Mass and width of unstable molecular state in quantum field theory

Applying resonance theory in the framework of relativistic quantum field theory, we investigate the temporal evolution of molecular state composed of two vector mesons as determined by the total Hamiltonian. Then exotic meson resonance $X(3915)$ is considered as a mixed state of two unstable molecular states $D^{*0}\bar{D}^{*0}$ and $D^{*+}D^{*-}$, and the corrected mass and width for resonance $X(3915)$ are calculated. In this actual calculation, we minutely show how to obtain the corrections for resonance and to exhibit the key features of dispersion relation in a new Feynman diagram. The numerical results are consistent with the experimental values

Quark Mass Ratios in ChPT with the Difference of Quark Condensates Considered

The chiral effective Lagrangian for pseudoscalar nonet is constructed in consideration of isospin breaking. And the difference of quark condensates is taken accounted. The SU(3) singlet eta0 is not taken as Goldstone-boson. The mixing with and without isospin symmetry is considered. The quark mass ratios are obtained through solving the mass equations of mesons. We estimate the change of quark mass ratios according to the change of the masses of pion+ and kaon+ to see how the electromagnetic corrections affect our results. It turn out that massless up quark is possible. The upper limit for mu/md is around 0.39. 2ms/(mu+md)=24.23~25.12. The values for quark condensate ratios and other constants are limited in narrow ranges.Comment: 10 page

Assistive diagnostic technology for congenital heart disease based on fusion features and deep learning

Introduction: Congenital heart disease (CHD) is a cardiovascular disorder caused by structural defects in the heart. Early screening holds significant importance for the effective treatment of this condition. Heart sound analysis is commonly employed to assist in the diagnosis of CHD. However, there is currently a lack of an efficient automated model for heart sound classification, which could potentially replace the manual process of auscultation.Methods: This study introduces an innovative and efficient screening and classification model, combining a locally concatenated fusion approach with a convolutional neural network based on coordinate attention (LCACNN). In this model, Mel-frequency spectral coefficients (MFSC) and envelope features are locally fused and employed as input to the LCACNN network. This model automatically analyzes feature map energy information, eliminating the need for denoising processes.Discussion: The proposed classification model in this study demonstrates a robust capability for identifying congenital heart disease, potentially substituting manual auscultation to facilitate the detection of patients in remote areas.Results: This study introduces an innovative and efficient screening and classification model, combining a locally concatenated fusion approach with a convolutional neural network based on coordinate attention (LCACNN). In this model, Mel-frequency spectral coefficients (MFSC) and envelope features are locally fused and employed as input to the LCACNN network. This model automatically analyzes feature map energy information, eliminating the need for denoising processes. To assess the performance of the classification model, comparative ablation experiments were conducted, achieving classification accuracies of 91.78% and 94.79% on the PhysioNet and HS databases, respectively. These results significantly outperformed alternative classification models

Bioactive nanocomposite coatings under visible light illumination promoted surface-mediated gene delivery

Gene delivery based on bioactive coatings on collagen has great potential for applications in bone repair. Meanwhile, controlled gene delivery at specific times/regions is essential for an efficient and complete bone reconstruction process. However, spatio-temporal regulation of gene release and delivery remains a great challenge. In this paper, we used visible light illumination to effectively regulate gene release and subsequent delivery into biological cells. A visible light responsive and bioactive nanocomposite coating (based on collagen/gold nanoparticles, e.g., Col/AuNPs) was prepared through hydrothermal and sol–gel processes and was used as a loading platform for complexes of enhanced green fluorescent protein and Lipofectamine2000 (LF/GFP). The results showed that the amount of immobilized LF/GFP was increased on Col/AuNPs and the release of pre-adsorbed LF/GFP was significantly enhanced in a spatio-temporal and controlled manner under visible light illumination. Moreover, the cellular intake of the released genes was improved, thus enhancing the gene expression efficiency of the cells. The mechanism of enhanced controlled gene delivery was attributed to the changes in collagen structures and rearrangement of cytoskeletal structures induced by the photothermal effect. The developed Col/AuNP composite coating is effective for both controlled surface-mediated gene delivery and gene-mediated bone repair

A Quantum Query Expansion Approach for Session Search

Recently, Quantum Theory (QT) has been employed to advance the theory of Information Retrieval (IR). Various analogies between QT and IR have been established. Among them, a typical one is applying the idea of photon polarization in IR tasks, e.g., for document ranking and query expansion. In this paper, we aim to further extend this work by constructing a new superposed state of each document in the information need space, based on which we can incorporate the quantum interference idea in query expansion. We then apply the new quantum query expansion model to session search, which is a typical Web search task. Empirical evaluation on the large-scale Clueweb12 dataset has shown that the proposed model is effective in the session search tasks, demonstrating the potential of developing novel and effective IR models based on intuitions and formalisms of QT

Smoking patterns and sociodemographic factors associated with tobacco use among Chinese rural male residents: a descriptive analysis

<p>Abstract</p> <p>Background</p> <p>Although evidence has shown high prevalence rates of tobacco use in the general urban populations in China, relatively little is known in its rural population. The purposes of this study were to examine smoking patterns and sociodemographic correlates of smoking in a sample of rural Chinese male residents.</p> <p>Methods</p> <p>The study employed a cross-sectional, multi-stage sampling design. Residents (N = 4,414; aged 15 years and older) were recruited from four geographic regions in China. Information on participants' tobacco use (of all forms), including their daily use, and sociodemographic characteristics were collected via survey questionnaires and the resultant data were analyzed using chi-square tests and logistic regression procedures.</p> <p>Results</p> <p>The overall smoking prevalence in the study sample was 66.8% (n = 2,950). Of these, the average use of tobacco products per day was 12.70 (SD = 7.99) and over 60% reported daily smoking of more than 10 cigarettes. Geographic regions of the study areas, age of the participants, marital status, ethnicity, education, occupation, and average personal annual income were found to be significantly associated with an increased likelihood of smoking among rural Chinese male residents.</p> <p>Conclusion</p> <p>There is a high smoking prevalence in the Chinese rural population and smoking behaviors are associated with important sociodemographic factors. Findings suggest the need for tobacco control and intervention policies aimed at reducing tobacco use in Chinese rural smoking populations.</p

On modeling rank-independent risk in estimating probability of Relevance

Estimating the probability of relevance for a document is fundamental in information retrieval. From a theoretical point of view, risk exists in the estimation process, in the sense that the estimated probabilities may not be the actual ones precisely. The estimation risk is often considered to be dependent on the rank. For example, the probability ranking principle assumes that ranking documents in the order of decreasing probability of relevance can optimize the rank effectiveness. This implies that a precise estimation can yield an optimal rank. However, an optimal (or even ideal) rank does not always guarantee that the estimated probabilities are precise. This means that part of the estimation risk is rank-independent. It imposes practical risks in the applications, such as pseudo relevance feedback, where different estimated probabilities of relevance in the first-round retrieval will make a difference even when two ranks are identical. In this paper, we will explore the effect and the modeling of such rank-independent risk. A risk management method is proposed to adaptively adjust the rank-independent risk. Experimental results on several TREC collections demonstrate the effectiveness of the proposed models for both pseudo-relevance feedback and relevance feedback