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Purpose: This study investigated the value of synchronous tele-ultrasonography (TUS) for naive operators in thyroid ultrasonography (US) examinations.Methods: Ninety-seven patients were included in this prospective, parallel-controlled trial. Thyroid scanning and diagnosis were completed by resident A independently, resident B with guidance from a US expert through synchronous TUS, and an on-site US expert. The on-site expert’s findings constituted the reference standard. Two other off-site US experts analyzed all data in a blind manner. Inter-operator consistency between the two residents and the on-site US expert for thyroid size measurements, nodule measurements, nodule features, American College of Radiology (ACR) Thyroid Imaging Reporting and Data System (TI-RADS) categories, and image quality was compared. Two questionnaires were completed to evaluate the clinical benefit.Results: Resident B detected more nodules consistent with the on-site expert than resident A did (89.4% vs. 56.5%, P0.75), while resident A achieved lower consistency in ACR TI-RADS categories, composition, echogenicity, margin, echogenic foci, and vascularity (all ICCs 0.40-0.75). Residents A and B had excellent consistency in target nodule measurements (all ICCs >0.75). Resident B achieved better performance than resident A for gray values, time gain compensation, depth, color Doppler adjustment, and the visibility of key information (all P

E-voting allows us to build a democratic business in most Internet of things (IoT) systems. For example, we may vote to choose a proper energy broker in a smart grid system. In this study, we focus on e-voting services in an Internet of energy (IoE) system, which is a new-style smart grid. A practical e-voting in IoE may focus on the properties of fairness, decentralization, eligibility, anonymity, compatibility, verifiability, and coercion resistance. It is difficult to fulfil all these properties simultaneously. Traditional voting schemes often use a public bulletin board or administrator in the voting process, which makes them become centralized. Services that offer e-voting via blockchain can make the voting schemes decentralized. However, many of them ignore the complexity of organizing the data of the transactions, which should be confirmed by the miners. Moreover, to the best of the authors’ knowledge, no works have tested the performance in the blockchain while considering practical use cases and constraints. Concerning all the challenges, we propose a practical anonymous voting scheme for IoE called IoEPAV. The proposed scheme fulfils all the mentioned design goals simultaneously. We tested IoEPAV both in different test networks of the Ethereum blockchain to give an overall evaluation. The practical evaluation can show that the proposed scheme is easy to be integrated into a real system like IoE. We also gave a comparison analysis with the state-of-the-art blockchain-based e-voting. All the results show that IoEPAV is decentralized, verifiable, anonymous, and highly efficient.

Development of solid oxide fuel cell (SOFC) anode with high resistance to coking and sulfur poisoning is highly desirable for the direct application of natural gas in SOFC. Herein, a (Cu, Sm)CeO2 anode with anchored Cu nanoparticles has been prepared. Most of Cu nanoparticles particle size ranges from 20 to 50 nm, which can increase the conductivity and catalytic activity of the anode. The Cu/CSCO10 supported cell exhibits a maximum power density of 404.6 mW/cm2 at 600 ⁰C when dry methane is used as fuel while its ohmic resistance is only 0.39 Ω·cm2. The single SOFC shows good stability when H2S content in the fuel is less than 150 ppm. Up to 900 hours of continuous stable operation with simulated natural gas and commercial natural gas as fuel prove the advantages and application potential of this anode.

In this highlight, the development of donor acceptor (D–A) MOF was briefly reviewed and summarized in the aspects of design, construction, and properties. Also, an outlook about the research and potential application of D–A MOF has been presented.

The convolutional neural network (CNN) is a feedforward neural network with deep structure and convolution operation. In the hyperspectral image (HSI) classification, CNN has demonstrated excellent performance in extracting spectral and spatial information. However, the inherent complexity and high dimension of HSIs still limit the performance of most neural network models. The powerful feature extraction ability of CNN is normally achieved by dozens or more layers, which brings a series of problems such as gradient vanishing, overfitting, and slow training speed. In order to address these problems, this article presents a CNN architecture-based stochastic depth residual network (SDRN), which is specially designed for HSI data. This model takes the original 3-D cube as the input and 3-D convolution is used to extract abundant spectral and spatial features through corresponding residual blocks. In order to reduce the training time, we adopt a stochastic depth strategy. For each small batch, a sublayer is randomly discarded by an identity function. During the testing stage, the residual network with complete depth is used. Experiments on three datasets and a comparison of the state-of-art methods show that SDRN has great advantages in accuracy and training time compared with state-of-the-art HSI classification methods.

In the simulation of building overheating risks, the use of typical meteorological years (TMY) can greatly reduce the simulation workload and accurately reflect the distribution of simulation results according to the weather conditions over a given period. However, all meteorological parameters in most current TMY methods use a uniform weighting factor which may make the simulation results against the actual simulation results of the period and negatively affect the accuracy of the evaluation results. In addition to differences in climate characteristics between climate zones, the sensitivity of different simulation results to external parameters will also be different. Therefore, a TMY method based on the Finkelstein-Schafer statistical method is proposed, which considers the climatic characteristics of different regions and the correlation with the output parameters of indoor simulation to select the typical month. The proposed method is demonstrated in the three future scenarios for the three cities in different climate zones in China. The results show that the traditional TMY method has an overestimated weight of solar radiation and wind speed and an undervalued weight of dry bulb temperature when indoor temperature-related indicators are the output target. Compared with the traditional TMY method, the TMY generated by the improved method is closer to the distribution characteristics of the long-term outdoor weather data. Furthermore, when using the improved TMY data to evaluate the overheating performance of the passive residential buildings, the difference of the results of the unmet degree hours, indoor overheating degree, and the overheating escalation factor between the long-term projected data and the TMY data can be reduced by 63%–67% compared with the traditional TMY data.

Bi-doped La1.5-xBixSr0.5Ni0.5Mn0.5O4+δ (LBSNM-x, x = 0, 0.05, 0.1, 0.15) was investigated as a potential air electrode for solid oxide electrolysis cell (SOEC). The effect of Bi doping on the structure, electrical conductivity, chemical compatibility with GDC electrolyte, electrochemical performance and thermal expansion coefficients (TECs) were investigated. XRD characterization results show that the solid solution content of Bi is less than or equal to 0.1. XPS characterization results indicate that Bi doping increases the oxygen vacancy content of LBSNM-x air electrode and thus greatly benefits its oxygen evolution reaction. Among the Bi-doped samples, LBSNM-0.1 electrode has the best electrochemical performance with its lowest Rp (polarization resistance) of 0.28 Ω cm2 at 800 °C based on LBSNM-0.1/GDC half-cell. LBSNM-0.1 single cell with 70%CO2 + 30%CO fuel gas feed on the fuel electrode has achieved current density of 811 mA cm−2 at 800 °C and 1.4 V, a 62.2% increase relative to that of LSNM single cell. In addition, LBSNM-0.1 single cell exhibits excellent stability at 800 °C and 1.3 V with 70%CO2 + 30%CO feed gas on the fuel electrode. These results prove that Bi-doped LBSNM-0.1 is an efficient air electrode for SOEC.

Electrolysis of CO2 into CO by using solid oxide electrolysis cells (SOECs) is an advanced and sustainable energy conversion technology in the production of chemical energy by electrical power. However, the development of SOECs is limited because of the lack of cathode with high activity and stability. Herein, La0.75Sr0.25-xCaxCr0.5Mn0.5O3/Gd0.1Ce0.9O2-δ (Cax-LSCM/GDC, x = 0, 0.05, 0.1 and 0.15) composites were obtained by introducing Ca into the A-site of LSCM and developed as desirable cathodes for high-temperature electrolysis of CO2. The electrode polarization resistance (Rp) for CO2 electrochemistry reduction of the half-cell with the Ca0.05-LSCM/GDC cathode shows the minimal values within different temperature and voltage ranges, this cathode has a Rp value of 0.98 Ω cm2 at 800 °C under an applied voltage of 1.0 V, which is decreased by 32% compared with that of the LSCM/GDC cathode under the same test condition. The current density of the full cell with the configuration of Ca0.05-LSCM/GDC|ScSZ|LSM/GDC achieves 31% increase at 1.2 V and 800 °C compared with that of LSCM/GDC cell. Moreover, Ca0.05-LSCM full cell remains fairly stable after 100 h of continuous operation in pure CO2 atmosphere. Results indicate that Ca doped LSCM may be a reliable cathode material for CO2 electrolysis in SOECs.