An infrared spectroscopic study of C7 intramolecular hydrogen bonds in peptides

The ir-spectra in the N-H stretching region of Piv-Pro-NHMe and Boc-Pro-NHMe have been studied in carbon tetrachloride and chloroform solutions over a wide range of concentrations. Based on the concentration dependence of the N-H stretching bands, it has been shown that the characteristic N-H stretching band due to the C7 intramolecular hydrogen bond is around 3335 cm-'. Intermolecular hydrogen bonding also occurs to a small extent in these peptides, giving rise to a slight concentration dependence of the N-H stretching bands. The band around 3335 cm-* need not necessarily be due to C7 hydrogen bonds alone as proposed by Tsuboi et al. or to intermolecular hydrogen bonding alone as proposed by Maxfield et al.; this conclusion is supported by studies on Boc-Leu-NHMe, which undergoes only intermolecular hydrogen bonding We have shown that 2-Aib-Aib-OMe and Z-Aib- Ala-OMe form C7 intramolecular hydrogen bonds in addition to C5 intramolecular hydrogen bonds. The present studies also show that all the peptides studied exist in more than one conformation in solution.

Conformations of dehydrophenylalanine containing peptides. NMR studies on three tripeptides with a central dehydrophenylalanyl residue.

Three tripeptides containing a central Z-dehydrophenylalanine residue (Δz-Phe), Boc-L-Phe-Δz-Phe-X-OMe (X = L-Val 1, L-Leu 2 and X = L-Ala 3) have been synthesized and their solution conformations investigated by 270 MHz 1H NMR spectroscopy. In all three peptides, conformations involving the X residue NH in an intramolecular hydrogen bond were favoured in CDCl3 solutions. Studies of the nuclear Overhauser effect (NOE) provided support for a Type II β turn conformation in these peptides with Phe and Δz-Phe occupying the i + 1 and i + 2 positions, respectively. Significantly different conformations lacking any intramolecular hydrogen bonds were observed for peptide 1 in (CD3)2SO. NOE results were consistent with a significant population of molecules having semi-extended conformations (ø > 100°) at the Δz-Phe residue.

Systems analysis for planning of air fleets and maintenance facilities

The high cost and extraordinary demands made on sophisticated air defence systems, pose hard challenges to the managers and engineers who plan the operation and maintenance of such systems. This paper presents a study aimed at developing simulation and systems analysis techniques for the effective planning and efficient operation of small fleets of aircraft, typical of the air force of a developing country. We consider an important aspect of fleet management: the problem of resource allocation for achieving prescribed operational effectiveness of the fleet. At this stage, we consider a single flying-base, where the operationally ready aircraft are stationed, and a repair-depot, where the planes are overhauled. An important measure of operational effectiveness is ‘ availability ’, which may be defined as the expected fraction of the fleet fit for use at a given instant. The tour of aircraft in a flying-base, repair-depot system through a cycle of ‘ operationally ready ’ and ‘ scheduled overhaul ’ phases is represented first by a deterministic flow process and then by a cyclic queuing process. Initially the steady-state availability at the flying-base is computed under the assumptions of Poisson arrivals, exponential service times and an equivalent singleserver repair-depot. This analysis also brings out the effect of fleet size on availability. It defines a ‘ small ’ fleet essentially in terms of the important ‘ traffic ’ parameter of service rate/maximum arrival rate.A simulation model of the system has been developed using GPSS to study sensitivity to distributional assumptions, to validate the principal assumptions of the analytical model such as the single-server assumption and to obtain confidence intervals for the statistical parameters of interest.

Modulus Spectroscopy and Dielectric Dispersion Studies in Alkali Metal Perchlorates

Alkali metal perchlorates (KClO4, RbClO4, and CsClO4) undergo a structural phase transition from the orthorhombic to the cubic phase at elevated temperatures. A detailed dielectric study of these crystals across the phase transition is carried out at different frequencies. The crystals are found to exhibit pronounced dielectric dispersion in the kHz frequency range. The results support the view that these transitions are of order–disorder type. The dielectric behaviour at temperatures above Tc is discussed in terms of modulus spectroscopy. An estimate of conductivity relaxation times above the phase transition temperatures made from modulus spectroscopy data gives values of 3.1, 12.2 and 17.7 μs for KClO4, RbClO4, and CsClO4, respectively.

Magnetic, electric, and dielectric properties of FeCo alloy nanoparticles dispersed in amorphous matrix

Nanocrystalline Fe53Co47 alloy was synthesized by a single-step transmetallation chemical method at room temperature. The Fe53Co47 alloy nanoparticles of 77 and 47 wt% were dispersed in silica matrix by the sol-gel process using tetraethyl orthosilcate. Structural studies reveal that the as-prepared alloy powders are in bcc phase and silica is in an amorphous state. The phase-transition temperature and Mossbauer spectra analysis of the Fe-Co alloy establishes the homogeneous alloy formation. A saturation magnetization of 218 emu/g was obtained for pure FeCo alloy at room temperature. Scanning electron microscopic analysis demonstrates the hollow-sphere morphology for FeCo alloy particles. Magnetic nanocomposite consisting of 47 wt% FeCo-silica shows enhanced thermal stability over the native FeCo alloy. Electrical and dielectric properties of 47 wt% FeCo-silica nanocomposites were investigated as a function of frequency and temperature. It was found that the dielectric constants and dielectric loss were stable throughout the measured temperature (310-373 K). Our results indicate that FeCo-silica nanocomposite is a promising candidate for high-frequency applications. (C) 2010 WILEY-VCH Verlag GmbH & Co. KGaA, Weinheim

Definitions and nomenclature of nucleic acid structure parameters

At an EMBO Workshop on DNA Curvature and Bending, held at Churchill College, Cambridge, on 10-15 September 1988, two sessions were scheduled on definitions of parameters used to describe the geometry of nucleic acid chains and helices, and a common nomenclature for these parameters. The most widely used library of helix analysis programs, HELIB (Fratini et al., 1982; Dickerson, 1985) suffers from the fact that the translations and rotations as defined are not fully independent and depend to a certain extent upon the choice of overall helix axis. Several research groups have been engaged independently in developing alternative programs for the geometrical analysis of polynucleotide chains, but with different definitions of quantities calculated and with widely different nomenclature even when the same parameter was involved.

Minimizing total completion time on a batch processing machine with job families

We consider the problem of minimizing the total completion time on a single batch processing machine. The set of jobs to be scheduled can be partitioned into a number of families, where all jobs in the same family have the same processing time. The machine can process at most B jobs simultaneously as a batch, and the processing time of a batch is equal to the processing time of the longest job in the batch. We analyze that properties of an optimal schedule and develop a dynamic programming algorithm of polynomial time complexity when the number of job families is fixed. The research is motivated by the problem of scheduling burn-in ovens in the semiconductor industry

Why the Definition of Eusociality Is Not Helpful to Understand Its Evolution and What Should We Do about It

The evolution of altruism is the central problem of the evolution of eusociality. The evolution of altruism is most likely to be understood by studying species that show altruism in spite of being capable of ''selfish'' individual reproduction. But the definition of eusociality groups together primitively eusocial species where workers retain the ability to reproduce on their own and highly eusocial species where workers have lost reproductive options. At the same time it separates the primitively eusocial species from semisocial species, species that lack life-time sterility and cooperatively breeding birds and mammals, in most of which, altruism and the associated social life are facultative. The definition of eusociality is also such that it is sometimes difficult to decide,what is eusocial and what is not. I therefore suggest that, (1) we expand the scope of eusociality to include semisocial species, primitively eusocial species, highly eusocial species as well as those cooperatively breeding birds and mammals where individuals give up substantial or all personal reproduction for aiding conspecifics, (2) there should be no requirement of overlap of generations or of life-time sterility and (3) the distinction between primitively and highly eusocial should continue, based on the presence or absence of morphological caste differentiation.

Demographic history of India and mtDNA-sequence diversity

The demographic history of India was examined by comparing mtDNA sequences obtained from members of three culturally divergent Indian subpopulations (endogamous caste groups). While an inferred tree revealed some clustering according to caste affiliation, there was no clear separation into three genetically distinct groups along caste lines. Comparison of pairwise nucleotide difference distributions, however, did indicate a difference in growth patterns between two of the castes. The Brahmin population appears to have undergone either a rapid expansion or steady growth. The low-ranking Mukri caste, however, may have either maintained a roughly constant population size or undergone multiple bottlenecks during that period. Comparison of the Indian sequences to those obtained from other populations, using a tree, revealed that the Indian sequences, along with ah other non-African samples, form a starlike cluster. This cluster may represent a major expansion, possibly originating in southern Asia, taking place at some point after modern humans initially left Africa.

Synthesis of nonlinear controller to recover an unstable aircraft from poststall regime

Dynamics of the aircraft configuration considered in this paper show a unique characteristic in that there are no stable attractors in the entire high angle-of-attack flight envelope. As a result, once the aircraft has departed from the normal flight regime, no standard technique can be applied to recover the aircraft. In this paper, using feedback linearization technique, a nonlinear controller is designed at high angles of attack, which is engaged after the aircraft departs from normal flight regime. This controller stabilizes the aircraft into a stable spin. Then a set of synthetic pilot inputs is applied to cause an automatic transition from the spin equilibrium to low angles of attack where the second controller is connected. This controller is a normal gain-scheduled controller designed to have a large domain of attraction at low angles of attack. It traps the aircraft into a low angle-of-attack level flight. This entire concept of recovery has been verified using six-degrees-of-freedom nonlinear simulation. Feedback linearization technique used to design a controller ensures internal stability only if the nonlinear plant has stable zero dynamics. Because zero dynamics depend on the selection of outputs, a new method of choosing outputs is described to obtain a plant that has stable zero dynamics. Certain important aspects pertaining to the implementation of a feedback linearization-based controller are also discussed.

Single-machine scheduling with past-sequence-dependent setup times and learning effects: a parametric analysis

In this article, we consider the single-machine scheduling problem with past-sequence-dependent (p-s-d) setup times and a learning effect. The setup times are proportional to the length of jobs that are already scheduled; i.e. p-s-d setup times. The learning effect reduces the actual processing time of a job because the workers are involved in doing the same job or activity repeatedly. Hence, the processing time of a job depends on its position in the sequence. In this study, we consider the total absolute difference in completion times (TADC) as the objective function. This problem is denoted as 1/LE, (Spsd)/TADC in Kuo and Yang (2007) ('Single Machine Scheduling with Past-sequence-dependent Setup Times and Learning Effects', Information Processing Letters, 102, 22-26). There are two parameters a and b denoting constant learning index and normalising index, respectively. A parametric analysis of b on the 1/LE, (Spsd)/TADC problem for a given value of a is applied in this study. In addition, a computational algorithm is also developed to obtain the number of optimal sequences and the range of b in which each of the sequences is optimal, for a given value of a. We derive two bounds b* for the normalising constant b and a* for the learning index a. We also show that, when a < a* or b > b*, the optimal sequence is obtained by arranging the longest job in the first position and the rest of the jobs in short processing time order.

A neural network based automatic generation control design through reinforcement learning

This paper presents the design and implementation of a learning controller for the Automatic Generation Control (AGC) in power systems based on a reinforcement learning (RL) framework. In contrast to the recent RL scheme for AGC proposed by us, the present method permits handling of power system variables such as Area Control Error (ACE) and deviations from scheduled frequency and tie-line flows as continuous variables. (In the earlier scheme, these variables have to be quantized into finitely many levels). The optimal control law is arrived at in the RL framework by making use of Q-learning strategy. Since the state variables are continuous, we propose the use of Radial Basis Function (RBF) neural networks to compute the Q-values for a given input state. Since, in this application we cannot provide training data appropriate for the standard supervised learning framework, a reinforcement learning algorithm is employed to train the RBF network. We also employ a novel exploration strategy, based on a Learning Automata algorithm,for generating training samples during Q-learning. The proposed scheme, in addition to being simple to implement, inherits all the attractive features of an RL scheme such as model independent design, flexibility in control objective specification, robustness etc. Two implementations of the proposed approach are presented. Through simulation studies the attractiveness of this approach is demonstrated.

Applying Genetic Algorithms to Optimize Power in Tiled SNUCA Chip Multicore Architectures

We propose a novel technique for reducing the power consumed by the on-chip cache in SNUCA chip multicore platform. This is achieved by what we call a "remap table", which maps accesses to the cache banks that are as close as possible to the cores, on which the processes are scheduled. With this technique, instead of using all the available cache, we use a portion of the cache and allocate lesser cache to the application. We formulate the problem as an energy-delay (ED) minimization problem and solve it offline using a scalable genetic algorithm approach. Our experiments show up to 40% of savings in the memory sub-system power consumption and 47% savings in energy-delay product (ED).

Applying Genetic Algorithms to Optimize Power in Tiled SNUCA Chip Multicore Architectures

We propose a novel technique for reducing the power consumed by the on-chip cache in SNUCA chip multicore platform. This is achieved by what we call a "remap table", which maps accesses to the cache banks that are as close as possible to the cores, on which the processes are scheduled. With this technique, instead of using all the available cache, we use a portion of the cache and allocate lesser cache to the application. We formulate the problem as an energy-delay (ED) minimization problem and solve it offline using a scalable genetic algorithm approach. Our experiments show up to 40% of savings in the memory sub-system power consumption and 47% savings in energy-delay product (ED).