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Introduction In this paper, a novel model for predicting the spread patterns of human and computer epidemics on the basis of complex temporal networks is presented. This research addresses existing challenges in forecasting behaviors and crisis escalation by analyzing the dissemination of viruses and malware via cumulative data and related behavioral patterns. Method The proposed model employs the analysis of historical data and temporal dependencies to simulate various interactions between networks and temporal influences, thereby predicting crisis scenarios. Results The proposed model, which considers various interactions between networks and temporal influences, is capable of better simulating nonlinear and complex behaviors. This innovation can assist researchers and decision-makers in developing more effective strategies for managing health and security crises. Ultimately, this research provides a deeper understanding of the processes underlying the spread of diseases and malware and can serve as a foundation for future studies in this field.

Understanding the complex interplay between contact networks in social host species and individual movement decisions is essential for designing effective disease control strategies in wild animals. We use a spatially explicit eco‐epidemiological individual‐based model to investigate the effect of host movement decisions on disease spread and persistence and reconcile findings with the patterns of a long‐term outbreak dataset. Using alternative mechanistic host movement submodels, in which decisions where to move are affected by either landscape structure or density of conspecifics, we validate simulations of disease spread against the known long‐term patterns of spread of classical swine fever in wild boar in Northern Germany by applying the same sampling scheme as in the field. We compare simulated with observed data using three key metrics: age class distribution of infected hosts, speed of pathogen spread, and spatial distribution patterns of infected individuals. We found two main movement strategies matching the observed pathogen spread and spatial patterns: correlated, habitat‐driven movement and competition‐driven movement. Furthermore, the only movement strategy that was able to recreate the observed trend in the age class distribution of infected host individuals was the implicit movement, purely based on host density. Our results show the significant impact of habitat composition and host population density on disease outbreak dynamics.

As a prominent forest pest on international quarantine lists, pine wilt disease (PWD) is characterized by its ease of transmission, rapid onset, high mortality rate, and the complexity of its prevention and control. The disease inflicts devastating damage on pine forest ecosystems and biodiversity in affected regions, resulting in substantial losses of ecological and economic value. This study uses 40 years of county-level data on PWD occurrences in China to investigate the historical spatiotemporal distribution patterns, the spreading process, and the impact of PWD on forest ecosystems. We divided the spread of PWD in China into three stages based on the changes in the number of affected areas. We used SaTScan spatial scanning to analyze the spatiotemporal distribution patterns and regional characteristics of the disease in each stage. Based on the spatial relationships of the affected areas, we identified two types of spread, namely continuous spread and leapfrogging spread, and conducted ecological models of the two spreading processes to describe the spread of PWD over the past 40 years. The results indicate that PWD has two major expansion periods in China. They show a diffusion pattern spreading from points to areas, ultimately forming four clusters with regional characteristics. Driving factors were selected for model construction based on the biological characteristics and spatiotemporal distribution patterns of PWD. The Susceptible (SIS) model and Random Forest (RF) model achieve good results in simulating continuous and leapfrog spread. By integrating the models of the two spreading processes, we can clearly quantify the 40-year spread of PWD in China. The long-term dynamic ecological modeling of PWD, based on historical dissemination characteristics, facilitates the development of disaster prediction models and the maintenance of forest ecosystems while also providing case studies for the invasion and spread of forest pests and pathogens.

Climate and infectious diseases each present critical challenges on a warming planet, as does the influence of climate on disease. Both are governed by nonlinear feedbacks, which drive multi‐annual cycles in disease outbreaks and weather patterns. Although climate and weather can influence infectious disease transmission and have spawned rich literature, the interaction between the independent feedbacks of these two systems remains less explored. Here, we demonstrate the potential for long‐lasting impacts of El Niño–Southern Oscillation (ENSO) events on disease dynamics using two approaches: interannual perturbations of a generic SIRS model to represent ENSO forcing, and detailed analysis of realistic specific humidity data in an SIRS model with endemic coronavirus (HCoV‐HKU1) parameters. Our findings reveal the importance of considering nonlinear feedbacks in susceptible population dynamics for predicting and managing disease risks associated with ENSO‐related weather variations. Plain Language Summary Many infectious diseases are sensitive to environmental conditions, such as temperature, precipitation, and humidity, and exhibit year‐to‐year variations in disease outbreaks. These variations can be affected by interannual climate variability driven by the El Niño‐Southern Oscillation (ENSO), as well as population immunity driven by previous outbreaks. This work models how these climate and disease factors may interact, and finds that effects of ENSO on disease can grow in magnitude and last beyond the duration of ENSO events due to the lasting effects of population immunity on infections. We also find that consecutive ENSO events, which often occur in reality, may have amplified multi‐year effects on infections. These results motivate high‐quality disease surveillance to more accurately estimate population immunity, which supports better prediction of climate‐related changes in infectious disease outbreaks. Key Points El Niño‐Southern Oscillation (ENSO) impacts on infectious disease outbreaks may last multiple years via lagged nonlinear effects on population immunity Lagged immune response may cause larger changes in disease the year after an ENSO event or after consecutive ENSO events Population immunity should therefore be considered when investigating the impacts of ENSO on infectious disease

In this paper, we address an extension of the classical two-dimensional bin packing (2BPP) that considers the spread of customer orders (2BPP-OS). The 2BPP-OS addresses a set of rectangular items, required from different customer orders, to be cut from a set of rectangular bins. All the items of a customer order are dispatched together to the next stage of production or distribution after its completion. The objective is to minimize the number of bins used and the spread of customer orders over the cutting process. The 2BPP-OS gains relevance in manufacturing environments that seek minimum waste solutions with satisfactory levels of customer service. We propose integer linear programming (ILP) models for variants of the 2BPP-OS that consider non-guillotine, 2-stage, restricted 3-stage, and unrestricted 3-stage patterns. We are not aware of integrated approaches for the 2BPP-OS in the literature despite its relevance in practical settings. Using a general-purpose ILP solver, the results show that the 2BPP-OS takes more computational effort to solve than the 2BPP, as it has to consider several symmetries that are often disregarded by the traditional 2BPP approaches. The solutions obtained by the proposed approaches have similar bin usage and significantly better metrics of customer satisfaction concerning the approaches that neglect the customer order spread.

Commodities flow through trade networks across the world, with trajectories that can be effectively modelled using approaches similar to those used in human mobility studies. Yet, documenting these movements comprehensively is challenging due to data sparsity, cost, and privacy constraints. Origin-destination (OD) matrices provide a widely used framework for representing mobility, although they inherently omit locations not directly observed as either origins or destinations. This incompleteness creates gaps across different geographical scales, constraining our ability to characterise movement patterns in underrepresented areas. In this study, we introduce a vector-field-based method to address these persistent data challenges. By transforming OD data into continuous vector fields, we capture spatial flow patterns more comprehensively than traditional network approaches, while also enabling robust analysis of mobility directions. Our approach incorporates interpolation techniques that handle incomplete and sparse datasets effectively; when approximately 500 out of 853 areas are removed, 189 areas (36%) maintain degree deviations of less than 15 degrees, showing that the general direction of flow is preserved for over one-third of the impacted areas and enabling continuous spatial analysis. We apply this framework to cattle trade data from Minas Gerais, Brazil. Cattle movements are particularly significant as they directly impact disease transmission, including foot-and-mouth disease. Accurately modelling these flows supports effective disease surveillance and preparedness, with benefits for both animal health and economic stability. Our analysis reveals distinct spatial clusters of trade behaviour, temporal patterns in flow directions, and seasonally varying critical points likely associated with known periodicities in cattle trade driven by breeding cycles, slaughter schedules, and fluctuations in global demand. While previous vector-field studies focused on human mobility, our framework addresses the distinct challenges of commodity flows, where aggregated OD data, sparse observations, and lack of data are the norm. It enables inference in unobserved areas which is a critical capability for modelling scenarios such as disease spread. This approach enhances our capacity to infer flow patterns from incomplete datasets and advances understanding of large-scale commodity trade dynamics.

Design patterns are important in object-oriented programming because they offer design motifs, elegant solutions to recurrent design problems, which improve the quality of software systems. Design motifs facilitate system maintenance by helping to understand design and implementation. However, after implementation, design motifs are spread throughout the source code and are thus not directly available to maintainers. We present DeMIMA, an approach to identify semi-automatically micro-architectures that are similar to design motifs in source code and to ensure the traceability of these micro-architectures between implementation and design. DeMIMA consists of three layers: two layers to recover an abstract model of the source code, including binary class relationships, and a third layer to identify design patterns in the abstract model. We apply DeMIMA to five open-source systems and, on average, we observe 34% precision for the considered 12 design motifs. Through the use of explanation-based constraint programming, DeMIMA ensures 100% recall on the five systems. We also apply DeMIMA on 33 industrial components.

Understanding spatial controls on wildfires is important when designing adaptive fire management plans and optimizing fuel treatment locations on a forest landscape. Previous research about this topic focused primarily on spatial controls for fire origin locations alone. Fire spread and behavior were largely overlooked. This paper contrasts the relative importance of biotic, abiotic, and anthropogenic constraints on the spatial pattern of fire occurrence with that on burn probability (i.e., the probability that fire will spread to a particular location). Spatial point pattern analysis and landscape succession fire model (LANDIS) were used to create maps to show the contrast. We quantified spatial controls on both fire occurrence and fire spread in the Midwest Ozark Highlands region, USA. This area exhibits a typical anthropogenic surface fire regime. We found that (1) human accessibility and land ownership were primary limiting factors in shaping clustered fire origin locations; (2) vegetation and topography had a negligible influence on fire occurrence in this anthropogenic regime; (3) burn probability was higher in grassland and open woodland than in closed-canopy forest, even though fire occurrence density was less in these vegetation types; and (4) biotic and abiotic factors were secondary descriptive ingredients for determining the spatial patterns of burn probability. This study demonstrates how fire occurrence and spread interact with landscape patterns to affect the spatial distribution of wildfire risk. The application of spatial point pattern data analysis would also be valuable to researchers working on landscape forest fire models to integrate historical ignition location patterns in fire simulation.

Understanding the dynamics of wildfire regimes is crucial for both regional forest management and predicting global interactions between fire regimes and climate. Accordingly, spatially explicit modeling of forest fire ecosystems is a very active field of research, including both generic and highly specific models. There is, however, a second field in which wildfire has served as a metaphor for more than 20 years: statistical physics. So far, there has been only limited interaction between these two fields of wildfire modeling. Here we show that two typical generic wildfire models from ecology are structurally equivalent to the most commonly used model from statistical physics. All three models can be unified to a single model in which they appear as special cases of regrowth‐dependent flammability. This local “ecological memory” of former fire events is key to self‐organization in wildfire ecosystems. The unified model is able to reproduce three different patterns observed in real boreal forests: fire size distributions, fire shapes, and a hump‐shaped relationship between disturbance intensity (average annual area burned) and diversity of succession stages. The unification enables us to bring together insights from both disciplines in a novel way and to identify limitations that provide starting points for further research.

Sutherland et al. (2020) used simulations from a physics-based numerical fire behaviour model to investigate the effect of the ignition protocol (namely length, direction and rate of ignition) on the spread rates measured in experimental fires. They concluded that the methods used by Cruz et al. (2015) were inadequate as the fires were not spreading at the pseudo-steady state when rate of spread measurements were made, thereby raising questions about the validity of several published experimental and modelling results. Fire spread measurement data from three different outdoor experimental burning studies conducted in grass fuels are used to show that, contrary to the claims of Sutherland et al. (2020), the fire behaviour data collected in Cruz et al. (2015) were from fires spreading in the pseudo-steady-state regime and thus are compatible with data from larger experimental plots. A discussion is presented addressing why Sutherland et al. (2020) simulations were unable to replicate real-world data.