Evaluating the role of composition and local structure on alkali out-diffusion in glasses for thin-film solar cells

The presence of alkali ions has reportedly improved the performance of CIGS/CZTS–based thin-film solar cells. The out-diffusion of the alkali ion, in particular, Na, from the glass substrate offers a facile scalable route of supplying the alkali ions during the growth of the absorber layer. In this work, we demonstrate the diffusion of different alkali ions (Li/Na/K) from composition tuned glasses with intentionally incorporated excess alkali ions into a thin Mo film, typically used as a bottom electrode in solar cells. We also evaluate the physical, mechanical, and thermal properties of the glasses for suitability as a substrate in thin-film deposition. The out-diffusion of alkali ions to the overlayer is found to be critically influenced by the composition and the local structure of the glasses. The Na ions exhibit the highest extent of diffusion among the alkali ions present in glass substrates, while that for the K-ions is the lowest. For the glasses with mixed alkali ions, the presence of Li facilitated the out-diffusion of Na, whereas K ions appear to inhibit the same. Differently with the existing reports, we show that the activation energy and the presence of Ca ions as additional modifiers play a crucial role in the transport mechanism of the ions. In addition, the synthesized glasses exhibit hardness of the order 5-7 GPa, density ~2.55 g cm^-3. The glass transition temperature lies between 535 and 580°C and the coefficient of thermal expansion 8.5-10 ppm/K, which is highly suitable for use as substrates in thin-film solar cells.     

Improving delamination resistance of carbon fiber reinforced polymeric composite by interface engineering using carbonaceous nanofillers through electrophoretic deposition: An assessment at different in-service temperatures

In this article, modification of carbon fiber surface by carbon based nanofillers (multi-walled carbon nanotubes [CNT], carbon nanofibers, and multi-layered graphene) has been achieved by electrophoretic deposition technique to improve its interfacial bonding with epoxy matrix, with a target to improve the mechanical performance of carbon fiber reinforced polymer composites. Flexural and short beam shear properties of the composites were studied at extreme temperature conditions; in-situ cryo, room and elevated temperature (−196, 30, and 120°C respectively). Laminate reinforced with CNT grafted carbon fibers exhibited highest delamination resistance with maximum improvement in flexural strength as well as in inter-laminar shear strength (ILSS) among all the carbon fiber reinforced epoxy (CE) composites at all in-situ temperatures. CNT modified CE composite showed increment of 9% in flexural strength and 17.43% in ILSS when compared to that of unmodified CE composite at room temperature (30°C). Thermomechanical properties were investigated using dynamic mechanical analysis. Fractography was also carried out to study different modes of failure of the composites.     

Effects of fiber surface grafting by functionalized carbon nanotubes on the interfacial durability during cryogenic testing and conditioning of CFRP composites

Carbon fiber reinforced epoxy (CE) composite is ideal for a cryogenic fuel storage tank in space applications due to its unmatched specific strength and modulus. In this article, inter-laminar shear strength (ILSS) of carbon fiber/epoxy (CE) composite is shown to be considerably improved by engineering the interface with carboxyl functionalized multi-walled carbon nanotube (FCNT) using electrophoretic deposition technique. FCNT deposited fibers from different bath concentrations (0.3, 0.5, and 1.0 g/L) were used to fabricate the laminates, which were then tested at room (30°C) and in-situ liquid nitrogen (LN) (−196°C) temperature as well as conditioning for different time durations (0.25, 0.5, 1, 6, and 12 h) followed by immediate RT testing to study the applicability of these engineered materials at the cryogenic environment. A maximum increment in ILSS was noticed at bath concentration of 0.5 g/L, which was ~21% and ~ 17% higher than neat composite at 30°C and − 196°C, respectively. Short-term LN conditioning was found to be detrimental due to developed cryogenic shock, which was further found to be compensated by cryogenic interfacial clamping upon long-term exposure.     

Effect ofθ-alumina formation on the growth kinetics of alumina-forming superalloys

Alumina-forming ODS superalloys are excellent oxidation-resistant materials. Their resistance relies upon the establishment of a stable, slow-growing, and adherent -alumina. In the present investigation, these alloys exhibited unstable and relatively less adherent -alumina phase, which increased the oxidation rate in the transient stage and converted into -alumina in the later part of the exposure. The oxide-growth process was found to depend upon various parameters such as temperature, time, and presence of an active elecment in the superalloy. Characterization carried out by XRD, SEM/EDAX, and AES on oxidized ODS and non-ODS alloys demonstrated a significant influence of the active element, Y, on the transformation of - to -alumina. SIMS analysis of two-stage oxidation at 900°C for two different durations evidently showed that the change in the transport process is due to -to--alumina transformation. On the basis of these results, a new and consistent mechanism is proposed to explain the influence of -alumina and its transformation on growth kinetics and the effect of yttrium on the transformation leading to good scale adherence and oxidation resistance.     

Interest point detection using imbalance oriented selection

Interest point detection has a wide range of applications, such as image retrieval and object recognition. Given an image, many previous interest point detectors first assign interest strength to each image point using a certain filtering technique, and then apply non-maximum suppression scheme to select a set of interest point candidates. However, we observe that non-maximum suppression tends to over-suppress good candidates for a weakly textured image such as a face image. We propose a new candidate selection scheme that chooses image points whose zero-/first-order intensities can be clustered into two imbalanced classes (in size), as candidates. Our tests of repeatability across image rotations and lighting conditions show the advantage of imbalance oriented selection. We further present a new face recognition application—facial identity representability evaluation—to show the value of imbalance oriented selection.     

Performance Analysis of Machine Learning Algorithms for Classifying Hand Motion-Based EEG Brain Signals

Brain-computer interfaces (BCIs) records brain activity using electroencephalogram (EEG) headsets in the form of EEG signals; these signals can be recorded, processed and classified into different hand movements, which can be used to control other IoT devices. Classification of hand movements will be one step closer to applying these algorithms in real-life situations using EEG headsets. This paper uses different feature extraction techniques and sophisticated machine learning algorithms to classify hand movements from EEG brain signals to control prosthetic hands for amputated persons. To achieve good classification accuracy, denoising and feature extraction of EEG signals is a significant step. We saw a considerable increase in all the machine learning models when the moving average filter was applied to the raw EEG data. Feature extraction techniques like a fast fourier transform (FFT) and continuous wave transform (CWT) were used in this study; three types of features were extracted, i.e., FFT Features, CWT Coefficients and CWT scalogram images. We trained and compared different machine learning (ML) models like logistic regression, random forest, k-nearest neighbors (KNN), light gradient boosting machine (GBM) and XG boost on FFT and CWT features and deep learning (DL) models like VGG-16, DenseNet201 and ResNet50 trained on CWT scalogram images. XG Boost with FFT features gave the maximum accuracy of 88%.     

EPR study of Cu2+ in Na2O-NaF-B2O3-Bi2O3 glasses

Electron paramagnetic resonance (EPR) studies have been performed on the glass system (30–x) NaF-xNa₂O-50B₂O₃-20Bi₂O₃ doped with CuO as a paramagnetic probe. The calculated g values indicate that Cu²⁺ is in a tetragonally elongated octahedral co-ordination, and the Jahn-Teller character gives rise to anisotropic hyperfine structure. The spin-Hamiltonian parameters and -bonding parameter, ², are calculated. Varying the fluoride ion concentration and its effect on these parameters is also discussed.     

A new approach for generating efficient sample from market basket data

Classical data mining algorithms require expensive passes over the entire database to generate frequent items and hence to generate association rules. With the increase in the size of database, it is becoming very difficult to handle large amount of data for computation. One of the solutions to this problem is to generate sample from the database that acts as representative of the entire database for finding association rules in such a way that the distance of the sample from the complete database is minimal. Choosing correct sample that could represent data is not an easy task. Many algorithms have been proposed in the past. Some of them are computationally fast while others give better accuracy. In this paper, we present an algorithm for generating a sample from the database that can replace the entire database for generating association rules and is aimed at keeping a balance between accuracy and speed. The algorithm that is proposed takes into account the average number of small, medium and large 1-itemset in the database and average weight of the transactions to define threshold condition for the transactions. Set of transactions that satisfy the threshold condition is chosen as the representative for the entire database. The effectiveness of the proposed algorithm has been tested over several runs of database generated by IBM synthetic data generator. A vivid comparative performance evaluation of the proposed technique with the existing sampling techniques for comparing the accuracy and speed has also been carried out.     

Identifying some important success factors in adopting agile software development practices

Agile software development (ASD) is an emerging approach in software engineering, initially advocated by a group of 17 software professionals who practice a set of “lightweight” methods, and share a common set of values of software development. In this paper, we advance the state-of-the-art of the research in this area by conducting a survey-based ex-post-facto study for identifying factors from the perspective of the ASD practitioners that will influence the success of projects that adopt ASD practices. In this paper, we describe a hypothetical success factors framework we developed to address our research question, the hypotheses we conjectured, the research methodology, the data analysis techniques we used to validate the hypotheses, and the results we obtained from data analysis. The study was conducted using an unprecedentedly large-scale survey-based methodology, consisting of respondents who practice ASD and who had experience practicing plan-driven software development in the past. The study indicates that nine of the 14 hypothesized factors have statistically significant relationship with “Success”. The important success factors that were found are: customer satisfaction, customer collaboration, customer commitment, decision time, corporate culture, control, personal characteristics, societal culture, and training and learning.