The traditional method of measuring the lettuce height is a manual measurement with instruments, which is greatly affected by human error.At present, researchers have proposed to use color cameras to obtain RGB images of lettuce, and to obtain the height of lettuce from the images. However, these tasks usually require camera calibration or a reference object with a known height, which is somewhat restrictive. Considering that deep neural networks have a powerful ability to feature extraction and expression, without camera calibration and reference objects, we try to use four networks of image recognition to explore the effect of deep learning on abstracting the lettuce height from RGB images. On the test set, including 80 images and height from 0.9 cm to 7.5 cm, we achieve a good result with a mean absolute error of 1.22 mm.

East China can be divided into two parts: the North China Block (or North China Craton) (NCC) and the South China Block (SCB). The mechanism for the destruction of the NCC and the large-scale magmatic activity in the SCB during the Mesozoic is a major focus in the geosciences; however, related scientific issues, such as gold mineralization in the NCC and the metallogenesis in the SCB, remain enigmatic. Recently, receiver function analysis and tomography have been used to define the vestiges of an upwelling mantle plume beneath the NCC and large-scale mantle upwelling beneath the SCB in the Mesozoic. In this study, the dynamic processes of the mantle plume and upwelling mantle are analysed, and the relationships between the mantle plume and gold mineralization as well as between mantle upwelling and metallogenesis are discussed. Finally, it is suggested that gold mineralization in the NCC might be linked to a Mesozoic mantle plume while metallogenesis in the SCB might be related to Mesozoic mantle upwelling.

Regarding transport of macroplastic (>5 mm) in rivers, its division over bifurcations is still an understudied topic. However, this is a critical knowledge to estimate plastic emission to oceans from rivers. To quantify the spatiotemporal variability of plastics in the Hong-Duong bifurcation, we executed a field campaign on a weekly basis and applied visual counting method where we count the floating plastics flowing through three cross sections to determine cross-sectional distribution of floating plastic and classify polymer categories of plastics from bridges located in the Hong-Duong bifurcation. These bridges include Nhat Tan located in northern Hanoi in the Red River, Long Bien is about 8km to the south from Nhat Tan in the Red River, and Dong Tru located in the tributary Duong River (~7km from Nhat Tan). We aim to determine the spatiotemporal changes of macroplastics across the Hong-Duong bifurcation over the period from May 2021 to November 2021. Until July 2021, we found that the total average macroplastic fluxes at the cross sections in Nhat Tan, Long Bien, and Dong Tru were 698, 159, and 113 items/hour, respectively. Notably, these values do not follow the expected plastic balance between total plastic flux in the parent river and its tributaries, which is likely explained by the accumulation, transport below the surface, or sedimentation of plastics in the space between measurement locations. Additionally, over three months of May, June, and July, the total average plastic fluxes in all cross sections showed an increasing trend (~10%). Furthermore, we also found that most plastics were distributed in the right side (downstream perspective) of Nhat Tan and Long Bien, while Dong Tru saw the opposite. Regarding the plastic classification, based on the River-OSPAR category, we found that food wrappers, polystyrene fragments, low density polyethylene (LDPE) bags, and polyethylene terephthalate (PET) bottles were the top 4 items. These findings together with the distribution of macroplastics along the cross sections are expected to apply in correlation analyses with hydrodynamic components in the bifurcation to determine their connectivity. This information is crucial for improving the efforts on quantifying macroplastic emissions from the Red River to the ocean which is still unknown up to now.

In order to count soybean seeds quickly and accurately, improve the speed of seed test and the level of soybean breeding , this dissertation developed a method of soybean seed counting based on VGG-Two (VGG-T). Firstly, in view of the lack of available image dataset in the field of soybean seed counting, a fast target point labeling method of combining pre-annotation based on digital image processing technology with manual correction annotation is proposed to speed up the establishment of publicly available soybean seed image dataset with annotation. This method only takes 197 minutes to mark 37,563 seeds, which saves 1,592 minutes than ordinary manual marking and replaces 96% of manual workload. Secondly, a method that would combine the density estimation-based methods and the convolution neural network (CNN)-based methods is developed to accurately estimate the seed count from an individual threshed seed image with a single perspective. Finally, the model is tested, and verify the effectiveness of the algorithm through three comparative experiments (with and without data enhancement, VGG16 and VGG-T, multiple sets of test set), which respectively provided 0.6 and 0.2 mean absolute error (MAE) in the original image and patch cases, while mean squared error (MSE) is 0.6 and 0.3. Compared with traditional image image morphology operations, ResNet18, ResNet18-T and vgg16, this method improves the accuracy of soybean seed counting.

To obtain phenotypic parameters by means of lossy measurement, we proposed a comprehensive and integrated approach to predict different parameters of four varieties of lettuces. By building different prediction models, we required predicted value of five phenotypic parameters of lettuce. Test results indicate that prediction models we have constructed are reliable and feasible. In addition,our methods can be better transferred to the research of other crops, and producers can adjust the growing environment of crops in time, so as to obtain higher yield.

Two cloud-resolving models (SCALE and VVM) take different pathways toward convective self-aggregation (CSA) in the radiative-convective equilibrium simulations, although they have similar domain-averaged properties. Analyses in the moisture space show that radiative cooling in the dry area mainly drives CSA in SCALE, while subsidence triggered by the convection in the moist region dominates in VVM. The change in the convective structures is found on the isentropic diagram in VVM, but this transition is unclear in SCALE. The object-based analysis provides that the convective systems with larger sizes are rare in SCALE. In contrast, large-size convective systems frequently develop in the moist region as CSA evolves in VVM. The large-size systems can efficiently drive the circulation between the dry and moist areas. The different pathways to CSA are associated with the transition of convective structures, which provides a new insight to understand CSA among cloud-resolving models based on the perspective of the mechanism.

Fractures and their connectivity are essential for fluid flow in low perme-ability formations. Abundant outcrops can only provide two-dimensional (2D) information, but subsurface fractures are three-dimensional (3D). The percola-tion status of 3D fracture networks and their 2D cross-section maps are rarely investigated simultaneously. In this work, we construct 3D fracture networks with their geometries characterized by different stochastic distributions. Then, we take cross-section maps to mimic real outcrops and label clusters to check the percolation status of 3D fracture networks and their 2D cross-section maps. The properties, reflecting the connectivity of two essential phases, are summarized and analyzed. We find that clustering effects impact local intersections significantly but have negligible impacts on fracture intensities of 3D fracture networks. The number of intersections per fracture is not a proper percolation parameter for complex 2D and 3D fracture networks. Fracture intensities are scale-dependent and usually decrease with increasing scales. The real fracture networks in the subsurface should be geometrically well-connected and pervasive if their outcrop maps are well connected. In particular, the fracture intensity of the real fracture network can be several times (at least 3.6 times) larger than the intensity at percolation. However, if outcrop maps are not well-connected, but their intensities are large enough (at least 0.43 times as large as the intensity at percolation), corresponding 3D fracture networks can also form a spanning cluster and show good connectivity with a high possibility.

Sun–Jupiter decade-scale magnetic entanglement emerges from Wilcox Solar Observatory 1975-2021 N–S ≲150 μT mean-field data, as a global response of solar magnetic fields to the magnetar-type evolution of Jupiter ~2001–onward global magnetoactivity discovered recently in the 1–6 month (385.8–64.3 nHz) band of Rieger resonance. At extreme ≲20% field variance, the sudden Jovian deviation is so high it forced solar magnetoactivity devolution into inverse-matching response, at effectively moderate ≲1.5% mean-field variance. Thus as Jupiter magnetoactivity evolved sinusoidally, the Sun began mirror-compensating ~2002 (the epoch of Abbe number drop), reducing its magnetoactivity in decreasingly sinusoidal fashion to solar cycle 24 extreme minimum. For check, 2004-2021 WIND mission data revealed <0.5-var% (<5-dB) calm ≲50 nT interplanetary magnetic field at L1, slightly undulated by the Jupiter evolution impulse, thus excluding solar wind and Sun as impulse sources (confirmed by statistical fidelity waning down Jupiter–L1–Sun diffusion vector spaces, as 10^7–10^3–10^2). Magnetic tangling of stars and hot (<0.1 AU) Jupiters was blamed previously for observed star superflaring 10^2–10^7 times more energetic than the strongest solar flare. Accordingly, the Sun ante-impulse locking is a shock-absorbing mechanism — routine shutter-response to Jupiter recurrent phasing into the flare-brown-dwarf state — with which the Sun enters a grand minimum (sleep mode). As Jupiter intermittently becomes an indirect driver of Earth’s climate, the Sun prepares to discharge stored energy as a non-extinction ~10^32-erg superflare (currently overdue). The mechanism, in which warm/cold Jupiters too trigger (mild) superflares, possibly defends stars against incoming Jupiters.

The recent Assam Earthquake has been previewed in the context of the recent seismicity pertaining to Kopili Fault of northeast India. Kopili fault of northeast India is known for its active seismicity. The ground motion parameters in the context of peak ground acceleration, peak ground velocity and peak spectral acceleration stand out for their implications. Considering the strong magnitude of the event, there is imminent need for earthquake resilient buildings as well as hazard mitigation strategies in order to tackle post-earthquake scenarios.

The production of H2O during metamorphism along active plate boundaries is inferred to contribute to low-frequency tectonic tremor seismicity. This study combines predictions from phase equilibria and mechanical modelling of coincident volume changes to investigate links of tremor with hydrofracturing and fluid migration under the actively forming Southern Alps, New Zealand. Our predicted location of metamorphic fluid production correlates with published geophysical images of inferred permeability enhancement, fluid accumulation and potential fluid flow. As the hanging-wall rocks are translated towards the surface by motion along the Alpine Fault, they can undergo metamorphic reactions that involve positive volume changes. Production of metamorphic fluids leads to hydrofracturing and the development of tremor hypocentres in regions along, and above deep reflectors of the Alpine Fault. The capacity of metamorphic rocks to generate or consume fluid along portions of the pressure–temperature path exerts a fundamental control on the distribution of stresses in the crust.

The fractal dimension and multifractal spectrum are widely used to characterize the complexity of natural fractures. However, a systematic investigation on the impact of different fracture properties (fracture lengths, orientations, center positions, system sizes) on the fractal and multifractal characterization of complex fracture networks is missing. We utilize an in-house developed DFN modeling software, HatchFrac, to construct stochastic fracture networks with prescribed distributions and systematically study the impact of four geometrical properties of fractures on the fractal and multifractal characterization. We calculate the single fractal dimension and multifractal spectrum with the box-counting method. The single fractal dimension, D, and the difference of singularity exponent, ∆α, are used to represent the fractal and multifractal patterns, respectively. We find that fracture lengths, orientations and system sizes have positive correlations with D and ∆α, while the system size has the most significant impact among the four parameters. D is uncorrelated with fracture positions (FD), which means that a single fractal dimension cannot capture the complexity caused by clustering effects. However, ∆α has a strong negative correlation with FD, which implies that clustering effects make fracture networks more complex, and ∆α can capture the difference. We also digitize 60 outcrop maps with a novel fracture detection algorithm and calculate their fractal dimension and multifractal spectrum. We find wide variations of D and ∆α on those outcrop maps, even for outcrops at similar scales. It means that a universal indicator for characterizing fracture networks at different scales or the same scale is almost impossible.

Focusing on non-destructive and automated acquisition of plant phenotypic parameters,this extended abstract proposed an end-to-end deep RNN based network structure for single perspective sparse raw point cloud regression task called DRN. It has been proven to achieve accuracy improvements in PointNet++ and PonitCNN when it comes to regression of lettuce plant height. We believe DRN structure is suitable for feature extraction from plant point cloud data and regression of spatial distance related plant phenotypes like plant height.

A primary source of uncertainty in terrestrial biosphere model (TBM) projection of carbon uptake and water cycling from ecosystems is the relationship between CO2 assimilation (A) and water loss via stomatal conductance (gs). A common mathematical framework for modeling this relationship is the “Unified Stomatal model”, which relates A to gs over environmental conditions and is governed by two terms, the stomatal slope (g1) and intercept (g0). Given their importance in determining the relationship between forest productivity and climate, an accurate and mechanistic understanding of the g1 and g0 parameters is crucial, particularly in wet tropical broadleaf forests where changes in water cycling could impact global weather patterns. These stomatal parameters are estimated using leaf-level gas exchange by two alternative methods: (1) a response curve where the environmental conditions are modified for a single leaf, or (2) a survey approach, where repeated measurements are made on multiple leaves over a diurnal range of environmental conditions. We compare the curve and survey approaches by conducting a comprehensive measurement campaign in which we paired diurnal gas exchange surveys with leaf level response curves for the estimation of g1 and g0 on six tropical species across a full range of leaf phenological stages. We examine how these different estimates impact model projection of gs, and how the consideration of a diurnal effect on g1 and g0 can improve predictions relative to a model using parameter estimates which are fixed over the photoperiod. Our results showed that age is an important factor to consider in estimates of g0, however there was no effect of leaf age on estimates of g1. The survey approach identified a diurnal trend associated with g1 and g0, which when accounted for improved model projections of diurnal trends in gs. We found that while both approaches yield equally statistically valid estimates of g1 and g0 at a fixed point in time, they are not directly comparable across diurnal timescales, where shifting water supply and carbon demand lead to dynamic canopy scale water use efficiency (WUE). These results suggest that to improve the accuracy of modelled gs in tropical forests, TBMs should recognize and implement diurnal variation in stomatal parameters which are associated with diurnal shifts in WUE.

The exhumation mechanism of the low-temperature/high-pressure (LT/HP) rocks, is critical for understanding the formation of the central Qiangtang metamorphic belt (CQMB), Tibetan Plateau, but it is still hotly debated. Here, we report field, petrological, phase-equilibria and petro-physical modelling data on the newly discovered lawsonite-bearing eclogites, epidote eclogite and lawsonite-bearing blueschists from the Lanling area in the CQMB. The mineral characteristics and phase equilibria modeling reveal that the LT/HP rocks record peak P-T conditions from peak pressure (Pmax) of 22.5–23.5 kbar at 460–480 °C to peak temperature (Tmax) of 530–550 °C at 20–22.5 kbar. Combined with previous documented geochronological data, a clockwise P-T-t path for these LT/HP rocks is obtained, which is characterized by pronounced heating decompression (~223–221 Ma), subsequent isothermal decompression (~221–219 Ma), and final cooling decompression (~219–212 Ma). Modeled densities and net buoyancies (defined as the density difference between Preliminary Reference Earth Model and LT/HP rocks) show that all LT/HP samples are buoyant at Pmax, but gradually become denser during heating decompression and evolve to neutrally or negatively buoyant around Tmax. Later mixing with lower-density garnet-phengite schists at Tmax, help the density of the exhuming LT/HP unit reduce to lower than that of the surrounding mantle again during continued isothermal decompression. We concluded that exhumation of eclogites and blueschists is short-lived (~10 Ma) and multi-stage buoyancy-driven characterized by early self-exhumation via diapiric rise and post-Tmax carried-exhumation along subduction channel.

Changes in the permeability of a fault zone are dependent upon the frequency of seismic waves, and is an important phenomenon whose mechanisms have not be completely elucidated to date. In this study, the tidal response of water level in well Chuan06 was considered as an indicator of the permeability of the fault zone, and the sensitivity of permeability changes to earthquakes with different epicentral distances were investigated. The results suggested that the permeability change induced by seismic waves is frequency dependent. In addition, based on the mechanisms of permeability changes induced by the seismic waves, the difference in sensitivity may be attributed to the different sizes of the fractures in the fault zone. These findings are likely to contribute to an understanding of hydrogeological responses and seismic activity induced by the earthquakes.

This paper describes an integrated statistical-hydrodynamic method to estimate tropical cyclone-induced coastal flood inundation hazard, applied to a coastal megacity-Shanghai, China. We identify three “worst-case” scenarios (extracted from over 5000 synthetic storms) that generate unprecedentedly high flood levels in Shanghai. Nevertheless, we find that the mainland Shanghai is relatively safe from coastal flooding under the current climate, thanks to its high-standard seawall protection. However, the city is expected to be increasingly at risk due to future sea level rise, with inundation two times and 20 times more likely to occur by mid-and late-21 st century, respectively, and inundation depth and area to greatly increase (e.g., 60-1360% increase in the inundation area for the “worst cases” by 2100). The low-lying and poorly-protected area (e.g. Chongming island) is likely to be moderately affected by flood events with long return periods at the current state but could be largely inundated in future sea-level-rise situations.

The growing depth and breadth of available data that span the solar-terrestrial environment place us at a tipping point – the potential of these data is immense but realizing that potential requires a new representation. A new network-based approach to represent data collected by power utilities along with information from the solar-terrestrial connection is used. The progress is generated as part of a new project within the National Science Foundation Convergence Accelerator program: “The Convergence Hub for the Exploration of Space Science (CHESS).” Results are shared from current data provided through the Electric Power Research Institute (EPRI) SUNBURST project linked to magnetometer data from the Super Magnetometer Initiative. These data are transformed into a network with GIC measurements and magnetometers as the nodes in order to answer a long-standing question: “How much more likely are deviations from the average current when there is active space weather”? To answer this question, periods of active space weather are identified in the magnetometer data, and these are compared to times of DC transients in the GIC data. The probability of a these transients is found to be , on average, 1.6 times higher during periods of active space weather than during quiet times. The most indicative magnetometers of these DC transients are often not the closest to where the GICs are measured.

The local or regional approach is often used in studies to detect the point of change of the mean in the hydro-climatic time series. These approaches have been applied by previous studies on a limited part of the Congo basin, in particular on the right bank of the Congo river. This study is a comprehensive spatio-temporal analysis over the Congo Basin that deals with the detection of the point of change of the mean in the precipitation time series using the Lee et Heghinian approach. The latter has been applied on 9021 precipitation time series over the Congo watershed which resulted in a spatio-temporal representation of the probabilities of the point of change of the mean. A clear cut of the study area unto distinctive zones with high or low probabilities has been obtained. The point of change of the mean in the precipitation time series occurred in 1969 or 1970 with high probabilities over two areas of the basin which correspond to the sub-basins of Oubangi and Kasai. These sub-basins are seemingly more vulnerable to climate change than other sub-basins such as the Batéké Plateau sub-basin as well as regions not yet studied.