Search

• A dynamic model for laser welding of aluminum alloy T-joints assisted by solder patch is proposed. • The temperature and flow fields during oscillating laser welding are calculated and analyzed. • The degassing condition of molten pool is improved during oscillating laser welding. • The simulation results are kept consistent with experimental results. During oscillating laser welding of aluminum alloy T-joints assisted by solder patch, it is found that the pore defect can be reduced and the high quality welded T-joints are obtained. To understand the effect of oscillating laser on the formation process of T-joint welds, a dynamic model for laser welding of aluminum alloy T-joints assisted by solder patch is developed in this paper to analyze the dynamic behaviors of keyhole and molten pool. Based on the dynamic model, the morphology characteristics of keyhole and the evolution processes of molten pool temperature and flow fields during conventional laser welding and oscillating laser welding are calculated and compared. The simulation results are found to be kept consistent with the experimental results. It is demonstrated that the keyhole is characterized with better morphology and higher stability and the degassing condition of molten pool is improved during oscillating laser welding compared with conventional laser welding. The molten pool temperature and flow fields are much more uniform during oscillating laser welding. The obtained results are efficient for suppressing weld defects and achieving high quality welding of aluminum alloy T-joints. [ABSTRACT FROM AUTHOR]

Objective: Different grades of meningiomas require different treatment strategies and have a different prognosis; thus, the noninvasive classification of meningiomas before surgery is of great importance. The purpose of this study was to explore the application value of magnetic resonance imaging (MRI) radiomics based on enhanced-T1-weighted (T1WI) images in the prediction of meningiomas grade.Materials and Methods: A total of 98 patients with meningiomas who were confirmed by surgical pathology and underwent preoperative routine MRI between January 2017 and December 2019 were analyzed. There were 82 cases of low-grade meningiomas (WHO grade I) and 16 cases of high-grade meningiomas (7 cases of WHO grade II and 9 cases of WHO grade III). These patients were randomly divided into a training group and test group according to 7:3 ratio. The lesions were manually delineated using ITK-SNAP software, and radiomics analysis were performed using the Analysis Kit (AK) software. A total of 396 tumor texture features were extracted. Subsequently, the LASSO algorithm was used to reduce the feature dimensions. Next, a prediction model was constructed using the Logistic Regression method and receiver operator characteristic was used to evaluate the prediction performance of the model.Results: A radiomics prediction model was constructed based on the selected nine characteristic parameters, which performed well in predicting the meningiomas grade. The accuracy rates in the training group and the test group were respectively 94.3% and 92.9%, the sensitivities were respectively 94.8%, and 91.7%, the specificities were respectively 91.7% and 100%, and the area under the curve values were respectively 0.958 and 0.948.Conclusion: The MRI radiomics method based on enhanced-T1WI images has a good predictive effect on the classification of meningiomas and can provide a basis for planning clinical treatment protocols. [ABSTRACT FROM AUTHOR]

Nutrient amendment increases plant productivity but the effects and mechanisms on soil organic carbon (SOC) accumulation and stability remain unclear, especially in nutrient deficient alpine ecosystem. Here, based on an experiment combining nitrogen (N) and phosphorus (P) input continuously for 15 years, we found that nutrient amendment did not affect total SOC content, but increased mineral-associated C with decreasing soil aggregate stability. Despite increased total phospholipid fatty acid (PLFA) and bacteria PLFA, nutrient amendment decreased soil enzyme activities involved in C cycling. The 13C NMR analyses showed that nutrient amendment decreased the aliphaticity but enhanced aromaticity of SOC. Structural equation models indicated that P availability (Olsen-P content) was most related to shifts in microbial community composition and decreased enzyme activities. Moreover, the concomitantly reduced aggregation and increased mineral-associated C were mainly attributable to the decrease of fungal biomass and increase of bacterial biomass. Together, interconnected factors such as increased acidity, aggregate destabilization, microbial community shift towards bacteria, and loss of oxidative enzyme activities could contribute to the overall response of SOC under intensive N and P input. In particular, available P rather than N may re-shape the pattern of physical and chemical stabilization of SOC, shifting from moderately physical protection to highly chemical stability, implicating the pivotal roles of P management in C cycling of alpine ecosystem. Graphical abstract Image 1 Highlights • Nutrient (nitrogen and phosphorus) amendment decreases soil aggregate stability. • Nutrient amendment increases mineral-associated C and SOC aromaticity. • SOC chemical stability was mainly controlled by P availability via microbial composition. • Nutrient enrichment leads to bacteria-dominated microbial community. [ABSTRACT FROM AUTHOR]

• A novel cross-domain fault diagnosis method for the rolling bearing is proposed. • The designed Adaptive threshold network as the backbone network of ATN-MAML. • The improved threshold function can effectively filter irrelevant information. • The model can obtain high accuracy even facing huge domain differences under limited data. Fault diagnosis of rotating machinery based on deep learning requires many identical labeled data to achieve satisfactory results. However, in engineering scenarios, few typical fault data have been manually marked, resulting in a poor on-site diagnosis consequence. Thus, training an efficient cross-domain diagnosis model under limited data is extremely challenging. This paper proposes a novel cross-domain fault diagnosis method based on model-agnostic meta -learning embedded in Adaptive threshold network (ATN-MAML). The threshold acquisition module is built to determine the signal's irrelevant information threshold adaptively. The designed threshold function is embedded in the network to filter out invalid information effectively as a non-linear layer. Then, MAML's training strategy is optimized to prevent overfitting. Finally, many cross-domain comparison experiments are conducted to verify the effectiveness of the proposed method on two datasets. The results show the model can maintain high accuracy even when the source and target domain data are from different devices. [ABSTRACT FROM AUTHOR]

• A leakage identification method for coal mine air supply pipeline systems is proposed. • The above method is realized by the joint noise reduction algorithm and ELM. • The joint noise reduction method comprises POVMD and wavelet denoising. • The separation point of low- and high-frequency modes is identified by MIE values. • Thresholds in the wavelet denoising method vary according to the decomposition layer. This work presents a leakage identification framework for the coal mine air supply pipeline system based on joint noise reduction and extreme learning machine (ELM). First, leakage vibration signals from a pipeline system are sampled. Then, vibration signals are decomposed by parameter-optimized variational mode decomposition (POVMD), where the mode number is determined via the energy difference principle, and the separation point of low- and high-frequency modes are identified by mutual information entropy. Next, the wavelet denoising method based on an improved threshold function is implemented on high-frequency modes. Then denoised vibration signals are reconstructed by denoised high-frequency modes and low-frequency modes. Subsequently, multi-scale entropy values are extracted and fed into ELM for training and testing. In the laboratory environment, the ELM model with 132 hidden nodes and the Sigmoid function shows the highest testing accuracy (94.17%), indicating that the proposed method is more practical and effective than other methods. [ABSTRACT FROM AUTHOR]

Nutrient availability may exert major controls over soil microbial respiration, especially in carbon (C)-rich, nitrogen (N)-limited ecosystems in high elevation regions, but how soil organic matter (SOM) decomposition and its temperature sensitivity respond to long-term N & P additions in alpine ecosystems remains unclear. We examined the impact of long-term (15 yr) N & P additions on soil microbial respiration and its temperature sensitivity (Q 10 ), and assessed the relative importance of nutrient-induced alterations in substrate quality and the microbial community composition in explaining the variation in soil respiration and temperature sensitivity. We found that N & P additions significantly reduced microbial respiration rates and cumulative C efflux, but increased the Q 10 (15/5 °C). Also, N & P additions reduced the biomass of the whole microbial community, gram negative bacteria and fungi, but increased the aromaticity and aliphaticity of soil organic C substrate. Across the treatments, averaged Q 10 was positively correlated with the complexity of SOM as characterized by 13 C-NMR, supporting the prediction based on kinetic theory that SOM with recalcitrant molecular structure is with high temperature sensitivity. Together, our results showed that changes in both substrate quality and soil microbial community induced by long-term nutrient inputs may alter the response of soil microbial respiration to elevated temperature. Because the positive effects of increasing temperature sensitivity for use of lower quality substrates on C emission may be offset by lower absolute rates at any one temperature, long-term N & P additions increase the uncertainty in predicting the net soil C losses in the scenario of warming on Tibetan Plateau. [ABSTRACT FROM AUTHOR]

• A dynamic model for laser welding of stake welded T-joints with a gap is developed. • The dynamic behaviors of molten pool and keyhole at different stages are discussed. • The molten pool depth shows the periodic fluctuation characteristic. • It is of great importance for improving the welding quality. The dynamic behaviors of molten pool and keyhole have the significant effects on weld quality and joint performance. To investigate the circular shaped oscillating laser welding (OLW) of dissimilar materials stake welded T-joints with a gap between the face plate (FP) and web plate (WP), a dynamic model of molten pool and keyhole for the OLW of dissimilar materials stake welded T-joints with a gap is developed and the dynamic behaviors of molten pool and keyhole during the weld formation process are analyzed and discussed in detail. Based on the calculated results, it is found that the laser beam mainly radiates on the front wall of keyhole and the hump is formed on the rear wall of keyhole relative to the moving direction of laser beam at the FP welding stage. At the gap penetration stage, the keyhole opening at the molten pool bottom of FP shows the trumpet-shaped characteristic and the molten pool is divided into the upper and lower parts by the gap. The formed gas cavity is separated and fused with the keyhole several times at the WP welding stage. The gap around the gas cavity is disappeared after the reintegration of the upper and lower parts of molten pool. The depth of molten pool shows the periodic fluctuation characteristic with time and the fluctuation period is nearly the same as the oscillation period of laser beam during the OLW process. [ABSTRACT FROM AUTHOR]

The laser energy distribution on base material (BM) surface plays an important role in the welding quality. To reduce pore and spatter welding defects during laser welding of aluminum alloy T-joints, a circular shaped oscillating laser beam welding (OLBW) method for aluminum alloy T-joints assisted with solder patch is proposed in this paper. An energy distribution model and a dynamic model for OLBW of aluminum alloy T-joints assisted with solder patch are established to analyze the influence of oscillation parameters on energy distributions of BM surface and dynamic behaviors of molten pool and keyhole. The energy distribution characteristics are obtained by the energy distribution model and the dynamic behaviors of molten pool and keyhole are calculated by the dynamic model for OLBW of aluminum alloy T-joints assisted with solder patch. The results show that the uniformity of energy distribution is improved with the increase in oscillation amplitude or frequency. The oscillation parameters can be adopted to adjust the keyhole stability and the temperature distributions, flow velocities and morphologies of molten pool, which is of great importance for aluminum alloy T-joints welding with high quality. • An energy distribution model and a dynamic model are proposed. • The energy distribution characteristics are calculated. • The dynamic behaviors of molten pool and keyhole are analyzed. • The proposed models are efficient for reducing the welding defects. [ABSTRACT FROM AUTHOR]

Anthropogenic nitrogen (N) input has overtaken natural N fixation as the largest reactive N source and is predicted to stimulate ecosystem carbon (C) sequestration. Most studies of N effects on soil C balance have focused on biological processes that control C input (plant production) and C output (microbial decomposition), but few have examined the general patterns of N effects on the physiochemical processes that regulate soil organic C persistence. We synthesized results from 87 publications that examined effects of experimental N input on soil aggregation, a key process controlling soil C persistence, and its related processes. Globally, N input significantly enhanced plant shoot and root biomass, and the formation of soil macroaggregates and their size (measured as mean weight diameter, MWD; P < 0.05). Surprisingly, N-enhancement of root biomass and soil aggregation primarily stemmed from urea applications. Although urea input reduced microaggregates, it increased macroaggregates (+6.9%) and MWD, likely due to enmeshment by urea-induced root growth (+20.5%). In contrast, other forms of N input (combined NH 4 +, NO 3 − and NH 4 NO 3) did not significantly affect root biomass, microaggregates or macroaggregates, but reduced microbial biomass C. Further, N-promotion of soil aggregation occurred mainly in croplands under low to moderate N input (<200 kg N ha−1 yr−1). Together, these results indicate that the form of N fertilizer exerts a primary control over N effects on plants, microbes, and soil aggregation. Our findings suggest that combination of urea fertilizers and reduced perturbations (e.g., reduced-tillage) may be key to enhance soil aggregation and organic C retention and persistence in vast agroecosystems. • A meta-analysis of N effects on roots and soil aggregates was conducted. • N input enhanced aggregate formation and stability likely by increasing root growth. • N form dominated the N effect on root biomass and soil aggregation. • N-promotion of soil aggregation occurred mainly in croplands with moderate N input. [ABSTRACT FROM AUTHOR]

A pilot-scale trial of four months was conducted to investigate the responses of heavy metal and nutrient to composting animal manure spiked with mushroom residues with and without earthworms. Results showed that earthworm activities accelerated organic matter mineralization (e.g. reduction in C/N ratio, increase in total concentrations of N, P, K) and humification (e.g. increase in humic acid concentration, humification ratio and humification index). Despite composting increased total heavy metal (i.e. As, Pb, Cu, Zn) concentrations irrespective of earthworm, the availability of heavy metals extracted by DTPA significantly ( P < 0.05) decreased particularly in treatments with earthworms introduced. The shift from available to unavailable fractions of heavy metals was either due to earthworm bioaccumulation, as indicated by total heavy metal concentrations being higher in earthworm tissues, or due to the formation of stable metal-humus complexes as indicated by the promotion of humification. Our results suggest that vermicomposting process could magnify the nutrient quality but relieve the heavy metals risk of agricultural organic wastes. [ABSTRACT FROM AUTHOR]