Energy-saving potential prediction models for large-scale building: A state-of-the-art review

2022 ◽  
Vol 156 ◽  
pp. 111992
Author(s):  
Xiu'e Yang ◽  
Shuli Liu ◽  
Yuliang Zou ◽  
Wenjie Ji ◽  
Qunli Zhang ◽  
...  
Keyword(s):  
Energy Saving ◽  
Large Scale ◽  
Prediction Models ◽  
State Of The Art ◽  
Energy Saving Potential

scholarly journals The influence of dataset homology and a rigorous evaluation strategy on protein secondary structure prediction

PLoS ONE ◽  
2021 ◽  
Vol 16 (7) ◽  
pp. e0254555
Author(s):  
Teng-Ruei Chen ◽  
Chia-Hua Lo ◽  
Sheng-Hung Juan ◽  
Wei-Cheng Lo
Keyword(s):  
Secondary Structure ◽  
Sequence Homology ◽  
Structure Prediction ◽  
Large Scale ◽  
Prediction Models ◽  
Secondary Structure Prediction ◽  
State Of The Art ◽  
Protein Secondary Structure ◽  
Substantial Improvement ◽  
Protein Secondary Structures

The secondary structure prediction (SSP) of proteins has long been an essential structural biology technique with various applications. Despite its vital role in many research and industrial fields, in recent years, as the accuracy of state-of-the-art secondary structure predictors approaches the theoretical upper limit, SSP has been considered no longer challenging or too challenging to make advances. With the belief that the substantial improvement of SSP will move forward many fields depending on it, we conducted this study, which focused on three issues that have not been noticed or thoroughly examined yet but may have affected the reliability of the evaluation of previous SSP algorithms. These issues are all about the sequence homology between or within the developmental and evaluation datasets. We thus designed many different homology layouts of datasets to train and evaluate SSP prediction models. Multiple repeats were performed in each experiment by random sampling. The conclusions obtained with small experimental datasets were verified with large-scale datasets using state-of-the-art SSP algorithms. Very different from the long-established assumption, we discover that the sequence homology between query datasets for training, testing, and independent tests exerts little influence on SSP accuracy. Besides, the sequence homology redundancy between or within most datasets would make the accuracy of an SSP algorithm overestimated, while the redundancy within the reference dataset for extracting predictive features would make the accuracy underestimated. Since the overestimating effects are more significant than the underestimating effect, the accuracy of some SSP methods might have been overestimated. Based on the discoveries, we propose a rigorous procedure for developing SSP algorithms and making reliable evaluations, hoping to bring substantial improvements to future SSP methods and benefit all research and application fields relying on accurate prediction of protein secondary structures.

scholarly journals Energy audit and conservation potential analysis of a large comprehensive commercial building

2018 ◽  
Vol 22 (Suppl. 2) ◽  
pp. 567-576
Author(s):  
Chunzhi Zhang ◽  
Nianxia Yuan ◽  
Qianjun Mao
Keyword(s):  
Energy Consumption ◽  
Energy Saving ◽  
Emission Reduction ◽  
Large Scale ◽  
Rapid Development ◽  
Recovery Period ◽  
Commercial Building ◽  
Energy Saving Potential ◽  
Consumption Data ◽  
Actual Energy Consumption

With the rapid development of large-scale public buildings, energy consumption has increased, of which the energy consumption of comprehensive commercial buildings can reach 10~20 times the common building energy consumption, and has great energy saving potential. In this paper, a large comprehensive commercial building in Chengdu is taken as an example to analyze the energy consumption through the actual energy consumption data, viewed from the energy-saving and emission-reduction and static investment payback period point. The results show that the energy saving rate of the building can be achieved by 32.64%, the emission reduction is 6196.52 t CO2 per year, and the investment recovery period is only about 0.90 years, which provides a reference for similar buildings.

Bioactivity Prediction Based on Matched Molecular Pair and Matched Molecular Series Methods

2020 ◽  
Vol 26 (33) ◽  
pp. 4195-4205
Author(s):  
Xiaoyu Ding ◽  
Chen Cui ◽  
Dingyan Wang ◽  
Jihui Zhao ◽  
Mingyue Zheng ◽  
...  
Keyword(s):  
Prediction Model ◽  
Large Scale ◽  
Prediction Models ◽  
Predictive Accuracy ◽  
Lead Optimization ◽  
Consensus Method ◽  
Molecular Pair ◽  
Bioactivity Prediction ◽  
Compound Synthesis ◽  
Consensus Modeling

Background: Enhancing a compound’s biological activity is the central task for lead optimization in small molecules drug discovery. However, it is laborious to perform many iterative rounds of compound synthesis and bioactivity tests. To address the issue, it is highly demanding to develop high quality in silico bioactivity prediction approaches, to prioritize such more active compound derivatives and reduce the trial-and-error process. Methods: Two kinds of bioactivity prediction models based on a large-scale structure-activity relationship (SAR) database were constructed. The first one is based on the similarity of substituents and realized by matched molecular pair analysis, including SA, SA_BR, SR, and SR_BR. The second one is based on SAR transferability and realized by matched molecular series analysis, including Single MMS pair, Full MMS series, and Multi single MMS pairs. Moreover, we also defined the application domain of models by using the distance-based threshold. Results: Among seven individual models, Multi single MMS pairs bioactivity prediction model showed the best performance (R2 = 0.828, MAE = 0.406, RMSE = 0.591), and the baseline model (SA) produced the most lower prediction accuracy (R2 = 0.798, MAE = 0.446, RMSE = 0.637). The predictive accuracy could further be improved by consensus modeling (R2 = 0.842, MAE = 0.397 and RMSE = 0.563). Conclusion: An accurate prediction model for bioactivity was built with a consensus method, which was superior to all individual models. Our model should be a valuable tool for lead optimization.

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