Finite field computation technique for exact solution of systems of linear equations and interval linear programming problems

An error-free computational approach is employed for finding the integer solution to a system of linear equations, using finite-field arithmetic. This approach is also extended to find the optimum solution for linear inequalities such as those arising in interval linear programming probloms.

Conformational Choices for the Stereochemically Constrained gamma-Amino Acid Residue Gabapentin: Theoretical Studies and Correlation With Experimental Results

Gabapentin (1-aminomethylcyclohexaneacetic acid, Gpn) is an achiral, conformationally constrained gamma amino acid residue. A survey of available crystal structures of Gpn peptides reveals that the torsion angles about the C-gamma-C-beta (theta(1)) and C-beta-C-alpha(theta(2)) bonds are overwhelmingly limited to gauche, gauche (g(+)g(+)/g(-)g(-)) conformations. The Gpn residue forms C-7 and C-9 hydrogen bonds in which the donor and acceptor atoms come from the flanking peptide units. In combination with alpha amino acid residues alpha gamma and gamma alpha segments can adopt C-12 hydrogen bonded structures. The conformational choices available to the Gpn residue have been probed using energy calculations, adopting a grid search strategy. Ramachandran phi-psi maps have been constructed for fixed values of theta(1) and theta(2), corresponding to the gauche and trans conformations. The sterically allowed and energetically favorable regions of conformational space have been defined and experimental observations compared. C-7 and C-9 hydrogen bonded conformational families have been identified using a grid search approach in which theta(1) and theta(2) values are varied over a range of +/- 10 degrees about ideal values at 1 degrees intervals. The theoretical analysis together with experimental observations for 59 Gpn residues from 35 crystal structures permits definition of the limited range of conformational possibilities at this gamma amino acid residue. .

Gabapentin: A Stereochemically Constrained gamma Amino Acid Residue in Hybrid Peptide Design

Nature has used the all-alpha-polypeptide backbone of proteins to create a remarkable diversity of folded structures. Sequential patterns of 20 distinct amino adds, which differ only in their side chains, determine the shape and form of proteins. Our understanding of these specific secondary structures is over half a century old and is based primarily on the fundamental elements: the Pauling alpha-helix and beta-sheet. Researchers can also generate structural diversity through the synthesis of polypeptide chains containing homologated (omega) amino acid residues, which contain a variable number of backbone atoms. However, incorporating amino adds with more atoms within the backbone introduces additional torsional freedom into the structure, which can complicate the structural analysis. Fortunately, gabapentin (Gpn), a readily available bulk drug, is an achiral beta,beta-disubstituted gamma amino add residue that contains a cyclohexyl ring at the C-beta carbon atom, which dramatically limits the range of torsion angles that can be obtained about the flanking C-C bonds. Limiting conformational flexibility also has the desirable effect of increasing peptide crystallinity, which permits unambiguous structural characterization by X-ray diffraction methods. This Account describes studies carried out in our laboratory that establish Gpn as a valuable residue in the design of specifically folded hybrid peptide structures. The insertion of additional atoms into polypeptide backbones facilitates the formation of intramolecular hydrogen bonds whose directionality is opposite to that observed in canonical alpha-peptide helices. If hybrid structures mimic proteins and biologically active peptides, the proteolytic stability conferred by unusual backbones can be a major advantage in the area of medicinal chemistry. We have demonstrated a variety of internally hydrogen-bonded structures in the solid state for Gpn-containing peptides, including the characterization of the C-7 and C-9 hydrogen bonds, which can lead to ribbons in homo-oligomeric sequences. In hybrid alpha gamma sequences, district C-12 hydrogen-bonded turn structures support formation of peptide helices and hairpins in longer sequences. Some peptides that include the Gpn residue have hydrogen-bond directionality that matches alpha-peptide helices, while others have the opposite directionality. We expect that expansion of the polypeptide backbone will lead to new classes of foldamer structures, which are thus far unknown to the world of alpha-polypeptides. The diversity of internally hydrogen-bonded structures observed in hybrid sequences containing Gpn shows promise for the rational design of novel peptide structures incorporating hybrid backbones.

The alpha method for solving differential algebraic inequality (DAI) systems

This paper describes an algorithm for ``direct numerical integration'' of the initial value Differential-Algebraic Inequalities (DAI) in a time stepping fashion using a sequential quadratic programming (SQP) method solver for detecting and satisfying active path constraints at each time step. The activation of a path constraint generally increases the condition number of the active discretized differential algebraic equation's (DAE) Jacobian and this difficulty is addressed by a regularization property of the alpha method. The algorithm is locally stable when index 1 and index 2 active path constraints and bounds are active. Subject to available regularization it is seen to be stable for active index 3 active path constraints in the numerical examples. For the high index active path constraints, the algorithm uses a user-selectable parameter to perturb the smaller singular values of the Jacobian with a view to reducing the condition number so that the simulation can proceed. The algorithm can be used as a relatively cheaper estimation tool for trajectory and control planning and in the context of model predictive control solutions. It can also be used to generate initial guess values of optimization variables used as input to inequality path constrained dynamic optimization problems. The method is illustrated with examples from space vehicle trajectory and robot path planning.

Modulating the preferred O-H center dot center dot center dot O hydrogen-bonding motif in a conformationally constrained environment through hydroxy-group derivatization

The crystal structures of three conformationally locked esters, namely the centrosymmetric tetrabenzoate of all-axial per-hydronaphthalene- 2,3,4a, 6,7,8a-hexaol, viz. trans-4a, 8a-dihydroxyperhydronaphthalene-2,3,6,7-tetrayl tetrabenzoate, C38H34O10, and the diacetate and dibenzoate of all-axial perhydronaphthalene-2,3,4a, 8a-tetraol, viz. (2R*,3R*,4aS*,8aS*)-4a, 8a-dihydroxyperhydronaphthalene-2,3-diyl diacetate, C-14-H22O6, and (2R*, 3R*, 4aS*, 8aS*)-4a, 8a-dihydroxyperhydronaphthalene- 2,3-diyl dibenzoate, C24H26O6, have been analyzed in order to examine the preference of their supramolecular assemblies towards competing inter-and intramolecular O-H center dot center dot center dot O hydrogen bonds. It was anticipated that the supramolecular assembly of the esters under study would adopt two principal hydrogen-bonding modes, namely one that employs intermolecular O-H center dot center dot center dot O hydrogen bonds (mode 1) and another that sacrifices those for intramolecular O-H center dot center dot center dot O hydrogen bonds and settles for a crystal packing dictated by weak intermolecular interactions alone (mode 2). Thus, while the molecular assembly of the two crystalline diacyl derivatives conformed to a combination of hydrogen-bonding modes 1 and 2, the crystal packing in the tetrabenzoate preferred to follow mode 2 exclusively.

A Nonlinear Approach for Re-entry Guidance of Reusable Launch Vehicles Using Model Predictive Static Programming

A nonlinear suboptimal guidance scheme is developed for the reentry phase of the reusable launch vehicles. A recently developed methodology, named as model predictive static programming (MPSP), is implemented which combines the philosophies of nonlinear model predictive control theory and approximate dynamic programming. This technique provides a finite time nonlinear suboptimal guidance law which leads to a rapid solution of the guidance history update. It does not have to suffer from computational difficulties and can be implemented online. The system dynamics is propagated through the flight corridor to the end of the reentry phase considering energy as independent variable and angle of attack as the active control variable. All the terminal constraints are satisfied. Among the path constraints, the normal load is found to be very constrictive. Hence, an extra effort has been made to keep the normal load within a specified limit and monitoring its sensitivity to the perturbation.

Genetic programming metamodel for rotating beams

This paper investigates the use of Genetic Programming (GP) to create an approximate model for the non-linear relationship between flexural stiffness, length, mass per unit length and rotation speed associated with rotating beams and their natural frequencies. GP, a relatively new form of artificial intelligence, is derived from the Darwinian concept of evolution and genetics and it creates computer programs to solve problems by manipulating their tree structures. GP predicts the size and structural complexity of the empirical model by minimizing the mean square error at the specified points of input-output relationship dataset. This dataset is generated using a finite element model. The validity of the GP-generated model is tested by comparing the natural frequencies at training and at additional input data points. It is found that by using a non-dimensional stiffness, it is possible to get simple and accurate function approximation for the natural frequency. This function approximation model is then used to study the relationships between natural frequency and various influencing parameters for uniform and tapered beams. The relations obtained with GP model agree well with FEM results and can be used for preliminary design and structural optimization studies.

Hybrid peptide design. Hydrogen bonded conformations in peptides containing the stereochemically constrained gamma-amino acid residue, gabapentin

he crystal structure of 12 peptides containing the conformationally constrained 1-(aminomethyl)cyclohexaneacetic acid, gabapentin (Gpn), are reported. In all the 39 Gpn residues conformationally characterized so far, the torsion angles about the C-alpha-C-beta and C-beta-C-gamma bonds are restricted to the gauche conformation (+/- 60 degrees). The Gpn residue is constrained to adopt folded conformations resulting in the formation of intramolecularly hydrogen-bonded structures even in short peptides. The peptides Boc-Ac(6)c-Gpn-OMe 1 and Boc-Gpn-Aib-Gpn-Aib-OMe 2 provide examples of C-7 conformation; peptides Boc-Gpn-Aib-OH 3, Boc-Ac(6)c-Gpn-OH 4, Boc-Val-Pro-Gpn-OH 5, Piv-Pro-Gpn-Val-OMe 6, and Boc-Gpn-Gpn-Leu-OMe 7 provide examples of C-9 conformation; peptide Boc-Ala-Aib-Gpn-Aib-Ala-OMe 8 provides an example of C-12 conformation and peptides Boc-beta Leu-Gpn-Val-OMe 9 and Boc-beta Phe-Gpn-Phe-OMe 10 provide examples of C-13 conformation. Gpn peptides provide examples of backbone expanded mimetics for canonical alpha-peptide turns like the gamma (C-7) and the beta (C-10) turns. The hybrid beta gamma sequences provide an example of a mimetic of the C-13 alpha-turn formed by three contiguous alpha-amino acid residues. Two examples of folded tripeptide structures, Boc-Gpn-beta Phe-Leu-OMe 11 and Boc-Aib-Gpn-beta Phg-NHMe 12, lacking internal hydrogen bonds are also presented. An analysis of available Gpn residue conformations provides the basis for future design of folded hybrid peptides.

Combining unsupervised and invigilated assessment of introductory programming

We compared student performance on large-scale take-home assignments and small-scale invigilated tests that require competency with exactly the same programming concepts. The purpose of the tests, which were carried out soon after the take home assignments were submitted, was to validate the students' assignments as individual work. We found widespread discrepancies between the marks achieved by students between the two types of tasks. Many students were able to achieve a much higher grade on the take-home assignments than the invigilated tests. We conclude that these paired assessments are an effective way to quickly identify students who are still struggling with programming concepts that we might otherwise assume they understand, given their ability to complete similar, yet more complicated, tasks in their own time. We classify these students as not yet being at the neo-Piagetian stage of concrete operational reasoning.

Optimal Reservoir Operation for Flood Control Using Folded Dynamic Programming

Folded Dynamic Programming (FDP) is adopted for developing optimalnreservoir operation policies for flood control. It is applied to a case study of Hirakud Reservoir in Mahanadi basin, India with the objective of deriving optimal policy for flood control. The river flows down to Naraj, the head of delta where a major city is located and finally joins the Bay of Bengal. As Hirakud reservoir is on the upstream side of delta area in the basin, it plays an important role in alleviating the severity of the flood for this area. Data of 68 floods such as peaks of inflow hydrograph, peak of outflow from reservoir during each flood, peak of flow hydrograph at Naraj and d/s catchment contribution are utilized. The combinations of 51, 54, 57 thousand cumecs as peak inflow into reservoir and 25.5, 20, 14 thousand cumecs respectively as,peak d/s catchment contribution form the critical combinations for flood situation. It is observed that the combination of 57 thousand cumecs of inflow into reservoir and 14 thousand cumecs for d/s catchment contribution is the most critical among the critical combinations of flow series. The method proposed can be extended to similar situations for deriving reservoir operating policies for flood control.

A new constraint programming approach for optimising a coal rail system

Because of the bottlenecking operations in a complex coal rail system, millions of dollars are costed by mining companies. To handle this issue, this paper investigates a real-world coal rail system and aims to optimise the coal railing operations under constraints of limited resources (e.g., limited number of locomotives and wagons). In the literature, most studies considered the train scheduling problem on a single-track railway network to be strongly NP-hard and thus developed metaheuristics as the main solution methods. In this paper, a new mathematical programming model is formulated and coded by optimization programming language based on a constraint programming (CP) approach. A new depth-first-search technique is developed and embedded inside the CP model to obtain the optimised coal railing timetable efficiently. Computational experiments demonstrate that high-quality solutions are obtainable in industry-scale applications. To provide insightful decisions, sensitivity analysis is conducted in terms of different scenarios and specific criteria. Keywords Train scheduling · Rail transportation · Coal mining · Constraint programming

Dynamic programming based optimum non-uniform samples for speech reconstruction and coding

Non-uniform sampling of a signal is formulated as an optimization problem which minimizes the reconstruction signal error. Dynamic programming (DP) has been used to solve this problem efficiently for a finite duration signal. Further, the optimum samples are quantized to realize a speech coder. The quantizer and the DP based optimum search for non-uniform samples (DP-NUS) can be combined in a closed-loop manner, which provides distinct advantage over the open-loop formulation. The DP-NUS formulation provides a useful control over the trade-off between bitrate and performance (reconstruction error). It is shown that 5-10 dB SNR improvement is possible using DP-NUS compared to extrema sampling approach. In addition, the close-loop DP-NUS gives a 4-5 dB improvement in reconstruction error.

A generalized Linear Programming based approach to optimal divisible load scheduling

In this paper we propose a general Linear Programming (LP) based formulation and solution methodology for obtaining optimal solution to the load distribution problem in divisible load scheduling. We exploit the power of the versatile LP formulation to propose algorithms that yield exact solutions to several very general load distribution problems for which either no solutions or only heuristic solutions were available. We consider both star (single-level tree) networks and linear daisy chain networks, having processors equipped with front-ends, that form the generic models for several important network topologies. We consider arbitrary processing node availability or release times and general models for communication delays and computation time that account for constant overheads such as start up times in communication and computation. The optimality of the LP based algorithms is proved rigorously.