A Platform for Ultra-Fast Proton Probing of Matter in Extreme Conditions.

Recent developments in ultrashort and intense laser systems have enabled the generation of short and brilliant proton sources, which are valuable for studying plasmas under extreme conditions in high-energy-density physics. However, developing sensors for the energy selection, focusing, transport, and detection of these sources remains challenging. This work presents a novel and simple design for an isochronous magnetic selector capable of angular and energy selection of proton sources, significantly reducing temporal spread compared to the current state of the art. The isochronous selector separates the beam based on ion energy, making it a potential component in new energy spectrum sensors for ions. Analytical estimations and Monte Carlo simulations validate the proposed configuration. Due to its low temporal spread, this selector is also useful for studying extreme states of matter, such as proton stopping power in warm dense matter, where short plasma stagnation time (<100 ps) is a critical factor. The proposed selector can also be employed at higher proton energies, achieving final time spreads of a few picoseconds. This has important implications for sensing technologies in the study of coherent energy deposition in biology and medical physics.

Reconstruction of lateral coherence and 2D emittance in plasma betatron X-ray sources.

X-ray sources have a strong social impact, being implemented for biomedical research, material and environmental sciences. Nowadays, compact and accessible sources are made using lasers. We report evidence of nontrivial spectral-angular correlations in a laser-driven betatron X-ray source. Furthermore, by angularly-resolved spectral measurements, we detect the signature of spatial phase modulations by the electron trajectories. This allows the lateral coherence function to be retrieved, leading to the evaluation of the coherence area of the source, determining its brightness. Finally, the proposed methodology allows the unprecedented reconstruction of the size of the X-ray source and the electron beam emittance in the two main emission planes in a single shot. This information will be of fundamental interest for user applications of new radiation sources.

Forecasting water temperature in lakes and reservoirs using seasonal climate prediction.

Seasonal climate forecasts produce probabilistic predictions of meteorological variables for subsequent months. This provides a potential resource to predict the influence of seasonal climate anomalies on surface water balance in catchments and hydro-thermodynamics in related water bodies (e.g., lakes or reservoirs). Obtaining seasonal forecasts for impact variables (e.g., discharge and water temperature) requires a link between seasonal climate forecasts and impact models simulating hydrology and lake hydrodynamics and thermal regimes. However, this link remains challenging for stakeholders and the water scientific community, mainly due to the probabilistic nature of these predictions. In this paper, we introduce a feasible, robust, and open-source workflow integrating seasonal climate forecasts with hydrologic and lake models to generate seasonal forecasts of discharge and water temperature profiles. The workflow has been designed to be applicable to any catchment and associated lake or reservoir, and is optimized in this study for four catchment-lake systems to help in their proactive management. We assessed the performance of the resulting seasonal forecasts of discharge and water temperature by comparing them with hydrologic and lake (pseudo)observations (reanalysis). Precisely, we analysed the historical performance using a data sample of past forecasts and reanalysis to obtain information about the skill (performance or quality) of the seasonal forecast system to predict particular events. We used the current seasonal climate forecast system (SEAS5) and reanalysis (ERA5) of the European Centre for Medium Range Weather Forecasts (ECMWF). We found that due to the limited predictability at seasonal time-scales over the locations of the four case studies (Europe and South of Australia), seasonal forecasts exhibited none to low performance (skill) for the atmospheric variables considered. Nevertheless, seasonal forecasts for discharge present some skill in all but one case study. Moreover, seasonal forecasts for water temperature had higher performance in natural lakes than in reservoirs, which means human water control is a relevant factor affecting predictability, and the performance increases with water depth in all four case studies. Further investigation into the skillful water temperature predictions should aim to identify the extent to which performance is a consequence of thermal inertia (i.e., lead-in conditions).

Are women more empathetic than men? A longitudinal study in adolescence.

Since the 1970s there has been a growing interest in analysing sex differences in psychological variables. Empirical studies and meta-analyses have contributed evidence on the differences between male and female individuals. More recently, the gender similarities hypothesis has supported the similarity of men and women in most psychological variables. This study contributes information on women's greater empathic disposition in comparison with men by means of a longitudinal design in an adolescent population. 505 male and female adolescents aged between 13 and 16 years were evaluated at two different moments (grade 2 and grade 3, lower secondary education). They completed the Index of Empathy for Children and Adolescents by Bryant and the Interpersonal Reactivity Index by Davis. The results confirm a greater empathic response in females than in males of the same age, differences growing with age. The sizes of the effect estimated in the second evaluation (average age 14 years) are large for emotional empathy and medium for cognitive empathy.

[Design and power analysis: n and confidence intervals of means]. / Diseño y análisis de la potencia: n y los intervalos de confianza de las medias.

In this study, we analyzed the validity of the conventional 80% power. The minimal sample size and power needed to guarantee non-overlapping (1-alpha)% confidence intervals for population means were calculated. Several simulations indicate that the minimal power for two means (m = 2) to have non-overlapping CIs is .80, for (1-alpha) set to 95%. The minimal power becomes .86 for 99% CIs and .75 for 90% CIs. When multiple means are considered, the required minimal power increases considerably. This increase is even higher when the population means do not increase monotonically. Therefore, the often adopted criterion of a minimal power equal to .80 is not always adequate. Hence, to guarantee that the limits of the CIs do not overlap, most situations require a direct calculation of the minimum number of observations that should enter in a study.