The Properties of Elasticity, Thermology, and Anisotropy in Pd-Based Alloys

This work is devoted to investigate the elasticity, anisotropy, plastic properties, and thermal conductivity of PdSnYb, PdSn2Yb and Heusler alloy Pd2SnYb via employing the first-principles. The magnetic properties of Pd2SnYb, PdSnYb and PdSn2Yb are obtained by the geometry optimization combining with spin polarization. And the stability of these three kinds of materials is ensured by comparing with the enthalpy of formation and binding energy. The Fermi energy has same trend with stability. The details of bulk and Young’s modulus are demonstrated in 3D plots, embodied the elastic anisotropies of PdSnYb, PdSn2Yb, and Pd2SnYb. The calculations of plastic properties are also anisotropic. And the minimum thermal conductivities are small enough for these three materials to be used as thermal barrier coatings.

Validation of virtual sensor-assisted Bayesian inference-based in-situ sensor calibration strategy for building HVAC systems

For building heating,ventilation and air-conditioning systems(HVACs),sensor faults significantly affect the operation and control.Sensors with accurate and reliable measurements are critical for ensuring the precise indoor thermal demand.Owing to its high calibration accuracy and in-situ effectiveness,a virtual sensor(VS)-assisted Bayesian inference(VS-BI)sensor calibration strategy has been applied for HVACs.However,the application feasibility of this strategy for wider ranges of different sensor types(within-control-loop and out-of-control-loop)with various sensor bias fault amplitudes,and influencing factors that affect the practical in-situ calibration performance are still remained to be explored.Hence,to further validate its in-situ calibration performance and analyze the influencing factors,this study applied the VS-BI strategy in a HVAC system including a chiller plant with air handle unit(AHU)terminal.Three target sensors including air supply(SAT),chilled water supply(CHS)and cooling water return(CWR)temperatures are investigated using introduced sensor bias faults with eight different amplitudes of[−2℃,+2℃]with a 0.5℃ interval.Calibration performance is evaluated by considering three influencing factors:(1)performance of different data-driven VSs,(2)the influence of prior standard deviationsσon in-situ sensor calibration and(3)the influence of data quality on in-situ sensor calibration from the perspective of energy conservation and data volumes.After comparison,a long short term memory(LSTM)is adopted for VS construction with determination coefficient R-squared of 0.984.Results indicate thatσhas almost no impact on calibration accuracy of CHS but scanty impact on that of SAT and CWR.The potential of using a prior standard deviationσto improve the calibration accuracy is limited,only 8.61%on average.For system within-control-loop sensors like SAT and CHS,VS-BI obtains relatively high in-situ sensor calibration accuracy if the data quality is relatively high.

Activation of catalase activity by a peroxisome-localized small heat shock protein Hsp17.6CⅡ

Plant catalases are important antioxidant enzymes and are indispensable for plant to cope with adverse environmental stresses. However, little is known how catalase activity is regulated especially at an organelle level. In this study, we identified that small heat shock protein Hsp17.6CⅡ(AT5G12020) interacts with and activates catalases in the peroxisome of Arabidopsis thaliana. Although Hsp17.6CⅡ is classified into the cytosol-located small heat shock protein subfamily, we found that Hsp17.6CⅡ is located in the peroxisome. Moreover, Hsp17.6CⅡ contains a novel non-canonical peroxisome targeting signal 1(PTS1), QKL, 16 amino acids upstream from the C-terminus. The QKL signal peptide can partially locate GFP to peroxisome, and mutations in the tripeptide lead to the abolishment of this activity. In vitro catalase activity assay and holdase activity assay showed that Hsp17.6CⅡ increases CAT2 activity and prevents it from thermal aggregation. These results indicate that Hsp17.6CⅡ is a peroxisome-localized catalase chaperone. Overexpression of Hsp17.6CⅡ conferred enhanced catalase activity and tolerance to abiotic stresses in Arabidopsis. Interestingly, overexpression of Hsp17.6CⅡ in catalase-deficient mutants,nca1-3 and cat2 cat3, failed to rescue their stress-sensitive phenotypes and catalase activity, suggesting that Hsp17.6CⅡ-mediated stress response is dependent on NCA1 and catalase activity. Overall, we identified a novel peroxisome-located catalase chaperone that is involved in plant abiotic stress resistance by activating catalase activity.

An improved transfer learning strategy for short-term cross-building energy prediction usingdata incremental

The available modelling data shortage issue makes it difficult to guarantee the performance of data-driven building energy prediction(BEP)models for both the newly built buildings and existing information-poor buildings.Both knowledge transfer learning(KTL)and data incremental learning(DIL)can address the data shortage issue of such buildings.For new building scenarios with continuous data accumulation,the performance of BEP models has not been fully investigated considering the data accumulation dynamics.DIL,which can learn dynamic features from accumulated data adapting to the developing trend of new building time-series data and extend BEP model's knowledge,has been rarely studied.Previous studies have shown that the performance of KTL models trained with fixed data can be further improved in scenarios with dynamically changing data.Hence,this study proposes an improved transfer learning cross-BEP strategy continuously updated using the coarse data incremental(CDI)manner.The hybrid KTL-DIL strategy(LSTM-DANN-CDI)uses domain adversarial neural network(DANN)for KLT and long short-term memory(LSTM)as the Baseline BEP model.Performance evaluation is conducted to systematically qualify the effectiveness and applicability of KTL and improved KTL-DIL.Real-world data from six-type 36 buildings of six types are adopted to evaluate the performance of KTL and KTL-DIL in data-driven BEP tasks considering factors like the model increment time interval,the available target and source building data volumes.Compared with LSTM,results indicate that KTL(LSTM-DANN)and the proposed KTL-DIL(LSTM-DANN-CDI)can significantly improve the BEP performance for new buildings with limited data.Compared with the pure KTL strategy LSTM-DANN,the improved KTL-DIL strategy LSTM-DANN-CDI has better prediction performance with an average performance improvement ratio of 60%.

A simple and precise method(Y2H-in-frame-seq) improves yeast two-hybrid screening with cDNA libraries

As the most powerful and widely used binary interaction assay,the yeast two-hybrid(Y2H) system has been developed for more than 30 years to improve the efficiency and quality of proteinprotein interaction(PPI) exploration and genome-wide interactome screening(Fields and Song.

A statistical-based online cross-system fault detection method for building chillers

Practical applications of data-driven fault detection(FD)are limited by their portability.The costs of model training and validation are extremely high when each system requires a model retrained on its own fault and fault-free data.Therefore,this paper proposes a statistical-based online cross-system FD method to address the problem of model portability.The proposed FD model can be cross-utilized between building chillers with various specifications while it only needs to update the original fault detection model by the normal operation data of the new chiller system,thus saving huge fault experimental costs for the fault detection of new chiller.First,a theoretical basis for the proposed cross-system fault detection method is presented.Then,experiments were conducted on three building chillers with different specifications.Both fault and fault-free data were collected from the three chillers.The development and validation of the proposed cross-system fault detection method are then conducted.Results show that the cross-system fault detection models perform well when used with different chillers.For instance,when the fault detection model of system#1 was cross-utilized to system#2,the detection accuracies of refrigerant leakage,refrigerant overcharge,and reduced evaporator water flow were 99.73%,90.17%,and 96.94%,respectively.Compared with original models,the detection accuracies were improved by 33.78%,84.07%,and 65.56%,respectively.Therefore,the proposed cross-system fault detection method has potential for online application to practical engineering FD.

Fabrication and characterization of Ba Ce_(0.8)Y_(0.2)O_(2.9)-Ce_(0.85)Sm_(0.15)O_(1.925) composite electrolytes for IT-SOFCs

The Ba Ce0.8Y0.2O2.9-Ce0.85Sm0.15O1.925 composite electrolytes were prepared with Ba Ce0.8Y0.2O2.9(BCY) and Ce0.85Sm0.15O1.925(SDC). The SDC and BCY powders were mixed in the weight ratio of 95:5, 85:15, and 75:25, respectively(named as BS95, BS85, and BS75). Because of the composite effect between the SDC and BCY phases, the BS95 and BS85 exhibit improved conductivity compared with the pure SDC and BCY. The conductivity of BS95 is higher than that of BS85, indicating that the composite effect of BS95 is greater than that of BS85. Nevertheless, the composite effect in BS75 does not exist. Hence, we conclude that the composite effect in the BCY-SDC composites will decrease with the increase of the amount of BCY and even disappear when the amount of BCY exceeds a certain value. In our case, the optimum composition of the composite electrolyte is 95 wt% SDC and 5 wt% BCY. The BS95 has the highest conductivity(σ1t=0.07808 S cm-1, at 800 °C) and the fuel cell based on the BS95 shows the best performance(the maximum power density reaches as high as 526 mw cm-2 at 750 °C). The encouraging results suggest that the BCY-SDC composites are the very promising electrolyte materials for IT-SOFCs.