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ABSTRACTAttosecond photoelectron spectroscopy has opened up for studying light–matter interaction on ultrafast time scales. It is often performed with interferometric experimental setups that require outstanding stability. We demonstrate and characterize in detail an actively stabilized, versatile, high spectral resolution attosecond beamline based on a Mach-Zehnder interferometer. The active stabilization keeps the interferometer ultra-stable for several hours with an RMS stability of 13 as and a total pump-probe delay scanning range of [Formula: see text] fs. A tunable femtosecond laser source to drive high-order harmonic generation allows for precisely addressing atomic and molecular resonances. Furthermore, the interferometer includes a spectral shaper in 4f-geometry in the probe arm as well as a tunable bandpass filter in the pump arm, which offer additional high flexibility in terms of tunability as well as narrowband or polychromatic probe pulses. We demonstrate the capabilities of the beamline via experiments using several variants of the RABBIT (reconstruction of attosecond beating by two photon transitions) technique. In this setup, the temporal-spectral resolution of photoelectron spectroscopy can reach a new level of accuracy and precision.

Understanding of photoionization dynamics, one of the fastest processes in nature, requires the characterization of all underlying ionization channels. Here the authors use an interferometry technique based on attosecond pulses to measure the phase and amplitude of the individual angular momentum channels in the photoionization of neon.

Here the authors report experiment and theory study of the photoionization of xenon inner shell 4d electron using attosecond pulses. They have identified two ionization paths - one corresponding to broad giant dipole resonance with short decay time and the other involving spin-flip transitions.

Ionization time delays are of interest in understanding the photoionization mechanism in atoms and molecules in ultra-short time scales. Here the authors investigate the angular dependence of photoionization time delays in the presence of an autoionizing resonance in argon atom using RABBITT technique.

In this study, we employ in situ, meteorological, and remote sensing data to estimate chlorophyll-a concentration at different depths in a South American freshwater ecosystem, focusing specifically on a lake in southern Chile known as Lake Maihue. For our analysis, we explored four different scenarios using three deep learning and traditional statistical models. These scenarios involved using field data (Scenario 1), meteorological variables (Scenario 2), and satellite data (Scenarios 3.1 and 3.2) to predict chlorophyll-a levels in Lake Maihue at three different depths (0, 15, and 30 m). Our choice of models included SARIMAX, DGLM, and LSTM, all of which showed promising statistical performance in predicting chlorophyll-a concentrations in this lake. Validation metrics for these models indicated their effectiveness in predicting chlorophyll levels, which serve as valuable indicators of the presence of algae in the water body. The coefficient of determination values ranged from 0.30 to 0.98, with the DGLM model showing the most favorable statistics in all scenarios tested. It is worth noting that the LSTM model yielded comparatively lower metrics, mainly due to the limitations of the available training data. The models employed, which use traditional statistical and machine learning models and meteorological and remote sensing data, have great potential for application in lakes in Chile and the rest of the world with similar characteristics. In addition, these results constitute a fundamental resource for decision-makers involved in the protection and conservation of water resource quality.

IntroductionIn dryland systems, biological soil crusts (biocrusts) can occupy large areas of plant interspaces, where they fix carbon following rain. Although distinct biocrust types contain different dominant photoautotrophs, few studies to date have documented carbon exchange over time from various biocrust types. This is especially true for gypsum soils. Our objective was to assess the carbon exchange of biocrust types established at the world’s largest gypsum dune field at White Sands National Park.MethodsWe sampled five different biocrust types from a sand sheet location in three different years and seasons (summer 2020, fall 2021, and winter 2022) for carbon exchange measurements in controlled lab conditions. Biocrusts were rehydrated to full saturation and light incubated for 30 min, 2, 6, 12, 24, and 36 h. Samples were then subject to a 12-point light regime with a LI-6400XT photosynthesis system to determine carbon exchange.ResultsBiocrust carbon exchange values differed by biocrust type, by incubation time since wetting, and by date of field sampling. Lichens and mosses had higher gross and net carbon fixation rates than dark and light cyanobacterial crusts. High respiration rates were found after 0.5 h and 2 h incubation times as communities recovered from desiccation, leveling off after 6 h incubation. Net carbon fixation of all types increased with longer incubation time, primarily as a result of decreasing respiration, which suggests rapid recovery of biocrust photosynthesis across types. However, net carbon fixation rates varied from year to year, likely as a product of time since the last rain event and environmental conditions preceding collection, with moss crusts being most sensitive to environmental stress at our study sites.DiscussionGiven the complexity of patterns discovered in our study, it is especially important to consider a multitude of factors when comparing biocrust carbon exchange rates across studies. Understanding the dynamics of biocrust carbon fixation in distinct crust types will enable greater precision of carbon cycling models and improved forecasting of impacts of global climate change on dryland carbon cycling and ecosystem functioning.

In this study, we combined machine learning and remote sensing techniques to estimate the value of chlorophyll-a concentration in a freshwater ecosystem in the South American continent (lake in Southern Chile). In a previous study, nine artificial intelligence (AI) algorithms were tested to predict water quality data from measurements during monitoring campaigns. In this study, in addition to field data (Case A), meteorological variables (Case B) and satellite data (Case C) were used to predict chlorophyll-a in Lake Llanquihue. The models used were SARIMAX, LSTM, and RNN, all of which showed generally good statistics for the prediction of the chlorophyll-a variable. Model validation metrics showed that all three models effectively predicted chlorophyll as an indicator of the presence of algae in water bodies. Coefficient of determination values ranging from 0.64 to 0.93 were obtained, with the LSTM model showing the best statistics in any of the cases tested. The LSTM model generally performed well across most stations, with lower values for MSE (2), RMSE (

The white gypsum dune ecosystem in the Tularosa Basin in south central New Mexico is the largest gypsum dune field on earth, covering 712.25 km2. White Sands National Monument in Otero County, New Mexico, protects approximately 40%, 297.85 km2, of this dune field. In 2006 the US National Park Service initiated a long term study of the Lepidoptera at White Sands National Monument, resulting in the discovery of two new species, Euxoa lafontainei Metzler & Forbes, n. sp. and Protogygia whitesandsensis Metzler & Forbes, n. sp. described herein. Adult moths and male and female genitalia are illustrated for Euxoa lafontainei, and adults and male genitalia are illustrated for Protogygia whitesandsensis and its relatives.