Identical Input Data Research Articles

A multi-vision monitoring framework for simultaneous real-time unmanned aerial monitoring of farmer activity and crop health

Current remote sensing technologies employing Unmanned Aerial Vehicles (UAVs) for farm monitoring have shown promise in characterizing the environment through diverse sensor systems, including hyperspectral cameras, LiDAR, thermal cameras, and RGB sensors. However, these solutions often specialize in either activity recognition or crop monitoring, but not both. To address this limitation and enhance efficacy, we propose a multi-vision monitoring (MVM) framework capable of simultaneously recognizing farm activities and assessing crop health. Our approach involves computer vision techniques that transform aerial videos into sequential images to extract essential environmental features. Central to our framework are two pivotal components: the Farmer Activity Recognition (FAR) algorithm and the Crop Image Analysis (CIA). The FAR algorithm introduces a novel feature extraction method capturing motion across various maps, enabling distinct feature sets for each activity. Meanwhile, the CIA component utilizes the normalized Triangular Greenness Index (nTGI) to estimate leave chlorophyll levels, an important indicator for crop health. By unifying these components, we achieve dual functionality—activity recognition and crop health estimation—using identical input data, thereby enhancing efficiency and versatility in farm monitoring. Our framework employs a diverse range of machine learning models, demonstrating the potential of our extracted features to address the defined problem effectively in unison.

Can models adequately reflect how long-term nitrogen enrichment alters the forest soil carbon cycle?

Abstract. Changes in the nitrogen (N) status of forest ecosystems can directly and indirectly influence their carbon (C) sequestration potential by altering soil organic matter (SOM) decomposition, soil enzyme activity, and plant–soil interactions. However, model representations of linked C–N cycles and SOM decay are not well validated against experimental data. Here, we use extensive data from the Fernow Experimental Forest long-term whole-watershed N fertilization study to compare the response to N perturbations of two soil models that represent decomposition dynamics differently (first-order decay versus microbially explicit reverse Michaelis–Menten kinetics). These two soil models were coupled to a common vegetation model which provided identical input data. Key responses to N additions measured at the study site included a shift in plant allocation to favor woody biomass over belowground carbon inputs, reductions in soil respiration, accumulation of particulate organic matter (POM), and an increase in soil C:N ratios. The vegetation model did not capture the often-observed shift in plant C allocation with N additions, which resulted in poor predictions of the soil responses. We modified the parameterization of the plant C allocation scheme to favor wood production over fine-root production with N additions, which significantly improved the vegetation and soil respiration responses. Additionally, to elicit an increase in the soil C stocks and C:N ratios with N additions, as observed, we modified the decay rates of the POM in the soil models. With these modifications, both models captured negative soil respiration and positive soil C stock responses in line with observations, but only the microbially explicit model captured an increase in soil C:N. Our results highlight the need for further model development to accurately represent plant–soil interactions, such as rhizosphere priming, and their responses to environmental change.

Mixed noise-guided mutual constraint framework for unsupervised anomaly detection in smart industries

Large-scale sensor and data acquisition systems, integrated with deep learning methodologies, play a pivotal role in enhancing the sustainability and security of smart city environments, exemplifying the critical significance of anomaly detection techniques. Anomaly detection in complex industrial scenarios presents various challenges, such as intricate working environments, limited anomaly samples, and lack of a priori information. Unsupervised anomaly detection based on knowledge distillation enables anomaly detection using only normal samples. However, the similarity in structure between teacher and student models, along with identical input data flow, hampers accurate anomaly detection and localization. To address these issues, we propose MNMC, an unsupervised anomaly detection model consisting of a mixed noise generation module emulating real defects, a mutual constraint module, and an anomaly segmentation module. Firstly, to enhance the student network’s ability to learn robust features, we construct a hybrid noise model comprising dead-leaves noise and perlin noise. This generates features with structural texture and distributional characteristics closer to real anomalies. Secondly, we design a mutual constraint framework to further improve the learning ability of the student network for normal features by constraining representations containing only a single noise. Lastly, for the detection of anomalies at different scales, we propose a new evaluation metric based on equal importance of normal and anomalous regions. Through ablation experiments, we demonstrate the effectiveness of the simulated real defect generation module and the mutual constraints module. Performance experiments on the MVTec dataset show that our method achieves competitive results compared to the current state-of-the-art anomaly detection methods.

Improved multimer prediction using massive sampling with AlphaFold in CASP15.

AlphaFold2 has revolutionized structure prediction by achieving high accuracy comparable to experimentally determined structures. However, there is still room for improvement, especially for challenging cases like multimers. A key to the success of AlphaFold is its ability to assess and rank its own predictions. Our basic idea for the Wallner group in CASP15 was to exploit this excellent scoring function in AlphaFold by massive sampling. To achieve this goal, we conducted AlphaFold runs using six different settings, using templates, without templates, and with an increased number of recycles for both multimer v1 and v2 weights. In all instances, we enabled dropout layers during inference, allowing for sampling of uncertainty and enhancing the diversity of the generated models. In total, 274 289 models were generated for the 38 targets in CASP15, with a median of 4810 models per target. Of these 38 targets, 10 were high quality, 11 were medium quality, 11 were acceptable, and only 6 were incorrect. The improvement over the baseline method, NBIS-AF2-multimer, is substantial, with the mean DockQ increasing from 0.43 to 0.56, with several targets showing a DockQ score increase of +0.6 units. Remarkable, considering Wallner and NBIS-AF2-multimer were using identical input data. The success can be attributed to the diversified sampling using dropout with different settings and, in particular, the use of multimer v1, which is much more susceptible to sampling compared with v2. The method is available here: http://wallnerlab.org/AFsample/.

Calculation of scan length and size-specific dose at longitudinal positions of body CT scans using dose equilibrium function.

Dose evaluation at longitudinal positions of body computed tomography (CT) scans is useful for CT quality assurance programs and patient organ dose evaluation. Accurate estimates depend on both patient size and scan length. To propose practical evaluation of the average dose to the transverse slab of an axial image slice for adult body CT examinations, considering not only patient size but also scan length, and to compare the results with those of Monte Carlo (Geant4) simulation [Dsim (z)] and size-specific dose estimates at longitudinal positions of scans [SSDE(z)] from international standards (IEC publication no. 62985). In a scan series, the total dose at each z-axis location was calculated using the input information identical to the SSDE(z) evaluation. Each axial image slice (slice thickness, 2.5 or 5mm) was first considered independently. Its z-axis coverage and CTDIvol (from the DICOM headers) were used to directly calculate a z-axis dose profile for the average dose over the cross-section of a water phantom, using the approach to equilibrium function. The phantom diameter was taken to be equal to the patient water equivalent diameter at that slice. The above was repeated at all slices and the dose at each z-axis location was accumulated from all profiles, referred to as Dcalc (z). For validation, we considered a cohort of 65 patients, who underwent chest and abdominopelvic examinations. The resultant Dcalc (z) was compared with Dsim (z) and SSDE(z), both available in a previous paper. Dcalc (z) evaluation could be used to accurately assess the scan range average dose, with an accuracy of 7.1%-8.7% for 65 patients in two examinations. On individual image slices, the maximum difference in magnitude between Dcalc (z) and Dsim (z) [and between SSDE(z) and Dsim (z) in parentheses] was 37.5% (85%) [two edges (2×5cm) of chest scan range], 17.8% (35.2%) (the remaining central region of chest scan), 26.8% (74.1%) [two edges (2×5cm) of abdominopelvic scan range], and 14.2% (22.5%) (the remaining central region of abdominopelvic scan). Identical input data are used for Dcalc (z) and SSDE(z) evaluations. The latter is limited to the z-axis locations within scan range. At each image slice, SSDE(z) is equivalent to the midpoint dose of a fixed-mA scan of 15-30cm (scan length). In contrast, Dcalc (z) considers dose accumulation from varying scan length (from sub-centimeter to about 1m) and tube current, and dose profile is also computed outside scan range. Besides greatly improving dose evaluation for individual image slices, Dcalc (z) allows for evaluating dose accumulation from multiple series, which typically span different scan ranges. Our proposal may assist CT manufacturers and dose index monitoring software in assessing dose at longitudinal positions of body CT scans.

The effect of spatial sampling on magnetic field modeling and helicity computation

Context. Nonlinear force-free (NLFF) modeling is regularly used to indirectly infer the 3D geometry of the coronal magnetic field, which is not otherwise accessible on a regular basis by means of direct measurements. Aims. We study the effect of binning in time-series NLFF modeling of individual active regions (ARs) in order to quantify the effect of a different underlying spatial sampling on the quality of modeling as well as on the derived physical parameters. Methods. We apply an optimization method to sequences of Solar Dynamics Observatory (SDO) Helioseismic and Magnetic Imager (HMI) vector magnetogram data at three different plate scales for three solar active regions to obtain nine NLFF model time series. From the NLFF models, we deduce active-region magnetic fluxes, electric currents, magnetic energies, and relative helicities, and analyze those with respect to the underlying spatial sampling. We calculate various metrics to quantify the quality of the derived NLFF models and apply a Helmholtz decomposition to characterize solenoidal errors. Results. At a given spatial sampling, the quality of NLFF modeling is different for different ARs, and the quality varies along the individual model time series. For a given AR, modeling at a certain spatial sampling is not necessarily of superior quality compared to that performed with a different plate scale. Generally, the NLFF model quality tends to be higher for larger pixel sizes with the solenoidal quality being the ultimate cause for systematic variations in model-deduced physical quantities. Conclusions. Optimization-based modeling using SDO/HMI vector data binned to larger pixel sizes yields variations in magnetic energy and helicity estimates of ≲30% on overall, given that concise checks ensure the physical plausibility and high solenoidal quality of the tested model. Spatial-sampling-induced differences are relatively small compared to those arising from other sources of uncertainty, including the effects of applying different data calibration methods, those of using vector data from different instruments, or those arising from application of different NLFF methods to identical input data.

Predicting fracture vertical growth containment using the width-induced net stress

The vertical growth of hydraulic fractures in layered formations is important from treatment-placement and well performance perspective. Most numerical fracture simulators generate fracture geometries by accounting for the in-situ stress and rock mechanical properties, along with key parameters related to fracturing fluids such as rheology and leakoff behavior, amongst various other critical parameters that can influence the outcome. Although the results from various simulators are generally reasonable, the fracture height estimates can vary significantly even for identical input data sets as shown in the literature. Because of the limitations of these simulators, some of the field observations, such as containment of fracture height growth across certain interfaces of contrasting material properties or weak interfacial bonding, are not replicated effectively, unless such an interface is pre-defined.In this study, the influence of fracture width on resultant stresses is incorporated in the calculation process while solving for vertical growth of hydraulic fractures. For this purpose, a modified equilibrium height growth model previously developed by the authors is used. The effects of viscous fluid movement in the fracture are also included in the calculation scheme.The outputs from the model were compared with the field-derived fracture heights that were inferred from micro seismic (MS) surveys conducted on various fracturing treatments that included both vertical and horizontal wells completed in layered formations. The analysis revealed a close agreement between the model-predicted and observed values of fracture height.While the authors acknowledge that predicting the containment of vertical growth of fracture across a plane is a challenging task given the complex nature of the problem, the paper discusses a method that offers a simpler approach to predicting the sites that may act as a constraint to the fracture growth during the stimulation treatments. This can aid in well-placement planning, perforation strategy, and hydraulic fracturing treatment design. The model can be readily deployed or incorporated in existing simulators.

New flight trajectory optimisation method using genetic algorithms

AbstractThis paper presents a new flight trajectory optimisation method, based on genetic algorithms, where the selected optimisation criterion is the minimisation of the total cost. The candidate flight trajectories evaluated in the optimisation process are defined as flight plans with two components: a lateral flight plan (the set of geographic points that define the flight trajectory track segments) and a vertical flight plan (the set of data that define the altitude and speed profiles, as well as the points where the altitude and/or speed changes occur). The lateral components of the candidate flight plans are constructed by selecting a set of adjacent nodes from a routing grid. The routing grid nodes are generated based on the orthodromic route between the flight trajectory’s initial and final points, a selected maximum lateral deviation from the orthodromic route and a selected grid node step size along and across the orthodromic route. Two strategies are investigated to handle invalid flight plans (relative to the aircraft’s flight envelope) and to compute their flight performance parameters. A first strategy is to assign a large penalty total cost to invalid flight profiles. The second strategy is to adjust the invalid flight plan parameters (altitude and/or speed) to the nearest limit of the flight envelope, with priority being given to maintaining the planned altitude. The tests performed in this study show that the second strategy is computationally expensive (requiring more than twice the execution time relative to the first strategy) and yields less optimal solutions. The performance of the optimal profiles identified by the proposed optimisation method, using the two strategies regarding invalid flight profile performance evaluation, were compared with the performance data of a reference flight profile, using identical input data: initial aircraft weight, initial and final aircraft geographic positions, altitudes and speed, cost index, and atmospheric data. The initial and final aircraft geographic positions, and the reference flight profile data, were retrieved from the FlightAware web site. This data corresponds to a real flight performed with the aircraft model used in this study. Tests were performed for six Cost Index values. Given the randomness of the genetic algorithms, the convergence to a global optimal solution is not guaranteed (the solution may be non-optimal or a local optima). For a better evaluation of the performance of the proposed method, ten test runs were performed for each Cost Index value. The total cost reduction for the optimal flight plans obtained using the proposed method, relative to the reference flight plan, was between 0.822% and 3.042% for the cases when the invalid flight profiles were corrected, and between 1.598% and 3.97% for the cases where the invalid profiles were assigned a penalty total cost.

Crop growth and soil water fluxes at erosion‐affected arable sites: Using weighing lysimeter data for model intercomparison

AbstractAgroecosystem models need to reliably simulate all biophysical processes that control crop growth, particularly the soil water fluxes and nutrient dynamics. As a result of the erosion history, truncated and colluvial soil profiles coexist in arable fields. The erosion‐affected field‐scale soil spatial heterogeneity may limit agroecosystem model predictions. The objective was to identify the variation in the importance of soil properties and soil profile modifications in agroecosystem models for both agronomic and environmental performance. Four lysimeters with different soil types were used that cover the range of soil variability in an erosion‐affected hummocky agricultural landscape. Twelve models were calibrated on crop phenological stages, and model performance was tested against observed grain yield, aboveground biomass, leaf area index, actual evapotranspiration, drainage, and soil water content. Despite considering identical input data, the predictive capability among models was highly diverse. Neither a single crop model nor the multi‐model mean was able to capture the observed differences between the four soil profiles in agronomic and environmental variables. The model's sensitivity to soil‐related parameters was apparently limited and dependent on model structure and parameterization. Information on phenology alone seemed insufficient to calibrate crop models. The results demonstrated model‐specific differences in the impact of soil variability and suggested that soil matters in predictive agroecosystem models. Soil processes need to receive greater attention in field‐scale agroecosystem modeling; high‐precision weighable lysimeters can provide valuable data for improving the description of soil–vegetation–atmosphere process in the tested models.

Hydrological Forecasts and Projections for Improved Decision-Making in the Water Sector in Europe

AbstractSimulations of water fluxes at high spatial resolution that consistently cover historical observations, seasonal forecasts, and future climate projections are key to providing climate services aimed at supporting operational and strategic planning, and developing mitigation and adaptation policies. The End-to-end Demonstrator for improved decision-making in the water sector in Europe (EDgE) is a proof-of-concept project funded by the Copernicus Climate Change Service program that addresses these requirements by combining a multimodel ensemble of state-of-the-art climate model outputs and hydrological models to deliver sectoral climate impact indicators (SCIIs) codesigned with private and public water sector stakeholders from three contrasting European countries. The final product of EDgE is a water-oriented information system implemented through a web application. Here, we present the underlying structure of the EDgE modeling chain, which is composed of four phases: 1) climate data processing, 2) hydrological modeling, 3) stakeholder codesign and SCII estimation, and 4) uncertainty and skill assessments. Daily temperature and precipitation from observational datasets, four climate models for seasonal forecasts, and five climate models under two emission scenarios are consistently downscaled to 5-km spatial resolution to ensure locally relevant simulations based on four hydrological models. The consistency of the hydrological models is guaranteed by using identical input data for land surface parameterizations. The multimodel outputs are composed of 65 years of historical observations, a 19-yr ensemble of seasonal hindcasts, and a century-long ensemble of climate impact projections. These unique, high-resolution hydroclimatic simulations and SCIIs provide an unprecedented information system for decision-making over Europe and can serve as a template for water-related climate services in other regions.

What Types of Cars Will We Be Driving? Methods of Forecasting Car Travel Demand by Vehicle Type

New technologies are emerging in the private vehicle market. Conventional propulsion systems are set to be replaced by alternative, more environment-friendly ones (e.g., electric vehicles), and certain new features, like autonomous driving, will possibly change the way private cars are employed. To assess the impact of such technologies, one must estimate how often and for which trips these vehicle types will be used. Another issue is the exact localization of certain vehicle types on the network, to assess environmental effects and identify where specific roadside infrastructure (e.g., charging stations) will be required. This paper presents four approaches to forecasting car usage by vehicle type using a macroscopic travel demand model in combination with a vehicle fleet or technology diffusion model. Integrating the two types of models requires tools ranging from assumptions and extrapolation of empirical data to synthetic or incremental discrete choice models. The approaches are employed in a case study forecasting travel demand using privately owned autonomous vehicles (AVs) in Germany in 2030. Despite identical input data, the estimated proportion of vehicle miles traveled (VMT) using AVs varies between 11% and 23% of overall car VMT, depending on the approach chosen. The reasons for this variation in results are investigated and some recommendations are given. To avoid the difficulties of fitting a synthetic model to observed data and to increase the accuracy of the results, the recommendation is to formulate the vehicle type choice as an incremental model added to the travel demand model.

Impact of the terrestrial reference frame on the determination of the celestial reference frame

Currently three up-to-date Terrestrial Reference Frames (TRF) are available, the ITRF2014 from IGN, the DTRF2014 from DGFI-TUM, and JTRF2014 from JPL. All use the identical input data of space-geodetic station positions and Earth orientation parameters, but the concept of combining these data is fundamentally different. The IGN approach is based on the combination of technique solutions, while the DGFI is combining the normal equation systems. Both yield in reference epoch coordinates and velocities for a global set of stations. JPL uses a Kalman filter approach, realizing a TRF through weekly time series of geocentric coordinates. As the determination of the CRF is not independent of the TRF and vice versa, the choice of the TRF might impact on the CRF. Within this work we assess this effect.We find that the estimated Earth orientation parameter (EOP) from DTRF2014 agree best with those from ITRF2014, the EOP resulting from JTRF2014 show besides clear yearly signals also some artifacts linked to certain stations. The estimated source position time series however, agree with each other better than ±1μas. When fixing EOP and station positions we can see the maximal effect of the TRF on the CRF. Here large systematics in position as well as proper motion arise. In case of ITRF2008 they can be linked to the missing data after 2008. By allowing the EOP and stations to participate in the adjustment, the agreement increases, however, systematics remain.

Comparing Quantity, Allocation and Configuration Accuracy of Multiple Land Change Models

The growing numbers of land change models makes it difficult to select a model at the beginning of an analysis, and is often arbitrary and at the researcher’s discretion. How to select a model at the beginning of an analysis, when multiple are suitable, represents a critical research gap currently understudied, where trade-offs of choosing one model over another are often unknown. Repeatable methods are needed to conduct cross-model comparisons to understand the trade-offs among models when the same calibration and validation data are used. Several methods to assess accuracy have been proposed that emphasize quantity and allocation, while overlooking the accuracy with which a model simulates the spatial configuration (e.g., size and shape) of map categories across landscapes. We compared the quantity, allocation, and configuration accuracy of four inductive pattern-based spatial allocation land change models (SLEUTH, GEOMOD, Land Change Modeler (LCM), and FUTURES). We simulated urban development with each model using identical input data from ten counties surrounding the growing region of Charlotte, North Carolina. Maintaining the same input data, such as land cover, drivers of change, and projected quantity of change, reduces differences in model inputs and allows for focus on trade-offs in different types of model accuracy. Results suggest that these four land change models produce representations of urban development with substantial variance, where some models may better simulate quantity and allocation at the trade-off of configuration accuracy, and vice versa. Trade-offs in accuracy exist with respect to the amount, spatial allocation, and landscape configuration of each model. This comparison exercise illustrates the range of accuracies for these models, and demonstrates the need to consider all three types of accuracy when assessing land change model’s projections.

Model of the Mechanical Behavior of Cementitious Matrices Reinforced with Nanomaterials

CNTs and CNFs have been introduced as a nanoscale reinforcing material to cementitious composites, for stiffening and strengthening the microstructure. This technology is motivated by the need to control crack initiation in the cementitious gel before it propagates into visible crack formations. Experimental evidence supports this concept; however, testing at the nanoscale may only be conducted through nanoindentation, which has a limited range only providing localized results that cannot be extrapolated to general stress states. To evaluate the restraining action of nanomaterials in the gel microstructure, a computational mechanistic model has been developed where the material phases (gel, nanotubes, and pores) are modeled explicitly allowing for natural randomness in their distribution and orientation. Repeated analysis with identical input data reproduces the statistical scatter observed in laboratory tests on identical material samples. The formulation uses a discrete element approach; the gel structure is represented by a random network of hydrates and successfully reproduces the known trends in mechanical behavior of cementitious materials (pressure and restraint sensitive material behavior) and the small ratio of tensile to compressive strength. Simulations illustrate that it is possible to computationally reproduce the measured properties and behavior of fiber-reinforced cement composites using information from simple laboratory tests.

A study of the surface roughness in elasto-plastic shrink fitted joint

The aim of the present study is to provide a methodology for improving the design of a shrink-fitted assembly by focusing on two objectives: (i) taking into account the surface roughness of components, (ii) introducing a non-classical hardening friction law in which tangential micro-slip displacements are considered. The calculations have been made for elasto-plastic properties of component in axisymmetric conditions. Both the loading and unloading processes have been considered. After finite element simulations, it is observed that overall static behaviour of the assembly is influenced considerably by the surface finish of the mating components. The current numerical results of the shrink fitted assembly received from the coarse mesh could be treated as contact benchmark for further finite element analyses using as closely as possible identical input data.

An integrated modeling approach for atmospheric corrosion in presence of a varying electrolyte film

A Dynamic Electrolyte Film Corrosion (DEFC) model is presented that integrates a model for uniform film condensation/evaporation in presence of salts with a simple model of iron corrosion. The focus is on the role of the film thickness. A limited number of measurements confirms that it is unavoidable to consider the hygroscopic effects. The model is used to simulate the role of sample thickness, heat transfer and changing rates of temperature and relative humidity in well defined climate chamber conditions. Obtained film thicknesses and corrosion rates are simulated using identical input data in absence of salt and for two salts (NaCl and a seawater-like mixture of 90%NaCl-10%MgCl2). The large indirect influence of salts on the corrosion is discussed. Also one-week simulations are performed based on recorded and averaged meteorological data. They show that averaged meteorologic data will lead to incorrect results.

MTJ-Based State Retentive Flip-Flop With Enhanced-Scan Capability to Sustain Sudden Power Failure

We present two non-volatile flip-flops (NVFFs) that incorporate magnetic tunnel junctions (MTJ) to ensure fast data storage and restoration from intentional and unintentional power outages. The proposed designs also facilitate enhanced scan mode testing capability by exploiting the nonvolatile latch to function as hold latch for delay testing. The proposed NVFF eliminates additional write drivers, and can operate at up to 2 GHz at 1.1 V, with 0.55 pJ of energy consumption in 22 nm predictive technology. We also address the issue of write asymmetry of MTJ through careful transistor upsizing to achieve near uniform write latency. A data-dependent power gating technique is proposed to mitigate the high static current during retention and back-to-back writing of the identical input data. The proposed gated NVFF achieves several orders of magnitude energy saving at the expense of 1.56X area compared to a standard enhanced scan flip-flop.

Increased Reliability of Nuclear Magnetic Resonance Protein Structures by Consensus Structure Bundles

Nuclear magnetic resonance (NMR) structures are represented by bundles of conformers calculated from different randomized initial structures using identical experimental input data. The spread among these conformers indicates the precision of the atomic coordinates. However, there is as yet no reliable measure of structural accuracy, i.e., how close NMR conformers are to the "true" structure. Instead, the precision of structure bundles is widely (mis)interpreted as a measure of structural quality. Attempts to increase precision often overestimate accuracy by tight bundles of high precision but much lower accuracy. To overcome this problem, we introduce a protocol for NMR structure determination with the software package CYANA, which produces, like the traditional method, bundles of conformers in agreement with a common set of conformational restraints but with a realistic precision that is, throughout a variety of proteins and NMR data sets, a much better estimate of structural accuracy than the precision of conventional structure bundles.

A New Algorithm for Reliable and General NMR Resonance Assignment

The new FLYA automated resonance assignment algorithm determines NMR chemical shift assignments on the basis of peak lists from any combination of multidimensional through-bond or through-space NMR experiments for proteins. Backbone and side-chain assignments can be determined. All experimental data are used simultaneously, thereby exploiting optimally the redundancy present in the input peak lists and circumventing potential pitfalls of assignment strategies in which results obtained in a given step remain fixed input data for subsequent steps. Instead of prescribing a specific assignment strategy, the FLYA resonance assignment algorithm requires only experimental peak lists and the primary structure of the protein, from which the peaks expected in a given spectrum can be generated by applying a set of rules, defined in a straightforward way by specifying through-bond or through-space magnetization transfer pathways. The algorithm determines the resonance assignment by finding an optimal mapping between the set of expected peaks that are assigned by definition but have unknown positions and the set of measured peaks in the input peak lists that are initially unassigned but have a known position in the spectrum. Using peak lists obtained by purely automated peak picking from the experimental spectra of three proteins, FLYA assigned correctly 96-99% of the backbone and 90-91% of all resonances that could be assigned manually. Systematic studies quantified the impact of various factors on the assignment accuracy, namely the extent of missing real peaks and the amount of additional artifact peaks in the input peak lists, as well as the accuracy of the peak positions. Comparing the resonance assignments from FLYA with those obtained from two other existing algorithms showed that using identical experimental input data these other algorithms yielded significantly (40-142%) more erroneous assignments than FLYA. The FLYA resonance assignment algorithm thus has the reliability and flexibility to replace most manual and semi-automatic assignment procedures for NMR studies of proteins.

Wetland versus open water evaporation: An analysis and literature review

Is the total evaporation from a wetland surface (including: open water evaporation, plant transpiration and wet/dry soil evaporation) similar, lower, or higher than evaporation from an open water surface under the same climatic conditions? This question has been the subject of long debate; the literature does not show a consensus. In this paper we contribute to the discussion in two steps. First, we analyse the evaporation from a wetland with emergent vegetation (Ea) versus open water evaporation (Ew) by applying the Penman–Monteith equation to identical climate input data, but with different biophysical characteristics of each surface. Second, we assess the variability of measured Ea/Ew through a literature review of selected wetlands around the globe.We demonstrate that the ratio Ea/Ew is site-specific, and a function of the biophysical properties of the wetland surface, which can also undergo temporal variability depending on local hydro-climate conditions. Second, we demonstrate that the Penman–Monteith model provides a suitable basis to interpret Ea/Ew variations. This implies that the assumption of wetland evaporation to behave similar to open water bodies is not correct. This has significant implications for the total water consumption and water allocation to wetlands in river basin management.