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Agroforestry (AF) is considered to be a sustainable land use practice as it combines agricultural production with multiple beneficial effects such as carbon sequestration, enhanced nutrient cycling and increased biodiversity. Quantification of these beneficial effects in temperate arable fields is still limited, however, and most studies focus on one sole parameter (i.e., impact on crop productivity, soil quality, biodiversity, etc.). Combined effects are only rarely considered, resulting in a lack of integrated quantification. Here we assess the effect of rows of walnut trees (Juglans regia L.) on soil organic carbon (SOC), soil nutrient status, the presence of potentially beneficial ground-dwelling arthropods and on the yield and quality of neighboring arable crops. Significantly higher SOC and soil nutrient concentrations were found near the trees, which is assumed to be primarily a result of tree leaf litter input. Abundance of macro-detritivorous arthropods was increased in and near the tree rows, whereas only limited effects of tree presence were found on the presence of the predatory arthropod taxa under study. The yield of all crops under study was reduced as a result of tree presence, with the strongest reductions observed for grain maize and sugar beet near the trees (<10 m). In addition, alteration of crop quality was observed near tree rows with decreased dry matter concentration of grain samples and increased crude protein concentration of winter cereals.

Uplift modeling is an instrument used to estimate the change in outcome due to a treatment at the individual entity level. Uplift models assist decision-makers in optimally allocating scarce resources. This allows the selection of the subset of entities for which the effect of a treatment will be largest and, as such, the maximization of the overall returns. The literature on uplift modeling mostly focuses on queries concerning the effect of a single treatment and rarely considers situations where more than one treatment alternative is utilized. This article surveys the current literature on multitreatment uplift modeling and proposes two novel techniques: the naive uplift approach and the multitreatment modified outcome approach. Moreover, a benchmarking experiment is performed to contrast the performances of different multitreatment uplift modeling techniques across eight data sets from various domains. We verify and, if needed, correct the imbalance among the pretreatment characteristics of the treatment groups by means of optimal propensity score matching, which ensures a correct interpretation of the estimated uplift. Conventional and recently proposed evaluation metrics are adapted to the multitreatment scenario to assess performance. None of the evaluated techniques consistently outperforms other techniques. Hence, it is concluded that performance largely depends on the context and problem characteristics. The newly proposed techniques are found to offer similar performances compared to state-of-the-art approaches.

Major Depression Disorder (MDD) and Generalized Anxiety Disorder (GAD) often co-occur, but the neurocognitive mechanisms of this co-occurrence remain unknown. Prominent views have pointed to attentional processes as potent mechanisms at play in MDD and GAD, respectively. Yet uncertainty remains regarding the very nature of attentional impairments in patients with co-occurring MDD and GAD. Inspired by contemporary models of attentional networks, we compared the three main attentional networks, namely the orienting, alerting, and executive networks of the Attention Network Task's model, in four groups of patients with, respectively, co-occurring DSM-5 MDD and GAD (n = 30), DSM-5 MDD only (n = 30), DSM-5 GAD only (n = 30), or free from any DSM-5 diagnosis (n = 30). To capture the multivariate nature of our data, we examined between-group differences in the attentional networks through a multivariate analysis of variance. Patients with co-occurring MDD and GAD exhibited more severe impairments in the executive control network than those with only one of the disorders. Although patients with MDD or GAD solely did not differ in terms of attentional impairments, both groups showed significantly more impairments in the executive control network than those free from any DSM-5 diagnosis (all Bonferonni-corrected post-hoc ps < 0.05). Our findings align with a longstanding staging approach to comorbidity whereby, via synergistic effects, co-occurring disorders produce more damages than the sum of each disorder. Here, for the first time, we extended this approach to the executive network of attention in the context of the co-occurrence between MDD and GAD. •Generalized anxiety disorder (GAD) and Major Depressive Disorder (MDD) often co-occur•Recent research has pointed to attentional networks as potent mechanisms of this co-occurrence•We tested whether patients with GAD and MDD differ from those with MDD or GAD solely•Patients with co-occurring GAD and MDD showed impaired executive network•They were significantly more impaired than those with MDD or GAD only

The notion of climate change anxiety has gained traction in the last years. Clayton & Karazsia (2020) recently developed the 22-item Climate Change Anxiety Scale (CAS), which assesses climate change anxiety via a four-factor structure. Yet other research has cast doubts on the very structure of the CAS by calling either for a shorter (i.e. 13 items) two-factor structure or for a shorter single-factor structure (i.e. 13 items). So far, these three different models have not yet been compared in one study. Moreover, uncertainty remains regarding the associations between the CAS and other psychological constructs, especially anxiety and depression. This project was designed to overcome these limitations. In a first preregistered study (n = 305), we translated the scale into French and tested, via confirmatory factor alyses (CFA), whether the French version would better fit with a four-, two-, or single-factor structure, as implied by previous works. We also examined how the CAS factors related to depression, anxiety, and environmental identity. In a second preregistered study, we aimed at replicating our comparison between the three CFA models in a larger sample (n = 905). Both studies pointed to a 13-item version of the scale with a two-factor structure as the best fitting model, with one factor reflecting cognitive and emotiol features of climate change anxiety and the other reflecting the related functiol impairments. Each factor exhibited a positive association with depression and environmental identity but not with general anxiety. We discuss how this two-factor structure impacts the conceptualization of climate change anxiety.

Deep neural network (DNN) architectures such as recurrent neural networks and transformers display outstanding performance in modeling sequential unstructured data. However, little is known about their merit to model customer churn with time-varying data. The paper provides a comprehensive evaluation of the ability of recurrent neural networks and transformers for customer churn prediction (CCP) using time-varying behavioral features in the form of recency, frequency, and monetary value (RFM). RFM variables are the backbone of CCP and, more generally, customer behavior forecasting. We examine alternative strategies for integrating time-varying and non-variant customer features in one network architecture. In this scope, we also assess hybrid approaches that incorporate the outputs of DNNs in conventional CCP models. Using a comprehensive panel data set from a large financial services company, we find recurrent neural networks to outperform transformer architectures when focusing on time-varying RFM features. This finding is confirmed when time-invariant customer features are included, independent of the specific form of feature integration. Finally, we find no statistical evidence that hybrid approaches (based on regularized logistic regression and extreme gradient boosting) improve predictive performance—highlighting that DNNs and especially recurrent neural networks are suitable standalone classifiers for CCP using time-varying RFM measures.

Despite the large-scale dissemination of mindfulness-based interventions, debates persist about the very nature of mindfulness. To date, one of the dominant views is the five-facet approach, which suggests that mindfulness includes five facets (i.e., Observing, Describing, Nonjudging, Nonreactivity, and Acting with Awareness). However, uncertainty remains regarding the potential interplay between these facets. In this study, we investigated the five-facet model via network analysis in an unselected sample ( n  = 1704). We used two distinct computational network approaches: a Gaussian graphical model (i.e., undirected) and a directed acyclic graph, with each model determining the relations between the facets and their relative importance in the network. Both computational approaches pointed to the facet denoting Acting with Awareness as playing an especially potent role in the network system. Altogether, our findings offer novel data-driven clues for the field's larger quest to ascertain the very foundations of mindfulness.

Environmental control in closed environment agricultural systems (CEA) is challenging due to the high energy demand and the dynamic interactions between plants and their heterogeneous phylloclimate. Optimization of crop production in CEA systems therefore requires a thorough understanding of whole-plant functioning and the interconnected plant-climate interactions. Such optimization is limited by an incomplete knowledge of how leaf-level measurements of gas exchange relate to whole-plant processes and how to scale-up point measurements of the heterogeneous environment to inform plant-level decisions. To address both, a dynamic whole-plant gas exchange system was developed to quantify the effect of temperature, relative humidity and light intensity on whole-plant photosynthetic and transpiration rates in lettuce (Lactuca sativa L.). Results showed that light intensity was the primary driver for whole-plant photosynthesis, with temperature optima increasing from 5 °C at a photosynthetic photon flux density (PPFD) of 150 µmol·m−2·s−1 to 13 °C at 400 µmolm−2·s−1. These optima for lettuce plants were 10 to 20 °C lower than those observed at leaf level due to a shifted balance between respiration and photosynthesis within the complex habitus of lettuce. The results showed a decoupling of transpiration and photosynthesis under high relative humidity, with vapour pressure deficit (VPD) values of 0.5 kPa or lower, which physically limited transpiration. The newly developed dynamic gas exchange system has proven to be a helpful tool for examining the relative importance and combined effects of environmental factors on whole-plant photosynthesis and transpiration. Potential future applications of this system include research on phylloclimate, implementation in production facilities, and validation of crop models.

Purpose – Retailers realize that customer churn detection is a critical success factor. However, no research study has taken into consideration that misclassifying a customer as a non-churner (i.e. predicting that (s)he will not leave the company, while in reality (s)he does) results in higher costs than predicting that a staying customer will churn. The aim of this paper is to examine the prediction performance of various cost-sensitive methodologies (direct minimum expected cost (DMECC), metacost, thresholding and weighting) that incorporate these different costs of misclassifying customers in predicting churn. Design/methodology/approach – Cost-sensitive methodologies are benchmarked on six real-life churn datasets from the retail industry. Findings – This article argues that total misclassification cost, as a churn prediction evaluation measure, is crucial as input for optimizing consumer decision making. The practical classification threshold of 0.5 for churn probabilities (i.e. when the churn probability is greater than 0.5, the customer is predicted as a churner, and otherwise as a non-churner) offers the worst performance. The provided managerial guidelines suggest when to use each cost-sensitive method, depending on churn levels and the cost level discrepancy between misclassifying churners versus non-churners. Practical implications – This research emphasizes the importance of cost-sensitive learning to improve customer retention management in the retail context. Originality/value – This article is the first to use the concept of misclassification costs in a churn prediction setting, and to offer recommendations about the circumstances in which marketing managers should use specific cost-sensitive methodologies.

Abstract Monitoring cellular behavior and eventually properly adapting cellular processes is key to handle the enormous complexity of today’s metabolic engineering questions. Hence, transcriptional biosensors bear the potential to augment and accelerate current metabolic engineering strategies, catalyzing vital advances in industrial biotechnology. The development of such transcriptional biosensors typically starts with exploring nature’s richness. Hence, in a first part, the transcriptional biosensor architecture and the various modi operandi are briefly discussed, as well as experimental and computational methods and relevant ontologies to search for natural transcription factors and their corresponding binding sites. In the second part of this review, various engineering approaches are reviewed to tune the main characteristics of these (natural) transcriptional biosensors, i.e., the response curve and ligand specificity, in view of specific industrial biotechnology applications, which is illustrated using success stories of transcriptional biosensor engineering.