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Este artigo tem como objetivo investigar e identificar os trade-offs que existem entre os atores no processo de concepção e gestão do uso e ocupação do solo na Área de Proteção Ambiental (APA) São Francisco Xavier (SFX). A investigação foi realizada com três grupos de atores sociais locais que ocupam e influenciam o uso e ocupação do solo - pequenos produtores de leite, proprietários de pousadas e agentes imobiliários. O estudo analisa o conhecimento deles em relação a função dessa categoria de unidade de conservação, o grau de participação no Conselho Gestor (CG) e os valores relativos atribuídos aos bens e serviços ecossistêmicos que cada um possui no planejamento da gestão da APA. Métodos mistos qualitativos e quantitativos foram utilizados, incluindo a observação participante. O principal resultado foi a identificação de divergências na atribuição de valores e prioridades em relação aos bens e serviços ecossistêmicos para a restauração da paisagem, portanto o planejamento ambiental do território precisa considerar trade-offs entre os diferentes atores – bem como os desafios para a participação do planejamento – a fim de garantir que os objetivos da APA sejam alcançados e mantidos a longo prazo.

Sediments act as a sink and a secondary source of contaminants, accounting for a central part of coastal and marine biogeochemical cycles. Phosphorus (P) is a macronutrient that governs primary productivity and phytoplankton growth, but excess P influx results in algae bloom and deteriorates aquatic ecosystems. This study assesses seasonal fluctuations, spatial distribution and fractions of P in the sediments of the southern Caspian Sea. In this study, at eight sampling points, composite samples of the surface (from 0 to 10 cm) seabed sediments were collected at 10 and 30 m water depths. The sampling campaigns were carried out in the four seasons and a total of 64 sediment samples were obtained. Total organic matter (TOM), total P (TP) concentration and particle size distribution were determined. Then, P was fractionated using a four-step sequential procedure to quantify the loosely bound P (LP), the reductant soluble P (FeP), the metallic oxide-bound P (AlP) and the calcium carbonate (CaCO3) bound P (CaP). The inorganic P (IP) pool refers to the sum of LP, FeP, AlP and CaP, while the organic P (OP) was calculated by subtracting IP from TP.The results show that seasonal fluctuations of mean TP were statistically insignificant (p-value > 0.05). Still, the highest levels were recorded in autumn (1555 mg kg-1), followed by winter (1405 mg kg-1), spring (1378 mg kg-1) and summer (1130 mg kg-1). These minor temporal variations in P levels are associated with the seasonal differences in the amount of runoff and the intensity of rivers discharging into the Caspian Sea, and thereby their sediment load and the physicochemical characteristics. The large riverine influx resulted in TP contamination hotspots in the river deltas of Anzali wetland, Babolrood and Sefidrud (northern Iran), where high loadings of suspended particles are discharged into the sea. The spatial TP distribution is thus site-specific and uneven. The main P fraction was CaP, reflecting the phosphate (PO43-) strong affinity for, and association with, calcium-bearing minerals. Only a minor fraction of P was determined as LP. The fraction of the mud-size particles was the main explanatory factor for the spatial distribution of overall low levels of non-residual (or bioavailable) P forms (i.e., LP, FeP and AlP) during spring and summer, while the sand fraction had the greatest explanatory value for the distribution of residual (non-bioavailable) P form (CaP) during autumn and winter. This study demonstrates that P bioavailability in sediments is mostly controlled by the physicochemical characteristics of the sediment material, which again is steered by seawater chemistry. A low content of bioavailable P fractions could therefore be related to the relatively low content of fine-grained (i.e. below 63 µm) particles in sediments of the southern Caspian Sea.

The study and application of transition metal hydrides (TMH), an active area of chemical research since the early 1960’s(1), has been broadly bifurcated into fields focused on energy storage through the reduction of protons to generate hydrogen(2,3) and in organic synthesis for the functionalization of unsaturated C–C, C–O, and C–N bonds.(4,5) In the former instance, electrochemical means for driving such reactivity has been commonplace since the 1950’s(6) but the use of stoichiometric exogenous organic- and metal-based reductants to harness the power of TMHs in synthetic chemistry remains the norm. In particular, Co-based TMHs have found widespread use for the derivatization of olefins and alkynes in complex molecule construction, often via a net hydrogen atom transfer (HAT).(7) Here, we show how an electrocatalytic approach inspired by decades of energy storage precedent can be leveraged in the context of modern organic synthesis. This strategy not only offers benefits in terms of sustainability and efficiency but also enables enhanced chemoselectivity and unique and tunable reactivity. Ten different reaction manifolds across dozens of substrates are thus exemplified, along with detailed mechanistic insight on this scalable electrochemical entry to Co-H generation that uniquely takes place through a low-valent intermediate.

There are large knowledge gaps concerning concentrations, sources, emissions, and spatial trends of mercury (Hg) in the atmosphere in developing regions of the Southern Hemisphere, particularly in urban areas. Filling these gaps is a prerequisite for assessing the effectiveness of international regulation and for enabling a better understanding of the global transport of Hg in the environment. Here we use a passive sampling technique to study the spatial distribution of gaseous elemental Hg (Hg(0), GEM) and assess emission sources in and around Dar es Salaam, Tanzania's largest city. Included in the study were the city's main municipal waste dumpsite and an e-waste processing facility as potential sources of GEM. To complement the GEM data and for a better overview of the Hg contamination status of Dar es Salaam, soil samples were collected from the same locations where passive air samplers were deployed and analysed for total Hg. Overall, GEM concentrations ranged between

In this paper, we report on the on-going development of a compact analytical instrument for future missions to the Moon, using the LIBS technique. The instrument, christened VOILA (Volatiles Identification by Laser Ablation), is part of the “LUVMI-X” scenario funded by the European Union’s Horizon 2020 programme which envisions the concept of a rover carrying a suite of instruments for detecting and characterizing volatiles as a component of lunar soil (“regolith”) at high latitudes on the Moon. Behaviour of the plasma created by LIBS target ablation depends on the prevalent atmospheric pressure. Mars atmospheric pressure is close to ideal for the LIBS technique. On bodies without atmosphere such as the Moon, the lack of confining pressure leads to the plasma dissipating quickly causing weaker signals. India has developed the first, modest LIBS instrument for operation on the Moon with however a number of limitations in its optical design. Moreover, the lander of the corresponding mission Chandrayaan-2 crashed during landing in 2019. Our VOILA is designed to be between the quite capable but large and massive Mars LIBS instruments of NASA (ChemCam on the CURIOSITY ANCE rover), and the very modest Indian LIBS for Chandrayaan- 2. The VOILA working range will vary between 0.3 and 0.5 m, with a laser pulse energy of ~15 mJ.

In this study, we investigate the characteristics of the LIBS plasma in Martian atmospheric conditions using stationary modeling of the LIBS plasma. LIBS spectra are simulated from a one-dimensional model of the plasma divided into two zones along the line of sight. The simulations are based on local thermal equilibrium and carried out using radiative transfer. The simulated spectra are fitted first to synthetic LIBS spectra to test the implementation and in a second step to time-resolved LIBS data of a carbonate sample obtained in experimentally simulated Martian atmospheric conditions. From the fits to the synthetic LIBS spectra, we confirm that plasmas with spatial gradients in the temperature and densities can be well described in a two-zone plasma model. From the fits to the measured data this observation also holds true. We find that the most of the emission lines are well described in the two-zone model and that the two-zone model is a significant improvement from the one-zone model. Furthermore, we obtain typical values of the plasma properties in the two-zone approximation from 500 ns to 1250 ns after plasma initiation and derive the effect of self-absorption, which is predicted to decrease the strongest pixel intensities by a factor of almost two orders of magnitudes.

The lunar south pole is of great interest for upcoming lunar exploration endeavors due to the detection of large reservoirs of water ice in the pole’s permanently shadowed regions [1], which could be utilized to reduce the costs of a sustained presence on the Moon [2]. A strong focus of future robotic exploration missions will therefore be on the detection of water and related volatiles. For this purpose, the project Lunar Volatiles Mobile Instrumentation – Extended (LUVMI-X) is developing an initial system design as well as payload and mobility breadboards for a small, lightweight rover [3]. One of the proposed payloads is the Volatiles Identification by Laser Analysis instrument (VOILA), which uses laser-induced breakdown spectroscopy (LIBS) to analyze the elemental composition of the lunar surface with an emphasis on the detection of hydrogen for the inference of the presence of water. VOILA is a joint project by OHB System AG, Laser Zentrum Hannover e.V., and the German Aerospace Center’s Institute of Optical Sensor Systems. It is designed to analyze targets on the lunar surface in front of the LUVMI-X rover at a variable focus between 300 mm to 500 mm, allowing for precise measurements under various measurement conditions. The spectrometer covers the wavelength range from 350 nm to 790 nm, which includes the hydrogen line at 656.3 nm as well as spectral lines of most major rock-forming elements. The breadboard laboratory setup for VOILA was recently completed and first measurements of Moon-relevant samples have been made. Here, we will show the results of these measurements and will discuss their meaning for the further improvement of the instrument design and for its potential use as a volatile-scouting instrument at the lunar south pole.[1] Li S. et al. (2018) PNAS, 36, 8907–8912. [2] Anand M. et al. (2012) Planet. Space Sci., 74, 42–48. [3] Gancet J. et al. (2019) ASTRA 2019. [4] Knight A. K. et al. (2000) Appl. Spectrosc., 54, 331–340. [5] Maurice S. et al. (2012) Space Sci. Rev., 170, 95–166. [6] Wiens R. C. et al. (2012) Space Sci. Rev., 170, 167–227. [7] Wiens R. C. et al. (2017) Spectroscopy, 32. [8] Ren X. et al. (2018) EPSC 2018, Abstract EPSC2018-759. [9] Laxmiprasad A. S. et al. (2013) Adv. Space Res., 52, 332–341. [10] Lasue J. et al. (2012) J. Geophys. Res., 117, E1.

This study assesses the occurrence of trace elements (TEs) in sediments of the southern Caspian Sea. A total of 16 shoreline sediment samples and 15 seabed sediment samples along five coastal transects were studied. The mean concentration of TEs follows the order of Zn V Cr Ni Cu Pb Co As Sb Mo Cd. The TEs had an uneven, heterogeneous distribution within the shoreline and seabed sampling sites. This is due to that the study area comprises a large number of different pollution sources, also different sediment physicochemical characteristics. Levels of individual TEs within the seabed sediment transects were higher where their shoreline sites had higher concentrations, reflecting that the coastal sites play an important role in diffusing the contaminants towards the sea. The main anthropogenic source of TEs in this highly populated region, especially in the western part, is likely a large number of discharge points of greywater entering the sea. In addition, dominant fishing industry, tourism, intense agriculture, and textile and paper industry, as well as several other commercial activities, contribute significantly to the overall loading of TEs. Based on the statistical analyses, the organic matter and mud fraction had a strong explanatory value for the spatial variation of Cu, while oxyhydroxides of Fe and Mn had good explanatory factors to govern the spatial variation of other TEs. Pb and Zn had a relatively high partition coefficient (Kd), reflecting the affinity of these elements to be sorbed to the sediment phase. Cd and Sb had lower Kd, tending to remain in the aqueous phase. Geochemical indices indicated high enrichment of Cd, Sb, Zn, and Pb at a number of sampling sites, reflecting potential local sources of contamination. The Sisangan recreational area was identified as the most contaminated site. From a public health perspective, the non-carcinogenic risk of TEs was significant only at this site. The carcinogenic risks of Pb(II) and As(III) in adults, and Pb(II), Cd(II), and As(III) in children, were tolerable.

Introduction With the confirmation of water ice in the lunar polar regions [1], the Moon has recently come into the focus of attention of international space agencies again. Volatiles, specifically water and hydrogen, are important resources both for life support and for potential applications as fuels and propellants for spacecraft. In-situ resource utilization (ISRU) of volatiles could significantly reduce the costs of a sustained presence on the Moon and could be beneficial for the future human deep space exploration of the solar system [2]. The detection of volatiles is therefore an important scientific goal for future robotic missions to the Moon. The LUVMI-X project (Lunar Volatiles Mobile Instrumentation – Extended) is developing an initial system design as well as payload and mobility breadboards for the detection of volatiles in the lunar polar region on a small, lightweight rover [3]. The LUVMI-X rover is shown in Figure 1. One proposed scientific payload is VOILA (Volatiles Identification by Laser Ablation), which is jointly developed by OHB System AG (OHB), Laser Zentrum Hannover (LZH), and the German Aerospace Center’s Institute of Optical Sensor Systems (DLR-OS). VOILA will use laser-induced breakdown spectroscopy (LIBS) to analyze the elemental composition of the lunar surface, with a special focus on detecting and quantifying hydrogen and oxygen as indicators for water. LIBS is a versatile technique that requires only optical access to its target [4]. A LIBS spectrum is obtained within seconds, making it well-suited for quick analyses of multiple targets in proximity to the rover. LIBS was first used in space by the ChemCam instrument on board NASA’s Curiosity rover on Mars [5, 6]. The first LIBS instrument on the Moon was supposed to operate on board the Pragyan rover of India’s Chandrayaan-2 mission [7]. However, the Chandrayaan-2 lander failed a soft landing in September 2019. Here, we present a summary of the VOILA instrument design and its intended capabilities for volatiles detection at the lunar south pole. Instrument Design With an expected mass of about 3 kg, VOILA is designed to be more lightweight than the ChemCam instrument, but heavier and slightly more complex than the LIBS instrument designed for the Chandrayaan-2 mission. It will include an optical head with a focusing mechanism designed for working distances between 30 cm and 50 cm, which allows for compensation of uneven terrain or different rover configurations. The optical head is mounted at the front of the rover body and can rotate horizontally to select multiple targets of interest at each rover location. VOILA uses an actively Q-switched pulsed Yb:YAG laser developed by LZH with a pulse energy of at least 15 mJ at a wavelength of 1030 nm to ablate sample material, generating a laser-induced plasma that can be spectrally analyzed. The VOILA spectrometer will have a wavelength range from 350 nm to 790 nm at a spectral resolution of about 0.5 nm, thereby covering atomic and ionic emission lines of the major rock forming elements (O, Si, Al, Fe, Mg, Ca, Na, K, Ti) and of hydrogen. LIBS measurements with VOILA are intended for the analysis of the topmost lunar surface layers. Simple depth profiling within millimeters could be achieved by repeated ablation of the same location on the surface. A single shot is expected to produce a crater of several millimeters depth due to the force of the outgoing shockwave in the regolith. Laboratory Studies Preliminary LIBS studies in near-vacuum (0.1 Pa of air) were made with the high-resolution LIBS setup at DLR-OS to determine the VOILA instrument requirements. Figure 2 shows a LIBS spectrum of lunar regolith simulant Exolith LMS-1 in which all major rock forming elements could be observed within 350 nm to 790 nm. A laser energy of 15 mJ/pulse was found to produce a very intense plasma when focused to a spot of 300 µm diameter. For a basalt/gypsum mixture with a water concentration of about 1.5 wt%, the H I line at 656.3 nm was successfully detected, see Figure 3. The signal to noise ratio is about 4.3 for this line, indicating that even lower concentrations can be detected. Furthermore, the high-resolution echelle spectrometer used for the measurements has a low throughput that will be exceeded by the VOILA instrument itself. Conclusion VOILA is a new LIBS instrument for the detection of volatiles at the lunar south pole. Initial experiments show that LIBS can be employed at low pressures and that clear signals of all major rock-forming elements and of hydrogen can be obtained in the specified spectral range of VOILA. The pulse energy of VOILA is at least 15 mJ, which is achievable by the laser prototype developed by LZH. The successful detection of hydrogen is promising, but the instrument should also be qualified with measurements of real water ice at low concentrations. Future studies should also investigate whether water ice can be distinguished from other hydrogen and oxygen sources. New results obtained with the VOILA demonstration setup developed by OHB, LZH and DLR will be presented at the conference. Acknowledgments This research was funded by the European Union’s Horizon 2020 research and innovation program under grant agreement No. 822018. References [1] Li S. et al. (2018) PNAS, 36, 8907–8912. [2] Anand M. et al. (2012) Planet. Space Sci., 74, 42–48. [3] Gancet J. et al. (2019) ASTRA 2019. [4] Knight A. K. et al. (2000) Appl. Spectrosc., 54, 331–340. [5] Maurice S. et al. (2012) Space Sci. Rev., 170, 95–166. [6] Wiens R. C. et al. (2012) Space Sci. Rev., 170, 167–227. [7] Laxmiprasad A. S. et al. (2013) Adv. Space Res., 52, 332–341. [8] Lasue J. et al. (2012) J. Geophys. Res., 117, E1.

With ChemCam on NASA's Curiosity rover, SuperCam on NASA's Perseverance rover, and MarSCoDe on the Zhurong rover of the China National Space Administration, there are currently three instruments on Mars that employ laser-induced breakdown spectroscopy (LIBS) to analyze the chemical composition of Martian rocks and soils. With more than 880,000 LIBS measurements on Mars by ChemCam alone, the LIBS technique has been proven to be uniquely qualified for the in-situ robotic exploration of planetary surfaces. Since the laser-induced plasma exhibits a complex spatiotemporal evolution that has a significant impact on the recorded LIBS spectra, fundamental investigations of the plasma propagation and the spatial distributions of the plasma emissions can provide important insight that can help to improve the analysis of Martian LIBS spectra. Here we present first results from our LIBS plasma imaging setup, which allows us to spatially and temporally resolve the spectral emissions from the laser-induced plasma in simulated Martian atmospheric conditions. Investigating a calcium sulfate sample, we find that the commonly applied assumption of a nearly isothermal and homogeneous plasma with a colder outer layer is not sufficient to describe the laser-induced plasma on Mars. Instead, different spectral features show unique spatial distributions that suggest a strong influence of the outgoing shock wave. After plasma formation, the plasma center is found to rapidly become colder and more rarefied than the outer plasma regions. Molecular emissions are found to originate in this cold plasma center.

The intensity of the molecular CaCl emission in LIBS spectra is examined in order to evaluate its suitability for the detection of chlorine in a Martian environment. Various mixtures resembling Martian targets with varying Cl content are investigated under simulated Martian conditions. The reactions leading to the formation of CaCl are modeled based on reaction kinetics and are used to fit the measured CaCl band intensities. MgCl bands are also investigated as potential alternatives to CaCl, but no MgCl bands can be identified in samples containing both Mg and Cl. The study confirms that CaCl is well suited for the indirect detection of chlorine, but finds a strong dependence on the concentrations of Ca and Cl in the sample. Spectra from samples with a high chlorine concentration can have low-intensity CaCl emission due to a deficiency of Ca. A qualitative estimate of the sample composition is possible based on the ratio of the band intensity of CaCl to the intensity of Ca emission lines. Time-resolved measurements show that the CaCl concentration in the plasma is highest after about 1 µs.

We propose an imitation learning methodology that allows robots to seamlessly retrieve and pass objects to and from human users. Instead of hand-coding interaction parameters, we extract relevant information such as joint correlations and spatial relationships from a single task demonstration of two humans. At the center of our approach is an interaction model that enables a robot to generalize an observed demonstration spatially and temporally to new situations. To this end, we propose a data-driven method for generating interaction meshes that link both interaction partners to the manipulated object. The feasibility of the approach is evaluated in a within user study which shows that human–human task demonstration can lead to more natural and intuitive interactions with the robot.

A photocatalytic system for the dearomative hydroarylation of benzene derivatives has been developed. Using a combination of an organic photoredox catalyst and an amine reductant, this process operates through a reductive radical-polar crossover mechanism where aryl halide reduction triggers a regioselective cyclization event, giving rise to a range of complex spirocyclic cyclohexadienes. This light-driven protocol functions at room temperature in a green solvent system (aq. MeCN), without the need for precious metal-based catalysts or reagents, or the generation of stoichiometric metal byproducts.

Chlorine and fluorine play an important role in the geological history of Mars due to their high concentration in Martian magmas and their influence on the generation and evolution of Martian basalts. Chlorine-bearing salts could also facilitate the formation of eutectic brines that could be important for the fluvial history of Mars. The LIBS instruments of ChemCam and SuperCam can detect emission lines of Cl and F, but the intensity of these emission lines is comparatively low, making it difficult to quantify them correctly. A promising alternative is the quantification by molecular emission of diatomic molecules like CaCl and CaF, which can be observed as intense molecular bands in LIBS spectra if Ca is also present. However, the nonlinear dependence of the band intensity on the concentrations of both elements needs to be considered. In this study, we expand upon our previous analysis of molecular bands by investigating samples which produce CaCl bands, CaF bands, or both. We find that the highest CaCl band intensities are found in samples containing more Ca than Cl, while the strongest CaF bands are found in samples with roughly equal concentrations of Ca and F. Both observations can be described by the model that we present here. We also find that the CaCl band is significantly stronger for a sample containing CaCl2 than it is for a sample containing the same concentrations of Ca and Cl in separate bonds. The opposite is true for the CaF band, which is significantly weaker for the sample containing CaF2 bonds than it is for the sample that does not contain CaF2 bonds. These matrix effects are partially attributed to fragmentation during the ablation process and differences in the dissociation energies. Furthermore, we observe that CaF formation is not affected by competing CaCl formation, while CaCl is strongly affected by competing CaF formation. All measurements are done in simulated Martian atmospheric conditions in order to assist the analysis of Martian LIBS data.

For in-situ geochemical analysis of the surface of Mars, laser-induced breakdown spectroscopy (LIBS) is a very useful technique and the first extraterrestrial LIBS instrument ChemCam will soon be followed by others. Appropriate normalization of real mission data that is taken under varying experimental and environmental conditions from diverse geologic samples is an ongoing topic. One approach is the scaling to emission line intensities of carbon and oxygen from the CO2-dominated low-pressure martian atmosphere as an internal standard. Here, we performed several experiments to examine the emission of carbon and oxygen from a simulated martian atmosphere on simple, mostly mono-elemental samples, and to compare the emission characteristics of the elements of both origins. Differences in laser irradiance were found to have the biggest impact on sample emission lines scaled to C and O emission of the atmosphere. The temporal behavior shown in time-resolved LIBS measurements is dominated by the degree of ionization, but the ratio of the neutral emission from the sample to neutral emission from the atmosphere also varies over time. The effect of different grain sizes was minor in comparison to the high intrinsic variation in the LIBS data. Different samples were found to affect the absolute intensity of atmospheric C(I) more than O(I). Furthermore, the C(I) emission was found to be inseparably superimposed by iron if the latter was present in the target. The results indicate limitations of the general suitability of atmospheric carbon and oxygen emission for normalization purposes of martian LIBS data.

Laser‐induced breakdown spectroscopy (LIBS) and Raman spectroscopy have a high potential for in situ geochemical and mineralogical analyses for planetary exploration, in particular in combination. The SuperCam instrument onboard NASA's Mars 2020 rover will use both techniques together on another planet for the first time. The high‐power pulsed LIBS laser ablates material, and a small luminous plasma is produced for spectral analysis. The laser–matter interaction and the plasma shock wave can alter the sample surface, and new molecules can be produced, which deposit close to the LIBS ablation crater. Subsequent Raman analysis might then not sample the original structure. Here, we investigated pure metals (Fe, Ni, and Ti), the iron‐containing oxides hematite and ilmenite, and a fragment of the Gibeon meteorite in terrestrial ambient conditions, in simulated Martian atmospheric conditions, and in vacuum. LIBS ablation craters and their close proximity were studied with subsequent Raman analysis. Our analysis shows that Earth and Mars atmosphere provide enough oxygen in the LIBS plasma to produce oxides with metals from the sample. These can then be observed in the Raman data. Also, carbon was seen in some of the Raman data from the sample after the LIBS measurement. On hematite, a reduction of the mineral, that is, the presence of magnetite, was observed inside the LIBS crater for terrestrial and Martian atmospheric conditions and in vacuum. For the analysis and correct interpretation of Raman data it is important to be aware that alteration could have occurred by a preceding LIBS measurement. Raman analysis of several positions close to the LIBS ablation crater can help to infer the original and a possibly altered structure.

Laser-induced breakdown spectroscopy (LIBS) receives increasing interest in the field of space exploration. Some volatile elements important to planetary research are, however, challenging to detect with conventional LIBS because their strongest emission lines lie in the vacuum-UV (VUV) spectral range below 200 nm. We demonstrate the potential of LIBS detection in the VUV range from 125 to 190 nm on one of the relevant challenging elements, sulfur (S), in a lunar context. With our laboratory set-up, we detected S in lunar analogues at concentrations ranging from 0.5 to 4.0 at% in a time-integrated configuration. The experiments were conducted in high vacuum (10−3 Pa) with laser parameters realistic for a space instrument (1064 nm, 25 mJ, 6 ns). The results indicate improved detection capabilities for S in a lunar geological context as compared to LIBS in the more common UV/VIS/NIR spectral range. We found limits of detection in the order of 0.5 at% S for all investigated sample series. Normalization by total intensity and by an internal standard, in our case by the emission signal of the O I triplet at 130 nm, was tested as well, but did not yield a noticeable improvement for our data. Besides S, we were also able to detect Al, Fe, O, Si and Ti from the lunar analogue matrix.

Interaction meshes are a promising approach for generating natural behaviors of virtual characters during ongoing user interactions. In this paper, we propose several extensions to the interaction mesh approach based on statistical analyses of the underlying example interactions. By applying principal component analysis and correlation analysis in addition to joint distance calculations, both the interaction mesh topology and the constraints used for mesh optimization can be generated in an automated fashion that accounts for the spatial and temporal contexts of the interaction. Copyright © 2015John Wiley & Sons, Ltd.

Physical human–robot interaction tasks require robots that can detect and react to external perturbations caused by the human partner. In this contribution, we present a machine learning approach for detecting, estimating, and compensating for such external perturbations using only input from standard sensors. This machine learning approach makes use of Dynamic Mode Decomposition (DMD), a data processing technique developed in the field of fluid dynamics, which is applied to robotics for the first time. DMD is able to isolate the dynamics of a nonlinear system and is therefore well suited for separating noise from regular oscillations in sensor readings during cyclic robot movements. In a training phase, a DMD model for behavior-specific parameter configurations is learned. During task execution, the robot must estimate the external forces exerted by a human interaction partner. We compare the DMD-based approach to other interpolation schemes. A variant, sparsity promoting DMD, is particularly well suited...

The paper focuses on political-geographical aspects of civil society and on the issues of politically engaged non-governmental organisations and civic initiatives in the Czech geographical community. It outlines the complex nature of civil society and different attitudes of researchers towards civil society and its political practice. Spatial aspects of civil society are pointed out, including the concepts of social capital and democracy. The importance of (liberal parliamentary) democracy is stressed, as well as aspects of deliberative and participatory democracy with a “vibrant” civil society. Further research should focus on the geographic factors of conflicts among civil society, state and business.

We present a data-driven imitation learning system for learning human-robot interactions from human-human demonstrations. During training, the movements of two interaction partners are recorded through motion capture and an interaction model is learned. At runtime, the interaction model is used to continuously adapt the robot's motion, both spatially and temporally, to the movements of the human interaction partner. We show the effectiveness of the approach on complex, sequential tasks by presenting two applications involving collaborative human-robot assembly. Experiments with varied object hand-over positions and task execution speeds confirm the capabilities for spatio-temporal adaption of the demonstrated behavior to the current situation.

Intention inference can be an essential step toward efficient human–robot interaction. For this purpose, we propose the Intention-Driven Dynamics Model (IDDM) to probabilistically model the generative process of movements that are directed by the intention. The IDDM allows the intention to be inferred from observed movements using Bayes’ theorem. The IDDM simultaneously finds a latent state representation of noisy and high-dimensional observations, and models the intention-driven dynamics in the latent states. As most robotics applications are subject to real-time constraints, we develop an efficient online algorithm that allows for real-time intention inference. Two human–robot interaction scenarios, i.e. target prediction for robot table tennis and action recognition for interactive humanoid robots, are used to evaluate the performance of our inference algorithm. In both intention inference tasks, the proposed algorithm achieves substantial improvements over support vector machines and Gaussian processes.

This video demonstrates a novel imitation learning approach for learning human-robot interactions from human-human demonstrations. During training, the movements of two human interaction partners are recorded via motion capture. From this, an interaction model is learned that inherently captures important spatial relationships as well as temporal synchrony of body movements between the two interacting partners. The interaction model is based on interaction meshes that were first adopted by the computer graphics community for the offline animation of interacting virtual characters. We developed a variant of interaction meshes that is suitable for real-time human-robot interaction scenarios. During humanrobot collaboration, the learned interaction model allows for adequate spatio-temporal adaptation of the robots behavior to the movements of the human cooperation partner. Thus, the presented approach is well suited for collaborative tasks requiring continuous body movement coordination of a human and a robot. The feasibility of the approach is demonstrated with the example of a cooperative Lego rocket assembly task.

In order to ensure safe operation, robots must be able to reliably detect behavior perturbations that result from unexpected physical interactions with their environment and human co-workers. While some robots provide firmware force sensors that generate rough force estimates, more accurate force measurements are usually achieved with dedicated force-torque sensors. However, such sensors are often heavy, expensive and require an additional power supply. In the case of lightweight manipulators, the already limited payload capabilities may be reduced in a significant way. This paper presents an experience-based approach for accurately estimating external forces being applied to a robot without the need for a force-torque sensor. Using Information Transfer, a subset of sensors relevant to the executed behavior are identified from a larger set of internal sensors. Models mapping robot sensor data to force-torque measurements are learned using a neural network. These models can be used to predict the magnitude and direction of perturbations from affordable, proprioceptive sensors only. Experiments with a UR5 robot show that our method yields force estimates with accuracy comparable to a dedicated force-torque sensor. Moreover, our method yields a substantial improvement in accuracy over force-torque values provided by the robot firmware.

Robotic manipulation tasks often require the control of forces and torques exerted on external objects. This paper presents a machine learning approach for estimating forces when no force sensors are present on the robot platform. In the training phase, the robot executes the desired manipulation tasks under controlled conditions with systematically varied parameter sets. All internal sensor data, in the presented case from more than 100 sensors, as well as the force exerted by the robot are recorded. Using Transfer Entropy, a statistical model is learned that identifies the subset of sensors relevant for torque estimation in the given task. At runtime, the model is used to accurately estimate the torques exerted during manipulations of the demonstrated kind. The feasibility of the approach is shown in a setting where a robotic manipulator operates a torque wrench to fasten a screw nut. Torque estimates with an accuracy of well below ±1Nm are achieved. A strength of the presented model is that no prior knowledge of the robot's kinematics, mass distribution or sensor instrumentation is required.

Aluminium mobilization from acidic forest soils in the remote Leigongshan area in southwestern China was investigated in the field and by a laboratory batch experiment using sequential extractions with HCl solutions with pH of approximately 3.4. Experimental data showed that strongly organically bound aluminium (Alp-Alcu) decreased significantly after sequential extraction, whereas exchangeable aluminium (Alex) increased in all three horizons. In the A-horizon with higher base saturation (BS), dissolved aluminium exchanged with base cations (BCs) on soil complex, causing lower aluminium release but significant leaching of BCs. In AB- and B-horizon with lower BS, more dissolved aluminium remained in solution. Al3+ activities in both experimental soil extracts and field soil waters strongly deviated from those predicted by the gibbsite model (i.e., pAl = pKsp + 3.0 pH) despite a significant increase in Al3+ with a decrease in pH. The soil organic-bound aluminium model, using Alcu and Alp to account for differences in the size of available Al pools in the soil, gave considerably better fits to both experimental and field data. SOM-Al model, approved by using Alex instead of organically bound aluminium (i.e., Alp and Alcu), could depict the changes in Al activities most successfully. We have concluded that Alp-Alcu is the main source of aqueous aluminium, whereas Alex plays an important role in regulating aluminium solubility during soil acidification.

Hepatopulmonary syndrome (HPS) and portopulmonary hypertension (PPHTN) are distinct clinical entities that may complicate liver disease. Although HPS and PPHTN are different, several reports describe 6 patients in whom both conditions have occurred, either concurrently or sequentially, sometimes with the onset of PPHTN after liver transplantation. The current report extends this sparse experience by reporting 2 patients who underwent liver transplantation for HPS and who developed pulmonary hypertension after liver transplantation. This experience calls for better understanding of the pathogenesis of HPS and PPHTN and ways to better predict their occurrence.

In physical human-robot interaction, robot behavior must be adjusted to forces applied by the human interaction partner. For measuring such forces, special-purpose sensors may be used, e.g. force-torque sensors, that are however often heavy, expensive and prone to noise. In contrast, we propose a machine learning approach for measuring external perturbations of robot behavior that uses commonly available, low-cost sensors only. During the training phase, behavior-specific statistical models of sensor measurements, so-called perturbation filters, are constructed using Principal Component Analysis, Transfer Entropy and Dynamic Mode Decomposition. During behavior execution, perturbation filters compare measured and predicted sensor values for estimating the amount and direction of forces applied by the human interaction partner. Such perturbation filters can therefore be regarded as virtual force sensors that produce continuous estimates of external forces.

In many settings, e.g. physical human-robot interaction, robotic behavior must be made robust against more or less spontaneous application of external forces. Typically, this problem is tackled by means of special purpose force sensors which are, however, not available on many robotic platforms. In contrast, we propose a machine learning approach suitable for more common, although often noisy sensors. This machine learning approach makes use of Dynamic Mode Decomposition (DMD) which is able to extract the dynamics of a nonlinear system. It is therefore well suited to separate noise from regular oscillations in sensor readings during cyclic robot movements under different behavior configurations. We demonstrate the feasibility of our approach with an example where physical forces are exerted on a humanoid robot during walking. In a training phase, a snapshot based DMD model for behavior specific parameter configurations is learned. During task execution the robot must detect and estimate the external forces exerted by a human interaction partner. We compare the DMD-based approach to other interpolation schemes and show that the former outperforms the latter particularly in the presence of sensor noise. We conclude that DMD which has so far been mostly used in other fields of science, particularly fluid mechanics, is also a highly promising method for robotics.