Slow-Fast Duffing Neural Mass Model

Epileptic seizures may be initiated by random neuronal fluctuations and/or by pathological slow regulatory dynamics of ion currents. This paper presents extensions to the Jansen and Rit neural mass model (JRNMM) to replicate paroxysmal transitions in intracranial electroencephalogram (iEEG) recordings. First, the Duffing NMM (DNMM) is introduced to emulate stochastic generators of seizures. The DNMM is constructed by applying perturbations to linear models of synaptic transmission in each neural population of the JRNMM. Then, the slow-fast DNMM is introduced by considering slow dynamics (relative to membrane potential and firing rate) of some internal parameters of the DNMM to replicate pathological evolution of ion currents. Through simulation, it is illustrated that the slow-fast DNMM exhibits transitions to and from seizures with etiologies that are linked either to random input fluctuations or pathological evolution of slow states. Estimation and optimization of a log likelihood function (LLF) using a continuous-discrete unscented Kalman filter (CD-UKF) and a genetic algorithm (GA) are performed to capture dynamics of iEEG data with paroxysmal transitions

Band-stop filter with suppression of requested number of spurious stopbands

Design method for band-stop filters (BSFs) that suppress a requested number of spurious bandstops and reduce ripples in the passbands below similar to 1 dB is proposed. BSF is designed in a form of a cascade of cells, each consisting of steps of equal electrical length, where the number of steps is used to control the number of suppressed spurious bandstops. Analytical formulas are developed that enable initial design of BSF for a given central frequency, depth, and bandwidth of the stopband. Varying the minimum characteristic impedances of initial cells, through an optimization using circuit simulation, the ripples in passbands are reduced below similar to 1 dB. Using the proposed theory, three filters in microstrip technology, with suppression of 3, 5, and 7 spurious stopbands respectively, were designed, fabricated, and measured. Good agreement between simulated and measured results has been observed. The proposed design can be recommended for filters having broad stop bandwidths, between 40 and 100%

Machine Learning-based Framework for Optimally Solving the Analytical Inverse Kinematics for Redundant Manipulators

Solving the analytical inverse kinematics (IK) of redundant manipulators in real time is a difficult problem in robotics since its solution for a given target pose is not unique. Moreover, choosing the optimal IK solution with respect to application-specific demands helps to improve the robustness and to increase the success rate when driving the manipulator from its current configuration towards a desired pose. This is necessary, especially in high-dynamic tasks like catching objects in mid-flights. To compute a suitable target configuration in the joint space for a given target pose in the trajectory planning context, various factors such as travel time or manipulability must be considered. However, these factors increase the complexity of the overall problem which impedes real-time implementation. In this paper, a real-time framework to compute the analytical inverse kinematics of a redundant robot is presented. To this end, the analytical IK of the redundant manipulator is parameterized by so-called redundancy parameters, which are combined with a target pose to yield a unique IK solution. Most existing works in the literature either try to approximate the direct mapping from the desired pose of the manipulator to the solution of the IK or cluster the entire workspace to find IK solutions. In contrast, the proposed framework directly learns these redundancy parameters by using a neural network (NN) that provides the optimal IK solution with respect to the manipulability and the closeness to the current robot configuration. Monte Carlo simulations show the effectiveness of the proposed approach which is accurate and real-time capable ($\approx$ \SI{32}{\micro\second}) on the KUKA LBR iiwa 14 R820

Response of Net Ecosystem Productivity of Three Boreal Forest Stands to Drought

In 2000-03, continuous eddy covariance measurements of carbon dioxide (CO2) flux were made above mature boreal aspen, black spruce, and jack pine forests in Saskatchewan, Canada, prior to and during a 3-year drought. During the 1st drought year, ecosystem respiration (R) was reduced at the aspen site due to the drying of surface soil layers. Gross ecosystem photosynthesis (GEP) increased as a result of a warm spring and a slow decrease of deep soil moisture. These conditions resulted in the highest annual net ecosystem productivity (NEP) in the 9 years of flux measurements at this site. During 2002 and 2003, a reduction of 6% and 34% in NEP, respectively, compared to 2000 was observed as the result of reductions in both R and GEP, indicating a conservative response to the drought. Although the drought affected most of western Canada, there was considerable spatial variability in summer rainfall over the 100-km extent of the study area; summer rainfalls in 2001 and 2002 at the two conifer sites minimized the impact of the drought. In 2003, however, precipitation was similarly low at all three sites. Due to low topographic position and consequent poor drainage at the black spruce site and the coarse soil with low water-holding capacity at the jack pine site almost no reduction in R, GEP, and NEP was observed at these two sites. This study shows that the impact of drought on carbon sequestration by boreal forest ecosystems strongly depends on rainfall distribution, soil characteristics, topography, and the presence of vegetation that is well adapted to these condition

Retrospective-Cost Adaptive Control of Uncertain Hammerstein-Wiener Systems with Memoryless and Hysteretic Nonlinearities

Peer Reviewedhttp://deepblue.lib.umich.edu/bitstream/2027.42/97108/1/AIAA2012-4449.pd

Seasonal variation in the canopy color of temperate evergreen conifer forests

Evergreen conifer forests are the most prevalent land cover type in North America. Seasonal changes in the color of evergreen forest canopies have been documented with near‐surface remote sensing, but the physiological mechanisms underlying these changes, and the implications for photosynthetic uptake, have not been fully elucidated. Here, we integrate on‐the‐ground phenological observations, leaf‐level physiological measurements, near surface hyperspectral remote sensing and digital camera imagery, tower‐based CO₂ flux measurements, and a predictive model to simulate seasonal canopy color dynamics. We show that seasonal changes in canopy color occur independently of new leaf production, but track changes in chlorophyll fluorescence, the photochemical reflectance index, and leaf pigmentation. We demonstrate that at winter‐dormant sites, seasonal changes in canopy color can be used to predict the onset of canopy‐level photosynthesis in spring, and its cessation in autumn. Finally, we parameterize a simple temperature‐based model to predict the seasonal cycle of canopy greenness, and we show that the model successfully simulates interannual variation in the timing of changes in canopy color. These results provide mechanistic insight into the factors driving seasonal changes in evergreen canopy color and provide opportunities to monitor and model seasonal variation in photosynthetic activity using color‐based vegetation indices

Arctic soil methane sink increases with drier conditions and higher ecosystem respiration

Arctic wetlands are known methane (CH4) emitters but recent studies suggest that the Arctic CH4 sink strength may be underestimated. Here we explore the capacity of well-drained Arctic soils to consume atmospheric CH4 using >40,000 hourly flux observations and spatially distributed flux measurements from 4 sites and 14 surface types. While consumption of atmospheric CH4 occurred at all sites at rates of 0.092 ± 0.011 mgCH4 m−2 h−1 (mean ± s.e.), CH4 uptake displayed distinct diel and seasonal patterns reflecting ecosystem respiration. Combining in situ flux data with laboratory investigations and a machine learning approach, we find biotic drivers to be highly important. Soil moisture outweighed temperature as an abiotic control and higher CH4 uptake was linked to increased availability of labile carbon. Our findings imply that soil drying and enhanced nutrient supply will promote CH4 uptake by Arctic soils, providing a negative feedback to global climate change

Chitosan/polyester-based scaffolds for cartilage tissue engineering: assessment of extracellular matrix formation

Naturally derived polymers have been extensively used in scaffold production for cartilage tissue engineering. The present work aims to evaluate and characterize extracellular matrix (ECM) formation in two types of chitosan-based scaffolds, using bovine articular chondrocytes (BACs). The influence of these scaffolds’ porosity, as well as pore size and geometry, on the formation of cartilagineous tissue was studied. The effect of stirred conditions on ECM formation was also assessed. Chitosan-poly(butylene succinate) (CPBS) scaffolds were produced by compression moulding and salt leaching, using a blend of 50% of each material. Different porosities and pore size structures were obtained. BACs were seeded onto CPBS scaffolds using spinner flasks. Constructs were then transferred to the incubator, where half were cultured under stirred conditions, and the other half under static conditions for 4 weeks. Constructs were characterized by scanning electron microscopy, histology procedures, immunolocalization of collagen type I and collagen type II, and dimethylmethylene blue assay for glycosaminoglycan (GAG) quantification. Both materials showed good affinity for cell attachment. Cells colonized the entire scaffolds and were able to produce ECM. Large pores with random geometry improved proteoglycans and collagen type II production. However, that structure has the opposite effect on GAG production. Stirred culture conditions indicate enhancement of GAG production in both types of scaffold.M.L. Alves da Silva would like to acknowledge the Portuguese Foundation for Science and Technology (FCT) for her grant (SFRH/BD/28708/2006), Marie Curie Actions-ALEA JACTA EST (MEST-CT-2004-008104), European NoE EXPERTISSUES (NMP3-CT-2004-500283), IP GENOSTEM (LSHB-CT-2003-503161) and CARTISCAFF (POCTI/SAUIBMA/58982