Metal adsorption by quasi cellulose xanthogenates derived from aquatic and terrestrial plant materials

The FTIR spectra, SEM-EDXA and copper adsorption capacities of the raw plant materials, alkali treated straws and cellulose xanthogenate derivatives of Eichhornia crassipes shoot, rape straw and corn stalk were investigated. FTIR spectra indicated that of the three plant materials, the aquatic biomass of Eichhornia crassipes shoot contained more O-H and C=O groups which accounted for the higher Cu²⁺ adsorption capacities of the raw and alkali treated plant material. SEM-EDXA indicated the incorporation of sulphur and magnesium in the cellulose xanthogenate. The Cu²⁺ adsorption capacities of the xanthogenates increased with their magnesium and sulphur contents. However more copper was adsorbed than that can be explained by exchange of copper with magnesium. Precipitation may contribute to the enhanced uptake of copper by the cellulose xanthogenate

Blockchain-Based Applications for Smart Grids: An Umbrella Review

This article presents an umbrella review of blockchain-based smart grid applications. By umbrella review, we mean that our review is based on systematic reviews of this topic. We aim to synthesize the findings from these systematic reviews and gain deeper insights into this discipline. After studying the systematic reviews, we find it imperative to provide a concise and authoritative description of blockchain technology because many technical inaccuracies permeate many of these papers. This umbrella review is guided by five research questions. The first research question concerns the types of blockchain-based smart grid applications. Existing systematic reviews rarely used a systematic method to classify these applications. To address this issue, we propose a taxonomy of these applications, first by differentiating them based on whether the application is focusing on functional or non-functional aspects of smart grid operations, and then by the specific functions or perspectives that the application aims to implement or enhance. The second research question concerns the roles that blockchain technology plays in smart grid applications. We synthesize the findings by identifying the most prominent benefits that blockchain technology could bring to these applications. We also take the opportunity to point out several common technical mistakes that pervade the blockchain literature, such as equating all forms of blockchains to data immutability. The third research question concerns the guidelines for deciding whether a blockchain-based solution would be useful to address the needs of smart grids. We synthesize the findings by proposing benefit- based guidelines. The fourth research question concerns the maturity levels of blockchain-based smart grid applications. We differentiate between academic-led and industry-led projects. We propose a five-level scale to evaluate the maturity levels. The ranking of the industry-led projects is performed through our own investigation. Our investigation shows that more than half of the industry-led projects mentioned in the systematic reviews are no longer active. Furthermore, although there are numerous news reports and a large number of academic papers published on blockchain-based smart grid applications, very few have been successfully embraced by the industry. The fifth research question concerns the open research issues in the development of blockchain-based smart grid applications. We synthesize the findings and provide our own analysis

A novel bearing multi-fault diagnosis approach based on weighted permutation entropy and an improved SVM ensemble classifier

Timely and accurate state detection and fault diagnosis of rolling element bearings are very critical to ensuring the reliability of rotating machinery. This paper proposes a novel method of rolling bearing fault diagnosis based on a combination of ensemble empirical mode decomposition (EEMD), weighted permutation entropy (WPE) and an improved support vector machine (SVM) ensemble classifier. A hybrid voting (HV) strategy that combines SVM-based classifiers and cloud similarity measurement (CSM) was employed to improve the classification accuracy. First, the WPE value of the bearing vibration signal was calculated to detect the fault. Secondly, if a bearing fault occurred, the vibration signal was decomposed into a set of intrinsic mode functions (IMFs) by EEMD. The WPE values of the first several IMFs were calculated to form the fault feature vectors. Then, the SVM ensemble classifier was composed of binary SVM and the HV strategy to identify the bearing multi-fault types. Finally, the proposed model was fully evaluated by experiments and comparative studies. The results demonstrate that the proposed method can effectively detect bearing faults and maintain a high accuracy rate of fault recognition when a small number of training samples are available

A Bayesian Failure Prediction Network Based on Text Sequence Mining and Clustering

The purpose of this paper is to predict failures based on textual sequence data. The current failure prediction is mainly based on structured data. However, there are many unstructured data in aircraft maintenance. The failure mentioned here refers to failure types, such as transmitter failure and signal failure, which are classified by the clustering algorithm based on the failure text. For the failure text, this paper uses the natural language processing technology. Firstly, segmentation and the removal of stop words for Chinese failure text data is performed. The study applies the word2vec moving distance model to obtain the failure occurrence sequence for failure texts collected in a fixed period of time. According to the distance, a clustering algorithm is used to obtain a typical number of fault types. Secondly, the failure occurrence sequence is mined using sequence mining algorithms, such as-PrefixSpan. Finally, the above failure sequence is used to train the Bayesian failure network model. The final experimental results show that the Bayesian failure network has higher accuracy for failure prediction

Impaired lipid metabolism in idiopathic pulmonary alveolar proteinosis

<p>Abstract</p> <p>Background</p> <p>It is well known that lipids abnormally accumulate in the alveoli during idiopathic pulmonary alveolar proteinosis (PAP). It is unclear, however, whether lipids also abnormally accumulate in serum. This study investigated the serum lipid panels in idiopathic PAP patients and explored the relationships between serum levels and the severity of idiopathic PAP.</p> <p>Methods and Results</p> <p>Clinical data including the level of serum lipids were evaluated in 33 non-diabetic idiopathic PAP patients and 157 healthy volunteers. Serum levels of triglyceride were higher in PAP patients than in healthy subjects (median: 192.00 mg/dl (<it>P</it>₂₅: 104.36, <it>P</it>₇₅: 219.00) <it>vs </it>119.56 mg/dl (<it>P</it>₂₅: 78.81, <it>P</it>₇₅: 193.03), <it>P </it>< 0.05), while high-density lipoprotein cholesterol (HDL-C) levels were lower in patients than in the control group (42.50 ± 10.30 <it>vs </it>51.34 ± 12.06 mg/dl, <it>P </it>< 0.01). Forced expiratory volume in one second and forced vital capacity in hypertriglyceridemia patients were lower than those in patients with normal triglyceride. Serum LDL-C and HDL-C ratio correlated negatively with PaO₂(r = -0.403, <it>P </it>< 0.05) and positively with lactate dehydrogenase (r = 0.381, <it>P </it>< 0.05).</p> <p>Conclusions</p> <p>PAP associates with high triglyceride and low HDL levels in the serum, and these lipids provide potential intervention strategy for treatment.</p

Diversity of NC10 bacteria associated with sediments of submerged Potamogeton crispus (Alismatales: Potmogetonaceae)

Background The nitrite-dependent anaerobic methane oxidation (N-DAMO) pathway, which plays an important role in carbon and nitrogen cycling in aquatic ecosystems, is mediated by “Candidatus Methylomirabilis oxyfera” (M. oxyfera) of the NC10 phylum. M. oxyfera-like bacteria are widespread in nature, however, the presence, spatial heterogeneity and genetic diversity of M. oxyfera in the rhizosphere of aquatic plants has not been widely reported. Method In order to simulate the rhizosphere microenvironment of submerged plants, Potamogeton crispus was cultivated using the rhizobox approach. Sediments from three compartments of the rhizobox: root (R), near-rhizosphere (including five sub-compartments of one mm width, N1–N5) and non-rhizosphere (>5 mm, Non), were sampled. The 16S rRNA gene library was used to investigate the diversity of M. oxyfera-like bacteria in these sediments. Results Methylomirabilis oxyfera-like bacteria were found in all three sections, with all 16S rRNA gene sequences belonging to 16 operational taxonomic units (OTUs). A maximum of six OTUs was found in the N1 sub-compartment of the near-rhizosphere compartment and a minimum of four in the root compartment (R) and N5 near-rhizosphere sub-compartment. Indices of bacterial community diversity (Shannon) and richness (Chao1) were 0.73–1.16 and 4–9, respectively. Phylogenetic analysis showed that OTU1-11 were classified into group b, while OTU12 was in a new cluster of NC10. Discussion Our results confirmed the existence of M. oxyfera-like bacteria in the rhizosphere microenvironment of the submerged plant P. crispus. Group b of M. oxyfera-like bacteria was the dominant group in this study as opposed to previous findings that both group a and b coexist in most other environments. Our results indicate that understanding the ecophysiology of M. oxyfera-like bacteria group b may help to explain their existence in the rhizosphere sediment of aquatic plant

Water vapor estimation based on 1-year data of E-band millimeter wave link in North China

Abstract. The amount of water vapor in the atmosphere is very small, but its content varies greatly in different humidity areas. The change in water vapor will affect the transmission of microwave link signals, and most of the water vapor is concentrated in the lower layer, so the water vapor density can be measured by the change in the near-ground microwave link transmission signal. This study collected 1-year data of the E-band millimeter wave link in Hebei, China, and used a model based on the International Telecommunication Union Radiocommunication Sector (ITU-R) to estimate the water vapor density. An improved method of extracting the water-vapor-induced attenuation value is also introduced. It has a higher time resolution, and the estimation error is lower than the previous method. In addition, this paper conducts the seasonal analysis of water vapor inversion for the first time. The monthly and seasonal evaluation index results show a high correlation between the retrieved water vapor density and the actual water vapor density value measured by the local weather station. The correlation value for the whole year is up to 0.95, the root mean square error is as low as 0.35 g m−3, and the average relative error is as low as 5.00 %. Compared with European Center for Medium-Range Weather Forecast (ECMWF) reanalysis, the correlation of the daily water vapor density estimation of the link has increased by 0.17, the root mean square error has been reduced by 3.14 g m−3, and the mean relative error has been reduced by 34.00 %. This research shows that millimeter wave backhaul link provides high-precision data for the measurement of water vapor density and has a positive effect on future weather forecast research. </jats:p