J. Appl. Cryst.の発刊に際して

The amount of data collected during synchrotron X-ray diffraction (XRD) experiments is constantly increasing. Most of the time, the data are collected with image detectors, which necessitates the use of image reduction/integration routines to extract structural information from measured XRD patterns. This step turns out to be a bottleneck in the data processing procedure due to a lack of suitable software packages. In particular, fast-running synchrotron experiments require online data reduction and analysis in real time so that experimental parameters can be adjusted interactively. Dioptas is a Python-based program for on-the-fly data processing and exploration of two-dimensional X-ray diffraction area detector data, specifically designed for the large amount of data collected at XRD beamlines at synchrotrons. Its fast data reduction algorithm and graphical data exploration capabilities make it ideal for online data processing during XRD experiments and batch post-processing of large numbers of images.

DIOPTAS: a program for reduction of two-dimensional X-ray diffraction data and data exploration

Tandem solar cells that pair silicon with a metal halide perovskite are a promising option for surpassing the single-cell efficiency limit. We report a monolithic perovskite/silicon tandem with a certified power conversion efficiency of 29.15%. The perovskite absorber, with a bandgap of 1.68 electron volts, remained phase-stable under illumination through a combination of fast hole extraction and minimized nonradiative recombination at the hole-selective interface. These features were made possible by a self-assembled, methyl-substituted carbazole monolayer as the hole-selective layer in the perovskite cell. The accelerated hole extraction was linked to a low ideality factor of 1.26 and single-junction fill factors of up to 84%, while enabling a tandem open-circuit voltage of as high as 1.92 volts. In air, without encapsulation, a tandem retained 95% of its initial efficiency after 300 hours of operation.

Monolithic perovskite/silicon tandem solar cell with >29% efficiency by enhanced hole extraction

On-line construction of suffix trees

In the near future, the targets for lithium-ion batteries concerning specific energy and cost can advantageously be met by introducing layered LiNixCoyMnzO2 (NCM) cathode materials with a high Ni content (x ≥ 0.6). Increasing the Ni content allows for the utilization of more lithium at a given cell voltage, thereby improving the specific capacity but at the expense of cycle life. Here, the capacity-fading mechanisms of both typical low-Ni NCM (x = 0.33, NCM111) and high-Ni NCM (x = 0.8, NCM811) cathodes are investigated and compared from crystallographic and microstructural viewpoints. In situ X-ray diffraction reveals that the unit cells undergo different volumetric changes of around 1.2 and 5.1% for NCM111 and NCM811, respectively, when cycled between 3.0 and 4.3 V vs Li/Li+. Volume changes for NCM811 are largest for x(Li) < 0.5 because of the severe decrease in interlayer lattice parameter c from 14.467(1) to 14.030(1) A. In agreement, in situ light microscopy reveals that delithiation leads to differe...

Anisotropic Lattice Strain and Mechanical Degradation of High- and Low-Nickel NCM Cathode Materials for Li-Ion Batteries

pyFAI is an open-source software package designed to perform azimuthal integration and, correspondingly, two-dimensional regrouping on area-detector frames for small- and wide-angle X-ray scattering experiments. It is written in Python (with binary submodules for improved performance), a language widely accepted and used by the scientific community today, which enables users to easily incorporate the pyFAI library into their processing pipeline. This article focuses on recent work, especially the ease of calibration, its accuracy and the execution speed for integration.This article will form part of a virtual special issue of the journal, presenting some highlights of the 12th Biennial Conference on High-Resolution X-ray Diffraction and Imaging (XTOP2014).

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The fast azimuthal integration Python library: pyFAI .

The Smith-Waterman (SW) algorithm is the only optimal local sequence alignment algorithm. There are many SW implementations on FPGA, which show speedups of up to 100x as compared to a general-purpose-processor (GPP). In this paper, we propose a design of the SW traceback, which is done in parallel with the matrix fill stage and which gives the optimal alignment after once scanning through the whole database. Beside that, we have proposed the hardware design for the RVEP SW FPGA implementation, which demonstrates that this solution can be realized with off-the-shelf FPGA boards.

/pdf/a-parallel-fpga-design-of-the-smith-waterman-traceback-4z99fndbqr.pdf

A parallel FPGA design of the Smith-Waterman traceback

In this paper we adapted a novel approach for accelerating the Smith-Waterman (S-W) algorithm using Recursive Variable Expansion (RVE), which exposes extra parallelism in the algorithm, as compared to any other technique The results demonstrate that applying the recursive variable expansion technique speeds up the performance by a factor of 136 to 141, as compared to traditional acceleration approaches at the cost of using 125 to 128 times more hardware resources

/pdf/hardware-implementation-of-the-smith-waterman-algorithm-13o8m4ioz5.pdf

Hardware implementation of the Smith-Waterman Algorithm using Recursive Variable Expansion

Many websites over the Internet are producing a variety of textual data; such as news, research articles, ebooks, personal blogs, and user reviews. In these websites, the textual data is so large that the process of finding pertinent information by a user often becomes cumbersome. To overcome this issue, “Text-based Recommendation Systems (RS)” are being developed. They are the systems with the capability to find the relevant information in a minimal time using text as the primary feature. There exist several techniques to build and evaluate such systems. And though a good number of surveys compile the general attributes of recommendation systems, there is still a lack of comprehensive literature review about the text-based recommendation systems. In this paper, we present a review of the latest studies on text-based RS. We have conducted this survey by collecting literature from preeminent digital repositories, that was published during the period 2010-2020. This survey mainly covers the four major aspects of the textual based recommendation systems used in the reviewed literature. The aspects are datasets, feature extraction techniques, computational approaches, and evaluation metrics. As benchmark datasets carry a vital role in any research, publicly available datasets are extensively reviewed in this paper. Moreover, for text-based RS many proprietary datasets are also used, which are not available in the public. But we have consolidated all the attributes of these publically available and proprietary datasets to familiarize these attributes to new researchers. Furthermore, the feature extraction methods from the text are briefed and their usage in the construction of text-based RS are discussed. Later, various computational approaches that use these features are also discussed. To evaluate these systems, some evaluation metrics are adopted. We have presented an overview of these evaluation metrics and diagramed them according to their popularity. The survey concludes that Word Embedding is the widely used feature selection technique in the latest research. The survey also deduces that hybridization of text features with other features enhance the recommendation accuracy. The study highlights the fact that most of the work is on English textual data, and News recommendation is the most popular domain.

/pdf/a-review-of-text-based-recommendation-systems-1kfmnv6n51.pdf

A Review of Text-Based Recommendation Systems

The majority of online comments/opinions are written in text-free format. Sentiment Analysis can be used as a measure to express the polarity (positive/negative) of comments/opinions. These comments/ opinions can be in different languages i.e. English, Urdu, Roman Urdu, Hindi, Arabic etc. Mostly, people have worked on the sentiment analysis of the English language. Very limited research work has been done in Urdu or Roman Urdu languages. Whereas, Hindi/Urdu is the third largest language in the world. In this paper, we focus on the sentiment analysis of comments/opinions in Roman Urdu. There is no publicly available Roman Urdu public opinion dataset. We prepare a dataset by taking comments/opinions of people in Roman Urdu from different websites. Three supervised machine learning algorithms namely NB (Naive Bayes), LRSGD (Logistic Regression with Stochastic Gradient Descent) and SVM (Support Vector Machine) have been applied on this dataset. From results of experiments, it can be concluded that SVM performs better than NB and LRSGD in terms of accuracy. In case of SVM, an accuracy of 87.22% is achieved.

Zubair Nawaz

Papers

The fast azimuthal integration Python library: pyFAI .

A parallel FPGA design of the Smith-Waterman traceback

Hardware implementation of the Smith-Waterman Algorithm using Recursive Variable Expansion

A Review of Text-Based Recommendation Systems

Sentiment Analysis for Roman Urdu