Spatial and temporal models in plant ecology: logic and causality

In the paper, I argue that in order to make credible ecological predictions for terrestrial ecosystems in a changing environment, we need empirical plant ecological models that are fitted to spatial and temporal ecological data. Here, it is advocated to use structural equation models in a hierarchical framework with latent variables. Furthermore, it is an advantage that the proposed hierarchical models are analogous to well-known theoretical epistemological models of how knowledge is obtained.

Event-driven temporal models for explanations - ETeMoX: explaining reinforcement learning

Modern software systems are increasingly expected to show higher degrees of autonomy and self-management to cope with uncertain and diverse situations. As a consequence, autonomous systems can exhibit unexpected and surprising behaviours. This is exacerbated due to the ubiquity and complexity of Artificial Intelligence (AI)-based systems. This is the case of Reinforcement Learning (RL), where autonomous agents learn through trial-and-error how to find good solutions to a problem. Thus, the underlying decision-making criteria may become opaque to users that interact with the system and who may require explanations about the system’s reasoning. Available work for eXplainable Reinforcement Learning (XRL) offers different trade-offs: e.g. for runtime explanations, the approaches are model-specific or can only analyse results after-the-fact. Different from these approaches, this paper aims to provide an online model-agnostic approach for XRL towards trustworthy and understandable AI. We present ETeMoX, an architecture based on temporal models to keep track of the decision-making processes of RL systems. In cases where the resources are limited (e.g. storage capacity or time to response), the architecture also integrates complex event processing, an event-driven approach, for detecting matches to event patterns that need to be stored, instead of keeping the entire history. The approach is applied to a mobile communications case study that uses RL for its decision-making. In order to test the generalisability of our approach, three variants of the underlying RL algorithms are used: Q-Learning, SARSA and DQN. The encouraging results show that using the proposed configurable architecture, RL developers are able to obtain explanations about the evolution of a metric, relationships between metrics, and were able to track situations of interest happening over time windows.

Geocoding and spatio-temporal modeling of long-term PM2.5 and NO2 exposure: the Mexican Teachers´ Cohort

Epidemiological studies on air pollution in Mexico often use the environmental concentrations of pollutants as measured by monitors closest to the home of participants as exposure proxies, yet this approach does not account for the space gradients of pollutants and ignores intra-city human mobility. This study aimed to develop high-resolution spatial and temporal models for predicting long-term exposure to PM2.5 and NO2 in ~16,500 participants from the Mexican Teachers’ Cohort study. We geocoded the home and work addresses of participants, and used secondary source information on geographical and meteorological variables as well as other pollutants to fit two generalized additive models capable of predicting monthly PM2.5 and NO2 concentrations during the 2004-2019 period. Both models were evaluated through 10-fold cross-validation, and showed high predictive accuracy with out-of-sample data and no overfitting (CV-RMSE=0.102 for PM2.5 and CV-RMSE=4.497 for NO2). Participants were exposed to a monthly average of 24.38 (6.78) mg/m3 of PM2.5 and 28.21 (8.00) ppb of NO2 during the study period. These models offer a promising alternative for estimating PM2.5 and NO2 exposure with high spatio-temporal resolution for epidemiological studies in the Mexico City Metropolitan Area.

A General Framework for Spatio-Temporal Modeling of Epidemics With Multiple Epicenters: Application to an Aerially Dispersed Plant Pathogen

The spread dynamics of long-distance-dispersed pathogens are influenced by the dispersal characteristics of a pathogen, anisotropy due to multiple factors, and the presence of multiple sources of inoculum. In this research, we developed a flexible class of phenomenological spatio-temporal models that extend a modeling framework used in plant pathology applications to account for the presence of multiple sources and anisotropy of biological species that can govern disease gradients and spatial spread in time. We use the cucurbit downy mildew pathosystem (caused by Pseudoperonospora cubensis) to formulate a data-driven procedure based on the 2008 to 2010 historical occurrence of the disease in the U.S. available from standardized sentinel plots deployed as part of the Cucurbit Downy Mildew ipmPIPE program. This pathosystem is characterized by annual recolonization and extinction cycles, generating annual disease invasions at the continental scale. This data-driven procedure is amenable to fitting models of disease spread from one or multiple sources of primary inoculum and can be specified to provide estimates of the parameters by regression methods conditional on a function that can accommodate anisotropy in disease occurrence data. Applying this modeling framework to the cucurbit downy mildew data sets, we found a small but consistent reduction in temporal prediction errors by incorporating anisotropy in disease spread. Further, we did not find evidence of an annually occurring, alternative source of P. cubensis in northern latitudes. However, we found a signal indicating an alternative inoculum source on the western edge of the Gulf of Mexico. This modeling framework is tractable for estimating the generalized location and velocity of a disease front from sparsely sampled data with minimal data acquisition costs. These attributes make this framework applicable and useful for a broad range of ecological data sets where multiple sources of disease may exist and whose subsequent spread is directional.

Comparison of Bayesian Spatio-temporal Models of Tuberculosis in Makassar, Indonesia

South Sulawesi province ranks sixth-highest in tuberculosis (TB) in Indonesia. Makassar ranks the highest in South Sulawesi. Spatio-temporal modelling can identify the areas with high risk as well as the temporal relative risk of disease. We analysed the tuberculosis cases data from Makassar City Health Office for 15 districts over seven years from 2012 to 2018. Seven models of Bayesian Spatio-temporal (BST) Conditional Autoregressive (CAR) were applied by using the measures of goodness of fit (GOF) namely, DIC and WAIC. The results showed that BST CAR localised model with G = 3 has the lowest DIC and BST CAR adaptive has the lowest WAIC. Based on the preferred model (Bayesian ST CAR localised with G=3), Panakukang district had the highest relative risk of TB in 2012, 2013, and 2014, while Makassar district had the highest relative risk of TB in 2015, 2016, and 2017. Mamajang had the highest relative risk of TB in 2018.

Spirals, spikes and spinning wheels: Temporal models challenging the sustainability agenda in relation to fast fashion consumption

Fast fashion has entered the political arena with specific reference to sustainability. To date the agenda has largely been informed by an examination of production methodologies and techniques documenting the rapid turnover of trends, the speed and efficiency of the production process and the lack of socially cohesive labour practices that it consistently engenders. Whilst governments seek to raise awareness and begin to generate initiatives to tackle the environmental fall out of fast fashion, this article turns its attention to the temporal patterns of consumer behaviour and why such a high percentage of what we buy is readily discarded soon after point of purchase. All stages in this linear model of consumption, it is argued, are shaped by a very specific relationship to time that ultimately informs our buying habits. Utilizing the work of the philosopher A. N. Whitehead and adopting a more psychosocial approach to fashion consumption, this article recognizes that even when purposefully seeking to consume sustainably, a greater need to align our use of time with a results-driven mindset locates the acquisition of something new as a highly achievable goal. As a consequence, rather than positioning the rationale for fashion purchases in the context of conspicuous consumption and emulation, here it functions to mitigate a lack of temporal control in other areas of our lives. In response, it is proposed that any successful attempts at tackling the problems associated with fast fashion must also seek to understand the temporal dynamics of consumption. For whilst governments’ attention is turned to ways to reduce the environmental impact associated with the production of clothing, increasing consumer demand derived from ‘neophilia’ will negate and indeed overturn any successes achieved. The conclusion will therefore suggest that promotional culture has a duty to explore ways in which it might engender greater emotional attachments to what we own. Future research into brand messaging, exploring the consequences of placing emphasis on quality over quantity and a subsequent potential deepening of a sense of brand loyalty, is also recommended as a way forward.

Trauma, time and information

PurposeIn this article the author would like to discuss information and the causal-temporal models as discussed in trauma theory and reports from trauma therapy. The article discusses two modes of temporality and the role of narrative explanations in informing the subject as to their past and present.Design/methodology/approachConceptual analysis.FindingsInformation in trauma has different meanings, partly as a result of different senses of temporality that make up explanations of trauma in trauma theory. One important meaning is that of explanation itself as a cause or a therapeutic cure for trauma.Research limitations/implicationsThe research proposes that trauma and trauma theory need to be understood in terms of the role of explanation, with explanation being understood as persuasion. This follows the historical genealogy of trauma theory from its origins in hypnosis and psychoanalysis.Originality/valueThe article examines the possibility of unconscious information and its effects in forming psychological subjectivity.