Water balance of selected floodplain lake basins in the Middle Bug River valley

This study is the first attempt in the literature on the subject of comparing water balance components for floodplain lake basins, depending on the type of a lake connection to the parent river. Research was carried out in the Bug River valley in 2007–2011 water years. Four types of connections were distinguished in the area under study. Simple water balance equation could only be used with regard to the lakes connected to the main river via the upstream crevasses. Detailed and individual water balance equations were developed with reference to the other types of lakes. Water gains and losses varied significantly in the lakes under study. Values of horizontal water balance components (inflow and outflow) of the floodplain lake in Wola Uhruska considerably prevailed over the vertical ones (precipitation and evaporation). Inflow of the Bug River waters was diverse during the time period under study and amounted from 600 000 to 2 200 000 m³ yr⁻¹. Volumes of precipitation and evaporation were rather stable and amounted to approx. 30 000 m³ yr⁻¹. The lowest disparity between horizontal and vertical water balance components was observed in the inter-levee lake. Both upstream inflow of rivers water and outflow from the lake (ranged from 0 in 2008 to 35 000 m³ yr⁻¹ in 2009) were usually an order of magnitude higher than precipitation and evaporation from the lake surface (700–800 m³ yr^−1). Study showed that the values and the proportion between aforementioned vertical and horizontal water balance elements were determined by the type of a lake connection to the Bug River. Storage volume showed no relationship to the type of connection, but resulted from individual features of the lakes (location within the valley, precipitation and evaporation volume, difference between water inflow and outflow)

Quantifying the computational capability of a nanomagnetic reservoir computing platform with emergent magnetization dynamics

Devices based on arrays of interconnected magnetic nano-rings with emergent magnetization dynamics have recently been proposed for use in reservoir computing applications, but for them to be computationally useful it must be possible to optimise their dynamical responses. Here, we use a phenomenological model to demonstrate that such reservoirs can be optimised for classification tasks by tuning hyperparameters that control the scaling and input rate of data into the system using rotating magnetic fields. We use task-independent metrics to assess the rings' computational capabilities at each set of these hyperparameters and show how these metrics correlate directly to performance in spoken and written digit recognition tasks. We then show that these metrics, and performance in tasks, can be further improved by expanding the reservoir's output to include multiple, concurrent measures of the ring arrays magnetic states

Dynamically‐driven emergence in a nanomagnetic system

Emergent behaviors occur when simple interactions between a system's constituent elements produce properties that the individual elements do not exhibit in isolation. This article reports tunable emergent behaviors observed in domain wall (DW) populations of arrays of interconnected magnetic ring‐shaped nanowires under an applied rotating magnetic field. DWs interact stochastically at ring junctions to create mechanisms of DW population loss and gain. These combine to give a dynamic, field‐dependent equilibrium DW population that is a robust and emergent property of the array, despite highly varied local magnetic configurations. The magnetic ring arrays’ properties (e.g., non‐linear behavior, “fading memory” to changes in field, fabrication repeatability, and scalability) suggest they are an interesting candidate system for realizing reservoir computing (RC), a form of neuromorphic computing, in hardware. By way of example, simulations of ring arrays performing RC approaches 100% success in classifying spoken digits for single speakers

The human optic nerve: fascicular organisation and connective tissue types along the extra-fascicular matrix

Fibres in the mammalian optic nerve are arranged into fascicles between which there is an extra-fascicular matrix containing connective tissue, a feature similar to that found in association with fibres in peripheral nerves, but not otherwise found in the CNS. The relationship between these major features of the nerve architecture are not known. We have addressed this question by examining the fascicular organisation of the optic nerve and the distribution of connective tissue and specific collagen types in the human. We have also examined the spatial development of connective tissue in the human nerve to determine when and from where it originates. Fibres are grouped into fascicles at all locations along the nerve, except intracranially, close to the chiasm where this pattern is lost. Relatively large fascicular numbers are found directly behind the eye and in the region of the optic canal, but decline in the mid-orbital segment of the nerve. Connective tissue is present in the extra-fascicular matrix throughout the fasciculated segment, but in many cases it does not fully encircle fascicles. The proportion of matrix occupied by connective tissue is similar along the length of the nerve (approximately 60%). Within the matrix, collagen types I, III, IV, V and VI are present throughout fasciculated regions. Staining for types V and VI appeared relatively weak compared with that for the other types. Although the collagen types in the nerve are similar to those at the lamina cribrosa and in peripheral nerves, they did not appear to be differentially distributed as in regions of the PNS. Connective tissue enters the nerve at a number of wide-spread locations early in development, consistent with the notion that it enters the nerve with the blood supply. It is present within the matrix before it is established at the lamina cribrosa

The human optic nerve: fascicular organisation and connective tissue types along the extra-fascicular matrix

Fibres in the mammalian optic nerve are arranged into fascicles between which there is an extra-fascicular matrix containing connective tissue, a feature similar to that found in association with fibres in peripheral nerves, but not otherwise found in the CNS. The relationship between these major features of the nerve architecture are not known. We have addressed this question by examining the fascicular organisation of the optic nerve and the distribution of connective tissue and specific collagen types in the human. We have also examined the spatial development of connective tissue in the human nerve to determine when and from where it originates. Fibres are grouped into fascicles at all locations along the nerve, except intracranially, close to the chiasm where this pattern is lost. Relatively large fascicular numbers are found directly behind the eye and in the region of the optic canal, but decline in the mid-orbital segment of the nerve. Connective tissue is present in the extra-fascicular matrix throughout the fasciculated segment, but in many cases it does not fully encircle fascicles. The proportion of matrix occupied by connective tissue is similar along the length of the nerve (approximately 60%). Within the matrix, collagen types I, III, IV, V and VI are present throughout fasciculated regions. Staining for types V and VI appeared relatively weak compared with that for the other types. Although the collagen types in the nerve are similar to those at the lamina cribrosa and in peripheral nerves, they did not appear to be differentially distributed as in regions of the PNS. Connective tissue enters the nerve at a number of wide-spread locations early in development, consistent with the notion that it enters the nerve with the blood supply. It is present within the matrix before it is established at the lamina cribrosa

Quantifying the computational capability of a nanomagnetic reservoir computing platform with emergent magnetisation dynamics

Devices based on arrays of interconnected magnetic nano-rings with emergent magnetization dynamics have recently been proposed for use in reservoir computing applications, but for them to be computationally useful it must be possible to optimise their dynamical responses. Here, we use a phenomenological model to demonstrate that such reservoirs can be optimised for classification tasks by tuning hyperparameters that control the scaling and input-rate of data into the system using rotating magnetic fields. We use task-independent metrics to assess the rings' computational capabilities at each set of these hyperparameters and show how these metrics correlate directly to performance in spoken and written digit recognition tasks. We then show that these metrics, and performance in tasks, can be further improved by expanding the reservoir's output to include multiple, concurrent measures of the ring arrays' magnetic states