Ultrathin Magnesium-based Coating as an Efficient Oxygen Barrier for Superconducting Circuit Materials

Scaling up superconducting quantum circuits based on transmon qubits necessitates substantial enhancements in qubit coherence time. Among the materials considered for transmon qubits, tantalum (Ta) has emerged as a promising candidate, surpassing conventional counterparts in terms of coherence time. However, the presence of an amorphous surface Ta oxide layer introduces dielectric loss, ultimately placing a limit on the coherence time. In this study, we present a novel approach for suppressing the formation of tantalum oxide using an ultrathin magnesium (Mg) capping layer deposited on top of tantalum. Synchrotron-based X-ray photoelectron spectroscopy (XPS) studies demonstrate that oxide is confined to an extremely thin region directly beneath the Mg/Ta interface. Additionally, we demonstrate that the superconducting properties of thin Ta films are improved following the Mg capping, exhibiting sharper and higher-temperature transitions to superconductive and magnetically ordered states. Based on the experimental data and computational modeling, we establish an atomic-scale mechanistic understanding of the role of the capping layer in protecting Ta from oxidation. This work provides valuable insights into the formation mechanism and functionality of surface tantalum oxide, as well as a new materials design principle with the potential to reduce dielectric loss in superconducting quantum materials. Ultimately, our findings pave the way for the realization of large-scale, high-performance quantum computing systems

New-Generation Quality and Safety Management of the Construction Industry

Construction is a complex human–machine–environment system, and the quality and safety management during construction faces numerous challenges. As the new-generation information technology develops, digital twin now can be used in construction to improve the quality and safety management and promote smart construction in China. Computability and controllability of the whole construction process is expected to be achieved using the digital twin technology; digital management of construction sites can be realized using advanced sensing, computing, and other technologies. In this article, we first investigate the demand for the application of digital twin into the construction quality management and analyze the research status and problems of the application. Subsequently, we propose a next-generation construction quality and safety management system that is composed of product intelligent design for construction quality and safety control, intelligent sensing and analysis of construction quality and safety status, data-driven construction quality and safety control, and construction quality management and dynamic supervision. Furthermore, we propose suggestions for the application of digital twin technology in the construction industry in China from the aspects of management, technology, as well as standards and specifications

Analysis of influencing factors of job demands of healthcare workers working in mobile cabin hospitals in China

Abstract Aim To explore the job demands of healthcare workers (HCWs) working in mobile cabin hospitals in Shanghai and identify the influencing factors. Design The study had a cross‐sectional design. Methods Using the convenience sampling method, we selected 1223 HCWs (medical team members) working in these mobile cabin hospitals during April–May 2022. The findings of the general information questionnaire and the hierarchy scale of job demands of HCWs working in mobile cabin hospitals were used for the investigation. Results The total score of job demands of the included HCWs was 132.26 ± 9.53; the average score of the items was 4.73 ± 0.34. Multivariate linear regression analyses showed that the following HCWs had significantly higher job demands: female HCWs and HCWs who received psychological training or intervention during the COVID‐19 pandemic, were satisfied with the doctor/nurse–patient relationship, received support from family members/friends/colleagues, believed that the risk of working in mobile cabin hospitals was high, had adapted to the working environment of mobile cabin hospitals and had college/undergraduate level of education. They would benefit from increased social support and better training in terms of psychological coping mechanisms(both theoretical knowledge and applicable skills) and COVID‐19 prevention,control and treatment abilities

Development and validation of a CT-based deep learning radiomics nomogram to predict muscle invasion in bladder cancer

Objective: This study aimed to develop a nomogram combining CT-based handcrafted radiomics and deep learning (DL) features to preoperatively predict muscle invasion in bladder cancer (BCa) with multi-center validation. Methods: In this retrospective study, 323 patients underwent radical cystectomy with pathologically confirmed BCa were enrolled and randomly divided into the training cohort (n = 226) and internal validation cohort (n = 97). And fifty-two patients from another independent medical center were enrolled as an independent external validation cohort. Handcrafted radiomics and DL features were constructed from preoperative nephrographic phase CT images. Least absolute shrinkage and selection operator (LASSO) regression was used to identify the most discriminative features in train cohort. Multivariate logistic regression was used to develop the predictive model and a deep learning radiomics nomogram (DLRN) was constructed. The predictive performance of models was evaluated by area under the curves (AUC) in the three cohorts. The calibration and clinical usefulness of DLRN were estimated by calibration curve and decision curve analysis. Results: The nomogram that incorporated radiomics signature and DL signature demonstrated satisfactory predictive performance for differentiating non-muscle invasive bladder cancer (NMIBC) from muscle invasive bladder cancer (MIBC), with an AUC of 0.884 (95 % CI: 0.813–0.953) in internal validation cohort and 0.862 (95 % CI: 0.756–0.968) in external validation cohort, respectively. Decision curve analysis confirmed the clinical usefulness of the nomogram. Conclusions: A CT-based deep learning radiomics nomogram exhibited a promising performance for preoperative prediction of muscle invasion in bladder cancer, and may be helpful in the clinical decision-making process

Validity of a multiphase CT-based radiomics model in predicting the Leibovich risk groups for localized clear cell renal cell carcinoma: an exploratory study

Abstract Objective To develop and validate a multiphase CT-based radiomics model for preoperative risk stratification of patients with localized clear cell renal cell carcinoma (ccRCC). Methods A total of 425 patients with localized ccRCC were enrolled and divided into training, validation, and external testing cohorts. Radiomics features were extracted from three-phase CT images (unenhanced, arterial, and venous), and radiomics signatures were constructed by the least absolute shrinkage and selection operator (LASSO) regression algorithm. The radiomics score (Rad-score) for each patient was calculated. The radiomics model was established and visualized as a nomogram by incorporating significant clinical factors and Rad-score. The predictive performance of the radiomics model was evaluated by the receiver operating characteristic curve, calibration curve, and decision curve analysis (DCA). Results The AUC of the triphasic radiomics signature reached 0.862 (95% CI: 0.809–0.914), 0.853 (95% CI: 0.785–0.921), and 0.837 (95% CI: 0.714–0.959) in three cohorts, respectively, which were higher than arterial, venous, and unenhanced radiomics signatures. Multivariate logistic regression analysis showed that Rad-score (OR: 4.066, 95% CI: 3.495–8.790) and renal vein invasion (OR: 12.914, 95% CI: 1.118–149.112) were independent predictors and used to develop the radiomics model. The radiomics model showed good calibration and discrimination and yielded an AUC of 0.872 (95% CI: 0.821–0.923), 0.865 (95% CI: 0.800–0.930), and 0.848 (95% CI: 0.728–0.967) in three cohorts, respectively. DCA showed the clinical usefulness of the radiomics model in predicting the Leibovich risk groups. Conclusions The radiomics model can be used as a non-invasive and useful tool to predict the Leibovich risk groups for localized ccRCC patients. Critical relevance statement The triphasic CT-based radiomics model achieved favorable performance in preoperatively predicting the Leibovich risk groups in patients with localized ccRCC. Therefore, it can be used as a non-invasive and effective tool for preoperative risk stratification of patients with localized ccRCC. Key points • The triphasic CT-based radiomics signature achieves better performance than the single-phase radiomics signature. • Radiomics holds prospects in preoperatively predicting the Leibovich risk groups for ccRCC. • This study provides a non-invasive method to stratify patients with localized ccRCC. Graphical Abstrac

Additional file 1 of Driving the implementation of hospital examination reservation system through hospital management

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