Search

Because of the relatively limited understanding of COVID-19 pathogenesis, immunological analysis for vaccine development is needed. Mice and macaques were immunized with an inactivated SARS-CoV-2 vaccine prepared by two inactivators. Various immunological indexes were tested, and viral challenges were performed on day 7 or 150 after booster immunization in monkeys. This inactivated SARS-CoV-2 vaccine was produced by sequential inactivation with formaldehyde followed by propiolactone. The various antibody responses and specific T cell responses to different viral antigens elicited in immunized animals were maintained for longer than 150 days. This comprehensive immune response could effectively protect vaccinated macaques by inhibiting viral replication in macaques and substantially alleviating immunopathological damage, and no clinical manifestation of immunopathogenicity was observed in immunized individuals during viral challenge. This candidate inactivated vaccine was identified as being effective against SARS-CoV-2 challenge in rhesus macaques., Graphical Abstract, This twice inactivated candidate SARS-CoV-2 vaccine was safe, effective in inducing immune responses and effective against SARS-CoV-2 challenge in rhesus macaques.

Data-driven approach has emerged as a popular research method for fault detection and diagnosis (FDD) of variable refrigerant flow system. However, one serious issue has been raised in recent studies, which shows that training classifiers with datasets which suffer of imbalanced class distributions can significantly influence the final classification accuracy. So, aiming at the imbalanced dataset environment problem of variable refrigerant flow (VRF) system based on data-driven fault detect and diagnosis, we conducted a study. First of all, through comparative research, it is concluded that the geometric mean accuracy of the fault detection in the VRF system has reduced an average of 42.49% due to the imbalanced data environment. Then we proposed a Principal Component Analysis-Synthetic Minority Oversampling Technique (PCA-SMOTE) method to save this problem, and verified the reliability and versatility of the proposed method with three types of modeling algorithms commonly used in VRF systems. The result shows that this method not only has obvious improvement effect, but also has strong versatility. In the end, we gradually reduce the number of minority from 50 to 10 to explore minimum fault data requirements of this method. The final result shows that this method can still ensure that common machine learning algorithms can effectively detect and diagnosis the VRF system fault when only 10 fault data are included.

Lithium metal has been regarded as the most ideal choice to be anode for rechargeable lithium batteries. However, lithium dendrites growth and unstable interface between lithium and electrolyte result in security risk and poor cyclic performance, hindering practical applications of lithium anode. By analyzing kinetic parameters and the morphology of initial lithium deposition, we find that In3Li13 substrate exhibits excellent lithiophilic nature with extremely small nucleation barrier of lithium. Thus, an integrated Li-rich Li-In anode with lithiophilic In3Li13 framework was designed and prepared by a facile one-step vacuum evaporation approach. The framework reduces the local current density and accommodates lithium in the interior space. Most importantly, the lithiophilic In3Li13 can guide uniform Li nucleation. Via the synergy of lithiophilic framework in structure and interface, the designed integrated Li-In anode exhibits excellent electrochemical performance. Symmetric cells with the Li-In anode stably cycle for over 230 hours, about four times the lifespan of the cell with the pure lithium anode. In the full cells with LiFePO4 cathode, the capacity retention is improved from 83.6% to 97.7% after 160 cycles with the Li-In anode. These attempts deepened the understanding of Li nucleation on lithiophilic substrate and provide new insight for designing high-performance lithium anode.

Compressor liquid floodback not only can drop the performance of the variable refrigerant flow (VRF) system, but also may cause mechanical failure. Therefore, this is necessary to perform a timely online diagnosis of that fault. The present paper proposes an online compressor liquid floodback fault diagnosis method for VRF system based on back-propagation neural network (BPNN), which fills the online compressor liquid floodback fault diagnosis knowledge gap. The proposed method main article context as follow: Firstly, the sensors in VRF system record and save the data every three seconds to form the raw dataset. Secondly, after preprocessing the raw dataset, the correlation analysis is used to filter the data variables. Thirdly, the BPNN model is established by using back-propagation neural network algorithm. The testing dataset was used to verify the reliability of the model. The online dataset was used to test the model's online diagnostic capability, and its results were also compared and analyzed to the Classification and Regression Tree (CART) model. This result indicates that the BPNN method has a low degree of over-fitting and high reliability, and its online diagnostic accuracy is up to 99.48%. By comparing results of the method BPNN and CART, it shows that the former is superior to the latter, regardless of the diagnostic accuracy or the online diagnostic stability.

This study presents a case study of office buildings using modal decomposition based ensemble learning method to forecast energy consumption of ground source heat pump systems (GSHP). Conventional machine learning methods have uncertainty in practical application as there are lots of stochastic terms in the structure of the algorithm. Therefore, ensemble learning models are proposed to ameliorate the problem. In this paper, the prediction potential of modal decomposition based ensemble learning models are investigated while providing a comprehensive comparison on different single models and ensemble learning models in the building field. Results show that the proposed VMD based ensemble learning models have remarkable advantages in GSHP energy consumption prediction comparing with other machine learning methods, and the prediction performances of VMD based ensemble learning models measured by RMSE and MAE are 30–50% better than common machine learning methods. This work is enlightening and indicates that VMD based ensemble learning models fit well with energy consumption prediction, which could bring more efficient and concise solutions for GSHP energy consumption predictions.

With the relatively serious global epidemic outbreak of SARS-CoV-2 infection, public concerns focus on not only clinical therapeutic measures and public quarantine for this disease but also the development of vaccines. The technical design of our SARS-CoV-2 inactivated vaccine provides a viral antigen that enables the exposure of more than one structural protein based upon the antibody composition of COVID-19 patients’ convalescent serum. This design led to valid immunity with increasing neutralizing antibody titers and a CTL response detected post-immunization of this vaccine by two injections in rhesus macaques. Further, this elicited immunoprotection in macaques enables not only to restrain completely viral replication in tissues of immunized animals, compared to the adjuvant control and those immunized by an RBD peptide vaccine, but also to significantly alleviate inflammatory lesion in lung tissues in histo-pathologic detection, compared to the adjuvant control with developed interstitial pneumonia. The data obtained from these macaques immunized with the inactivated vaccine or RBD peptide vaccine suggest that immunity with a clinically protective effect against SARS-CoV-2 infection should include not only specific neutralizing antibodies but also specific CTL responses against at least the S and N antigens.

Variable refrigerant flow (VRF) systems faults are inevitable due to installation errors, degradation, and other reasons. It is of great value to quantitatively understand the impact of faults on VRF systems performance and to clarify the changing trends of variables under different types of faults through experiments. In particular, the experimental analysis of simultaneous faults situations is helpful to improve the fault detection and diagnosis technology of VRF systems. There have been some previous experimental studies on the impact of faults, but none of them concerns modern VRF systems and their simultaneous faults. This paper presents results from a laboratory study of a VRF system with different types of faults. It provides the first published results of combinations of triple simultaneous faults, in addition to previously untested types of double simultaneous faults. The quantitative impact of the three crucial performance parameters, e.g. cooling capacity, system power, and COP, of the system under different faults has been analyzed. In addition, the quantitative influence and variation trend of system parameter variables during single fault and simultaneous fault are summarized. Results show that the outdoor fouling fault has the greatest impact, which can cause a 47.6% COP drop and 80.27% cooling capacity reduction. The influence of the simultaneous fault on the variable trend is superimposed and offset, but the trend influence of some faults also has a dominant characteristic.

Lithium metal has been considered as an attractive anode for high energy density lithium battery because of high specific capacity and the lowest redox potential. However, the growth of Li dendrites during cycling has hindered the practical application of Li metal battery (LMB). Herein, TTFEB is adopted as a bifunctional electrolyte additive to improve the performance of Li metal anode, which is beneficial for the surface chemistry of anode and the property of the electrolyte. A LiF-rich SEI layer and enhanced Li+ migration in electrolyte can be achieved with 2% TTFEB, which result in uniform Li deposition without uncontrollable Li dendrites. With controlled Li deposition in TTFEB-containing electrolyte, a significantly improved Coulombic efficiency (~ 96%) in Li|Cu cell, long lifetime (over 1000 h), and weak polarization in Li|Li cell can be obtained. Moreover, excellent cycling performance and rate capability of Li|LiFePO4 cells with TTFEB are also presented in this work. Our strategy provides new insights on the diverse function of electrolyte additives for rechargeable LMB.

Sensor fault detection is essential to maintain operations of heating, ventilation, and air conditioning systems (HVACs) in buildings. Data-driven sensor fault detection methods are becoming increasingly popular recently. However, for the relatively limited modelling data available in the heating, ventilation, and air conditioning system, it is a major challenge to extract fault-discrimination information (FDI) from limited data to develop a data-driven model with higher fault detection performance and lower false-alarm rate. Therefore, this study proposed an improved Stacking (IStacking) sensor fault detection method using fault-discrimination information. Fault-discrimination-information is extracted from original samples by subtracting the statistics with the threshold of each single model. Four different single models (i.e., principal component analysis, one-class support vector machine , K-Means clustering, and autoencoder) are employed to develop the ensemble learning detection method with relatively high generalization ability using a Stacking training manner. Sensor bias faults from two different sources are used for validation. Area under the ROC (receiver operating characteristic) curve (AUC) and false positive rate (FPR) are used to evaluate the detection performance and false-alarm rate. Results indicated that IStacking outperforms the four single models and traditional Stacking when the added biases are ranged [-4 °C, −2 °C] and [3.7 °C, 4 °C]. Since single models are stacked and fault-discrimination information is used to replace the original input, IStacking averagely increases Area under the ROC curve by 2.85% and decreases false positive rate by 5.76%.

Due to the frequently changed outdoor weather conditions and indoor requirements, heating, ventilation and air conditioning (HVAC) experiences faulty operations inevitably throughout its lifespan. Therefore, it is important to monitor and diagnose HVAC fault operations. Recently, deep learning methods have attracted more attentions for their guarantee of better diagnosis performance under various system configurations and operating conditions. However, these methods are black-box models which though highly accurate for fault diagnosis but are extremely hard to explain. To overcome the disadvantage of poor interpretability of deep learning black-box models, this study therefore proposes a novel explainable deep learning based fault diagnosis method that is suitable for HVACs. To maintain HVAC operational information and variable locations of all chiller input data samples, proposed method is established with three characteristics: 1) the pooling layer is excluded, 2) the size of convolution filter kernel is set as 1, and 3) use softsign as an activation function. Considering the resulting impacts of HVAC faults on system operating variables, a new Absolute Gradient-weighted Class Activation Mapping (Grad-Absolute-CAM) method is proposed to visualize the fault diagnosis criteria and make the model explainable by providing the fault-discriminative information. The proposed method is validated using fault experimental dataset of a typical building HVAC system (i.e., chiller) from the ASHRAE research project 1043 (RP-1043). The fault diagnosis accuracy is over 98.5% for seven chiller faults. Results indicates that it is capable of interpreting the model work mechanism by activation feature maps and explaining the fault diagnosis criteria by Grad-Absolute-CAM.

This study investigated 3D concrete printing of permanent formwork for concrete column construction. The effect of different hydroxypropyl methyl cellulose (HPMC) contents (0, 0.0003 and 0.0006 by mass of binder) and the water-to-binder (W/B) ratios (0.27, 0.29 and 0.31) on the rheological properties, structural build-up and mechanical performance were studied using several mixtures for manufacture of the permanent formworks. The results showed that the mixture with the HPMC = 0.0006 and the W/B = 0.27 showed the maximum static yield stress, largest thixotropy and maximum green strength, and thereby selected as the optimum mixture. The plastic failure of the optimum mixture was also predicted using a thixotropy model and was compared with the experimental results. Subsequently, three concrete columns with different longitudinal steel reinforcement ratios (0.0%, 1.9% and 2.5%) were constructed using the printed concrete as the permanent formwork and tested in compression. Good bonding was observed at the interface of the cast-in-place concrete and the printed concrete permanent formwork. In addition, it was observed that the initial stiffness, the maximum bearing capacity and the corresponding longitudinal displacement of the concrete columns increased, as the longitudinal reinforcement ratio increased. The counterpart concrete columns using the conventional formworks were also constructed and tested for comparison. In comparison, the concrete columns made using the printed concrete as the permanent formwork obtained a higher stiffness and bearing capacity than the counterpart conventional concrete columns. The reasons for the differences are explained.

1,3,6-Hexanetricarbonitrile (HTN) has been investigated as an electrolyte additive to improve the electrochemical performance of the Li 1.2 Ni 0.13 Co 0.13 Mn 0.54 O 2 cathode at high operating voltage (4.8 V). Linear sweep voltammetry (LSV) results indicate that HTN can improve the oxidation potential of the electrolyte. The influences of HTN on the electrochemical behaviors and surface properties of the cathode at high voltage have been investigated by galvanostatic charge/discharge test, electrochemical impedance spectroscopy (EIS), and ex-situ physical characterizations. Charge-discharge results demonstrate that the capacity retention of the Li 1.2 Ni 0.13 Co 0.13 Mn 0.54 O 2 cathode in 1% HTN-containing electrolyte after 150 cycles at 0.5 C is improved to 92.3%, which is much higher than that in the standard electrolyte (ED). Combined with the theoretical calculation, ICP tests, XRD and XPS analysis, more stable and homogeneous interface film is confirmed to form on the cathode surface with incorporation of HTN, meanwhile, the electrolyte decomposition and the cathode structural destruction are restrained effectively upon cycling at high voltage, leading to improved electrochemical performance of Li 1.2 Ni 0.13 Co 0.13 Mn 0.54 O 2 cathode.

The current 3D concrete printing (3DCP) technology is limited by the reinforcing methods. Conventional steel reinforcement is hard to be incorporated in the 3DCP process. To overcome this limitation, this study aims to develop 3D-printable engineered cementitious composites (ECCs) exhibiting ultra-high tensile strain capacity of more than 8%, which can be used for digital construction of ‘self-reinforced’ concrete structures, reducing the reliance on the conventional steel reinforcement. Different volume fractions of polyethylene fibers (1%, 1.5% and 2%) were used to reinforce the ECC matrix. The fresh properties (including the workability, rheological properties and buildability) and the hardened properties (including the compressive and flexural strengths, and the uniaxial tensile performance), along with the microstructure of the developed 3D printable ECCs were experimentally investigated. Conventionally mold-cast ECCs were also prepared and tested for comparison purposes. The results showed that the developed 3D printable PE-ECCs exhibited strong strain-hardening behavior with the tensile strength and tensile strain capacity of up to 5.7 MPa and 11.4%, respectively. In addition, the results showed that the 3D printed PE-ECCs exhibited superior tensile performance to the conventionally mold-cast PE-ECC counterparts. This finding is in good agreement with the results of the microstructural analysis. Keywords: 3D printing, ECCs, PE fiber, Rheological properties, Ultra-high tensile strain capacity, Mechanical properties

Engineered cementitious composites (ECC) is emerging as promising candidate cementitious materials for 3D concrete printing (3DCP). This paper experimentally investigated the bending behaviors of 3D printed ECC beams with different geometric configuration. A novel mix design for ECC with ultra-high molecular weight polyethylene (PE) fiber was developed and the PE-ECC beams were casted by two methods, i.e., mold-cast and extrusion-based 3D printing. The four-point bending tests were conducted for plain ECC beams, reinforced concrete (RC) beams as well as cast-in-situ unreinforced ECC beam. The influences of different parameters including fabrication methods, print angles and superposition types were particularly investigated. The anisotropic mechanical properties under bending of printed beams and bending capacity of cast in place reinforced concrete beams were also investigated. Also, the strain distribution of longitudinal steel reinforcement in reinforced concrete as well as printed ECC beam were studied. The results indicated that the printed ECC beam presented a ductile failure mode accompanied with strain hardening behaviors. Also, the ECC beams exposed to z direction loading showed better mechanical anisotropic properties when compared with those exposed to x direction.

Establishing fault diagnosis models for HVAC systems to reduce building energy waste and restore buildings to their intended levels of performance is very necessary. While, it is a challenging task to establish a multi-fault decoupling diagnostic model for a variable refrigerant flow system, especially considering the system's unsteady defrosting process and sensor faults. In view of this, this study carried out 27 sets of experiments and obtained 58850 fault and 10889 normal operational data samples to analyze the impact of the unsteady defrosting process and developed a multi-fault decoupling fault diagnostic model considering defrosting and sensor fault based on convolutional neural network. The confusion matrix, geometric mean accuracy (GMA), false alarm rate (FAR) and other indicators are used to comprehensively evaluate model diagnosis results. The results show that the proposed convolutional neural network (CNN) model has excellent performance in multi-fault coupling diagnosis considering defrosting and sensor biases. The recognition accuracy of the CNN model for each fault are all higher than 97.5%. The GMA of the CNN model is as high as 98.46%, which is higher than 20.04%, 11.565% and 6.36% compared to the decision model, the support vector machine model and the multilayer perceptron model. In addition, the FAR of CNN model is only 0.58%, which is 8.93%, 4.05% and 1.62% lower than the other three models.

Triphenyl phosphite (TPPi) was investigated as an electrolyte additive for high-voltage lithium-ion batteries using lithium-rich layered oxide cathode in this paper. The cyclic stability of Li 1.16 Ni 0.2 Co 0.1 Mn 0.54 O 2 was improved significantly with the adoption of 0.2 wt. % TPPi. The capacity retention of Li 1.16 Ni 0.2 Co 0.1 Mn 0.54 O 2 was improved significantly, especially at elevated temperature, from 69.0% to 89.6% after 90 cycles. Electrochemical and physical characterizations indicated that TPPi was oxidized at 4.2 V (vs. Li/Li + ), which was lower than that of the solvents and lithium extraction from Li 1.16 Ni 0.2 Co 0.1 Mn 0.54 O 2 , and the oxidization resulting in an artificial cathode interface film, which effectively inhibited the decomposition of traditional electrolyte and protected Li 1.16 Ni 0.2 Co 0.1 Mn 0.54 O 2 material from destruction.

HVAC systems occupy a large part of the building’s energy consumption. The development of fault detection and diagnosis (FDD) techniques for HVAC system is becoming increasingly essential for building energy saving. The present paper proposes a comparison study on the basic data-driven methods for variable refrigerant flow (VRF) system fault diagnosis. And five widely used data-driven methods were analyzed, which were decision tree (DT), support vector machines (SVM), clustering (CL), shallow neural networks (SNN), and deep neural networks (DNN). The six common types of fault data in VRF system and three evaluation indexes were used to compare the performance of the proposed five FDD methods in single fault and multiple faults. Results indicate that the single fault diagnosis performance of all methods is better than multiple fault diagnosis. The performances of DNN, SNN, and SVM are better than CART and CL no matter single fault or multiple fault diagnosis. Among them, we believe that the SVM method is preferred for single fault diagnosis and the DNN method is preferred for multiple fault diagnosis. And the performance of method CL is the worst, especially in the case of multiple fault classification, the accuracy is only about 48%. The study results are dedicated to providing a reference for subsequent research in FDD of VRF system. Some important remarks are finally concluded in this paper.

Detailed information for Method section (including the description of virus strains, summary of long-term phase III clinical trail, the ethics and protocol of longâ term observation of phase III clinical trial, the consent form and additional supplemental data. Figure S1 legendA total of 1,100 participants, receiving either the vaccine or placebo, were observed during the surveillance period. The cumulative curves for the cases of HFMD induced by enterovirus 71 (EV71), coxsackievirus A16 (CA16), and other enteroviruses were estimated as a percentage among these participants with Kaplanâ Meier survival curves during the period from the receipt of the first dose until 24 months thereafter. The inset shows the same data on an enlarged y-axis. Differences in the distributions of cases of HFMD among the individuals who received the placebo and those who received the vaccine were evaluated with a log-rank test. (A) Cumulative curve for the cases of HFMD induced by EV71. (B) Cumulative curve for the cases of HFMD induced by CA16. (C) Cumulative curve for the cases of HFMD induced by other enteroviruses. (PDF 2302 kb)

Enterovirus 71 (EV71), a major causative agent of hand-foot-and-mouth disease (HFMD), causes outbreaks among children in the Asia-Pacific region. A vaccine is urgently needed. Based on successful pre-clinical work, phase I and II clinical trials of an inactivated EV71 vaccine, which included the participants of 288 and 660 respectively, have been conducted. In the present study, the immune response and the correlated modulation of gene expression in the peripheral blood mononuclear cells (PBMCs) of 30 infants (6 to 11 months) immunized with this vaccine or placebo and consented to join this study in the phase II clinical trial were analyzed. The results showed significantly greater neutralizing antibody and specific T cell responses in vaccine group after two inoculations on days 0 and 28. Additionally, more than 600 functional genes that were up- or down-regulated in PBMCs were identified by the microarray assay, and these genes included 68 genes associated with the immune response in vaccine group. These results emphasize the gene expression profile of the immune system in response to an inactivated EV71 vaccine in humans and confirmed that such an immune response was generated as the result of the positive mobilization of the immune system. Furthermore, the immune response was not accompanied by the development of a remarkable inflammatory response. Clinical Trial Registration: NCT01391494 and NCT01512706.

Background To investigate the long-term effects on immunity of an inactivated enterovirus 71 (EV71) vaccine and its protective efficacy. Methods A sub-cohort of 1,100 volunteers from Guangxi Province in China was eligible for enrolment and randomly administered either the EV71 vaccine or a placebo on days 0 and 28 in a phase III clinical trial and then observed for the following 2 years with approval by an independent ethics committee of Guangxi Zhuang Autonomous Region, China. Serum samples from the 350 participants who provided a full series of blood samples (at all the sampling points) within the 2-year period were collected. Vaccine-induced immune effects, including the neutralizing antibody titres and cross-protection against different genotypes of EV71, were examined. This study also evaluated the protective efficacy of this vaccine based upon clinical diagnosis. Results This sub-cohort showed a >60 % drop-out rate over 2 years. The seroconversion rates among the 161 immunized subjects remained >95 % at the end of study. The geometric mean titres of neutralizing antibodies (anti-genotype C4) 360 days after vaccination in 350 subjects were 81.0 (subjects aged 6–11 months), 98.4 (12–23 months), 95.0 (24–35 months), and 81.8 (36–71 months). These titres subsequently increased to 423.1, 659.0, 545.0, and 321.9, respectively, at 540 days post-immunization (d.p.i.), and similar levels were maintained at 720 d.p.i. Higher IFN-γ/IL-4-specific responses to the C4 genotype of EV71 and cross-neutralization reactivity against major EV71 genotype strains were observed in the vaccine group compared to those in the placebo group. Five EV71-infected subjects were observed in the placebo-treated control group and none in the vaccine-immunized group in per-protocol analysis. Conclusion These results are consistent with the induction of dynamic immune responses and protective efficacy of the vaccine against most circulating EV71 strains. Trial registration number Clinicaltrials.gov, NCT01569581, Trial registration date: March 2012 Electronic supplementary material The online version of this article (doi:10.1186/s12916-015-0448-7) contains supplementary material, which is available to authorized users.