Computational approach to localization using global energy minimization

Damage localization induced by strain softening can be predicted by the direct minimization of a global energy function. This article concerns the computational strategy for implementing this principle for softening materials such as concrete. Instead of using heuristic global optimization techniques, our strategies are a hybrid of local optimization methods with a path-finding approach to ensure a global optimum. With admissible nodal displacements being independent variables, it is easy to deal with the geometric (mesh) constraint conditions. The direct search optimization methods recover the localized solutions for a range of softening lattice models which are representative of quasi-brittle structures

Stereochemical studies on cyclic peptides. XIII. Energy minimization studies on cyclic hexapeptides having hydrogen bonds

The basic cyclic hexapeptide conformations which accommodate hydrogen bonded β and γ turns in the backbone have been worked out using stereochemical criteria and energy minimization procedures. It was found that cyclic hexapeptides can be made up of all possible combinations of 4 ± 1 hydrogen bonded types I, I', II and II' β turns, giving rise to symmetric conformations having twofold and inversion symmetries as well as nonsymmetric structures. Conformations having exclusive features of 3 ± 1 hydrogen bonded γ turns were found to be possible in threefold and S6 symmetric cyclic hexapeptides. The results show that the cyclic hexapeptides formed by the linking of two β turn tripeptide fragments differ mainly in (a) the hydrogen bonding scheme present in the β turn tripeptides and (b) the conformation at the α-carbon atoms where the two tripeptide fragments link. The different hydrogen bonding schemes found in the component β turns are: 1) a β turn with only a 4 ± 1 hydrogen bond, 2) a type I or I' β turn with 4 ± 1 and 3 ± 1 hydrogen bonds occurring in a bifurcated form and 3) a type II or II' β turn having both the 4 ± 1 and the 3 ± 1 hydrogen bonds with the same acceptor oxygen atom. The conformation at the linking α-carbon atoms was found to lie either in the extended region or in the 3 ± 1 hydrogen bonded γ turn or inverse γ turn regions. Further, the threefold and the S6 symmetric conformations have three γ turns interleaved by three extended regions or three inverse γ turns, respectively. The feasibility of accommodating alanyl residues of both isomeric forms in the CHP minima has been explored. Finally, the available experimental data are reviewed in the light of the present results.

Stereochemical studies on cyclic peptides: Detailed energy minimization studies on hydrogen bonded all-trans cyclic pentapeptide backbones

Conformational studies have been carried out on hydrogenbonded all-trans cyclic pentapeptide backbone. Application of a combination of grid search and energy minimization on this system has resulted in obtaining 23 minimum energy conformations, which are characterized by unique patterns of hydrogen bonding comprising of β- and γ-turns. A study of the minimum energy conformationsvis-a-vis non-planar deviation of the peptide units reveals that non-planarity is an inherent feature in many cases. A study on conformational clustering of minimum energy conformations shows that the minimum energy conformations fall into 6 distinct conformational families. Preliminary comparison with available X-ray structures of cyclic pentapeptide indicates that only some of the minimum energy conformations have formed crystal structures. The set of minimum energy conformations worked out in the present study can form a consolidated database of prototypes for hydrogen bonded backbone and be useful for modelling cyclic pentapeptides both synthetic and bioactive in nature.

The structures of bacteriorhodopsin with different retinal-Schiff base orientations--computer modeling and energy minimization studies

Bacteriorhodopsin has been the subject of intense study in order to understand its photochemical function. The recent atomic model proposed by Henderson and coworkers based on electron cryo-microscopic studies has helped in understanding many of the structural and functional aspects of bacteriorhodopsin. However, the accuracy of the positions of the side chains is not very high since the model is based on low-resolution data. In this study, we have minimized the energy of this structure of bacteriorhodopsin and analyzed various types of interactions such as - intrahelical and interhelical hydrogen bonds and retinal environment. In order to understand the photochemical action, it is necessary to obtain information on the structures adopted at the intermediate states. In this direction, we have generated some intermediate structures taking into account certain experimental data, by computer modeling studies. Various isomers of retinal with 13-cis and/or 15-cis conformations and all possible staggered orientations of Lys-216 side chain were generated. The resultant structures were examined for the distance between Lys-216-schiff base nitrogen and the carboxylate oxygen atoms of Asp-96 - a residue which is known to reprotonate the schiff base at later stages of photocycle. Some of the structures were selected on the basis of suitable retinal orientation and the stability of these structures were tested by energy minimization studies. Further, the minimized structures are analyzed for the hydrogen bond interactions and retinal environment and the results are compared with those of the minimized rest state structure. The importance of functional groups in stabilizing the structure of bacteriorhodopsin and in participating dynamically during the photocycle have been discussed.

Refining near-native protein-protein docking decoys by local resampling and energy minimization

How to refine a near-native structure to make it closer to its native conformation is an unsolved problem in protein-structure and protein-protein complex-structure prediction. In this article, we first test several scoring functions for selecting locally resampled near-native protein-protein docking conformations and then propose a computationally efficient protocol for structure refinement via local resampling and energy minimization. The proposed method employs a statistical energy function based on a Distance-scaled Ideal-gas REference state (DFIRE) as an initial filter and an empirical energy function EMPIRE (EMpirical Protein-InteRaction Energy) for optimization and re-ranking. Significant improvement of final top-1 ranked structures over initial near-native structures is observed in the ZDOCK 2.3 decoy set for Benchmark 1.0 (74% whose global rmsd reduced by 0.5 angstrom or more and only 7% increased by 0.5 angstrom or more). Less significant improvement is observed for Benchmark 2.0 (38% versus 33%). Possible reasons are discussed.

Energy minimization in multi-task software-defined sensor networks

Abstract—
After a decade of extensive research on application-specific wireless sensor networks (WSNs), the recent development of information and communication technologies makes it practical to realize the software-defined sensor networks (SDSNs), which are able to adapt to various application requirements and to fully explore the resources of WSNs. A sensor node in SDSN is able to conduct multiple tasks with different sensing targets simultaneously. A given sensing task usually involves multiple sensors to achieve a certain quality-of-sensing, e.g., coverage ratio. It is significant to design an energy-efficient sensor scheduling and management strategy with guaranteed quality-of-sensing for all tasks. To this end, three issues are investigated in this paper: 1) the subset of sensor nodes that shall be activated, i.e., sensor activation, 2) the task that each sensor node shall be assigned, i.e., task mapping, and 3) the sampling rate on a sensor for a target, i.e., sensing scheduling. They are jointly considered and formulated as a mixed-integer with quadratic constraints programming (MIQP) problem, which is then reformulated into a mixed-integer linear programming (MILP) formulation with low computation complexity via linearization. To deal with dynamic events such as sensor node participation and departure, during SDSN operations, an efficient online algorithm using local optimization is developed. Simulation results show that our proposed online algorithm approaches the globally optimized network energy efficiency with much lower rescheduling time and control overhead.

Unifying energy minimization and mutual information maximization for robust 2D/3D registration of X-ray and CT images

Similarity measure is one of the main factors that affect the accuracy of intensity-based 2D/3D registration of X-ray fluoroscopy to CT images. Information theory has been used to derive similarity measure for image registration leading to the introduction of mutual information, an accurate similarity measure for multi-modal and mono-modal image registration tasks. However, it is known that the standard mutual information measure only takes intensity values into account without considering spatial information and its robustness is questionable. Previous attempt to incorporate spatial information into mutual information either requires computing the entropy of higher dimensional probability distributions, or is not robust to outliers. In this paper, we show how to incorporate spatial information into mutual information without suffering from these problems. Using a variational approximation derived from the Kullback-Leibler bound, spatial information can be effectively incorporated into mutual information via energy minimization. The resulting similarity measure has a least-squares form and can be effectively minimized by a multi-resolution Levenberg-Marquardt optimizer. Experimental results are presented on datasets of two applications: (a) intra-operative patient pose estimation from a few (e.g. 2) calibrated fluoroscopic images, and (b) post-operative cup alignment estimation from single X-ray radiograph with gonadal shielding.

Leveraging heterogeneity for energy minimization in data centers

Energy consumption in data centers is nowadays a critical objective because of its dramatic environmental and economic impact. Over the last years, several approaches have been proposed to tackle the energy/cost optimization problem, but most of them have failed on providing an analytical model to target both the static and dynamic optimization domains for complex heterogeneous data centers. This paper proposes and solves an optimization problem for the energy-driven configuration of a heterogeneous data center. It also advances in the proposition of a new mechanism for task allocation and distribution of workload. The combination of both approaches outperforms previous published results in the field of energy minimization in heterogeneous data centers and scopes a promising area of research.

Top-down free-energy minimization on protein potential energy landscapes

The hierarchical properties of potential energy landscapes have been used to gain insight into thermodynamic and kinetic properties of protein ensembles. It also may be possible to use them to direct computational searches for thermodynamically stable macroscopic states, i.e., computational protein folding. To this end, we have developed a top-down search procedure in which conformation space is recursively dissected according to the intrinsic hierarchical structure of a landscape's effective-energy barriers. This procedure generates an inverted tree similar to the disconnectivity graphs generated by local minima-clustering methods, but it fundamentally differs in the manner in which the portion of the tree that is to be computationally explored is selected. A key ingredient is a branch-selection algorithm that takes advantage of statistically predictive properties of the landscape to guide searches down the tree branches that are most likely to lead to the physically relevant macroscopic states. Using the computational folding of a β-hairpin-forming peptide as an example, we show that such predictive properties indeed exist and can be used for structure prediction by free-energy global minimization.

Energy Optimization of Memory Intensive Parallel Workloads

Energy consumption is an important concern in modern multicore processors. The energy consumed by a multicore processor during the execution of an application can be minimized by tuning the hardware state utilizing knobs such as frequency, voltage etc. The existing theoretical work on energy minimization using Global DVFS (Dynamic Voltage and Frequency Scaling), despite being thorough, ignores the time and the energy consumed by the CPU on memory accesses and the dynamic energy consumed by the idle cores. This article presents an analytical energy-performance model for parallel workloads that accounts for the time and the energy consumed by the CPU chip on memory accesses in addition to the time and energy consumed by the CPU on CPU instructions. In addition, the model we present also accounts for the dynamic energy consumed by the idle cores. The existing work on global DVFS for parallel workloads shows that using a single frequency for the entire duration of a parallel application is not energy optimal and that varying the frequency according to the changes in the parallelism of the workload can save energy. We present an analytical framework around our energy-performance model to predict the operating frequencies (that depend upon the amount of parallelism) for global DVFS that minimize the overall CPU energy consumption. We show how the optimal frequencies in our model differ from the optimal frequencies in a model that does not account for memory accesses. We further show how the memory intensity of an application affects the optimal frequencies.

Integrated solar energy and absorption cooling model for HVAC (heating, ventilating, and air conditioning) applications in buildings

The demands in production and associate costs at power generation through non renewable resources are increasing at an alarming rate. Solar energy is one of the renewable resource that has the potential to minimize this increase. Utilization of solar energy have been concentrated mainly on heating application. The use of solar energy in cooling systems in building would benefit greatly achieving the goal of non-renewable energy minimization. The approaches of solar energy heating system research done by initiation such as University of Wisconsin at Madison and building heat flow model research conducted by Oklahoma State University can be used to develop and optimize solar cooling building system. The research uses two approaches to develop a Graphical User Interface (GUI) software for an integrated solar absorption cooling building model, which is capable of simulating and optimizing the absorption cooling system using solar energy as the main energy source to drive the cycle. The software was then put through a number of litmus test to verify its integrity. The litmus test was conducted on various building cooling system data sets of similar applications around the world. The output obtained from the software developed were identical with established experimental results from the data sets used. Software developed by other research are catered for advanced users. The software developed by this research is not only reliable in its code integrity but also through its integrated approach which is catered for new entry users. Hence, this dissertation aims to correctly model a complete building with the absorption cooling system in appropriate climate as a cost effective alternative to conventional vapor compression system.

A cyber-physical approach to combined HW-SW monitoring for improving energy efficiency in data centers

High-Performance Computing, Cloud computing and next-generation applications such e-Health or Smart Cities have dramatically increased the computational demand of Data Centers. The huge energy consumption, increasing levels of CO2 and the economic costs of these facilities represent a challenge for industry and researchers alike. Recent research trends propose the usage of holistic optimization techniques to jointly minimize Data Center computational and cooling costs from a multilevel perspective. This paper presents an analysis on the parameters needed to integrate the Data Center in a holistic optimization framework and leverages the usage of Cyber-Physical systems to gather workload, server and environmental data via software techniques and by deploying a non-intrusive Wireless Sensor Net- work (WSN). This solution tackles data sampling, retrieval and storage from a reconfigurable perspective, reducing the amount of data generated for optimization by a 68% without information loss, doubling the lifetime of the WSN nodes and allowing runtime energy minimization techniques in a real scenario.

Conformation, energy, and folding ability of selected amino acid sequences.

Evolutionary selection of sequences is studied with a knowledge-based Hamiltonian to find the design principle for folding to a model protein structure. With sequences selected by naive energy minimization, the model structure tends to be unstable and the folding ability is low. Sequences with high folding ability have only the low-lying energy minimum but also an energy landscape which is similar to that found for the native sequence over a wide region of the conformation space. Though there is a large fluctuation in foldable sequences, the hydrophobicity pattern and the glycine locations are preserved among them. Implications of the design principle for the molecular mechanism of folding are discussed.