Multiple myeloma as a challenging multidimensional random process: a data-driven web-based application for treatment selection
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The complex structure of aquatic food webs emerges from a few assembly rules
Food-web theory assumes that larger-bodied predators generally select larger prey. This allometric rule fails to explain a considerable fraction of trophic links in aquatic food webs. Here we show that food-web constraints result in guilds of predators that vary in size but have specialized on prey of the same size, and that the distribution of such specialist guilds explains about one-half of the food-web structure. We classified 517 pelagic species into five predator functional groups. Most of these follow three prey selection strategies: a guild following the allometric rule whereby larger predators eat larger prey and two guilds of specialists that prefer either smaller or larger prey than predicted by the allometric rule. Such coexistence of non-specialist and specialist guilds independent from taxa or body size points towards structural principles behind ecological complexity. We show that the pattern describes >90% of observed linkages in 218 food webs in 18 aquatic ecosystems worldwide. The pattern can be linked to eco-evolutionary constraints to prey exploitation and provides a blueprint for more effective food-web models.
Post-processing methods for delay embedding and feature scaling of reservoir computers
Reservoir computing is a machine learning method that is well-suited for complex time series prediction tasks. Both delay embedding and the projection of input data into a higher-dimensional space play important roles in enabling accurate predictions. We establish simple post-processing methods that train on past node states at uniformly or randomly-delayed timeshifts. These methods improve reservoir computer prediction performance through increased feature dimension and/or better delay embedding. Here we introduce the multi-random-timeshifting method that randomly recalls previous states of reservoir nodes. The use of multi-random-timeshifting allows for smaller reservoirs while maintaining large feature dimensions, is computationally cheap to optimise, and is our preferred post-processing method. For experimentalists, all our post-processing methods can be translated to readout data sampled from physical reservoirs, which we demonstrate using readout data from an experimentally-realised laser reservoir system.
Mobilizing power quality and reliability measurements for electricity equity and justice in Africa
In sub-Saharan Africa, urban electricity inequities manifesting as poor power quality and reliability (PQR) are prevalent. Yet, granular PQR data and frameworks for assessing PQR inequities and guiding equitable electricity interventions remain sparse. To address this gap, we present a conceptual framework that leverages energy justice, capability and multidimensional poverty theories alongside concepts relating to power systems to quantify PQR inequities in sub-Saharan Africa. To demonstrate our framework and using 1 year’s worth of remotely sensed PQR data from Accra, Ghana, we assessed the distributive scale of PQR inequities, explored how multidimensional poverty exacerbates these inequities and examined the impact of PQR on households’ domestic capabilities. We found wider patterns of PQR inequities and a link between poor PQR and neighbourhoods with higher multidimensional poverty. We conclude that using remotely sensed data combined with justice and capability frameworks offers a powerful method for revealing PQR inequities and driving sustainable energy transitions.
Dynamic optimizers for complex industrial systems via direct data-driven synthesis
The chemical process industry (CPI) faces significant challenges in improving sustainability and efficiency while maintaining conservative principles for managing cost, complexity, and uncertainty. This work introduces a data-driven approach to dynamic real-time optimization (D-RTO) that addresses the aforementioned concerns by directly extracting process optimization policies from historical plant data. Our method constructs a value function to evaluate trajectory quality and employs weighted regression to derive improved policies. When applied to a plant-wide industrial process control problem, the proposed optimizer demonstrates superior performance in adapting to disturbances while maintaining stability and product quality. These results challenge conventional assumptions regarding the potential of data-driven optimization in the CPI. Although limitations exist due to the black-box nature of neural networks, this study presents a promising avenue for enhancing operational efficiency in industrial settings. The proposed approach offers a practical solution for process optimization, as it leverages readily available historical data and does not require extensive modeling efforts. By demonstrating significant efficiency improvement on a realistic industrial benchmark problem, this work paves the way for the adoption of data-driven optimization techniques in real-world CPI applications.
The mechanism effects of root exudate on microbial community of rhizosphere soil of tree, shrub, and grass in forest ecosystem under N deposition
Forests are composed of various plant species, and rhizosphere soil microbes are driven by root exudates. However, the interplay between root exudates, microbial communities in the rhizosphere soil of canopy trees, understory shrubs, grasses, and their responses to nitrogen (N) deposition remains unclear. Pinus tabulaeformis, Rosa xanthina, and Carex lancifolia were used to investigate root exudates, rhizosphere soil microbial communities, and their responses to N application in forest ecosystem. Root exudate abundances of P. tabulaeformis were significantly higher than that of R. xanthina and C. lancifolia, with carbohydrates dominating P. tabulaeformis and R. xanthina root exudates, fatty acids prevailing in C. lancifolia root exudates. Following N application, root exudate abundances of P. tabulaeformis and R. xanthina initially increased before decreasing, whereas those of C. lancifolia decreased. Microbial number of rhizosphere soil of C. lancifolia was higher than that of P. tabulaeformis and R. xanthina, but there was insignificant variation of rhizosphere soil microbial diversity among plant species. N application exerted promotional and inhibitory impacts on bacterial and fungal numbers, respectively, while bacterial and fungal diversities were increased by N application. Overall, N application had negative effects on root exudates of P. tabulaeformis, inhibiting rhizosphere soil microbial populations. N application suppressed rhizosphere soil microbial populations by increasing root exudates of R. xanthina. Conversely, N application elevated nutrient content in the rhizosphere soil of C. lancifolia, reducing root exudates and minimally promoting microbial populations. This study highlights the importance of understory vegetation in shaping soil microbial communities within forests under N deposition.
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