Effect of density and total weight on flow depth, velocity, and stresses in loess debris flows

Debris flows that involve loess material produce important damage around the world. However, the kinematics of such processes are poorly understood. To better understand these kinematics, we used a flume to measure the kinematics of debris flows with different mixture densities and weights. We used sensors to measure pore fluid pressure and total normal stress. We measured flow patterns, velocities, and depths using a high-speed camera and laser range finder to identify the temporal evolution of the flow behavior and the corresponding peaks. We constructed fitting functions for the relationships between the maximum values of the experimental parameters. The hydrographs of the debris flows could be divided into four phases: increase to a first minor peak, a subsequent smooth increase to a second peak, fluctuation until a third major peak, and a final continuous decrease. The flow depth, velocity, total normal stress, and pore fluid pressure were strongly related to the mixture density and total mixture weight. We defined the corresponding relationships between the flow parameters and mixture kinematics. Linear and exponential relationships described the maximum flow depth and the mixture weight and density, respectively. The flow velocity was linearly related to the weight and density. The pore fluid pressure and total normal stress were linearly related to the weight, but logarithmically related to the density. The regression goodness of fit for all functions was >0.93. Therefore, these functions are accurate and could be used to predict the consequences of loess debris flows. Our results provide an improved understanding of the effects of mixture density and weight on the kinematics of debris flows in loess areas, and can help landscape managers prevent and design improved engineering solutions.Peer ReviewedPostprint (published version

A sufficient maximum principle for backward stochastic systems with mixed delays

In this paper, we study the problem of optimal control of backward stochastic differential equations with three delays (discrete delay, moving-average delay and noisy memory). We establish the sufficient optimality condition for the stochastic system. We introduce two kinds of time-advanced stochastic differential equations as the adjoint equations, which involve the partial derivatives of the function $f$ and its Malliavin derivatives. We also show that these two kinds of adjoint equations are equivalent. Finally, as applications, we discuss a linear-quadratic backward stochastic system and give an explicit optimal control. In particular, the stochastic differential equations with time delay are simulated by means of discretization techniques, and the effect of time delay on the optimal control result is explained

Global Stability of Positive Periodic Solutions and Almost Periodic Solutions for a Discrete Competitive System

A discrete two-species competitive model is investigated. By using some preliminary lemmas and constructing a Lyapunov function, the existence and uniformly asymptotic stability of positive almost periodic solutions of the system are derived. In addition, an example and numerical simulations are presented to illustrate and substantiate the results of this paper

Antagonising Wnt/β-catenin signalling ameliorates lens-capsulotomy-induced retinal degeneration in a mouse model of diabetes

AIMS/HYPOTHESIS: Cataract surgery in diabetic individuals worsens pre-existing retinopathy and triggers the development of diabetic ocular complications, although the underlying cellular and molecular pathophysiology remains elusive. We hypothesise that lens surgery may exaggerate pre-existing retinal inflammation in diabetes, which may accelerate neurovascular degeneration in diabetic eyes.METHODS: Male heterozygous Ins2Akita mice (3 months of age) and C57BL/6 J age-matched siblings received either lens capsulotomy (to mimic human cataract surgery) or corneal incision (sham surgery) in the right eye. At different days post surgery, inflammation in anterior/posterior ocular tissues was assessed by immunohistochemistry and proinflammatory gene expression in the retina by quantitative PCR (qPCR). Degenerative changes in the retina were evaluated by electroretinography, in vivo examination of retinal thickness (using spectral domain optical coherence tomography [SD-OCT]) and morphometric analysis of retinal neurons. The therapeutic benefit of neutralising Wnt/β-catenin signalling following lens capsulotomy was evaluated by intravitreal administration of monoclonal antibody against the co-receptor low-density lipoprotein receptor-related protein 6 (LRP6) (Mab2F1; 5 μg/μl in each eye).RESULTS: Lens capsulotomy triggered the early onset of retinal neurodegeneration in Ins2Akita mice, evidenced by abnormal scotopic a- and b-wave responses, reduced retinal thickness and degeneration of outer/inner retinal neurons. Diabetic Ins2Akita mice also had a higher number of infiltrating ionised calcium-binding adapter molecule 1 (IBA1)/CD68+ cells in the anterior/posterior ocular tissues and increased retinal expression of inflammatory mediators (chemokine [C-C motif] ligand 2 [CCL2] and IL-1β). The expression of β-catenin was significantly increased in the inner nuclear layer, ganglion cells and infiltrating immune cells in Ins2Akita mice receiving capsulotomy. Neutralisation of Wnt/β-catenin signalling by Mab2F1 ameliorated ocular inflammation and prevented capsulotomy-induced retinal degeneration in the Ins2Akita mouse model of diabetes.CONCLUSIONS/INTERPRETATION: Targeting the canonical Wnt/β-catenin signalling pathway may provide a novel approach for the postoperative management of diabetic individuals needing cataract surgery.</p

Scalar Transport over a Forest Edge with Impact of Vertical Distribution of Foliage and Source Types

The dynamics of forest-boundary-layer interactions over a forest edge have been an interesting research topic in recent years. A better understanding of edge flow has implications for the siting and interpretation of flux measurements near a forest edge. In the present study, we use large-eddy simulations with the newly developed multi-layer canopy model MCANOPY to investigate canopy flows, or more specifically scalar transfer, over a forest edge. Two important impact factors for edge flows are considered: the vertical distribution of foliage and the scalar source/sink distribution. The simulations show that both factors have non-ignored impacts on the scalar transfer over a forest edge. For plants with a deep, and sparse trunk space, a strong and long sub-canopy jet is observed, which qualitatively changes the flow dynamics and thus the scalar distributions. For relatively uniform distributed foliage plants, a strong flow convergence is found near the leading edge, which dominates the edge flow patterns and leads to a scalar flux peak region at the canopy top. Investigation has shown that the scalar concentrations are mainly affected by the flow advection rather than the turbulent for plants in our simulations with leaf area density of 4. The scalar fluxes are mainly affected by the vertical gradient of scalar concentration since the turbulence near a forest edge is qualitatively similar. In consistent with previous studies, the uniform ground scalar source shows the most pronounced spatial variations. While considering the source from the MCANOPY model that is close to reality, the behavior of scalars (i.e., CO2 and water vapor) is even more complicated owing to the interaction between flow dynamics and scalar source/sink distributions. It implies that both the scalar source distributions and canopy structures should be considered when interpreting flux measurement near a forest edge. The work here has important implications for interpreting measurement data and improves the understanding of scalar transfer over a forest edge

Relation between land cover and landslide susceptibility in Val d'Aran, Pyrenees (Spain): historical aspects, present situation and forward prediction

The effects of land use and land cover (LULC) dynamics on landslide susceptibility are not fully understood. This study evaluates the influence of LULC on landslide susceptibility and assesses the historic and future LULC changes in a high mountain region. A detailed inventory map showing the distribution of landslides was prepared based on the 2013 episode in Val d'Aran, Pyrenees (Spain). This inventory showed that LULC clearly affected landslide susceptibility. Both the number of landslides and the landslide density triggered in grassland and meadow was highest (52% and 2.0 landslides/km2). In contrast, the landslide density in areas covered by forest and shrubs was much lower (15% and 0.4 landslides/km2, and 23% and 1.7 landslides/km2, respectively). Historical changes of LULC between 1946 and 2013 were determined by comparing aerial photographs. The results indicated that the forest and shrub areas increased by 68 and 65%, respectively; whereas grassland and scree areas decreased by 33 and 52%. Urban area also increased by 532%, especially between 1990 and 2001. Future LULC was predicted until 2097 using TerrSet software. The results showed that the forest area and urban area increased by 57 and 43%, severally; while shrubs, grassland and scree area decreased by 28, 46 and 78%, respectively. Heuristic and deterministic models were applied to create susceptibility maps, which classified the study area into four susceptibility degrees from very low to high. The maps were validated by the 2013 landslide dataset and showed satisfactory results using receiver operating characteristics curves and density graph method. Then, susceptibility maps until 2097 were calculated by the heuristic model and results revealed that landslide susceptibility will decrease by 48% for high-susceptible areas. In contrast, the areas of very-low susceptibility degree will increase 95%, while medium and low-susceptible areas will be more or less constant. This study only includes the effect of future LULC changes on the landslide susceptibility and does not analyze the future impacts of climate changes and the variation of rainfall conditions. Nevertheless, the results may be used as support for land management guidelines to reduce the risk of slope instabilities.Postprint (author's final draft

Architecture and key technologies of coalmine underground vision computing

It has always been a common demand to stay away from the harsh environment with narrow space, numerous devices, complex operation process, and hidden hazards, and realize intelligent unmanned mining in the coal industry. To achieve this goal, it is very necessary for us to develop an effective theory of vision computing for underground coalmine applications. Its main task is to build effective models or frameworks for perceiving, describing, recognizing and understanding the environment of underground coalmine, and let intelligent equipment get 3D environment information in coalmine from images or videos. To effectively develop this theory and make it better for intelligent development of coalmine, this paper first analyzed the similarities and differences about computer vision and visual computing in coalmine, and proposed its composition architecture. And then, this paper introduced in detail the key technologies involved in visual computing in coalmine including visual perception and light field computing, feature extraction and feature description, semantic learning and vision understanding, 3D vision reconstruction, and sense computing integration and edge intelligence, which is followed by typical application cases of visual computing in coalmines. Finally, the development trend and prospect of underground visual computing in coalmine was given. In this section, this paper focused on concluding the key challenges and introducing two valuable applications including coalmine Augmented Reality/Mixed Reality and parallel intelligent mining. With the breakthrough of underground vision computing, it will play a more and more important role in the intelligent development of coal mines