Energy and AI最新文献

{"title":"Machine learning-based estimation of icing losses in wind farms: Applications in pre-construction energy yield assessment","authors":"Sabin Stefanov ,&nbsp;Daniel Gallacher ,&nbsp;Alasdair McDonald ,&nbsp;Ajit C. Pillai ,&nbsp;Michael Togneri","doi":"10.1016/j.egyai.2026.100750","DOIUrl":"10.1016/j.egyai.2026.100750","url":null,"abstract":"<div><div>A substantial share of current and planned wind energy projects are located in cold climates, where ice accretion on turbine blades degrades aerodynamic performance and leads to significant power losses. Moreover, the processes of ice accretion and ablation are governed by complex, time-dependent physics, making the estimation of icing losses difficult and thereby introducing uncertainties during pre-construction energy yield assessments, as well as complicating strategic decision-making. Although several approaches currently exist, these models for estimating ice-related losses rely on input variables with high measurement uncertainty, require on-site meteorological mast data, or define icing events in ways that are inconsistent with industry standards. To address these limitations, this study proposes a machine learning framework that predicts icing losses using low uncertainty and widely available meteorological inputs — namely temperature, relative humidity, wind speed and precipitation. The framework is trained and validated using data from nine wind farms in Finland and Sweden. Two training strategies are examined: a global model that employs aggregated data from multiple sites, and a local ensemble composed of farm-specific models. Each strategy is evaluated using three learning architectures: linear regression, Light Gradient Boosting Machine (LightGBM), and Extreme Gradient Boosted Trees (XGBoost). Among all combinations, the global XGBoost model demonstrates the strongest performance, achieving a relative mean absolute error of 22.67% in total energy loss estimation across the dataset, closely matching the capabilities of commercial state-of-the-art tools. Interpretability analysis further indicates that the model generally relies on features in a manner consistent with the established physical understanding, thereby enhancing confidence in its predictive ability. Taken together, the proposed framework offers a robust and practical approach for quantifying icing losses during pre-construction energy yield assessments, providing a valuable tool for wind energy developers in cold climates.</div></div>","PeriodicalId":34138,"journal":{"name":"Energy and AI","volume":"24 ","pages":"Article 100750"},"PeriodicalIF":9.6,"publicationDate":"2026-05-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147797649","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Data-driven machine learning modelling for the manufacturing of the fuel electrode support in solid oxide cells","authors":"Tan Le-Dinh ,&nbsp;Hartmut Schlenz ,&nbsp;Norbert H. Menzler ,&nbsp;Alejandro A. Franco ,&nbsp;Olivier Guillon","doi":"10.1016/j.egyai.2026.100687","DOIUrl":"10.1016/j.egyai.2026.100687","url":null,"abstract":"<div><div>The industry-relevant fabrication of supports in fuel-electrode supported Solid Oxide Cells (SOCs) by tape casting typically involves a multi-stage process, demanding precise control over tape thickness and density. However, conventional SOC manufacturing processes are resource-intensive and often rely on industry/R&amp;D unpublished knowledge and trial-and-error practices to achieve the target properties of the resulting tape. Hence, machine learning (ML) was employed for predicting the thickness and density across three distinct stages of the fabrication process: tape casting, sintering, and NiO-reduction process. Our developed ML models (e.g., Extra Trees and Ridge Regressions) demonstrate exceptional accuracy (<span><math><mrow><msup><mrow><mtext>R</mtext></mrow><mrow><mn>2</mn></mrow></msup><mo>&gt;</mo><mn>0</mn><mo>.</mo><mn>9</mn></mrow></math></span>) for each specific prediction task. Concurrently, experimental data analysis was conducted to elucidate the impact of the manufacturing parameters on the tape properties. Our data-driven ML approach offers a pathway towards achieving precise tape property control and advancing more efficient SOC support manufacturing.</div></div>","PeriodicalId":34138,"journal":{"name":"Energy and AI","volume":"24 ","pages":"Article 100687"},"PeriodicalIF":9.6,"publicationDate":"2026-05-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146174327","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"A generative AI-Enhanced hybrid framework for reliable long-term photovoltaic power forecasting","authors":"Yue Qi ,&nbsp;Chengwei Lou ,&nbsp;Jin Yang","doi":"10.1016/j.egyai.2026.100760","DOIUrl":"10.1016/j.egyai.2026.100760","url":null,"abstract":"<div><div>Accurate long-horizon photovoltaic (PV) power forecasting remains constrained by the absence of reliable future meteorological data and the limited adaptability of existing models. To address these challenges, this study proposes an end-to-end forecasting workflow that integrates year-ahead meteorological data synthesis, optimisation-driven model tuning, and hybrid deep-learning architecture design into a single coherent process. The framework employs a Conditional Time Generative Adversarial Network (CTimeGAN) in combination with a Large-Language-Model-based module to generate physically consistent, trend-aware meteorological sequences that embed plausible future variations while preserving statistical consistency. To improve predictive performance, an Enhanced Alpha Evolution Optimiser (EAEO) featuring diversity-adaptive exploration and rank-weighted updating is employed to tune a Transformer–LSTM hybrid network that jointly captures global dependencies and temporal dynamics. Comparative analyses show that the proposed framework consistently achieves superior performance across all evaluated hybrid architectures, demonstrating improved accuracy and robustness in long-term forecasting. Compared to a baseline LSTM model, the proposed method reduces MAE and RMSE by approximately 40–46%, while <span><math><msup><mrow><mi>R</mi></mrow><mrow><mn>2</mn></mrow></msup></math></span> increases by around 38%. The framework provides a scalable, privacy-aware solution for high-resolution, year-ahead PV power forecasting, supporting energy storage scheduling, demand response optimisation, and the reliable integration of distributed renewable resources in future net-zero power systems.</div></div>","PeriodicalId":34138,"journal":{"name":"Energy and AI","volume":"24 ","pages":"Article 100760"},"PeriodicalIF":9.6,"publicationDate":"2026-05-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147849835","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Physics-guided, noise-resilient and interpretable machine learning framework for fault detection and diagnosis in variable refrigerant flow systems","authors":"Muhammad Reshaeel ,&nbsp;Vinod Khadkikar ,&nbsp;Mohamed I.Hassan Ali","doi":"10.1016/j.egyai.2026.100766","DOIUrl":"10.1016/j.egyai.2026.100766","url":null,"abstract":"<div><div>Fault detection and diagnosis (FDD) in variable refrigerant flow (VRF) systems is typically driven by improving classification accuracy using complex machine learning (ML) models trained on clean datasets, with limited consideration of robustness to sensor noise, generalization under distributional shift, and interpretability. Moreover, existing studies predominantly focus on refrigerant charge and fouling faults, while critical component-level hard faults remain underexplored. To address these gaps, this study proposes a Gaussian noise-resilient, interpretable, and physics-guided ML framework for component-level VRF FDD using only built-in sensor telemetry. The framework introduces noise-resilient physics-informed (NRPI) variants of three conventional classifiers (RF, SVC, and XGBoost), integrating curriculum-based Gaussian noise augmentation to explicitly account for noise and a post-hoc physics-guided probabilistic rule to refine class-specific predictions in a modular manner. A comprehensive three-stage evaluation protocol including hold-out testing, independent offline validation, and controlled noise-based stress testing was employed to assess accuracy, offline generalization, and noise robustness. NRPI variants consistently outperform their baseline counterparts with NRPI-RF and NRPI-SVC achieving the highest mean macro-F1 of 99.10% and 99.13% across different level of sensor noise. Averaged across the different noise tests, NRPI‑XGBoost achieved the largest macro‑F1 gain (38.15%) over its baseline variant, while NRPI‑RF and NRPI‑SVC improved their macro-F1 scores by 11.98% and 4.38%, respectively. Ablation and interpretability analyses reveal that noise-aware training enhances global robustness under additive Gaussian noise, while the physics-guided rule improves class-specific reliability. Overall, the proposed framework provides an accurate, robust and interpretable pathway for component-level FDD in VRF systems.</div></div>","PeriodicalId":34138,"journal":{"name":"Energy and AI","volume":"24 ","pages":"Article 100766"},"PeriodicalIF":9.6,"publicationDate":"2026-05-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147849839","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Energy-efficient machine learning approaches for enhanced biofuel (Biodiesel) production: A review","authors":"Myung-Kyu Song ,&nbsp;Deok-Won Kim ,&nbsp;Swabhiman Mohanty ,&nbsp;Moon Son ,&nbsp;Sean S. Lee ,&nbsp;El-Sayed Salama ,&nbsp;Xiangkai Li ,&nbsp;Ramesh Kumar ,&nbsp;Byong‑Hun Jeon","doi":"10.1016/j.egyai.2026.100696","DOIUrl":"10.1016/j.egyai.2026.100696","url":null,"abstract":"<div><div>Biodiesel represents a sustainable alternative to fossil fuels and aligns with long-term decarbonization goals. Integrating economic efficiency with sustainability requires a reinvention of process technology and artificial intelligence (AI)-based advanced prediction tools to maximize performance and minimize costs and carbon footprint. Under conservative estimates of 1% efficiency enhancement in an AI-driven optimization process, a 100-million kg/year microalgal biodiesel plant could save 3.6–5.7 million MJ/year of energy, corresponding to 457–719 tonnes of CO₂eq reductions. However, these transitions become challenging due to the significant energy consumption required for AI modeling of nonlinear, complex datasets in energy production systems. This review focuses on current progress in designing energy-efficient AI models using green computing systems to advance smart energy systems. Four essential integrated pillars have been identified and discussed for implementation in an AI model framework that minimizes environmental impacts in biofuel synthesis. These pillars are Real-Time Process Optimization and Control, Predictive Intelligence and Resilience, Sustainable and Scalable Architecture, and Trustworthy and Compliant AI. A four-stage roadmap has been proposed to develop a low-energy framework for process optimization and predictive control. This roadmap includes moving from basic data infrastructure and pilot trials to the integration of key performance indicator dashboards, a human-assisted, semi-automated co-pilot phase, and, finally, a fully autonomous, closed-loop control system. Ultimately, AI tools offer a pathway to lower their own carbon footprint by following a clear roadmap and turning traditional production systems into next-generation biodiesel manufacturing platforms through cutting-edge technologies and a focus on creating incremental value through continual validation.</div></div>","PeriodicalId":34138,"journal":{"name":"Energy and AI","volume":"24 ","pages":"Article 100696"},"PeriodicalIF":9.6,"publicationDate":"2026-05-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146174361","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"A data-driven digital twin approach for real-time flux density prediction in solar power towers using ray-level deep learning corrections","authors":"Sergio Díaz-Alonso,&nbsp;Christian Raeder,&nbsp;Allen Charly,&nbsp;Bernhard Hoffschmidt","doi":"10.1016/j.egyai.2026.100763","DOIUrl":"10.1016/j.egyai.2026.100763","url":null,"abstract":"<div><div>Concentrating solar technologies offer significant potential for sustainable and dispatchable heat, power and fuel production, especially through solar power tower systems. However, accurate flux density measurement at the receiver remains a critical barrier to the commercial deployment of this technology. Current direct and indirect methods are disruptive, expensive and inapplicable to cavity receivers. In order to overcome these challenges, this work proposes a computational data-driven method that couples four convolutional neural networks operating sequentially with a Monte Carlo ray-tracing simulator to correct both the power and directional components of simulated rays at the receiver aperture plane. Ray direction data are compressed from over 100 × 10⁶ parameters to 2028 values through statistical distribution fitting, enabling near-real-time inference on standard computing hardware. The power correction achieves accuracies up to 95.0 % at the aperture plane, while direction predictions exceed 80.0 % Pearson correlation for the three ray direction components across 63 diverse meteorological conditions spanning direct normal irradiance values from 100.0 to 900.0 W/m². The corrected rays are subsequently projected into the three-dimensional cavity receiver geometry, yielding an integrated flux prediction accuracy of 80.9 % compared to experimental camera-based measurements at the Solarturm Jülich facility. An aggregated training strategy across 931 meteorological conditions and multiple heliostat configurations ensures generalization without cost-extensive per-heliostat calibration. These results demonstrate that ray-level deep learning correction provides a universal, non-disruptive, and industrially scalable solution for flux density prediction in both open and cavity solar receivers.</div></div>","PeriodicalId":34138,"journal":{"name":"Energy and AI","volume":"24 ","pages":"Article 100763"},"PeriodicalIF":9.6,"publicationDate":"2026-05-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147797648","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Robust explainable forecasting via heterogeneous sensor fusion with applications in residential peak load management","authors":"Raluca Laura Portase,&nbsp;Rodica Potolea","doi":"10.1016/j.egyai.2026.100764","DOIUrl":"10.1016/j.egyai.2026.100764","url":null,"abstract":"<div><div>High electricity consumption peaks in the residential sector can lead to expensive grid upgrades and higher consumer costs. Traditional forecasting methods often lack the contextual awareness to predict the behaviour-driven spikes. To address this problem, this paper proposes a simulated robust, closed-loop architecture for residential demand-side management that integrates multi-sensor fusion and machine learning. We perform a proof-of-concept evaluation of our proposed system on a publicly available household electricity consumption dataset. In our experimental evaluation, by leveraging internal and external environmental data as proxies for human activity, our system identifies high-demand peaks at the household level. We investigate the forecasting window from the perspective of the stability–sensitivity trade-off for peak and minimum consumption. Integrating heterogeneous sensors reduced peak forecasting errors by 29%. We perform a detailed analysis of the required sensors and their placement to improve peak detection, and evaluate sensor robustness to assess system performance under various hardware or network failure models. Simulation results in a limited-data scenario demonstrate a peak reduction of up to 36.36% without compromising essential household functions or total energy efficiency.</div></div>","PeriodicalId":34138,"journal":{"name":"Energy and AI","volume":"24 ","pages":"Article 100764"},"PeriodicalIF":9.6,"publicationDate":"2026-05-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147849838","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Conditional diffusion modeling for probabilistic behind-the-meter PV disaggregation","authors":"Marc Jené-Vinuesa ,&nbsp;Hussain Kazmi ,&nbsp;Mònica Aragüés-Peñalba ,&nbsp;Andreas Sumper","doi":"10.1016/j.egyai.2026.100692","DOIUrl":"10.1016/j.egyai.2026.100692","url":null,"abstract":"<div><div>Distributed behind-the-meter (BTM) PV systems are expanding rapidly, yet their generation is often metered together with demand, resulting in operational challenges for grid operators. This paper introduces a novel generative diffusion-based methodology for probabilistic BTM PV disaggregation that directly learns the distribution of PV generation from low-resolution smart meter data with 30-minute granularity. The proposed conditional diffusion model combines a domain-specific conditioning strategy with a sequential U-Net architecture to generate multiple plausible daily PV profiles, enabling both accurate point estimates and well-calibrated prediction intervals. On a real-world Australian benchmark, the model outperforms traditional deterministic baselines and representative probabilistic alternatives, achieving a mean absolute scaled error (MASE) of 0.954 and a coverage probability of 61.5% for the 50% prediction interval. Sensitivity studies highlight the effect of conditioning signals and training data volume, while cross-dataset experiments confirm generalization to Dutch residential data. By disaggregating BTM PV and quantifying uncertainty, this study demonstrates the potential of generative models to enhance grid visibility and support applications such as load forecasting, planning, and flexibility assessment.</div></div>","PeriodicalId":34138,"journal":{"name":"Energy and AI","volume":"24 ","pages":"Article 100692"},"PeriodicalIF":9.6,"publicationDate":"2026-05-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146174322","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Physics-informed neural networks for methane sorption: Cross-gas transfer learning, ensemble collapse under physics constraints, and Monte Carlo Dropout uncertainty quantification","authors":"Mohammad Nooraiepour ,&nbsp;Zezhang Song ,&nbsp;Wei Li ,&nbsp;Sarah Perez","doi":"10.1016/j.egyai.2026.100757","DOIUrl":"10.1016/j.egyai.2026.100757","url":null,"abstract":"<div><div>Accurate methane sorption prediction across heterogeneous coal ranks requires models that combine thermodynamic consistency, efficient knowledge transfer across data-scarce geological systems, and calibrated uncertainty estimates, capabilities that are rarely addressed together in existing frameworks. We present a physics-informed transfer learning framework that adapts a hydrogen sorption PINN to methane sorption prediction via Elastic Weight Consolidation, coal-specific feature engineering, and a three-phase curriculum that progressively balances transfer preservation with thermodynamic fine-tuning. Trained on 993 equilibrium measurements from 114 independent coal experiments spanning lignite to anthracite, the framework achieves R<span><math><mrow><msup><mrow></mrow><mrow><mn>2</mn></mrow></msup><mo>=</mo><mn>0</mn><mo>.</mo><mn>932</mn></mrow></math></span> on held-out coal samples, a 227% improvement over pressure-only classical isotherms, while hydrogen pre-training delivers 18.9% lower RMSE and 19.4% faster convergence than random initialization. A comparison of five Bayesian uncertainty quantification approaches reveals a systematic divergence in performance across physics-constrained architectures. Monte Carlo Dropout achieves well-calibrated uncertainty (ECE, <span><math><mrow><mo>=</mo><mn>0</mn><mo>.</mo><mn>101</mn></mrow></math></span>, <span><math><mrow><msub><mrow><mi>ρ</mi></mrow><mrow><mi>s</mi></mrow></msub><mo>=</mo><mn>0</mn><mo>.</mo><mn>708</mn></mrow></math></span>) at minimal overhead (<span><math><mrow><mn>1</mn><mo>.</mo><mn>5</mn><mo>×</mo></mrow></math></span> inference cost), while deep ensembles – regardless of architectural diversity or initialization strategy – exhibit performance degradation because shared physics constraints narrow the admissible solution manifold, thereby attenuating the functional disagreement required for reliable ensemble-based epistemic uncertainty estimation. SHAP and ALE analyses confirm that the learned representations remain physically interpretable with established coal sorption mechanisms: moisture–volatile interactions are most influential (17.2% importance), pressure–temperature coupling captures thermodynamic co-dependence, and 11 of 12 features exhibit non-monotonic effects. These results identify Monte Carlo Dropout as the best-performing uncertainty quantification method in this physics-constrained transfer-learning framework, and demonstrate cross-gas transfer learning as a data-efficient strategy for geological material modeling.</div></div>","PeriodicalId":34138,"journal":{"name":"Energy and AI","volume":"24 ","pages":"Article 100757"},"PeriodicalIF":9.6,"publicationDate":"2026-05-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147849722","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Physics-informed distributed reinforcement learning for privacy-aware voltage regulation using local smart meter data","authors":"Dong Liu ,&nbsp;Juan S. Giraldo ,&nbsp;Peter Palensky ,&nbsp;Pedro P. Vergara","doi":"10.1016/j.egyai.2026.100768","DOIUrl":"10.1016/j.egyai.2026.100768","url":null,"abstract":"<div><div>Centralized reinforcement learning-based voltage regulation in distribution networks is becoming increasingly difficult due to the growing penetration of distributed energy resources, high computational burden, repeated power flow calculations, and increasing privacy concerns. This paper proposes a physics-informed fully distributed reinforcement learning framework that enables autonomous voltage regulation using only local smart meter data. A Thevenin-equivalent-based local voltage estimation model and a hybrid correction mechanism are developed to support accurate local decision-making without synchronized global measurements or centralized power flow solvers. A lightweight coordination mechanism is further introduced to refine the actions of independently trained local agents. Case studies show that the proposed framework reduces voltage violations by approximately 80%, achieves performance close to that of power flow-based training environments, and achieves a training speedup of about 6<span><math><mo>×</mo></math></span>. The results also indicate that the relaxation factors in the reward function and the coordination scaler are critical to voltage regulation efficiency, whereas the discount factor has a smaller impact. These findings demonstrate the practicality of the proposed framework for privacy-aware fully distributed voltage regulation.</div></div>","PeriodicalId":34138,"journal":{"name":"Energy and AI","volume":"24 ","pages":"Article 100768"},"PeriodicalIF":9.6,"publicationDate":"2026-05-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147849837","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}