With global wind energy capacity ramping up, accurately predicting damage equivalent loads (DELs) and fatigue across wind turbine populations is critical, not only for ensuring the longevity of existing wind farms but also for the design of new farms. However, the estimation of such quantities of interests is hampered by the inherent complexity in modeling critical underlying processes, such as the aerodynamic wake interactions between turbines that increase mechanical stress and reduce useful lifetime. While high-fidelity computational fluid dynamics and aeroelastic models can capture these effects, their computational requirements limits real-world usage. Recently, fast machine learning-based surrogates which emulate more complex simulations have emerged as a promising solution. Yet, most surrogates are task-specific and lack flexibility for varying turbine layouts and types. This study explores the use of graph neural networks (GNNs) to create a robust, generalizable flow and DEL prediction platform. By conceptualizing wind turbine populations as graphs, GNNs effectively capture farm layout-dependent relational data, allowing extrapolation to novel configurations. We train a GNN surrogate on a large database of PyWake simulations of random wind farm layouts to learn basic wake physics, then fine-tune the model on limited data for a specific unseen layout simulated in HAWC2Farm for accurate adapted predictions. This transfer learning approach circumvents data scarcity limitations and leverages fundamental physics knowledge from the source low-resolution data. The proposed platform aims to match simulator accuracy, while enabling efficient adaptation to new higher-fidelity domains, providing a flexible blueprint for wake load forecasting across varying farm configurations.

As the field of migration studies evolves in the digital age, big data analytics emerge as a potential game-changer, promising unprecedented granularity, timeliness, and dynamism in understanding migration patterns. However, the epistemic value added by this data explosion remains an open question. This paper critically appraises the claim, investigating the extent to which big data augments, rather than merely replicates, traditional data insights in migration studies. Through a rigorous literature review of empirical research, complemented by a conceptual analysis, we aim to map out the methodological shifts and intellectual advancements brought forth by big data. The potential scientific impact of this study extends into the heart of the discipline, providing critical illumination on the actual knowledge contribution of big data to migration studies. This, in turn, delivers a clarified roadmap for navigating the intersections of data science, migration research, and policymaking.

Machine-learning techniques are used in this BJPsych special issue on precision medicine in attempts to create statistical models that make clinically relevant predictions for individual patients. In this primer, we outline five key points that are helpful for a new reader to consider in order to engage with the field and evaluate the literature. These points include the consideration of why we are interested in new statistical approaches, how they may produce individualised predictions, what caveats need to be kept in-mind and why the interest and engagment of clinicians and clinical researchers is critical to successful model development and implementation. We hope that the following primer will provide shared understanding to encourage dialogue between clinical and methodological fields.

Wild radish (Raphanus raphanistrum L.) is a weed found globally in agricultural systems. The facultative winter annual nature of this plant and high genetic variability makes modeling its growth and phenology difficult. In the present study, R. raphanistrum natural seedbanks exhibited a biphasic pattern of emergence, with emergence peaks occurring in both fall and spring. Traditional sigmoidal models were inadequate to fit this pattern, regardless of the predictive environmental variable, and a corresponding biphasic model (sigmoidal + Weibull) was used to describe emergence based on the best parameters. Each best-fit chronological, thermal, and hydrothermal model accounted for at least 85% of the variation of the validation data. Observations on phenology progression from four cohorts were used to create a common model that described all cohorts adequately. Different phenological stages were described using chronological, thermal, hydrothermal, daylength-dependent thermal time, and daylength-dependent hydrothermal time. Integrating daylength and temperature into the models was important for predicting reproductive stages of R. raphanistrum.

Nondestructive means for estimating air potato (also known as air yam; Dioscorea bulbifera L.) biomass will help gauge its management efficacy over time. We developed allometric equations to estimate total and fractional biomass components and densities of aerial bulbils and underground tubers of field-grown D. bulbifera in Florida. We selected four naturally infested sites representing its southern, central, and northern distribution in Florida and measured three independent variables (vine densities, stem diameters, and top heights) of 84 (21 site−1) discrete D. bulbifera patches during late October to early December of 2012. We destructively harvested D. bulbifera biomass, sorted by tubers, stems, leaves, and bulbils; counted units of bulbils and underground tubers (dependent variables); and dried to a constant weight. Mean percentages of tuber, stem, leaf, and bulbil fractions in total biomass were 42.0, 15.6, 26.0, and 16.4, respectively. We developed a parameterized multiplicative prediction model and regression equation for each dependent variable. Slopes of relationships among independent and dependent variables varied by biomass and density (bulbil and tuber) of plant components. Multiplied values of independent variables: all three for total, tuber, stem, and leaf biomass; two (vine base diameter*patch height) for bulbil biomass; two (vine density*patch height) for bulbil density; and only one (stem density) for tuber density provided best (R2-based) prediction values. These models will provide nondestructive methods for estimating biomass components and density of vegetative propagules of naturally growing D. bulbifera. Models are critical for understanding the performance of D. bulbifera in its exotic range, estimating biomass to project control costs, and comparing biomass components and bulbil/tuber densities during pre- and postmanagement periods to gauge control efficacy.

The adenovirus vaccine and benzathine penicillin G (BPG) have been used by the US military to prevent acute respiratory diseases (ARD) in trainees, though these interventions have had documented manufacturing problems. We fit Poisson regression and random forest models (RF) to 26 years of weekly ARD incidence data to explore the impact of the adenovirus vaccine and BPG prophylaxis on respiratory disease burden. Adenovirus vaccine availability was among the most important predictors of ARD in the RF, while BPG was the ninth most important. BPG was a significant protective factor against ARD (incidence rate ratio (IRR) = 0.68; 95% confidence interval (CI) 0.67–0.70), but less so than either the old or new adenovirus vaccine (IRR = 0.39, 95% CI 0.38–0.39 and IRR = 0.11, 95% CI 0.11–0.11), respectively. These results suggest that BPG is moderately predictive of, and significantly protective against ARD, though to a lesser extent than either the old or new adenovirus vaccine.

As researchers and land managers increasingly seek to understand plant invasions and the external (climate) and internal (plant genetics) conditions that govern the process, new insight is helping to answer the elusive question of what makes some invasions successful and others not. Plant invasion success or failure is based on a combination of evolutionary and ecological processes. Abiotic (e.g., climate) and biotic (e.g., plant competition) conditions in the environment and plant genetics (e.g., fitness) combine in either decreasing or increasing invasion, yet it has proven challenging to know exactly which of these conditions leads to success for a given species, even when a wealth of empirical data is available. Further, current regional distribution models for invasive plant species rarely consider biotic and fitness interactions, instead focusing primarily on abiotic conditions. The crucial role of all three factors (climate dynamics, invader fitness, and ecosystem resistance) must not be ignored. Here we construct a three-factor invasion framework from which we develop conceptual models using empirical studies for yellow starthistle, nonnative common reed, and musk thistle, three dissimilar but commonly occurring invasive plant species in North America. We identify how components of the invasion process—rapid population increase, established local dominance, and rapid range expansion—are influenced by ecosystem resistance, invader fitness, and/or climate dynamics, a set of broadly defined factors for each of the three invasive plant species. Our framework can be used to (1) establish research priorities, (2) address gaps in theoretical understanding, and (3) identify invasion process components that can be targeted to improve management. Building on previous models, our unifying framework, which can be used for assessing any invasive plant species having sufficient empirical data, simultaneously shows the influence of ecosystem resistance, invader fitness, and climate dynamics factors on the invasion process.

On-farm implementation of IWM includes the biological and economic rationalization of weed management decisions. This, in turn, requires the ability to predict how weed populations and pressures will change with farm management options such as tillage method. We have developed a set of algorithms that simulate seedling emergence of mixed populations of green and redroot pigweed on the basis of the ecophysiological responses of seed germination and shoot elongation to temperature and, for germination, to moisture. The algorithms were calibrated using field data from 1993 and evaluated with data from two locations in 1994 and 1995. Over both sites and years, for four tillage systems, cumulative emergence was predicted with an overall root mean square error of 1.7%. Overprediction in one year was attributed to moisture shortage. Errors were greater for moldboard plowed plots than for those no-tilled. This decreased ability to predict emergence with increased tillage (soil disturbance) suggests that the algorithms should be modified to account for increased heterogeneity in weed seed distribution and soil moisture within disturbed soil. The algorithms, which were explicitly designed for incorporation within a crop growth model (e.g., CROPSIM), could become a useful part of a decision support system to rationalize weed management. Importantly, they could help predict changes in weed populations as farm management is adjusted, thereby reducing economic and environmental risk in agroecosystems.

This study examines water system characteristics, managers' attitudes and perceptions toward water conservation, and future planning strategies that influence the adoption of water conservation programs for urban and rural communities. We surveyed water system managers in Oklahoma, Arkansas, Tennessee, and Florida; and we parameterized predictive adoption models for price-based (PC) and nonprice-based (NPC) conservation programs. Notably, results suggest that information about the price elasticity of water demand for a community does encourage PC and NPC adoption; and we found no evidence that PC and NPC adoption is jointly considered by water systems.

We address the effect of spatial scale and temporal variation on model generality when forming predictive models for fish assignment using a new data mining approach, Random Forests (RF), to variable biological markers (parasite community data). Models were implemented for a fish host-parasite system sampled along the Mediterranean and Atlantic coasts of Spain and were validated using independent datasets. We considered 2 basic classification problems in evaluating the importance of variations in parasite infracommunities for assignment of individual fish to their populations of origin: multiclass (2–5 population models, using 2 seasonal replicates from each of the populations) and 2-class task (using 4 seasonal replicates from 1 Atlantic and 1 Mediterranean population each). The main results are that (i) RF are well suited for multiclass population assignment using parasite communities in non-migratory fish; (ii) RF provide an efficient means for model cross-validation on the baseline data and this allows sample size limitations in parasite tag studies to be tackled effectively; (iii) the performance of RF is dependent on the complexity and spatial extent/configuration of the problem; and (iv) the development of predictive models is strongly influenced by seasonal change and this stresses the importance of both temporal replication and model validation in parasite tagging studies.

Dengue activity depends on fluctuations in Aedes populations which in turn are known to be influenced by climate factors including temperature, humidity and rainfall. It has been hypothesized that haze may reduce dengue transmission. Due to its geographical location Singapore suffers almost every year from hazes caused by wildfires from Indonesia. Such hazes have a significant impact on pollution indexes in Singapore. We set out to study the relationship of dengue activity and haze (measured as pollution standard index) in Singapore, using ARIMA models. We ran different univariate models, each encompassing a different lag period for the effects of haze and temperature (from lag 0 to lag 12 weeks). We analysed the data on a natural logarithmic scale to stabilize the variance and improve the estimation. No association between dengue activity and haze was found. Our findings do not lend support to the hypothesis that haze is associated with reduced dengue activity in Singapore.