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In this work, we study the strong decays of the newly observed Ξ ( 1620 ) 0 assuming that it is a meson-baryon molecular state of Λ K ¯ and Σ K ¯ . We consider four possible spin-parity assignments J P = 1 / 2 ± and 3 / 2 ± for the Ξ ( 1620 ) 0 , and evaluate its partial decay width into Ξ π and Ξ π π via hadronic loops with the help of effective Lagrangians. In comparison with the Belle data, the calculated decay width favors the spin-party assignment 1 / 2 - while the other spin-parity assignments do not yield a decay width consistent with data in the molecule picture. We find that about 52–68% of the total width comes from the K ¯ Λ channel, while the rest is provided by the K ¯ Σ channel. As a result, both channels are important in explaining the strong decay of the Ξ ( 1620 ) 0 . In addition, the transition Ξ ( 1620 ) 0 → π Ξ is the main decay channel in the J P = 1 / 2 - case, which almost saturates the total width. These information are helpful to further understand the nature of the Ξ ( 1620 ) 0 .

Background Pulmonary embolism (PE) is life-threatening and requires timely and accurate diagnosis, yet current imaging methods, like computed tomography pulmonary angiography, present limitations, particularly for patients with contraindications to iodinated contrast agents. We aimed to develop a quantitative texture analysis pipeline using machine learning (ML) based on non-contrast thoracic computed tomography (CT) scans to discover intensity and textural features correlated with regional lung perfusion (Q) physiology and pathology and synthesize voxel-wise Q surrogates to assist in PE diagnosis. Methods We retrospectively collected 99m Tc-labeled macroaggregated albumin Q-SPECT/CT scans from patients suspected of PE, including an internal dataset of 76 patients (64 for training, 12 for testing) and an external testing dataset of 49 patients. Quantitative CT features were extracted from segmented lung subregions and underwent a two-stage feature selection pipeline. The prior-knowledge-driven preselection stage screened for robust and non-redundant perfusion-correlated features, while the data-driven selection stage further filtered features by fitting ML models for classification. The final classification model, trained with the highest-performing PE-associated feature combination, was evaluated in the testing cohorts based on the Area Under the Curve (AUC) for subregion-level predictability. The voxel-wise Q surrogate was then synthesized using the final selected feature maps (FMs) and model score maps (MSMs) to investigate spatial distributions. The Spearman correlation coefficient (SCC) and Dice similarity coefficient (DSC) were used to assess the spatial consistency between FMs or MSMs and Q-SPECT scans. Results The optimal model performance achieved an AUC of 0.863 during internal testing and 0.828 on the external testing cohort. The model identified a combination containing 14 intensity and textural features that were non-redundant, robust, and capable of distinguishing between high- and low-functional lung regions. Spatial consistency assessment in the internal testing cohort showed moderate-to-high agreement between MSMs and reference Q-SPECT scans, with median SCC of 0.66, median DSCs of 0.86 and 0.64 for high- and low-functional regions, respectively. Conclusions This study validated the feasibility of using quantitative texture analysis and a data-driven ML pipeline to generate voxel-wise lung perfusion surrogates, providing a radiation-free, widely accessible alternative to functional lung imaging in managing pulmonary vascular diseases. Clinical trial number Not applicable.

A bstract Detailed studies of two-neutrino double electron capture (2 ν DEC) is a crucial step towards searching for the neutrinoless mode to explore the Majorana nature of neutrinos. We have measured precisely the half-life of the 2 ν DEC process in 124 Xe, utilizing a total exposure of 1.73 tonne year from the commissioning run and the first science run of the PandaX-4T experiment. A time-dependent background model in the O (10 keV) energy is constructed for the first time in PandaX-4T data. With an unbinned maximum likelihood fit, we determine the half-life of the 2 ν DEC process to be (1.03 ± 0.15 stat ± 0.08 sys ) × 10 22 yr. Furthermore, we have evaluated the capture fraction for both electrons captured from the K shell ( KK ) to be (65 ± 5)%, which aligns with the 124 Xe nuclear model calculations within 1.8 σ .

Abstract In this work, we study the strong decays of the newly observed\\varXi (1620)⁰Ξ ( 1620 ) 0 assuming that it is a meson-baryon molecular state of\\varLambda K̄Λ K ¯ and\\varSigma K̄Σ K ¯ . We consider four possible spin-parity assignmentsJᴾ=1/2^(±)J P = 1 / 2 ± and3/2^(±)3 / 2 ± for the\\varXi (1620)⁰Ξ ( 1620 ) 0 , and evaluate its partial decay width into\\varXi π Ξ π and\\varXi π π Ξ π π via hadronic loops with the help of effective Lagrangians. In comparison with the Belle data, the calculated decay width favors the spin-party assignment1/2⁻1 / 2 - while the other spin-parity assignments do not yield a decay width consistent with data in the molecule picture. We find that about 52–68% of the total width comes from theK̄\\varLambda K ¯ Λ channel, while the rest is provided by theK̄\\varSigma K ¯ Σ channel. As a result, both channels are important in explaining the strong decay of the\\varXi (1620)⁰Ξ ( 1620 ) 0 . In addition, the transition\\varXi (1620)⁰→ π \\varXi Ξ ( 1620 ) 0 → π Ξ is the main decay channel in theJᴾ=1/2⁻J P = 1 / 2 - case, which almost saturates the total width. These information are helpful to further understand the nature of the\\varXi (1620)⁰Ξ ( 1620 ) 0 .

In this work, we study the strong decays of the newly observed$$\\varXi (1620)^0$$Ξ ( 1620 ) 0 assuming that it is a meson-baryon molecular state of$$\\varLambda {\\bar{K}}$$Λ K ¯ and$$\\varSigma {\\bar{K}}$$Σ K ¯ . We consider four possible spin-parity assignments$$J^P=1/2^{\\pm }$$J P = 1 / 2 ± and$$3/2^{\\pm }$$3 / 2 ± for the$$\\varXi (1620)^0$$Ξ ( 1620 ) 0 , and evaluate its partial decay width into$$\\varXi \\pi $$Ξ π and$$\\varXi \\pi \\pi $$Ξ π π via hadronic loops with the help of effective Lagrangians. In comparison with the Belle data, the calculated decay width favors the spin-party assignment$$1/2^-$$1 / 2 - while the other spin-parity assignments do not yield a decay width consistent with data in the molecule picture. We find that about 52–68% of the total width comes from the$${\\bar{K}}\\varLambda $$K ¯ Λ channel, while the rest is provided by the$${\\bar{K}}\\varSigma $$K ¯ Σ channel. As a result, both channels are important in explaining the strong decay of the$$\\varXi (1620)^0$$Ξ ( 1620 ) 0 . In addition, the transition$$\\varXi (1620)^0\\rightarrow \\pi \\varXi $$Ξ ( 1620 ) 0 → π Ξ is the main decay channel in the$$J^{P}=1/2^{-}$$J P = 1 / 2 - case, which almost saturates the total width. These information are helpful to further understand the nature of the$$\\varXi (1620)^0$$Ξ ( 1620 ) 0 .

In this work, we study the strong decays of the newly observed [Formula omitted] assuming that it is a meson-baryon molecular state of [Formula omitted] and [Formula omitted]. We consider four possible spin-parity assignments [Formula omitted] and [Formula omitted] for the [Formula omitted], and evaluate its partial decay width into [Formula omitted] and [Formula omitted] via hadronic loops with the help of effective Lagrangians. In comparison with the Belle data, the calculated decay width favors the spin-party assignment [Formula omitted] while the other spin-parity assignments do not yield a decay width consistent with data in the molecule picture. We find that about 52-68% of the total width comes from the [Formula omitted] channel, while the rest is provided by the [Formula omitted] channel. As a result, both channels are important in explaining the strong decay of the [Formula omitted]. In addition, the transition [Formula omitted] is the main decay channel in the [Formula omitted] case, which almost saturates the total width. These information are helpful to further understand the nature of the [Formula omitted].

Purpose: To develop a deep learning framework based on a hybrid dataset to enhance the quality of CBCT images and obtain accurate HU values. Materials and Methods: A total of 228 cervical cancer patients treated in different LINACs were enrolled. We developed an encoder–decoder architecture with residual learning and skip connections. The model was hierarchically trained and validated on 5279 paired CBCT/planning CT images and tested on 1302 paired images. The mean absolute error (MAE), peak signal to noise ratio (PSNR), and structural similarity index (SSIM) were utilized to access the quality of the synthetic CT images generated by our model. Results: The MAE between synthetic CT images generated by our model and planning CT was 10.93 HU, compared to 50.02 HU for the CBCT images. The PSNR increased from 27.79 dB to 33.91 dB, and the SSIM increased from 0.76 to 0.90. Compared with synthetic CT images generated by the convolution neural networks with residual blocks, our model had superior performance both in qualitative and quantitative aspects. Conclusions: Our model could synthesize CT images with enhanced image quality and accurate HU values. The synthetic CT images preserved the edges of tissues well, which is important for downstream tasks in adaptive radiotherapy.