We study a diffusion process on a three-dimensional comb under stochastic resetting. We consider three different types of resetting: global resetting from any point in the comb to the initial position, resetting from a finger to the corresponding backbone, and resetting from secondary fingers to the main fingers. The transient dynamics along the backbone in all three cases is different due to the different resetting mechanisms, finding a wide range of dynamics for the mean-squared displacement. For the particular geometry studied herein, we compute the stationary solution and the mean-square displacement and find that the global resetting breaks the transport in the three directions. Regarding the resetting to the backbone, the transport is broken in two directions but it is enhanced in the main axis. Finally, the resetting to the fingers enhances the transport in the backbone and the main fingers but reaches a steady value for the mean-squared displacement in the secondary fingers.

This review addresses issues of various drift–diffusion and inhomogeneous advection problems with and without resetting on comblike structures. Both a Brownian diffusion search with drift and an inhomogeneous advection search on the comb structures are analyzed. The analytical results are verified by numerical simulations in terms of coupled Langevin equations for the comb structure. The subordination approach is one of the main technical methods used here, and we demonstrated how it can be effective in the study of various random search problems with and without resetting.

Premise: Functional plant ecology seeks to understand how functional traits govern species distributions, community assembly, and ecosystem functions. While global trait datasets have advanced the field, substantial gaps remain, and extracting trait information from text in books, research articles, and online sources via machine learning offers a valuable complement to costly field campaigns.Methods: We propose a natural language processing pipeline that extracts traits from unstructured species descriptions by using classification models for categorical traits and question‐answering models for numerical traits. The pipeline's performance is evaluated on two large databases with over 50,000 species descriptions, utilizing approaches ranging from a keyword search to large language models.Results: Our final optimized pipeline used a transformer architecture and obtained a mean precision of 90.8% (range 81.6–97%) and a mean recall of 88.6% (77.4–97%) across five categorical traits, representing a 9.83% increase in precision and 42.35% increase in recall over a regular expression‐based approach. The question‐answering model yielded a normalized mean absolute error of 10.3% averaged across three numerical traits.Discussion: The natural language processing pipeline we propose has the potential to facilitate the digitization and extraction of large amounts of plant functional trait information residing in scattered textual descriptions.

With the ever increasing interconnectedness among countries and industries, globalization has empowered economies and promoted international trade, capital flow and labor mobility, leading to improved products and services. However, the growing interdependence has also propelled an inherent reliance on joint cooperation which has considerably influenced the complexity of global value chains (GVCs). This plays a significant role in policy decisions, raising questions about trade risks that originate from such interdependence. In this paper, we study the impact of network linkage disturbances on the output supply and input demand of countries. We model the network interconnectedness of countries according to the latest 2016 release of the World Input–Output Database (WIOD) that includes data tables for the period 2000-2014 covering 43 countries as well as a model for the Rest of the World (ROW). We assess the shock distributions across the world economy by quantifying the changes in the network linkages using sensitivity analysis. Our contribution is in the definition of a shock tensor with the purpose of evaluating the impact of link sensitivity. The shock tensor is a straightforward yet comprehensive tool that allows us to obtain ample results at various levels of granularity when combining it with aggregation operators. Our study introduces a novel methodology that enables us to acquire input and output link sensitivities for all country pairings when an economic shock initiates or concludes within a country of interest. This innovative approach also facilitates the analysis of evolving trends in these link sensitivities, providing a comprehensive understanding of the dynamics of shock propagation across the global network. Taking advantage of the time-series nature of the WIOD, our results reveal illustrative visualizations and quantative measures that characterize patterns of shock distribution and relationships among countries throughout the period from 2000 to 2014. Our methodology and results not only uncover valuable trends but also establish a structured approach to better understand the aggregate effects of shock distributions. Thus, this study could be helpful for policy makers to assess trade relationships between countries and obtain quantitative insights for making informed decisions as well as explore the overall state of the globalization as a whole.

Functional plant ecology aims to understand how functional traits govern the distribution of species along environmental gradients, the assembly of communities, and ecosystem functions and services. The rapid rise of functional plant ecology has been fostered by the mobilization and integration of global trait datasets, but significant knowledge gaps remain about the functional traits of the ∼380,000 vascular plant species worldwide. The acquisition of urgently needed information through field campaigns remains challenging, time-consuming and costly. An alternative and so far largely untapped resource for trait information is represented by texts in books, research articles and on the internet which can be mobilized by modern machine learning techniques. Here, we propose a natural language processing (NLP) pipeline that automatically extracts trait information from an unstructured textual description of a species and provides a confidence score. To achieve this, we employ textual classification models for categorical traits and question answering models for numerical traits. We demonstrate the proposed pipeline on five categorical traits (growth form, life cycle, epiphytism, climbing habit and life form), and three numerical traits (plant height, leaf length, and leaf width). We evaluate the performance of our new NLP pipeline by comparing results obtained using different alternative modeling approaches ranging from a simple keyword search to large language models, on two extensive databases, each containing more than 50,000 species descriptions. The final optimized pipeline utilized a transformer architecture to obtain a mean precision of 90.8% (range 81.6-97%) and a mean recall of 88.6% (77.4-97%) on the categorical traits, which is an average increase of 21.4% in precision and 57.4% in recall compared to a standard approach using regular expressions. The question answering model for numerical traits obtained a normalized mean absolute error of 10.3% averaged across all traits. The NLP pipeline we propose has the potential to facilitate the digitalization and extraction of large amounts of plant functional trait information residing in scattered textual descriptions. Additionally, our study adds to an emerging body of NLP applications in an ecological context, opening up new opportunities for further research at the intersection of these fields.