A machine learning model was created to predict the electron spectrum generated by a GeV-class laser wakefield accelerator. The model was constructed from variational convolutional neural networks, which mapped the results of secondary laser and plasma diagnostics to the generated electron spectrum. An ensemble of trained networks was used to predict the electron spectrum and to provide an estimation of the uncertainty of that prediction. It is anticipated that this approach will be useful for inferring the electron spectrum prior to undergoing any process that can alter or destroy the beam. In addition, the model provides insight into the scaling of electron beam properties due to stochastic fluctuations in the laser energy and plasma electron density.

Ensuring the successful treatment of tuberculosis (TB) is an essential public health responsibility of national TB programs. This case study describes how the Department of Health and Family Welfare, Kerala state, successfully prevented the disruptions in TB treatment when an unprecedented massive flood, declared as “a calamity of severe nature,” completely disrupted normal operations in the state during August 2018. Unanticipated floods led to the displacement and relocation of more than 1.5 million citizens. The state has ensured continuity of TB treatment for all notified drug sensitive and drug-resistant TB patients (9608 and 434, respectively), including those who were displaced and relocated. A real-time web-enabled, case-based patient management information system has helped preserve the entire patient information, available at multiple levels. Routine strength of the program, including good rapport with patients, frontline multipurpose health workers and treatment supporters, high literacy rate of general population, and well-integrated primary health care system delivering TB services, enabled ensuring continuity of care during the disaster situation. The success of the post-flood TB control measures in Kerala affirms the importance of maintaining an integrated and strong TB control component with general health system ownership.

The eastern Arabian Sea is influenced by both the advection of upwelled water from the western Arabian Sea and winter convective mixing. Therefore, sediments collected from the eastern Arabian Sea can help to understand the long-term seasonal hydrographic changes. We used the planktonic foraminifera census and stable isotopic ratio (δ18O) from sediments drilled during the International Ocean Discovery Program Expedition 355 to reconstruct surface hydrographic changes in the eastern Arabian Sea during the last 350 kyr. The increased abundance of Globigerina bulloides suggests enhanced advection of upwelled water during the latter half of MIS7 and the beginning of MIS6, as a result of a strengthened summer monsoon. A large drop in upwelling and/or advection of upwelled water from the western Arabian Sea is inferred during the subsequent interval of MIS6, based on the rare presence of G. bulloides. The comparable relative abundance of Neogloboquadrina dutertrei, G. bulloides and Globigerinoides ruber suggests that during the early part of MIS5, hydrographic conditions were similar to today. The upwelling decreased and winter convection increased with the progress of the glacial interval. A good coherence between planktonic foraminiferal assemblage-based monsoon stacks from both the eastern and western Arabian Sea suggests a coeval response of the entire northern Arabian Sea to the glacial–interglacial changes. The glacial–interglacial difference in δ18Osw-ivc was at a maximum with 4–5 psu change in salinity during Termination 2 and 3, and a minimum during Termination 4. The significantly reduced regional contribution to the glacial–interglacial change in δ18Osw-ivc during Termination 4 suggests a lesser change in the monsoon.