Straw mulching is a widespread practice for reducing the soil carbon loss caused by erosion. However, the effects of straw mulching on dissolved organic matter (DOM) runoff loss from black soil are ...not well studied. How straw mulching affects the composition and loss of runoff DOM by changing soil aggregates remains largely unclear. Here, a straw mulching treatment was compared to a no mulching treatment (as a control) on sloping farmland with black soil erosion in Northeast China. We divided the soil into large macroaggregates (>2 mm), small macroaggregates (0.25–2 mm), and microaggregates (<0.25 mm). After five rain events, the effects of straw mulching on the concentration (characterized by dissolved organic carbon (DOC)) and composition (analyzed by fluorescence spectroscopy) of runoff and soil aggregate DOM were studied. The results showed that straw mulching reduced the runoff amount by 54.7%. Therefore, although straw mulching increased the average DOC concentration in runoff, it reduced the total runoff DOM loss by 48.3%. The composition of runoff DOM is similar to that of soil, as both contain humic-like acid and protein-like components. With straw mulching treatment, the protein-like components in small macroaggregates accumulated and the protein-like components in runoff declined with rain events. Fluorescence spectroscopy technology may help in understanding the hydrological paths of rain events by capturing the dynamic changes of runoff and soil DOM characteristics. A variation partitioning analysis (VPA) indicated that the DOM concentration and composition of microaggregates explained 68.2% of the change in runoff DOM from no mulching plots, while the change in runoff DOM from straw mulching plots was dominated by small macroaggregates at a rate of 55.1%. Taken together, our results demonstrated that straw mulching reduces the fragmentation of small macroaggregates and the loss of microaggregates, thus effecting DOM compositions in soil and reducing the DOM loss in runoff. These results provide a theoretical basis for reducing carbon loss in sloping farmland.
The development of mutually reinforcing solar‐driven interfacial evaporation (SDIE) and integrated functional materials/systems to achieve efficient production of freshwater and energy/matters ...simultaneously under extremely high solar utilization is in high demand. Herein, an integrated SDIE reaction system (reduced graphene oxide (rGO)‐palladium (Pd) catalytic evaporator, rGO‐Pd) is first reported, where SDIE and the integrated catalytic reaction are mutually reinforced. The apparent utilization of solar to thermal energy by the integrated SDIE reaction system is a combination of evaporative utilization and catalytic utilization. The reaction heat released by the rGO‐Pd catalytic evaporator enhances its anti‐salt water production performance to a record of 12.7 L m−2 h−1, surpassing the reported performance of other integrated SDIE reaction systems. In the rGO‐Pd catalytic evaporator, the synergetic effect of photothermal and rapid mass transfer significantly increases the catalytic activity (turnover frequency) of Pd catalysts up to a record 125.07 min−1, which is about 3.75 times of the condition without light. This integrated SDIE reaction system can effectively and simultaneously produce freshwater, salt, and catalyzed chemicals after evaporating water to dryness. This study paves the way for SDIE's high‐performance applications in future integrated water, energy, and environmental systems.
For the first time, a reduced graphene oxide‐palladium catalytic evaporator is reported, where solar‐driven interfacial evaporation (SDIE) and the integrated catalytic reaction are mutually reinforced, which makes the apparent utilization of solar to thermal energy a combination of evaporative utilization and catalytic utilization. This study paves the way for SDIE's high‐performance applications in future integrated water, energy, and environmental systems.
In skin lesion detection systems, a large number of labeled images are usually required to achieve high segmentation accuracy (ACC), which hinders the effectiveness and timeliness of disease ...diagnosis. To this end, a high-precision skin lesion detection system is proposed in this article. The hardware part of the system adopts a modular design, fully considering ergonomics, portability, and miniaturization. In the software part, an active learning ensemble with a multimodel fusion method (ALEM) is proposed to achieve efficient and accurate skin lesion region segmentation. The core idea of ALEM is to use multiple uncertainty strategies of active learning to obtain the most uncertain pixels to be marked in the skin lesion image when marking image pixels. The experiment shows that the average Dice coefficient (DIC) and average Jaccard index (JAI) of ALEM on International Skin Imaging Collaboration (ISIC)-2016 are 82.81% and 92.4%, respectively, and that on ISIC-2017 are 87.51% and 79.26%, respectively. It is worth noting that ALEM still outperforms in tests with only 80% of the training data and no more than 15% pixel annotation per image on average. Our system achieves an average AUC of 91.01% on the ISIC2017 and is tested for effectiveness on real skin. The skin lesion detection system developed in this article is expected to bring convenience to doctors and patients and speed up the diagnosis of diseases.
Eddy current pulsed thermography (ECPT) has attracted much attention in nondestructive testing for its noncontact and large field of view. However, the ECPT images usually suffer from the thermal ...diffusion blurs. The fusion of temperature spatial and temporal features is the hotspot in the new research of ECPT enhancing methods, but these two features are contradictory on the speed and the accuracy of algorithms. Specifically, spatial features process fast but perform poorly in accuracy and antinoise ability, while temporal features are usually calculated from the whole ECPT video sequence, which inevitably increases the demand for data storage and the time cost, especially in the detection of large workpieces, such as engine blades or pressure pipelines. In this article, an enhanced diffusion-based method (EDBM) is proposed to solve this issue, which maps the temporal features through the spatial features of a single ECPT image, significantly reduces the input data volume and shows great potential in ECPT online detection. Experiments on multiple artificial and natural samples verify that, compared with raw ECPT image, the proposed EDBM can reduce root-mean-square error by 51.7%-86.5% (73.2% on average) and improve signal-to-noise ratio by 3.70-16.8 times (6.82 times on average), which performs better than the commonly spatial-based and temporal-based ECPT enhancement algorithms, such as enhanced Canny and independent component analysis, close to the latest sparse-model decomposition methods, but with two orders of magnitude less time cost.
Previous MRI studies confirmed abnormalities in the limbic-cortical-striatal-pallidal-thalamic (LCSPT) network or limbic-cortico-striatal-thalamic-cortical (LCSTC) circuits in patients with major ...depressive disorder (MDD), but few studies have investigated the subcortical structural abnormalities. Therefore, we sought to determine whether focal subcortical grey matter (GM) changes might be present in MDD at an early stage. We recruited 30 first episode, untreated patients with major depressive disorder (MDD) and 26 healthy control subjects. Voxel-based morphometry was used to evaluate cortical grey matter changes, and automated volumetric and shape analyses were used to assess volume and shape changes of the subcortical GM structures, respectively. In addition, probabilistic tractography methods were used to demonstrate the relationship between the subcortical and the cortical GM. Compared to healthy controls, MDD patients had significant volume reductions in the bilateral putamen and left thalamus (FWE-corrected, p < 0.05). Meanwhile, the vertex-based shape analysis showed regionally contracted areas on the dorsolateral and ventromedial aspects of the bilateral putamen, and on the dorsal and ventral aspects of left thalamus in MDD patients (FWE-corrected, p < 0.05). Additionally, a negative correlation was found between local atrophy in the dorsal aspects of the left thalamus and clinical variables representing severity. Furthermore, probabilistic tractography demonstrated that the area of shape deformation of the bilateral putamen and left thalamus have connections with the frontal and temporal lobes, which were found to be related to major depression. Our results suggested that structural abnormalities in the putamen and thalamus might be present in the early stages of MDD, which support the role of subcortical structure in the pathophysiology of MDD. Meanwhile, the present study showed that these subcortical structural abnormalities might be the potential trait markers of MDD.
Development of a tumor is a very complex process, and invasion and metastasis of malignant tumors are hallmarks and are difficult problems to overcome. The tumor microenvironment plays an important ...role in controlling tumor fate and autophagy induced by the tumor microenvironment is attracting more and more attention. Autophagy can be induced by several stressors in the tumor microenvironment and autophagy modifies the tumor microenvironment, too. Autophagy has dual roles in tumor growth. In this review, we discussed the interaction between autophagy and the tumor microenvironment and the paradoxical roles of autophagy on tumor growth at different stages of tumor development.
A code inspection station for Codes on Complex Backgrounds was developed for a beverage packaging line. The system consists of an image acquisition system, embedded industrial computer, control ...system, power supply system, and human-machine interface. The in-line testing results of false detection and omission detection rates demonstrated that the proposed solution can fully meet the production requirements. To the best of our knowledge, this report describes the first time that deep learning has been applied to the industrial defect inspection for the plastic container industry.
Display omitted
•Proposed a CNN based in-line inspection of codes on complex backgrounds.•DL was applied to the defect inspection for the container industry first time.•Transfer learning enabled the trained model to inspect different kinds of packages.•Achieved both excellent performance in accuracy and computational complexity.
Machine vision technologies have been widely used for automating the product quality control, but the defect inspection for codes on complex backgrounds is still a challenging task in the plastic container industry. In this work, an efficient and accurate inspection solution based on deep learning was proposed aiming at the detection of codes on complex backgrounds for the plastic container such as beverage packages. Firstly, image processing algorithms such as the region translation method, morphological processing, and image matching technology based on SIFT (Scale Invariant Feature Transform) features were implemented to generate synthetic defective samples, which moderated the class-imbalance problem. Data augmentation strategies were used to increase the amount of training data. Secondly, the ShuffleNet V2 framework was adapted to inspect inkjet codes on complex backgrounds. Additionally, the transfer learning was used to transfer the trained model to other inspection tasks for different kinds of packages. Finally, the proposed approach was built onto an in-line code inspection apparatus for the plastic container industry, and an accuracy of 0.9988 was achieved. The in-line testing results of false detection and omission detection rates demonstrated that the proposed solution can fully meet the production requirements. To the best of our knowledge, this report describes the first time that deep learning has been applied to the industrial defect inspection for the plastic container industry.
Hepatocellular carcinoma (HCC) is one of the most common cancers in the world and is often associated with a poor prognosis. The main reason for this poor prognosis is that inconspicuous early ...symptoms lead to delayed diagnosis. Treatment options for advanced HCC remain limited and ineffective. In this context, the exploration of the immune microenvironment in HCC becomes attractive. In this study, we divided HCC into immune cell and non-immune cell subtypes, by single-cell sequencing analysis of GEO dataset GSE146115. We found differentially expressed genes in the two subtypes, which we used to construct a prognostic model for HCC through Cox and Lasso regressions. Our prognostic model can accurately evaluate the prognosis of HCC patients, and provide a reference for the design of immunotherapy for HCC.
Abstract
Single Event Gate Rupture (SEGR) is one of the most severe problems that SiC MOSFETs experience in the space radiation environment. The influence of drain bias (
V
DS
) and gate bias (
V
GS
...) on SEGR was explored in this work using the fluctuation of leakage current when the device was irradiated at various biases. The source of leakage current is isolated and determined through testing, and the influence mechanisms of
V
GS
and
V
DS
effects on SEGR are further explored using TCAD simulation. The investigation demonstrates that while the drain bias can indirectly enhance the potential of SiC-side oxide, the gate bias can directly alter the potential of metal-side oxide during heavy ion irradiation. When gate bias and drain bias are combined, a strong electric field is generated in the gate oxide, resulting in SEGR in SiC MOSFETs. In addition to single event burnout, the SEGR effect is a significant issue for SiC MOSFETs due to their high susceptibility to heavy ion irradiation.
We present a new seismic reflection dataset and use it to characterize fossil mud volcanoes in the southwestern Songliao graben basin in northeastern China. The results reveal a link between mud ...volcanism and slab rollback along the eastern Asian margin. This study focuses on the upper 3 km of the Lujiapu sub-basin, which lies in the southwestern Songliao basin. The base of the sequence consists of Lower Cretaceous siliciclastic deposits that are penetrated by mud volcano fluidization pipes. These deposits are overlain by Upper Cretaceous strata that have been deformed by the mud volcanoes. The sequence is capped by undeformed Neogene–Quaternary sediments. These observations constrain the timing of mud volcanism to the Santonian–Campanian (~87–72 Ma). During Late Cretaceous diapirism, normal faulting at the tops of the mud volcanoes formed horsts and grabens. Surface anticlines and inversion structures near the mud volcano source layer indicate that basin inversion occurred during deposition of the Sifangtai and Mingshui formations (Campanian). Similar structures are also identified in the central Songliao basin. Thus, we propose that slab flattening to deepening during paleo-Pacific Plate subduction led to basin inversion. This process triggered the migration of high-pressure fluids and brecciated plastic rocks along pre-existing normal faults, which in turn produced the mud volcano system.
PDF
