Error, Confidence, and Cost in Engineering Computations

The questions that one should answer in engineering computations - deterministic, probabilistic/randomized, as well as heuristic - are (i) how good the computed results/outputs are and (ii) how much the cost in terms of amount of computation and the amount of storage utilized in getting the outputs is. The absolutely errorfree quantities as well as the completely errorless computations done in a natural process can never be captured by any means that we have at our disposal. While the computations including the input real quantities in nature/natural processes are exact, all the computations that we do using a digital computer or are carried out in an embedded form are never exact. The input data for such computations are also never exact because any measuring instrument has inherent error of a fixed order associated with it and this error, as a matter of hypothesis and not as a matter of assumption, is not less than 0.005 per cent. Here by error we imply relative error bounds. The fact that exact error is never known under any circumstances and any context implies that the term error is nothing but error-bounds. Further, in engineering computations, it is the relative error or, equivalently, the relative error-bounds (and not the absolute error) which is supremely important in providing us the information regarding the quality of the results/outputs. Another important fact is that inconsistency and/or near-consistency in nature, i.e., in problems created from nature is completely nonexistent while in our modelling of the natural problems we may introduce inconsistency or near-inconsistency due to human error or due to inherent non-removable error associated with any measuring device or due to assumptions introduced to make the problem solvable or more easily solvable in practice. Thus if we discover any inconsistency or possibly any near-inconsistency in a mathematical model, it is certainly due to any or all of the three foregoing factors. We do, however, go ahead to solve such inconsistent/near-consistent problems and do get results that could be useful in real-world situations. The talk considers several deterministic, probabilistic, and heuristic algorithms in numerical optimisation, other numerical and statistical computations, and in PAC (probably approximately correct) learning models. It highlights the quality of the results/outputs through specifying relative error-bounds along with the associated confidence level, and the cost, viz., amount of computations and that of storage through complexity. It points out the limitation in error-free computations (wherever possible, i.e., where the number of arithmetic operations is finite and is known a priori) as well as in the usage of interval arithmetic. Further, the interdependence among the error, the confidence, and the cost is discussed.

Optimal placement of Distributed Generation for a projected load increase using Relative Electrical Distance approach

There is a lot of pressure on all the developed and second world countries to produce low emission power and distributed generation (DG) is found to be one of the most viable ways to achieve this. DG generally makes use of renewable energy sources like wind, micro turbines, photovoltaic, etc., which produce power with minimum green house gas emissions. While installing a DG it is important to define its size and optimal location enabling minimum network expansion and line losses. In this paper, a methodology to locate the optimal site for a DG installation, with the objective to minimize the net transmission losses, is presented. The methodology is based on the concept of relative electrical distance (RED) between the DG and the load points. This approach will help to identify the new DG location(s), without the necessity to conduct repeated power flows. To validate this methodology case studies are carried out on a 20 node, 66kV system, a part of Karnataka Transco and results are presented.

Unusual co-crystal of isonicotinamide: the structural landscape in crystal engineering

The idea of a structural landscape is based on the fact that a large number of crystal structures can be associated with a particular organic molecule. Taken together, all these structures constitute the landscape. The landscape includes polymorphs, pseudopolymorphs and solvates. Under certain circumstances, it may also include multicomponent crystals (or co-crystals) that contain the reference molecule as one of the components. Under still other circumstances, the landscape may include the crystal structures of molecules that are closely related to the reference molecule. The idea of a landscape is to facilitate the understanding of the process of crystallization. It includes all minima that can, in principle, be accessed by the molecule in question as it traverses the path from solution to the crystal. Isonicotinamide is a molecule that is known to form many co-crystals. We report here a 2 : 1 co-crystal of this amide with 3,5-dinitrobenzoic acid, wherein an unusual N-H center dot center dot center dot N hydrogen-bonded pattern is observed. This crystal structure offers some hints about the recognition processes between molecules that might be implicated during crystallization. Also included is a review of other recent results that illustrate the concept of the structural landscape.

Crystal Engineering in the Desiraju Research Group in Bangalore

Over the years, crystal engineering has transformed into a mature and multidisciplinary subject. New understanding, challenges, and opportunities have emerged in the design of complex structures and structure-property evaluation. Revolutionary pathways adopted by many leaders have shaped and directed this subject. In this short essay to celebrate the 60th birthday of Prof. Gautam R. Desiraju, we, his current research group members, contemplate the development of some of the topics explored by our group in the context of the overall subject. These topics, though not entirely new, are of significant interest to the crystal engineering community.

Demonstrating the Usefulness of CAELinux for Computer Aided Engineering using an Example of the Three Dimensional Reconstruction of a Pig Liver

CAELinux is a Linux distribution which is bundled with free software packages related to Computer Aided Engineering (CAE). The free software packages include software that can build a three dimensional solid model, programs that can mesh a geometry, software for carrying out Finite Element Analysis (FEA), programs that can carry out image processing etc. Present work has two goals: 1) To give a brief description of CAELinux 2) To demonstrate that CAELinux could be useful for Computer Aided Engineering, using an example of the three dimensional reconstruction of a pig liver from a stack of CT-scan images. One can note that instead of using CAELinux, using commercial software for reconstructing the liver would cost a lot of money. One can also note that CAELinux is a free and open source operating system and all software packages that are included in the operating system are also free. Hence one can conclude that CAELinux could be a very useful tool in application areas like surgical simulation which require three dimensional reconstructions of biological organs. Also, one can see that CAELinux could be a very useful tool for Computer Aided Engineering, in general.

Point Spread Function Engineering for Super-Resolution Single-Photon and Multiphoton Fluorescence Microscopy

Super-resolution imaging techniques are of paramount interest for applications in bioimaging and fluorescence microscopy. Recent advances in bioimaging demand application-tailored point spread functions. Here, we present some approaches for generating application-tailored point spread functions along with fast imaging capabilities. Aperture engineering techniques provide interesting solutions for obtaining desired system point spread functions. Specially designed spatial filters—realized by optical mask—are outlined both in a single-lens and 4Pi configuration. Applications include depth imaging, multifocal imaging, and super-resolution imaging. Such an approach is suitable for fruitful integration with most existing state-of-art imaging microscopy modalities.

Nanoindentation in Crystal Engineering: Quantifying Mechanical Properties of Molecular Crystals

Nanoindentation is a technique for measuring the elastic modulus and hardness of small amounts of materials. This method, which has been used extensively for characterizing metallic and inorganic solids, is now being applied to organic and metalorganic crystals, and has also become relevant to the subject of crystal engineering, which is concerned with the design of molecular solids with desired properties and functions. Through nanoindentation it is possible to correlate molecular-level properties such as crystal packing, interaction characteristics, and the inherent anisotropy with micro/macroscopic events such as desolvation, domain coexistence, layer migration, polymorphism, and solid-state reactivity. Recent developments and exciting opportunities in this area are highlighted in this Minireview.