Vertical root distribution and root cohesion of typical tree species on the Loess Plateau, China

Black locust（Robinia pseudoacacia L.） and Chinese pine（Pinus tabulaeformis Carr.） are two woody plants that are widely planted on the Loess Plateau for controlling soil erosion and land desertification. In this study, we conducted an excavation experiment in 2008 to investigate the overall vertical root distribution characteristics of black locust and Chinese pine. We also performed triaxial compression tests to evaluate the root cohesion（additional soil cohesion increased by roots） of black locust. Two types of root distribution, namely, vertical root（VR） and horizontal root（HR）, were used as samples and tested under four soil water content（SWC） conditions（12.7%, 15.0%, 18.0% and 20.0%, respectively）. Results showed that the root lengths of the two species were mainly concentrated in the root diameter of 5–20 mm. A comparison of root distribution between the two species indicated that the root length of black locust was significantly greater than that of Chinese pine in nearly all root diameters, although the black locust used in the comparison was 10 years younger than the Chinese pine. Root biomass was also significantly greater in black locust than in Chinese pine, particularly in the root diameters of 3–5 and 5–10 mm. These two species were both found to be deep-rooted. The triaxial compression tests showed that root cohesion was greater in the VR samples than in the HR samples. SWC was negatively related to both soil shear strength and root cohesion. These results could provide useful information on the architectural characteristics of woody root system and expand the knowledge on shallow slope stabilization and soil erosion control by plant roots on the Loess Plateau.

Effects of deficit irrigation with saline water on spring wheat growth and yield in arid Northwest China

Field experiments were conducted in 2008 and 2009 to study the effects of deficit irrigation with saline water on spring wheat growth and yield in an arid region of Northwest China. Nine treatments included three salinity levels sl, s2 and s3 （0.65, 3.2, and 6.1 dS/m） in combination with three water levels wl, w2 and w3 （375, 300, and 225 mm）. In 2008, for most treatments, deficit irrigation showed adverse effects on wheat growth; meanwhile, the effect of saline irrigation was not apparent. In 2009, growth parameters of wl treatments were not always optimal under saline irrigation. At 3.2 and 6.1 dS/m in 2008, the highest yield was obtained by wl treatments, however, in 2009, the weight of 1,000 grains and wheat yield both followed the order w2 〉 wl 〉 w3. In this study, spring wheat was sensitive to water deficit, especially at the booting to grain-filling stages, but was not significantly affected by saline irrigation and the combination of the two factors. The results demonstrated that 300-mm irrigation water with a salinity of less than 3.2 dS/m is suitable for wheat fields in the study area.

Enzyme activities and gene expression of starch metabolism provide insights into grape berry development

Grapes are categorized as a non-climacteric type of fruit which its ripening is not associated to important rises in respiration and ethylene synthesis.The starch metabolism shares a certain role in the carbohydrate metabolic pathways during grape berry development,and is regarded as an important transient pool in the pathway of sugar accumulation.However,the comprehensive role of starch and its contribution to the quality and flavor of grape berry have not been explored thoroughly.In this study,the expression levels of genes enzyme activities and carbohydrate concentrations related to starch metabolism,were analyzed to understand the molecular mechanism of starch accumulation during grape berry development.The results indicated that starch granules in grape berry were located at the chloroplast in the sub-epidermal tissues,acting as the temporary reserves of photosynthetic products to meet the needs for berry development,and relatively high starch contents could be detected at véraison stage.Moreover,both ADP-glucose pyrophosphorylase(EC 2.7.7.27)and sucrose phosphate synthase(EC 2.3.1.14)involved in starch synthesis displayed elevated gene expression and enzymes activities in the sub-epidermal tissue,whileα-andβ-amylases involved in its degradation were highly transcribed and active in the central flesh,explaining the absence of starch in this last tissue.Change in the gene expression and activities of ADP-glucose pyrophosphorylase,β-amylase and sucrose phosphate synthase revealed that they were regulated by the circadian rhythms in the fruitlets compared with those in the leaves.Both the morphological,enzymological and transcriptional data in this study provide advanced understandings on the function of starch during berry development and ripening that are so important for berry quality.This study will further facilitate our understanding of the sugar metabolism in grape berry as well as in other plant species.

Efficient extraction of phenol from wastewater by ionic micro-emulsion method:Anionic and cationic

Phenolic wastewater is one of the priorities in the field of wastewater treatment,which poses a serious threat to the human health and nature environment.In this paper,cationic cetyltrimethylammonium bromide(CTAB)and anionic sodium oleate(Na OL)microemulsions were utilized to extract phenol from the wastewater.The optimal extraction factors were investigated by exploring the effects of microemulsion composition ratio and extraction conditions on the phenol extraction performance.Furthermore,the enhanced extraction mechanism of phenol by cations microemulsions is illustrated by studying the extraction process of cationic and anionic microemulsions in the extraction of phenol.The optimum components were obtained:surfactant concentration of 0.2 mol·L^(-1),isoamyl alcohol volume of 30%,internal aqueous phase concentration of CTAB microemulsion of 0.05 mol·L^(-1),and internal aqueous phase concentration of Na OL microemulsion of 0.09 mol·L^(-1).The extraction efficiencies were 96.44%and 82.0%when using CTAB and Na OL microemulsions under optimal conditions(water-emulsion ratio of 5,contact time of 9 min,extraction temperature of 298.15 K,and p H of 9),confirming the enhanced extraction of phenol by CTAB cationic microemulsion.It was analyzed that the enhanced extraction of CTAB microemulsion was due to the electrostatic adsorption of cations with phenol root ions.

Generative pre-trained transformers(GPT)-based automated data mining for building energy management:Advantages,limitations and the future

Advanced data mining methods have shown a promising capacity in building energy management.However,in the past decade,such methods are rarely applied in practice,since they highly rely on users to customize solutions according to the characteristics of target building energy systems.Hence,the major barrier is that the practical applications of such methods remain laborious.It is necessary to enable computers to have the human-like ability to solve data mining tasks.Generative pre-trained transformers(GPT)might be capable of addressing this issue,as some GPT models such as GPT-3.5 and GPT-4 have shown powerful abilities on interaction with humans,code generation,and inference with common sense and domain knowledge.This study explores the potential of the most advanced GPT model(GPT-4)in three data mining scenarios of building energy management,i.e.,energy load prediction,fault diagnosis,and anomaly detection.A performance evaluation framework is proposed to verify the capabilities of GPT-4 on generating energy load prediction codes,diagnosing device faults,and detecting abnormal system operation patterns.It is demonstrated that GPT-4 can automatically solve most of the data mining tasks in this domain,which overcomes the barrier of practical applications of data mining methods in this domain.In the exploration of GPT-4,its advantages and limitations are also discussed comprehensively for revealing future research directions in this domain.

End-to-end data-driven modeling framework for automated and trustworthy short-term building energy load forecasting

Conventional automated machine learning(AutoML)technologies fall short in preprocessing low-quality raw data and adapting to varying indoor and outdoor environments,leading to accuracy reduction in forecasting short-term building energy loads.Moreover,their predictions are not transparent because of their black box nature.Hence,the building field currently lacks an AutoML framework capable of data quality enhancement,environment self-adaptation,and model interpretation.To address this research gap,an improved AutoML-based end-to-end data-driven modeling framework is proposed.Bayesian optimization is applied by this framework to find an optimal data preprocessing process for quality improvement of raw data.It bridges the gap where conventional AutoML technologies cannot automatically handle missing data and outliers.A sliding window-based model retraining strategy is utilized to achieve environment self-adaptation,contributing to the accuracy enhancement of AutoML technologies.Moreover,a local interpretable model-agnostic explanations-based approach is developed to interpret predictions made by the improved framework.It overcomes the poor interpretability of conventional AutoML technologies.The performance of the improved framework in forecasting one-hour ahead cooling loads is evaluated using two-year operational data from a real building.It is discovered that the accuracy of the improved framework increases by 4.24%–8.79%compared with four conventional frameworks for buildings with not only high-quality but also low-quality operational data.Furthermore,it is demonstrated that the developed model interpretation approach can effectively explain the predictions of the improved framework.The improved framework offers a novel perspective on creating accurate and reliable AutoML frameworks tailored to building energy load prediction tasks and other similar tasks.

Federated learning-based short-term building energy consumption prediction method for solving the data silos problem

Transfer learning is an effective method to predict the energy consumption of information-poor buildings by learning transferable knowledge from operational data of information-rich buildings.However,it is not recommended to directly use the operational data without protection due to the risk of leaking occupants’privacy.To address this problem,this study proposes a federated learning-based method to learn transferable knowledge from building operational data without privacy leaking.It trains a transferable federated model based on the operational data from the buildings similar to the target building with limited data.An advanced secure aggregation algorithm is adopted in the training process to ensure that no one can infer private information from the training data.The federated model obtained is evaluated by comparing it with the standalone model without federated learning based on 13 similar office buildings from the Building Data Genome Project.The results show that the federated model outperforms the standalone model concerning the prediction accuracy and training time.On average,the federated model achieves a 25.4%decrease in CV-RMSE when the target building has limited operational data.Even if the target building has no operational data,the federated model still achieves acceptable accuracy(CV-RMSE is 22.2%).Meanwhile,the training time of the federated model is 90%less than that of the standalone model.The research insights can help develop federated learning-based methods for solving the data silos problem in building energy management.The methodology and analysis procedures are reproducible and all codes and data sets are available on Github.

A real-time abnormal operation pattern detection method for building energy systems based on association rule bases

Expert systems are effective for anomaly detection in building energy systems.However,it is usually inefficient to establish comprehensive rule bases manually for complex building energy systems.Association rule mining is available to accelerate the establishment of the rule bases due to its powerful capability of discovering rules from numerous data.This paper proposes a real-time abnormal operation pattern detection method towards building energy systems.It can benefit from both expert systems and association rule mining.Association rules are utilized to establish association rule bases of abnormal and normal operation patterns.The established rule bases are then utilized to develop an expert system for real-time detection of abnormal operation patterns.The proposed method is applied to an actual chiller plant for evaluating its performance.Results show that 15 types of known abnormal operation patterns and 11 types of unknown abnormal operation patterns are detected successfully by the proposed method.

Clustering compression-based computation-efficient calibration method for digital twin modeling of HVAC system

Digital twin is regarded as the next-generation technology for the effective operation of heating,ventilation and air conditioning(HVAC)systems.It is essential to calibrate the digital twin models to match them closely with real physical systems.Conventional real-time calibration methods cannot satisfy such requirements since the computation loads are beyond acceptable tolerances.To address this challenge,this study proposes a clustering compression-based method to enhance the computation efficiency of digital twin model calibration for HVAC systems.This method utilizes clustering algorithms to remove redundant data for achieving data compression.Moreover,a hierarchical multi-stage heuristic model calibration strategy is developed to accelerate the calibration of similar component models.Its basic idea is that once a component model is calibrated by heuristic methods,its optimal solution is utilized to narrow the ranges of parameter probability distributions of similar components.By doing so,the calibration process can be guided,so that fewer iterations would be used.The performance of the proposed method is evaluated using the operational data from an HVAC system in an industrial building.Results show that the proposed clustering compression-based method can reduce computation loads by 97%,compared to the conventional calibration method.And the proposed hierarchical heuristic model calibration strategy is capable of accelerating the calibration process after clustering and saves 14.6%of the time costs.

A review of data mining technologies in building energy systems:Load prediction,pattern identification,fault detection and diagnosis

With the advent of the era of big data,buildings have become not only energy-intensive but also data-intensive.Data mining technologies have been widely utilized to release the values of massive amounts of building operation data with an aim of improving the operation performance of building energy systems.This paper aims at making a comprehensive literature review of the applications of data mining technologies in this domain.In general,data mining technologies can be classified into two categories,i.e.,supervised data mining technologies and unsupervised data mining technologies.In this field,supervised data mining technologies are usually utilized for building energy load prediction and fault detection/diagnosis.And unsupervised data mining technologies are usually utilized for building operation pattern identification and fault detection/diagnosis.Comprehensive discussions are made about the strengths and shortcomings of the data mining-based methods.Based on this review,suggestions for future researches are proposed towards effective and efficient data mining solutions for building energy systems.

Probabilistic graphical models in energy systems:A review

Probabilistic graphical models(PGMs)can effectively deal with the problems of energy consumption and occupancy prediction,fault detection and diagnosis,reliability analysis,and optimization in energy systems.Compared with the black-box models,PGMs show advantages in model interpretability,scalability and reliability.They have great potential to realize the true artificial intelligence in energy systems of the next generation.This paper intends to provide a comprehensive review of the PGM-based approaches published in the last decades.It reveals the advantages,limitations and potential future research directions of the PGM-based approaches for energy systems.Two types of PGMs are summarized in this review,including static models(SPGMs)and dynamic models(DPGMs).SPGMs can conduct probabilistic inference based on incomplete,uncertain or even conflicting information.SPGM-based approaches are proposed to deal with various management tasks in energy systems.They show outstanding performance in fault detection and diagnosis of energy systems.DPGMs can represent a dynamic and stochastic process by describing how its state changes with time.DPGM-based approaches have high accuracy in predicting the energy consumption,occupancy and failures of energy systems.In the future,a unified framework is suggested to fuse the knowledge-driven and data-driven PGMs for achieving better performances.Universal PGM-based approaches are needed that can be adapted to various energy systems.Hybrid algorithms would outperform the basic PGMs by integrating advanced techniques such as deep learning and first-order logic.

A multi agent-based optimal control method for combined cooling and power systems with thermal energy storage

Combined cooling,heating and power(CCHP)systems have been considered as a potential energy saving technology for buildings due to their high energy efficiency and low carbon emission.Thermal energy storage(TES)can improve the energy efficiency of CCHP systems,since they reduce the mismatch between the energy supply and demand.However,it also increases the complexity of operation optimization of CCHP systems.In this study,a multi-agent system(MAS)-based optimal control method is proposed to minimize the operation cost of CCHP systems combined with TES.Four types of agents,i.e.,coordinator agents,building agents,energy management agents and optimization agents,are implemented in the MAS to cooperate with each other.The operation optimization problem is solved by the genetic algorithm.A simulated system is utilized to validate the performance of the proposed method.Results show that the operation cost reductions of 10.0%on a typical summer day and 7.7%on a typical spring day are achieved compared with a rule-based control method.A sensitivity analysis is further performed and results show that the optimal operation cost does not change obviously when the rated capacity of TES exceeds a threshold.