An accurate and simultaneous ab initio prediction for both light nuclei and nuclear matter has been a longstanding challenge in nuclear physics, due to the significant uncertainties associated with the three-nucleon f...An accurate and simultaneous ab initio prediction for both light nuclei and nuclear matter has been a longstanding challenge in nuclear physics, due to the significant uncertainties associated with the three-nucleon forces.In this Letter, we develop the relativistic quantum Monte Carlo methods for the nuclear ab initio problem, and calculate the ground-state energies of A ≤ 4 nuclei using the two-nucleon Bonn force with an unprecedented high accuracy. The present relativistic results significantly outperform the nonrelativistic results with only twonucleon forces. We demonstrate that both light nuclei and nuclear matter can be well described simultaneously in the relativistic ab initio calculations, even in the absence of three-nucleon forces, and a correlation between the properties of light A ≤ 4 nuclei and the nuclear saturation is revealed. This provides a quantitative understanding of the connection between the light nuclei and nuclear matter saturation properties.展开更多
Bonn Hauptbahnhof ist ein Bahnhof in Bonn an der linken Rheinstrecke und zugleich Endpunkt der Voreifelbahn.Er ist in die Bahnhofskategorie 2(Fernverkehrssystemhalt) eingeordnet und hat IC-,EC- und ICE-Anbindungen.Unt...Bonn Hauptbahnhof ist ein Bahnhof in Bonn an der linken Rheinstrecke und zugleich Endpunkt der Voreifelbahn.Er ist in die Bahnhofskategorie 2(Fernverkehrssystemhalt) eingeordnet und hat IC-,EC- und ICE-Anbindungen.Unter dem eigentlichen Hauptbahnhof der Deutschen Bahn befindet sich eine Stadtbahnanlage.T(a|¨)glich halten in Bonn bis zu 82 Fernverkehrs- und 238 Nahverkehrsz(u|¨)ge.Rund 40.000 Reisende benutzen den Bahnhof t(a|¨)glich zum ein-,aus- oder umsteigen.展开更多
Der Flughafen K(o|¨)ln/Bonn Konrad Adenauer(auch K(o|¨)lnBonn Airport oder Flughafen K(o|¨)ln-Wahn) ist ein deutscherVerkehrsflughafen und liegf am s(u|¨)d(o|¨)stlichen Stadtrand vonK(o|¨...Der Flughafen K(o|¨)ln/Bonn Konrad Adenauer(auch K(o|¨)lnBonn Airport oder Flughafen K(o|¨)ln-Wahn) ist ein deutscherVerkehrsflughafen und liegf am s(u|¨)d(o|¨)stlichen Stadtrand vonK(o|¨)ln und zum Teil in Troisdorf.Das Flughafengel(a|¨)nde wird展开更多
Brain signal analysis from electroencephalogram(EEG)recordings is the gold standard for diagnosing various neural disorders especially epileptic seizure.Seizure signals are highly chaotic compared to normal brain sign...Brain signal analysis from electroencephalogram(EEG)recordings is the gold standard for diagnosing various neural disorders especially epileptic seizure.Seizure signals are highly chaotic compared to normal brain signals and thus can be identified from EEG recordings.In the current seizure detection and classification landscape,most models primarily focus on binary classification—distinguishing between seizure and non-seizure states.While effective for basic detection,these models fail to address the nuanced stages of seizures and the intervals between them.Accurate identification of per-seizure or interictal stages and the timing between seizures is crucial for an effective seizure alert system.This granularity is essential for improving patient-specific interventions and developing proactive seizure management strategies.This study addresses this gap by proposing a novel AI-based approach for seizure stage classification using a Deep Convolutional Neural Network(DCNN).The developed model goes beyond traditional binary classification by categorizing EEG recordings into three distinct classes,thus providing a more detailed analysis of seizure stages.To enhance the model’s performance,we have optimized the DCNN using two advanced techniques:the Stochastic Gradient Algorithm(SGA)and the evolutionary Genetic Algorithm(GA).These optimization strategies are designed to fine-tune the model’s accuracy and robustness.Moreover,k-fold cross-validation ensures the model’s reliability and generalizability across different data sets.Trained and validated on the Bonn EEG data sets,the proposed optimized DCNN model achieved a test accuracy of 93.2%,demonstrating its ability to accurately classify EEG signals.In summary,the key advancement of the present research lies in addressing the limitations of existing models by providing a more detailed seizure classification system,thus potentially enhancing the effectiveness of real-time seizure prediction and management systems in clinical settings.With its inherent classification performance,the proposed approach represents a significant step forward in improving patient outcomes through advanced AI techniques.展开更多
基金supported in part by the National Natural Science Foundation of China (Grant Nos. 12141501, 123B2080, 12435006, 12475117, and 11935003)the National Key Laboratory of Neutron Science and Technology (Grant No. NST202401016)+2 种基金the National Key R&D Program of China (Grant No. 2024YFE0109803)the High-performance Computing Platform of Peking Universitythe funding support from the State Key Laboratory of Nuclear Physics and Technology, Peking University (Grant No. NPT2023ZX03)。
文摘An accurate and simultaneous ab initio prediction for both light nuclei and nuclear matter has been a longstanding challenge in nuclear physics, due to the significant uncertainties associated with the three-nucleon forces.In this Letter, we develop the relativistic quantum Monte Carlo methods for the nuclear ab initio problem, and calculate the ground-state energies of A ≤ 4 nuclei using the two-nucleon Bonn force with an unprecedented high accuracy. The present relativistic results significantly outperform the nonrelativistic results with only twonucleon forces. We demonstrate that both light nuclei and nuclear matter can be well described simultaneously in the relativistic ab initio calculations, even in the absence of three-nucleon forces, and a correlation between the properties of light A ≤ 4 nuclei and the nuclear saturation is revealed. This provides a quantitative understanding of the connection between the light nuclei and nuclear matter saturation properties.
文摘Bonn Hauptbahnhof ist ein Bahnhof in Bonn an der linken Rheinstrecke und zugleich Endpunkt der Voreifelbahn.Er ist in die Bahnhofskategorie 2(Fernverkehrssystemhalt) eingeordnet und hat IC-,EC- und ICE-Anbindungen.Unter dem eigentlichen Hauptbahnhof der Deutschen Bahn befindet sich eine Stadtbahnanlage.T(a|¨)glich halten in Bonn bis zu 82 Fernverkehrs- und 238 Nahverkehrsz(u|¨)ge.Rund 40.000 Reisende benutzen den Bahnhof t(a|¨)glich zum ein-,aus- oder umsteigen.
文摘Der Flughafen K(o|¨)ln/Bonn Konrad Adenauer(auch K(o|¨)lnBonn Airport oder Flughafen K(o|¨)ln-Wahn) ist ein deutscherVerkehrsflughafen und liegf am s(u|¨)d(o|¨)stlichen Stadtrand vonK(o|¨)ln und zum Teil in Troisdorf.Das Flughafengel(a|¨)nde wird
基金funded by the Researchers Supporting Program at King Saud University(RSPD2024R809).
文摘Brain signal analysis from electroencephalogram(EEG)recordings is the gold standard for diagnosing various neural disorders especially epileptic seizure.Seizure signals are highly chaotic compared to normal brain signals and thus can be identified from EEG recordings.In the current seizure detection and classification landscape,most models primarily focus on binary classification—distinguishing between seizure and non-seizure states.While effective for basic detection,these models fail to address the nuanced stages of seizures and the intervals between them.Accurate identification of per-seizure or interictal stages and the timing between seizures is crucial for an effective seizure alert system.This granularity is essential for improving patient-specific interventions and developing proactive seizure management strategies.This study addresses this gap by proposing a novel AI-based approach for seizure stage classification using a Deep Convolutional Neural Network(DCNN).The developed model goes beyond traditional binary classification by categorizing EEG recordings into three distinct classes,thus providing a more detailed analysis of seizure stages.To enhance the model’s performance,we have optimized the DCNN using two advanced techniques:the Stochastic Gradient Algorithm(SGA)and the evolutionary Genetic Algorithm(GA).These optimization strategies are designed to fine-tune the model’s accuracy and robustness.Moreover,k-fold cross-validation ensures the model’s reliability and generalizability across different data sets.Trained and validated on the Bonn EEG data sets,the proposed optimized DCNN model achieved a test accuracy of 93.2%,demonstrating its ability to accurately classify EEG signals.In summary,the key advancement of the present research lies in addressing the limitations of existing models by providing a more detailed seizure classification system,thus potentially enhancing the effectiveness of real-time seizure prediction and management systems in clinical settings.With its inherent classification performance,the proposed approach represents a significant step forward in improving patient outcomes through advanced AI techniques.
