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To ensure the integrity and reliability of structures, it is of great importance to understand the fatigue damage mechanism of materials under different amplitude loading conditions. For this purpose, a nonlinear fatigue damage accumulation model based on material configurational forces is developed in this paper. Specially, since the material configurational force is defined on the material space point and directly associated with the evolution of material configuration, it is introduced as an internal variable to describe fatigue damage accumulation. Additionally, the corresponding relation between configurational force and stress state is presented. Meanwhile, the damage increasing among every loading cycle, that is, the damage rate, is dependent on the stress state and damage state variable. Therefore, the formulation of damage rate is proposed by a function of material configurational force and damage state variable. Further, numerical implementation of this proposed nonlinear fatigue damage accumulation model has been carried out through two notched structures to describe the fatigue damage evolution and predict the fatigue life of materials. The results indicate that the numerical results are consistent with previous experimental data. Highlights: A nonlinear damage accumulation model under uniaxial fatigue is proposed.The configurational force is introduced as an internal variable to describe the damage.The numerical results of the proposed model show adequate accuracy. [ABSTRACT FROM AUTHOR]

When human adults make decisions (e.g., wearing a seat belt), we often consider the negative consequences that would ensue if our actions were to fail, even if we have never experienced such a failure. Do the same considerations guide our understanding of other people's decisions? In this paper, we investigated whether adults, who have many years of experience making such decisions, and 6‐ and 7‐year‐old children, who have less experience and are demonstrably worse at judging the consequences of their own actions, conceive others' actions as motivated both by reward (how good reaching one's intended goal would be), and by what we call "danger" (how badly one's action could end). In two pre‐registered experiments, we tested whether adults and 6‐ and 7‐year‐old children tailor their predictions and explanations of an agent's action choices to the specific degree of danger and reward entailed by each action. Across four different tasks, we found that children and adults expected others to negatively appraise dangerous situations and minimize the danger of their actions. Children's and adults' judgments varied systematically in accord with both the degree of danger the agent faced and the value the agent placed on the goal state it aimed to achieve. However, children did not calibrate their inferences about how much an agent valued the goal state of a successful action in accord with the degree of danger the action entailed, and adults calibrated these inferences more weakly than inferences concerning the agent's future action choices. These results suggest that from childhood, people use a degree of danger and reward to make quantitative, fine‐grained explanations and predictions about other people's behavior, consistent with computational models on theory of mind that contain continuous representations of other agents' action plans. [ABSTRACT FROM AUTHOR]

Because of the possibility of getting the right answers for the wrong reasons, the predictive performance of a complex systems model is not by itself a reliable indicator of hypothesis quality for the purposes of scientific learning about processes. The predictive performance of a structurally adequate model should be an emergent property of its functional performance. In this context, any Pareto trade‐off between measures of predictive performance versus functional performance indicates process‐level error in the model; this trade‐off, if it exists, indicates that the model's predictions are right for the wrong functional reasons. This paper demonstrates a novel concept based on information theory that is capable of attributing observed errors to specific processes. To demonstrate that the concept and method hold true for models and observations of real systems, we employ a minimal single‐parameter‐variation sensitivity analysis using a sophisticated ecohydrology model, MLCan, for a well‐monitored field site (Bondville IL Ameriflux Soybean). We identify both functional and predictive error in MLCan, and also evidence of the hypothesized tradeoffs between the two. This trade‐off indicates structural error within MLCan. For example, the sensible heat flux process can be calibrated to achieve good predictive performance at the cost of poor functional performance. In contrast, we find little structural error for processes driven by solar radiation, which appear "right for the right reasons." This method could be applied broadly to pinpoint process error and structural error in a wide range of system models, beyond the ecohydrological scope demonstrated here. Key Points: Information theory can attribute model error to structural and process‐level sourcesPareto‐style trade‐offs exist between functional and predictive performanceThe MLCan ecohydrology model demonstrates both well‐posed and ill‐posed trade‐offs between different processes [ABSTRACT FROM AUTHOR]

Although current exemplar models of category learning are flexible and can capture how different features are emphasized for different categories, they still lack the flexibility to adapt to local changes in category learning, such as the effect of different sequences of study. In this paper, we introduce a new model of category learning, the Sequential Attention Theory Model (SAT-M), in which the encoding of each presented item is influenced not only by its category assignment (global context) as in other exemplar models, but also by how its properties relate to the properties of temporally neighboring items (local context). By fitting SAT-M to data from experiments comparing category learning with different sequences of trials (interleaved vs. blocked), we demonstrate that SAT-M captures the effect of local context and predicts when interleaved or blocked training will result in better testing performance across three different studies. Comparatively, ALCOVE, SUSTAIN, and a version of SAT-M without locally adaptive encoding provided poor fits to the results. Moreover, we evaluated the direct prediction of the model that different sequences of training change what learners encode and determined that the best-fit encoding parameter values match learners' looking times during training. [ABSTRACT FROM AUTHOR]

In two recent papers, Rose and Nichols present evidence in favor of the view that humans represent category essences in terms of a telos, such as honey‐making, and not in terms of scientific essences, such as bee DNA. Here, I challenge their interpretation of the evidence and show that it is directly predicted by the main theory they seek to undermine. I argue that their results can be explained as instances of diagnostic reasoning about scientific essences. [ABSTRACT FROM AUTHOR]

This paper is derived from a keynote talk given at the Google's 2020 Flood Forecasting Meets Machine Learning Workshop. Recent experiments applying deep learning to rainfall‐runoff simulation indicate that there is significantly more information in large‐scale hydrological data sets than hydrologists have been able to translate into theory or models. While there is a growing interest in machine learning in the hydrological sciences community, in many ways, our community still holds deeply subjective and nonevidence‐based preferences for models based on a certain type of "process understanding" that has historically not translated into accurate theory, models, or predictions. This commentary is a call to action for the hydrology community to focus on developing a quantitative understanding of where and when hydrological process understanding is valuable in a modeling discipline increasingly dominated by machine learning. We offer some potential perspectives and preliminary examples about how this might be accomplished. Key Points: Hydrology lacks scale‐relevant theories, but deep learning experiments suggest that these theories should existThe success of machine learning for hydrological forecasting has potential to decouple science from modelingIt is up to hydrologists to clearly show where and when hydrological theory adds value to simulation and forecasting [ABSTRACT FROM AUTHOR]

Clustering is indispensable in the quest for robust vegetation classification schemes that aim to partition, summarise and communicate patterns. However, clustering solutions are sensitive to methods and data and are therefore unstable, a feature that is usually attributed to noise. Viewed through a central‐tendency lens, noise is defined as the degree of departure from type, which is problematic since vegetation types are abstractions of continua, and so noise can only be quantified relative to the particular solution at hand. Graph theory models the structure of vegetation data based on the interconnectivity of samples. Through a graph‐theoretic lens, the causes of instability can be quantified in absolute terms via the degree of connectivity among objects. We simulated incremental increases in sampling intensity in a dataset over five iterations and assessed classification stability across successive solutions derived using algorithms implementing, respectively, models of central‐tendency and interconnectivity. We used logistic regression to model the likelihood of a sample changing groups between iterations as a function of distance to the centroid and degree of interconnectivity. Our results show that the degree to which samples are interconnected is a more powerful predictor of instability than the degree to which they deviate from their nearest centroid. The removal of weakly interconnected samples resulted in more stable classifications, although solutions with many clusters were apparently inherently less stable than those with few clusters, and improvements in stability flowing from the removal of outliers declined as the number of clusters increased. Our results reinforce the fact that clusters abstracted from continuous data are inherently unstable and that the quest for stable, fine‐scale classifications from large regional datasets is illusory. Nevertheless, our results show that using models better suited to the analysis of continuous data may yield more stable classifications of the available data. [ABSTRACT FROM AUTHOR]

Habitat fragmentation and isolation threaten the survival of several wide‐ranging species, such as tigers, through increased risk from diseases, disasters, climate change, and genetic depression. Identification of the habitat most likely to achieve connectivity among protected areas is vital for the long‐term persistence of tigers. We aimed to improve the mapping of potential transfrontier protected area corridors for tigers by connecting sites within the Terai Arc Landscape in Nepal and to those in India, highlighting targeted conservation actions needed along these corridors to maintain long‐term connectivity. We used least‐cost corridor modeling and circuit theory to identify potential corridors and bottlenecks in the study area. The landscape's resistance to tigers' movement was gathered from expert opinions to inform corridor modeling. We identified nine potential tiger corridors in the Terai Arc Landscape—Nepal that aligned strongly with the remaining intact habitats of the Siwalik landscape, which could facilitate tiger movement. Banke‐Bardia and Chitwan‐Parsa‐Valimiki complexes and Lagga‐Bhagga and Khata corridors were identified as high‐priority conservation cores and corridors. While our model exhibited congruence with most established corridors in the landscape, it has identified the need to enhance existing corridors to improve landscape connectivity. Several pinch points posing an increased risk to connectivity were identified. Most of these were located near the protected area boundaries and along the Nepal–India border. The Siwalik landscape holds the key to long‐term connectivity in the study area; however, immediate conservation attention is needed, particularly at pinch points, to secure this connectivity for tigers. Validation of identified corridors through empirical research and their conservation is a priority. [ABSTRACT FROM AUTHOR]

The cracks formed by the breaking and collapse of the overlying strata are the oxygen supply channels for the continuous combustion of coal fires. The development of cracks is of great significance for determining the scope of the fire area and controlling the fire efficiently and accurately. To study the development law of cracks, a breaking model of the overlying strata in the shallow coal seam fire area is established on the basis of beam theory considering the coupling effect of gravity load and temperature field. The physical model of the coal fire is established based on the similarity principle. The temperature data from rock strata in the model are monitored by the infrared thermal imager. The dynamic distribution function of the roof temperature field is obtained by fitting. According to the breaking model theory, the position of the maximum tensile stress on the roof of the shallow fire area is the middle of the lower surface. The theoretical calculation value of the first breaking distance of the roof is 0.575 m, which is consistent with the experimental results of the physical model. The maximum tensile stress of the roof under the thermal‐mechanical coupling is slightly less than that under the load only. For shallow coal seams, the thermal stress generated by the coal fire increases the first breaking distance of the roof. [ABSTRACT FROM AUTHOR]

The Movement Disorder Society revised version of the Unified Parkinson's Disease Rating Scale (MDS‐UPDRS) parts 2 and 3 reflect patient‐reported functional impact and clinician‐reported severity of motor signs of Parkinson's disease (PD), respectively. Total scores are common clinical outcomes but may obscure important time‐based changes in items. We aim to analyze longitudinal disease progression based on MDS‐UPRDS parts 2 and 3 item‐level responses over time and as functions of Hoehn & Yahr (H&Y) stages 1 and 2 for subjects with early PD. The longitudinal item response theory (IRT) modeling is a novel statistical method addressing limitations in traditional linear regression approaches, such as ignoring varying item sensitivities and the sum score balancing out improvements and declines. We utilized a harmonized dataset consisting of six studies with 3573 subjects with early PD and 14,904 visits, and mean follow‐up time of 2.5 years (±1.57). We applied both a unidimensional (each part separately) and multidimensional (both parts combined) longitudinal IRT models. We assessed the progression rates for both parts, anchored to baseline H&Y stages 1 and 2. Both the uni‐ and multidimensional longitudinal IRT models indicate significant worsening time effects in both parts 2 and 3. Baseline H&Y stage 2 was associated with significantly higher baseline severities, but slower progression rates in both parts, as compared with stage 1. Patients with baseline H&Y stage 1 demonstrated slower progression in part 2 severity compared to part 3, whereas patients with baseline H&Y stage 2 progressed faster in part 2 than part 3. The multidimensional model had a superior fit compared to the unidimensional models and it had excellent model performance. [ABSTRACT FROM AUTHOR]

We present an evolutionary game theory model in which polarization, antipathy, and political activism are simultaneous consequences of the evolution of individuals' ideologies and their attitudes toward other ideologies. We show that the evolutionary process is likely to result in a vicious path with individuals becoming increasingly extreme and polarized on the ideological spectrum and the society ending up with two politically engaged groups sharing no common grounds and strong hatred against each other. [ABSTRACT FROM AUTHOR]

A nonlinear model is proposed in light of the theory of isodamage curves, to assess the cumulative fatigue damage under multistage loading. The isodamage curves were developed through the theory of the S‐N fatigue damage envelope, proposed by Pavlou. The adopted functional form of the fatigue damage is a commonly accepted general form. In the present work, a stress function for the fatigue life exponent is derived with the aid of the S‐N curve only. In the proposed nonlinear model, no adjusting parameters are necessary for fatigue damage estimation. The fatigue life prediction of the derived model is compared with experimental results of various alloys and existing models. The comparison is made for C35, C45, Al‐2024‐T42, 15HM, A336 GR 5, and A387 GR22 for two‐stage loading high‐low and low‐high, and Al 6082‐T6 for multistage loading. Highlights: Nonlinear damage function in light of the theory of the S‐N fatigue damage envelopeValidity of the model in both HCF and LCFVerification of the new fatigue model with various metallic materialsDiscussion of the model effectivenes in relation to the known fatigue damaging process [ABSTRACT FROM AUTHOR]

The purpose of this review is to discuss the development and the contribution of the experiments and numerical simulations in compression‐induced failure characteristics of flawed rock specimens. Investigation is essential to understand the fundamental failures occurring in a rock bridge, for assessing anticipated and actual performances of the structures built on or in rock masses. The review begins by representing and discussing compression‐induced crack initiation and growth in flawed rocks and explaining the significance of studying these issues in Section 1. The crack initiation and growth behaviors in flawed rocks under uniaxial, biaxial, and triaxial compressions are described in depth in Section 2 where it is expected to distinguish 2‐D crack growth from 3‐D crack growth. After that, the failure characteristics of flawed rock specimens due to temperature treatments and hydraulic pressure are systematically reviewed. Numerical studies of the compression‐induced failure characteristics of flawed rock specimens based on different numerical theories and numerical models are comprehensively evaluated in Section 3. In Section 4, the new findings obtained recently from experimental and numerical studies are drawn. Finally, some aspects of prospective research and a brief summary are presented in Sections 5 and 6. [ABSTRACT FROM AUTHOR]

The use of traits is growing in ecology and biodiversity informatics, with initiatives to collate trait data and integrate it into biodiversity databases. A need to develop better predictive capacity for how species respond to environmental change has in part motivated this focus. Functional traits are of most interest—those with a defined link to individual survival, development, growth and reproduction.Non‐trivial challenges arise immediately in deciding which functional traits to prioritise and how to characterise them. Here we discuss the advantages of a theoretical perspective for defining functional traits in the context of dynamical systems models of energy and mass exchange that link organisms to their environments. We argue that the theoretical frameworks upon which such models are built (biophysical ecology, metabolic theory) provide clear criteria to decide upon functional trait definitions, measurement requirements and associated metadata, via their mathematical connection to model parameters and state variables, and thus to system performance (survival, development, growth and reproduction).We distinguish 'descriptive' traits from 'functional' traits by dividing the latter into four classes—parameter, model, threshold and estimation—according to whether they are model parameters, define model structure, are threshold state variables or can be used to estimate model parameters.We develop a decision tree for this classification and illustrate it in the context of mammalian heat exchange but emphasise the scheme's generality to any kind of organism.We show how a theoretical perspective may change how we prioritise traits for collection and databasing in ways that are not necessarily more difficult to achieve, especially with new technologies, and provide clear guidance for requisite metadata. The use of theoretically driven criteria for prioritising the collection of functional trait data will maximise the generality, quality and consistency of trait databases for comparative analyses. Such databases will simultaneously facilitate the development of integrated predictive modelling frameworks across multiple organisational scales from individuals to ecosystems. ​ A free Plain Language Summary can be found within the Supporting Information of this article. [ABSTRACT FROM AUTHOR]

Introduction: In response to the COVID‐19 pandemic, health systems had to quickly adopt a process for enabling targeted and patient‐centered care delivery. This case study describes the utilization of Harrison's open‐systems model to create an approach for rapid adoption of existing telehealth technologies in a large scale academic medical center. Methods: An internal group of organizational developers, was enlisted to enable this effort. Local networks were employed and organized into focus groups to rapidly assess and address barriers to adoption and informal interviews with executive leadership were conducted to align organizational goals. Interventions include rapid deployment of focused and data driven provider, staff and patient support bolstered by effective communication and resource management. Results: There was an increase in the number of patient portal activation codes by 75% during the month of March. The number of activation codes generated expectedly decreased in April as many patients now had activated patient portals. The video visit volume as a result of provider self‐scheduling increased went from a baseline of 0 to over 600 clinical visits. Discussion: Experienced organizational development programs can facilitate adoption of change. The faculty practice of CUIMC has years of experience with supporting wide scale operational change centered on technology. In this case, providing engaged networks with tailored content that is focused on the process and available technology promoted rapid adoption and optimization. Conclusion: In the setting of profound external pressure, experience with the ability to focus on tailoring training and support to the culture of the organization helped to rapidly increase the availability and success of telehealth visits for a large scale academic medical center. [ABSTRACT FROM AUTHOR]

Khemlani et al. (2018) mischaracterize logic in the course of seeking to show that mental model theory (MMT) can accommodate a form of inference (I, let us label it) they find in a high percentage of their subjects. We reveal their mischaracterization and, in so doing, lay a landscape for future modeling by cognitive scientists who may wonder whether human reasoning is consistent with, or perhaps even capturable by, reasoning in a logic or family thereof. Along the way, we note that the properties touted by Khemlani et al. as innovative aspects of MMT-based modeling (e.g., nonmonotonicity) have for decades been, in logic, acknowledged and rigorously specified by families of (implemented) logics. Khemlani et al. (2018) further declare that I is "invalid in any modal logic." We demonstrate this to be false by our introduction (Appendix A) of a new propositional modal logic (within a family of such logics) in which I is provably valid, and by the implementation of this logic. A second appendix, B, partially answers the two-part question, "What is a formal logic, and what is it for one to capture empirical phenomena?". [ABSTRACT FROM AUTHOR]

Traditional occupancy models that record detection/non‐detection (DND) of a species typically rely on either spatial or temporal survey replication to estimate model parameters. Recording the time until a species is first encountered after starting a survey is often possible with little extra effort and such time‐to‐detection (TTD) surveys may be more efficient than pure DND surveys. Using continuous time data, TTD occupancy models can in theory estimate occupancy and detection parameters using a single TTD survey. However, the robustness and general applicability of TTD models has not been widely addressed and their effectiveness in different study systems remains unknown.We use simulations and bird data of 63 species from a field study in the Karoo region of South Africa to explicitly compare estimates of occupancy, detection and species richness between DND and TTD models under various levels of survey replication and for species with different occupancy and detection characteristics.Simulations revealed that for inconspicuous species single survey TTD models can perform better or equally as well as DND models with a higher number of replicates. This effect was attenuated in widespread species. The benefits of TTD models were more pronounced at low survey replicates and performance of the two methods converged quickly as the number of survey replicates increased. However, results from the field data showed that a single TTD survey was not adequate to reliably estimate occupancy, detection and species richness; especially in rare and inconspicuous species. Increasing the number of TTD surveys to two replicates improved the models substantially.Our results demonstrate that the general utility of TTD surveys depends on the characteristics of the species considered in the study. A single TTD survey may be sufficient in some study designs but is unlikely to be sufficient in most multi‐species field scenarios where communities are made up of species that have a wide range of detection and occupancy probabilities. TTD surveys do provide benefits, however, in that data can be used to construct detection curves which can be used to guide survey effort in the design of future studies. [ABSTRACT FROM AUTHOR]

Collaborative fundraising mechanisms—known as joint fundraising appeals—have received increasing attention as innovative ways to reduce fundraising competition between aid agencies. While some aid agencies participate in joint fundraising activities, others consider that the disadvantages outweigh the benefits. Based on the analysis of two‐stage sequential game theory models, the article identifies key factors that affect aid agencies' decisions about participation in a joint fundraising mode. Our analysis indicates that expected effects of the joint fundraising mode depend on aid agencies' efficiency levels. For efficient aid agencies, the joint mode functions as a buffer to prevent them from fierce competition. In the case of inefficient aid agencies, the competitive mode saves aid agencies from underinvestment in fundraising activities. We classify representative allocation rules currently applied in the humanitarian context into three types. Our analysis shows that these allocation rules only lead to beneficial allocations for limited circumstances. Following the analysis of the three representative allocation rules, the present work also analyzes aid agencies' negotiation‐based allocation without predetermined allocation rules, using generalized Nash bargaining. [ABSTRACT FROM AUTHOR]

In active contour models (snakes), various vector force fields replacing the gradient of the original external energy in the equations of motion are a popular way to extract the object boundary. Gradient descent method is usually used to obtain the equations of motion by minimising the energy functional. However, it always suffers from local minimum in extracting complex geometries because of non-convex functional. Gradient descent method with adaptive momentum term is proposed in this study. First, an acceleration function of evolution is defined. Then, the adaptive momentum term is obtained by calculating the product between the edge stopping function and the defined acceleration function. Finally, adaptive momentum is compatible with the snakes. The edge stopping function is used to decide the influence region of the momentum, whereas the defined acceleration function determines the magnitude of the momentum. It is used to extract the complex geometries (such as deep concavity) when adding the adaptive momentum into some snakes, such as gradient vector field or vector field convolution snakes. On the other hand, the proposed method also accelerates the rate of convergence. It can be applied to extract a single object in real images. The experimental results show that the proposed method is effective and efficient. [ABSTRACT FROM AUTHOR]

Ecologists now recognize that the classical theory of population and community ecology is severely limited by its inability to represent the effects of individual adaptive behavior, but we are still looking for ways to accommodate adaptive behavior in models and theory. This presentation directly addressed one of the most fundamental problems with integrating the theory of behavioral ecology with higher ecological levels: The fitness-optimization approaches that dominate behavioral ecology cannot be used in IBMs that include such fundamental elements of population ecology as competition among individuals. The SPT theory presented by Harvey is a modification of the classical fitness-optimization theory of behavioral ecology, in which we assume organisms have behaviors because those behaviors convey fitness. [Extracted from the article]