Proton Conduction in Metal-Organic Frameworks and Related Modularly Built Porous Solids

Proton-conducting materials are an important component of fuel cells. Development of new types of proton-conducting materials is one of the most important issues in fuel-cell technology. Herein, we present newly developed proton-conducting materials, modularly built porous solids, including coordination polymers (CPs) or metalorganic frameworks (MOFs). The designable and tunable nature of the porous materials allows for fast development in this research field. Design and synthesis of the new types of proton-conducting materials and their unique proton-conduction properties are discussed.

Gene expression programming-fuzzy logic method for crop type classification

Crop type classification using remote sensing data plays a vital role in planning cultivation activities and for optimal usage of the available fertile land. Thus a reliable and precise classification of agricultural crops can help improve agricultural productivity. Hence in this paper a gene expression programming based fuzzy logic approach for multiclass crop classification using Multispectral satellite image is proposed. The purpose of this work is to utilize the optimization capabilities of GEP for tuning the fuzzy membership functions. The capabilities of GEP as a classifier is also studied. The proposed method is compared to Bayesian and Maximum likelihood classifier in terms of performance evaluation. From the results we can conclude that the proposed method is effective for classification.

Generalized model predictive static programming and its application to 3D impact angle constrained guidance of air-to-surface missiles

A new `generalized model predictive static programming (G-MPSP)' technique is presented in this paper in the continuous time framework for rapidly solving a class of finite-horizon nonlinear optimal control problems with hard terminal constraints. A key feature of the technique is backward propagation of a small-dimensional weight matrix dynamics, using which the control history gets updated. This feature, as well as the fact that it leads to a static optimization problem, are the reasons for its high computational efficiency. It has been shown that under Euler integration, it is equivalent to the existing model predictive static programming technique, which operates on a discrete-time approximation of the problem. Performance of the proposed technique is demonstrated by solving a challenging three-dimensional impact angle constrained missile guidance problem. The problem demands that the missile must meet constraints on both azimuth and elevation angles in addition to achieving near zero miss distance, while minimizing the lateral acceleration demand throughout its flight path. Both stationary and maneuvering ground targets are considered in the simulation studies. Effectiveness of the proposed guidance has been verified by considering first order autopilot lag as well as various target maneuvers.

Modification of Extended Open Frameworks with Fluorescent Tags for Sensing Explosives: Competition between Size Selectivity and Electron Deficiency

Three new electron-rich metal-organic frameworks (MOF-1-MOF-3) have been synthesized by employing ligands bearing aromatic tags. The key role of the chosen aromatic tags is to enhance the -electron density of the luminescent MOFs. Single-crystal X-ray structures have revealed that these MOFs form three-dimensional porous networks with the aromatic tags projecting inwardly into the pores. These highly luminescent electron-rich MOFs have been successfully utilized for the detection of explosive nitroaromatic compounds (NACs) on the basis of fluorescence quenching. Although all of the prepared MOFs can serve as sensors for NACs, MOF-1 and MOF-2 exhibit superior sensitivity towards 4-nitrotoluene (4-NT) and 2,4-dinitrotoluene (DNT) compared to 2,4,6-trinitrotoluene (TNT) and 1,3,5-trinitrobenzene (TNB). MOF-3, on the other hand, shows an order of sensitivity in accordance with the electron deficiencies of the substrates. To understand such anomalous behavior, we have thoroughly analyzed both the steady-state and time-resolved fluorescence quenching associated with these interactions. Determination of static Stern-Volmer constants (K-S) as well as collisional constants (K-C) has revealed that MOF-1 and MOF-2 have higher K-S values with 4-NT than with TNT, whereas for MOF-3 the reverse order is observed. This apparently anomalous phenomenon was well corroborated by theoretical calculations. Moreover, recyclability and sensitivity studies have revealed that these MOFs can be reused several times and that their sensitivities towards TNT solution are at the parts per billion (ppb) level.

Robust Reentry Guidance of a Reusable Launch Vehicle Using Model Predictive Static Programming

A robust suboptimal reentry guidance scheme is presented for a reusable launch vehicle using the recently developed, computationally efficient model predictive static programming. The formulation uses the nonlinear vehicle dynamics with a spherical and rotating Earth, hard constraints for desired terminal conditions, and an innovative cost function having several components with associated weighting factors that can account for path and control constraints in a soft constraint manner, thereby leading to smooth solutions of the guidance parameters. The proposed guidance essentially shapes the trajectory of the vehicle by computing the necessary angle of attack and bank angle that the vehicle should execute. The path constraints are the structural load constraint, thermal load constraint, bounds on the angle of attack, and bounds on the bank angle. In addition, the terminal constraints include the three-dimensional position and velocity vector components at the end of the reentry. Whereas the angle-of-attack command is generated directly, the bank angle command is generated by first generating the required heading angle history and then using it in a dynamic inversion loop considering the heading angle dynamics. Such a two-loop synthesis of bank angle leads to better management of the vehicle trajectory and avoids mathematical complexity as well. Moreover, all bank angle maneuvers have been confined to the middle of the trajectory and the vehicle ends the reentry segment with near-zero bank angle, which is quite desirable. It has also been demonstrated that the proposed guidance has sufficient robustness for state perturbations as well as parametric uncertainties in the model.

Construction of Bis-pyrazole Based Co(II) Metal-Organic Frameworks and Exploration of Their Chirality and Magnetic Properties

In continuation of our interest in pyrazole based multifunctional metal-organic frameworks (MOFs), we report herein the construction of a series of Co(II) MOFs using a bis-pyrazole ligand and various benzene polycarboxylic acids. Employment of different acids has resulted in different architectures ranging from a two-dimensional grid network, porous nanochannels with interesting double helical features such as supramolecular chicken wire, to three-dimensional diamondoid networks. One of the distinguishing features of the network is their larger dimensions which can be directly linked to a relatively larger size of the ligand molecule. Conformational flexibility of the ligand also plays a decisive role in determining both the dimensionality and topology of the final structure. Furthermore, chirality associated with helical networks and magnetic properties of two MOFs have also been investigated.

Multi-Temporal Satellite Image Analysis Using Gene Expression Programming

This paper discusses an approach for river mapping and flood evaluation to aid multi-temporal time series analysis of satellite images utilizing pixel spectral information for image classification and region-based segmentation to extract water covered region. Analysis of Moderate Resolution Imaging Spectroradiometer (MODIS) satellite images is applied in two stages: before flood and during flood. For these images the extraction of water region utilizes spectral information for image classification and spatial information for image segmentation. Multi-temporal MODIS images from ``normal'' (non-flood) and flood time-periods are processed in two steps. In the first step, image classifiers such as artificial neural networks and gene expression programming to separate the image pixels into water and non-water groups based on their spectral features. The classified image is then segmented using spatial features of the water pixels to remove the misclassified water region. From the results obtained, we evaluate the performance of the method and conclude that the use of image classification and region-based segmentation is an accurate and reliable for the extraction of water-covered region.

Multicomponent Assembly of Fluorescent-Tag Functionalized Ligands in Metal-Organic Frameworks for Sensing Explosives

Detection of trace amounts of explosive materials is significantly important for security concerns and pollution control. Four multicomponent metal organic frameworks (MOFs-12, 13, 23, and 123) have been synthesized by employing ligands embedded with fluorescent tags. The multicomponent assembly of the ligands was utilized to acquire a diverse electronic behavior of the MOFs and the fluorescent tags were strategically chosen to enhance the electron density in the MOFs. The phase purity of the MOFs was established by PXRD, NMR spectroscopy, and finally by singlecrystal XRD. Single-crystal structures of the MOFs-12 and 13 showed the formation of three-dimensional porous networks with the aromatic tags projecting inwardly into the pores. These electron-rich MOFs were utilized for detection of ex- plosive nitroaromatic compounds (NACs) through fluorescence quenching with high selectivity and sensitivity. The rate of fluorescence quenching for all the MOFs follows the order of electron deficiency of the NACs. We also showed the detection of picric acid (PA) by luminescent MOFs is not always reliable and can be misleading. This attracts our attention to explore these MOFs for sensing picryl chloride (PC), which is as explosive as picric acid and used widely to prepare more stable explosives like 2,4,6-trinitroaniline from PA. Moreover, the recyclability and sensitivity studies indicated that these MOFs can be reused several times with parts per billion (ppb) levels of sensitivity towards PC and 2,4,6-trinitrotoluene (TNT).

Generalized Model Predictive Static Programming and Angle-Constrained Guidance of Air-to-Ground Missiles

A new generalized model predictive static programming technique is presented for rapidly solving a class of finite-horizon nonlinear optimal control problems with hard terminal constraints. Two key features for its high computational efficiency include one-time backward integration of a small-dimensional weighting matrix dynamics, followed bya static optimization formulation that requires only a static Lagrange multiplier to update the control history. It turns out that under Euler integration and rectangular approximation of finite integrals it is equivalent to the existing model predictive static programming technique. In addition to the benchmark double integrator problem, usefulness of the proposed technique is demonstrated by solving a three-dimensional angle-constrained guidance problem for an air-to-ground missile, which demands that the missile must meet constraints on both azimuth and elevation angles at the impact point in addition to achieving near-zero miss distance, while minimizing the lateral acceleration demand throughout its flight path. Simulation studies include maneuvering ground targets along with a first-order autopilot lag. Comparison studies with classical augmented proportional navigation guidance and modern general explicit guidance lead to the conclusion that the proposed guidance is superior to both and has a larger capture region as well.

Optimal control of grinding mill circuit using model predictive static programming: A new nonlinear MPC paradigm

The recently developed reference-command tracking version of model predictive static programming (MPSP) is successfully applied to a single-stage closed grinding mill circuit. MPSP is an innovative optimal control technique that combines the philosophies of model predictive control (MPC) and approximate dynamic programming. The performance of the proposed MPSP control technique, which can be viewed as a `new paradigm' under the nonlinear MPC philosophy, is compared to the performance of a standard nonlinear MPC technique applied to the same plant for the same conditions. Results show that the MPSP control technique is more than capable of tracking the desired set-point in the presence of model-plant mismatch, disturbances and measurement noise. The performance of MPSP and nonlinear MPC compare very well, with definite advantages offered by MPSP. The computational speed of MPSP is increased through a sequence of innovations such as the conversion of the dynamic optimization problem to a low-dimensional static optimization problem, the recursive computation of sensitivity matrices and using a closed form expression to update the control. To alleviate the burden on the optimization procedure in standard MPC, the control horizon is normally restricted. However, in the MPSP technique the control horizon is extended to the prediction horizon with a minor increase in the computational time. Furthermore, the MPSP technique generally takes only a couple of iterations to converge, even when input constraints are applied. Therefore, MPSP can be regarded as a potential candidate for online applications of the nonlinear MPC philosophy to real-world industrial process plants. (C) 2014 Elsevier Ltd. All rights reserved.

A smart approach to achieve an exceptionally high loading of metal nanoparticles supported by functionalized extended frameworks for efficient catalysis

The problem associated with metal nanoparticle (NP) agglomeration when trying to achieve a high loading amount has been solved by a new method of functionalization of MOFs' pores with terminal alkyne moieties. The alkynophilicity of the Au3+ ions has been utilized successfully for an exceptionally high loading (similar to 50 wt%) of Au-NPs on supported functionalized MOFs.

Optimality Conditions and Duality for Semi-Infinite Mathematical Programming Problem with Equilibrium Constraints

This article considers a semi-infinite mathematical programming problem with equilibrium constraints (SIMPEC) defined as a semi-infinite mathematical programming problem with complementarity constraints. We establish necessary and sufficient optimality conditions for the (SIMPEC). We also formulate Wolfe- and Mond-Weir-type dual models for (SIMPEC) and establish weak, strong and strict converse duality theorems for (SIMPEC) and the corresponding dual problems under invexity assumptions.

GLOBAL REGISTRATION OF MULTIPLE POINT CLOUDS USING SEMIDEFINITE PROGRAMMING

Consider N points in R-d and M local coordinate systems that are related through unknown rigid transforms. For each point, we are given (possibly noisy) measurements of its local coordinates in some of the coordinate systems. Alternatively, for each coordinate system, we observe the coordinates of a subset of the points. The problem of estimating the global coordinates of the N points (up to a rigid transform) from such measurements comes up in distributed approaches to molecular conformation and sensor network localization, and also in computer vision and graphics. The least-squares formulation of this problem, although nonconvex, has a well-known closed-form solution when M = 2 (based on the singular value decomposition (SVD)). However, no closed-form solution is known for M >= 3. In this paper, we demonstrate how the least-squares formulation can be relaxed into a convex program, namely, a semidefinite program (SDP). By setting up connections between the uniqueness of this SDP and results from rigidity theory, we prove conditions for exact and stable recovery for the SDP relaxation. In particular, we prove that the SDP relaxation can guarantee recovery under more adversarial conditions compared to earlier proposed spectral relaxations, and we derive error bounds for the registration error incurred by the SDP relaxation. We also present results of numerical experiments on simulated data to confirm the theoretical findings. We empirically demonstrate that (a) unlike the spectral relaxation, the relaxation gap is mostly zero for the SDP (i.e., we are able to solve the original nonconvex least-squares problem) up to a certain noise threshold, and (b) the SDP performs significantly better than spectral and manifold-optimization methods, particularly at large noise levels.