Self-calibrated atom-interferometer gyroscope by modulating atomic velocities.

Atom-interferometer gyroscopes have attracted much attention for their long-term stability and extremely low drift. For such high-precision instruments, self-calibration to achieve an absolute rotation measurement is critical. In this work, we propose and demonstrate the self-calibration of an atom-interferometer gyroscope. This calibration is realized by using the detuning of the laser frequency to control the atomic velocity, thus modulating the scale factor of the gyroscope. The modulation determines the order and the initial phase of the interference stripe, thus eliminating the ambiguity caused by the periodicity of the interferometric signal. This self-calibration method is validated through a measurement of the Earth's rotation rate, and a relative uncertainty of 162 ppm is achieved. Long-term stable and self-calibrated atom-interferometer gyroscopes have important applications in the fields of fundamental physics, geophysics, and long-time navigation.

Evaluating the Demand for Nucleic Acid Testing in Different Scenarios of COVID-19 Transmission: A Simulation Study.

INTRODUCTION: The 2019 novel coronavirus (COVID-19) has been recognized as the most severe human infectious disease pandemic in the past century. To enhance our ability to control potential infectious diseases in the future, this study simulated the influence of nucleic acid testing on the transmission of COVID-19 across varied scenarios. Additionally, it assessed the demand for nucleic acid testing under different circumstances, aiming to furnish a decision-making foundation for the implementation of nucleic acid screening measures, the provision of emergency materials, and the allocation of human resources. METHODS: Considering the transmission dynamics of COVID-19 and the preventive measures implemented by countries, we explored three distinct levels of epidemic intensity: community transmission, outbreak, and sporadic cases. Integrating the theory of scenario analysis, we formulated six hypothetical epidemic scenarios, each corresponding to possible occurrences during different phases of the pandemic. We developed an improved SEIR model, validated its accuracy using real-world data, and conducted a comprehensive analysis and prediction of COVID-19 infections under these six scenarios. Simultaneously, we assessed the testing resource requirements associated with each scenario. RESULTS: We compared the predicted number of infections simulated by the modified SEIR model with the actual reported cases in Israel to validate the model. The root mean square error (RMSE) was 350.09, and the R-squared (R2) was 0.99, indicating a well-fitted model. Scenario 4 demonstrated the most effective prevention and control outcomes. Strengthening non-pharmaceutical interventions and increasing nucleic acid testing frequency, even under low testing capacity, resulted in a delayed epidemic peak by 78 days. The proportion of undetected cases decreased from 77.83% to 31.21%, and the overall testing demand significantly decreased, meeting maximum demand even with low testing capacity. The initiation of testing influenced case detection probability. Under high testing capacity, increasing testing frequency elevated the detection rate from 36.40% to 77.83%. Nucleic acid screening proved effective in reducing the demand for testing resources under diverse epidemic prevention and control strategies. While effective interventions and nucleic acid screening measures substantially diminished the demand for testing-related resources, varying degrees of insufficient testing capacity may still persist. CONCLUSIONS: The nucleic acid detection strategy proves effective in promptly identifying and isolating infected individuals, thereby mitigating the infection peak and extending the time to peak. In situations with constrained testing capacity, implementing more stringent measures can notably decrease the number of infections and alleviate resource demands. The improved SEIR model demonstrates proficiency in predicting both reported and unreported cases, offering valuable insights for future infection risk assessments. Rapid evaluations of testing requirements across diverse scenarios can aptly address resource limitations in specific regions, offering substantial evidence for the formulation of future infectious disease testing strategies.

Optimal resource allocation model for COVID-19: a systematic review and meta-analysis.

BACKGROUND: A lack of health resources is a common problem after the outbreak of infectious diseases, and resource optimization is an important means to solve the lack of prevention and control capacity caused by resource constraints. This study systematically evaluated the similarities and differences in the application of coronavirus disease (COVID-19) resource allocation models and analyzed the effects of different optimal resource allocations on epidemic control. METHODS: A systematic literature search was conducted of CNKI, WanFang, VIP, CBD, PubMed, Web of Science, Scopus and Embase for articles published from January 1, 2019, through November 23, 2023. Two reviewers independently evaluated the quality of the included studies, extracted and cross-checked the data. Moreover, publication bias and sensitivity analysis were evaluated. RESULTS: A total of 22 articles were included for systematic review; in the application of optimal allocation models, 59.09% of the studies used propagation dynamics models to simulate the allocation of various resources, and some scholars also used mathematical optimization functions (36.36%) and machine learning algorithms (31.82%) to solve the problem of resource allocation; the results of the systematic review show that differential equation modeling was more considered when testing resources optimization, the optimization function or machine learning algorithm were mostly used to optimize the bed resources; the meta-analysis results showed that the epidemic trend was obviously effectively controlled through the optimal allocation of resources, and the average control efficiency was 0.38(95%CI 0.25-0.51); Subgroup analysis revealed that the average control efficiency from high to low was health specialists 0.48(95%CI 0.37-0.59), vaccines 0.47(95%CI 0.11-0.82), testing 0.38(95%CI 0.19-0.57), personal protective equipment (PPE) 0.38(95%CI 0.06-0.70), beds 0.34(95%CI 0.14-0.53), medicines and equipment for treatment 0.32(95%CI 0.12-0.51); Funnel plots and Egger's test showed no publication bias, and sensitivity analysis suggested robust results. CONCLUSION: When the data are insufficient and the simulation time is short, the researchers mostly use the constructor for research; When the data are relatively sufficient and the simulation time is long, researchers choose differential equations or machine learning algorithms for research. In addition, our study showed that control efficiency is an important indicator to evaluate the effectiveness of epidemic prevention and control. Through the optimization of medical staff and vaccine allocation, greater prevention and control effects can be achieved.

Core Structure-Oriented Asymmetric α-Allenylic Alkylation of Amino Acid Esters Enabled by Chiral Aldehyde/Palladium Catalysis.

Aiming at the reported chiral synthons leading to manzacidins A and D, here we report a highly efficient catalytic asymmetric α-allenylic alkylation reaction of NH2-unprotected amino acid esters that is promoted by combined chiral aldehyde/palladium catalysis. Fifty examples of unnatural α,α-disubstituted amino acid esters are reported with good-to-excellent yields and stereoselectivities. Based on this methodology, a key intermediate leading to manzacidin C and its other three stereoisomers is prepared accordingly.

A network meta-analysis of risk factors of infection among close contacts of COVID-19.

Objective: We aimed to use network meta-analysis to compare the impact of infection risk factors of close contacts with COVID-19, identify the most influential factors and rank their subgroups. It can provide a theoretical basis for the rapid and accurate tracking and management of close contacts. Methods: We searched nine databases from December 1, 2019 to August 2, 2023, which only took Chinese and English studies into consideration. Odd ratios (ORs) were calculated from traditional meta-estimated secondary attack rates (SARs) for different risk factors, and risk ranking of these risk factors was calculated by the surface under the cumulative ranking curve (SUCRA). Results: 25 studies with 152647 participants identified. Among all risk factors, the SUCRA of type of contact was 69.6 % and ranked first. Among six types of contact, compared with transportation contact, medical contact, social contact and other, daily contact increased risk of infection by 12.11 (OR: 12.11, 95 % confidence interval (CI): 6.51-22.55), 7.76 (OR: 7.76, 95 % CI: 4.09-14.73), 4.65 (OR: 4.65, 95 % CI: 2.66-8.51) and 8.23 OR: 8.23, 95 % CI: 4.23-16.01) times, respectively. Overall, SUCRA ranks from highest to lowest as daily contact (94.7 %), contact with pollution subjects (78.4 %), social contact (60.8 %), medical contact (31.8 %), other (27.9 %), transportation contact (6.4 %). Conclusion: The type of contact had the greatest impact on COVID-19 close contacts infection among the risk factors we included. Daily contact carried the greatest risk of infection among six types of contact, followed by contact with pollution subjects, social contact, other, medical contact and transportation contact. The results can provide scientific basis for rapid assess the risk of infection among close contacts based on fewer risk factors and pay attention to high-risk close contacts during management, thereby reducing tracking and management costs.

Compact multi-channel radio frequency pulse-sequence generator with fast-switching capability for cold-atom interferometers.

Cold-atom interferometers have matured into a powerful tool for fundamental physics research, and they are currently moving from realizations in the laboratory to applications in the field. A radio frequency (RF) generator is an indispensable component of these devices for controlling lasers and manipulating atoms. In this work, we developed a compact RF generator for fast switching and sweeping the frequencies and amplitudes of atomic-interference pulse sequences. In this generator, multi-channel RF signals are generated using a field-programmable gate array (FPGA) to control eight direct digital synthesizers (DDSs). We further propose and demonstrate a method for pre-loading the parameters of all the RF pulse sequences to the DDS registers before their execution, which eliminates the need for data transfer between the FPGA and DDSs to change RF signals. This sharply decreases the frequency-switching time when the pulse sequences are running. Performance characterization showed that the generated RF signals achieve a 100 ns frequency-switching time and a 40 dB harmonic-rejection ratio. The generated RF pulse sequences were applied to a cold-atom-interferometer gyroscope, and the contrast of atomic interference fringes was found to reach 38%. This compact multi-channel generator with fast frequency/amplitude switching and/or sweeping capability will be beneficial for applications in field-portable atom interferometers.