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PurposeProcess mining tools can help discover and improve the business processes of urban community services from historical service event records. However, for the community service domains with small datasets, the effects of process mining are generally limited due to process incompleteness and data noise. In this paper, a cross-domain knowledge transfer method is proposed to help service process discovery with small datasets by making use of rich knowledge in similar domains with large datasets.Design/methodology/approachFirst, ontology modeling is used to reduce the effects of cross-domain semantic ambiguity on knowledge transfer. Second, association rules (of the activities in the service processes) are extracted with Bayesian network. Third, applicable association rules are retrieved using an applicability assignment function. Further, the retrieved association rules in domains with large datasets are mapped to those with a small dataset using a linear programming method, with a heuristic miner being adopted to generate the process model.FindingsThe proposed method is verified based on the empirical data of 10 service domains from Beidaihe, China. Results show that process discovery performance of all 10 domains were improved with the overall robustness score, precision, recall and F1 score increased by 13%, 13%, 17% and 15%, respectively. For the domains with only small datasets, the cross-domain knowledge transfer method outperforms popular state-of-the art methods.Originality/valueThe limitations of sample sizes are greatly reduced. This scheme can be followed to establish business process management systems of community services with reasonable performance and limited sample sizes.

Additional file 1. PRISMA Network Meta-analysis Checklist.

Additional file 2: Table S1. Strategy of this meta-analysis. Table S2. Inclusion and exclusion criteria in each involved study. Table S3. Head-to-head comparisons of incidence of post-dural puncture headache at 48 hours after surgery. Table S4. Head-to-head comparisons of incidence of post-dural puncture headache at 24 hours after surgery. Table S5. Head-to-head comparisons of severity of post-dural puncture headache at 24 hours after surgery. Table S6. Head-to-head comparisons of severity of post-dural puncture headache at 48 hours after surgery. Table S7. Head-to-head comparisons of severity of post-dural puncture headache at 72 hours after surgery. Table S8. Head-to-head comparisons of incidence of postoperative nausea and vomiting. Table S9. Assessment of publication bias for network meta-analysis. Figure S1. Risk of bias summary. Figure S2. Risk of bias graph. Figure S3. Inconsistency test of cumulative incidence of post-dural puncture headache within 7 days. Figure S4. Inconsistency test of incidence of post-dural puncture headache at 24 hours after surgery. Figure S5. Inconsistency test of incidence of post-dural puncture headache at 48 hours after surgery. Figure S6. Inconsistency test of severity of post-dural puncture headache at 24 hours after surgery. Figure S7. Inconsistency test of severity of post-dural puncture headache at 48 hours after surgery. Figure S8. Inconsistency test of severity of post-dural puncture headache at 72 hours after surgery. Figure S9. Inconsistency test of postoperative nausea and vomiting. Figure S10. Funnel plot of the outcomes.

This study aims to investigate the characteristics and kinetics of cellulose degradation in oil-impregnated pressboard (OIP) under simultaneous thermal-electrical-mechanical stress accelerated aging. Thus, the multi-stress aging experimental setup is designed and established. OIP multi-stress aging experiments are performed at 130 °C, a DC electric voltage of +6 kV, vibration amplitude of ${10}{-}{50}\ \mu\mathrm{m}$ and vibration frequency of 100–300 Hz. The aging characteristics of pressboard and oil are then measured, including the degree of polymerization and aging by-products (i.e., furfural, moisture, acidity and dissolved gas). Investigations on aging characteristics reveal that thermal stress is still the dominant factor of cellulose degradation, while electrical and mechanical stresses serve as aging acceleration factors. The simultaneous thermal-electrical-mechanical aging of OIP is 10-24% faster than the single thermal aging. Moreover, the kinetics of cellulose degradation is also proposed to predict the bond scission fraction of cellulose with a goodness-of-fitting over 0.97. Finally, the correlations between the parameters in the kinetics of cellulose degradation and mechanical stress reveal that the increase of vibration amplitude generates more weak and amorphous links available for degradation, while the increase of vibration frequency accelerates the reaction rate.

Summary The use of electronic cigarettes (ECs) has grown exponentially over the past few years in China. To obtain the basic information of Chinese EC users, a questionnaire survey was performed to collect the data that describe characteristics of study populations, including daily usage, consumption preference, and risk perception. Volunteers were recruited from Beijing, Shanghai, Guangzhou, and Shenzhen cities of China. In total, among the 511 participants that were included in the analysis, 491 participants (95%) used refillable ECs in the past 3 months. There was a strong association between EC use and traditional cigarette smoking. The majority of participants were dual users and the number of cigarettes smoked decreased with the use of ECs. After using an EC, 20.7% of conventional cigarette smokers attempted to quit smoking. Young people were the primary users across all geographic locations. Specifically, the daily usage for the participants who were 18–39 years old, was higher than those over 40 years old. The majority of participants (59.9%) preferred fruit flavors and 57.8% of them used 1–5 mg/mL nicotine-concentration liquids. Most EC users are motived to quit smoking, and 74.6% claimed that the ECs had no irritant effect on the oral cavity. A total of 50.5% of family members of the study participants reported an improvement of health status while the participants used ECs instead of traditional cigarettes. Overall, the results of this Chinese survey indicate that the per-day cigarette consumption of the dual users decreased whereas their proportion of smokers of low-“tar” cigarettes increased.

Summary With the rapid popularity and increasing numbers of E-cigarette (EC) users, debates on possible health risks and regulation of EC products have attracted attention in public. As a useful means to help assessing the risks of EC consumers, puffing topography studies have come to be an important tool globally. In this paper, a cross-sectional epidemiological study on puffing topography of 511 EC consumers in four cities of China was conducted in January 2018. Data acquisition was based on a questionnaire and a Clinical Research Support System (CReSS) Pocket device for the smoking behavior recording. The results showed that EC consumers in the four cities were mainly young males, most of whom preferred tank systems with zero or low nicotine concentrations (1–5 mg/mL) in the liquid. The average puff volume, puff duration and inter-puff interval (IPI) were 87.2 mL, 1.97 s and 22.0 s, respectively. The factors that may have affected puffing topography such as product type, nicotine concentration, using time, smoking status and battery power were studied. The results suggest that the EC product type and the concentration of nicotine in EC liquids were the main factors that affected the puffing topography of the users. This study was the first study on the puff topography of Chinese EC consumers with a large number of participants. The results may not only help to more deeply understand smoking behavior and to better assess the potential risks for the EC users, but they may also supply useful information for the development of smoking machine regimes for ECs.

Coronary microvascular disease (CMVD) is common in patients with cardiovascular risk factors and is associated with an increased risk of adverse cardiovascular events. Although the study of CMVD in modern medicine is ongoing, there is still no effective treatment for it. Traditional Chinese medicine (TCM) has some clinical advantages based on syndrome differentiation and individualized treatment. In this review, we review the clinical significance, pathogenesis, and current treatments of CMVD and systematically summarize the clinical efficacy and potential action mechanisms of TCM for CMVD. In addition, the scientific problems that need to be solved urgently and the research strategy of TCM for CMVD are described. CMVD has great clinical significance, but there are still many gaps in the related research. This review aims to attract the attention of clinicians to CMVD and promote research on CMVD in TCM.

Summary Smoking is mainly sustained by nicotine dependence (ND), which varies across ethnic groups principally due to genetic as well as environmental factors. The Fagerström Test for Nicotine Dependence (FTND) and biomarkers of tobacco exposure are two important approaches to assess ND. However, the relationship between ND and FTND of Chinese smokers has not been studied. The aim of this study was to assess the relationship between FTND scores and nicotine, cotinine, 3′-hydroxycotinine (3HC) and nicotine metabolite ratio (NMR, the concentration ratio of 3HC to cotinine) in Chinese smokers. FTND was carried out and general characteristics were collected using a self-administered smoking questionnaire with 289 smokers. Nicotine, cotinine and 3HC in urine were simultaneously determined by liquid chromatography–mass spectrometry/mass spectrometry (LC-MS/MS). The concentrations of nicotine, cotinine and 3HC in the urine of smokers with a high FTND score were higher than in the urine of those with a low FTND score. There were significant correlations between urinary biomarker and FTND scores. Except for FTND item 2 (difficulty to refrain), the other items showed significant associations with the urinary biomarkers. No relationship was found between the nicotine metabolite ratio (NMR, 3′-hydroxycotinine/cotinine) and FTND scores or general characteristics of the participants. In conclusion, biomarkers of tobacco exposure levels are significantly associated with FTND scores. However, FTND Item 2 and NMR were not found to be associated with nicotine dependence in Chinese smokers.

Coronavirus disease 2019 (COVID-19) is an acute respiratory infectious disease caused by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), and it has become a public health concern worldwide. In addition to respiratory symptoms, some COVID‑19 patients also show various gastrointestinal symptoms and even consider gastrointestinal symptoms to be the first manifestation. A large amount of evidence has shown that SARS-CoV-2 infection could disrupt the gut microbiota balance, and disorders of the gut microbiota could aggravate the condition of COVID-19 patients. Therefore, maintaining the gut microbiota balance is expected to become a potential new therapeutic target for treating COVID-19. Traditional Chinese medicine (TCM) has significant effects in all stages of the prevention and treatment of COVID-19. It can adjust the gut microbiota and is an ideal intestinal microecological regulator. This review summarizes the advantages and clinical efficacy of TCM in the treatment of COVID-19 and expounds on the relationship between TCM and the gut microbiota, the relationship between COVID-19 and the gut microbiota, the mechanism of gut microbiota disorders induced by SARS-CoV-2, the relationship between cytokine storms and the gut microbiota, and the role and mechanism of TCM in preventing and treating COVID-19 by regulating the gut microbiota to provide new research ideas for TCM in the prevention and treatment of COVID-19.

This study aims to examine and explain the differences at city level in cumulative COVID-19 cases and time from first to last infection during the first 6 weeks of the epidemic in China.A quantitative study is conducted in China based on the multisource spatial data of 315 Chinese cities. Firstly, the spatial discrepancy of COVID-19 transmission was examined based on spatial autocorrelation analysis and hot pot analysis. Next, a comprehensive indicator framework was established by including a wide range of factors such as human mobility, geographical features, public health measures, and residents' awareness. Finally, multivariate regression models using these variables were constructed to identify the determinants of COVID-19 transmission.Significant spatial discrepancy of transmission was proved, and 10 determinants were identified.The transmission consequence (measured by the number of cumulative cases) was mostly correlated with the migration scale from Wuhan, followed by socioeconomic factors. Transmission duration (measured as the time from the first to last case within the city) was mostly determined by total migration scale and lockdown speed, which suggests that timely implementation of public health measures facilitated fast control of transmission. Residents' attention to COVID-19 was proved to be not only helpful for reducing confirmed cases, but also in favor of rapid transmission control. Altitude produced slight but significant effect on transmission duration. These conclusions are expected to provide decision support for the local governments of China and other jurisdictions.

Additional file 3: Supplementary Figure 3. The regulation of differential expressed mRNAs, miRNAs, circRNAs and lncRNAs.

Additional file 2: Table S2. Akaike information criterion for the prognostic signature.

Additional file 6: Supplementary Figure 6. The differential expressed mRNAs, top GO annotation and KEGG analysis. a. The volcanic map of differential expressed mRNAs. b. The top GO annotation of differential expressed mRNAs. c. The KEGG analysis of differential expressed mRNAs.

Additional file 1: Supplementary Figure 1. The transcriptome assembly quality control. a, The base quality of sequence. b, The pie chart for Rfam sequence category. c, the Biological Replicate quality control. d, The length distribution of counts of total sRNAs in this study.

Additional file 1: TableS1. PRISMA Network Meta-analysis Checklist. TableS2. Strategy of this meta-analysis. TableS3. Head-to-head comparisons of cesarean delivery rate. Table S4. Head-to-head comparisons oftime from intervention-to-birth. TableS5. Head-to-head comparisons of achieving vaginal delivery within 24 hours.Table S6. Head-to-head comparisonsof Bishop score increment. Table S7. Head-to-head comparisons of uterine hyperstimulation with fetal heart ratechanges. Table S8. Head-to-headcomparisons of oxytocin augmentation. TableS9. Head-to-head comparisons of instrumental delivery. Table S10. Head-to-head comparisons of meconium-stained amnioticfluid. Table S11. Head-to-headcomparisons of chorioamnionitis. TableS12. Head-to-head comparisons of postpartum hemorrhage. Table S13. Head-to-head comparisons ofApgar score

Additional file 4: Supplementary Figure 4. The construction of regulatory networks contains lncRNA, circRNA, miRNA and mRNA in peanut response to root -knot nematode.

With the global trend to establish smart distributed-generation grids, the level of AC-voltage harmonics is significantly increasing due to power electronic converters interfacing renewable energy sources with the grid. Such harmonics will not only degrade the power quality and increase the losses in the electricity grids but will also have a severe impact on oil impregnated paper (OIP) in power transformer bushings. While much attention has been given to investigate the deterioration of OIP under thermal and electrical stresses, the impact of voltage harmonics on the OIP structure is still uncertain and calls for more investigations. Therefore, this paper is aimed at investigating the variations of the multilevel structure of an OIP aged under high levels of AC-voltage harmonics. In this context, degree of polymerization of the aged OIP is measured to reveal the changes of OIP secondary structure. Also, X-ray diffraction, deuteration treatment and Fourier transform infra-red spectroscopy are conducted to analyze the changes of the OIP aggregate structure during the aging process. Further experiments are performed to investigate the variation of the microscopic appearance, thermal behavior and dielectric properties, including scanning electronic microscopy, thermal gravity analysis, differential scanning calorimetry and frequency domain spectroscopy. This investigation on the variation of the multilevel structure of aged OIP not only promotes further understanding to the aging behavior and mechanism of OIP under AC-voltage harmonics, but also proposes crystallinity and accessibility as two new effective aging indicators for the OIP structure.

Coronavirus disease 2019 (COVID-19) is an emergent infectious pneumonia caused by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), which is highly contagious and pathogenic. COVID-19 has rapidly swept across the world since it was first discovered in December 2019 and has drawn significant attention worldwide. During the early stages of the outbreak in China, traditional Chinese medicines (TCMs) were involved in the whole treatment process. As an indispensable part of TCM, Chinese patent medicines (CPMs) played an irreplaceable role in the prevention and treatment of this epidemic. Their use has achieved remarkable therapeutic efficacy during the period of medical observation and clinical treatment of mild, moderate, severe, and critical cases and during convalescence. In order to better propagate and make full use of the benefits of TCM in the treatment of COVID-19, this review will summarize the potential target of SARS-CoV-2 as well as the theoretical basis and clinical efficacy of recommended 22 CPMs by the National Health Commission and the Administration of TCM and local provinces or cities in the treatment of COVID-19. Additionally, the study will further analyze the drug composition, potential active ingredients, potential targets, regulated signaling pathways, and possible mechanisms for COVID-19 through anti-inflammatory and immunoregulation, antiviral, improve lung injury, antipyretic and organ protection to provide meaningful information about the clinical application of CPMs.

In community emergency management, it is crucial to generate evacuation task plans (ETPs) to help reduce risks in complex disaster situations. Case-driven and model-driven approaches have their respective advantages in generating ETPs, which can complement each other. With case-driven approach, historical experience can be fully used to establish the relationship between typhoon scenarios and historical ETPs. Through model-driven approach, the continuity of ETPs can be guaranteed when required information to operate the plans is missing. This study aims at proposing an integrated approach that can combine the benefits of both case-driven approaches and model-driven approaches. Based on the structural modeling of evacuation tasks, this paper proposes an integrated approach to generate ETPs for community typhoon emergencies. Finally, a case that is based on actual problems is provided to verify the reasonability and effectiveness of the proposed method.

Transformer insulation paper is a key indicator for transformer remaining operational life. Paper decomposition is evaluated using the degree of polymerization (DP) which calls for samples of insulation paper from operating transformers. Since collecting such paper samples is extremely difficult, other indicators have been used to indirectly reveal the DP value of insulation paper. This includes dissolved gases in transformer oil such as carbon oxides and hydrocarbon gases, furan compounds, methanol, ethanol, and moisture along with some oil characteristics such as interfacial tension. However, for the same oil sample, these individual parameters lead to different DP values. This is attributed to the lack of accuracy of the established mathematical and artificial intelligence models correlating DP with each of the above mentioned individual parameters. This paper presents a self-learning method to estimate the DP value of transformer insulation paper based on multiple transformer oil aging parameters. The proposed method comprises data processing, fuzzy c-means and linear regression. Results reveal that estimating the DP value based on multiple aging parameters is more accurate than estimating it using one single parameter as per the current practice. The proposed method not only helps to understand the correlation between multiple oil aging parameters and the DP value of paper insulation, but also promotes the establishment of more accurate life assessment models for power transformers based on these oil aging parameters.

Distributed optical fiber vibration sensing (DVS) can measure vibration information along with an optical fiber. Accurate classification of vibration events is a key issue in practical applications of DVS. In this paper, we propose a convolutional neural network (CNN) to analyze DVS data and achieve high-accuracy event recognition fully. We conducted experiments outdoors and collected more than 10,000 sets of vibration data. Through training, the CNN acquired the features of the raw DVS data and achieved the accurate classification of multiple vibration events. The recognition accuracy reached 99.9% based on the time–space data, a higher than used time-domain, frequency–domain, and time–frequency domain data. Moreover, considering that the performance of the DVS and the testing environment would change over time, we experimented again after one week to verify the method’s generalization performance. The classification accuracy using the previously trained CNN is 99.2%, which is of great value in practical applications.

The discrepancy between expected and actual food product features can be an important indicator of food quality and thus may inform consumer decisions. Using mixed method design, this paper explores whether and how expectation discrepancy affects food consumers' quality perception and purchase intentions under food safety risk situation. In the first phase, a qualitative study based on in-depth semi structured interviews (N = 15) was conducted. The interview results revealed that consumers in general responded negatively towards the existence of expectation discrepancies as it led to poor perceived food quality. It was further revealed that the discrepancy in expectation dimensions had a stronger effect on consumers’ perceptions as compared to the discrepancy in expectation extent. In the second phase extensive online survey (N = 2172), it was confirmed that expectation discrepancy had significantly affected quality perceptions and purchase intentions. Moreover, perceived quality played a mediating role between expectation discrepancy and purchase intentions. In addition, expectation proportion (measured with the importance of specific food feature) was shown to strengthen (moderate) the impact of expectation discrepancy. Furthermore, the effects of expectation discrepancy were stronger for high-risk food (versus low risk food). The current paper shows that the effects of expectation discrepancy play a highly significant role in forming perceptions about food safety, therefore, precise marketing strategies can be made by clustering consumers according to the expectation proportions.

A convolutional neural network is proposed to process Brillouin gain spectrum, enabling extract a spatial-resolution higher than the used pump pulse width determined theoretical spatial-resolution. A 0.5-m hotspot is accurately retrieved using 40-ns pump pulse.

In the field of Internet of Things, there is an urgent need for sensors with large-scale sensing capability for scenarios such as intelligent monitoring of production lines and urban infrastructure. Brillouin optical time domain analysis (BOTDA) sensors, which can monitor thousands of continuous points simultaneously, show great advantages in these applications. We propose a convolutional neural network (CNN) to process the data of conventional Brillouin optical time domain analysis (BOTDA) sensors, which achieves unprecedented performance improvement that allows to directly retrieve higher spatial resolution (SR) from the sensing system that use long pump pulses. By using the simulated Brillouin gain spectrums (BGSs) as the CNN input and the corresponding high SR BFS as the output target, the trained CNN is able to obtain a SR higher than the theoretical value determined by the pump pulse width. In the experiment, the CNN accurately retrieves 0.5-m hotspots from the measured BGS with pump pulses from 20 to 50 ns, and the acquired BFS is in great agreement with 45/40 ns differential pulse-width pair (DPP) measurement results. Compared with the DPP technique, the proposed CNN demonstrates a 2-fold improvement in BFS uncertainty with only half the measurement time. In addition, by changing the training datasets, the proposed CNN can obtain tunable high SR retrieval based on conventional BOTDA sensors that use long pulses without any requirement of hardware modifications. The proposed data post-processing approach paves the way to enable novel high spatial resolution BOTDA sensors, which brings substantial improvement over the state-of-the-art techniques in terms of system complexity, measurement time and reliability, etc., Comment: 7 pages, 6 figures

The DC transformer(DCT), which has been regarded as the core power electronics equipment, has gained increasing importance in the modern DC power distribution system. This paper presents a systematic design and stability analysis of the DCT back-to-back system, which consist of two DCT working as voltage source and current source respectively. The DCT average model including the current limiting inductor is proposed in this paper. In addition, the stability of the back-to-back system is analyzed according to the Nyquist criterion. At last, the effectiveness of the proposed system model is validated by the simulation and experiment results.

Rainstorms easily can cause serious secondary disasters such as waterlogging, debris flow, and flood, and the combination of these secondary disasters makes cities more vulnerable. However,...

At present, the oil-paper insulation structure is mainly composed of traditional mineral oil and cellulose insulation paper. The hots pot of future research is to select better insulation oil to improve the overall insulation life of the system. Compared with traditional mineral insulation oil, gas to liquid(GTL) oil made from base oil refined by natural gas liquefaction technology by Fischer-Tropsch(FT) method are characterized by high oxidative stability and low sulfur content. In this paper, the space charge accumulation, distribution and attenuation characteristics of oil-impregnated paper composed of GTL oil and traditional mineral oil were studied. The space trap energy level and trap density distribution of two oil-impregnated papers were obtained according to isothermal decay current(IDC) method. The experimental results show that in the voltage-on stage, traditional mineral oil-impregnated paper is more prone to charge injection and the injection speed is faster. In the voltage-off stage, the traditional mineral oil-impregnated paper attenuates the charge faster, and the GTL oil-impregnated paper has more residual charge. Moreover, the trap level of GTL oil-impregnated paper is deeper than that of traditional mineral oil-impregnated paper.

We propose a new class of semiparametric regression models of mean residual life for censored outcome data. The models, which enable us to estimate the expected remaining survival time and generalize commonly used mean residual life models, also conduct covariate dimension reduction. Using the geometric approaches in semiparametrics literature and the martingale properties with survival data, we propose a flexible inference procedure that relaxes the parametric assumptions on the dependence of mean residual life on covariates and how long a patient has lived. We show that the estimators for the covariate effects are root-$n$ consistent, asymptotically normal, and semiparametrically efficient. With the unspecified mean residual life function, we provide a nonparametric estimator for predicting the residual life of a given subject, and establish the root-$n$ consistency and asymptotic normality for this estimator. Numerical experiments are conducted to illustrate the feasibility of the proposed estimators. We apply the method to analyze a national kidney transplantation dataset to further demonstrate the utility of the work., Comment: 73 pages, 9 figures

A 3300V/62.5A low loss FS-IGBT was developed by using the carrier storage layer structure and laser annealing process on the back p+ collector, which could lower the saturation voltage. The effects of laser annealing energy and P+ collector dose on dynamic and static state were studied. Considering the trade-off of the turn-off loss and the saturation voltage, the IGBT was finally designed with the 2.35V saturation voltage, 132mJ turn-off loss.

Eggs continue to be significant in terms of the world economy and human nutrition. The aim of this study was to estimate the prevalence of foodborne pathogens in a cross section of egg production types in China and to examine the virulence features of the isolated pathogens. Three hundred eggs from three provinces were tested for Salmonella , Escherichia coli , Staphylococcus aureus , and Campylobacter . Two eggs were positive for Salmonella , one was positive for both E. coli and S. aureus , and none were positive for Campylobacter . When comparing different egg laying systems, there were no significant differences in findings between packaged and unpackaged eggs and between battery cage and cage-free eggs, but there were significant differences in the findings among pathogens and among provinces. Other potential foodborne pathogens were identified in addition to the targeted pathogens. The virulence of Serratia marcescens was higher than that of the isolated Salmonella Typhimurium, and the virulence of Staphylococcus saprophyticus was comparable to that of the isolated S. aureus . These findings are important for local risk assessments concerning possible human foodborne infections via cross-contamination of eggs.

It was reported that the main obstacle of Li2ZrO3 as high-temperature CO2 absorbents is the very slow CO2 sorption kinetics, which are ascribed to the gradual formation of compact zirconia and carbonate shells along with inner unreacted lithium zirconate cores; accordingly, the “sticky” Li+ and O2– ions have to travel a long distance through the solid shells by diffusion. We report here that three-dimensional interconnected nanoporous Li2ZrO3 exhibiting ultrafast kinetics is promising for CO2 sorption. Specifically, nanoporous Li2ZrO3 (LZ-NP) exhibited a rapid sorption rate of 10.28 wt %/min with an uptake of 27 wt % of CO2. Typically, the k1 values of LZ-NP (kinetic parameters extracted from sorption kinetics) were nearly 1 order of magnitude higher than the previously reported conventional Li2ZrO3 reaction systems. Its sorption capacity of 25 wt % within ∼4 min is 2 orders of magnitude faster than those obtained using spherical Li2ZrO3 powders. Furthermore, nanoporous Li2ZrO3 exhibited good stability ov...

Transformer life management has gained remarkable recognition due to its crucial role for safe operation of the power grid. Since transformer life is dominated by insulating state of its oil-paper insulation, the DP value has been traditionally regarded as the primary indicator. However, the DP sampling procedure is destructible and inconvenient. In this paper, we present an effective principal component regression method to assess the insulating state of oil-paper through indirectly estimating the degree of polymerization based on the aging characteristic parameter in oil. Thermal aging experiments were firstly conducted on palm oil and mineral oil impregnated paper, respectively. After aging experiments, aging characteristic parameters of oil were tested, including moisture, acidity, 2-furan, surface tension, dissolved gas-in-oil analysis, methanol, and ethanol. Principal component regression method was then performed to find the principal component and build a relationship between the degree of polymerization and the aging characteristic parameters of oil. After computation, the estimation of degree of polymerization value can be obtained with a high goodness of fitting, which is 0.92 and 0.78 for mineral oil and palm oil impregnated paper respectively. The estimation could be improved by data processing.

As a potential alternative to mineral oil used in transformer, palm oil is an eco-friendly choice while its aging behavior and internal aging mechanism are still unclear. Since the failure in transformer oil-paper insulation is mainly caused by heat, with the purpose of better understanding the similarities and differences of palm oil compared with mineral oil during transformer operation, oil-papers respectively made from these two oils were thermally aged for different duration and then experimentally analyzed. Through the analysis of experimental results, the following conclusions were drawn. Quite different aging mechanisms were revealed for each oil-paper insulation material. That means mineral oil had been degraded by scission of main chains followed with oxidation, whereas palm oil had been degraded by scission of ester bonds followed with loss of terminal carbonyl. The different degradation mechanisms further lead to different changes in dielectric properties during thermal aging. The electron transition energy of mineral oil is more pronounced with the increase of aging degree. Besides, palm oil has a shorter wetting time in paper, and the local damage can be repaired more easily after partial discharge. On the contrary, longer wetting time of mineral oil in paper relates to easily damaged for mineral oil-paper insulation in partial discharge. This work clearly shows that palm oil should be a good substitute for traditional mineral oil not only is it eco-friendly, but also possesses even better performance to some certain extent.

The precise classification of community service cases is the most fundamental aspect of intelligent community service systems. However, data imbalance makes it challenging to achieve the desired level of precise classification. Existing transfer learning methods use open Internet knowledge for identifying case features and mining potential feature relations. However, community service cases have the characteristics such as short text length and non-public content, which restrict the transfer learning modes. In this paper, a cross-region transfer learning method is proposed to solve the classification problem of cases with small datasets in data imbalance situation while considering the perspective of regional cooperation. First, an ontology modeling method is applied to standardize the case features, reducing the effect of semantic ambiguity on transfer results. Secondly, to improve the effectiveness of source domain classification, this paper utilizes an extended marginal fisher analysis where the distance is measured by the inner product between data. Next, the mapping matrix from target domain to source domain is learned through domain adaptation. Finally, the method is verified based on the empirical data from Lanzhou and Beidaihe in China. Experimental results on classifiers show the proposed method helps regions to improve case classification rates significantly through knowledge complementation. The proposed approach can be followed to build case-based community service systems of reasonable accuracy and limited sample sizes.

In community safety service, big data governance is the prime mode to achieve community safety big data sharing and service value increasing. Although existing researches have preliminarily established the general big data governance framework, identification and governance of specific sharing problems lack comprehensive and systematic scenario description. Applying software engineering method, this paper proposes a kind of scenario expression model of big data governance in community safety service. Considering the common features of big data governance scenarios, construct the meta-scenario model of big data governance in community safety services. Considering the scenario expression difference under different levels, scales and particle sizes, construct the full view scenario model of big data governance in community safety services by meta-models nesting to complete the scenario expression under different applying situation. Finally, a use case is proposed to verify the rationality and effectiveness of the scenario expression models.

The interactions between solute atoms and crystalline defects such as vacancies, dislocations, and grain boundaries play an essential role in determining physical, chemical and mechanical properties of solid-solution alloys. Here we present a universal correlation between two electronic factors and the solute-defect interaction energies in binary alloys of body-centered-cubic (bcc) refractory metals (such as W and Ta) with transition-metal substitutional solutes. One electronic factor is the bimodality of the d-orbital local density of states for a matrix atom at the substitutional site, and the other is related to the hybridization strength between the valance sp- and d-bands for the same matrix atom. Remarkably, the correlation is independent of the types of defects and the locations of substitutional sites, following a linear relation for a particular pair of solute-matrix elements. Our findings provide a novel and quantitative guidance to engineer the solute-defect interactions in alloys based on electronic structures.

Based on scenario analysis, this paper makes a definition of natural disaster scenario type and concept from different aspects, describes the mechanization of the development of natural disaster chain scenario and applies Dynamic Bayesian Network. It uses probability assessment to analyze the transformation rules in the development of natural disaster chain scenario, to make up the shortage of the traditional method to capture the dynamic changes of the natural disaster chain, and to provide support for the prediction, decision and response of the natural disasters.

Avian pathogenic E. coli (APEC) is the etiologic agent of avian colibacillosis, the most common disease responsible for chicken morbidity in the world. Although multiple virulence-associated factors were identified, their prevalence in Algeria is still poorly known. In the present research, 92 avian pathogenic E. coli (APEC) isolates were recovered from broilers with clinical signs and lesions of colibacillosis. In addition, 32 E. coli isolates collected from feces of healthy birds (AFEC) were included for comparison. All isolates were investigated by PCR for the presence of a total of 11 virulence-associated genes described for avian pathogenic (iroN, ompT, hlyF, iss, iutA, and fimC) and diarrheagenic E. coli (eae, stx, elt/est, ipaH, and aggR). The sensitivity of 39 APEC isolates to 16 antibiotics was also determined using antimicrobial pretreated microplates. Here, we report that 98% of the examined isolates host at least one of the tested virulence factors. The most prevalent genes in APEC were iutA (90.6%), ompT (86.9%), and iss (85.8%); whereas, iutA (78.1%), fimC (78.1%), and iroN (68.7%) were the highest prevalent genes in AFEC. Our data showed that none of the AFEC isolates harbor any of the tested diarrheagenic genes. Moreover, only elt/est (5.4%), stx (2.1%), and ipaH (2.1%) genes were carried by APEC isolates. We further established that ceftazodime, ceftiofur, mequindox, amoxicillin/clavulanic acid, and meropenem were the most efficient antibiotics against the analyzed APEC isolates. Overall, our findings provide more insights about APEC and AFEC virulence potential in Algeria which could participate in the fight against colibacillosis.