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The computing and storage requirements of the energy and intensity frontiers will grow significantly during the Run 4 & 5 and the HL-LHC era. Similarly, in the intensity frontier, with larger trig ger readouts during supernovae explosions, the Deep Underground Neutrino Experiment (DUNE) will have unique computing challenges that could be addressed by the use of parallel and accelerated dataprocessing capabilities. Most of the requirements of the energy and intensity frontier experiments rely on increasing the role of high performance computing (HPC) in the HEP community. In this presentation, we will describe our ongoing efforts that are focused on using HPC resources for the next generation HEP experiments. The HEPCCE (High Energy Physics-Center for Computational Excellence) IOS (Input/Output and Storage) group has been developing approaches to map HEP data to the HDF5 , an IO library optimized for the HPC platforms to store the intermediate HEP data. The complex HEP data products are serialized using ROOT to allow for experiment independent general mapping approaches of the HEP data to the HDF5 format. The mapping approaches can be optimized for high performance parallel IO. Similarly, simpler data can be directly mapped into the HDF5, which can also be suitable for offloading into the GPUs directly. We will present our works on both complex and simple data model models.

The HL-LHC run is anticipated to start at the end of this decade and will pose a significant challenge for the scale of the HEP software and computing infrastructure. The mission of the U.S. CMS Software & Computing Operations Program is to develop and operate the software and computing resources necessary to process CMS data expeditiously and to enable U.S. physicists to fully participate in the physics of CMS. We have developed a strategic plan to prioritize R&D efforts to reach this goal for the HL-LHC. This plan includes four grand challenges: modernizing physics software and improving algorithms, building infrastructure for exabyte-scale datasets, transforming the scientific data analysis process and transitioning from R&D to operations. We are involved in a variety of R&D projects that fall within these grand challenges. In this talk, we will introduce our four grand challenges and outline the R&D program of the U.S. CMS Software & Computing Operations Program.

Background: Chilli veinal mottle virus is a remarkable threat to chilli crops worldwide including Pakistan. Methods: In the present study, 18 chilli pepper genotypes were catalogued against ChiVMV ATIPK isolate by using DAS-ELISA with virus-specific polyclonal antisera. Results: Out of 18 genotypes, four genotypes viz., Loungi pepper, Sanam, Red Pepper, and CV-1 were categorized as highly susceptible followed by three susceptible (Soofi, Royal Wonder, Pepper PLSQ) and four (Yolo wonder, Bhakkar Local, CIM-506, CR-25) were moderately susceptible. The plants of these genotypes exhibited characteristic symptoms of ChiVMV and accumulated a high level of virus titer. The rest of three (California wonder, Gola Peshawar, Red top) and four (CV-7, Hot Pepper, Doni Pepper, Ghotki) genotypes were grouped as resistant and moderately resistant, respectively. Three resistant and three highly susceptible genotypes were further appraised for genetic diversity by RAPD markers and a total of 75 bands were scored, 76% of them unveiled polymorphism with an amplification range of 5–9 bands. Genetic analysis revealed 44–78.67% similarities with Mean Similarity Index (MSI) ranging from 59.46 to 65.86%. Most of the primers were highly informative with PIC value fluctuated from 0.388 to 0.666, with an average value of 0.512. Cluster analysis further confirmed and clustered both groups of genotypes in a separate clade. Conclusion: It is concluded that genotypes; California Wonder and Red Top showed diverse response from other genotypes, not only based on symptoms and ELISA but also on markers' basis. Therefore, these genotypes could be useful as ChiVMV resistant sources in the chilli breeding program.

We examine whether US mutual funds managed by females have a higher portfolio liquidity than those of their male counterparts. Single female managers' holdings are 8–25% more liquid than those of single‐male‐managed funds. When there is a transition from a male to a female manager, fund holdings liquidity increases compared with a male to male transition. The findings are consistent with the risk‐averse and conservative decision‐making behaviour of female managers. We do not find evidence to support the excessive trading hypothesis that predicts a higher portfolio liquidity for overconfident male fund managers. Our findings add to growing evidence that gender affects professionals' investment choices. [ABSTRACT FROM AUTHOR]

A number of strontium-based iron oxides are available for the industrial use. Among them, strontium hexaferrite cobalt iron oxide (Sr2Co2Fe12O22) is preferred over others due to its better magnetic and electrical properties than others. This material is also extensively used for the absorption of microwave radiations. This study was conducted to account for the synthesis and characterization of Sr2Co2Fe12O22 nanoparticles and to report their biocompatibility in albino mice. Nanoparticles were synthesised by normal microemulsion, XRD analysis confirmed the single and scanning electron microscopy (SEM) revealed average particle size ranged between 30 and 50 nm. Nine-week-old male mice were intraperitoneally administered with 50 mg/mL of solvent/kg body weight of strontium hexaferrite cobalt iron oxide nanoparticles for 22 days. Control group was maintained in parallel. A series of neurological tests (rota rod, light and dark box, open field and Morris water maze) were conducted in both groups. Blood samples were collected from direct cardiac puncture, and parameters of complete blood count, serum biochemical parameters and antioxidant were determined in liver and brain tissues of all subjects. Analysis of result revealed that all studied neurological test performances varied nonsignificantly (P > 0.05) between the two treatments except clockwise rotations during open field test that were significantly reduced (P = 0.05) in Strontium hexaferrite Cobalt Iron Oxide nanoparticle-treated male albino mice than in the control group. All studied complete blood count and serum parameters varied nonsignificantly (P > 0.05) between two treatments. It was observed that superoxide dismutase concentration was significantly higher (P = 0.05) in the liver of nanoparticle-treated male mice. In conclusion, we are reporting that applied dose of strontium hexaferrite cobalt iron oxide nanoparticles is affecting the exploratory behaviour and antioxidant metabolites of male albino mice.

Modern experiments in high energy physics analyze millions of events recorded in particle detectors to select the events of interest and make measurements of physics parameters. These data can often be stored as tabular data in files with detector information and reconstructed quantities. Most current techniques for event selection in these files lack the scalability needed for high performance computing environments. We describe our work to develop a high energy physics analysis framework suitable for high performance computing. This new framework utilizes modern tools for reading files and implicit data parallelism. Framework users analyze tabular data using standard, easy-to-use data analysis techniques in Python while the framework handles the file manipulations and parallelism without the user needing advanced experience in parallel programming. In future versions, we hope to provide a framework that can be utilized on a personal computer or a high performance computing cluster with little change to the user code.

High Energy Physics (HEP) experiments generally employ sophisticated statistical methods to present results in searches of new physics. In the problem of searching for sterile neutrinos, likelihood ratio tests are applied to short-baseline neutrino oscillation experiments to construct confidence intervals for the parameters of interest. The test statistics of the form Δχ2 is often used to form the confidence intervals, however, this approach can lead to statistical inaccuracies due to the small signal rate in the region-of-interest. In this paper, we present a computational model for the computationally expensive Feldman-Cousins corrections to construct a statistically accurate confidence interval for neutrino oscillation analysis. The program performs a grid-based minimization over oscillation parameters and is written in C++. Our algorithms make use of vectorization through Eigen3, yielding a single-core speed-up of 350 compared to the original implementation, and achieve MPI data parallelism by employing DIY. We demonstrate the strong scaling of the application at High-Performance Computing (HPC) sites. We utilize HDF5 along with HighFive to write the results of the calculation to file.

In their recent measurement of the neutrino oscillation parameters, NOvA uses a sample of approximately 25 million reconstructed spills to search for electron-neutrino appearance events. These events are stored in an n-tuple format, in 250 thousand ROOT files. File sizes range from a few hundred KiB to a few MiB; the full dataset is approximately 1.4 TiB. These millions of events are reduced to a few tens of events by the application of strict event selection criteria, and then summarized by a handful of numbers each, which are used in the extraction of the neutrino oscillation parameters. The NOvA event selection code is currently a serial C++ program that reads these n-tuples. The current table data format and organization and the selection/ reduction processing involved provides us with an opportunity to explore alternate approaches to represent the data and implement the processing. We represent our n-tuple data in HDF5 format that is optimized for the HPC environment and which allows us to use the machine’s high-performance parallel I/O capabilities. We use MPI, numpy and h5py to implement our approach and compare the performance with the existing approach. We study the performance implications of using thousands of small files of different sizes as compared with one large file using HPC resources. This work has been done as part of the SciDAC project, “HEP analytics on HPC” in collaboration with the ASCR teams at ANL and LBNL.