Complex Natural Background Research Articles

Multiple isotopes reveal the driving forces of nitrogen cycling from freshwater to brackish water

Rivers play a crucial role in global nitrogen (N) cycling, but revealing the driving mechanism of N cycling remains challenging because of the complex natural background gradients. The Qiantang River Basin provides an opportunity to elucidate the driving mechanism due to the complex climatic and hydrological conditions. In this study, the multiple stable isotopes suggested that the conservative mixing of two end members was insufficient to explain the complex behavior of N in both seasons. In-soil processes were the primary N cycling processes that controlled riverine N loading during the wet season, whereas in-stream N biological transformation processes (nitrification and assimilation) were more prevalent during the dry season. The results of MixSIAR revealed that soil sources (soil N and N fertilizer) contributed the most to NO3− during the wet season, accounting for 64.3 %, followed by manure and sewage (31.6 %) and atmospheric precipitation (4.1 %). During the dry season, manure and sewage were the predominant contributors to NO3− (52.1 %), followed by soil N (26.6 %), N fertilizer (18.8 %), and atmospheric precipitation (2.5 %). The relationships between d-excess and δ15N-NH4+ or δ15N-NO3− suggested that both climatic and hydrological conditions would be the driving forces regulating the N transportation and transformation in this basin, leading to the high spatiotemporal heterogeneity in N loading and isotopic compositions. In the wet season, precipitation patterns served as the primary driving forces regulating in-soil biological processes and soil leaching. While the hydrological conditions, especially water residence time, were the crucial factors controlling in-stream biological processes during the dry season. This study elucidates N sources, biotransformation processes, and their driving forces from freshwater to brackish water, which has applications for understanding the N fate from terrene to ocean.

Research on the identification and detection of field pests in the complex background based on the rotation detection algorithm.

As a large agricultural and population country, China's annual demand for food is significant. The crop yield will be affected by various natural disasters every year, and one of the most important factors affecting crops is the impact of insect pests. The key to solving the problem is to detect, identify and provide feedback in time at the initial stage of the pest. In this paper, according to the pest picture data obtained through the pest detection lamp in the complex natural background and the marking categories of agricultural experts, the pest data set pest rotation detection (PRD21) in different natural environments is constructed. A comparative study of image recognition is carried out through different target detection algorithms. The final experiment proves that the best algorithm for rotation detection improves mean Average Precision by 18.5% compared to the best algorithm for horizontal detection, reaching 78.5%. Regarding Recall, the best rotation detection algorithm runs 94.7%, which is 7.4% higher than horizontal detection. In terms of detection speed, the rotation detection time of a picture is only 0.163s, and the model size is 66.54MB, which can be embedded in mobile devices for fast detection. This experiment proves that rotation detection has a good effect on pests' detection and recognition rate, which can bring new application value and ideas, provide new methods for plant protection, and improve grain yield.

Quantifying proportions of different material sources to loess based on a grid search and Monte Carlo model: A case study of the Ili Valley, Central Asia

The Ili Valley is among the main distribution areas of loess deposits in Xinjiang Province, Central Asia, while the provenance of Ili loess remains under debate. In this study, samples from near-surface loess, two types of topsoil and modern riverbed sediment were analyzed for their concentrations of major and trace geochemical elements to determine the relative proportions of different provenances of loess deposits in different zones of the Ili Valley. The results obtained by the grid search technology and Monte Carlo model indicated that the proximal material is dominated in the Ili loess. Alluvial-diluvial sediments as the main local material source have significantly influenced loess in the western region of Ili Valley. Moreover, this influence gradually decreases in the eastern region and the Zhaosu Basin. The proportion of modern riverbed sediment in the eastern Ili Valley is significantly lower than that in the Zhaosu Basin and is lowest in the western Ili Valley. However, the proportion of dust and topsoil type-II with the mean value of 11.8% and 7.2%, respectively, is highest in the western Ili Valley and lowest in the Zhaosu Basin. The complex natural background of the Ili Valley can be used to interpret the quantitative results and the geochemical characteristics of Ili loess from different regions. The reliability of the proposed method can be assessed by environmental indicators such as grain size, and geomorphic-hydrological background and other published records.

Development of acid-resistant winter rye Kiprez variety

The current trend of winter rye breeding for the conditions of northern agriculture is development of aluminum and acid-tolerant cultivars. Studies were carried out in the central zone of the Kirov region in 1991-2019. The complex natural provocative background was used (aluminum ion content 25.5-26.7 mg/100 g of soil at a pH of salt extract 3.6-3.8; annual epiphytotic development of pink snow mold Microdochium nivale (Fr.). Winter-resistant and adaptive cv. Kirovskaya 89 served as the initial material. A rigid natural provocative background made it possible to select the best plants (500 pieces) that were transplanted in spring to an isolated area. Subsequent negative selections, field and laboratory assessments made it possible to create the Kiprez population. During the period of 1995-2013 population reproduction was carried out on a provocative background and 5 cycles of negative selection with simultaneous study in competitive varietal testing on two soil backgrounds. The first selection cycle contributed to an increase in the yield of the new population under conditions of edaphic stress over the original cv. Kirovskaya 89 by 12.6%; the second and third by 19.5 and 29.3%, respectively. Further selection did not show a significant effect. Since 2010, the further improvement of the cv. Kiprez has been carried out using individual-family selection and paired crosses. A multi-year competitive test (2014-2019) showed the advantage of the cv. Kiprez over the standard and the initial cultivar. The average yield increase to the Kirovskaya 89 variety under conditions of slightly acidic soils (pH − 5.3-5.7; Al3+ − 5.0-6.5) was 0.50 t / ha (Kirov) and 0.75 t/ha (Falenki); in the conditions of edaphic stress − 1.91 t / ha (Falenki). A combination of aluminum- and acid tolerance of the cultivar with drought resistance was revealed. In 2014, with a moisture deficit (31% of the norm) in the grain filling and ripening phase, cv. Kiprez exceeded significantly the cv. Falenskaya 4 (standard) by 0.82 tons/ha (19.1%) in a acidic background; initial cv. Kirovskaya 89 - by 1.97 tons/ha (62.7%). Cv. Kiprez is recommended for cultivation on low-fertile soils in harsh hydrothermal conditions of northern agriculture.

Влияние почвенной кислотности на урожайность озимой ржи и возможности эдафической селекции

The review of the results of investigations on influence of soil acidity on winter rye productivity was done. The pattern of breeding process for creation of aluminum- and acid-resistant winter rye varieties under conditions of North-East of Russian Non-Chernozem zone was substantiated. Different methods of estimation such as cell and tissue cultures, method of nutrition solutions, soil culture, field study are used for screening of agricultural crop varieties on the level of resistance to abiotic stress. Winter rye is the least sensitive to soil acidity. However, higher soil acidity decreases winter hardiness and regenerating ability of rye after snow mould (M. nivale) damage that leads to shortfall of its production up to 40%. Creation of acid-resistant rye varieties is a perspective direction in breeding research especially for the conditions of north crop farming. In Russia, rye edaphic breeding began in the North-East Agricultural Research Institute in 1980 and it is now the only research institute in the country involved in this work. Studies are conducted under provocative conditions of field, greenhouse and laboratory experiments. Aluminum- and acid-resistant rye varieties were created - Regina (method of cells and tissues) and Kiprez (field method of multiple selections on natural rigid provocative background). Under conditions of natural soil background these varieties exceeded the initial variety Kirovskaya 89 in productivity by 0.24 t/ha and 0.72 t/ha correspondingly. Study on provocative soil conditions proved the superiority of variety Kiprez in aluminum- and acid-resistance. Additional productivity compared to the standard Falensky 4 was 0.26-0.32 t/ha, to the variety Kirovskaya 89 - 0.78-1.63 t/ha. Variety Regina was inferior to the standard Falenskaya 4 in productivity. In 2016 variety Kiprez was transferred to the State varietal test. Based on studies carried out in Federal Agricultural Research Center of the North-East the plan of creation of aluminum- and acid-resistant winter rye varieties with the use of complex natural provocative background (Н + and Al 3+ ; M. nivale) was developed and implemented. The varieties being created are subjected to obligatory test under rigid field provocative conditions. Acid-resistant varieties are reliable reserve for increasing the winter rye grain production on low-fertile acid soils of Non-Chernozem zone of Russia and are valuable sources of aluminum- and acid-resistance in breeding programs in other regions of the country.

Natural vision effects on contrast sensitivity and their correlation with macaque V1 activity

In a series of physiological and psychophysical experiments, our lab has previously shown that there are significant differences in brain representations and visual perception between natural vision and vision in laboratory experiments. Complex natural scenes and saccadic eye movements alter vision relative to experiments with simple stimuli flashed during prolonged fixation. Here we show that natural vision has significant effects on contrast sensitivity and that these effects are predictable from patterns of activation in macaque V1. Animals were trained to perform a 2AFC contrast discrimination task while recordings from V1 were made with 96 electrode Utah arrays. Conditions in which saccades brought stimuli into receptive fields were compared with other conditions that required fixation before stimuli were flashed. In both conditions the Gabor target stimuli were positioned on a complex natural scene background. We find, with similar visual input, that saccades across a complex scene alter contrast sensitivity relative to sensitivity with flashed stimuli. Specifically, contrast sensitivity is worse at lower spatial frequencies (1-4 cy/deg) but similar at higher spatial frequencies when a saccade brings the Gabor target into view. This effect is comparable to the reduced contrast sensitivity our lab has reported in similar human psychophysics experiments. The differences between "saccade" and "flash" contrast sensitivity functions is consistent with the effects of saccades across complex scenes on V1 spiking and LFP activity and the spatial frequency dependence of neural changes. Importantly, the spatial frequency dependence of the effects appears related to the statistics of natural scenes rather than magno/parvo processing differences. These findings are important for the characterization of V1 responses and perception in natural vision and for the clinical assessment of contrast sensitivity. Meeting abstract presented at VSS 2014

Understanding leaves in natural images – A model-based approach for tree species identification

With the aim of elaborating a mobile application, accessible to anyone and with educational purposes, we present a method for tree species identification that relies on dedicated algorithms and explicit botany-inspired descriptors. Focusing on the analysis of leaves, we developed a working process to help recognize species, starting from a picture of a leaf in a complex natural background. A two-step active contour segmentation algorithm based on a polygonal leaf model processes the image to retrieve the contour of the leaf. Features we use afterwards are high-level geometrical descriptors that make a semantic interpretation possible, and prove to achieve better performance than more generic and statistical shape descriptors alone. We present the results, both in terms of segmentation and classification, considering a database of 50 European broad-leaved tree species, and an implementation of the system is available in the iPhone application Folia.1https://itunes.apple.com/app/folia/id547650203.1

Man-made Object Detection Based on Texture Visual Perception

Based on human visual attention mechanism and texture visual perception, this paper proposes a method for man-made object detection by extracting texture and geometry structure features. Followed by clustering the texture feature, geometry structure feature is obtained to realize final detection. Then a man-made object detection scheme is designed, by which typical man-made objects in complex natural background, including airplanes, tanks and vehicles can be detected. The experiments sustain that the proposed method is effective and rational.

Man-made Object Detection Based on Texture Clustering and Geometric Structure Feature Extracting

Automatic aerial image interpretation is one of new rising high-tech application fields, and it’s proverbially applied in the military domain. Based on human visual attention mechanism and texture visual perception, this paper proposes a new approach for man-made object detection and marking by extracting texture and geometry structure features. After clustering the texture feature to realize effective image segmentation, geometry structure feature is obtained to achieve final detection and marking. Thus a man-made object detection methodology is designed, by which typical man-made objects in complex natural background, including airplanes, tanks and vehicles can be detected. The experiments sustain that the proposed method is effective and rational.

Computable general equilibrium models for sustainability impact assessment: Status quo and prospects

Sustainability Impact Assessment ( SIA) of economic, environmental, and social effects triggered by governmental policies has become a central requirement for policy design. The three dimensions of SIA are inherently intertwined and subject to trade-offs. Quantification of trade-offs for policy decision support requires numerical models in order to assess systematically the interference of complex interacting forces that affect economic performance, environmental quality, and social conditions. This paper investigates the use of computable general equilibrium (CGE) models for measuring the impacts of policy interference on policy-relevant economic, environmental, and social (institutional) indicators. We find that operational CGE models used for energy–economy–environment (E3) analyses have a good coverage of central economic indicators. Environmental indicators such as energy-related emissions with direct links to economic activities are widely covered, whereas indicators with complex natural science background such as water stress or biodiversity loss are hardly represented. Social indicators stand out for very weak coverage, mainly because they are vaguely defined or incommensurable. Our analysis identifies prospects for future modeling in the field of integrated assessment that link standard E3-CGE-models to themespecific complementary models with environmental and social focus.

Measuring Sustainable Development: The Use of Computable General Equilibrium Models

Sustainability Impact Assessment (SIA) of economic, environmental, and societal effects triggered by governmental policies has become a central requirement for policy design. The three dimensions of SIA are inherently intertwined and subject to trade-offs. Quantification of trade-offs for policy decision support requires numerical models in order to assess systematically the interference of complex interacting forces that affect economic performance, environmental quality, and societal conditions. This paper investigates the use of computable general equilibrium (CGE) models for measuring the impacts of policy interference on policy-relevant economic, environmental, and social (institutional) indicators.We find that operational CGE models used for energy-economy-environment (E3) analyses have a good coverage of central economic indicators. Environmental indicators such as energy-related emissions with direct links to economic activities are widely covered, whereas indicators with complex natural science background such as water stress or biodiversity loss are hardly represented. Societal indicators stand out for very weak coverage, not at last because they are vaguely defined or incommensurable. Our analysis identifies prospects for future modeling in the field of integrated assessment that link standard E3-CGE-models totheme-specific complementary models with environmental and societal focus.

Defining the notion of visual pattern for predicting visual target distinctness in a complex rural background

The goal of this paper is to analyze a new notion of visual pattern that captures the primary details for the task of quantifying the visual distinctness of targets in complex natural scenes. We show that given an objective function, which is defined as the mean of the fraction of correctly classified targets across a number of datasets and whose maximization is desired, the optimal notion of visual pattern in a complex natural background can be defined as congruence in a certain statistical structure at attentional points across a range of 2-D frequency bands. We draw this conclusion from several experiments in which the best definition of visual pattern is estimated based on the relation between the visual target distinctness measured by human observers and a compu- tational distance that applies a simple decision rule to the differences between segregated visual patterns. © 2000 Society of Photo-Optical Instrumen- tation Engineers. (S0091-3286(00)01802-X)