Search

The Microwave Radiation Imager (MWRI) onboard the FengYun satellite plays a crucial role in global change monitoring and numerical weather prediction. Estimating and correcting geolocation errors are important to retrieving accurate geophysical variables. However, the instantaneous field of view (IFOV) inter-channel deviation, which is mainly caused by the structure mounting error and measurement error of feedhorns, is less studied. In this present study, we constructed a general theoretical model to automatically estimate the IFOV inter-channel deviations suitable for conical-scanning instruments. The model can automatically detect the along-track and across-track vectors that pass through the land–sea boundary points and are perpendicular to the actual coastlines. Regarding the midpoints of the vectors as the brightness temperature (Tb) inflection points, the IFOV inter-channel deviation is the pixel offset or distance of the maximum gradients of the Tb near the inflection points for each channel relative to the 89-GHz V-pol channel. We tested the model’s operational performance using the FY-3G/MWRI-Rainfall Mission (MWRI-RM) observations. Considering that parameter uploading adjusted the IFOV inter-channel deviations, the model’s validity was verified by comparing the adjustments calculated by the model with the theoretical changes caused by parameter uploading. The result shows that the differences between them for all window channels are less than 100 m, indicating the model’s effectiveness in evaluating the IFOV inter-channel deviation for the MWRI-RM. Furthermore, the estimated on-orbit IFOV inter-channel deviations for the MWRI-RM show that all channel deviations are less than 1 km, meeting the instrument’s design requirement of 2 km. We believe this study will provide a foundation for IFOV inter-channel registration of passive microwave payloads and spatial matching of multiple payloads.

This study introduces an innovative model-order reduction (MOR) technique that integrates boundary element and finite element methodologies, streamlining the analysis of wideband vibro-acoustic interactions within aquatic and aerial environments. The external acoustic phenomena are efficiently simulated via the boundary element method (BEM), while the finite element method (FEM) adeptly captures the dynamics of vibrating thin-walled structures. Furthermore, the integration of isogeometric analysis within the finite element/boundary element framework ensures geometric integrity and maintains high-order continuity for Kirchhoff–Love shell models, all without the intermediary step of meshing. Foundational to our reduced-order model is the application of the second-order Arnoldi method coupled with Taylor expansions, effectively eliminating the frequency dependence of system matrices. The proposed technique significantly enhances the computational efficiency of wideband vibro-acoustic coupling analyses, as demonstrated through numerical simulations.

Channidae (snakehead fish) is a family of medium-to-large freshwater carnivorous fish and contain the genus, Channa. Here, the complete mitogenomes of two Channa fish were determined and comparatively analyzed with the mitogenomes of 16 other Channidae fish species. The two newly sequenced complete mitogenomes were circular DNA molecules with sizes of 16,953 bp (Channa burmanica; OP954106) and 16,897 bp (Channa aurantimaculata; OQ134162). The mitogenomes were composed of 37 genes and one D-loop region. Positive AT skews and negative GC skews were found in the mitogenomes. Most protein-coding genes (PCGs) started with the conventional start codon, ATG; however, the sequence of the stop codon was variable. There was no obvious difference in relative synonymous codon usage among the two mitogenomes, and the two species shared a similar number of codon usage of mitogenomic PCGs, which was also similar to the mean values for the other 15 species of Channa. All Ka/Ks values were cox1 had the lowest value, and atp8 had the highest. All of the tRNAs were typical clover structures, except trnS1. Phylogenetic analysis showed that C. burmanica and C. aurantimaculata shared a close relationship and that they were also closely related to C. gachua. These findings enrich the gene database of Channidae species, clarify the mitochondrial genome structure of the two species, and provide basic data for invasive biological surveillance in the future.

The COVID-19 pandemic over recent years has shown a great need for the rapid, low-cost, and on-site detection of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2). In this study, an aptamer-based colloidal gold nanoparticle lateral flow test strip was well developed to realize the visual detection of wild-type SARS-CoV-2 spike proteins (SPs) and multiple variants. Under the optimal reaction conditions, a low detection limit of SARS-CoV-2 S proteins of 0.68 nM was acquired, and the actual detection recovery was 83.3% to 108.8% for real-world samples. This suggests a potential tool for the prompt detection of SARS-CoV-2 with good sensitivity and accuracy, and a new method for the development of alternative antibody test strips for the detection of other viral targets.

Controlled-release urea (CRU) fertilizers are widely used in agricultural production to reduce conventional nitrogen (N) fertilization-induced agricultural greenhouse gas emissions (GHGs) and improve N use efficiency (NUE). However, the long-term effects of different CRU fertilizers on GHGs and crop yields in vegetable fields remain relatively unexplored. This study investigated the variations in GHG emissions at four growth stages of lettuce in the spring and autumn seasons based on a five-year field experiment in the North China Plain. Four treatments were setup: CK (without N application), U (conventional urea—N application), ON (20% reduction in urea—N application), CRU (20% reduction in polyurethane-coated urea without topdressing), and DCRU (20% reduction in polyurethane-coated urea containing dicyandiamide [DCD] without topdressing). The results show that N application treatments significantly increased the GHG emissions and the lettuce yield and net yield, and DCRU exhibited the lowest N2O and CO2 emissions, the highest lettuce yield and net yield, and the highest lettuce N content of the N application treatments. When compared to U, the N2O emission peak under CRU and DCRU treatments was notably decreased and delayed, and their average N2O emission fluxes were significantly reduced by 10.20–20.72% and 17.51–29.35%, respectively, leading to a significant reduction in mean cumulative N2O emissions during the 2017–2021 period. When compared to U, the CO2 fluxes of DCRU significantly decreased by 8.0–16.54% in the seedling period, and mean cumulative CO2 emission decreased by 9.28%. Moreover, compared to U, the global warming potential (GWP) and greenhouse gas intensity (GHGI) of the DCRU treatment was significantly alleviated by 9.02–17.13% and 16.68–20.36%, respectively. Compared to U, the N content of lettuce under DCRU was significantly increased by 6.48–17.25%, and the lettuce net yield was also significantly increased by 5.41–7.71%. These observations indicated that the simple and efficient N management strategy to strike a balance between enhancing lettuce yields and reduce GHG emissions in open-field lettuce fields could be obtained by applying controlled-release urea containing DCD without topdressing.

Layered double oxides are widely employed in catalyzing the aldol condensation for producing biofuels, but its selectivity and stability need to be further improved. Herein, a novel MCM-41-supported Mg–Al-layered double oxide (LDO/MCM-41) was prepared via the in situ integration of a sol–gel process and coprecipitation, followed by calcination. This composite was first employed to catalyze the self-condensation of cyclopentanone for producing high-density cycloalkane precursors. LDO/MCM-41 possessed large specific surface area, uniform pore size distribution, abundant medium basic sites and Bronsted acid sites. Compared with the bulk LDO, LDO/MCM-41 exhibited a higher selectivity for C10 and C15 oxygenates at 150 °C (93.4% vs. 84.6%). The selectivity for C15 was especially enhanced on LDO/MCM-41, which was three times greater than that on LDO. The stability test showed that naked LDO with stronger basic strength had a rapid initial activity, while it suffered an obvious deactivation due to its poor carbon balance. LDO/MCM-41 with lower basic strength had an enhanced stability even with a lower initial activity. Under the optimum conditions (50% LDO loading, 170 °C, 7 h), the cyclopentanone conversion on LDO/MCM-41 reached 77.8%, with a 60% yield of C10 and 15.2% yield of C15.

This paper introduces the novel application of the mass and force lumping technique to enhance the finite element discretization of the fully intrinsic beam formulation. In our aeroelastic system model, 2-D unsteady aerodynamics were incorporated alongside simple calculations for thrust and gravity. Through the central difference discretization method, the discretized system was thoroughly examined, shedding light on the advantages of the mass and force lumping approach. With the use of a first-order lumping method, we successfully reconstructed the inertia matrices, external forces, and moments. The resulting equations are more systematically structured, facilitating the extraction of a regular state-space linear system using the direct index reduction method post-linearization. Numerical results further confirm that the proposed techniques can effectively capture the nonlinear dynamics of aeroelastic systems, enabling equation reconstruction and leading to significant benefits in system order reduction and flight dynamical analysis.

With the development of the smart grid, the traditional defect detection methods in transmission lines are gradually shifted to the combination of robots or drones and deep learning technology to realize the automatic detection of defects, avoiding the risks and computational costs of manual detection. Lightweight embedded devices such as drones and robots belong to small devices with limited computational resources, while deep learning mostly relies on deep neural networks with huge computational resources. And semantic features of deep networks are richer, which are also critical for accurately classifying morphologically similar defects for detection, helping to identify differences and classify transmission line components. Therefore, we propose a method to obtain advanced semantic features even in shallow networks. Combined with transfer learning, we change the image features (e.g., position and edge connectivity) under self-supervised learning during pre-training. This allows the pre-trained model to learn potential semantic feature representations rather than relying on low-level features. The pre-trained model then directs a shallow network to extract rich semantic features for downstream tasks. In addition, we introduce a category semantic fusion module (CSFM) to enhance feature fusion by utilizing channel attention to capture global and local information lost during compression and extraction. This module helps to obtain more category semantic information. Our experiments on a self-created transmission line defect dataset show the superiority of modifying low-level image information during pre-training when adjusting the number of network layers and embedding of the CSFM. The strategy demonstrates generalization on the publicly available PASCAL VOC dataset. Finally, compared with state-of-the-art methods on the synthetic fog insulator dataset (SFID), the strategy achieves comparable performance with much smaller network depths.

Depth maps captured by traditional consumer-grade depth cameras are often noisy and low-resolution. Especially when upsampling low-resolution depth maps with large upsampling factors, the resulting depth maps tend to suffer from vague edges. To address these issues, we propose a multi-channel progressive attention fusion network that utilizes a pyramid structure to progressively recover high-resolution depth maps. The inputs of the network are the low-resolution depth image and its corresponding color image. The color image is used as prior information in this network to fill in the missing high-frequency information of the depth image. Then, an attention-based multi-branch feature fusion module is employed to mitigate the texture replication issue caused by incorrect guidance from the color image and inconsistencies between the color image and the depth map. This module restores the HR depth map by effectively integrating the information from both inputs. Extensive experimental results demonstrate that our proposed method outperforms existing methods.

Power load forecasting plays an important role in power systems, and the accuracy of load forecasting is of vital importance to power system planning as well as economic efficiency. Power load data are nonsmooth, nonlinear time-series and “noisy” data. Traditional load forecasting has low accuracy and curves not fitting the load variation. It is not well predicted by a single forecasting model. In this paper, we propose a novel model based on the combination of data mining and deep learning to improve the prediction accuracy. First, data preprocessing is performed. Second, identification and correction of anomalous data, normalization of continuous sequences, and one-hot encoding of discrete sequences are performed. The load data are decomposed and denoised using the double decomposition modal (LVMD) strategy, the load curves are clustered using the double weighted fuzzy C-means (DBFCM) algorithm, and the typical curves obtained are used as load patterns. In addition, data feature analysis is performed. A convolutional neural network (CNN) is used to extract data features. A bidirectional long short-term memory (BLSTM) network is used for prediction, in which the number of hidden layer neurons, the number of training epochs, the learning rate, the regularization coefficient, and other relevant parameters in the BLSTM network are optimized using the influenza virus immunity optimization algorithm (IVIA). Finally, the historical data of City H from 1 January 2016 to 31 December 2018, are used for load forecasting. The experimental results show that the novel model based on LVMD-DBFCM load c1urve clustering combined with CNN-IVIA-BLSTM proposed in this paper has an error of only 2% for electric load forecasting.

Unmanned aerial vehicle (UAV) tilt photography technology has gradually become a new technical means of disaster risk identification. This technology combines UAVs, satellite remote sensing, and ground online monitoring systems to establish an integrated space–sky–Earth system that can be used for tailings pond risk identification. With the use of this system for visual interpretation, water body identification, and monitoring data analysis, multiple types of monitoring parameters of a typical tailings pond in China, such as the seepage line and surface deformation, were obtained. Moreover, intelligent fusion analysis was performed of multisource data to outline the problems affecting tailings safety in the process of elevation expansion and irregular ore discharge of the tailings pond. Warning values of different levels were obtained to assess the overall safety condition of the tailings pond, and the proposed technology was verified. The research results could provide a new basis for accurate evaluation of the running state of tailings ponds and offer an effective remote monitoring means for tailings pond enterprises and supervisory departments.

Micro(nano)plastics are emerging contaminants that have been shown to cause various ecotoxicological effects on soil biota. Earthworms, as engineers of the ecosystem, play a fundamental role in soil ecosystem processes and have been used as model species in ecotoxicological studies. Research that evaluates micro(nano)plastic toxicity to earthworms has increased greatly over the last decade; however, only few studies have been conducted to highlight the current knowledge and evolving trends of this topic. This study aims to visualize the research status and knowledge structure of the relevant literature. Bibliometrics and visualization analyses were conducted using co-citations, cooperation networks and cluster analysis. The results showed that micro(nano)plastic toxicity to earthworms is an emerging and increasingly popular topic, with 78 articles published from 2013 to 2022, the majority of which were published in the last two years. The most prolific publications and journals involved in this topic were also identified. In addition, the diversity of cooperative relationships among different countries and institutions confirmed the evolution of this research field, in which China contributed substantially. The high-frequency keywords were then determined using co-occurrence analysis, and were identified as exposure, bioaccumulation, soil, pollution, toxicity, oxidative stress, heavy metal, microplastic, Eisenia foetida and community. Moreover, a total of eight clusters were obtained based on topic knowledge clustering, and these included the following themes: plastic pollution, ingestion, combined effects and the biological endpoints of earthworms and toxic mechanisms. This study provides an overview and knowledge structure of micro(nano)plastic toxicity to earthworms so that future researchers can identify their research topics and potential collaborators.

Open vegetable fields in China are a major anthropogenic source of nitrous oxide (N2O) emissions due to excessive nitrogen (N) fertilization. A 4 yr lettuce experiment was conducted to determine the impacts of controlled-release fertilizers (CRFs) and nitrification inhibitors (NIs) on lettuce yield, N2O emissions and net economic benefits. Five treatments included (i) no N fertilizer (CK), (ii) conventional urea at 255 kg N ha–1 based on farmers’ practice (FP), (iii) conventional urea at 204 kg N ha–1 (OPT), (iv) CRF at 204 kg N ha–1 (CU) and (v) CRF (204 kg N ha–1) added with NI (CUNI). No significant differences were found in the lettuce yields among different N fertilization treatments. Compared with FP, the cumulative N2O emissions were significantly decreased by 8.1%, 38.0% and 42.6% under OPT, CU and CUNI, respectively. Meanwhile, the net benefits of OPT, CU and CUNI were improved by USD 281, USD 871 and USD 1024 ha–1 compared to CN, respectively. This study recommends the combined application of CRF and NI at a reduced N rate as the optimal N fertilizer management for the sustainable production of vegetables in China with the lowest environmental risks and the greatest economic benefits.

In complex driving scenarios, automated vehicles should behave reasonably and respond adaptively with high computational efficiency. In this paper, a computational efficient motion planning method is proposed, which considers traffic interaction and accelerates calculation. Firstly, the behavior is decided by connecting the points on the unequally divided road segments and lane centerlines, which simplifies the decision-making process in both space and time span. Secondly, as the dynamic vehicle model with changeable longitudinal velocity is considered in the trajectory generation module, the C/GMRES algorithm is used to accelerate the calculation of trajectory generation and realize on-line solving in nonlinear model predictive control. Meanwhile, the motion of other traffic participants is more accurately predicted based on the driver’s intention and kinematics vehicle model, which enables the host vehicle to obtain a more reasonable behavior and trajectory. The simulation results verify the effectiveness of the proposed method.

Soil macrofauna, such as Spirobolus bungii, are an important component of ecosystems. However, systematic studies of the genetic diversity, population genetic structure, and the potential factors affecting the genetic differentiation of S. bungii are lacking. We performed a population genetic study of 166 individuals from the mountains to the south of the Yangtze River, north of the Yangtze River in Nanjing city, and near Tianjin city, in order to investigate the correlations between geographical distance and genetic diversity. A total of 1182 bp of COX2 and Cytb gene sequences of mitochondrial DNA, and 700 bp of the 18S rRNA gene sequence were analyzed. There were two haplotypes and one variable site in the 18S rRNA gene, and 28 haplotypes and 78 variable sites in the COX2 and Cytb genes. In this study, the 18S rRNA gene was used for species identification, and mtDNA (concatenated sequences with Cytb and COX2) was used for population genetic analysis. Structure cluster analysis indicated that the genetic structures of the different populations of S. bungii tended to be consistent at small geographical scales. Phylogenetic trees revealed that the haplotypes were clearly divided into three branches: the area south of the Yangtze River, the area to the north of the Yangtze River in Nanjing, and the area in Tianjin. Large geographical barriers and long geographical distance significantly blocked gene flow between populations of S. bungii. Our results provide a basic theoretical basis for subsequent studies of millipede taxonomy and population genetic evolution.

We sequenced the complete mitochondrial genomes of Lycosa shansia, and Lycosa singoriensis by combining Sanger and next-generation sequencing methods and analyzed the sequenced genomes in order to explore the phylogenetic placement and the mitogenome composition and evolution of these species. The mitochondrial genome of L. shansia was 14,638 bp, whereas that of L. singoriensis was 13,686 bp. The type of genes and direction of the coding strand present in the mitogenomes were the same as those in other species of Lycosoidea, including two ribosomal RNA genes (rRNAs), 22 transfer RNA genes (tRNAs), and 13 protein-coding genes (PCGs). The mitogenomes of the two species exhibited negative AT and positive GC skews. This indicated that the nucleotide compositions of the mitogenomes of L. singoriensis and L. shansia tended to be T and G. Both the mean and median values of Ka/Ks of ATP8 were the highest among the 13 protein-coding genes, indicating that it might have evolved more rapidly than the other protein-coding genes in both species. ATP8 may have undergone more relaxed selection constraints and accumulated more mutations. In addition, many tRNAs lacked T and D stem loops; a few had no acceptor stems. The assessed species were recovered nested within Lycosidae with high support. The present findings will be useful for future studies on the mitogenome evolution of spiders.

To apply EEG-based brain-machine interfaces during rehabilitation, separating various tasks during motor imagery (MI) and assimilating MI into motor execution (ME) are needed. Previous studies were focusing on classifying different MI tasks based on complex algorithms. In this paper, we implement intelligent, straightforward, comprehensible, time-efficient, and channel-reduced methods to classify ME versus MI and left- versus right-hand MI. EEG of 30 healthy participants undertaking motional tasks is recorded to investigate two classification tasks. For the first task, we first propose a “follow-up” pattern based on the beta rebound. This method achieves an average classification accuracy of 59.77% ± 11.95% and can be up to 89.47% for finger-crossing. Aside from time-domain information, we map EEG signals to feature space using extraction methods including statistics, wavelet coefficients, average power, sample entropy, and common spatial patterns. To evaluate their practicability, we adopt a support vector machine as an intelligent classifier model and sparse logistic regression as a feature selection technique and achieve 79.51% accuracy. Similar approaches are taken for the second classification reaching 75.22% accuracy. The classifiers we propose show high accuracy and intelligence. The achieved results make our approach highly suitable to be applied to the rehabilitation of paralyzed limbs.

Estuarine environmental capacity is the foundation for coastal biological diversity and self-purification capacity. Hence, studies on the marine environmental capacity (MEC) are the foundation for the total discharge control and water quality improvement of land-based pollutants. In the article, A calibrated two-dimensional hydrodynamic model was used to study the environmental characteristics of Sanmen Bay, including the tides, the residual currents, the tidal prism, and water exchange abilities. The model results were used to estimate the environmental capacity of the bay. Taking the pollution problem in Sanmen Bay as an example, the method of response factor, the sub-unit control method, and the phased control method were used to estimate the environmental capacity, pollutant amounts, and the pollutant reduction in the bay. The concentrations of COD, inorganic nitrogen, and acid salt in Sanmen Bay are spatially varied, with higher values occurring in the western part and in the inner bay. The half exchange time of the whole bay is about 23 days, and the exchange time of 95% water body is about 60 days. The evaluation of MEC cannot only provide technical support for the offshore aquaculture industries but also provide a scientific basis for the total control of terrigenous pollutants in coastal cities in Southern Zhejiang Province.

Top tensioned risers (TTRs) and steel catenary risers (SCRs) have been widely used in the field of marine engineering. They are commonly used to transport fluids from subsea wells to surface platforms. Under the action of ocean currents, these risers are often subjected to vortex-induced vibrations (VIVs), which might lead to serious fatigue damage. In this study, VIV around TTR and SCR were numerically simulated using the computational fluid dynamics software FLUENT when the Reynolds number was 4000. In the calculations, the full hexahedron grid and large eddy simulation were used to ensure calculation accuracy from the boundary conditions, as well as solution control. The shape, frequency, and amplitude of VIV produced by TTRs and SCRs at different times and depths were simulated.

The influence on tunnel construction of karst and underground rivers is always an important problem in tunnel engineering. In order to demonstrate the rationality of backfill parameters and the effectiveness of supports under the influence of groundwater when a tunnel passes through a large karst cave, the finite element software FLAC3D was used for numerical analysis. Converting the mean values and standard deviations of mechanical response results of the surrounding rock and the supports on multiple sections into the ECULID distance from the origin point on a two-dimensional plane as evaluation indexes, the influence of the reinforcement parameters on the mechanical response of the surrounding rock and the supports was analyzed by orthogonal experiments. Based on fuzzy decision theory, by regarding the ECULID distance between the simulated result of each group and the global optimal value of the multiple evaluation index as the comprehensive evaluation index, a backfill parameter design method was proposed. By comparing the results which used optimal and worst parameters in the FLAC3D, 10 times and 2.5 times differences in longitudinal and horizontal displacement were observed, respectively. Then, the optimal backfill parameters were applied to the actual project for verification. The field monitoring results showed that the optimal backfill parameters can effectively reduce the displacement around the tunnel. After constructing a diversion for the underground river, the water flow in the karst cave did not rise during a rainstorm. This study provides a reference for the design and construction of other projects in the future.

Most current approaches applied for the essential identification of adulteration in edible vegetable oils are of limited practical benefit because they require long analysis times, professional training, and costly instrumentation. The present work addresses this issue by developing a novel simple, accurate, and rapid identification approach based on the magnetic resonance relaxation fingerprints obtained from low-field nuclear magnetic resonance spectroscopy measurements of edible vegetable oils. The relaxation fingerprints obtained for six types of edible vegetable oil, including flaxseed oil, olive oil, soybean oil, corn oil, peanut oil, and sunflower oil, are demonstrated to have sufficiently unique characteristics to enable the identification of the individual types of oil in a sample. By using principal component analysis, three characteristic regions in the fingerprints were screened out to create a novel three-dimensional characteristic coordination system for oil discrimination and adulteration identification. Univariate analysis and partial least squares regression were used to successfully quantify the oil adulteration in adulterated binary oil samples, indicating the great potential of the present approach on both identification and quantification of edible oil adulteration.

To create approximately spherical structures with curved sidewalls, this paper presents a method for building a series of decreasing slopes along the sidewall of a circular truncated cone. The multistep ring-edge etching technology of first reducing the concentric mask and then cutting the top off to create a mesa shape can be used to form the slopes. This wet-etching method avoids the constraints of crystallographic properties with surfactant-added Tetramethylammonium hydroxide (TMAH), enabling the manufacture of successive given inclination angles, the precise modulation of the spherical curvature by reduction design of concentric masks, and the setting of etching time. The newly approximated spherical Si microstructure patterns can be used for microlenses, quartz crystal resonators, micropulleys, and other applications. The present research is an approach to fabricate advanced microelectromechanical systems (MEMS) curved-surface structures, extending the range of 3D structures fabricated by silicon wet etching.

Stellite 6 alloy has excellent wear resistance, corrosion resistance, and oxidation resistance, however the difficulties in traditional processing limit its wide application. Additive manufacturing technology that has emerged in recent years is expected to provide a new way for the processing of stellite 6 alloy. In this study, two square thin-walled stellite 6 parts were fabricated through the wire arc additive manufacturing technology. At the same time, the effect of stress relief annealing on the mechanical performance of the fabricated stellite 6 part was studied and compared with the corresponding casting part. The results indicate that the additive manufacturing stellite 6 components exhibit satisfactory quality and appearance. Moreover, the microstructure of the additive manufacturing part is much finer than that of the casting part. From the substrate to the top region of the additive manufacturing part, the morphology of the dendrites changes from columnar to equiaxed, and the hardness increases firstly and then decreases gradually. In addition, the average hardness of the additive manufacturing part is ~7–8 HRC higher than the casting part. The ultimate tensile strength and yield strength is ~150MPa higher than the casting part, while the elongation is almost the same. The stress relief annealing has no significant effect on the hardness of the AM part, but it can slightly improve the strength.

In this study, Multiwalled carbon nanotubes (MWCNTs) were oxidized by a mixture of sulfuric acid and nitric acid (V:V = 3:1) at 70 °C for 1, 2, and 4 h, respectively. The oxidized MWCNTs were characterized by N2 adsorption, Fourier-transform infrared spectroscopy (FT-IR), X-ray photoelectron spectroscopy (XPS), thermal gravimetric analysis (TGA), and Raman spectroscopy to determine the oxidation degree. The dispersion of the MWCNTs was investigated by UV-vis-NIR, SEM, and dynamic light scattering measurements. Results show that sulfonitric treatment increased the surface area and total pore volume and reduced the average pore diameter of MWCNTs. The treatment promoted the formation of oxidized species on the surface MWCNTs, as identified by FT-IR, TGA, and X-ray photoelectron spectroscopy measurements, and more oxygen-containing functional groups were generated when treatment time was extended. Moreover, a general relationship between oxidation degree and dispersibility of MWCNTs in water was established. UV-vis-NIR and dynamic light scattering measurements and SEM images revealed that MWCNTs with higher oxidation degree showed better dispersibility in water.

Effective feature selection can help improve the classification performance in bearing fault diagnosis. This paper proposes a novel feature selection method based on bearing fault diagnosis called Feature-to-Feature and Feature-to-Category- Maximum Information Coefficient (FF-FC-MIC), which considers the relevance among features and relevance between features and fault categories by exploiting the nonlinearity capturing capability of maximum information coefficient. In this method, a weak correlation feature subset obtained by a Feature-to-Feature-Maximum Information Coefficient (FF-MIC) matrix and a strong correlation feature subset obtained by a Feature-to-Category-Maximum Information Coefficient (FC-MIC) matrix are merged into a final diagnostic feature set by an intersection operation. To evaluate the proposed FF-FC-MIC method, vibration data collected from two bearing fault experiment platforms (CWRU dataset and CUT-2 dataset) were employed. Experimental results showed that accuracy of FF-FC-MIC can achieve 97.50%, and 98.75% on the CWRU dataset at the motor speeds of 1750 rpm, and 1772 rpm, respectively, and reach 91.75%, 94.69%, and 99.07% on CUT-2 dataset at the motor speeds of 2000 rpm, 2500 rpm, 3000 rpm, respectively. A significant improvement of FF-FC-MIC has been confirmed, since the p-values between FF-FC-MIC and the other methods are 1.166 × 10 − 3 , 2.509 × 10 − 5 , and 3.576 × 10 − 2 , respectively. Through comparison with other methods, FF-FC-MIC not only exceeds each of the baseline feature selection method in diagnosis accuracy, but also reduces the number of features.

Wire feeding additive manufacturing (WFAM) has broad application prospects because of its advantages of low cost and high efficiency. However, with the mode of lateral wire feeding, including wire and laser additive manufacturing, gas tungsten arc additive manufacturing etc., it is easy to generate macro defects on the surface of the components because of the anisotropy of melted wire, which limits the promotion and application of WFAM. In this work, gas tungsten arc additive manufacturing with lateral wire feeding is proposed to investigate the mechanisms of macro defects. The results illustrate that the defect forms mainly include side spatters, collapse, poor flatness, and unmelted wire. It was found that the heat input, layer thickness, tool path, and wire curvature can have an impact on the macro defects. Side spatters are the most serious defects, mainly because the droplets cannot be transferred to the center of the molten pool in the lateral wire feeding mode. This research indicates that the macro defects can be controlled by optimizing the process parameters. Finally, block parts without macro defects were fabricated, which is meaningful for the further application of WFAM.