8 research outputs found

    The Influencing Factors Analysis of Aquaculture Mechanization Development in Liaoning, China

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    Promoting the mechanization of aquaculture is one of the most important supporting measures to ensure the high-quality development of the aquaculture industry in China. In order to solve the problems of predominantly manual work and to decrease the costs of aquaculture, the influencing factors of China’s aquaculture mechanization were systematically analyzed. The triple bottom theory was selected, and three aspects were identified, including environmental, economic, and social aspects. Through the literature review, the Delphi method, and the analytic hierarchy process, the comprehensive evaluation indicator system, including 18 influencing factors, was proposed. Moreover, the fuzzy comprehensive evaluation method was combined with the model to solve the evaluation results. A case study in Liaoning Province was offered and, according to the analysis results, the economic aspect at the first level was the most critical factor; the financial subsidy for the purchase of aquaculture machinery, the energy consumption of the machinery and equipment, and the promotion and use of aquaculture technology were the most important factors and had the greatest impact on the development of aquaculture mechanization in China. The effective implementation paths and countermeasures were proposed, such as the promotion of mechanized equipment and the enhancement of the machinery purchase subsidies, in order to provide an important decision-making basis for the improvement of the level of aquaculture mechanization

    JUNO Sensitivity to Invisible Decay Modes of Neutrons

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    International audienceWe explore the bound neutrons decay into invisible particles (e.g., n→3νn\rightarrow 3 \nu or nn→2νnn \rightarrow 2 \nu) in the JUNO liquid scintillator detector. The invisible decay includes two decay modes: n→inv n \rightarrow { inv} and nn→inv nn \rightarrow { inv} . The invisible decays of ss-shell neutrons in 12C^{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 νˉe\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 τ/B(n→inv)>5.0×1031 yr\tau/B( n \rightarrow { inv} ) > 5.0 \times 10^{31} \, {\rm yr} and τ/B(nn→inv)>1.4×1032 yr\tau/B( nn \rightarrow { inv} ) > 1.4 \times 10^{32} \, {\rm yr}

    Prediction of Energy Resolution in the JUNO Experiment

    No full text
    International audienceThis paper presents the energy resolution study in the JUNO experiment, incorporating the latest knowledge acquired during the detector construction phase. The determination of neutrino mass ordering in JUNO requires an exceptional energy resolution better than 3% at 1 MeV. To achieve this ambitious goal, significant efforts have been undertaken in the design and production of the key components of the JUNO detector. Various factors affecting the detection of inverse beta decay signals have an impact on the energy resolution, extending beyond the statistical fluctuations of the detected number of photons, such as the properties of liquid scintillator, performance of photomultiplier tubes, and the energy reconstruction algorithm. To account for these effects, a full JUNO simulation and reconstruction approach is employed. This enables the modeling of all relevant effects and the evaluation of associated inputs to accurately estimate the energy resolution. The study reveals an energy resolution of 2.95% at 1 MeV. Furthermore, the study assesses the contribution of major effects to the overall energy resolution budget. This analysis serves as a reference for interpreting future measurements of energy resolution during JUNO data taking. Moreover, it provides a guideline in comprehending the energy resolution characteristics of liquid scintillator-based detectors

    Prediction of Energy Resolution in the JUNO Experiment

    No full text
    International audienceThis paper presents the energy resolution study in the JUNO experiment, incorporating the latest knowledge acquired during the detector construction phase. The determination of neutrino mass ordering in JUNO requires an exceptional energy resolution better than 3% at 1 MeV. To achieve this ambitious goal, significant efforts have been undertaken in the design and production of the key components of the JUNO detector. Various factors affecting the detection of inverse beta decay signals have an impact on the energy resolution, extending beyond the statistical fluctuations of the detected number of photons, such as the properties of liquid scintillator, performance of photomultiplier tubes, and the energy reconstruction algorithm. To account for these effects, a full JUNO simulation and reconstruction approach is employed. This enables the modeling of all relevant effects and the evaluation of associated inputs to accurately estimate the energy resolution. The study reveals an energy resolution of 2.95% at 1 MeV. Furthermore, the study assesses the contribution of major effects to the overall energy resolution budget. This analysis serves as a reference for interpreting future measurements of energy resolution during JUNO data taking. Moreover, it provides a guideline in comprehending the energy resolution characteristics of liquid scintillator-based detectors

    Prediction of Energy Resolution in the JUNO Experiment

    No full text
    International audienceThis paper presents the energy resolution study in the JUNO experiment, incorporating the latest knowledge acquired during the detector construction phase. The determination of neutrino mass ordering in JUNO requires an exceptional energy resolution better than 3% at 1 MeV. To achieve this ambitious goal, significant efforts have been undertaken in the design and production of the key components of the JUNO detector. Various factors affecting the detection of inverse beta decay signals have an impact on the energy resolution, extending beyond the statistical fluctuations of the detected number of photons, such as the properties of liquid scintillator, performance of photomultiplier tubes, and the energy reconstruction algorithm. To account for these effects, a full JUNO simulation and reconstruction approach is employed. This enables the modeling of all relevant effects and the evaluation of associated inputs to accurately estimate the energy resolution. The study reveals an energy resolution of 2.95% at 1 MeV. Furthermore, the study assesses the contribution of major effects to the overall energy resolution budget. This analysis serves as a reference for interpreting future measurements of energy resolution during JUNO data taking. Moreover, it provides a guideline in comprehending the energy resolution characteristics of liquid scintillator-based detectors

    JUNO Sensitivity to Invisible Decay Modes of Neutrons

    No full text
    International audienceWe explore the bound neutrons decay into invisible particles (e.g., n→3νn\rightarrow 3 \nu or nn→2νnn \rightarrow 2 \nu) in the JUNO liquid scintillator detector. The invisible decay includes two decay modes: n→inv n \rightarrow { inv} and nn→inv nn \rightarrow { inv} . The invisible decays of ss-shell neutrons in 12C^{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 νˉe\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 τ/B(n→inv)>5.0×1031 yr\tau/B( n \rightarrow { inv} ) > 5.0 \times 10^{31} \, {\rm yr} and τ/B(nn→inv)>1.4×1032 yr\tau/B( nn \rightarrow { inv} ) > 1.4 \times 10^{32} \, {\rm yr}

    JUNO Sensitivity to Invisible Decay Modes of Neutrons

    No full text
    International audienceWe explore the bound neutrons decay into invisible particles (e.g., n→3νn\rightarrow 3 \nu or nn→2νnn \rightarrow 2 \nu) in the JUNO liquid scintillator detector. The invisible decay includes two decay modes: n→inv n \rightarrow { inv} and nn→inv nn \rightarrow { inv} . The invisible decays of ss-shell neutrons in 12C^{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 νˉe\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 τ/B(n→inv)>5.0×1031 yr\tau/B( n \rightarrow { inv} ) > 5.0 \times 10^{31} \, {\rm yr} and τ/B(nn→inv)>1.4×1032 yr\tau/B( nn \rightarrow { inv} ) > 1.4 \times 10^{32} \, {\rm yr}
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