Towards a Reference Architecture with Modular Design for Large-scale Genotyping and Phenotyping Data Analysis: A Case Study with Image Data

With the rapid advancement of computing technologies, various scientific research communities have been extensively using cloud-based software tools or applications. Cloud-based applications allow users to access software applications from web browsers while relieving them from the installation of any software applications in their desktop environment. For example, Galaxy, GenAP, and iPlant Colaborative are popular cloud-based systems for scientific workflow analysis in the domain of plant Genotyping and Phenotyping. These systems are being used for conducting research, devising new techniques, and sharing the computer assisted analysis results among collaborators. Researchers need to integrate their new workflows/pipelines, tools or techniques with the base system over time. Moreover, large scale data need to be processed within the time-line for more effective analysis. Recently, Big Data technologies are emerging for facilitating large scale data processing with commodity hardware. Among the above-mentioned systems, GenAp is utilizing the Big Data technologies for specific cases only. The structure of such a cloud-based system is highly variable and complex in nature. Software architects and developers need to consider totally different properties and challenges during the development and maintenance phases compared to the traditional business/service oriented systems. Recent studies report that software engineers and data engineers confront challenges to develop analytic tools for supporting large scale and heterogeneous data analysis. Unfortunately, less focus has been given by the software researchers to devise a well-defined methodology and frameworks for flexible design of a cloud system for the Genotyping and Phenotyping domain. To that end, more effective design methodologies and frameworks are an urgent need for cloud based Genotyping and Phenotyping analysis system development that also supports large scale data processing. In our thesis, we conduct a few studies in order to devise a stable reference architecture and modularity model for the software developers and data engineers in the domain of Genotyping and Phenotyping. In the first study, we analyze the architectural changes of existing candidate systems to find out the stability issues. Then, we extract architectural patterns of the candidate systems and propose a conceptual reference architectural model. Finally, we present a case study on the modularity of computation-intensive tasks as an extension of the data-centric development. We show that the data-centric modularity model is at the core of the flexible development of a Genotyping and Phenotyping analysis system. Our proposed model and case study with thousands of images provide a useful knowledge-base for software researchers, developers, and data engineers for cloud based Genotyping and Phenotyping analysis system development

Probing the NMSSM via Higgs boson signatures from stop cascade decays at the LHC

Higgs signatures from the cascade decays of light stops are an interesting possibility in the next to minimal supersymmetric standard model (NMSSM). We investigate the potential reach of the light stop mass at the 13 TeV run of the LHC by means of five NMSSM benchmark points where this signature is dominant. These benchmark points are compatible with current Higgs coupling measurements, LHC constraints, dark matter relic density and direct detection constraints. We consider single and di-lepton search strategies, as well as the jet-substructure technique to reconstruct the Higgs bosons. We find that one can probe stop masses up to 1.2 TeV with 300 $\rm fb^{-1}$ luminosity via the di-lepton channel, while with the jet-substructure method, stop masses up to 1 TeV can be probed with 300 $\rm fb^{-1}$ luminosity. We also investigate the possibility of the appearance of multiple Higgs peaks over the background in the fat-jet mass distribution, and conclude that such a possibility is viable only at the high luminosity run of 13 TeV LHC.Comment: 20 pages, 5 figures; Two figures updated, typos corrected. Matched with the published versio

Software Design Change Artifacts Generation through Software Architectural Change Detection and Categorisation

Software is solely designed, implemented, tested, and inspected by expert people, unlike other engineering projects where they are mostly implemented by workers (non-experts) after designing by engineers. Researchers and practitioners have linked software bugs, security holes, problematic integration of changes, complex-to-understand codebase, unwarranted mental pressure, and so on in software development and maintenance to inconsistent and complex design and a lack of ways to easily understand what is going on and what to plan in a software system. The unavailability of proper information and insights needed by the development teams to make good decisions makes these challenges worse. Therefore, software design documents and other insightful information extraction are essential to reduce the above mentioned anomalies. Moreover, architectural design artifacts extraction is required to create the developer’s profile to be available to the market for many crucial scenarios. To that end, architectural change detection, categorization, and change description generation are crucial because they are the primary artifacts to trace other software artifacts. However, it is not feasible for humans to analyze all the changes for a single release for detecting change and impact because it is time-consuming, laborious, costly, and inconsistent. In this thesis, we conduct six studies considering the mentioned challenges to automate the architectural change information extraction and document generation that could potentially assist the development and maintenance teams. In particular, (1) we detect architectural changes using lightweight techniques leveraging textual and codebase properties, (2) categorize them considering intelligent perspectives, and (3) generate design change documents by exploiting precise contexts of components’ relations and change purposes which were previously unexplored. Our experiment using 4000+ architectural change samples and 200+ design change documents suggests that our proposed approaches are promising in accuracy and scalability to deploy frequently. Our proposed change detection approach can detect up to 100% of the architectural change instances (and is very scalable). On the other hand, our proposed change classifier’s F1 score is 70%, which is promising given the challenges. Finally, our proposed system can produce descriptive design change artifacts with 75% significance. Since most of our studies are foundational, our approaches and prepared datasets can be used as baselines for advancing research in design change information extraction and documentation

Fabrication and characterization of crystalline cubic bismuth zinc niobate pyrochlore (Bi<SUB>1.5</SUB>ZnNb<SUB>1.5</SUB>O<SUB>7</SUB>) nanoparticles derived by sol-gel

Here, we report the fabrication and characterization of crystalline cubic bismuth zinc niobate pyrochlore, Bi1.5ZnNb1.5O7, (BZN) nanoparticles. A novel sol-gel method is used for the synthesis of air-stable and precipitate-free diol-based sols of BZN which was dried at 150&#176;C and partially calcined at 350 &#176; C/1 h to decompose organics and bring down the free energy barrier for crystallization. Annealed at 450-700 &#176; C/1 h, BZN powder exhibited nanocrystalline morphology. The average BZN nanoparticle size were about 6, 60 and 85 nm for the samples annealed at 450, 600 and 700 &#176; C/1 h, respectively as observed by transmission electron microscope (TEM). The crystallinity and phase formation of the as synthesized nanoparticles were confirmed by the selected-area electron diffraction (SAED), X-ray diffraction (XRD) and high resolution TEM (HRTEM) analysis. Energy-dispersive X-ray spectroscopy (EDX) analysis demonstrated that stoichiometric Bi1.5ZnNb1.5O7 was formed

Application of Speaker Recognition on Biometric

Speaker recognition is the process of determining which registered speaker provides a given utterance followed by the process of accepting or rejecting the identity claim of a speaker. This paper reports on an experimental study involving signal processing in both time and frequency domain, and to receive a small bit of insight into the principles of speech analysis. This was accomplished by recording four speech segments from each person in our classroom, all of them varying slightly. Comparisons and analysis were then made on each signal, depending upon the instructions given by Dr. Qi

Size effect on the lattice parameter of KCl during mechanical milling

The size effect on the lattice parameter of ionic KCl nanocrystals was studied systematically during mechanical milling of pure KCl powder under vacuum. The results suggest anomalous lattice expansion, with the lattice parameter increasing from 6.278&#197; at d=6&#956; m to 6.30307&#197; at d=85nm. The defects generated during ball milling of KCl and surface stress are deemed to be responsible for this lattice parameter expansion

Design, Synthesis and Biological Evaluation of novel Hedgehog Inhibitors for treating Pancreatic Cancer

Hedgehog (Hh) pathway is involved in epithelial-mesenchymal transition (EMT) and cancer stem cell (CSC) maintenance resulting in tumor progression. GDC-0449, an inhibitor of Hh pathway component smoothened (Smo) has shown promise in the treatment of various cancers including pancreatic cancer. However, the emergence of resistance during GDC-0449 treatment with numerous side effects limits its use. Therefore, here we report the design, synthesis and evaluation of novel GDC-0449 analogs using N-[3-(2-pyridinyl) phenyl] benzamide scaffold. Cell-based screening followed by molecular simulation revealed 2-chloro-N1-[4-chloro-3-(2-pyridinyl)phenyl]-N4,N4-bis(2-pyridinylmethyl)-1,4- benzenedicarboxamide (MDB5) as most potent analog, binding with an extra interactions in seventransmembrane (7-TM) domain of Smo due to an additional 2-pyridylmethyl group than GDC-0449. Moreover, MDB5 was more efficient in inhibiting Hh pathway components as measured by Gli-1 and Shh at transcriptional and translational levels. Additionally, a significant reduction of ALDH1, CD44 and Oct-3/4, key markers of pancreatic CSC was observed when MIA PaCa-2 cells were treated with MDB5 compared to GDC-0449. In a pancreatic tumor mouse model, MDB5 containing nanoparticles treated group showed significant inhibition of tumor growth without loss in body weight. These evidence highlight the enhanced Hh pathway inhibition and anticancer properties of MDB5 leaving a platform for mono and/or combination therapy

Application of digital image analysis for monitoring the behavior of factors that control the rock fragmentation in opencast bench blasting: A case study conducted over four opencast coal mines of the Talcher Coalfields, India

Drilling and blasting play a very important role in driving the economy of opencast mines, as various mining activities related to strata handling are dependent on the size of the rock mass created due to blasting. Thus the analysis of fragments created from rock explosion is essential in order to monitor its compatibility with the deployed mining machineries/HEMMs (such as shovel, dumper, dragline, etc.). As over fragmentation as well as under fragmentation both tend to increase the cost of mining, the generation of fragment size in the desired range is necessary. Several factors control the rock fragmentation in blasting, such as the burden, bench height/drilling depth, stemming column, powder factor and hole diameter. The assessment of rock fragmentation with respect to the aforementioned parameters helps to enhance the blast performance and, hence, this study intends to carry out digital image analysis for monitoring the mean fragment size and boulder percentage. A highly consistent result has been obtained using forty blasting datasets carried out in the four different opencast mines of the Talcher Coalfield (India), namely Balram OCP, Ananta OCP, Lakhanpur OCP, and Lajkura OCP. Keywords: Digital image analysis, Rock fragmentation, Bench blasting, Mean fragment size, Boulder percentag

Embedded Emotion-based Classification of Stack Overflow Questions Towards the Question Quality Prediction

Abstract-Software developers often ask questions in Stack Overflow Q &amp; A site, and their posted questions sometimes do not meet the standard guidelines. As a consequence, some of the questions are edited by expert users, some of them are down-voted, or some are even deleted permanently. Besides, the users (i.e., developers) might not get the expected solutions for their problems. In this paper, we study up-voted and down-voted questions from Stack Overflow, and analyze the relationship of embedded emotions with question quality. We use Sentiment140 API for identifying embedded emotions in the question texts, and then apply Feed-Forward Multilayer Perceptron (MLP) and Support Vector Machine (SVM) on the emotion data for developing a quality prediction model. Experiments using 38,920 Stack Overflow questions suggest about 70% precision and about 74% recall for our model with 10-fold cross-validation, and these findings clearly reveal the impact of human emotions upon the quality of a question