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Abstract Time reversal exploits the invariance of electromagnetic wave propagation in reciprocal and lossless media to localize radiating sources. Time‐reversed measurements are back‐propagated in a simulated domain and converge to the unknown source location. The focusing time (i.e. the simulation instant at which the fields converge to the source location) and the source location can be identified using field maxima, entropy, time kurtosis, and space kurtosis. This paper analyses the spatial energy‐density distribution of time‐reversed electromagnetic fields by introducing a convergence metric based on the spatial average and variance of the energy density. It is analytically proven that the proposed metric identifies the focusing time and the source location, with direct links to the source frequency content. The analytical results are verified in a free‐space numerical simulation and the proposed metric is then compared to existing ones in a simulated inhomogeneous medium. Next, this metric is applied and compared in an experimental case study to localize electromagnetic interference sources. The proposed metric outperforms existing ones to identify the focusing time and can also be used to locate the source. Finally, because of its tensorial nature, it can handle anisotropic media, opening the door to quantitative analyses of time‐reversal focusing in metamaterials.

Time-sensitive networks, as in the context of IEEE-TSN and IETF-Detnet, require bounds on worst-case delays. Various network analysis tools compute such bounds; these tools are based on different methods and provide delay bounds that are all valid but may differ; furthermore, it is generally not known which tool will provide the best bound. To obtain the best possible bound, users need to implement multiple pieces of code with a different syntax for every tool, which is impractical and error-prone. To address this issue, we present Saihu, a Python interface that integrates the three most frequently used worst-case network analysis tools: xTFA, DiscoDNC, and Panco. They altogether implement six analysis methods. Saihu provides a general interface that enables defining a network in a single file and executing all tools simultaneously without any modification. Saihu further exports analysis results as formatted reports automatically and allows quick generation of certain types of networks. With its simplified steps of execution, Saihu reduces the burden on users and makes it accessible for anyone working with time-sensitive networks. An introductory video is available at https://youtu.be/MiOhLay8Kr4.

AIM OF THIS STUDY: General internal medicine is a crucial element in healthcare systems. Understanding how many people are and will be working in this field is important to maintain and improve quality for patients in healthcare systems. This can provide a basis for political decisions. METHODS: We conducted a cross-sectional study to analyse the current and future workforce of generalists (general practitioners and internists in hospitals) in Switzerland. The Swiss Society of General Internal Medicine (SSGIM) distributed a survey to all members. Respondents were asked about their current average workload in 2023 and planned workload in 2033. The responses were used to calculate full-time equivalent (FTE) for the current and future workforce of generalists and to extrapolate FTE for all active SSGIM members. To model the demand by 2033, we derived different scenarios. RESULTS: Of all 6,232 active SSGIM members, 2,030 (33%) participated: 46% female, 25% (largest age group) 56-65 years old, 19% still in postgraduate training. The average workload in 2023 was 78% for female and 87% for male generalists; the FTE extrapolated to all active SSGIM members in 2023 was 5,246. By 2033, 1,935 FTEs (36%) will retire, 502 FTEs (10%) will reduce their workload, 116 FTEs (2%) will increase their workload and 2,800 FTEs (53%) will remain in the workforce with the same workload as in 2023. To maintain the same workforce as in 2023, 2,321 new FTEs (44%) will be needed by 2033. To fill this gap of 232 FTE new generalists per year, we modelled different scenarios with assumptions of interest, workload, migration and dropouts. CONCLUSIONS: Within only one decade, 44% of the current workforce of generalists will disappear, mainly due to retirement and decreased workload. To fill this gap, various scenarios need to be incorporated. Politicians are called upon to create the political framework to create attractive training and working conditions for generalists to address the future demand for healthcare services.

Background: Women may receive suboptimal pain management compared with men, and this disparity might be related to gender stereotypes. Objectives: To assess the influence of patient gender on the management of acute low back pain. Design: We assessed pain management by 231 physicians using an online clinical vignette describing a consultation for acute low back pain in a female or male patient. The vignette was followed by a questionnaire that assessed physicians’ management decisions and their gender stereotypes. Methods: We created an online clinical vignette presenting a patient with acute low back pain and assessed the influence of a patient’s gender on pain management. We investigated gender-related stereotyping regarding pain care by emergency physicians using the Gender Role Expectation of Pain questionnaire. Results: Both male and female physicians tended to consider that a typical man was more sensitive to pain, had less pain endurance, and was more willing to report pain than a typical woman. These stereotypes did not translate into significant differences in pain management between men and women. However, women tended to be referred less often for imaging examinations than men and were also prescribed lower doses of ibuprofen and opioids. The physician’s gender had a modest influence on management decisions, female physicians being more likely to prescribe ancillary examinations. Conclusion: We observed gender stereotypes among physicians. Our findings support the hypothesis that social characteristics attributed to men and women influence pain management. Prospective clinical studies are needed to provide a deeper understanding of gender stereotypes and their impact on clinical management.

Time reversal exploits the invariance of electromagnetic wave propagation in reciprocal and lossless media to localize radiating sources. Time‐reversed measurements are back‐propagated in a simulated domain and converge to the unknown source location. The focusing time (i.e. the simulation instant at which the fields converge to the source location) and the source location can be identified using field maxima, entropy, time kurtosis, and space kurtosis. This paper analyses the spatial energy‐density distribution of time‐reversed electromagnetic fields by introducing a convergence metric based on the spatial average and variance of the energy density. It is analytically proven that the proposed metric identifies the focusing time and the source location, with direct links to the source frequency content. The analytical results are verified in a free‐space numerical simulation and the proposed metric is then compared to existing ones in a simulated inhomogeneous medium. Next, this metric is applied and compared in an experimental case study to localize electromagnetic interference sources. The proposed metric outperforms existing ones to identify the focusing time and can also be used to locate the source. Finally, because of its tensorial nature, it can handle anisotropic media, opening the door to quantitative analyses of time‐reversal focusing in metamaterials. [ABSTRACT FROM AUTHOR]