A Formal Approach for RT-DVS Algorithms Evaluation Based on Statistical Model Checking

Energy saving is a crucial concern in embedded real time systems. Many RT-DVS algorithms have been proposed to save energy while preserving deadline guarantees. This paper presents a novel approach to evaluate RT-DVS algorithms using statistical model checking. A scalable framework is proposed for RT-DVS algorithms evaluation, in which the relevant components are modeled as stochastic timed automata, and the evaluation metrics including utilization bound, energy efficiency, battery awareness, and temperature awareness are expressed as statistical queries. Evaluation of these metrics is performed by verifying the corresponding queries using UPPAAL-SMC and analyzing the statistical information provided by the tool. We demonstrate the applicability of our framework via a case study of five classical RT-DVS algorithms.</jats:p

Genomic sequencing combined with marker-assisted breeding effectively eliminates potential linkage drag of a target gene: a case study in tobacco

Linkage drag frequently impedes the utilization of beneficial genes from wild species in crop improvement. The N gene from Nicotiana glutinosa confers strong resistance to tobacco mosaic virus (TMV) but introduces linkage drag when introgressed into cultivated tobacco (Nicotiana tabacum). To address this issue, we sequenced the TMV-resistant flue-cured tobacco line 0970A and carried out comparative genomic analysis. Additionally, we used molecular markers to screen a BC4F1 population derived from the cross between 0970A and an elite flue-cured tobacco variety CB-1 (recurrent parent). As a result of sequencing 0970A, the N gene was located at the end of chromosome Nt11. The comparative genomic analysis showed that 0970A inherited approximately 3.74 Mb of N. glutinosa DNA (N-fragment) from its donor, Coker 176. From screening the BC4F1 population with molecular markers, a recombinant was identified. This recombinant had a significantly reduced N-fragment (~270 kb), which minimized the linkage drag while still maintaining resistance to TMV. This research indicates that the combination of genome sequencing and marker-assisted breeding can be successfully applied to reduce linkage drag. The findings offer valuable resources for breeding tobacco with resistance to TMV

Real-time Monitoring for the Next Core-Collapse Supernova in JUNO

Core-collapse supernova (CCSN) is one of the most energetic astrophysical events in the Universe. The early and prompt detection of neutrinos before (pre-SN) and during the SN burst is a unique opportunity to realize the multi-messenger observation of the CCSN events. In this work, we describe the monitoring concept and present the sensitivity of the system to the pre-SN and SN neutrinos at the Jiangmen Underground Neutrino Observatory (JUNO), which is a 20 kton liquid scintillator detector under construction in South China. The real-time monitoring system is designed with both the prompt monitors on the electronic board and online monitors at the data acquisition stage, in order to ensure both the alert speed and alert coverage of progenitor stars. By assuming a false alert rate of 1 per year, this monitoring system can be sensitive to the pre-SN neutrinos up to the distance of about 1.6 (0.9) kpc and SN neutrinos up to about 370 (360) kpc for a progenitor mass of 30$M_{\odot}$ for the case of normal (inverted) mass ordering. The pointing ability of the CCSN is evaluated by using the accumulated event anisotropy of the inverse beta decay interactions from pre-SN or SN neutrinos, which, along with the early alert, can play important roles for the followup multi-messenger observations of the next Galactic or nearby extragalactic CCSN.Comment: 24 pages, 9 figure

Detection of the Diffuse Supernova Neutrino Background with JUNO

As an underground multi-purpose neutrino detector with 20 kton liquid scintillator, Jiangmen Underground Neutrino Observatory (JUNO) is competitive with and complementary to the water-Cherenkov detectors on the search for the diffuse supernova neutrino background (DSNB). Typical supernova models predict 2-4 events per year within the optimal observation window in the JUNO detector. The dominant background is from the neutral-current (NC) interaction of atmospheric neutrinos with 12C nuclei, which surpasses the DSNB by more than one order of magnitude. We evaluated the systematic uncertainty of NC background from the spread of a variety of data-driven models and further developed a method to determine NC background within 15\% with {\it{in}} {\it{situ}} measurements after ten years of running. Besides, the NC-like backgrounds can be effectively suppressed by the intrinsic pulse-shape discrimination (PSD) capabilities of liquid scintillators. In this talk, I will present in detail the improvements on NC background uncertainty evaluation, PSD discriminator development, and finally, the potential of DSNB sensitivity in JUNO

JUNO Sensitivity to Invisible Decay Modes of Neutrons

We explore the bound neutrons decay into invisible particles (e.g., $n\rightarrow 3 \nu$ or $nn \rightarrow 2 \nu$) in the JUNO liquid scintillator detector. The invisible decay includes two decay modes: $n \rightarrow { inv}$ and $nn \rightarrow { inv}$. The invisible decays of $s$-shell neutrons in $^{12}{\rm C}$ will leave a highly excited residual nucleus. Subsequently, some de-excitation modes of the excited residual nuclei can produce a time- and space-correlated triple coincidence signal in the JUNO detector. Based on a full Monte Carlo simulation informed with the latest available data, we estimate all backgrounds, including inverse beta decay events of the reactor antineutrino $\bar{\nu}_e$, natural radioactivity, cosmogenic isotopes and neutral current interactions of atmospheric neutrinos. Pulse shape discrimination and multivariate analysis techniques are employed to further suppress backgrounds. With two years of exposure, JUNO is expected to give an order of magnitude improvement compared to the current best limits. After 10 years of data taking, the JUNO expected sensitivities at a 90% confidence level are $\tau/B( n \rightarrow { inv} ) > 5.0 \times 10^{31} \, {\rm yr}$ and $\tau/B( nn \rightarrow { inv} ) > 1.4 \times 10^{32} \, {\rm yr}$.Comment: 28 pages, 7 figures, 4 table

Potential of Core-Collapse Supernova Neutrino Detection at JUNO

JUNO is an underground neutrino observatory under construction in Jiangmen, China. It uses 20kton liquid scintillator as target, which enables it to detect supernova burst neutrinos of a large statistics for the next galactic core-collapse supernova (CCSN) and also pre-supernova neutrinos from the nearby CCSN progenitors. All flavors of supernova burst neutrinos can be detected by JUNO via several interaction channels, including inverse beta decay, elastic scattering on electron and proton, interactions on C12 nuclei, etc. This retains the possibility for JUNO to reconstruct the energy spectra of supernova burst neutrinos of all flavors. The real time monitoring systems based on FPGA and DAQ are under development in JUNO, which allow prompt alert and trigger-less data acquisition of CCSN events. The alert performances of both monitoring systems have been thoroughly studied using simulations. Moreover, once a CCSN is tagged, the system can give fast characterizations, such as directionality and light curve

Results from the Intergovernmental Panel on Climatic Change Photochemical Model Intercomparison (PhotoComp)

Results from the Intergovernmental Panel on Climatic Change (IPCC) tropospheric photochemical model intercomparison (PhotoComp) are presented with a brief discussion of the factors that may contribute to differences in the modeled behaviors of HOx cycling and the accompanying O-3 tendencies. PhotoComp was a tightly controlled model experiment in which the IPCC 1994 assessment sought to determine the consistency among models that are used to predict changes in tropospheric ozone, an important greenhouse gas, Calculated tropospheric photodissociation rates displayed significant differences, with a root-mean-square (rms) error of the reported model results ranging from about +/-6-9% of the mean (for O-3 and NO2) to up to +/-15% (H2O2 and CH2O). Models using multistream methods in radiative transfer calculations showed distinctly higher rates for photodissociation of NO2 and CH2O compared to models using two-stream methods, and this difference accounted for up to one third of the rms error for these two rates, In general, some small but systematic differences between models were noted for the predicted chemical tendencies in cases that did not include reactions of nonmethane hydrocarbons (NMHC). These differences in modeled O-3 tendencies in some cases could be identified, for example, as being due to differences in photodissociation rates, but in others they could not and must be ascribed to unidentified errors. O-3 tendencies showed rms errors of about +/-10% in the moist, surface level cases with NOx concentrations equal to a few tens of parts per trillion by volume. Most of these model to model differences can be traced to differences in the destruction of O-3 due to reaction with HO2. Differences in HO2, in turn, are likely due to (1) inconsistent reaction rates used by the models for the conversion of HO2 to H2O2 and (2) differences in the model-calculated photolysis of H2O2 and CH2O. In the middle tropospheric ''polluted'' scenario with NOx concentrations larger than a few parts per billion by volume, O-3 tendencies showed rms errors of +/-10-30%. These model to model differences most likely stem from differences in the calculated rates of O-3 photolysis to O(D-1), which provides about 80% of the HOx source under these conditions. The introduction of hydrocarbons dramatically increased both the rate of NOx loss and its model to model differences, which, in turn, are reflected in an increased spread of predicted O-3. Including NMHC in the simulation approximately doubled the rms error for O-3 concentration

The Negative Intelligence–Religiosity Relation: New and Confirming Evidence

Zuckerman et al. (2013) conducted a meta-analysis of 63 studies that showed a negative intelligence–religiosity relation (IRR). As more studies have become available and because some of Zuckerman et al.’s (2013) conclusions have been challenged, we conducted a new meta-analysis with an updated data set of 83 studies. Confirming previous conclusions, the new analysis showed that the correlation between intelligence and religious beliefs in college and noncollege samples ranged from −.20 to −.23. There was no support for mediation of the IRR by education but there was support for partial mediation by analytic cognitive style. Thus, one possible interpretation for the IRR is that intelligent people are more likely to use analytic style (i.e., approach problems more rationally). An alternative (and less interesting) reason for the mediation is that tests of both intelligence and analytic style assess cognitive ability. Additional empirical and theoretical work is needed to resolve this issue. </jats:p