The exceptional sediment load of fine-grained dispersal systems: Example of the Yellow River, China

Sedimentary dispersal systems with fine-grained beds are common, yet the physics of sediment movement within them remains poorly constrained. We analyze sediment transport data for the best-documented, fine-grained river worldwide, the Huanghe (Yellow River) of China, where sediment flux is underpredicted by an order of magnitude according to well-accepted sediment transport relations. Our theoretical framework, bolstered by field observations, demonstrates that the Huanghe tends toward upper-stage plane bed, yielding minimal form drag, thus markedly enhancing sediment transport efficiency. We present a sediment transport formulation applicable to all river systems with silt to coarse-sand beds. This formulation demonstrates a remarkably sensitive dependence on grain size within a certain narrow range and therefore has special relevance to silt-sand fluvial systems, particularly those affected by dams

Suspended-sediment induced stratification inferred from concentration and velocity profile measurements in the lower Yellow River, China

Despite a multitude of models predicting sediment transport dynamics in open‐channel flow, self‐organized vertical density stratification that dampens flow turbulence due to the interaction between fluid and sediment, has not been robustly validated with field observations from natural rivers. Turbulence‐suppressing density stratification can develop in channels with low channel‐bed slope and high sediment concentration. As the Yellow River, China, maintains one of the highest sediment loads in the world for a low sloping system, this location is ideal for documenting particle and fluid interactions that give rise to density stratification. Herein, we present analyses from a study conducted over a range of discharge conditions (e.g., low flow, rising limb, and flood peak) from a lower reach of the Yellow River, whereby water samples were collected at targeted depths to measure sediment concentration and, simultaneously, velocity measurements were collected throughout the flow depth. Importantly, sediment concentration varied by an order of magnitude between base and flood flows. By comparing measured concentration and velocity profiles to predictive models, we show that the magnitude of density stratification increases with sediment concentration. Furthermore, a steady‐state calculation of sediment suspension is used to determine that sediment diffusivity increases with grain size. Finally, we calculate concentration and velocity profiles, showing that steady‐state sediment suspensions are reliably predicted over a range of stratification conditions larger than had been previously documented in natural river flows. We determine that the magnitude of density stratification can be predicted by a function considering an entrainment parameter, sediment concentration, and bed slope

Suspended-sediment induced stratification inferred from concentration and velocity profile measurements in the lower Yellow River, China

Despite a multitude of models predicting sediment transport dynamics in open‐channel flow, self‐organized vertical density stratification that dampens flow turbulence due to the interaction between fluid and sediment, has not been robustly validated with field observations from natural rivers. Turbulence‐suppressing density stratification can develop in channels with low channel‐bed slope and high sediment concentration. As the Yellow River, China, maintains one of the highest sediment loads in the world for a low sloping system, this location is ideal for documenting particle and fluid interactions that give rise to density stratification. Herein, we present analyses from a study conducted over a range of discharge conditions (e.g., low flow, rising limb, and flood peak) from a lower reach of the Yellow River, whereby water samples were collected at targeted depths to measure sediment concentration and, simultaneously, velocity measurements were collected throughout the flow depth. Importantly, sediment concentration varied by an order of magnitude between base and flood flows. By comparing measured concentration and velocity profiles to predictive models, we show that the magnitude of density stratification increases with sediment concentration. Furthermore, a steady‐state calculation of sediment suspension is used to determine that sediment diffusivity increases with grain size. Finally, we calculate concentration and velocity profiles, showing that steady‐state sediment suspensions are reliably predicted over a range of stratification conditions larger than had been previously documented in natural river flows. We determine that the magnitude of density stratification can be predicted by a function considering an entrainment parameter, sediment concentration, and bed slope

ON CORRELATING BIRD MIGRATION TRAJECTORY WITH CLIMATE CHANGES

Climate changes are expected to affect bird migration in several aspects including timing changes, breeding and migration orientation. The correlation analysis of several climate conditions (e.g. temperature, wind, humidity, etc) and bird migration trajectory is the key for explaining bird behavior during migration. Moreover, the resulting correlation can be used for predicting new bird behavior according to climate changes. In this paper we propose an integrated solution for correlating bird migration trajectory with climate conditions. This solution is composed by two orthogonal and complementary methods. The first method concerns discovering regions where birds are used to stop during their migration. The second method is based on a machine learning algorithm for classifying bird stops according to climate conditions. A real bird migration scenario was used for assessing the accuracy of the integrated solution

Gene Selection for Multiclass Prediction by Weighted Fisher Criterion

Gene expression profiling has been widely used to study molecular signatures of many diseases and to develop molecular diagnostics for disease prediction. Gene selection, as an important step for improved diagnostics, screens tens of thousands of genes and identifies a small subset that discriminates between disease types. A two-step gene selection method is proposed to identify informative gene subsets for accurate classification of multiclass phenotypes. In the first step, individually discriminatory genes (IDGs) are identified by using one-dimensional weighted Fisher criterion (wFC). In the second step, jointly discriminatory genes (JDGs) are selected by sequential search methods, based on their joint class separability measured by multidimensional weighted Fisher criterion (wFC). The performance of the selected gene subsets for multiclass prediction is evaluated by artificial neural networks (ANNs) and/or support vector machines (SVMs). By applying the proposed IDG/JDG approach to two microarray studies, that is, small round blue cell tumors (SRBCTs) and muscular dystrophies (MDs), we successfully identified a much smaller yet efficient set of JDGs for diagnosing SRBCTs and MDs with high prediction accuracies (96.9% for SRBCTs and 92.3% for MDs, resp.). These experimental results demonstrated that the two-step gene selection method is able to identify a subset of highly discriminative genes for improved multiclass prediction

Universal relation with regime transition for sediment transport in fine-grained rivers

Fine-grained sediment (grain size under 2,000 μm) builds floodplains and deltas, and shapes the coastlines where much of humanity lives. However, a universal, physically based predictor of sediment flux for fine-grained rivers remains to be developed. Herein, a comprehensive sediment load database for fine-grained channels, ranging from small experimental flumes to megarivers, is used to find a predictive algorithm. Two distinct transport regimes emerge, separated by a discontinuous transition for median bed grain size within the very fine sand range (81 to 154 μm), whereby sediment flux decreases by up to 100-fold for coarser sand-bedded rivers compared to river with silt and very fine sand beds. Evidence suggests that the discontinuous change in sediment load originates from a transition of transport mode between mixed suspended bed load transport and suspension-dominated transport. Events that alter bed sediment size near the transition may significantly affect fluviocoastal morphology by drastically changing sediment flux, as shown by data from the Yellow River, China, which, over time, transitioned back and forth 3 times between states of high and low transport efficiency in response to anthropic activities

Universal relation with regime transition for sediment transport in fine-grained rivers

Fine-grained sediment (grain size under 2,000 μm) builds floodplains and deltas, and shapes the coastlines where much of humanity lives. However, a universal, physically based predictor of sediment flux for fine-grained rivers remains to be developed. Herein, a comprehensive sediment load database for fine-grained channels, ranging from small experimental flumes to megarivers, is used to find a predictive algorithm. Two distinct transport regimes emerge, separated by a discontinuous transition for median bed grain size within the very fine sand range (81 to 154 μm), whereby sediment flux decreases by up to 100-fold for coarser sand-bedded rivers compared to river with silt and very fine sand beds. Evidence suggests that the discontinuous change in sediment load originates from a transition of transport mode between mixed suspended bed load transport and suspension-dominated transport. Events that alter bed sediment size near the transition may significantly affect fluviocoastal morphology by drastically changing sediment flux, as shown by data from the Yellow River, China, which, over time, transitioned back and forth 3 times between states of high and low transport efficiency in response to anthropic activities

Cooperative ecological adaptive cruise control for plug-in hybrid electric vehicle based on approximate dynamic programming

Eco-driving control generates significant energy-saving potential in car-following scenarios. However, the influence of preceding vehicle may impose unnecessary velocity waves and deteriorate fuel economy. In this research, a learning-based method is exploited to achieve satisfied fuel economy for connected plug-in hybrid electric vehicles (PHEVs) with the advantage of vehicle-to-vehicle communication system. A data-driven energy consumption model is leveraged to generate reinforcement signals for approximate dynamic programming (ADP) with the consideration of nonlinear efficiency characteristics of hybrid powertrain system. An advanced ADP scheme is designed for connected PHEVs driving in car-following scenarios. Additionally, the cooperative information is incorporated to further improve the fuel economy of the vehicle under the premise of driving safety. The proposed method is mode-free and showcases acceptable computational efficiency as well as adaptability. The simulation results demonstrate that the fuel economy during car-following processes is remarkably improved through cooperative driving information, thereby partially paving the theoretical basis for energy-saving transportation

High-Dose Dexamethasone Alters the Increase in Interleukin-16 Level in Adult Immune Thrombocytopenia

Adult primary immune thrombocytopenia (ITP) is an autoimmune-mediated haemorrhagic disorder. Interleukin-16 (IL-16) can directly affect cellular or humoural immunity by mediating the cellular cross-talk among T cells, B cells and dendritic cells. Several studies have focused on IL-16 as an immunomodulatory cytokine that takes part in Th1 polarization in autoimmune diseases. In this study, we investigated IL-16 expression in the bone marrow supernatant and plasma of ITP patients and healthy controls. What's more, we detected IL-16 expression in ITP patients with the single-agent 4-day high-dose dexamethasone (HD-DXM) therapy. In patients with active ITP, bone marrow supernatant and plasma IL-16 levels increased (P < 0.05) compared with those of healthy controls. In the meantime, the mRNA expression in BMMCs (pro-IL-16, caspase-3) and PBMCs (pro-IL-16, caspase-3 and T-bet) of ITP patients was increased (P < 0.05) relative to those of healthy controls. In patients who responded to HD-DXM therapy, both plasma IL-16 levels and gene expression in PBMCs (pro-IL-16, caspase-3, and T-bet) were decreased (P < 0.05). In summary, the abnormal level of IL-16 plays important roles in the pathogenesis of ITP. Regulating Th1 polarization associated with IL-16 by HD-DXM therapy may provide a novel insight for immune modulation in ITP