Application of FTIR spectroscopy for monitoring water quality in a hypertrophic aquatic ecosystem (Lake Auensee, Leipzig)

FTIR spectroscopy as molecular fingerprint has been used to assess macromolecular and ele-mental stoichiometry as well as growth rates of phytoplankton cells. Chemometric models have been developed to extract quantitative information from FTIR spectra to reveal macro-molecular composition (of proteins, carbohydrates and lipids), C:N ratio, and growth potential. In this study, we tested these chemometric models based on lab-cultured algal species in mon-itoring changes of phytoplankton community structure in a hypertrophic lake (Lake Auensee, Leipzig, Germany), where a seasonal succession of spring green algal bloom followed by cya-nobacterial dominance in summer can be commonly observed. Our results demonstrated that green algae reacted to environmental changes such as nitrogen limitation (due to imbalanced nitrogen and phosphorus supply) with restricted growth by changing carbon allocation from protein synthesis to storage carbohydrates and/or lipids, and increased C:N ratio. By contrast, cyanobacteria proliferated under nitrogen limiting conditions. Furthermore, the FTIR-based growth potential of green alga matched well with the population biomass determined by the Chl-a concentration. However, the predicted growth potential based on FTIR spectroscopy cannot describe the realistic growth development of cyanobacteria in this lake. These results revealed that green algae and cyanobacteria have different strategies of C-allocation stoichi-ometry and growth patterns in response to environmental changes. These taxon-specific re-sponses may explain at a molecular level why green algae bloomed in the spring under condi-tions with sufficient nutrient, lower pH and lower water temperature; while cyanobacteria overgrew green algae and dominated in the summer under conditions with limited nutrient availability, higher pH and higher water temperature. In addition, the applicability of these chemometric models for predicting field cyanobacterial growth is of limited value. This may be attributed to other special adaptation properties of cyanobacterial species under stress growth conditions. We used flow cytometry to isolate functional algal groups from the water samples. Despite some drawbacks of the flow cytometry combined FTIR spectroscopy tech-nique, this method provides prospects of monitoring water quality and early warning of harmful algal blooms

Distributed sampled-data control of nonholonomic multi-robot systems with proximity networks

This paper considers the distributed sampled-data control problem of a group of mobile robots connected via distance-induced proximity networks. A dwell time is assumed in order to avoid chattering in the neighbor relations that may be caused by abrupt changes of positions when updating information from neighbors. Distributed sampled-data control laws are designed based on nearest neighbour rules, which in conjunction with continuous-time dynamics results in hybrid closed-loop systems. For uniformly and independently initial states, a sufficient condition is provided to guarantee synchronization for the system without leaders. In order to steer all robots to move with the desired orientation and speed, we then introduce a number of leaders into the system, and quantitatively establish the proportion of leaders needed to track either constant or time-varying signals. All these conditions depend only on the neighborhood radius, the maximum initial moving speed and the dwell time, without assuming a prior properties of the neighbor graphs as are used in most of the existing literature.Comment: 15 pages, 3 figure

Energy Efficiency Bounds for Photonic Analog to Digital Converters

Many efforts have examined the prospect of photonic based analog to digital converters (ADCs) and shown that they can substantially outperform their electronic counterparts in terms of speed and resolution. In this paper we analyse the power consumption of photonic ADCs, which has not been meaningfully examined in previous literature yet is a critical figure of merit for analog to digital conversion. Firstly, we show that in a quantum noise limited regime photonic based converters cannot exceed the efficiency of conventional electronic designs in any reasonable operating environment. However, we further show that the exceptional performance of photonic ADCs at high frequencies may allow them to outperform high sampling rate electronic ADCs on a Schreier figure of merit basis, whose performance is limited by technological constraints such as clock jitter and the switching speed of the integrated circuit technology

Distributed Least Squares Algorithm for Continuous-time Stochastic Systems Under Cooperative Excitation Condition

In this paper, we study the distributed adaptive estimation problem of continuous-time stochastic dynamic systems over sensor networks where each agent can only communicate with its local neighbors. A distributed least squares (LS) algorithm based on diffusion strategy is proposed such that the sensors can cooperatively estimate the unknown time-invariant parameter vector from continuous-time noisy signals. By using the martingal estimation theory and Ito formula, we provide upper bounds for the estimation error of the proposed distributed LS algorithm, and further obtain the convergence results under a cooperative excitation condition. Compared with the existing results, our results are established without using the boundedness or persistent excitation (PE) conditions of regression signals. We provide simulation examples to show that multiple sensors can cooperatively accomplish the estimation task even if any individual can not

Global Convergence of Online Identification for Mixed Linear Regression

Mixed linear regression (MLR) is a powerful model for characterizing nonlinear relationships by utilizing a mixture of linear regression sub-models. The identification of MLR is a fundamental problem, where most of the existing results focus on offline algorithms, rely on independent and identically distributed (i.i.d) data assumptions, and provide local convergence results only. This paper investigates the online identification and data clustering problems for two basic classes of MLRs, by introducing two corresponding new online identification algorithms based on the expectation-maximization (EM) principle. It is shown that both algorithms will converge globally without resorting to the traditional i.i.d data assumptions. The main challenge in our investigation lies in the fact that the gradient of the maximum likelihood function does not have a unique zero, and a key step in our analysis is to establish the stability of the corresponding differential equation in order to apply the celebrated Ljung's ODE method. It is also shown that the within-cluster error and the probability that the new data is categorized into the correct cluster are asymptotically the same as those in the case of known parameters. Finally, numerical simulations are provided to verify the effectiveness of our online algorithms

Steady state behavior of the free recall dynamics of working memory

This paper studies a dynamical system that models the free recall dynamics of working memory. This model is a modular neural network with n modules, named hypercolumns, and each module consists of m minicolumns. Under mild conditions on the connection weights between minicolumns, we investigate the long-term evolution behavior of the model, namely the existence and stability of equilibriums and limit cycles. We also give a critical value in which Hopf bifurcation happens. Finally, we give a sufficient condition under which this model has a globally asymptotically stable equilibrium with synchronized minicolumn states in each hypercolumn, which implies that in this case recalling is impossible. Numerical simulations are provided to illustrate our theoretical results. A numerical example we give suggests that patterns can be stored in not only equilibriums and limit cycles, but also strange attractors (or chaos)

Allocating Capacity with Demand Competition: Fixed Factor Allocation*

We consider a supply chain consisting of a supplier and two retailers. The supplier sells a single product to the retailers, who, in turn, retail the product to customers. The supplier has limited production capacity, and the retailers compete for the supplier’s capacity and are duopolists engaged in Cournot competition for their customers. When the sum of the retailers’ orders exceeds the supplier’s capacity, the supplier allocates his capacity according to a preannounced allocation rule. We propose a new capacity allocation rule, fixed factor allocation, which incorporates the ideas of proportional and lexicographic allocations: it prioritizes retailers as in lexicographic allocation, but guarantees only a fixed proportion of the total available capacity to the prioritized retailer. We show that (1) the fixed factor allocation rule incorporates lexicographic and proportional allocations from the perspectives of the supplier and the supply chain; (2) under fixed factor allocation, the supply chain profit is not affected by the allocation factor when it is greater than a threshold; (3) the retailers share the supply chain profit with the supplier depending on the value of the allocation factor; and (4) the fixed factor allocation coordinates the supply chain when the market size is sufficiently large. We also compare fixed factor with proportional and lexicographic allocations, respectively. Furthermore, we demonstrate how the supplier can optimize his capacity level and wholesale price under fixed factor allocation.Peer Reviewedhttps://deepblue.lib.umich.edu/bitstream/2027.42/137548/1/deci12234.pdfhttps://deepblue.lib.umich.edu/bitstream/2027.42/137548/2/deci12234_am.pd