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We investigate the effect of ultrasound on the evaporation and crystallization of sessile NaCl solution droplets which were positioned in traveling or standing wave ultrasound field. The experimental results indicated that the ultrasound field can significantly accelerate the evaporation rate of the sessile droplets and refine the crystal grains. By adjusting the distance between the sessile droplets and the ultrasound emitter, it is found that, in traveling wave ultrasound field, the sessile droplet evaporation time and the time for the appearance of NaCl grains exhibited a fluctuating increase as the droplet-emitter distance increased. While in the standing wave ultrasound, the sessile droplet evaporation rate increases with the increasing droplet-emitter distance. Overall, the traveling wave ultrasound field has a stronger effect on grain refinement of the sessile droplets than the standing wave ultrasound field. The grain refinement is attributed to the decrease of critical nucleation radius caused by ultrasound energy and the increase of the nucleation rate caused by the accelerated evaporation rate. In addition, the breakage of grains caused by ultrasonic cavitation would also lead to grain refinement.

Ti-bearing blast furnace slag is a kind of solid waste produced by Pangang Group Company through the blast furnace smelting method. A variety of valuable components can be extracted from the Ti-bearing blast furnace slag after acidolysis with concentrated sulphuric acid. In order to study the kinetics of acidolysis, this paper investigated the effects of the acidolysis temperature, acid-slag ratio and raw material particle size on the overall extraction rate of Ti4+, Mg2+ and Al3+ components at different reaction times, and simulated the acidolysis process by using the unreacted shrinking core model. The results showed that the acidolysis process was controlled by internal diffusion with an apparent activation energy of 19.05 kJ mol–1 and the semi-empirical kinetic equation of the acidolysis process was obtained.

This study aims to develop a comprehensive strategy that accurately screens and quantifies quality markers (Q-markers) by combining LC-MS metabolomics with fingerprint-effect relationships, chemometrics, and QAMS methods for the quality control in traditional Chinese medicine compound preparations (CMP). First, the chemical compositions in Jinlian Qingre granules (JQG) were analyzed and identified using ultrahigh-performance liquid chromatography-high resolution mass spectrometry (UHPLC-HRMS). Next, the HPLC fingerprint of JQG was established, and chemometrics were employed to evaluate JQG quality. Then, potential anti-inflammatory bioactive constituents were screened using a fingerprinting-effect relationship model utilizing partial least squares regression (PLSR) and a back propagation neural network optimized using a genetic algorithm (GA-BPNN). On this basis, Q-markers of JQG were determined and analyzed using quantitative analysis of multi-components by the single marker (QAMS) employing three components as reference substances (RS). 24 chemical constituents in JQG were characterized using UHPLC-HRMS. Twenty-four common peaks were assigned to the fingerprint. Six compounds, mangiferin, 2′'-O-beta-L-galactopyranosylorientin, orientin, veratric acid, vitexin, and harpagoside, identified from the common peaks associated with anti-inflammatory efficacy, could be used as Q-markers of JQG. The content determination results displayed no significant difference in the content of Q-markers in 20 batches of JQG samples measured using the external standard method and QAMS. Q-markers could be efficiently screened and determined by integrating multidisciplinary technologies. This comprehensive strategy could be beneficial for the thorough evaluation of CMP.

Multispectral satellite data allow experts to discriminate rock units based on their spectral signature characteristics. Here, Sentinel-2, ASTER and the Landsat-8 Operational Land Imager (OLI) were assessed for lithological mapping by using a random forest (RF) classifier for a study area located in Xitieshan, Northwest China. The classification accuracy of Sentinel-2 was 60.71%, which was 5.24% and 4.77% higher than the accuracies for ASTER and the Landsat-8 OLI, respectively. Three image enhancement techniques, namely, principal component analysis (PCA), independent component analysis (ICA) and minimum noise fraction (MNF), were used with grey-level cooccurrence matrices (GLCMs) to increase the quality of the input datasets. The ICA could discriminate between rock unit datasets better than the other approaches. In contrast, GLCM performed poorly when used independently. The overall classification accuracies were 60.71%, 62.63%, 64.34%, 65.21% and 58.87% for the 10 bands of Sentinel-2, PCA, MNF, ICA and GLCM, respectively. Then, five datasets were combined as a single group and applied in RF classification. Sentinel-2 obtained an overall accuracy of 73.96% and performed better than the other single-dataset approaches used in this study. Furthermore, the classification result of RF was achieved better performance than that of the support vector machine algorithm (SVM). During feature selection processing, ReliefF, the most successful pre-processing algorithm, was employed to preliminarily perform feature screening. Then, the optimal dataset was selected on the basis of the importance ranking of RF. A total of 20 more important predictors were selected from 114 original features using the ReliefF-RF model. These predictors were used in the lithological mapping, and an overall accuracy of 77.63% was reached.

Abstract Drug–target interaction (DTI) prediction is a crucial step in drug discovery and repositioning as it reduces experimental validation costs if done right. Thus, developing in-silico methods to predict potential DTI has become a competitive research niche, with one of its main focuses being improving the prediction accuracy. Using machine learning (ML) models for this task, specifically network-based approaches, is effective and has shown great advantages over the other computational methods. However, ML model development involves upstream hand-crafted feature extraction and other processes that impact prediction accuracy. Thus, network-based representation learning techniques that provide automated feature extraction combined with traditional ML classifiers dealing with downstream link prediction tasks may be better-suited paradigms. Here, we present such a method, DTi2Vec, which identifies DTIs using network representation learning and ensemble learning techniques. DTi2Vec constructs the heterogeneous network, and then it automatically generates features for each drug and target using the nodes embedding technique. DTi2Vec demonstrated its ability in drug–target link prediction compared to several state-of-the-art network-based methods, using four benchmark datasets and large-scale data compiled from DrugBank. DTi2Vec showed a statistically significant increase in the prediction performances in terms of AUPR. We verified the "novel" predicted DTIs using several databases and scientific literature. DTi2Vec is a simple yet effective method that provides high DTI prediction performance while being scalable and efficient in computation, translating into a powerful drug repositioning tool.

Abstract Two-dimensional (2D) chemical fingerprints are widely used as binary features for the quantification of structural similarity of chemical compounds, which is an important step in similarity-based virtual screening (VS). Here, using an eigenvalue-based entropy approach, we identified 2D fingerprints with little to no contribution to shaping the eigenvalue distribution of the feature matrix as related ones and examined the degree to which these related 2D fingerprints influenced molecular similarity scores calculated with the Tanimoto coefficient. Our analysis identified many related fingerprints in publicly available fingerprint schemes and showed that their presence in the feature set could have substantial effects on the similarity scores and bias the outcome of molecular similarity analysis. Our results have implication in the optimal selection of 2D fingerprints for compound similarity analysis and the identification of potential hits for compounds with target biological activity in VS.

Abstract In silico prediction of drug–target interactions is a critical phase in the sustainable drug development process, especially when the research focus is to capitalize on the repositioning of existing drugs. However, developing such computational methods is not an easy task, but is much needed, as current methods that predict potential drug–target interactions suffer from high false-positive rates. Here we introduce DTiGEMS+, a computational method that predicts Drug–Target interactions using Graph Embedding, graph Mining, and Similarity-based techniques. DTiGEMS+ combines similarity-based as well as feature-based approaches, and models the identification of novel drug–target interactions as a link prediction problem in a heterogeneous network. DTiGEMS+ constructs the heterogeneous network by augmenting the known drug–target interactions graph with two other complementary graphs namely: drug–drug similarity, target–target similarity. DTiGEMS+ combines different computational techniques to provide the final drug target prediction, these techniques include graph embeddings, graph mining, and machine learning. DTiGEMS+ integrates multiple drug–drug similarities and target–target similarities into the final heterogeneous graph construction after applying a similarity selection procedure as well as a similarity fusion algorithm. Using four benchmark datasets, we show DTiGEMS+ substantially improves prediction performance compared to other state-of-the-art in silico methods developed to predict of drug-target interactions by achieving the highest average AUPR across all datasets (0.92), which reduces the error rate by 33.3% relative to the second-best performing model in the state-of-the-art methods comparison.

Constructing mobile robot milling systems with multiple mounting surfaces for large cabins still has several unsolved issues, such as huge economic and time costs, unpredictable milling accuracy and milling time. Hence, a multi-objective optimization method for constructing a mobile robot milling system of large cabins is proposed in the current paper. Firstly, mathematical models of constructing the system and the optimization objective function are established. Thereafter, a multi-objective optimization method for the mobile robot milling system construction based on NSGA-II (Fast Non-dominated Sorting Genetic Algorithm) is proposed. Finally, feasibility and validity of the proposed method are verified through comparing the optimization result with two practical mobile robot systems. Results show that the proposed method is able to estimate different combinations’ milling accuracy, cost and time consumption.

In this study, graphene and N-doped graphene nanosheets were synthesized through the shock-induced reduction of CO2 using a cylindrical shock-loading apparatus. The mixture of solid CO2 and Mg powder was filled in the pre-cooled sample tube and then impacted by a shock-driven cylindrical flyer tube. The impact generated a shockwave that propagated into the mixed precursor, inducing a chemical reaction between CO2 and Mg at a high shock pressure and high shock temperature. The recovered black powders were characterized via various techniques, confirming the presences of few-layer graphene. The mechanism is carefully shown to be that CO2 was reduced by Mg to form few-layer graphene under shock-induced high pressure and high temperature. By adding carbamide as an N source, this synthetic route was also applied to synthesize N-doped graphene nanosheets. Moreover, the yield and mass of the graphene materials in this study are up to 40% and 0.5 g, respectively. This study showed an efficient and easy-to-scale-up route to prepare few-layer graphene and N-doped few-layer graphene through shock synthesis.

In order to study the feasibility and superiority of foam lightweight concrete (FLC) for existing buried high-pressure gas pipeline section under soft soil roadbed, this paper takes the buried existing high-pressure gas pipeline section under the second phase of the Foqingcong Expressway project as the engineering background, and designs two soft soil roadbed treatment schemes, the pile composite foundation method, and the foam lightweight concrete (FLC). Through bearing capacity and settlement calculations, it was confirmed that FLC is feasible for soft soil roadbeds in the existing buried high-pressure gas pipe section, and the three major aspects of analysis, namely the safety benefits, economic benefits and ecological benefits based on the LCA carbon emission calculation, showed that the FLC soft soil roadbed treatment method can reduce the cost by 12% compared with the pile composite foundation treatment method, which is about 1.07 million RMB, and the carbon emission is reduced by about one third. This is a clear benefit advantage. Finally, the feasibility of FLC for buried high-pressure gas pipe sections under both soft soil roadbeds was further verified by field measurements of settlement and earth pressure, which has broad application prospects.

Shewanella putrefaciens is a specific spoilage bacterium for fish during cold storage. To better understand the molecular mechanisms of cold stress adaptation of S. putrefaciens, tandem mass tag- (TMT-) based quantitative proteomic analysis was performed to detect the effects of cold stress on protein expression profiles in S. putrefaciens which had been cultivated at 4°C and 30°C, respectively. A total of 266670 peptide spectrum matching numbers were quantified proteins after data analysis. Of the 2292 proteins quantitatively analyzed, a total of 274 were found to be differentially expressed (DE) under cold stress compared with the nonstress control. By integrating the results of Kyoto Encyclopedia of Genes and Genomes (KEGG) analyses, 9 common KEGG terms were found notable for the cold-responsive proteins. Generally, the DE proteins involved in carbohydrate, amino acid, and fatty acid biosynthesis and metabolism were significantly upregulated, leading to a specific energy conservation survival mode. The DE proteins related to DNA repair, transcription, and translation were upregulated, implicating change of gene expression and more protein biosynthesis needed in response to cold stress.

Dynamic safety factors during the construction of an overlapping tunnel-group metro station were studied in the framework of the strength-reduction finite element method. Based on the equivalent plastic strain and displacement of surrounding rock, its damage mode under typical excavation conditions was investigated. The aim of this investigation was to provide information for the design activities concerning the supporting system of the station and the pre-reinforcement of its surrounding ground. The accuracy of the model was assessed by comparing the ground settlements obtained from on-site monitoring with those from the numerical model. The analysis results show that the safety factor reaches the minimum when the No. 3 guide hole of the station hall is excavated. Thus, this is the most dangerous construction step. During this step, the plastic zone penetration phenomenon occurs in the surrounding rock, which is sandwiched between the hall and the platform of the station. In this case, both the deformation of the surrounding rock and the internal forces of the lining increase. The surrounding rock in the sidewall loses its stability. Thereafter, the primary support plays a role of stabilizing the guide hole.

In the present, microwave field has been widely used in chemical processes as an important means of intensification. The heterogeneous catalysts coupling with microwave has been shown to have many advantages, such as high catalytic performance and stability. Our objective is to focus an up-to-date overview concerning the advances in the special microwave effects of the heterogeneous catalytic reactions including special thermal effect, microwave plasma, enhanced active groups, and the flexibility of structure. This review systematically states the action mechanism and some practical application of microwave-induced catalytic process. Finally, the potential research directions in the field of microwave-induced catalysis are prospected.

An innovative hybrid energy system consisting of a waste-to-energy unit and a coal-fired power unit is designed to enhance the energy recovery of waste and decrease the investment costs of waste-to-energy unit. In this integrated design, partial cold reheat steam of the coal-fired unit is heated by the waste-to-energy boiler’s superheater. The heat required for partial preheated air of waste-to-energy unit and its feedwater are supplied by the feedwater of CFPU. In addition, an additional evaporator is deployed in the waste-to-energy boiler, of which the outlet stream is utilized to provide the heat source for the urea hydrolysis unit of coal-fired power plant. The stand-alone and proposed designs are analyzed and compared through thermodynamic and economic methods. Results indicate that the net total energy efficiency increases from 41.84% to 42.12%, and the net total exergy efficiency rises from 41.19% to 41.46% after system integration. Moreover, the energy efficiency and exergy efficiency of waste-to-energy system are enhanced by 10.48% and 9.92%, respectively. The dynamic payback period of new waste-to-energy system is cut down from 11.39 years to 5.48 years, and an additional net present value of $14.42 million is got than before.

This study aims to clarify the mechanism of exfoliation of graphene through electrical pulsed wire discharge (PWD) of a graphite strip, made by the compression of inexpensive expanded graphite in water. The explosion of the graphite strip was visualized using a high-speed video camera. During the energized heating of the sample, explosions, accompanied by shock waves due to expansion of gas inside the sample, occurred at various locations of the sample, and the sample started to expand rapidly. The exfoliated graphene was observed as a region with low light transmittance. The PWD phenomenon of graphite strips, a type of porous material, is reasonably explained by the change in electrical resistivity of the sample during discharge and the light emission due to energy transition of the excited gas.

Planetary gearboxes are more and more widely used in large and complex construction machinery such as those used in aviation, aerospace fields, and so on. However, the movement of the gear is a typical complex motion and is often under variable conditions in real environments, which may make vibration signals of planetary gearboxes nonlinear and nonstationary. It is more difficult and complex to achieve fault diagnosis than to fix the axis gearboxes effectively. A fault diagnosis method for planetary gearboxes based on improved complementary ensemble empirical mode decomposition (ICEEMD)-time-frequency information entropy and variable predictive model-based class discriminate (VPMCD) is proposed in this paper. First, the vibration signal of planetary gearboxes is decomposed into several intrinsic mode functions (IMFs) by using the ICEEMD algorithm, which is used to determine the noise component by using the magnitude of the entropy and to remove the noise components. Then, the time-frequency information entropy of intrinsic modal function under the new decomposition is calculated and regarded as the characteristic matrix. Finally, the fault mode is classified by the VPMCD method. The experimental results demonstrate that the method proposed in this paper can not only solve the fault diagnosis of planetary gearboxes under different operation conditions, but can also be used for fault diagnosis under variable operation conditions. Simultaneously, the proposed method is superior to the wavelet entropy method and variational mode decomposition (VMD)-time-frequency information entropy.

Basalt fiber has a great advantage on the mechanical properties and durability of reactive powder concrete (RPC) because of its superior mechanical properties and chemical corrosion resistance. In this paper, basalt fiber was adopted to modified RPC, and plain reactive powder concrete (PRPC), basalt fiber reactive powder concrete (BFRPC) and steel fiber reactive powder concrete (SFRPC) were prepared. The mechanical properties and freeze–thaw durability of BFRPC with different basalt fiber contents were tested and compared with PRPC and SFRPC to investigate the effects of basalt fiber contents and fiber type on the mechanical properties and freeze–thaw durability of RPC. Besides, the mass loss rate and compressive strength loss rate of RPC under two freeze–thaw conditions (fresh-water freeze–thaw and chloride-salt freeze–thaw) were tested to evaluate the effects of freeze–thaw conditions on the freeze–thaw durability of RPC. The experiment results showed that the mechanical properties and freeze–thaw resistance of RPC increased as the basalt fiber content increase. Compared with the fresh-water freeze–thaw cycle, the damage of the chloride-salt freeze–thaw cycle on RPC was great. Based on the freeze–thaw experiment results, it was found that SFRPC was sensitive to the corrosion of chloride salts and compared with the steel fiber, the improvement of basalt fiber on the freeze–thaw resistance of RPC was great.

N-doped porous carbon encapsulated NiO/Ni composite nanomaterials (N-doped NiO/Ni@C) was successfully obtained by a one-step solution combustion method. This study demonstrates a one-step combustion method to synthesize n-doped porous carbon encapsulated NiO/Ni composite nanomaterials, using carbon dioxide as the carbon source, nickel nitrate as the nickel source, and hydrazine hydrate as the reaction solution. Spherical NiO nanoparticles with a particle size of 20 nm were uniformly distributed in the carbon matrix. The load of NiO/Ni can be controlled by the amount of nickel nitrate. The range of carbon content of recovered samples is 69–87 at%. The content of incorporated nitrogen for recovered samples is 1.94 at%. As the anode of lithium ion battery, the composite material exhibits high capacity, excellent multiplier performance and stable circulation performance. N-doped NiO/Ni@C-2 was applied to lithium ion batteries, and its reversible capacity maximum is 980 mAh g−1 after 100 cycles at the current density of 0.1 A g−1. Its excellent electrochemical properties imply its high potential application for high-performance lithium-ion battery anode materials.

In this study, the adsorption performances of graphene before and after modification to H2S and CH4 molecules were studied using first principles with the density functional theory (DFT) method. The most stable adsorption configuration, the adsorption energy, the density of states, and the charge transfer are discussed to research the adsorption properties of intrinsic graphene (IG), Ni-doped graphene (Ni−G), vacancy defect graphene (DG), and graphene oxide (G−OH) for H2S and CH4. The weak adsorption and charge transfer of IG achieved different degrees of promotion by doping the Ni atom, setting a single vacancy defect, and adding oxygen-containing functional groups. It can be found that a single vacancy defect significantly enhances the strength of interaction between graphene and adsorbed molecules. DG peculiarly shows excellent adsorption performance for H2S, which is of great significance for the study of a promising sensor for H2S gas.

Herein, we report a one-dimensional (1D) S-doped K3Ti5NbO14@TiO2 (STNT) core-shell heterostructured composite with an enhanced photocatalytic degradation activity under visible light, which was prepared by a simple reassembly-calcination method using thiourea as the S source. The anisotropically shaped rods are favorable for the rapid transport of photogenerated charge carriers. The substitution of Ti4+ by S6+ is primarily incorporated into the lattice of the TiO2 shell so as to create an intra-band-gap state below the conduction band (CB) position, giving rise to Ti−O−S bonds and thus the visible light response. The presence of electron-deficient S atoms is of benefit to the decreased recombination rate of photogenerated electrons and holes by capturing electrons (e−). Meanwhile, a tight close interface between K3Ti5NbO14 and TiO2 was formed to achieve a nano-heterojunction structure, leading to the fostered separation of its interfacial photogenerated electrons and holes. The visible-light-driven photocatalytic degradation of methylene blue (MB) by STNT composites is higher than that by pure K3Ti5NbO14, owing to the synergistic effects of S doping and heterojunction. A possible photocatalytic mechanism was proposed with a reasonable discussion. This work may provide an insight into constructing highly efficient core-shell photocatalysts used toward sustainable environmental remediation and resource shortages.

Basalt fibers are widely used in the modification of concrete materials due to its excellent mechanical properties and corrosion resistance. In this study, the basalt fibers were used to modify reactive powder concrete (RPC). The effect of four mix proportion parameters on the working and mechanical properties of basalt fiber reactive powder concrete (BFRPC) was evaluated by the response surface methodology (RSM). The fluidity, flexural and compressive strength were tested and evaluated. A statistically experimental model indicated that D (the silica fume to cement ratio) was the key of interactions between factors, affecting other factors and controlling properties of BFRPC. The increase in basalt fiber content had a remarkable effect on increasing the flexural and compressive strength when D = 0.2. The addition of basalt fiber obviously improved the mechanical properties of RPC. While when D = 0.4, the decrease of fiber content and the increase of quartz sand content could increase the compressive strength.

Fracture mechanics is one of the most important approaches to structural safety analysis. Modeling the fracture process zone (FPZ) is critical to understand the nonlinear cracking behavior of heterogeneous quasi-brittle materials such as concrete. In this work, a nonlinear extended scaled boundary finite element method (X-SBFEM) was developed incorporating the cohesive fracture behavior of concrete. This newly developed model consists of an iterative procedure to accurately model the traction distribution within the FPZ accounting for the cohesive interactions between crack surfaces. Numerical validations were conducted on both of the concrete beam and dam structures with various loading conditions. The results show that the proposed nonlinear X-SBFEM is capable of modeling the nonlinear fracture propagation process considering the effect of cohesive interactions, thereby yielding higher precisions than the linear X-SBFEM approach.