Search

To study the static software defect detection system, based on the traditional static software defect detection system design, a new static software defect detection system design based on big data technology is proposed. The proposed method can optimize the distribution of test resources and improve the quality of software products by predicting the potential defect program modules and design the software and hardware of the static software defect detection system of big data technology. It is found that the traditional static software defect detection system design based on code source data takes a long time, averaging 65 h /day. However, the traditional static software defect detection system based on deep learning has a short detection time, averaging 35 h/day. In this article, the detection time of the static software defect detection system based on big data is shorter than that of the other two traditional system designs, with an average of 15 h/day. Because the system design adjusts the operating state of the system, it improves the accuracy of data operation. On the premise of data collection, the system inspection research is completed, which ensures the operational safety of software data, alleviates the contradiction between system and data to a high degree, improves the efficiency of system operation, reduces unnecessary operations, further shortens the time required for inspection, improves the system performance, and has higher research and operation value.

The QR code recognition often faces the challenges of uneven background fluctuations, inadequate illuminations, and distortions due to the improper image acquisition method. This makes the identification of QR codes difficult, and therefore, to deal with this problem, artificial intelligence-based systems came into existence. To improve the recognition rate of QR image codes, this article adopts an improved adaptive median filter algorithm and a QR code distortion correction method based on backpropagation (BP) neural networks. This combination of artificial intelligence algorithms is capable of fitting the distorted QR image into the geometric deformation pattern, and QR code recognition is accomplished. The two-dimensional code distortion is addressed in this study, which was a serious research issue in the existing software systems. The research outcomes obtained after emphasizing on the preprocessing stage of the image revealed that a significant improvement of 14% is observed for the reading rate of QR image code, after processing by the system algorithm in this article. The artificial intelligence algorithm adopted has a certain effect in improving the recognition rate of the two-dimensional code image.

Forests are the largest carbon sink in terrestrial ecosystems, and the impact of nitrogen (N) deposition on this carbon sink depends on the fate of external N inputs. However, the patterns and driving factors of N retention in different forest compartments remain elusive. In this study, we synthesized 408 observations from global forest 15N tracer experiments to reveal the variation and underlying mechanisms of 15N retention in plants and soils. The results showed that the average total ecosystem 15N retention in global forests was 63.04 ± 1.23%, with the soil pool being the main N sink (45.76 ± 1.29%). Plants absorbed 17.28 ± 0.83% of 15N, with more allocated to leaves (5.83 ± 0.63%) and roots (5.84 ± 0.44%). In subtropical and tropical forests, 15N was mainly absorbed by plants and mineral soils, while the organic soil layer in temperate forests retained more 15N. Additionally, forests retained more N15H4+$$ {}^{15}\mathrm{N}{\mathrm{H}}_4^{+} $$than N15O3−$$ {}^{15}\mathrm{N}{\mathrm{O}}_3^{-} $$, primarily due to the stronger capacity of the organic soil layer to retain N15H4+$$ {}^{15}\mathrm{N}{\mathrm{H}}_4^{+} $$. The mechanisms of 15N retention varied among ecosystem compartments, with total ecosystem 15N retention affected by N deposition. Plant 15N retention was influenced by vegetative and microbial nutrient demands, while soil 15N retention was regulated by climate factors and soil nutrient supply. Overall, this study emphasizes the importance of climate and nutrient supply and demand in regulating forest N retention and provides data to further explore the impacts of N deposition on forest carbon sequestration. [ABSTRACT FROM AUTHOR]

Elevated nitrogen (N) deposition and changes in reduced or oxidized component contribution greatly affect soil acidification. China has experienced a significant transformation of N deposition components from NHx to NOy over the past 40 years, but the effects of component transformation on soil acidification are poorly understood. Therefore, long‐term monitoring data and literature on N deposition, combined with the results of isotope experiments, were used to explore the contributions of different N forms on soil acidification in China's forests. Here, all processes related to NHx and NOy, including the transformation to NH4+ and NO3− and subsequent N cycling in the soil, were considered. We found that N‐induced soil acidification in 80% area of China's forests was dominated by NHx deposition, and the other areas (South China) were dominated by NOy deposition in 2010s. From 1980 to 2019, the average contribution of NHx was higher than that of NOy but the latter contribution continued to increase. Meanwhile, the results showed that soil acidification increased with the decrease of the ratio of NHx to NOy (RNHx/NOy), which is mainly because NOy is more easily leached in the form of NO3− than that of NHx under the influence of different plant preferences and soil retention rates, resulting in a higher net proton production of NOy. Our research has powerful implications for policymaking, provides a theoretical basis for formulating different N reduction policies in different regions, and points out that the synergistic effect of RNHx/NOy changes should be considered to alleviate soil acidification. Key Points: Most forest soil was dominated by the contribution of NHx deposition, causing soil acidificationThe contribution of NOy deposition to soil acidification increased continuously during 1980–2019Decreased ratio of NHx to NOy deposition has led to aggravated forest soil acidification [ABSTRACT FROM AUTHOR]

Nitrous oxide (N2O) is an influential greenhouse gas (GHG) and an unregulated ozone‐depleting substance. The extent to which N2O emissions from natural forest soils have been enhanced by high atmospheric nitrogen (N) deposition in China during past decades is unclear; however, assessing land‐related N2O emissions for 2060 national "carbon neutrality" goal is urgent. In this study, we proposed a "gray box" conceptual model and deduced a linear relationship between soil N2O emissions and N deposition on a large scale. On this basis, we created a "process‐augmented data‐driven" approach using which we combined experimental N addition data from 38 Chinese forests with N deposition observation data to estimate regional soil N2O emissions. We found that the N2O emission budget of eastern China forest soils averaged 0.24 ± 0.07 TgN yr−1 from 1996 to 2015; of this, ∼36% (0.087 TgN yr−1) was directly induced by N deposition. Soil N2O emission rates (RN2O) fluctuated slightly after 2006, and the RN2Os of different ecoregions were significantly different (p < 0.001). Soil factors and N‐deposition‐related factors dominated the spatial variation of RN2O. Our findings provide country‐specific and ecoregion‐specific emission factors for national GHG inventories in China. Our approach bridges the gap between site‐level experiments and demand for regional N2O emissions, and is applicable for estimating N2O emissions in other countries/regions. Meanwhile, more N addition experiments are needed to comprehensively understand N cycling processes and extend the predictive capability of the approach. Plain Language Summary: Nitrous oxide (N2O) is a greenhouse gas that is around 300 times more powerful than carbon dioxide. As many countries and regions are aiming to achieve zero net carbon emission ("carbon neutrality") by the mid‐21st century, we need to evaluate the emissions of nitrous oxide to build an accurate picture of the current situation. However, it is difficult to estimate the emission of N2O from natural soils because the spatial variation is high. Inspired by the linkage between elemental nitrogen (N) that enters an ecosystem and N that leaves the ecosystem, we proposed a conceptual model and deduced a linear relationship between N deposition and soil N2O emissions. On the basis, we used experimental N addition data from various sites to quantify the linear relationship. Then, the N2O emission and its variation was estimated with dynamic N deposition data. In eastern China forests where the N deposition rate is exceptionally high, around 0.24 million tons of N2O‐N was emitted annually from 1996 to 2015, around 36% of which was directly induced by N deposition. From another perspective, soil N2O emissions in this region are expected to decline owing to pollution control practices and decreasing N deposition. Key Points: Eastern China forest soils emitted 0.24 TgN2O‐N yr−1 from 1996 to 2015N deposition directly induced ∼36% of emitted N2O from eastern China forest soilsDirect contribution of N deposition to soil N2O emissions increased from tropical to boreal forests [ABSTRACT FROM AUTHOR]

To study the influence of heeling angles on the lubrication flow field characteristics and the variation law of the churning power loss of ship, based on the Moving Particle Semi-implicit method, the lubrication flow field characteristics of marine reducer of eight kinds of conditions were analyzed. The results show that, under same input shaft speeds, initial lubricating oil volume and heeling angles, the lubrication performance of the marine reducer is better in the moving forward condition than in the backward condition. The high-speed splashed oil in moving forward condition is mainly located in the meshing zone between the output gear and the transmission gears, while the high-speed splashed oil in the backward condition is mainly located at the intersection of the splashed surface and the circumferential surface of the output gear. By analyzing the variation of the number of particles on the initial liquid surface under different working conditions, it is found that the number of particles splashed in the gearbox is the largest and the lubrication effect is the best when the heeling angle is 30°.The oil churning power loss of the gear increases with the increase of the heave angle. At the same heeling angle, the fluctuation range of the oil churning power loss in the moving forward and in the backward conditions of the marine reducer tends to converge, and their mean values are roughly the same. [ABSTRACT FROM AUTHOR]