Unusual Two-Step Assembly of a Minimalistic Dipeptide-Based Functional Hypergelator

Self-assembled peptide hydrogels represent the realization of peptide nanotechnology into biomedical products. There is a continuous quest to identify the simplest building blocks and optimize their critical gelation concentration (CGC). Herein, a minimalistic, de novo dipeptide, Fmoc-Lys(Fmoc)-Asp, as an hydrogelator with the lowest CGC ever reported, almost fourfold lower as compared to that of a large hexadecapeptide previously described, is reported. The dipeptide self-assembles through an unusual and unprecedented two-step process as elucidated by solid-state NMR and molecular dynamics simulation. The hydrogel is cytocompatible and supports 2D/3D cell growth. Conductive composite gels composed of Fmoc-Lys(Fmoc)-Asp and a conductive polymer exhibit excellent DNA binding. Fmoc-Lys(Fmoc)-Asp exhibits the lowest CGC and highest mechanical properties when compared to a library of dipeptide analogues, thus validating the uniqueness of the molecular design which confers useful properties for various potential applications.     

A precise sensor fault detection technique using statistical techniques for wireless body area networks

One of the major challenges in wireless body area networks (WBANs) is sensor fault detection. This paper reports a method for the precise identification of faulty sensors, which should help users identify true medical conditions and reduce the rate of false alarms, thereby improving the quality of services offered by WBANs. The proposed sensor fault detection (SFD) algorithm is based on Pearson correlation coefficients and simple statistical methods. The proposed method identifies strongly correlated parameters using Pearson correlation coefficients, and the proposed SFD algorithm detects faulty sensors. We validated the proposed SFD algorithm using two datasets from the Multiparameter Intelligent Monitoring in Intensive Care database and compared the results to those of existing methods. The time complexity of the proposed algorithm was also compared to that of existing methods. The proposed algorithm achieved high detection rates and low false alarm rates with accuracies of 97.23% and 93.99% for Dataset 1 and Dataset 2, respectively.     

An application-specific protocol architecture for wireless microsensor networks

Networking together hundreds or thousands of cheap microsensor nodes allows users to accurately monitor a remote environment by intelligently combining the data from the individual nodes. These networks require robust wireless communication protocols that are energy efficient and provide low latency. We develop and analyze low-energy adaptive clustering hierarchy (LEACH), a protocol architecture for microsensor networks that combines the ideas of energy-efficient cluster-based routing and media access together with application-specific data aggregation to achieve good performance in terms of system lifetime, latency, and application-perceived quality. LEACH includes a new, distributed cluster formation technique that enables self-organization of large numbers of nodes, algorithms for adapting clusters and rotating cluster head positions to evenly distribute the energy load among all the nodes, and techniques to enable distributed signal processing to save communication resources. Our results show that LEACH can improve system lifetime by an order of magnitude compared with general-purpose multihop approaches.     

Approximate channel identification via δ‐signed correlation

A method of approximate channel identification is proposed that is based on a simplification of the correlation estimator. Despite the numerical simplification (no multiplications or additions are required, only comparisons and an accumulator), the performance of the proposed estimator is not significantly worse than that of the standard correlation estimator. A free (user selectable) parameter moves ‘smoothly’ from a situation with small sum‐squared channel estimation error but hard‐to‐identify channel peaks, to one with a larger sum‐squared estimation error but easy‐to‐identify channel peaks. The proposed estimator is shown to be biased and its behaviour is analysed in a number of situations. Applications of the proposed estimator to sparsity detection, symbol timing recovery and to the initialization of blind equalizers are suggested. Copyright © 2002 John Wiley & Sons, Ltd.     

On-line emission and economic load dispatch using adaptive Hopfield neural network

This paper presents an adaptive Hopfield neural network (AHNN) based methodology, where the slope of the activation function is adjusted, for finding approximate Pareto solutions for the multi-objective optimization problem of emission and economic load dispatch (EELD). We have placed emphasis on finding solutions quickly, rather than the global Pareto solutions, so that our algorithm can be employed in large on-line power systems where variations in load are quite frequent. To enable faster convergence, adaptive learning rates have been developed by using energy function and applied to the slope adjustment method. The proposed algorithm has been tested on selected IEEE bus benchmark systems. The convergence of AHNN is found to be nearly 50% faster than the non-adaptive version.     

Forced convection subcooled boiling of binary mixtures

A mode based on an additive mechanism of heat transfer is proposed for forced convection subcooled boiling of binary mixtures. The contributing modes of heat transfer are: (i) the heat transferred as latent heat by the rising bubbles, (ii) the heat transferred as the heat contained in the superheated thermal layer that is removed from the surface in the wake of the rising bubbles and (iii) the single phase forced convection heat transfer from the heating surface not influenced by the bubbles. Experimental data from the literature on binary systems show good agreement with the model, validating the postulated mechanism.     

Foundations of data fusion for automation

This article discusses about the developing paradigms of data for sensor-actuator networks that perform engineering tasks. We provide a new foundation for data fusion based on two concepts: a conceptual framework and the goal-seeking paradigm. The conceptual framework emphasizes the dominant structures in the system. The goal-seeking paradigm is a mechanism for representing system evolution that explicitly manages uncertainty. The goal-seeking formulation for data fusion helps to distinguish between subjective decisions that can be executed by computers. These notions are useful for critical tasks, such as security management in large-scale distributed systems. Investigations in this area, and further refinement of the goal-seeking formulation for instrumentation and measurement applications, are likely to future systems that facilitate holistic user decision making.     

Advances in software engineering

Software is the key technology in applications as diverse as accounting, hospital management, aviation, and nuclear power. Application advances in different domains such as these-each with different requirements-have propelled software development from small batch programs to large, real-time programs with multimedia capabilities. To cope, software's enabling technologies have undergone tremendous improvement in hardware, communications, operating systems, compilers, databases, programming languages, and user interfaces, among others. In turn, those improvements have fueled even more advanced applications. Improvements in VLSI technology and multimedia, for example, have resulted in faster, more compact computers that significantly widened the range of software applications. Database and user interface enhancements, on the other hand, have spawned more interactive and collaborative development environments. Such changes have a ripple effect on software development processes as well as on software techniques and tools. In this article, we highlight software development's crucial methods and techniques of the past 30 years