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Modeling and computer simulations, we claim, should be considered core philosophical methods. More precisely, we will defend two theses. First, philosophers should use simulations for many of the same reasons we currently use thought experiments. In fact, simulations are superior to thought experiments in achieving some philosophical goals. Second, devising and coding computational models instill good philosophical habits of mind. Throughout the paper, we respond to the often implicit objection that computer modeling is “not philosophical.”

Driving range and fast charge capability of electric vehicles are heavily dependent on the 3D microstructure of lithium-ion batteries (LiBs) and substantial fundamental research is required to optimise electrode design for specific operating conditions. Here we have developed a full microstructure-resolved 3D model using a novel X-ray nano-computed tomography (CT) dual-scan superimposition technique that captures features of the carbon-binder domain. This elucidates how LiB performance is markedly affected by microstructural heterogeneities, particularly under high rate conditions. The elongated shape and wide size distribution of the active particles not only affect the lithium-ion transport but also lead to a heterogeneous current distribution and non-uniform lithiation between particles and along the through-thickness direction. Building on these insights, we propose and compare potential graded-microstructure designs for next-generation battery electrodes. To guide manufacturing of electrode architectures, in-situ X-ray CT is shown to reliably reveal the porosity and tortuosity changes with incremental calendering steps. The 3D microstructure of the electrode predominantly determines the electrochemical performance of Li-ion batteries. Here, the authors show that the microstructural heterogeneities lead to non-uniform Li insertion and current distribution while graded-microstructures improve the performance.

The complexities of modern biomedicine are rapidly increasing. Thus, modeling and simulation have become increasingly important as a strategy to understand and predict the trajectory of pathophysiology, disease genesis, and disease spread in support of clinical and policy decisions. In such cases, inappropriate or ill-placed trust in the model and simulation outcomes may result in negative outcomes, and hence illustrate the need to formalize the execution and communication of modeling and simulation practices. Although verification and validation have been generally accepted as significant components of a model’s credibility, they cannot be assumed to equate to a holistic credible practice, which includes activities that can impact comprehension and in-depth examination inherent in the devel-opment and reuse of the models. For the past several years, the Committee on Credible Practice of Modeling and Simulation in Healthcare, an interdisciplinary group seeded from a U.S. interagency initiative, has worked to codify best practices. Here, we provide Ten Rules for credible practice of modeling and simulation in healthcare developed from a comparative analysis by the Committee’s multidisciplinary membership, followed by a large stakeholder com-munity survey. These rules establish a unified conceptual framework for modeling and simulation design, implementation, evaluation, dissemination and usage across the modeling and simulation life-cycle. While biomedical science and clinical care domains have somewhat different requirements and expectations for credible practice, our study converged on rules that would be useful across a broad swath of model types. In brief, the rules are: (1) Define context clearly. (2) Use contextually appropriate data. (3) Evaluate within context. (4) List limitations explicitly. (5) Use version control. (6) Document appropriately. (7) Disseminate broadly. (8) Get independent reviews. (9) Test competing imple-mentations. (10) Conform to standards. Although some of these are common sense guidelines, we have found that many are often missed or misconstrued, even by seasoned practitioners. Computational models are already widely used in basic science to generate new biomedical knowledge. As they penetrate clinical care and healthcare policy, contributing to personalized and precision medicine, clinical safety will require established guidelines for the credible practice of modeling and simulation in healthcare.

This paper presents an approach of unsupervised learning of clusters from a citation database, and applies it to a large corpus of articles in philosophy to give an account of the structure of the discipline. Following a list of journals from the PhilPapers-archive, 68,152 records were downloaded from the Reuters Web of Science-Database. Their citation data was processed using dimensionality reduction and clustering. The resulting clusters were identified, and the results are graphically represented. They suggest that the division of analytic and Continental philosophy in the considered timespan is overstated; that analytical, in contrast to Continental philosophy does not form a coherent group in recent philosophy; and that metaphors about the disciplinary structure should focus on the coherence and interconnectedness of a multitude of smaller and larger subfields.

In recent years, 3D imaging became an increasingly popular screening modality for high-throughput plant phenotyping. The 3D scans provide a rich source of information about architectural plant organization which cannot always be derived from multi-view projection 2D images. On the other hand, 3D scanning is associated with a principle inaccuracy by assessment of geometrically complex plant structures, for example, due the loss of geometrical information on reflective, shadowed, inclined and/or curved leaf surfaces. Here, we aim to quantitatively assess the impact of geometrical inaccuracies in 3D plant data on phenotypic descriptors of four different shoot architectures, including tomato, maize, cucumber, and arabidopsis. For this purpose, virtual laser scanning of synthetic models of these four plant species was used. This approach was applied to simulate different scenarios of 3D model perturbation, as well as the principle loss of geometrical information in shadowed plant regions. Our experimental results show that different plant traits exhibit different and, in general, plant type specific dependency on the level of geometrical perturbations. However, some phenotypic traits are tendentially more or less correlated with the degree of geometrical inaccuracies in assessing 3D plant architecture. In particular, integrative traits, such as plant area, volume, and physiologically important light absorption show stronger correlation with the effectively visible plant area than linear shoot traits, such as total plant height and width crossover different scenarios of geometrical perturbation. Our study addresses an important question of reliability and accuracy of 3D plant measurements and provides solution suggestions for consistent quantitative analysis and interpretation of imperfect data by combining measurement results with computational simulation of synthetic plant models.

High-frequency repetitive transcranial magnetic stimulation (rTMS) to the left dorsolateral prefrontal cortex (L-DLPFC) shows promise as a treatment for treatment-resistant depression in adolescents. Conventional rTMS coil placement strategies include the 5 cm, the Beam F3, and the magnetic resonance imaging (MRI) neuronavigation methods. The purpose of this study was to use electric field (E-field) models to compare the three targeting approaches to a computational E-field optimization coil placement method in depressed adolescents. Ten depressed adolescents (4 females, age: 15.9±1.1) participated in an open-label rTMS treatment study and were offered MRI-guided rTMS five times per week over 6–8 weeks. Head models were generated based on individual MRI images, and E-fields were simulated for the four targeting approaches. Results showed a significant difference in the induced E-fields at the L-DLPFC between the four targeting methods (χ2=24.7, p<0.001). Post hoc pairwise comparisons showed that there was a significant difference between any two of the targeting methods (Holm adjusted p<0.05), with the 5 cm rule producing the weakest E-field (46.0±17.4V/m), followed by the F3 method (87.4±35.4V/m), followed by MRI-guided (112.1±14.6V/m), and followed by the computational approach (130.1±18.1V/m). Variance analysis showed that there was a significant difference in sample variance between the groups (K2=8.0, p<0.05), with F3 having the largest variance. Participants who completed the full course of treatment had median E-fields correlated with depression symptom improvement (r=−0.77, p<0.05). E-field models revealed limitations of scalp-based methods compared to MRI guidance, suggesting computational optimization could enhance dose delivery to the target.

The present paper introduces a simple framework for modeling the relationship between environmental states, perceptual states, and action. The framework represents situations where an agent’s perceptual state forms the basis for choosing an action, and what action the agent performs determines the agent’s payoff, as a function of the environmental conditions in which the action is performed. The framework is used as the basis for a simulation study of the sorts of correspondence between perceptual and environmental states that are important for successful navigation of the world. Some of the results are surprising and conflict with long held views about the kind of perception-to-environment correspondence that is important for knowledge of the world. The results also raise doubts concerning the view that our perceptual states provide a basis for knowledge of the real structure of the external world.

This paper examines different mechanisms for adjudicating disagreement about distributive justice. It begins with a case where individuals have deeply conflicting convictions about distributive justice and must make a social choice regarding the distribution of goods. Four mechanisms of social choice are considered: social contract formation, Borda count vote, simple plurality vote, and minimax bargaining. I develop an agent-based model which examines which mechanisms lead to the greatest degree of satisfying justice-based preferences over the course iterated social choices. Agents are ascribed two kinds of motivations: they wish to realize justice and to receive a greater package of goods. Each agent seeks to realize her ideal distribution, and the failure to do so leaves agents “disappointed,” resulting in their trading off the pursuit of gains in justice in favor of gains in self-interest. Mechanisms are assessed using the metric of how many agents remain interested in justice over the course of iterated adjudication. The mechanisms are also examined under some non-ideal conditions, such as the presence of power asymmetries or strategic behavior. Several significant results are addressed: social contract formation and simple plurality voting are robust under the conditions considered, bargaining is a highly ineffective means of adjudicating distributive disagreement, and lastly allowing for concessions in justice for gains in self-interest proves to be a crucial mechanism for ensuring the stability of resolutions.

Recent studies have shown that the specific application of transcranial direct current stimulation (tDCS) over the cerebellum can modulate cerebellar activity. In parallel, transcutaneous spinal DC stimulation (tsDCS) was found to be able to modulate conduction along the spinal cord and spinal cord functions. Of particular interest is the possible use of these techniques in pediatric age, since many pathologies and injuries, which affect the cerebellar cortex as well as spinal cord circuits, are diffuse in adults as well as in children. Up to now, experimental studies of cerebellar and spinal DC stimulation on children are completely missing and therefore there is a lack of information about the safety of this technique as well as the appropriate dose to be used during the treatment. Therefore, the knowledge of electric quantities induced into the cerebellum and over the spinal cord during cerebellar tDCS and tsDCS, respectively, is required. This work attempts to address this issue by estimating through computational techniques, the electric field distributions induced in the target tissues during the two stimulation techniques applied to different models of children of various ages and gender. In detail, we used four voxel child models, aged between 5- and 8-years. Results revealed that, despite inter-individual differences, the cerebellum is the structure mainly involved by cerebellar tDCS, whereas the electric field generated by tsDCS can reach the spinal cord also in children. Moreover, it was found that there is a considerable spread toward the anterior area of the cerebellum and the brainstem region for cerebellar tDCS and in the spinal nerve for spinal direct current stimulation. Our study therefore predicts that the electric field spreads in complex patterns that strongly depend on individual anatomy, thus giving further insight into safety issues and informing data for pediatric investigations of these stimulation techniques.