Thermodynamics of Ion Separation by Electrosorption

We present a simple, top-down approach for the calculation of minimum energy consumption of electrosorptive ion separation using variational form of the (Gibbs) free energy. We focus and expand on the case of electrostatic capacitive deionization (CDI), and the theoretical framework is independent of details of the double-layer charge distribution and is applicable to any thermodynamically consistent model, such as the Gouy-Chapman-Stern (GCS) and modified Donnan (mD) models. We demonstrate that, under certain assumptions, the minimum required electric work energy is indeed equivalent to the free energy of separation. Using the theory, we define the thermodynamic efficiency of CDI. We explore the thermodynamic efficiency of current experimental CDI systems and show that these are currently very low, less than 1% for most existing systems. We applied this knowledge and constructed and operated a CDI cell to show that judicious selection of the materials, geometry, and process parameters can be used to achieve a 9% thermodynamic efficiency (4.6 kT energy per removed ion). This relatively high value is, to our knowledge, by far the highest thermodynamic efficiency ever demonstrated for CDI. We hypothesize that efficiency can be further improved by further reduction of CDI cell series resistances and optimization of operational parameters

Tuberculoma of brain-study of prospective clinical EEG, CT scan data of fifty two patients

52 patients with tuberculomas of brain above 12 years of age were studied during 1981 to 1988. These patients presented with focal or generalised seizures with or without focal neurological signs or raised ICT and had an abnormal EEG which correlated with the site of lesion in the CT Scan. Follow up with serial CT Scans was at intervals of 3, 6, 9, 12 and 18 months. A minimum of 3 scans for each patient were available for analysis

A fuzzy DEMATEL approach based on intuitionistic fuzzy information for evaluating knowledge transfer effectiveness in GSD projects

The offshore/onsite teams' effectiveness of knowledge transfer is significantly measured by various kinds of factors. In this paper, we propose a knowledge transfer (KT) assessment framework which integrates four criteria for evaluating the KT effectiveness of GSD teams. These are: knowledge, team, technology, and organisation factors. In this context, we present a fuzzy DEMATEL approach for assessing GSD teams KT effectiveness based on intuitionistic fuzzy numbers (IFNs). In this approach, decision makers provide their subjective judgments on the criteria, characterised on the basis of intuitionistic fuzzy sets. Moreover, intuitionistic fuzzy sets used in the fuzzy DEMATEL approach can effectively assess the KT effectiveness criteria and rank the alternatives. Subsequently, the entire process is illustrated with GSD teams' KT evaluation criteria samples, and the factors are ranked using fuzzy linguistic variables which are mapped to IFNs. Afterwards, the IFNs are converted into their corresponding basic probability assignments (BPAs) and then the Dempster-Shafer theory is used to combine the group decision making process. Besides, illustrative applicability and usefulness of the proposed approach in group decision making process for the evaluation of multiple criteria under fuzzy environment has been tested by software professionals at Inowits Software Organisation in India

A light-in-flight single-pixel camera for use in the visible and short-wave infrared

This is the final version. Available on open access from the Optical Society of America via the DOI in this recordSingle-pixel cameras reconstruct images from a stream of spatial projection measurements recorded with a single-element detector, which itself has no spatial resolution. This enables the creation of imaging systems that can take advantage of the ultra-fast response times of single-element detectors. Here we present a single-pixel camera with a temporal resolution of 200 ps in the visible and short-wave infrared wavelengths, used here to study the transit time of distinct spatial modes transmitted through few-mode and orbital angular momentum mode conserving optical fiber. Our technique represents a way to study the spatial and temporal characteristics of light propagation in multimode optical fibers, which may find use in optical fiber design and communications.Engineering and Physical Sciences Research Council (EPSRC)European Union Horizon 2020Office of Naval Research (ONR)National Science Foundation (NSF

Preface

Requirements Engineering (RE) is the process of discovering, documenting, and managing the requirements for a computer-based syste

Fog computing: Concepts, principles and related paradigms

© Springer International Publishing AG, part of Springer Nature 2018. Fog Computing, sometimes also referred to as Edge Computing, extends the Cloud Computing paradigm to lower latency, improve location awareness, provide better support for mobility and increase business agility. There is necessarily a requirement for these attributes in this age of the Internet of Things (IoT) where, according to one estimate, there will be close to 50 billion interconnected smart devices by 2020, and the amount of Big Data generated by these devices is expected to grow to around 200 exabytes per year by 2020. The core characteristic of the Fog Computing architecture is that it provides compute and data analytics services more immediately and close to the physical devices that generate such data, i.e. at the Edge of the network, and thus bypassing the wider Internet. In this chapter, we discuss the concepts and principles of Fog paradigm as well as the related paradigms and technologies, present the difference between the Cloud and Fog architectures and briefly discuss the OpenFog Reference Architecture. Hopefully, this chapter will set a scene for the various Fog-related topics presented in the rest of this book

Combining Fine- and Coarse-Grained Classifiers for Diabetic Retinopathy Detection

Visual artefacts of early diabetic retinopathy in retinal fundus images are usually small in size, inconspicuous, and scattered all over retina. Detecting diabetic retinopathy requires physicians to look at the whole image and fixate on some specific regions to locate potential biomarkers of the disease. Therefore, getting inspiration from ophthalmologist, we propose to combine coarse-grained classifiers that detect discriminating features from the whole images, with a recent breed of fine-grained classifiers that discover and pay particular attention to pathologically significant regions. To evaluate the performance of this proposed ensemble, we used publicly available EyePACS and Messidor datasets. Extensive experimentation for binary, ternary and quaternary classification shows that this ensemble largely outperforms individual image classifiers as well as most of the published works in most training setups for diabetic retinopathy detection. Furthermore, the performance of fine-grained classifiers is found notably superior than coarse-grained image classifiers encouraging the development of task-oriented fine-grained classifiers modelled after specialist ophthalmologists.Comment: Pages 12, Figures

Software engineering approach to bug prediction models using machine learning as a service (MLaaS)

The presence of bugs in a software release has become inevitable. The loss incurred by a company due to the presence of bugs in a software release is phenomenal. Modern methods of testing and debugging have shifted focus from “detecting” to “predicting” bugs in the code. The existing models of bug prediction have not been optimized for commercial use. Moreover, the scalability of these models has not been discussed in depth yet. Taking into account the varying costs of fixing bugs, depending on which stage of the software development cycle the bug is detected in, this paper uses two approaches - one model which can be employed when the 'cost of changing code' curve is exponential and the other model can be used otherwise. The cases where each model is best suited are discussed. This paper proposes a model that can be deployed on a cloud platform for software development companies to use. The model in this paper aims to predict the presence or absence of a bug in the code, using machine learning classification models. Using Microsoft Azure's machine learning platform this model can be distributed as a web service worldwide, thus providing Bug Prediction as a Service (BPaaS)

Characterization of X-Linked SNP genotypic variation in globally distributed human populations

An analysis of X-linked genetic variation in human populations provides insights into population structure and demographic patterns