campus areas, offering a foundation for creating high-quality outdoor environments with restorative and social functions.
Keyword ：
image semantic segmentation image semantic segmentation interpretable machine learning interpretable machine learning landscape optimization landscape optimization university campus university campus
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| GB/T 7714 | Zhuang, Xiaowen , Tang, Zhenpeng , Lin, Shuo et al. Prediction and Optimization of the Restoration Quality of University Outdoor Spaces: A Data-Driven Study Using Image Semantic Segmentation and Explainable Machine Learning [J]. | BUILDINGS , 2025 , 15 (16) . |
| MLA | Zhuang, Xiaowen et al. "Prediction and Optimization of the Restoration Quality of University Outdoor Spaces: A Data-Driven Study Using Image Semantic Segmentation and Explainable Machine Learning" . | BUILDINGS 15 . 16 (2025) . |
| APA | Zhuang, Xiaowen , Tang, Zhenpeng , Lin, Shuo , Ding, Zheng . Prediction and Optimization of the Restoration Quality of University Outdoor Spaces: A Data-Driven Study Using Image Semantic Segmentation and Explainable Machine Learning . | BUILDINGS , 2025 , 15 (16) . |
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As universities become increasingly open, campuses are no longer only places for study and daily life for students and faculty, but also essential spaces for public visits and cultural identity. Traditional perception evaluation methods that rely on manual surveys are limited by sample size and subjective bias, making it challenging to reveal differences in experiences between groups (students/visitors) and the complex relationships between spatial elements and perceptions. This study uses a comprehensive open university in China as a case study to address this. It proposes a research framework that combines street-view image semantic segmentation, perception survey scores, and interpretable machine learning with sample augmentation. First, full-sample modeling is used to identify key image semantic features influencing perception indicators (nature, culture, aesthetics), and then to compare how students and visitors differ in their perceptions and preferences across campus spaces. To overcome the imbalance in survey data caused by group-space interactions, the study applies the CTGAN method, which expands minority samples through conditional generation while preserving distribution authenticity, thereby improving the robustness and interpretability of the model. Based on this, attribution analysis with an interpretable decision tree algorithm further quantifies semantic features' contribution, direction, and thresholds to perceptions, uncovering heterogeneity in perception mechanisms across groups. The results provide methodological support for perception evaluation of campus functional zones and offer data-driven, human-centered references for campus planning and design optimization.
Keyword ：
campus functional zones campus functional zones campus space campus space image semantic segmentation image semantic segmentation SHAP SHAP student-visitor differences student-visitor differences
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| GB/T 7714 | Zhuang, Xiaowen , Cai, Yi , Tang, Zhenpeng et al. Optimizing University Campus Functional Zones Using Landscape Feature Recognition and Enhanced Decision Tree Algorithms: A Study on Spatial Response Differences Between Students and Visitors [J]. | BUILDINGS , 2025 , 15 (19) . |
| MLA | Zhuang, Xiaowen et al. "Optimizing University Campus Functional Zones Using Landscape Feature Recognition and Enhanced Decision Tree Algorithms: A Study on Spatial Response Differences Between Students and Visitors" . | BUILDINGS 15 . 19 (2025) . |
| APA | Zhuang, Xiaowen , Cai, Yi , Tang, Zhenpeng , Ding, Zheng , Gan, Christopher . Optimizing University Campus Functional Zones Using Landscape Feature Recognition and Enhanced Decision Tree Algorithms: A Study on Spatial Response Differences Between Students and Visitors . | BUILDINGS , 2025 , 15 (19) . |
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The European Union Emissions Trading System (EU ETS) serves as the cornerstone of European climate policy, providing a critical mechanism for mitigating greenhouse gas emissions. Accurate forecasting of the carbon allowance prices within the market is essential for policymakers, enterprises, and investors. To address the need for interval-valued time series modeling and forecasting in the carbon market, this paper proposes a Transformer-based multi-task learning framework that integrates online news and search engine data information to forecast interval-valued EU carbon allowance futures prices. Empirical evaluations demonstrate that the proposed framework achieves superior predictive accuracy for short-term forecasting and remains robust under high market volatility and economic policy uncertainty compared to single-task learning benchmarks. Furthermore, ablation experiments indicate that incorporating news sentiment intensity and search index effectively enhances the framework's predictive performance. Interpretability analysis highlights the critical role of specific temporal factors, while the time-varying variable importance analysis further underscores the influence of carbon allowance close prices and key energy market variables and also recognizes the contributions of news sentiment. In summary, this study provides valuable insights for policy management, risk hedging, and portfolio decision-making related to interval-valued EU carbon prices and offers a robust forecasting tool for carbon market prediction.
Keyword ：
carbon prices carbon prices interpretability interpretability interval forecasting interval forecasting interval-valued time series interval-valued time series multi-task learning multi-task learning sentiment analysis sentiment analysis
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| GB/T 7714 | Liu, Dinggao , Wang, Liuqing , Lin, Shuo et al. A Novel Multi-Task Learning Framework for Interval-Valued Carbon Price Forecasting Using Online News and Search Engine Data [J]. | MATHEMATICS , 2025 , 13 (3) . |
| MLA | Liu, Dinggao et al. "A Novel Multi-Task Learning Framework for Interval-Valued Carbon Price Forecasting Using Online News and Search Engine Data" . | MATHEMATICS 13 . 3 (2025) . |
| APA | Liu, Dinggao , Wang, Liuqing , Lin, Shuo , Tang, Zhenpeng . A Novel Multi-Task Learning Framework for Interval-Valued Carbon Price Forecasting Using Online News and Search Engine Data . | MATHEMATICS , 2025 , 13 (3) . |
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There exists a complex interplay between the aggregate risk of the stock market and the risks inherent to various industries. However, existing studies have largely overlooked the disparities in risk contagion effects between different industries and the overall market across diverse time scales, and there remains instability in delineating risk frequency domains. Consequently, it becomes challenging to fully unravel the intricate correlation dynamics between different industries and the broader stock market across varying temporal dimensions. Therefore, this paper takes the China stock market as a representative of emerging markets, selects eleven distinct industry indices along with the Shanghai-Shenzhen 300 Index (CSI 300 Index), and introduces the Variable Mode Decomposition (VMD) algorithm to extract intricate information from time series data. Additionally, the Fuzzy Entropy (FE) algorithm is employed to effectively reconstruct different frequency domains. Furthermore, this paper integrates the strengths of the Generalized Autoregressive Conditional Heteroskedasticity (GARCH) model, the Copula function, and the Conditional Value at Risk (CoVaR) model. By constructing the novel VMD-FE-GARCH-Copula-CoVaR hybrid model, this research aims to explore the risk contagion characteristics of various industries and the Shanghai-Shenzhen 300 Index across different time scales, offering a fresh perspective for the paper of risk contagion within stock market industries. © The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature 2025.
Keyword ：
Commerce Commerce Financial markets Financial markets Marketplaces Marketplaces Risk assessment Risk assessment
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| GB/T 7714 | Linjie, Zhan , Zhenpeng, Tang . Multiscale risk spillover analysis of China’s stock market industry: evidence supported by a novel hybrid model based on signal decomposition technology [J]. | Soft Computing , 2025 , 29 (2) : 559-577 . |
| MLA | Linjie, Zhan et al. "Multiscale risk spillover analysis of China’s stock market industry: evidence supported by a novel hybrid model based on signal decomposition technology" . | Soft Computing 29 . 2 (2025) : 559-577 . |
| APA | Linjie, Zhan , Zhenpeng, Tang . Multiscale risk spillover analysis of China’s stock market industry: evidence supported by a novel hybrid model based on signal decomposition technology . | Soft Computing , 2025 , 29 (2) , 559-577 . |
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The prediction of agricultural commodity futures returns is crucial for understanding global economic trends, alleviating inflationary pressures, and optimizing investment portfolios. However, current research that uses full-sample decomposition to predict agricultural futures returns suffers from data leakage, and the resulting forecast bias leads to overly optimistic outcomes. Additionally, previous studies have lacked a comprehensive consideration of key economic variables that influence agricultural prices. To address these issues, this study proposes the "Rolling VMD-LASSO-Mixed Ensemble" forecasting framework and compares its performance with "Rolling VMD" against univariate models, "Rolling VMD-LASSO" against "Rolling VMD", and "Rolling VMD-LASSO-Mixed Ensemble" against "Rolling VMD-LASSO". Empirical results show that, on average, "Rolling VMD" improved MSE, MAE, Theil U, ARV, and DA by 3.05%, 1.09%, 1.52%, 2.96%, and 11.11%, respectively, compared to univariate models. "Rolling VMD-LASSO" improved these five indicators by 2.11%, 1.15%, 1.09%, 2.13%, and 1.00% over "Rolling VMD". The decision tree-based "Rolling VMD-LASSO-Mixed Ensemble" outperformed "Rolling VMD-LASSO" by 1.98%, 0.96%, 1.28%, 2.55%, and 4.18% in the five metrics. Furthermore, the daily average return, maximum drawdown, Sharpe ratio, Sortino ratio, and Calmar ratio based on prediction results also show that "Rolling VMD" outperforms univariate forecasting, "Rolling VMD-LASSO" outperforms "Rolling VMD", and "Rolling VMD-LASSO-Mixed Ensemble" outperforms "Rolling VMD-LASSO". This study provides a more accurate and robust forecasting framework for the global agricultural futures market, offering significant practical value for investor risk management and policymakers in stabilizing prices.
Keyword ：
agricultural futures return prediction agricultural futures return prediction dynamic factors screen dynamic factors screen investment performance investment performance mixed ensemble mixed ensemble rolling VMD algorithm rolling VMD algorithm
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| GB/T 7714 | Ye, Yiling , Zhuang, Xiaowen , Yi, Cai et al. Enhancing Agricultural Futures Return Prediction: Insights from Rolling VMD, Economic Factors, and Mixed Ensembles [J]. | AGRICULTURE-BASEL , 2025 , 15 (11) . |
| MLA | Ye, Yiling et al. "Enhancing Agricultural Futures Return Prediction: Insights from Rolling VMD, Economic Factors, and Mixed Ensembles" . | AGRICULTURE-BASEL 15 . 11 (2025) . |
| APA | Ye, Yiling , Zhuang, Xiaowen , Yi, Cai , Liu, Dinggao , Tang, Zhenpeng . Enhancing Agricultural Futures Return Prediction: Insights from Rolling VMD, Economic Factors, and Mixed Ensembles . | AGRICULTURE-BASEL , 2025 , 15 (11) . |
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The energy sector is central to global decarbonization, and the effective allocation of financial resources is vital to its development. However, the impact of financial resources on energy development remains ambiguous in existing literature. This study bridges this gap by theoretically and empirically analyzing the role of the financial resources allocated to the energy industry. We develop a novel theoretical model that integrates the energy and financial sectors, subsequently deriving an inverted U-shaped relationship between financial resources and energy development. Empirically, we use an energy-related indicator to capture financial resources directed towards the energy sector and validate the hypothesis using panel data from 30 Chinese provinces from 2006 to 2019. The result remains robust after addressing endogeneity concerns and a series of robustness tests. Heterogeneity analysis further shows that the inverted U-shaped relationship is more pronounced in regions with abundant energy resources and high financial development, while the effect in those characterized by underdeveloped financial systems is insignificant. Mechanism analysis reveals that financial resources influence energy development through financing constraints and investment efficiency. This study advances our understanding of the role of finance in energy sector development, offering key policy implications for optimizing financial allocation. Financial institutions should establish appropriate support thresholds to ensure optimal financial resource allocation to the energy sector, while addressing financing constraints and improving investment efficiency. Support strategies should be tailored to regional conditions, such as energy endowments and financial development levels.
Keyword ：
Energy industry Energy industry Financial resource allocation Financial resource allocation Inverted U -shape Inverted U -shape
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| GB/T 7714 | Tang, Zhenpeng , Lin, Xinyi , Long, Houyin . The effectiveness of financial industry in promoting the high-quality development of energy industry [J]. | JOURNAL OF ASIAN ECONOMICS , 2025 , 99 . |
| MLA | Tang, Zhenpeng et al. "The effectiveness of financial industry in promoting the high-quality development of energy industry" . | JOURNAL OF ASIAN ECONOMICS 99 (2025) . |
| APA | Tang, Zhenpeng , Lin, Xinyi , Long, Houyin . The effectiveness of financial industry in promoting the high-quality development of energy industry . | JOURNAL OF ASIAN ECONOMICS , 2025 , 99 . |
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<正>作为长期关注农村金融改革的学者，本人有幸深度参与省政协“提升金融服务水平，助力打造特色农业产业集群”协商课题，这个课题着力于解决涉农金融服务痛点，本质上是要破解“大农业观”落地过程中的金融梗阻。在调研过程中，省政协搭建的多部门、多领域协同创新平台，生动诠释了社会主义协商民主的制度优势。通过参与专题协商会，我更深切感受到政协民主协商的独特价值：协商会场外，依托政协委员、政府部门、高校专家学者等跨界团队，通过实地走访、座谈交流、问卷调查等多种方法，深度剖析涉农金融服务的结构性困境；
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| GB/T 7714 | 唐振鹏 . 民主协商汇众智 信用生态开新局 [J]. | 政协天地 , 2025 , 1 (06) : 11 . |
| MLA | 唐振鹏 . "民主协商汇众智 信用生态开新局" . | 政协天地 1 . 06 (2025) : 11 . |
| APA | 唐振鹏 . 民主协商汇众智 信用生态开新局 . | 政协天地 , 2025 , 1 (06) , 11 . |
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Crude oil and grain, as two pivotal global commodities, exhibit significant price co-movement that profoundly affects national economic stability and food security. From the perspective of systems theory, the energy and grain markets do not exist in isolation but rather form a highly coupled complex system, characterized by nonlinear feedback, cross-market risk contagion, and cascading effects. This study systematically investigates the transmission mechanisms from international crude oil prices to the domestic prices of Chinese four major grains, employing the DY spillover index, Vector Error Correction Model (VECM), and a mediation effect framework. The empirical findings reveal three key insights. First, rising international crude oil prices significantly strengthen the pass-through of global grain prices to domestic markets, while simultaneously weakening the effectiveness of domestic price stabilization policies. Second, higher crude oil prices amplify international-to-domestic price spillovers by increasing maritime freight costs, a key channel in global grain trade logistics. Third, elevated oil prices stimulate demand for renewable biofuels, including biodiesel and ethanol, thereby boosting international demand for corn and soybeans and intensifying the transmission of price fluctuations in these commodities to the domestic market. These findings reveal the key pathways through which shocks in the energy market affect food security and highlight the necessity of studying the "energy-food" coupling mechanism within a systems framework, enabling a more comprehensive understanding of cross-market risk transmission.
Keyword ：
biofuel consumption biofuel consumption crude oil price crude oil price DY spillover index DY spillover index grain price transmission grain price transmission shipping cost shipping cost
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| GB/T 7714 | Zhuang, Xiaowen , Wang, Sikai , Tang, Zhenpeng et al. Food Security Under Energy Shock: Research on the Transmission Mechanism of the Effect of International Crude Oil Prices on Chinese and US Grain Prices [J]. | SYSTEMS , 2025 , 13 (10) . |
| MLA | Zhuang, Xiaowen et al. "Food Security Under Energy Shock: Research on the Transmission Mechanism of the Effect of International Crude Oil Prices on Chinese and US Grain Prices" . | SYSTEMS 13 . 10 (2025) . |
| APA | Zhuang, Xiaowen , Wang, Sikai , Tang, Zhenpeng , Fu, Zhenhan , Dong, Baihua . Food Security Under Energy Shock: Research on the Transmission Mechanism of the Effect of International Crude Oil Prices on Chinese and US Grain Prices . | SYSTEMS , 2025 , 13 (10) . |
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A university campus is a composite built environment integrating research, daily life, culture, and ecological green space. Its landscape elements shape environmental perception and overall spatial quality. This study assesses spatial quality by identifying key features and optimizing their joint effects across three perceptions: safety, comfort, and belonging. Using a Chinese campus, we captured street-view images, applied semantic segmentation to quantify elements (grass, trees, buildings, roads, sidewalks), and used explainable machine learning with data augmentation to identify the features most relevant to these perceptions. This study then employed fuzzy-set Qualitative Comparative Analysis (fsQCA) to reveal configuration pathways that enhance spatial quality. Results show that data augmentation mitigates class imbalance and improves prediction accuracy. Key features include sky, river, bridge, people, grass, and sidewalks, and path analysis indicates that greater sky openness and higher densities of people, roads, sidewalks, and grass, together with fewer buildings, cars, and bare earth, enhance safety, comfort, and belonging. This study delivers globally transferable design rules and a replicable, policy-ready workflow that enables evidence-based campus upgrades across diverse regions.
Keyword ：
campus buildings campus buildings combined effects combined effects data augmentation data augmentation landscape features landscape features
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| GB/T 7714 | Zhuang, Xiaowen , Cai, Yi , Tang, Zhenpeng et al. Identification and Configuration Optimization of Key Campus Landscape Features Using Augmentation-Based Machine Learning and Configuration Analysis [J]. | BUILDINGS , 2025 , 15 (21) . |
| MLA | Zhuang, Xiaowen et al. "Identification and Configuration Optimization of Key Campus Landscape Features Using Augmentation-Based Machine Learning and Configuration Analysis" . | BUILDINGS 15 . 21 (2025) . |
| APA | Zhuang, Xiaowen , Cai, Yi , Tang, Zhenpeng , Ding, Zheng , Gan, Christopher . Identification and Configuration Optimization of Key Campus Landscape Features Using Augmentation-Based Machine Learning and Configuration Analysis . | BUILDINGS , 2025 , 15 (21) . |
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This study aims to forecast high-frequency electricity prices using a hybrid machine learning decomposition model. Incorporating four types of weather data-temperature, humidity, wind gusts, and wind speed-the model predicts electricity prices at a fifteen-minute frequency in the Belgian market. Initially, a combination of the Reverse Unrestricted-MIDAS (RU-MIDAS) model and machine learning is tested but fails to produce satisfactory predictive outcomes. To address the complex characteristics of electricity price data and mitigate data leakage, a new forecasting framework, "Mixed-Frequency Rolling Singular Spectrum Analysis with Machine Learning" (MF-RSSA-ML), is proposed. An empirical analysis is conducted on three rolling decomposition algorithms. Rolling Empirical Ensemble Mode Decomposition performs poorly in both error metrics and the Diebold-Mariano (DM) test, while rolling Empirical Mode Decomposition improves error metrics but still fails the DM test. In contrast, the "MF-RSSA-ML" framework demonstrates superior predictive performance, improving MAE, RMSE, and SMAPE by up to 31.43%, 26.04%, and 20.79%, respectively. The results of the DM test provide additional evidence supporting the superiority of MF-RSSA-ML compared to the other two decomposition approaches. By effectively integrating weather-related influences, the "MF-RSSA-ML" system provides accurate high-frequency electricity price forecasts. The findings offer valuable insights for generators, wholesalers, and consumers in optimizing electricity production and consumption decisions.
Keyword ：
Electricity price forecasting Electricity price forecasting High-frequency data High-frequency data Machine learning Machine learning Mixed-frequency data prediction Mixed-frequency data prediction Rolling decomposition Rolling decomposition Weather changes Weather changes
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| GB/T 7714 | Tang, Zhenpeng , Wang, Liuqing , Cai, Yi et al. Forecasting high-frequency electricity price with hybrid machine learning decomposition model [J]. | ANNALS OF OPERATIONS RESEARCH , 2025 . |
| MLA | Tang, Zhenpeng et al. "Forecasting high-frequency electricity price with hybrid machine learning decomposition model" . | ANNALS OF OPERATIONS RESEARCH (2025) . |
| APA | Tang, Zhenpeng , Wang, Liuqing , Cai, Yi , Abedin, Mohammad Zoynul . Forecasting high-frequency electricity price with hybrid machine learning decomposition model . | ANNALS OF OPERATIONS RESEARCH , 2025 . |
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