The Makran accretionary prism developed in the north-western part of the Indian Ocean as a consequence of the subduction of the Arabian Sea since Late Cretaceous times. It extends from southern Iran to the Baluchistan region of Pakistan where it joins the Chaman-Ornach-Nal left-lateral strike-slip fault systems to the north and the Owen Fracture Zone-Murray Ridge transtensional (right-lateral) system to the south in a complex triple junction near the city of Karachi. In September to October of 2004, we surveyed most of the accretionary complex off Pakistan with R/V Marion Dufresne. We achieved a nearly continuous bathymetric mapping of the prism and the subduction trench from 62°30′E to the triple junction near 62°30′E together with nearly 1000 km of seismic reflection (13 lines) and we took 18 piston cores in different geological settings. One of the main results is that the frontal part of the Makran accretionary prism is less two-dimensional than previously expected. We interpret the along-strike tectonic variation as a consequence of lateral variations in sediment deposition as well as a consequence of the under-thrusting of a series of basement highs and finally of the vicinity to the triple junction

Triboelectric nanogenerators (TENGs) can be incorporated into modern electronic devices requiring sustainable, renewable, and reliable microscale energy sources. We report the first use of human hair, which is known to be a highly triboelectric material, for the fabrication of biobased TENGs. Ethanolic NaOH was used to dissolve hair, and two simple fabrication techniques, bar- and spin-coating methods, were used to prepare hair-based films on electrode substrates. The dissolved hair paste has somewhat different chemical composition from original hair, but the hair-based film has almost the same level as the untreated human hair in the triboelectric series. The spin-coated film is thinner and has more even surface compared with the bar-coated one, and exhibits better performance in triboelectric generation. The TENG using a spin-coated hair film produced the maximum peak-to-peak voltage of 103 V and the power density of 60 mW m–2 across a 1.2 MΩ resistor; using the TENG device, an array of LEDs was in situ lighted without the aid of energy-collecting capacitors. A biowaste human hair offers the advantage of easy accessibility and processability into a highly tribopositive material for TENGs, and our work thus broadens the choice of positive tribo-materials and offers a novel approach for fabricating cost-effective, high efficiency, and biobased TENGs.

Descriptor systems consisting of a large number of differential-algebraic equations (DAEs) usually arise from the discretization of partial differential-algebraic equations. This paper presents an efficient algorithm for solving the coupled Sylvester equation that arises in converting a system of linear DAEs to ordinary differential equations. A significant computational advantage is obtained by exploiting the structure of the involved matrices. The proposed algorithm removes the need to solve a standard Sylvester equation or to invert a matrix. The improved performance of this new method over existing techniques is demonstrated by comparing the number of floating-point operations and via numerical examples.

Liquid-solid contact electrification is a useful mechanism to harvest wasted micromechanical energy. In this study, we investigate how the surface properties of a solid substrate affect contact electrification efficiency. Substrate surfaces were modified from hydrophilic to hydrophobic by changing the density of self-assembled monolayers (SAMs) on a SiO2 surface. A substrate with a partially-covered SAM exhibited superior performance. The partially-covered SAM substrate is hydrophobic enough to induce quick dewetting of water from the surface and sufficiently electronegative to induce a high charge density on the surface. The quick dewetting results from the aliphatic tail groups of the SAM and -OH groups make the SiO2 surface electronegative; these two competing properties can be simultaneously obtained by optimizing the SAM density. Our findings contribute to the understanding of contact electrification in liquid-solid-type energy-harvesting devices and advance the strategies to maximize the electrification efficiency by optimizing surface geometries and properties.

In predictive control, a quadratic program (QP) needs to be solved at each sampling instant. We present a new warm-start strategy to solve a QP with an interior-point method whose data is slightly perturbed from the previous QP. In this strategy, an initial guess of the unknown variables in the perturbed problem is determined from the computed solution of the previous problem. We demonstrate the effectiveness of our warm-start strategy to a number of online benchmark problems. Numerical results indicate that the proposed technique depends upon the size of perturbation and it leads to a reduction of 30-74% in floating point operations compared to a cold-start interior-point method.

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Abstract-Interior point methods (IPMs) have proven to be an efficient way of solving quadratic programming problems in predictive control. A linear system of equations needs to be solved in each iteration of an IPM. The ill-conditioning of this linear system in the later iterations of the IPM prevents the use of an iterative method in solving the linear system due to a very slow rate of convergence; in some cases the solution never reaches the desired accuracy. In this paper we propose the use of a well-conditioned, approximate linear system, which increases the rate of convergence of the iterative method. The computational advantage is obtained by the use of an inexact Newton method along with the use of novel preconditioners. Numerical results indicate that the computational complexity of our proposed method scales quadratically with the number of states and linearly with the horizon length.

Abstract.A method is proposed for reducing the cost of computing search directions in an interior point method for a quadratic program. The KKT system is partitioned and modified, based on the ratios of the slack variables and dual variables associated with the inequality constraints, to produce a smaller, approximate linear system. Analytical and numerical results are included that suggest the distribution of eigenvalues of the new, approximate system matrix is improved, which makes it more amenable to being solved with an iterative linear solver. For this purpose, new preconditioners are also presented to allow iterative methods, such as MINRES, to be used. Numerical results indicate that the computational complexity of the proposed method scales well when applied to a finite horizon discrete-time optimal control problem with linear dynamics, quadratic cost, and linear inequality constraints, which arises in model predictive control applications.Key words. interior point methods, quadratic programming, ill-conditioning, large-scale problems, inexact methods, iterative methods, predictive control AMS subject classifications. 90C51, 90C20, 90C06, 15A12, 49M15, 49N05 DOI. 10.1137/11082960X1. Introduction. Interior point methods (IPMs) have proved to be an efficient way of solving linear, quadratic, and nonlinear programming problems. Quadratic programs (QPs) arise in many applications, such as least-squares regression with linear constraints, robust data fitting, support vector machines, and predictive control problems [5,14,15,21]. They also appear in solving a nonlinear programming problem with sequential quadratic programming, in which a series of QPs is solved [24, Chap. 18].Many primal-dual IPMs for solving a QP involve finding a solution to the KarushKuhn-Tucker (KKT) conditions by a Newton-type method [30], in which a linear system of equations is formed and solved at each IPM iteration. Solving this linear system is the main contributor to the total computational cost of an IPM.The linear system that arises in this process is often sparse and large. Its size is usually reduced by block elimination, which provides alternative linear systems, as reviewed in section 2. However, the reduction in size may adversely affect the sparsity of the system matrix. In section 3 we present a new method that replaces the linear system with a smaller, approximate linear system, in which sparsity has been preserved. An upper bound on the error introduced, which decreases with each IPM iteration, is also presented. A nice property of this approximate linear system is that