•A new type of carrier based on the low alkali cementitious material was developed.•The carrier was very effective in preserving the bacterial activity.•Cracks up to 417 μm was healed completely in ...28 days by loading bacteria in the carrier.
Self-healing based on microbially-induced calcium carbonate precipitation has been proposed as a smart and environmentally friendly strategy for the repair of concrete cracks. It is advisable to incorporate bacteria-based healing agents in fresh state concrete during mixing. Although the selected bacteria are alkaliphilic spore-forming strains, they are still vulnerable to the harsh environment of concrete. In this paper, we developed a protective carrier for the bacteria by using calcium sulphoaluminate cement, which is a type of low alkali, fast hardening cementitious material. By regulating the composition of the carrier material and the content of healing agents, the compatibility of the carrier with both the healing agents and the concrete matrix was optimized. The carrier, which acted as a support for the bacteria, was effective in preserving the bacterial activity over a long period of time. After embedding this bacteria-based self-healing system in concrete, cracks up to 417 μm with a crack closure near 100% was achieved in 28 days. Compared with plain mortar, the regain ratios of the compressive strength and water tightness increased 130% and 50%, respectively. The research suggests the potential application of this novel microbial self-healing system in extending the life span of concrete.
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GEOZS, IJS, IMTLJ, KILJ, KISLJ, NLZOH, NUK, OILJ, PNG, SAZU, SBCE, SBJE, UL, UM, UPCLJ, UPUK, ZRSKP
Brain signals refer to the biometric information collected from the human brain. The research on brain signals aims to discover the underlying neurological or physical status of the individuals by ...signal decoding. The emerging deep learning techniques have improved the study of brain signals significantly in recent years. In this work, we first present a taxonomy of non-invasive brain signals and the basics of deep learning algorithms. Then, we provide the frontiers of applying deep learning for non-invasive brain signals analysis, by summarizing a large number of recent publications. Moreover, upon the deep learning-powered brain signal studies, we report the potential real-world applications which benefit not only disabled people but also normal individuals. Finally, we discuss the opening challenges and future directions.
Rotating machines have been widely used in industrial engineering. The fault diagnosis of rotating machines plays a vital important role to reduce the catastrophic failures and heavy economic loss. ...However, the measured vibration signal of rotating machinery often represents non-linear and non-stationary characteristics, resulting in difficulty in the fault feature extraction. As a statistical measure, entropy can quantify the complexity and detect dynamic change through taking into account the non-linear behavior of time series. Therefore, entropy can be served as a promising tool to extract the dynamic characteristics of rotating machines. Recently, many studies have applied entropy in fault diagnosis of rotating machinery. This paper aims to investigate the applications of entropy for the fault characteristics extraction of rotating machines. First, various entropy methods are briefly introduced. Its foundation, application, and some improvements are described and discussed. The review is divided into eight parts: Shannon entropy, Rényi entropy, approximate entropy, sample entropy, fuzzy entropy, permutation entropy, and other entropy methods. In each part, we will review the applications using the original entropy method and the improved entropy methods, respectively. In the end, a summary and some research prospects are given.
The Qinghai-Tibetan Plateau is a very large land unit and an important terrestrial ecosystem within the Eurasian continent. Because of the harsh climate associated with the high altitude, alpine ...meadows on the plateau are susceptible to degradation from overgrazing. For this region, and for other alpine meadow pastures internationally, there is a need to define the sustainable stocking rate, to develop sound policy pertaining to future land use. Here we report biomass and liveweight gain per animal and per ha for pastures grazed by yaks at high, medium, or low stocking rates over 4 growing seasons from 2010 to 2013. Measures of herbage nutritive value are reported. The influence of inter-year variation in precipitation on standing herbage biomass was also evaluated. Higher precipitation increased standing herbage biomass and herbage nutritive value, indicating that vegetation suffered summer water deficit even in this environment. The sustainable stocking rate in this environment was determined to be approximately 1 yak ha-1 (grown from 80 kg to 120 kg liveweight in 90 d). At this stocking rate, yak weight gain per ha was 88% of that achieved at higher stocking rates typically used by farmers, but with little or no evidence of land degradation.
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DOBA, IZUM, KILJ, NUK, PILJ, PNG, SAZU, SIK, UILJ, UKNU, UL, UM, UPUK
Community question answering (CQA) represents the type of Web applications where people can exchange knowledge via asking and answering questions. One significant challenge of most real-world CQA ...systems is the lack of effective matching between questions and the potential good answerers, which adversely affects the efficient knowledge acquisition and circulation. On the one hand, a requester might experience many low-quality answers without receiving a quality response in a brief time; on the other hand, an answerer might face numerous new questions without being able to identify the questions of interest quickly. Under this situation, expert recommendation emerges as a promising technique to address the above issues. Instead of passively waiting for users to browse and find their questions of interest, an expert recommendation method raises the attention of users to the appropriate questions actively and promptly. The past few years have witnessed considerable efforts that address the expert recommendation problem from different perspectives. These methods all have their issues that need to be resolved before the advantages of expert recommendation can be fully embraced. In this survey, we first present an overview of the research efforts and state-of-the-art techniques for the expert recommendation in CQA. We next summarize and compare the existing methods concerning their advantages and shortcomings, followed by discussing the open issues and future research directions.
Soybean (Glycine max) is a photoperiod-sensitive and self-pollinated species. Days to flowering (DTF) and maturity (DTM), duration of flowering-to-maturity (DFTM) and plant height (PH) are crucial ...for soybean adaptability and yield. To dissect the genetic architecture of these agronomically important traits, a population consisting of 309 early maturity soybean germplasm accessions was genotyped with the Illumina Infinium SoySNP50K BeadChip and phenotyped in multiple environments. A genome-wide association study (GWAS) was conducted using a mixed linear model that involves both relative kinship and population structure.
The linkage disequilibrium (LD) decayed slowly in soybean, and a substantial difference in LD pattern was observed between euchromatic and heterochromatic regions. A total of 27, 6, 18 and 27 loci for DTF, DTM, DFTM and PH were detected via GWAS, respectively. The Dt1 gene was identified in the locus strongly associated with both DTM and PH. Ten candidate genes homologous to Arabidopsis flowering genes were identified near the peak single nucleotide polymorphisms (SNPs) associated with DTF. Four of them encode MADS-domain containing proteins. Additionally, a pectin lyase-like gene was also identified in a major-effect locus for PH where LD decayed rapidly.
This study identified multiple new loci and refined chromosomal regions of known loci associated with DTF, DTM, DFTM and/or PH in soybean. It demonstrates that GWAS is powerful in dissecting complex traits and identifying candidate genes although LD decayed slowly in soybean. The loci and trait-associated SNPs identified in this study can be used for soybean genetic improvement, especially the major-effect loci associated with PH could be used to improve soybean yield potential. The candidate genes may serve as promising targets for studies of molecular mechanisms underlying the related traits in soybean.
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DOBA, IZUM, KILJ, NUK, PILJ, PNG, SAZU, SIK, UILJ, UKNU, UL, UM, UPUK
Leaf Area Index (LAI) is an important parameter which can be used for crop growth monitoring and yield estimation. Many studies have been carried out to estimate LAI with remote sensing data obtained ...by sensors mounted on Unmanned Aerial Vehicles (UAVs) in major crops; however, most of the studies used only a single type of sensor, and the comparative study of different sensors and sensor combinations in the model construction of LAI was rarely reported, especially in soybean. In this study, three types of sensors, i.e., hyperspectral, multispectral, and LiDAR, were used to collect remote sensing data at three growth stages in soybean. Six typical machine learning algorithms, including Unary Linear Regression (ULR), Multiple Linear Regression (MLR), Random Forest (RF), eXtreme Gradient Boosting (XGBoost), Support Vector Machine (SVM) and Back Propagation (BP), were used to construct prediction models of LAI. The results indicated that the hyperspectral and LiDAR data did not significantly improve the prediction accuracy of LAI. Comparison of different sensors and sensor combinations showed that the fusion of the hyperspectral and multispectral data could significantly improve the predictive ability of the models, and among all the prediction models constructed by different algorithms, the prediction model built by XGBoost based on multimodal data showed the best performance. Comparison of the models for different growth stages showed that the XGBoost-LAI model for the flowering stage and the universal models of the XGBoost-LAI and RF-LAI for three growth stages showed the best performances. The results of this study might provide some ideas for the accurate estimation of LAI, and also provide novel insights toward high-throughput phenotyping of soybean with multi-modal remote sensing data.
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IZUM, KILJ, NUK, PILJ, PNG, SAZU, UL, UM, UPUK
Exploring the spatiotemporal variation in vegetation phenology in the Yellow River basin has significant scientific significance for ecological governance, protection, and construction in the region. ...In this study, the GOSIF and GOSIF_GPP remote sensing products from 2001 to 2020 were selected, and the double logistic curve fitting and dynamic threshold method were used to obtain three vegetation phenology parameters: start of season (SOS), end of season (EOS), and length of season (LOS) in the growing season of the Yellow River basin over 20 years. The results showed the following: (1) The vegetation phenology in the Yellow River basin exhibited an obvious zonal distribution pattern from east to west and from south to north. The SOS was mainly concentrated in March-April, and 33.83% of the regions showed a significant advance. The EOS mainly ended in October-November, 7.46% of the region showed a significant delay. The LOS was approximately 150-200 days, with 28.20% showing significant prolongation. (2) SOS showed a gradual delay from east to west and from south to north, while EOS showed a gradual advance from east to west and from south to north. (3) The correlation between SOS and temperature and precipitation is not strong, and the influence of temperature and precipitation on SOS is not strong; EOS is highly correlated with temperature (69.76%), but not with precipitation (94.39%). Temperature is the dominant factor in EOS variation. In conclusion, temperature is the main factor affecting the interannual phenological changes in the Yellow River Basin.
Abstract
Soil water and root distribution following revegetation are key research topics in water-limited ecosystems. However, little is known about the interaction between soil water and root ...distribution in deep soils under different precipitation conditions. Knowledge of the root–soil water relationship of revegetated land and its response to precipitation is crucial for the management of water resources and ecological restoration worldwide, including on the Chinese Loess Plateau. In this study, we investigated soil water and root distribution under apple orchard and black locust down a 10 m soil profile and exposed to different amounts of annual precipitation on the Loess Plateau. The results showed that soil water content (SWC) under two typical planted forests both significantly decreased as the mean annual precipitation (MAP) decreased. SWC spatial variation is demarcated by a 500–550 mm precipitation threshold, being relatively high when MAP > 550 mm but extremely low when MAP < 500 mm. In apple orchards, the depth above which 50% of the roots were present increased with increasing precipitation, but in black locust it became shallower. The results of a linear mixed model revealed a significant relationship between fine root length density and SWC depletion degree for black locust irrespective of the amount of precipitation, but it was only found in the 200–1000 cm soil layers with MAP > 550 mm and the 0–200 cm soil layers with MAP < 550 mm for apple orchards. The MAP × depth interaction was significant with respect to SWC depletion degree for MAP > 550 mm, but not for MAP < 550 mm in both vegetation types. These findings add to our current understanding of the root–soil water relationship of species used for revegetation and highlight the need to assess the long-term effect of revegetation on soil water consumption in water-limited ecosystems.
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