This paper builds a DSGE model for a small open economy (SOE) in which the central bank systematically intervenes both the domestic currency bond and the FX markets using two policy rules: a Taylor-type rule and a second rule in which the operational target is the rate of nominal currency depreciation. For this, the instruments used by the central bank (bonds and international reserves) must be included in the model, as well as the institutional arrangements that determine the total amount of resources the central bank can use. The ‘corner’ regimes in which only one of the policy rules is used are particular cases of th e model. The model is calibrated and implemented in Dynare for 1) simple policy rules, 2) optimal simple policy rules, and 3) optimal policy under commitment. Numerical losses are obtained for ad-hoc loss functions for different sets of central bank preferences (styles). The results show that the losses are systematically lower when both policy rules are used simultaneously, and much lower for the usual preferences (in which only inflation and/or output stabilization matter). It is shown that this result is basically due to the central bank’s enhanced ability, when it uses the two policy rules, to influence capital flows through the effects of its actions on the endogenous risk premium in the (risk-adjusted) interest parity equation.

The Regional hourly Advanced Baseline Imager (ABI) and Visible Infrared Imaging Radiometer Suite (VIIRS) Emissions (RAVE) algorithm operationally generates biomass burning (fire) emissions of carbon dioxide (CO2), carbon monoxide (CO), fine particulate matter (PM2.5), total particulate matter (TPM), sulfur dioxide (SO2), organic carbon (OC), black carbon (BC), nitrogen oxides (NOx), ammonia (NH3 ), methane (CH4) and volatile organic compounds (VOCs) for every hour each day. Additionally, hourly mean fire radiative power (FRP), hourly fire radiative energy (FRE), and dry mass (DM) consumed are also provided. RAVE was produced in response to the National Weather Service (NWS) Environmental Modeling Center (EMC) request for fire detections, fire radiative power, and hourly emissions for EMC regional air quality forecast models. The product files are in netCDF-4 format and the North American domain of interest is 3.5 deg N to 81.8 deg N and 144.96 deg E to 27.84 deg W. The product has a latency of one hour, a refresh rate of one hour, and the two spatial resolutions are 3 km and 13 km.

Traditional pastures in temperate regions face limitations such as reduced growth and nutritional quality during the summer season. Plantain (P. lanceolata L.) offers advantages like increased yield and decreased nitrogen losses from grazing ruminants. Effective grazing management is essential for pasture health, and defoliation frequency and intensity play a pivotal role. This study aimed to evaluate plantain’s regrowth, yield, and morpho-physiological and chemical responses under different defoliation frequencies and intensities, with the goal of enhancing its management in pastures. The study was conducted in pots within a controlled-environment growth chamber, examining the impact of three defoliation frequencies (based on extended leaf length: 15, 25 and 35 cm) and two defoliation intensities (5 and 8 cm of residual heights) with four replicates (24 pots as experimental units). The variables of interest were morphological characteristics, dry matter (DM) accumulation, herbage chemical composition, growth rate traits, and photosynthetic parameters. Defoliation frequency affected plantain’s growth and nutritional composition. More frequent cuts (15 cm) resulted in lower DM yield per cut and lower stem content, while less frequent cuts (35 cm) produced higher values. Defoliation intensity influenced the proportion of leaves and stems in the total DM, with 5 cm cuts favoring leaves. Nutrient content was also affected by defoliation frequency, with less frequent cuts (35 cm) showing lower crude protein concentration and metabolizable energy content but higher neutral detergent fiber and water-soluble carbohydrate concentration. Plantain’s growth rate variables were mainly influenced by defoliation frequency, with less frequent cuts promoting faster leaf appearance and growth of new leaves. The basal fluorescence variables and chlorophyll content were affected by cutting frequency, being highest when cut less frequently (35 cm), while no differences were found in the actual quantum efficiency among different defoliation frequencies and intensities. The fraction of light dedicated to non-photochemical quenching was highest when cut less frequently and more intensively. Overall, defoliation at 25 cm of extended leaf length balanced plantain forage quality and regrowth capacity.

IntroductionSurveys conducted internationally have found widespread interest in artificial intelligence (AI) amongst medical students. No similar surveys have been conducted in Western Australia (WA) and it is not known how medical students in WA feel about the use of AI in healthcare or their understanding of AI. We aim to assess WA medical students’ attitudes towards AI in general, AI in healthcare, and the inclusion of AI education in the medical curriculum.MethodsA digital survey instrument was developed based on a review of available literature and consultation with subject matter experts. The survey was piloted with a group of medical students and refined based on their feedback. We then sent this anonymous digital survey to all medical students in WA (approximately 1539 students). Responses were open from the 7th of September 2021 to the 7th of November 2021. Students’ categorical responses were qualitatively analysed, and free text comments from the survey were qualitatively analysed using open coding techniques.ResultsOverall, 134 students answered one or more questions (8.9% response rate). The majority of students (82.0%) were 20–29 years old, studying medicine as a postgraduate degree (77.6%), and had started clinical rotations (62.7%). Students were interested in AI (82.6%), self-reported having a basic understanding of AI (84.8%), but few agreed that they had an understanding of the basic computational principles of AI (33.3%) or the limitations of AI (46.2%). Most students (87.5%) had not received teaching in AI. The majority of students (58.6%) agreed that AI should be part of medical training and most (72.7%) wanted more teaching focusing on AI in medicine. Medical students appeared optimistic regarding the role of AI in medicine, with most (74.4%) agreeing with the statement that AI will improve medicine in general. The majority (56.6%) of medical students were not concerned about the impact of AI on their job security as a doctor. Students selected radiology (72.6%), pathology (58.2%), and medical administration (44.8%) as the specialties most likely to be impacted by AI, and psychiatry (61.2%), palliative care (48.5%), and obstetrics and gynaecology (41.0%) as the specialties least likely to be impacted by AI. Qualitative analysis of free text comments identified the use of AI as a tool, and that doctors will not be replaced as common themes.ConclusionMedical students in WA appear to be interested in AI. However, they have not received education about AI and do not feel they understand its basic computational principles or limitations. AI appears to be a current deficit in the medical curriculum in WA, and most students surveyed were supportive of its introduction. These results are consistent with previous surveys conducted internationally.