BRICS and the New American Imperialism

"BRICS is a grouping of the five major emerging economies of Brazil, Russia, India, China and South Africa. Volume five in the Democratic Marxism series, BRICS and the New American Imperialism challenges the mainstream understanding of BRICS and US dominance to situate the new global rivalries engulfing capitalism. It offers novel analyses of BRICS in the context of increasing US induced imperial chaos, deepening environmental crisis tendencies (such as climate change and water scarcity), contradictory dynamics inside BRICS countries and growing subaltern resistance. The authors revisit contemporary thinking on imperialism and anti-imperialism, drawing on the work of Rosa Luxemburg, one of the leading theorists after Marx, who attempted to understand the expansionary nature of capitalism from the heartlands to the peripheries. The richness of Luxemburg’s pioneering work inspires most of the volume’s contributors in their analyses of the dangerous contradictions of the contemporary world as well as forms of democratic agency advancing resistance. While various forms of resistance are highlighted, among them water protests, mass worker strikes, anti-corporate campaigning and forms of cultural critique, this volume grapples with the challenge of renewing anti-imperialism beyond the NGO-driven World Social Forum and considers the prospects of a new horizontal political vessel to build global convergence. It also explores the prospects of a Fifth International of Peoples and Workers.

Determining optimal configuration for turbine generator cooler

Configuration problems with hierarchical decision-tree structures are difficult to encode for solution using simple genetic algorithms. The chromosomes typically require fitness-evaluating schemes with steep gradients to optima. Solutions get stuck at local optima. We used a GA that can control the expression of over-specified chromosomes for exploring the multilevel search-space. Experiments to configure a turbine generator cooler are performed and results are reported. © 2002 IEEE

Anomaly detection in multidimensional data using negative selection algorithm

While dealing with sensitive personnel data, the data have to be maintained to preserve integrity and usefulness. The mechanisms of the natural immune system are very promising in this area, it being an efficient anomaly or change detection system. This paper reports anomaly detection results with single and multidimensional data sets using the negative selection algorithm developed by Forrest et al. (1994). © 2002 IEEE

Optimum complexity neural networks for anomaly detection task

In this paper we study the performance of compressed data for classification and anomaly detection. We use networks of various complexities for our purpose, guided by the data itself rather than one uniform-complexity network for the entire data set

MILA - Multilevel Immune Learning Algorithm

The biological immune system is an intricate network of specialized tissues, organs, cells, and chemical molecules. T-cell-dependent humoral immune response is one of the complex immunological events, involving interaction of B cells with antigens (Ag) and their proliferation, differentiation and subsequent secretion of antibodies (Ab). Inspired by these immunological principles, we proposed a Multilevel Immune Learning Algorithm (MILA) for novel pattern recognition. It incorporates multiple detection schema, clonal expansion and dynamic detector generation mechanisms in a single framework. Different test problems are studied and experimented with MILA for performance evaluation. Preliminary results show that MILA is flexible and efficient in detecting anomalies and novelties in data patterns. © Springer-Verlag Berlin Heidelberg 2003

Digital PCR Modeling for Maximal Sensitivity, Dynamic Range and Measurement Precision

<div><p>The great promise of digital PCR is the potential for unparalleled precision enabling accurate measurements for genetic quantification. A challenge associated with digital PCR experiments, when testing unknown samples, is to perform experiments at dilutions allowing the detection of one or more targets of interest at a desired level of precision. While theory states that optimal precision (P_o) is achieved by targeting ~1.59 mean copies per partition (λ), and that dynamic range (R) includes the space spanning one positive (λ_L) to one negative (λ_U) result from the total number of partitions (n), these results are tempered for the practitioner seeking to construct digital PCR experiments in the laboratory. A mathematical framework is presented elucidating the relationships between precision, dynamic range, number of partitions, interrogated volume, and sensitivity in digital PCR. The impact that false reaction calls and volumetric variation have on sensitivity and precision is next considered. The resultant effects on sensitivity and precision are established via Monte Carlo simulations reflecting the real-world likelihood of encountering such scenarios in the laboratory. The simulations provide insight to the practitioner on how to adapt experimental loading concentrations to counteract any one of these conditions. The framework is augmented with a method of extending the dynamic range of digital PCR, with and without increasing n, via the use of dilutions. An example experiment demonstrating the capabilities of the framework is presented enabling detection across 3.33 logs of starting copy concentration.</p></div