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Nitrogen (N) is among the main nutrients widely used in agriculture worldwide; however, its administration and management can be challenging. Excess nitrogen is harmful to plant health and the environment, requiring effective monitoring of leaf nitrogen concentration (LNC) in field crops. Remote sensing stands out as a valuable tool in this context. This study contributed to the monitoring of LNC by implementing a machine learning algorithm based on the processing of reflectance data from Sentinel-2 (S2) satellites obtained via spectral resampling. For this purpose, five independent datasets containing leaf reflectance measurements collected by spectroradiometers were resampled to the spectral resolution of the sensors onboard the S2 satellites. LNC prediction models were developed from the resampled datasets, using Support Vector Regression (SVR) and Random Forest Regression (RFR), with 75% of the data from each set used to train a model and the remaining 25% for validation. The models demonstrated good predictive power, with the Root Mean Squared Error (RMSE) ranging from 0.39 to 0.94%. Furthermore, this study investigated the transferability of the models' predictive power by using 100% of the data from each set for training and validating predictions on the other sets. To improve transferability, the Transfer Component Analysis (TCA) technique was applied to adapt domains between the sets. This analysis revealed favorable results, especially with the TCA-SVR and TCA-RFR combinations, highlighting a greater capacity to extract transferable spectral features between different leaf reflectance datasets. It was concluded that spectral resampling does not hinder the development of effective LNC prediction models. Aligning this resampling with the resolution of Sentinel-2 sensors, resulted in more efficient monitoring of LNC, eliminating the need to individually reference each sampling point. This approach simplified the monitoring process, reduced both time and costs, and was directly beneficial to producers.

Under unbalanced grid conditions, the DC side voltage of pulse width modulation (PWM) rectifier will overshoot during start-up and reference voltage transients, which may lead to system instability. In this paper, a deadbeat predictive power control (DPPC) with fuzzy PI compound controller (FPCC) and power predictive corrector (PPC) is proposed to solve that problem. Firstly, the parameters of the PI controller are adjusted online by the fuzzy control rule of the FPCC to eliminate the overshoot of the DC side voltage and contribute to faster dynamic responses, which thereby could correct the reference active power. Secondly, the static error between the reference and system active powers is reduced by accumulative predictive errors in the PPC. The simulation is carried out under ideal and unbalanced grid conditions. The result shows that the proposed control scheme can effectively eliminate the overshoot of DC voltage, reduce the static error of active power, and improve the dynamic response and anti-interference ability of the rectifier.

Purpose Partial least squares (PLS) has been introduced as a “causal-predictive” approach to structural equation modeling (SEM), designed to overcome the apparent dichotomy between explanation and prediction. However, while researchers using PLS-SEM routinely stress the predictive nature of their analyses, model evaluation assessment relies exclusively on metrics designed to assess the path model’s explanatory power. Recent research has proposed PLSpredict, a holdout sample-based procedure that generates case-level predictions on an item or a construct level. This paper offers guidelines for applying PLSpredict and explains the key choices researchers need to make using the procedure. Design/methodology/approach The authors discuss the need for prediction-oriented model evaluations in PLS-SEM and conceptually explain and further advance the PLSpredict method. In addition, they illustrate the PLSpredict procedure’s use with a tourism marketing model and provide recommendations on how the results should be interpreted. While the focus of the paper is on the PLSpredict procedure, the overarching aim is to encourage the routine prediction-oriented assessment in PLS-SEM analyses. Findings The paper advances PLSpredict and offers guidance on how to use this prediction-oriented model evaluation approach. Researchers should routinely consider the assessment of the predictive power of their PLS path models. PLSpredict is a useful and straightforward approach to evaluate the out-of-sample predictive capabilities of PLS path models that researchers can apply in their studies. Research limitations/implications Future research should seek to extend PLSpredict’s capabilities, for example, by developing more benchmarks for comparing PLS-SEM results and empirically contrasting the earliest antecedent and the direct antecedent approaches to predictive power assessment. Practical implications This paper offers clear guidelines for using PLSpredict, which researchers and practitioners should routinely apply as part of their PLS-SEM analyses. Originality/value This research substantiates the use of PLSpredict. It provides marketing researchers and practitioners with the knowledge they need to properly assess, report and interpret PLS-SEM results. Thereby, this research contributes to safeguarding the rigor of marketing studies using PLS-SEM.

1. Habitat selection models usually assume that the spatial distributions of animals depend positively on the distributions of resources and negatively on interference. However, the presence of conspecifics at a given location also signals safety and the availability of resources. This may induce followers to select contiguous patches and causes animals to cluster. Resource availability, interference and attraction therefore jointly lead to self-organized patterns in foraging animals. 2. We analyse the distribution of foraging shorebirds at landscape level on the basis of a resource-based model to establish, albeit indirectly, the importance of conspecific attraction and interference. 3. At 23 intertidal sites with a mean area of 170 ha spread out over the Dutch Wadden Sea, the spatial distribution of six abundant shorebird species was determined. The location of individuals and groups was mapped using a simple method based on projective geometry, enabling fast mapping of low-tide foraging shorebird distributions. We analysed the suitability of these 23 sites in terms of food availability and travel distances to high tide roosts. 4. We introduce an interference sensitivity scale which maps interference as a function of inter-individual distance. We thus obtain interference-insensitive species, which are only sensitive to interference at short inter-individual distances (and may thus pack densely) and interference-sensitive species which interfere over greater inter-individual distances (and thus form sparse flocks). 5. We found that interference-insensitive species like red knot (Calidris canutus) and dunlins (Calidris alpina) are more clustered than predicted by the spatial distribution of their food resources. This suggests that these species follow each other when selecting foraging patches. In contrast, curlew (Numenius arquata) and grey plover (Pluvialis squatarola), known to be sensitive to interference, form sparse flocks. Hence, resource-based models have better predictive power for interference-sensitive species than for interference-insensitive species. 6. It follows from our analysis that monitoring programmes, habitat selection models and statistical analyses should also consider the mechanisms of self-organization.

Purpose This study aims to propose guidelines for the joint use of partial least squares structural equation modeling (PLS-SEM) and fuzzy-set qualitative comparative analysis (fsQCA) to combine symmetric and asymmetric perspectives in model evaluation, in the hospitality and tourism field. Design/methodology/approach This study discusses PLS-SEM as a symmetric approach and fsQCA as an asymmetric approach to analyze structural and configurational models. It presents guidelines to conduct an fsQCA based on latent construct scores drawn from PLS-SEM, to assess how configurations of exogenous constructs produce a specific outcome in an endogenous construct. Findings This research highlights the advantages of combining PLS-SEM and fsQCA to analyze the causal effects of antecedents (i.e., exogenous constructs) on outcomes (i.e., endogenous constructs). The construct scores extracted from the PLS-SEM analysis of a nomological network of constructs provide accurate input for performing fsQCA to identify the sufficient configurations required to predict the outcome(s). Complementing the assessment of the model’s explanatory and predictive power, the fsQCA generates more fine-grained insights into variable relationships, thereby offering the means to reach better managerial conclusions. Originality/value The application of PLS-SEM and fsQCA as separate prediction-oriented methods has increased notably in recent years. However, in the absence of clear guidelines, studies applied the methods inconsistently, giving researchers little direction on how to best apply PLS-SEM and fsQCA in tandem. To address this concern, this study provides guidelines for the joint use of PLS-SEM and fsQCA.

© 2019 The Authors. Ecological Monographs published by Wiley Periodicals, Inc. on behalf of Ecological Society of America This is an open access article under the terms of the Creative Commons Attribution License, which permits use, distribution and reproduction in any medium, provided the original work is properly cited.

Statistical modeling is a powerful tool for developing and testing theories by way of causal explanation, prediction, and description. In many disciplines there is near-exclusive use of statistical modeling for causal explanation and the assumption that models with high explanatory power are inherently of high predictive power. Conflation between explanation and prediction is common, yet the distinction must be understood for progressing scientific knowledge. While this distinction has been recognized in the philosophy of science, the statistical literature lacks a thorough discussion of the many differences that arise in the process of modeling for an explanatory versus a predictive goal. The purpose of this article is to clarify the distinction between explanatory and predictive modeling, to discuss its sources, and to reveal the practical implications of the distinction to each step in the modeling process.

Finite element (FE) model studies have made important contributions to our understanding of functional biomechanics of the lumbar spine. However, if a model is used to answer clinical and biomechanical questions over a certain population, their inherently large inter-subject variability has to be considered. Current FE model studies, however, generally account only for a single distinct spinal geometry with one set of material properties. This raises questions concerning their predictive power, their range of results and on their agreement with in vitro and in vivo values. Eight well-established FE models of the lumbar spine (L1-5) of different research centers around the globe were subjected to pure and combined loading modes and compared to in vitro and in vivo measurements for intervertebral rotations, disc pressures and facet joint forces. Under pure moment loading, the predicted L1-5 rotations of almost all models fell within the reported in vitro ranges, and their median values differed on average by only 2° for flexion-extension, 1° for lateral bending and 5° for axial rotation. Predicted median facet joint forces and disc pressures were also in good agreement with published median in vitro values. However, the ranges of predictions were larger and exceeded those reported in vitro, especially for the facet joint forces. For all combined loading modes, except for flexion, predicted median segmental intervertebral rotations and disc pressures were in good agreement with measured in vivo values. In light of high inter-subject variability, the generalization of results of a single model to a population remains a concern. This study demonstrated that the pooled median of individual model results, similar to a probabilistic approach, can be used as an improved predictive tool in order to estimate the response of the lumbar spine.

Executioner-caspase activation has been considered a point-of-no-return in apoptosis. However, numerous studies report survival from caspase activation after treatment with drugs or radiation. An open question is whether cells can recover from direct caspase activation without pro-survival stress responses induced by drugs. To address this question, we engineered a HeLa cell line to express caspase-3 inducibly and combined it with a quantitative caspase activity reporter. While high caspase activity levels killed all cells and very low levels allowed all cells to live, doses of caspase activity sufficient to kill 15 to 30% of cells nevertheless allowed 70 to 85% to survive. At these doses, neither the rate, nor the peak level, nor the total amount of caspase activity could accurately predict cell death versus survival. Thus, cells can survive direct executioner-caspase activation, and variations in cellular state modify the outcome of potentially lethal caspase activity. Such heterogeneities may underlie incomplete tumor cell killing in response to apoptosis-inducing cancer treatments.

This article addresses the question of which metaphysical paradigm is most suitable for gaining a deeper understanding of our conscious inner life and bringing us closer to a powerful theory of consciousness (TOC). To answer this question, the key characteristics of a strong theory, namely, predictive and explanatory power, are used to evaluate various paradigms. The predictive power of a TOC relies primarily on how accurately it can state the conditions under which a physical system is capable of forming conscious states, whereas the explanatory power of a TOC reflects the degree to which the theory makes it intelligible why conscious states are formed under the stated conditions. It proves expedient for the evaluation to divide the paradigms into two classes: physicalism and non-physicalism. From the physicalist point of view, consciousness is reducible to the physical, while non-physicalism is predicated on the assumption that consciousness is fundamental and irreducible to physical properties. The analysis reveals that a TOC built on the physicalist paradigm has the potential to achieve high predictive power but fails to unfold explanatory power. It is demonstrated that the non-physicalist paradigm has clear advantages over physicalism when it comes to developing a powerful TOC. These findings make a strong case for initiating a paradigm shift that replaces the prevailing physicalist stance with a non-physicalist approach. Such a paradigm shift does not make the prominent neuroscientific theories obsolete. Rather, it places these theories in a broader context and entails a reinterpretation of the neurophysiological indicators of consciousness.