User Attitudes on E-Books Collection in Mahatma Gandhi University Library: A Case Study

A study on the user’s attitudes, interest and understanding of e-book collections. The sample taken for the study is the E-book collection developed at the Mahatma Gandhi University Library and its users. The survey assessed the impact of the e-books on teaching learning and research. The problems associated with the use of e-books aware also identified. The inference of the study are that the user’s require an orientation on different types of collections, and also extended facilities for access which is possible in this format

Precise Meaning of a Word through Number Understanding: Introduction

An introduction of a novel approach that can translate the meaning of a word in different language exactly. The method was used by employing a simple logic of number summation through the conversion of alphabet of a language into a number.  The understanding of precise meaning of a word is shown from 2 different languages and alphabets to three types of languages. In addition, it was invented that it is also possible to have accurate synonym of a word in 4 kinds of languages. This simple technique suggests that by explaining the understanding and meaning of a word in number, the scholars can obtain exact similarity from a word.  &nbsp

On Mixed-Initative Planning and Control for Autonomous Underwater Vehicles

Supervision and control of Autonomous underwater vehicles (AUVs) has traditionally been focused on an operator determining a priori the sequence of waypoints of a single vehicle for a mission. As AUVs become more ubiquitous as a scientific tool, we envision the need for controlling multiple vehicles which would impose less cognitive burden on the operator with a more abstract form of human-in-the-loop control. Such mixed-initiative methods in goal-oriented commanding are new for the oceanographic domain and we describe the motivations and preliminary experiments with multiple vehicles operating simultaneously in the water, using a shore-based automated planner

Quantum Chemical Simulation of Molecular Structures for High Efficiency Solar Cells

Organic fluorophores are important component in the present day optical and photo electronic devices such as displays, solar cells, light emitting diodes, etc due to several characteristics including large choice of emission and absorption wavelengths window, high absorption cross section, and possibility of synthesizing them from abundant renewable sources. Various dyes such as xanthenes, azo, porphyrins,indolines, Ru complex dyes are evaluated for the above applications. Among them, renewable energy devices under the photovoltaic protocol have become particularly interesting due to its potential to be fabricated at lower cost. Dyes are the important component of dye-sensitized solar cells (DSSCs), in which the dyes are the primary absorbers of solar energy. Upon light excitation, the photoexcited electrons in the dyes are injected to a metal oxide semiconducting nanostructure from where it is collected. A large choice of dyes is tested for the DSC application; however, the state-of the-art DSC employs a porphyrin dye conjugated to mesoporous TiO particles. Photovoltaic conversion efficiency as high as ~12% and open voltage above 1 V has been typically achieved in DSCs employing porphyrin dyes in mesoporousTiO 22 andCuI/ tri iodide electrolyte. Compared to the conventional ruthenium based bypyridyl dicarboxylic acid dyes, porphyrin dyes are attractive because of their low cost and extended absorption wavelength window. A survey of literature shows that there are little study on understanding deeply the structure and properties of porphyrin dyes using quantum chemical methods. We studied the properties of different prophyrin dyes to enhance the efficiency of DSSC using ab-initio quantum chemical methods. Quantum chemical calculations under the framework of DFT were employed to study the difference in ground and excited state properties of porphyrin dyes. DFT calculations were performed with the use of Becke’s three parameter hybrid methods [Becke, 1993] with the Lee, Yang and Parr (B3LYP) gradient corrected correlation functional [Lee et al., 2008] using the Gaussian 09W program packages [Frisch, et al., 2009].Geometry optimizations were carried out using the standard double-ζ quality lanl2dz basis sets [Hay and Wadt, 1985] followed by harmonic frequency calculations and simulating their IR spectra. Discrete spectra of excitation energies and corresponding oscillator strengths were obtained by the time-dependent DFT (TDDFT) method including energy singlet transitions [Scalmani, et al., 2006]. Molecular volumesof molecules were obtained from the Gaussian output file of the optimized geometry. Additional TDDFT single point energy calculation is used to map the electron density of the ground and excited states of the dyes. The porphyrin molecules were modeled with and without Zn as central atom and phenyl groups as a meso substituent. Fig.1a summarizes the results of the calculations. Porphyrin, Zn porphyrin complex,tetraphenyl porphyrin, and tetraphenyl Zn porphyrin complex were considered in this study. All these molecules were experimentally found in literature. The reliability of the optimized geometry was further checked by harmonic frequency calculations at the B3LYP/lanl2dz level of DFT. The simulated IR spectrum of the optimized structure showed only real frequencies thereby confirming a minimum energy structure. Absorption wavelength windows and oscillator strengths of these dyes were obtained by the time-dependent DFT (TDDFT) method. UV-Vis spectrum show light absorption range is almost from 200-600, shown in fig 1b

User Attitudes on E-Books Collection in Mahatma Gandhi University Library: A Case Study

A study on the user’s attitudes, interest and understanding of e-book collections. The sample taken for the study is the E-book collection developed at the Mahatma Gandhi University Library and its users. The survey assessed the impact of the e-books on teaching learning and research. The problems associated with the use of e-books aware also identified. The inference of the study are that the user’s require an orientation on different types of collections, and also extended facilities for access which is possible in this format

Fault Localization for Buggy Deep Learning Framework Conversions in Image Recognition

When deploying Deep Neural Networks (DNNs), developers often convert models from one deep learning framework to another (e.g., TensorFlow to PyTorch). However, this process is error-prone and can impact target model accuracy. To identify the extent of such impact, we perform and briefly present a differential analysis against three DNNs widely used for image recognition (MobileNetV2, ResNet101, and InceptionV3) converted across four well-known deep learning frameworks (PyTorch, Keras, TensorFlow (TF), and TFLite), which revealed numerous model crashes and output label discrepancies of up to 72%. To mitigate such errors, we present a novel approach towards fault localization and repair of buggy deep learning framework conversions, focusing on pre-trained image recognition models. Our technique consists of four stages of analysis: 1) conversion tools, 2) model parameters, 3) model hyperparameters, and 4) graph representation. In addition, we propose various strategies towards fault repair of the faults detected. We implement our technique on top of the Apache TVM deep learning compiler, and we test it by conducting a preliminary fault localization analysis for the conversion of InceptionV3 from TF to TFLite. Our approach detected a fault in a common DNN converter tool, which introduced precision errors in weights, reducing model accuracy. After our fault localization, we repaired the issue, reducing our conversion error to zero

DeltaNN: Assessing the Impact of Computational Environment Parameters on the Performance of Image Recognition Models

Image recognition tasks typically use deep learning and require enormous processing power, thus relying on hardware accelerators like GPUs and TPUs for fast, timely processing. Failure in real-time image recognition tasks can occur due to sub-optimal mapping on hardware accelerators during model deployment, which may lead to timing uncertainty and erroneous behavior. Mapping on hardware accelerators is done using multiple software components like deep learning frameworks, compilers, and device libraries, that we refer to as the computational environment. Owing to the increased use of image recognition tasks in safety-critical applications like autonomous driving and medical imaging, it is imperative to assess their robustness to changes in the computational environment, as the impact of parameters like deep learning frameworks, compiler optimizations, and hardware devices on model performance and correctness is not yet well understood. In this paper we present a differential testing framework, DeltaNN, that allows us to assess the impact of different computational environment parameters on the performance of image recognition models during deployment, post training. DeltaNN generates different implementations of a given image recognition model for variations in environment parameters, namely, deep learning frameworks, compiler optimizations and hardware devices and analyzes differences in model performance as a result. Using DeltaNN, we conduct an empirical study of robustness analysis of three popular image recognition models using the ImageNet dataset. We report the impact in terms of misclassifications and inference time differences across different settings. In total, we observed up to 72% output label differences across deep learning frameworks, and up to 81% unexpected performance degradation in terms of inference time, when applying compiler optimizations

Tin oxide as an emerging electron transport medium in perovskite solar cells

Electron transport medium (ETM) is one of the most important components determining the photovoltaic performance of organic-inorganic halide perovskite solar cells (PSCs). Among the metal oxide semiconductors, anatase (TiO2) is the most common material used as ETM in PSCs to facilitate charge collection as well as to support a thin perovskite absorber layer. Production of conductive crystalline TiO2 requires relatively higher temperatures (400–500 °C) which limits its application to glass substrates coated with fluorine tin oxide (FTO) as other tin oxides (e.g. indium tin oxide) degrade at temperatures above 300 °C. Furthermore, this renders it unsuitable for flexible devices, often based on low-temperature flexible plastic substrates. Pure tin oxide, one of the earliest metal oxide semiconductors, is often used in myriad electronic devices and has shown outstanding characteristics as an ETM in PSC systems. Thus, tin oxide can be considered a viable alternative to TiO2 due to its excellent electron mobility and higher stability than other alternatives such as zinc oxide. This review article gives a brief history of ETMs in PSC systems and reviews recent developments in the use of tin oxide in both pure and composite form as ETMs. Efficiencies of up to 21% have been reported in tin oxide based PSCs with photovoltages of up to ~1214 mV

Principles of materials circular economy

Material sourcing, processing, usage, and end-use management play a substantial role in present-day life; however, the sustainability concerns call for adaptation of “materials circular economy” to provide the materials’ share of the solutions to the existential threats. This Matter of Opinion puts together ten principles of materials circular economy as a guide for the materials community at large, including researchers, engineers, designers, manufacturers, businesses, and policy makers, to review and update. We hope that these ten principles and associated future editions will be helpful to eliminate the materials-related existential threats