Transforming U.S. Particle Physics Education: A Snowmass 2021 Study

The pursuit of knowledge in particle physics requires constant learning. As new tools become available, new theories are developed, and physicists search for new answers with ever-evolving methods. However, it is the case that formal educational systems serve as the primary training grounds for particle physicists. Graduate school (and undergraduate school to a lesser extent) is where researchers learn most of the technical skills required for research, develop scientific problem-solving abilities, learn how to establish themselves in their field, and begin developing their career. It is unfortunate, then, that the skills gained by physicists during their formal education are often mismatched with the skills actually required for a successful career in physics. We performed a survey of the U.S. particle physics community to determine the missing elements of graduate and undergraduate education and to gauge how to bridge these gaps. In this contributed paper, part of the 2021-22 Snowmass Community Planning Exercise, we report the results of this survey. We also recommend several specific community actions to improve the quality of particle physics education; the "community" here refers to physics departments, national labs, professional societies, funding agencies, and individual physicists.Comment: contribution to Snowmass 202

Analysis of 83mKr prompt scintillation signals in the PIXeY detector

Prompt scintillation signals from 83mKr calibration sources are a useful metric to calibrate the spatial variation of light collection efficiency and electric field magnitude of a two phase liquid-gas xenon time projection chamber. Because 83mKr decays in two steps, there are two prompt scintillation pulses for each calibration event, denoted S1a and S1b. We study the ratio of S1b to S1a signal sizes in the Particle Identification in Xenon at Yale (PIXeY) experiment and its dependence on the time separation between the two signals (Δ t), notably its increase at low Δ t. In PIXeY data, the Δ t dependence of S1b/S1a is observed to exhibit two exponential components: one with a time constant of 0.05 ± 0.02 μ s, which can be attributed to processing effects and pulse overlap and one with a time constant of 10.2 ± 2.2 μs that increases in amplitude with electric drift field, the origin of which is not yet understood

Nuclear Recoil Scintillation Linearity of a High Pressure 4^4He Gas Detector

We investigate scintillation linearity of a commercial high pressure $^4$He gas detector using monoenergetic 2.8 MeV neutrons from a deuterium-deuterium fusion neutron generator. The scintillation response of the detector was measured for a range of recoil energies between 83 keV and 626 keV by tagging neutrons scattering into fixed angles with a far-side organic scintillator detector. Detailed Monte Carlo simulations were compared to experimental data to determine the linearity of the detector response by comparing the scaling of the energy deposits in the simulations to the detector output. In this analysis, a linear scintillation response corresponds to a consistent value for the scaling factor between simulated energy deposits and experimental data for several different scattering angles. We demonstrate that the detector can be used to detect fast neutron interactions down to 83 keV recoil energies and can be used to characterize low-energy neutron sources, one of its potential applications

Metabolic alteration in obese diabetes rats upon treatment with Centella asiatica extract

Ethnopharmacological relevance: ‘Pegaga’ is a traditional Malay remedy for a wide range of complaints. Among the 'pegaga’, Centella asiatica has been used as a remedy for diabetes mellitus. Thus, we decided to validate this claim by evaluating the in vivo antidiabetic property of C. asiatica (CA) on T2DM rat model using the holistic 1H NMR-based metabolomics approach. Method: In this study, an obese diabetic (mimic of T2DM condition) animal model was developed using Sprague–Dawley rats fed with a high-fat diet and induced into diabetic condition by the treatment of a low dose of streptozotocin (STZ). The effect of C. asiatica extract on the experimental animals was followed based on the changes observed in the urinary and serum metabolites, measured by 1H NMR of urine and blood samples collected over the test period. Results: A long-term treatment of obese diabetic rats with CA extract could reverse the glucose and lipid levels, as well as the tricarboxylic acid cycle and amino acid metabolic disorders, back towards normal states. Biochemical analysis also showed an increase of insulin production in diabetic rats upon treatment of CA extract. Conclusion: This study has provided evidence that clearly supported the traditional use of CA as a remedy for diabetes. NMR-based metabolomics was successfully applied to show that CA produced both anti-hyperglycemic and anti-hyperlipidemic effects on a rat model. In addition to increasing the insulin secretion, the CA extract also ameliorates the metabolic pathways affected in the induced diabectic rats. This study further revealed the potential usage of CA extract in managing diabetes mellitus and the results of this work may contribute towards the further understanding of the underlying molecular mechanism of this herbal remedy

Hybrid multicriteria fuzzy classification of network traffic patterns, anomalies, and protocols

© 2017, Springer-Verlag London Ltd., part of Springer Nature. Traffic classification in computer networks has very significant roles in network operation, management, and security. Examples include controlling the flow of information, allocating resources effectively, provisioning quality of service, detecting intrusions, and blocking malicious and unauthorized access. This problem has attracted a growing attention over years and a number of techniques have been proposed ranging from traditional port-based and payload inspection of TCP/IP packets to supervised, unsupervised, and semi-supervised machine learning paradigms. With the increasing complexity of network environments and support for emerging mobility services and applications, more robust and accurate techniques need to be investigated. In this paper, we propose a new supervised hybrid machine-learning approach for ubiquitous traffic classification based on multicriteria fuzzy decision trees with attribute selection. Moreover, our approach can handle well the imbalanced datasets and zero-day applications (i.e., those without previously known traffic patterns). Evaluating the proposed methodology on several benchmark real-world traffic datasets of different nature demonstrated its capability to effectively discriminate a variety of traffic patterns, anomalies, and protocols for unencrypted and encrypted traffic flows. Comparing with other methods, the performance of the proposed methodology showed remarkably better classification accuracy

Ethnic differences in the association of fat and lean mass with bone mineral density in the Singapore population

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