•A multiscale deep feature learning method with hybrid models is proposed to forecast daily inflow.•Both EEMD and FT techniques are applied for multiscale feature extraction.•A DBN is used as a deep ...feature learning approach.•Hybrid D-NNs are employed for features forecasting.•This method improves inflow forecasting accuracy due to the capacity of understanding sophisticated features sufficiently.
Inflow forecasting applies data supports for the operations and managements of reservoirs. A multiscale deep feature learning (MDFL) method with hybrid models is proposed in this paper to deal with the daily reservoir inflow forecasting. Ensemble empirical mode decomposition and Fourier spectrum are first employed to extract multiscale (trend, period and random) features, which are then represented by three deep belief networks (DBNs), respectively. The weights of each DBN are subsequently applied to initialize a neural network (D-NN). The outputs of the three-scale D-NNs are finally reconstructed using a sum-up strategy toward the forecasting results. A historical daily inflow series (from 1/1/2000 to 31/12/2012) of the Three Gorges reservoir, China, is investigated by the proposed MDFL with hybrid models. For comparison, four peer models are adopted for the same task. The results show that, the present model overwhelms all the peer models in terms of mean absolute percentage error (MAPE=11.2896%), normalized root-mean-square error (NRMSE=0.2292), determination coefficient criteria (R2=0.8905), and peak percent threshold statistics (PPTS(5)=10.0229%). The addressed method integrates the deep framework with multiscale and hybrid observations, and therefore being good at exploring sophisticated natures in the reservoir inflow forecasting.
The objectives of this study were to synthesize aluminum hydroxide modified palygorskite nano-composites (Al-PG) and to investigate their suitability as adsorbents to remove phosphate from aqueous ...solution. The nano-composites were characterized by XRD, XRF and TEM. The characterization results showed that aluminum hydroxide gel was successfully loaded onto palygorskites (PGs) with diameters of nanometers, and the crystal composition of PG had not been changed after modification. The effects of modified mass ratios, pH, co-existing anions, and initial phosphate concentrations on phosphate removal were investigated by batch experiments. The Freundlich model provided a better description for the adsorption process than the Langmuir model. The maximum phosphate adsorption capacity was 16.86 mg g
−1
for Al-PG, while it was 4.08 mg g
−1
for natural PG. Of the adsorption isotherms and thermodynamic studies considered, the adsorption of phosphate by Al-PG was chemisorption, endothermic and spontaneous. Kinetic studies indicated that the adsorption of phosphate onto Al-PG can be fitted by a pseudo-second-order kinetic model very well. Thus, the cost-effective and high adsorption capacity of Al-PG has wide potential use in phosphate removal from aqueous solutions.
Aluminum hydroxide was successfully loading on PG. The cost-effective and high adsorption capacity of Al-PG had widely potential utilization on phosphate removal.
Attribute reduction is an important application of rough set theory. Most existing rough set models do not consider the weight information of attributes in information systems. In this paper, we ...first study the weight of each attribute in information systems by using data binning method and information entropy theory. Then, we propose a new rough set model of weighted neighborhood probabilistic rough sets (WNPRSs) and investigate its basic properties. Meanwhile, the dependency degree formula of an attribute relative to an attribute subset is defined based on WNPRSs. Subsequently, we design a novel attribute reduction method by using WNPRSs and the corresponding algorithm is also given. Finally, to evaluate the performance of the proposed algorithm, we conduct a data experiment and compare it with other existing attribute reduction algorithms on eight public datasets. Experimental result demonstrates that the proposed attribute reduction algorithm is effective and performs better than some of the existing algorithms.
Seismic landslides are the most common and highly destructive earthquake-triggered geological hazards. They are large in scale and occur simultaneously in many places. Therefore, obtaining landslide ...information quickly after an earthquake is the key to disaster mitigation and relief. The survey results show that most of the landslide-information extraction methods involve too much manual participation, resulting in a low degree of automation and the inability to provide effective information for earthquake rescue in time. In order to solve the abovementioned problems and improve the efficiency of landslide identification, this paper proposes an automatic landslide identification method named improved U-Net model. The intelligent extraction of post-earthquake landslide information is realized through the automatic extraction of hierarchical features. The main innovations of this paper include the following: (1) On the basis of the three RGB bands, three new bands, DSM, slope, and aspect, with spatial information are added, and the number of feature parameters of the training samples is increased. (2) The U-Net model structure is rebuilt by adding residual learning units during the up-sampling and down-sampling processes, to solve the problem that the traditional U-Net model cannot fully extract the characteristics of the six-channel landslide for its shallow structure. At the end of the paper, the new method is used in Jiuzhaigou County, Sichuan Province, China. The results show that the accuracy of the new method is 91.3%, which is 13.8% higher than the traditional U-Net model. It is proved that the new method is effective and feasible for the automatic extraction of post-earthquake landslides.
Pigs are one of the most important economic livestock. Gut microbiota is not only critical to the health but also the production efficiency of pigs. Manipulating gut microbiota relies on the full ...view of gut microbiome and the understanding of drive forces shaping microbial communities. 16s rDNA sequencing was used to profile microbiota along the longitudinal and radical axes to obtain the topographical map of microbiome in different intestinal compartments in young pigs. Alpha and beta-diversities revealed distinct differences in microbial compositions between the distal ileum and cecum and colon, as well as between the lumen and mucosa.
and
dominated in the ileum, constituting 95 and 80% of the luminal and mucosa-attached microbiome. Transitioning from the small intestine to the large intestine, luminal
increased from 1.69 to 45.98% in the cecum and 40.09% in the colon, while mucosal
raised from 9 to 35.36% and 27.96%. Concurrently, luminal
and
and mucosal-attached
remarkably decreased. By co-occurrence network analyses,
and
were recognized as the central nodes of luminal microbial network, and
and
were identified as mucosal central nodes. Co-abundance was uncovered among
, and
in the luminal and mucosal microbiome, while opportunistic pathogens from γ-
in the mucosa. Strong co-exclusion was shown between
with
-centered microbial groups in the lumen. Redundancy analysis found bile acids and short chain fatty acids explained 37.1 and 41% of variations in the luminal microbial composition, respectively. Primary bile acid, taurine- and glycine- conjugated bile acids were positively correlated with
, whereas secondary bile acids, acetate, propionate, butyrate, and valerate were positively correlated with
. Functional analyses demonstrated that
, and
were positively correlated with gene functions related to amino acids, energy, cofactors and vitamins metabolism, which are indispensable for the hosts. These results suggested site specific colonization and co-occurrence of swine gut microbiome closely relate to the microenvironment in each niche. Interactions of core gut microbiome greatly contributed to metabolism and/or immunity in the swine intestine.
The non-receptor protein tyrosine phosphatase (PTP) SHP2, encoded by PTPN11, plays an essential role in RAS-mitogen-activated protein kinase (MAPK) signaling during normal development. It has been ...perplexing as to why both enzymatically activating and inactivating mutations in PTPN11 result in human developmental disorders with overlapping clinical manifestations. Here, we uncover a common liquid-liquid phase separation (LLPS) behavior shared by these disease-associated SHP2 mutants. SHP2 LLPS is mediated by the conserved well-folded PTP domain through multivalent electrostatic interactions and regulated by an intrinsic autoinhibitory mechanism through conformational changes. SHP2 allosteric inhibitors can attenuate LLPS of SHP2 mutants, which boosts SHP2 PTP activity. Moreover, disease-associated SHP2 mutants can recruit and activate wild-type (WT) SHP2 in LLPS to promote MAPK activation. These results not only suggest that LLPS serves as a gain-of-function mechanism involved in the pathogenesis of SHP2-associated human diseases but also provide evidence that PTP may be regulated by LLPS that can be therapeutically targeted.
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•Disease-associated mutations endow SHP2 liquid-liquid phase separation capability•SHP2 LLPS is driven by electrostatic interactions mediated by PTP domain•SHP2 allosteric inhibitors block SHP2 LLPS by locking SHP2 in closed conformation•Mutant SHP2 can recruit and activate WT SHP2 in LLPS to promote MAPK activation
Disease-associated mutants of a critical phosphatase in the RAS-MAPK pathway undergo phase separation through a dominant gain-of-function mechanism, explaining how both enzymatically activating and inactivating mutations dysregulate the pathway and can be therapeutically targeted.
•A method is proposed for daily urban water demand forecasting.•Both multi-scale analysis and nonlinear mapping are applied for forecasting.•This method can improve forecasting accuracy for daily ...water demand.
Water is one of the most important resources for economic and social developments. Daily water demand forecasting is an effective measure for scheduling urban water facilities. This work proposes a multi-scale relevance vector regression (MSRVR) approach to forecast daily urban water demand. The approach uses the stationary wavelet transform to decompose historical time series of daily water supplies into different scales. At each scale, the wavelet coefficients are used to train a machine-learning model using the relevance vector regression (RVR) method. The estimated coefficients of the RVR outputs for all of the scales are employed to reconstruct the forecasting result through the inverse wavelet transform. To better facilitate the MSRVR forecasting, the chaos features of the daily water supply series are analyzed to determine the input variables of the RVR model. In addition, an adaptive chaos particle swarm optimization algorithm is used to find the optimal combination of the RVR model parameters. The MSRVR approach is evaluated using real data collected from two waterworks and is compared with recently reported methods. The results show that the proposed MSRVR method can forecast daily urban water demand much more precisely in terms of the normalized root-mean-square error, correlation coefficient, and mean absolute percentage error criteria.
While alkyl radicals have been well demonstrated to undergo both 1,5- and 1,6-hydrogen atom abstraction (HAA) reactions, 1,4-HAA is typically a challenging process both entropically and ...enthalpically. Consequently, chemical transformations based on 1,4-HAA have been scarcely developed. Guided by the general mechanistic principles of metalloradical catalysis (MRC), 1,4-HAA has been successfully incorporated as a key step, followed by 4-exo-tet radical substitution (RS), for the development of a new catalytic radical process that enables asymmetric 1,4-C–H alkylation of diazoketones for stereoselective construction of cyclobutanone structures. The key to success is the optimization of the Co(II)-based metalloradical catalyst through judicious modulation of D 2-symmetric chiral amidoporphyrin ligand to adopt proper steric, electronic, and chiral environments that can utilize a network of noncovalent attractive interactions for effective activation of the substrate and subsequent radical intermediates. Supported by an optimal chiral ligand, the Co(II)-based metalloradical system, which operates under mild conditions, is capable of 1,4-C–H alkylation of α-aryldiazoketones with varied electronic and steric properties to construct chiral α,β-disubstituted cyclobutanones in good to high yields with high diastereoselectivities and enantioselectivities, generating dinitrogen as the only byproduct. Combined computational and experimental studies have shed light on the mechanistic details of the new catalytic radical process, including the revelation of facile 1,4-HAA and 4-exo-tet-RS steps. The resulting enantioenriched α,β-disubstituted cyclobutanones, as showcased with several enantiospecific transformations to other types of cyclic structures, may find useful applications in stereoselective organic synthesis.
One major limiting factor for sediment microbial fuel cells (SMFC) is the low oxygen reduction rate in the cathode. The use of the photosynthetic process of the algae is an effective strategy to ...increase the oxygen availability to the cathode. In this study, SMFCs were constructed by introducing the algae (Chlorella vulgaris) to the cathode, in order to generate oxygen in situ. Cyclic voltammetry and dissolved oxygen analysis confirmed that C. vulgaris in the cathode can increase the dissolved oxygen concentration and the oxygen reduction rate. We showed that power generation of SMFC with algae-assisted cathode was 21 mW m−2 and was further increased to 38 mW m−2 with additional carbon nanotube coating in the cathode, which was 2.4 fold higher than that of the SMFC with bare cathode. This relatively simple method increases the oxygen reduction rate at a low cost and can be applied to improve the performance of SMFCs.
•C. vulgaris is efficient in situ oxygenators for the oxygen reduction reaction.•Carbon nanotube strengthens the oxygen reduction rate from C. vulgaris release.•C. vulgaris can be cultivated in SMFC without any further addition of CO2.•We report a simple method for improving the performance of SMFC.
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