Methods for Detecting Early Warnings of Critical Transitions in Time Series Illustrated Using Simulated Ecological Data

Many dynamical systems, including lakes, organisms, ocean circulation patterns, or financial markets, are now thought to have tipping points where critical transitions to a contrasting state can happen. Because critical transitions can occur unexpectedly and are difficult to manage, there is a need for methods that can be used to identify when a critical transition is approaching. Recent theory shows that we can identify the proximity of a system to a critical transition using a variety of so-called `early warning signals', and successful empirical examples suggest a potential for practical applicability. However, while the range of proposed methods for predicting critical transitions is rapidly expanding, opinions on their practical use differ widely, and there is no comparative study that tests the limitations of the different methods to identify approaching critical transitions using time-series data. Here, we summarize a range of currently available early warning methods and apply them to two simulated time series that are typical of systems undergoing a critical transition. In addition to a methodological guide, our work offers a practical toolbox that may be used in a wide range of fields to help detect early warning signals of critical transitions in time series data.

A Series of Trifacial Pd6 Molecular Barrels with Porphyrin Walls

Three new nanoscopic trigonal prisms, (tmen)6Pd6(H2L)3](NO3)12 (1), (Meen)6Pd6(H2L)3](NO3)12 (2), and (2,2'-bipy)6Pd6(H2L)3](NO3)12 (3), have been synthesized in excellent yields through single-step metalligand-coordination-driven self-assembly using 5,10,15,20-tetrakis(3-pyridyl)porphyrin (H2L) as a donor and cis-blocked PdII 90 degrees acceptors. These complexes were fully characterized by spectroscopic studies and single-crystal X-ray diffraction. All of these barrels quantitatively bind ZnII ions in the N4 pockets of the porphyrin walls at room temperature. Their corresponding zinc-embedded complexes, (tmen)6Pd6(ZnL)3](NO3)12 (1?a), (Meen)6Pd6(ZnL)3](NO3)12 (2?a), and (2,2'-bipy)6Pd6(ZnL)3](NO3)12 (3?a), were synthesized under ambient conditions by the post-synthetic binding of ZnII ions into the H2N4 pockets of the porphyrin walls of these complexes. These zinc-embedded complexes were characterized by electronic absorption, fluorescence emission, 1H NMR spectroscopy, as well as elemental analysis. Complexes 13 exhibited considerable microporosity in their solid state. Complex 1 was an efficient adsorbent for nitrogen gas and EtOH, MeOH, and water vapors.

Natural convection in a series of thermally interacting cavities with wall mounted heaters

Laminar natural convection in a series of thermally interacting cavities is numerically studied. Each cavity consists of a conducting bottom wall with a surface mounted heater. The side walls of the cavities are isothermally cooled. Each cavity thermally interacts with its adjacent cavities through the conducting walls. Flow and heat transfer characteristics are studied in detail for various Rayleigh numbers. The convection characteristics in multiple cavities are compared with those in single independent cavity. The thermal interaction between the cavities results in lower temperatures compared with those in independent cavities. While heat is rejected into the adjacent upper cavity through some portion of the conducting wall, heat is received from the adjacent cavity through the remaining portion of the wall. The influence of substrate conductivity on heat exchange between adjacent cavities are examined. Substrate conductivity shows strong effect on temperature distribution. When cooling at both vertical sides is changed to one side cooling, the heat transfer characteristics are changed drastically and many interesting flow features are observed. Effects of cavity aspect ratio is studied and higher heat transfer rates are observed at higher aspect ratios. Correlations for dimensionless temperature maximum and average Nusselt number are presented in terms of Rayleigh number.

Combination of similarity measures for time series classification using genetic algorithms

Time series classification deals with the problem of classification of data that is multivariate in nature. This means that one or more of the attributes is in the form of a sequence. The notion of similarity or distance, used in time series data, is significant and affects the accuracy, time, and space complexity of the classification algorithm. There exist numerous similarity measures for time series data, but each of them has its own disadvantages. Instead of relying upon a single similarity measure, our aim is to find the near optimal solution to the classification problem by combining different similarity measures. In this work, we use genetic algorithms to combine the similarity measures so as to get the best performance. The weightage given to different similarity measures evolves over a number of generations so as to get the best combination. We test our approach on a number of benchmark time series datasets and present promising results.

Taylor series expansion based multidimensional image reconstruction for confocal and 4pi microscopy

We propose and experimentally demonstrate a three-dimensional (3D) image reconstruction methodology based on Taylor series approximation (TSA) in a Bayesian image reconstruction formulation. TSA incorporates the requirement of analyticity in the image domain, and acts as a finite impulse response filter. This technique is validated on images obtained from widefield, confocal laser scanning fluorescence microscopy and two-photon excited 4pi (2PE-4pi) fluorescence microscopy. Studies on simulated 3D objects, mitochondria-tagged yeast cells (labeled with Mitotracker Orange) and mitochondrial networks (tagged with Green fluorescent protein) show a signal-to-background improvement of 40% and resolution enhancement from 360 to 240 nm. This technique can easily be extended to other imaging modalities (single plane illumination microscopy (SPIM), individual molecule localization SPIM, stimulated emission depletion microscopy and its variants).

Estimation of Series Capacitance for a Three-Phase Transformer Winding From Its Measured Frequency Response

Recently, authors published a method to indirectly measure series capacitance (C-s) of a single, isolated, uniformly wound transformer winding, from its measured frequency response. The next step was to implement it on an actual three-phase transformer. This task is not as straightforward as it might appear at first glance, since the measured frequency response on a three-phase transformer is influenced by nontested windings and their terminal connections, core, tank, etc. To extract the correct value of C-s from this composite frequency response, the formulation has to be reworked to first identify all significant influences and then include their effects. Initially, the modified method and experimental results on a three-phase transformer (4 MVA, 33 kV/433 V) are presented along with results on the winding considered in isolation (for cross validation). Later, the method is directly implemented on another three-phase unit (3.5 MVA, 13.8 kV/765 V) to show repeatability.

On the unramified principal series of GL(3) over non-archimedean local fields

Let F be a non-archimedean local field and let O be its ring of integers. We give a complete description of the irreducible constituents of the restriction of the unramified principal series representations of GL(3)(F) to GL(3)(O). (C) 2013 Elsevier Inc. All rights reserved.

Predictability of nonstationary time series using wavelet and EMD based ARMA models

Research has been undertaken to ascertain the predictability of non-stationary time series using wavelet and Empirical Mode Decomposition (EMD) based time series models. Methods have been developed in the past to decompose a time series into components. Forecasting of these components combined with random component could yield predictions. Using this ideology, wavelet and EMD analyses have been incorporated separately which decomposes a time series into independent orthogonal components with both time and frequency localizations. The component series are fit with specific auto-regressive models to obtain forecasts which are later combined to obtain the actual predictions. Four non-stationary streamflow sites (USGS data resources) of monthly total volumes and two non-stationary gridded rainfall sites (IMD) of monthly total rainfall are considered for the study. The predictability is checked for six and twelve months ahead forecasts across both the methodologies. Based on performance measures, it is observed that wavelet based method has better prediction capabilities over EMD based method despite some of the limitations of time series methods and the manner in which decomposition takes place. Finally, the study concludes that the wavelet based time series algorithm can be used to model events such as droughts with reasonable accuracy. Also, some modifications that can be made in the model have been discussed that could extend the scope of applicability to other areas in the field of hydrology. (C) 2013 Elesvier B.V. All rights reserved.

New Structural Topologies in a Series of 3d Metal Complexes with Isomeric Phenylenediacetates and 1,3,5-Tris(1-imidazolyl)benzene Ligand: Syntheses, Structures, and Magnetic and Luminescence Properties

In this article we present the syntheses, characterizations, magnetic and luminescence properties of five 3d-metal complexes, Co(tib)(1,2-phda)](n)center dot(H2O)(n) (1), Co-3(tib)(2)(1,3-phda)(3)(H2O)](n)center dot(H2O)(2n) (2), Co-5(tib)(3)(1,4-phda)(5)(H2O)(3)](n)center dot(H2O)(7n) (3), Zn-3(tib)(2)(1,3-phda)(3)](n)center dot(H2O)(4n) (4), and Mn(tib)(2)(H2O)(2)](n)center dot(1,4-phdaH)(2n)center dot(H2O)(4n) (5), obtained from the use of isomeric phenylenediacetates (phda) and the neutral 1,3,5-tris(1-imidazolyl)benzene (tib) ligand. Single crystal X-ray structures showed that 1 constitutes 3,5-connected 2-nodal nets with a double-layered two-dimensional (2D) structure, while 2 forms an interpenetrated 2D network (3,4-connected 3-nodal net). Complex 3 has a complicated three-dimensional structure with 10-nodal 3,4,5-connected nets. Complex 4, although it resembles 2 in stoichiometry and basic building structures, forms a very different overall 2D assembly. In complex 5 the dicarboxylic acid, upon losing only one of the acidic protons, does not take part in coordination; instead it forms a complicated hydrogen bonding network with water molecules. Magnetic susceptibility measurements over a wide range of temperatures revealed that the metal ions exchange very poorly through the tib ligand, but for the Co(II) complexes the effects of nonquenched orbital contributions are prominent. The 3d(10) metal complex 4 showed strong luminescence with lambda(max) = 415 nm (lambda(ex) = 360 nm).

Study on the Effect of Dead Time and its Compensation for Bus-Clamping PWM Techniques

Dead-time is provided in between the gating signals of the top and bottom semiconductor switches in an inverter leg to prevent the shorting of DC bus. Due to this dead time, there is a significant unwanted change in the output voltage of the inverter. The effect is different for different pulse width modulation (PWM) methodologies. The effect of dead-time on the output fundamental voltage is studied theoretically as well as experimentally for bus-clamping PWM methodologies. Further, experimental observations on the effectiveness of dead-time compensation are presented.

Fuzzy Classification of Time Series Data

The problem of classification of time series data is an interesting problem in the field of data mining. Even though several algorithms have been proposed for the problem of time series classification we have developed an innovative algorithm which is computationally fast and accurate in several cases when compared with 1NN classifier. In our method we are calculating the fuzzy membership of each test pattern to be classified to each class. We have experimented with 6 benchmark datasets and compared our method with 1NN classifier.

Role of Vertex Index in Substructure Identification and Activity Prediction: A Study on Antitubercular Activity of a Series of Acid Alkyl Ester Derivatives

Tuberculosis (TB) is a life threatening disease caused due to infection from Mycobacterium tuberculosis (Mtb). That most of the TB strains have become resistant to various existing drugs, development of effective novel drug candidates to combat this disease is a need of the day. In spite of intensive research world-wide, the success rate of discovering a new anti-TB drug is very poor. Therefore, novel drug discovery methods have to be tried. We have used a rule based computational method that utilizes a vertex index, named `distance exponent index (D-x)' (taken x = -4 here) for predicting anti-TB activity of a series of acid alkyl ester derivatives. The method is meant to identify activity related substructures from a series a compounds and predict activity of a compound on that basis. The high degree of successful prediction in the present study suggests that the said method may be useful in discovering effective anti-TB compound. It is also apparent that substructural approaches may be leveraged for wide purposes in computer-aided drug design.

Solar thermal power system augmented with LHP

The present work reports the study of the bubble formation dynamics in the compensation chamber (CC) of the evaporator in Loop Heat Pipes. A series of experiments were conducted at different heat loads and bubbles in the CC were visualized. Bubbles diameter, frequency and velocity were measured and correlated against heat loads. Temperatures were measured at various locations and heat transfer coefficient was calculated. Performance of the LHP evaporator was evaluated at different heat loads. (C) 2013 The Authors. Published by Elsevier Ltd. This is an open access article under the CC BY-NC-ND license

Time-Domain Approach to Estimate Series Capacitance of an Isolated Phase Winding of a Transformer

Information available in frequency response data is equivalently available in the time domain as a response due to an impulse excitation. The idea to pursue this equivalence to estimate series capacitance is linked to the well-known fact that under impulse excitation, the line/neutral current in a transformer has three distinct components, of which, the initial capacitive component is the first to manifest, followed by the oscillatory and inductive components. Of these, the capacitive component is temporally well separated from the rest-a crucial feature permitting its direct access and analysis. Further, the winding initially behaves as a pure capacitive network, so the initial component must obviously originate from only the (series and shunt) capacitances. With this logic, it should therefore be possible to estimate series capacitance, just by measuring the initial capacitive component of line current and the total shunt capacitance. The principle of the method and details of its implementation on two actual isolated transformerwindings (uniformly wound) are presented. For implementation, a low-voltage recurrent surge generator, a current probe, and a digital oscilloscope are all that is needed. The method is simple and requires no programming and needs least user intervention, thus paving the way for its widespread use.