Effect of transients in nuclear fission on multiplicity of prescission neutrons

Transients in the fission of highly excited nuclei are studied in the framework of the Langevin equation. Time-dependent fission widths are calculated which show that after the initial transients, a steady flow towards the scission point is established not only for nuclei which have fission barriers but also for nuclei which have no fission barrier. It is shown from a comparison of the transient time and the fission life time that fission changes from a diffusive to a transient dominated process over a certain transition region as a function of the spin of the fissioning nucleus. Multiplicities of prescission neutrons are calculated in a statistical model with as well as without a single swoop description of fission and they are found to differ in the transition region. We however find that the difference is marginal and hence a single swoop picture of fission though not strictly valid in the transition region can still be used in the statistical model calculations.Comment: 15 pages including 7 figures, to appear in The European Physical Journal

Petrography of Middle Jurassic to Early Cretaceous sandstones in the Kutch Basin, western India: Implications on provenance and basin evolution

Abstract This paper investigates the provenance of Middle Jurassic to Early Cretaceous sediments in the Kutch Basin, western India, on the basis of mineralogical investigations of sandstones composition (Quartz–Feldspar–Lithic (QFL) fragment), Zircon–Tourmaline–Rutile (ZTR) index, and mineral chemistry of heavy detrital minerals of the framework. The study also examines the compositional variation of the sandstone in relation to the evolution of the Kutch Basin, which originated as a rift basin during the Late Triassic and evolved into a passive margin basin by the end Cretaceous. This study analyzes sandstone samples of Jhumara, Jhuran and Bhuj Formations of Middle Jurassic, Upper Jurassic and Lower Cretaceous, respectively, in the Kutch Mainland. Sandstones record a compositional evolution from arkosic to subarkosic as the feldspar content decreases from 68% in the Jhumara Formation to 27% in the Bhuj Formation with intermediate values in the Jhuran Formation. The QFL modal composition indicates basement uplifted and transitional continental settings at source. Heavy mineral content of these sandstones reveals the occurrence of zircon, tourmaline, rutile, garnet, apatite, monazite and opaque minerals. Sub-rounded to well-rounded zircon grains indicate a polycyclic origin. ZTR indices for samples in Jhumara, Jhuran and Bhuj Formations are 25%, 30% and 50% respectively. Chemistry of opaque minerals reveals the occurrence of detrital varieties such as ilmenite, rutile, hematite/magnetite and pyrite, in a decreasing order of abundances. Chemistry of ilmenites in the Jhumara Formation reveals its derivation from dual felsic igneous and metabasic source, while those in Jhuran and Bhuj Formations indicate a metabasic derivation. Chemistry of garnet reveals predominantly Fe-rich (almandine) variety of metabasic origin. X-ray microscopic study provides the percentage of heavy minerals ranging from 3% to 5.26%. QFL detrital modes reflect the evolution of the basin from an active rift to a passive margin basin during the Mesozoic. Integration of results from QFL modal composition of the sandstones, heavy mineral analysis and mineral chemistry, suggests sediment supply from both northern and eastern highlands during the Middle Jurassic. The uplift along the Kutch Mainland Fault in the Early Cretaceous results in curtailment of sediment input from north

Evaporation residue cross-sections as a probe for nuclear dissipation in the fission channel of a hot rotating nucleus

Evaporation residue cross-sections are calculated in a dynamical description of nuclear fission in the framework of the Langevin equation coupled with statistical evporation of light particles. A theoretical model of one-body nuclear friction which was developed earlier, namely the chaos-weighted wall formula, is used in this calculation for the 224Th nucleus. The evaporation residue cross-section is found to be very sensitive to the choice of nuclear friction. The present results indicate that the chaotic nature of the single-particle dynamics within the nuclear volume may provide an explanation for the strong shape-dependence of nuclear friction which is usually required to fit experimental data.Comment: 12 pages including 4 figure

Characterization of Sro1, a novel stress responsive protein in Schizosaccharomyces pombe

The large amount of available genome sequencing data presents a huge challenge in the form of orphan sequences. This study reports the detailed functional characterization of one such orphan sequence in Schizosaccharomyces pombe. We identified this gene as a prominently upregulated 1.4 kb transcript in a screen for Cigarette smoke extract responsive genes in S. pombe and named it Stress Responsive Orphan 1 (Sro1). We report various functions of Sro1 in regulation of cellular behaviour under stress conditions. We show that this gene (Sro1) responds to a variety of stress conditions and that the expression of the gene is regulated mainly through the stress activated protein kinase (SAPK) Sty1 and its downstream transcription factor Atf1. Deletion of Sro1 also significantly alters the reactive oxygen species (ROS) generation profiles and the cell-cycle progression of S. pombe during stress conditions. The stress-specific alteration of the ROS generation profiles and checkpoint activation resulting from deletion of the gene suggest that Sro1 might be a key player in determining cellular responses/fate under stress conditions

The Manufacturing Data and Machine Learning Platform: Enabling Real-time Monitoring and Control of Scientific Experiments via IoT

IoT devices and sensor networks present new opportunities for measuring, monitoring, and guiding scientific experiments. Sensors, cameras, and instruments can be combined to provide previously unachievable insights into the state of ongoing experiments. However, IoT devices can vary greatly in the type, volume, and velocity of data they generate, making it challenging to fully realize this potential. Indeed, synergizing diverse IoT data streams in near-real time can require the use of machine learning (ML). In addition, new tools and technologies are required to facilitate the collection, aggregation, and manipulation of sensor data in order to simplify the application of ML models and in turn, fully realize the utility of IoT devices in laboratories. Here we will demonstrate how the use of the Argonne-developed Manufacturing Data and Machine Learning (MDML) platform can analyze and use IoT devices in a manufacturing experiment. MDML is designed to standardize the research and operational environment for advanced data analytics and AI-enabled automated process optimization by providing the infrastructure to integrate AI in cyber-physical systems for in situ analysis. We will show that MDML is capable of processing diverse IoT data streams, using multiple computing resources, and integrating ML models to guide an experiment.Comment: Two page demonstration paper. Accepted to WFIoT202

End-to-end AI framework for interpretable prediction of molecular and crystal properties

We introduce an end-to-end computational framework that allows for hyperparameter optimization using the DeepHyper library, accelerated model training, and interpretable AI inference. The framework is based on state-of-the-art AI models including CGCNN, PhysNet, SchNet, MPNN, MPNN-transformer, and TorchMD-NET. We employ these AI models along with the benchmark QM9, hMOF, and MD17 datasets to showcase how the models can predict user-specified material properties within modern computing environments. We demonstrate transferable applications in the modeling of small molecules, inorganic crystals and nanoporous metal organic frameworks with a unified, standalone framework. We have deployed and tested this framework in the ThetaGPU supercomputer at the Argonne Leadership Computing Facility, and in the Delta supercomputer at the National Center for Supercomputing Applications to provide researchers with modern tools to conduct accelerated AI-driven discovery in leadership-class computing environments. We release these digital assets as open source scientific software in GitLab, and ready-to-use Jupyter notebooks in Google Colab.Comment: 20 pages, 10 images, 6 tables; v2: accepted to Machine Learning: Science and Technolog

Multiscale modeling of interaction of alane clusters on Al(111) surfaces: A reactive force field and infrared absorption spectroscopy approach

We have used reactive force field (ReaxFF) to investigate the mechanism of interaction of alanes on Al(111) surface. Our simulations show that, on the Al(111) surface, alanes oligomerize into larger alanes. In addition, from our simulations, adsorption of atomic hydrogen on Al(111) surface leads to the formation of alanes via H-induced etching of aluminum atoms from the surface. The alanes then agglomerate at the step edges forming stringlike conformations. The identification of these stringlike intermediates as a precursor to the bulk hydride phase allows us to explain the loss of resolution in surface IR experiments with increasing hydrogen coverage on single crystal Al(111) surface. This is in excellent agreement with the experimental works of Go et al. [ E. Go, K. Thuermer, and J. E. Reutt-Robey, Surf. Sci. 437, 377 (1999) ]. The mobility of alanes molecules has been studied using molecular dynamics and it is found that the migration energy barrier of Al_(2)H_6 is 2.99 kcal/mol while the prefactor is D_0 = 2.82 × 10^(−3) cm^2/s. We further investigated the interaction between an alane and an aluminum vacancy using classical molecular dynamics simulations. We found that a vacancy acts as a trap for alane, and eventually fractionates/annihilates it. These results show that ReaxFF can be used, in conjunction with ab initio methods, to study complex reactions on surfaces at both ambient and elevated temperature conditions

Psychosocial and quality of life assessment in cancer patients: a pilot study in Indian set up

Background: Routine screening for distress is internationally recommended as a standard of care among cancer patients. This study was conducted to assess the level of stress and determine the association between quality of life (QOL) with demographic, socio-economic status, treatment phase, cancer stage, etc.Methods: An observational study, performed in the department of Clinical Oncology, Nayati Multi Super Speciality Hospital, Mathura, India. Data of 62 histopathologically proven cancer patients between Nov 2016 and July 2018, were analyzed. This pilot study was conducted to assess the QOL and stress levels of cancer patients by using scales of WHOQOL-BREF, QSC-R23 and Hamilton scale. Results: Among 62 cancer patients, high distress along with poor QOL was seen maximum in males, 40-60 year age group and educated. In majority of domains, high distress was found in middle class, whereas poor QOL was found in Lower class in Environmental domain (p<0.01). We found higher distress in nuclear families (p<0.05). High distress was seen in cancer patients who were aware of illness and was found to statistically significant. Poor QOL in stage 4 was found to be statistically significant in Psychological domain of WHOQOL-BREF. High distress was found in patients undergoing treatment in all patients as compared to Pre-treatment phase and Post-treatment phase (p<0.05).Conclusion: To assess psychological stress in cancer patients using all three scales we could not obtain a conclusive result covering all dimensions of QOL. So, in our next study authors plan to develop one indigenous new scale