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Global collections of synthesized flux tower data such as FLUXNET have accelerated scientific progress beyond the eddy covariance community. However, remaining data issues in FLUXNET data pose challenges for users, particularly for multi-site synthesis and modelling activities. Here, we present complementary consistency flags (C2Fs) for flux tower data, which rely on multiple indications of inconsistency among variables, along with a methodology to detect discontinuities in time series. The C2F relates to carbon and energy fluxes, as well as to core meteorological variables, and consists of the following: (1) flags for daily data values, (2) flags for entire-site variables, and (3) flags at time stamps that mark large discontinuities in the time series. The flagging is primarily based on combining outlier scores from a set of predefined relationships among variables. The methodology to detect break points in the time series is based on a non-parametric test for the difference in distributions of model residuals. Applying C2F to the FLUXNET 2015 dataset reveals the following: (1) among the considered variables, gross primary productivity and ecosystem respiration data were flagged most frequently, in particular during rain pulses under dry and hot conditions. This information is useful for modelling and analysing ecohydrological responses. (2) There are elevated flagging frequencies for radiation variables (shortwave, photosynthetically active, and net). This information can improve the interpretation and modelling of ecosystem fluxes with respect to issues in the driver. (3) The majority of long-term sites show temporal discontinuities in the time series of latent energy, net ecosystem exchange, and radiation variables. This should be useful for carefully assessing the results in terms of interannual variations in and trends of ecosystem fluxes. The C2F methodology is flexible for customizing and allows for varying the desired strictness of consistency. We discuss the limitations of the approach that can present starting points for future developments.

Describing the coupling of nitrogen (N), phosphorus (P), and carbon (C) cycles of land ecosystems requires understanding microbial element use efficiencies of soil organic matter (SOM) decomposition. These efficiencies are studied by the Soil Enzyme Steady Allocation Model (SESAM) at the decadal scale. The model assumes that the soil microbial communities and their element use efficiencies develop towards an optimum where the growth of the entire community is maximized. Specifically, SESAM approximated this growth optimization by allocating resources to several SOM-degrading enzymes proportional to the revenue of these enzymes, called the Relative approach. However, a rigorous mathematical treatment of this approximation has been lacking so far. Therefore, in this study we derive explicit formulas of enzyme allocation that maximize the total return from enzymatic processing, called the Optimal approach. Further, we derive another heuristic approach that prescribes the change of allocation without the need of deriving a formulation for the optimal allocation, called the Derivative approach. When comparing predictions across these approaches, we found that the Relative approach was a special case of the Optimal approach valid at sufficiently high microbial biomass. However, at low microbial biomass, it overestimated allocation to the enzymes having lower revenues compared to the Optimal approach. The Derivative-based allocation closely tracked the Optimal allocation. These findings increase our confidence in conclusions drawn from SESAM studies. Moreover, the new developments extend the range of conditions at which valid conclusions can be drawn. Further, based on these findings we formulated the constrained enzyme hypothesis. This hypothesis provides a complementary explanation why some substrates in soil are preserved over decades, although they are often decomposed within a few years in incubation experiments. This study shows how optimality considerations lead to simplified models, new insights, and new hypotheses. This is another step in deriving a simple representation of an adaptive microbial community, which is required for coupled stoichiometric C–N–P dynamic models that are aimed to study decadal processes beyond the ecosystem scale.

Throughfall heterogeneity induced by the redistribution of precipitation in vegetation canopies has repeatedly been hypothesized to affect the variation in the soil water content and runoff behavior, especially in forests. However, observational studies relating the spatial variation in the soil water content directly to net precipitation are rare, and few confirm modeling hypotheses. Here, we investigate whether throughfall patterns affect the spatial heterogeneity in the soil water response in the main rooting zone. We assessed rainfall, throughfall and soil water content (at two depths, 7.5 and 27.5 cm) on a 1 ha temperate mixed-beech forest plot in Germany during the 2015 and 2016 growing seasons using independent, high-resolution, stratified, random designs. Because the throughfall and soil water content cannot be measured at the same location, we used kriging to derive the throughfall values at the locations where the soil water content was measured. We first explored the spatial variation and temporal stability of throughfall and soil water patterns and subsequently evaluated the effects of input (throughfall), soil properties (field capacity and macroporosity), and vegetation parameters (canopy cover and distance to the next tree) on the soil water content and dynamics. Throughfall spatial patterns were related to canopy density. Although spatial autocorrelation decreased with increasing event sizes, temporally stable throughfall patterns emerged, leading to reoccurring higher- and lower-input locations across precipitation events. Linear mixed-effects model analysis showed that soil water content patterns were poorly related to spatial patterns of throughfall and that they were more influenced by unidentified, but time constant, factors. Instead of the soil water content itself, the patterns of its increase after rainfall corresponded more closely to throughfall patterns: more water was stored in the soil in areas where throughfall was elevated. Furthermore, soil moisture patterns themselves affected the local soil water response, probably by mediating fast drainage and runoff. Locations with a low topsoil water content tended to store less of the input water, indicating preferential flow. In contrast, locations with a high water content stored less water in the subsoil. Moreover, the distance to the next tree and macroporosity modified how much water was retained in soil storage. Overall, throughfall patterns imprinted less on the soil water content and more on the soil water dynamics shortly after rainfall events; therefore, percolation rather than the soil water content may depend on the small-scale spatial heterogeneity in canopy input patterns.

Globally, soils store two to three times as much carbon as currently resides in the atmosphere, and it is critical to understand how soil greenhouse gas (GHG) emissions and uptake will respond to ongoing climate change. In particular, the soil-to-atmosphere CO2 flux, commonly though imprecisely termed soil respiration (RS ), is one of the largest carbon fluxes in the Earth system. An increasing number of high-frequency RS measurements (typically, from an automated system with hourly sampling) have been made over the last two decades; an increasing number of methane measurements are being made with such systems as well. Such high frequency data are an invaluable resource for understanding GHG fluxes, but lack a central database or repository. Here we describe the lightweight, open-source COSORE (COntinuous SOil REspiration) database and software, that focuses on automated, continuous and long-term GHG flux datasets, and is intended to serve as a community resource for earth sciences, climate change syntheses and model evaluation. Contributed datasets are mapped to a single, consistent standard, with metadata on contributors, geographic location, measurement conditions and ancillary data. The design emphasizes the importance of reproducibility, scientific transparency and open access to data. While being oriented towards continuously measured RS , the database design accommodates other soil-atmosphere measurements (e.g. ecosystem respiration, chamber-measured net ecosystem exchange, methane fluxes) as well as experimental treatments (heterotrophic only, etc.). We give brief examples of the types of analyses possible using this new community resource and describe its accompanying R software package.

Abstract Multiple trauma patients with severe chest trauma are at increased risk for tracheostomy. While the risk factors associated with the need for tracheostomy are well established in the general critical care population, they have not yet been validated in a cohort of patients suffering severe thoracic trauma. This retrospective cohort study analysed data on patients aged 18 years or older who were admitted to one of the six participating academic level I trauma centres with multiple injuries, including severe thoracic trauma (AISThorax ≥ 3) between 2010 and 2014. A multivariable binary regression was used to identify predictor variables for tracheostomy and to develop the Tracheostomy in Thoracic Trauma Prediction Score (T3P-Score). The study included 1019 adult thoracic trauma patients, of whom 165 underwent tracheostomy during their intensive care unit (ICU) stay. Prehospital endotracheal intubation (adjusted OR [AOR]: 2.494, 95% CI [1.412; 4.405]), diagnosis of pneumonia during the ICU stay (AOR: 4.374, 95% CI [2.503; 7.642]), duration of mechanical ventilation (AOR: 1.008/hours of intubation, 95% CI [1.006; 1.009]), and an AISHead ≥ 3 (AOR 1.840, 95% CI [1.039; 3.261]) were independent risk factors for tracheostomy. Patients with sepsis had a lower risk of tracheostomy than patients without sepsis (AOR 0.486, 95% CI [0.253; 0.935]). The T3P-Score had high predictive validity for tracheostomy (ROCAUC = 0.938, 95% CI [0.920, 0.956]; Nagelkerke’s R2 was 0.601). The T3P-Score’s specificity was 0.68, and the sensitivity was 0.96. The severity of thoracic trauma did not predict the need for tracheostomy. Follow-up studies should validate the T3P-Score in external data sets and study the reasons for the reluctant use of tracheostomy in patients with severe thoracic trauma and subsequent sepsis. Trial registration : The study was applied for and registered a priori with the respective ethics committees.

Phosphorus (P) availability affects the response of terrestrial ecosystems to environmental and climate change (e.g., elevated CO2), yet the magnitude of this effect remains uncertain. This uncertainty arises mainly from a lack of quantitative understanding of the soil biological and geochemical P cycling processes, particularly the P exchange with soil mineral surfaces, which is often described by a Langmuir sorption isotherm. We first conducted a literature review on P sorption experiments and terrestrial biosphere models (TBMs) using a Langmuir isotherm. We then developed a new algorithm to describe the inorganic P exchange between soil solution and soil matrix based on the double-surface Langmuir isotherm and extracted empirical equations to calculate the sorption capacity and Langmuir coefficient. We finally tested the conventional and new models of P sorption at five beech forest sites in Germany along a soil P stock gradient using the QUINCY (QUantifying Interactions between terrestrial Nutrient CYcles and the climate system) TBM. We found that the conventional (single-surface) Langmuir isotherm approach in most TBMs largely differed from P sorption experiments regarding the sorption capacities and Langmuir coefficients, and it simulated an overly low soil P-buffering capacity. Conversely, the double-surface Langmuir isotherm approach adequately reproduced the observed patterns of soil inorganic P pools. The better representation of inorganic P cycling using the double-surface Langmuir approach also improved simulated foliar N and P concentrations as well as the patterns of gross primary production and vegetation carbon across the soil P gradient. The novel model generally reduces the estimates of P limitation compared with the conventional model, particularly at the low-P site, as the model constraint of slow inorganic P exchange on plant productivity is reduced.

The input of liquid water to terrestrial ecosystems is composed of rain and non-rainfall water (NRW). The latter comprises dew, fog, and the adsorption of atmospheric vapor on soil particle surfaces. Although NRW inputs can be relevant to support ecosystem functioning in seasonally dry ecosystems, they are understudied, being relatively small, and therefore hard to measure. In this study, we apply a partitioning routine focusing on NRW inputs over 1 year of data from large, high-precision weighing lysimeters at a semi-arid Mediterranean site. NRW inputs occur for at least 3 h on 297 d (81 % of the year), with a mean diel duration of 6 h. They reflect a pronounced seasonality as modulated by environmental conditions (i.e., temperature and net radiation). During the wet season, both dew and fog dominate NRW, while during the dry season it is mostly the soil adsorption of atmospheric water vapor. Although NRW contributes only 7.4 % to the annual water input, NRW is the only water input to the ecosystem during 15 weeks, mainly in the dry season. Benefitting from the comprehensive set of measurements at our experimental site, we show that our findings are in line with (i) independent measurements and (ii) independent model simulations forced with (near-) surface energy and moisture measurements. Furthermore, we discuss the simultaneous occurrence of soil vapor adsorption and negative eddy-covariance-derived latent heat fluxes. This study shows that NRW inputs can be reliably detected through high-resolution weighing lysimeters and a few additional measurements. Their main occurrence during nighttime underlines the necessity to consider ecosystem water fluxes at a high temporal resolution and with 24 h coverage.

Understanding the coupling of nitrogen (N) and carbon (C) cycles of land ecosystems requires understanding microbial element use efficiencies of soil organic matter (SOM) decomposition. Whereas important controls of those efficiencies by microbial community adaptations have been shown at the scale of a soil pore, a simplified representation of those controls is needed at the ecosystem scale. However, without abstracting from the many details, models are not identifiable; i.e. they cannot be fitted without ambiguities to observations. There is a need to find, implement, and validate abstract simplified formulations of theses processes. Therefore, we developed the Soil Enzyme Allocation Model (SEAM). The model explicitly represents community adaptation strategies of resource allocation to extracellular enzymes and enzyme limitations on SOM decomposition. They thus provide an abstraction from several microbial functional groups to a single holistic microbial community. Here we further simplify SEAM using a quasi-steady-state assumption for extracellular enzyme pools to derive the Soil Enzyme Steady Allocation Model (SESAM) and test whether SESAM can provide the same decadal-term predictions as SEAM. SESAM reproduced the priming effect, the SOM banking mechanism, and the damping of fluctuations in carbon use efficiency with microbial competition as predicted by SEAM and other more detailed models. This development is an important step towards a more parsimonious representation of soil microbial effects in global land surface models.

The global land surface absorbs about a third of anthropogenic emissions each year, due to the difference between two key processes: ecosystem photosynthesis and respiration. Despite the importance of these two processes, it is not possible to measure either at the ecosystem scale during the daytime. Eddy-covariance measurements are widely used as the closest 'quasi-direct' ecosystem-scale observation from which to estimate ecosystem photosynthesis and respiration. Recent research, however, suggests that current estimates may be biased by up to 25%, due to a previously unaccounted for process: the inhibition of leaf respiration in the light. Yet the extent of inhibition remains debated, and implications for estimates of ecosystem-scale respiration and photosynthesis remain unquantified. Here, we quantify an apparent inhibition of daytime ecosystem respiration across the global FLUXNET eddy-covariance network and identify a pervasive influence that varies by season and ecosystem type. We develop partitioning methods that can detect an apparent ecosystem-scale inhibition of daytime respiration and find that diurnal patterns of ecosystem respiration might be markedly different than previously thought. The results call for the re-evaluation of global terrestrial carbon cycle models and also suggest that current global estimates of photosynthesis and respiration may be biased, some on the order of magnitude of anthropogenic fossil fuel emissions.

Since the release of ChatGPT in November 2022, large language model-based chatbots have attracted much attention. Although businesses value their potential for efficiency gains, academics are concerned about their effects on learning and assessments. This Case enables instructors to integrate large language model-based chatbots into the curriculum. Students assume the role of a professional accountant who retrieves an artificial intelligence-written text that has to be evaluated and improved before presenting it to a client. The analysis and writing activity help students gain domain-specific accounting knowledge and improve their writing skills. Students also improve their computer literacy as they learn to engage with artificial intelligence effectively, assess generated output, and evaluate output quality. These learnings enable students to judge in which business scenarios chatbots are beneficial and when original human contributions will likely remain superior. Finally, this activity expands other cognitive skills, such as critical thinking and judgment. JEL Classifications: A22; A23; M40; M41; M49. [ABSTRACT FROM AUTHOR]

Abstract Ventricular tachycardia storm is a potentially lethal condition with limited treatment options. Failed ablation is associated with a fourfold mortality increase in this population. Stereotactic body radiotherapy was proposed in these cases. We report a case where radiotherapy was safely performed, leading to the elimination of adequate shocks.

Flood risk management in Germany follows an integrative approach in which both private households and businesses can make an important contribution to reducing flood damage by implementing property-level adaptation measures. While the flood adaptation behavior of private households has already been widely researched, comparatively less attention has been paid to the adaptation strategies of businesses. However, their ability to cope with flood risk plays an important role in the social and economic development of a flood-prone region. Therefore, using quantitative survey data, this study aims to identify different strategies and adaptation drivers of 557 businesses damaged by a riverine flood in 2013 and 104 businesses damaged by pluvial or flash floods between 2014 and 2017. Our results indicate that a low perceived self-efficacy may be an important factor that can reduce the motivation of businesses to adapt to flood risk. Furthermore, property-owners tended to act more proactively than tenants. In addition, high experience with previous flood events and low perceived response costs could strengthen proactive adaptation behavior. These findings should be considered in business-tailored risk communication.

Eine Einführung zum qualitativen Forschen mit Vignetten, mit der übersichtlich sowie nahe am empirischen Material unterschiedliche Leser:innengruppen angesprochen werden und mit der das Potenzial für verschiedenste Methoden und Studiendesigns in der qualitativen Sozialforschung abgedeckt wird, ist noch nicht geschrieben. MIKO-SCHEFZIG versucht in "Forschen mit Vignetten" Facetten der qualitativen Vignettenforschung aufzuzeigen und anhand eigener Studien anschaulich zu vermitteln. Im Mittelpunkt stehen dabei Konzepte der Situation und ein partizipativ-transformativer Anspruch. Die empirisch fundierte Darstellung hat ihre Stärken in der lesenswerten Darlegung zum Forschungsdesign und -anspruch, darüber hinaus werden wiederholt die allgemeinen Möglichkeiten, Herausforderungen und Grenzen des Forschens mit Vignetten thematisiert. Das Buch erfüllt jedoch insgesamt nur im Ansatz den Charakter einer allgemeinen Einführung

Viscoelastic and surface properties of polymer blends prepared as models of hot melt pressure sensitive adhesives (HMPSA) were investigated and the correlation to tack, peel, and shear adhesion properties was discussed. Three different types of tackifiers, which present a softening point of ca. 100 °C as well as different compatibility degree with the midblock and the end-block of poly(styrene-block-isoprene-block-styrene) (SIS), were employed. A good association degree between the midblock and the tackifiers was observed. Surface energy was used to evaluate the blend compatibility trend. The blends presented lower surface energy than the pure materials due to non-polar and acid-base interactions. This is a feature of compatible blends. PSA character was observed based on the obtained results of tack and peel for the blends and it is a consequence of the adequate degree of compatibility of the tackifiers and the midblock. The compatibility of the tackifiers and the end-block influenced shear adhesion. Rosin ester resin showed a better association with the styrenic domains. The second maximum of the loss modulus (G″) curve for a blend containing rosin resin shifted to lower temperatures in comparison to blends containing the other investigated resins. The lowest holding power values were measured for the blends containing rosin ester resin. The styrene domains were pronouncedly disturbed by this resin.

Soil CO2 efflux is the second-largest carbon flux in terrestrial ecosystems. Its feedback to climate determines model predictions of the land carbon sink, which is crucial to understanding the future of the earth system. For understanding and quantification, however, observations by the most widely applied chamber measurement method need to be aggregated to larger temporal and spatial scales. The aggregation is hampered by random error that is characterized by occasionally large fluxes and variance heterogeneity that is not properly accounted for under the typical assumption of normally distributed fluxes. Therefore, we explored the effect of different distributional assumptions on the aggregated fluxes. We tested the alternative assumption of lognormally distributed random error in observed fluxes by aggregating 1 year of data of four neighboring automatic chambers at a Mediterranean savanna-type site. With the lognormal assumption, problems with error structure diminished, and more reasonable prediction intervals were obtained. While the differences between distributional assumptions diminished when aggregating data of single chambers to an annual value, differences were important on short timescales and were especially pronounced when aggregating across chambers to plot level. Hence we recommend as a good practice that researchers report plot-level fluxes with uncertainties based on the lognormal assumption. Model data integration studies should compare predictions and observations of soil CO2 efflux on a log scale. This study provides methodology and guidance that will improve the analysis of soil CO2 efflux observations and hence improve understanding of soil carbon cycling and climate feedbacks.

Plant–soil interactions, such as the coupling of plants' below-ground biomass allocation with soil organic matter (SOM) decomposition, nutrient release and plant uptake, are essential to understand the response of carbon (C) cycling to global changes. However, these processes are poorly represented in the current terrestrial biosphere models owing to the simple first-order approach of SOM cycling and the ignorance of variations within a soil profile. While the emerging microbially explicit soil organic C models can better describe C formation and turnover, at present, they lack a full coupling to the nitrogen (N) and phosphorus (P) cycles with the soil profile. Here we present a new SOM model – the Jena Soil Model (JSM) – which is microbially explicit, vertically resolved and integrated with the N and P cycles. To account for the effects of nutrient availability and litter quality on decomposition, JSM includes the representation of enzyme allocation to different depolymerisation sources based on the microbial adaptation approach as well as of nutrient acquisition competition based on the equilibrium chemistry approximation approach. Herein, we present the model structure and basic features of model performance in a beech forest in Germany. The model reproduced the main SOM stocks and microbial biomass as well as their vertical patterns in the soil profile. We further tested the sensitivity of the model to parameterisation and showed that JSM is generally sensitive to changes in microbial stoichiometry and processes.

The insect repellent ethyl butylacetylaminopropionate (IR3535) was used as a functional additive for poly (l-lactic acid) (PLLA) to modify its structure and mechanical properties and achieve insect repellency. PLLA/IR3535 mixtures at various compositions were prepared via melt extrusion. In the analyzed composition range of 0 to 23 m% IR3535, PLLA and IR3535 were miscible at the length scale represented by the glass transition temperature. Addition of IR3535 resulted in a significant decrease in the glass transition temperature of PLLA, as well as in the elastic modulus, indicating its efficiency as a plasticizer. All mixtures were amorphous after extrusion, though PLLA/IR3535 extrudates with an IR3535 content between 18 and 23 m% crystallized during long-term storage at ambient temperature, due to their low glass transition temperature. Quantification of the release of IR3535 into the environment by thermogravimetric analysis at different temperatures between 50 and 100 °C allowed the estimation of the evaporation rate at lower temperatures, suggesting an extremely low release rate with a time constant of the order of magnitude of 1–2 years at body temperature.