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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.

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.

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.

Ensemble learning models have been widely used for wind power forecasting to facilitate efficient dispatching of power systems. However, traditional ensemble methods cannot always function well due to insufficient accuracy and diversity of base learners, ignorance of ensemble pruning, as well as the lack of adaptation capability. Therefore, a novel probabilistic wind power forecasting method is proposed based on selective ensemble of finite mixture Gaussian process regression models (SEFMGPR). First, a set of diverse local Gaussian process regression (GPR) models are constructed through multimodal perturbation mechanism, i.e., perturbing the training data and input attributes simultaneously. Then, a set of finite mixture GPR models (FMGPR) is built by integrating local GPR models through finite mixture mechanism (FMM). Next, the highly influential FMGPR models are selected using genetic algorithm (GA) based ensemble pruning. When a new test sample comes, the component predictions from the selected FMGPR models are adaptively combined by using FMM again and the probabilistic prediction results of the SEFMGPR model are obtained. Besides, an incremental adaptation mechanism is used to alleviate performance degradation of SEFMGPR. The application results from a real wind farm dataset show that SEFMGPR outperforms the traditional global and ensemble wind power prediction methods, and can maintain high prediction accuracy by effectively handling time-varying changes of wind power data.

Fudong Rao,1,&ast; Zongjie Wang,2,&ast; Xijuan Chen,3 Linwei Liu,4 Bin Qian,1 Yanhua Guo4 1Department of Anesthesiology, People’s Hospital Affiliated to Fujian University of Traditional Chinese Medicine, Fuzhou, Fujian, People’s Republic of China; 2Department of Anesthesiology, Longyan First Hospital Affiliated to Fujian Medical University, Longyan, Fujian, People’s Republic of China; 3Department of Nephrology, Hematology and Pediatrics, People’s Hospital Affiliated to Fujian University of Traditional Chinese Medicine, Fuzhou, Fujian, People’s Republic of China; 4Department of Anesthesiology, Shengli Clinical Medical College of Fujian Medical University, Fuzhou, Fujian, People’s Republic of China&ast;These authors contributed equally to this workCorrespondence: Bin QianDepartment of Anesthesiology, People’s Hospital Affiliated to Fujian University of Traditional Chinese Medicine, No. 602, Middle of Bayiqi Road, Fuzhou, 350004, People’s Republic of ChinaTel +86-158800 5016Fax +86-591-86250061Email 1547945960@qq.comYanhua GuoDepartment of Anesthesiology, Shengli Clinical Medical College of Fujian Medical University, No. 134, Dongjie, Fuzhou, 350001, People’s Republic of ChinaTel +86-8050333902Fax +86-591-88217841Email guoyanhua19@126.comPurpose: Ultrasound-guided thoracic paravertebral block (TPVB) has become increasingly popular for postoperative analgesia after breast surgery. We designed this prospective, randomized, double-blind, placebo-controlled trial to test the hypothesis that TPVB is superior to placebo in improving the patient quality of recovery following modified radical mastectomy.Patients and Methods: Sixty-eight female patients undergoing elective unilateral modified radical mastectomy were enrolled. Patients were randomized to receive preoperative ultrasound-guided TPVB with 0.5% ropivacaine (TPVB group, n=34) or 0.9% saline (Control group, n=34). The primary outcome was quality of recovery, measured 24 h after surgery using the 40-item Quality of recovery questionnaire (QoR-40). Secondary outcomes were the area under the curve of the visual analog scale pain scores over 24 h, postoperative 24-h morphine consumption, time to first rescue analgesia, length of post-anesthesia care unit stay, postoperative nausea and vomiting, and patient satisfaction.Results: The global QoR-40 score 24 h postoperatively (median [interquartile range]) was 173 [170– 177] in the TPVB group and 161 [160– 164] in the control group (P< 0.001), respectively, with a median difference (95% confidence interval) of 11 (9– 14). Compared with the control group, preoperative TPVB decreased the area under the curve of the visual analog scale pain scores over 24 h, reduced postoperative 24-h morphine consumption, prolonged the time to first rescue analgesia, shortened the length of post-anesthesia care unit stay, lessened postoperative nausea and vomiting, and improved the patient satisfaction.Conclusion: A single preoperative injection of TPVB with ropivacaine enhances the quality of recovery and postoperative analgesia in patients following modified radical mastectomy.Keywords: ultrasonography, thoracic paravertebral block, quality of recovery, regional anesthesia, pain management, breast cancer surgery

In eukaryotes, Maf1 is an essential and specific negative regulator of RNA polymerase (Pol) III. Pol III, which synthesizes transfer RNAs (tRNAs), is suppressed by Maf1 under conditions of nutrient starvation or environmental stress. Here, we identified M. oryzae MoMaf1, a homolog of ScMaf1 in budding yeast. A heterogeneous complementation assay revealed that MoMaf1 fully restored growth defects in the ΔScmaf1 mutant under SDS stress. Disrupting MoMAF1 elevated the tRNA content and increased sensitivity to cell wall agents. Moreover, the ΔMomaf1 mutant exhibited reduced vegetative growth, conidiogenesis, and pathogenicity. Interestingly, we found that MoMaf1 undergoes nuclear–cytoplasmic shuffling, through which MoMaf1 accumulates in nuclei under nutrient deficiency or upon the interaction of M. oryzae with rice. Therefore, this study helps to elucidate the pathogenic molecular mechanism of M. oryzae.

As our dependence on intelligent machines continues to grow, so does the demand for more transparent and interpretable models. In addition, the ability to explain the model generally is now the gold standard for building trust and deployment of artificial intelligence systems in critical domains. Explainable artificial intelligence (XAI) aims to provide a suite of machine learning techniques that enable human users to understand, appropriately trust, and produce more explainable models. Selecting an appropriate approach for building an XAI-enabled application requires a clear understanding of the core ideas within XAI and the associated programming frameworks. We survey state-of-the-art programming techniques for XAI and present the different phases of XAI in a typical machine learning development process. We classify the various XAI approaches and, using this taxonomy, discuss the key differences among the existing XAI techniques. Furthermore, concrete examples are used to describe these techniques that are mapped to programming frameworks and software toolkits. It is the intention that this survey will help stakeholders in selecting the appropriate approaches, programming frameworks, and software toolkits by comparing them through the lens of the presented taxonomy.

Some individuals with acute pancreatitis (AP) suffer from pancreatic necrosis. Diabetes affects the severity of AP, but whether diabetes influences pancreatic necrosis is unclear. This study aims to investigate the clinical characteristics of AP patients with and without diabetes as well as analyze the risk factors of pancreatic necrosis. A total of 625 AP patients participated in the study. Clinical and laboratory data were retrieved. Multivariate logistic regression analysis was used to identify the risk factors for pancreatic necrosis. ROC curves assess the accuracy of indicators for predicting pancreatic necrosis in AP. AP patients with diabetes had high BMI, CTSI scores, pancreatitis severity, WBC, neutrophil, CRP, triacylglycerols and glucose levels. Diabetes, serum calcium and D-dimer were independent risk factors for pancreatic necrosis. Pancreatic necrosis in diabetes patients is also associated with sex and age. D-dimer is a better predictor of pancreatic necrosis in AP patients than serum calcium. Diabetic patients are more likely to suffer severe AP. Serum calcium and D-dimer are independent predictors for pancreatic necrosis. Furthermore, low serum calcium, high D-dimer levels, younger age and female sex are independent risk factors for pancreatic necrosis in AP patients with diabetes.