This study examines how partisan identities shape citizens’ perceptions of democratic and undemocratic behavior by politicians. Drawing on theories of partisan-motivated reasoning, we argue that partisan identities serve as powerful directional goals that unconsciously bias how citizens evaluate political behaviors against democratic principles. Crucially, we theorize that affective polarization amplifies these partisan rationalization biases by strengthening in-party attachment and out-party animosity. Through a survey experiment in South Korea, we find that citizens systematically rationalize their democratic perceptions based on partisan affiliation, perceiving both regular and undemocratic behaviors by co-partisan politicians as more democratic than similar behaviors by out-partisan politicians. Remarkably, citizens even view undemocratic behaviors by co-partisan politicians as more democratic than regular behaviors by out-partisan politicians. Furthermore, we demonstrate that affective polarization significantly amplifies these partisan rationalization biases. Citizens with higher levels of affective polarization are more susceptible to partisan rationalization of democratic perceptions and show stronger perceptual biases compared to those with lower affective polarization. These findings help explain how partisan rationalization and affective polarization can distort citizens’ perceptions of democracy, potentially undermining their ability to serve as a check against democratic backsliding.

Abstract Cryptocurrency is becoming very important in global finance, especially in developing countries. It can help more people get financial services and make financial transactions transparent. But it uses a lot of electricity, which can harm the environment. This paper looks at how cryptocurrency can help economic growth while also being friendly to nature. Using simple math models, tables, examples, and studies, it suggests ways to balance technology and the environment. Keywords: Cryptocurrency, Sustainable Development, Emerging Economies, Financial Inclusion, Blockchain.

The development of socio-cognitive skills depends on understanding others' behavioral cues. We studied 43 toddlers (21 girls, 22 boys, aged 10-37 months; 74% White Hispanic), including 21 deaf and hard of hearing (DHH) children, using data collected in Catalonia (Spain) between November 2022 and July 2023. An experimenter indicated a hidden toy's location to an assistant either by a point with gaze or by gaze only. Both DHH and hearing toddlers understood the point with gaze cue. However, only the hearing group interpreted the gaze-only cue by reliably searching for the hidden toy. These findings reveal early language-related differences in social-cognitive development that may underlie later challenges in complex theory of mind reasoning.

ABSTRACT This article reconstructs Thomas Hobbes’s theory of nonverbal reasoning and examines its implications for animal cognition. While Hobbes is often read as reducing reason to verbal computation, close analysis of Leviathan, De Corpore, and earlier texts reveals a more nuanced account in which reasoning originates as the imaginative “computation” of phantasms. While acknowledging discrepancies between different works, the article emphasizes their underlying coherence and development. It argues that Hobbes recognizes a form of nonverbal animal reasoning grounded in perceptual “comparison,” though he denies animals the capacity to engage in reasoning involving mnemonic or imaginary content. This account reveals a structural continuity between human and animal logical thinking, while also highlighting the limitations of the latter. The article situates Hobbes within early modern debates on reason, prudence, and universals, showing how his radical nominalism reinforces the primacy of imagination in reasoning. In doing so, it clarifies Hobbes’s contribution to the philosophy of mind and underscores his relevance to ongoing debates about the nature and limits of animal cognition.

Abstract In recent years, models of a fortiori argumentation from the field of artificial intelligence and law, developed to describe legal case-based reasoning based on precedent, have been successfully applied to improve interpretability of data-driven decision systems. To aid with these applications, we further develop the theory of a fortiori case-based reasoning by extending the knowledge representations on which these models operate. More specifically, we modify the representations to accommodate incomplete information, as well as to incorporate both dimensional (as opposed to binary) and hierarchical (as opposed to unstructured) information. This results in four models—one for each combination of accommodating dimensional or hierarchical information. We investigate their formal properties, and find they are monotonic with respect to the addition of new precedents and of new facts, and that some are conservative extensions of other models. In addition, we exemplify each through a running example from the penitentiary law domain.

IntroductionDepression is a highly prevalent mental disorder worldwide, and its cognitive rigidity—characterized by persistent negative beliefs resistant to countervailing evidence—remains a critical puzzle in philosophical psychiatry and clinical psychology. Existing theories of abductive reasoning have struggled to explain why similar adversities lead to rigid negative cognition in some individuals but adaptive coping in others.MethodsDrawing on a hierarchical reconstruction of Peircean abduction, this study develops a three-layer model of depressive inferential pathology. The theoretical framework integrates insights from embodied cognition, existential phenomenology, and epistemic consequentialism to analyze the formal structure of depressive reasoning.ResultsThe model identifies three interlocking inferential failures: (1) First-order pathology: Fixation of negatively self-referential abductive explanations, narrowing explanatory space; (2) Core pathology: Failure of meta-abduction, eliminating reflective revision of explanatory practices and cognitive adaptability; (3) Inferential extension: Rigid abductive conclusions are treated as absolute premises for destructive deductive reasoning, generating self-negating conclusions and closed ruminative loops. This model unifies clinical phenomena such as rumination and cognitive distortion, and clarifies the transition from situational responses to chronic pathology.DiscussionThe findings suggest that depressive cognitive rigidity stems not from negative belief content alone, but from structural defects in reasoning. Effective intervention should focus on restoring meta-abductive capacity—treating one’s own explanatory practices as revisable—complementing traditional approaches that target belief correction. This framework bridges philosophical psychiatry and clinical theory, offering a unified account of depression’s cognitive persistence and resistance to intervention.

ABSTRACT For counselors, the welfare of clients and the protection of the community are grounded in comprehension and implementation of ethical principles in practice. Counselors face increasing ethics complexity as organizational and political influences conflict with client’s self-interest. Two thought-experiments involving a Tarasoff-like dilemma were used to explore factors related to ethical decision-making by clinicians in definitive and ambiguous duty-to-warn situations. Outcomes demonstrated that legal, moral, and social factors influenced ethical decision-making in a non-probability sample of 221 U.S. counselors who participated in the thought-experiment study. Increasing ethical uncertainty was related to a decrease in direct action, stronger influence of moral and social factors, and the stronger influence of ethical self-efficacy on decision-making. This study bridges the cognitive model of moral reasoning and social cognitive theory, extending the concept of agency into the ethical domain and suggesting refinements to moral reasoning models. Implications are discussed.

Wildland fire behaviour is strongly governed by the coupled effects of wind and terrain slope, yet the literature remains fragmented across experimental, empirical, mathematical, and physics-based modelling traditions. A systematic scoping review with narrative synthesis was performed (Web of Science, Scopus, and Google Scholar plus citation chaining), screening studies for explicit wind–slope treatment with reported forcings and outcomes. Across more than 150 studies, slope benches, wind tunnels, trenches/canyons, and field burns show that upslope–wind alignment promotes flame attachment and a shift from radiation-led to convection-led preheating (often near 20–30° slopes and moderate winds), whereas opposing or downslope forcing lifts flames and suppresses spread; confined geometries can trigger eruptive acceleration. Mathematical analogues and empirical models provide fast predictions using compact wind/slope modifiers and enable scenario and burn-probability mapping but typically prescribe coupling and miss regime transitions. Physics-based LES/CFD and coupled atmosphere–fire systems resolve terrain–flow feedback sand can yield reduced-order laws suitable for embedding into operational tools, albeit at higher computational cost and with validation gaps. Benchmarks are consolidated, approaches are compared using a common rubric (fidelity, validation, applicability, cost, and operational utility), and priorities are identified for cross-scale datasets, firebrand transport in complex terrain, and real-time coupled prediction.

Abstract Background: Combination therapy aimed at modulating the tumor microenvironment (TME) is a robust strategy to improve response to checkpoint inhibition and broaden indications for use in patients with metastatic breast cancer. The epigenetic modulator, entinostat when combined with dual checkpoint blockade; nivolumab and ipilimumab demonstrated promising results in our Phase Ib trial (NCI-0944) and in our recent meta-analysis (AACR-3225, 2025). Use of a cancer systems immunology approach accelerated discovery of the complex mechanism of response to treatment. Lastly, we demonstrate the potential for math modeling to predict response similar to RECIST, and inform organ specific response, a powerful tool with potential for use toward a more personalized approach for patients. Methods: To identify mechanisms of response to combination therapy in a preclinical model, we used knowledge-guided subclustering of single-cell RNA-sequencing data and cell circuits analysis to predict salient interactions. Multiparametric flow cytometry and ex-vivo immune suppression assays were used to validate preclinical findings. Imaging mass cytometry and bulk RNA sequencing of patient samples were used to validate findings in patients. Derivation of dynamic mathematical models of tumor-immune modulatory responses were then fit through innovative use of both preclinical (sequencing) and clinical (proteomics) data - to predict that a combination of effects on the TME is necessary for response. Adaptation of this mathematical model was then made to determine its potential for use in prediction of site-specific response in metastatic lesions. We introduced methods employing posterior parameter sampling and simulation to create virtual tumor populations, enabling extrapolation beyond the data to predict probabilities of response in metastatic lesions, even when no data exists at that site. Results: In the mouse lung TME, we identified 39 cell states and salient interactions, of which myeloid, T cell, and B cell subpopulations were most affected by treatment in mice bearing metastatic breast cancer treated with combination therapy. Functional immunologic assays verified inhibition of the ICAM pathway in myeloid cells partially recapitulated treatment effects on CD8+ T cells. We also found evidence that treatment increased anti-tumor IgG production. Analysis of patient biopsies via spatial proteomics corroborated preclinical findings: in responders, we observed increased B cell activation, mature tertiary lymphoid structures (TLS), and increased CD8+ T cell—macrophage distances with treatment. We demonstrated clinical utility of the mathematical model via Bayesian parameter inference with clinical responses measured by RECIST. This revealed that only the immunosuppression parameters were predictive of response; parameters controlling cytotoxicity were uninformative. We also show that the model can predict response at sites that have yet to develop disease—a tool which could be considered for future trials to predict overall response rates based on mechanism of drug response. Conclusions: We conclude that epigenetic modulation via HDACi induces a carefully orchestrated set of changes in plasma cells and CD8 T cells with MDSCs and macrophages to sensitize the TME to checkpoint inhibition. Significant changes in TLS formation and macrophage -T cell interactions in biopsies from patient responders validate findings. More broadly we provide a framework for the discovery of cell-cell interactions that control responses in complex TMEs. We also demonstrate how interdisciplinary data integration fuels this new field of cancer systems immunology to accelerate discovery of mechanisms of successful immunotherapeutic response in previously unresponsive solid tumor types. Citation Format: E. T. Roussos Torres, E. Gonzalez, J. Kreger, Y. Liu, X. Wu, A. Barbetta, A. G. Baugh, B. Al-Zubeidy, J. Jang, S. M. Shin, Z. M. Zhang, V. Stearns, R. M. Connolly, W. Jin Ho, J. Emamaullee, A. L. MacLean. Cancer systems immunology unravels complexity of reversing immune suppression and predicts beyond RECIST in metastatic breast cancer [abstract]. In: Proceedings of the San Antonio Breast Cancer Symposium 2025; 2025 Dec 9-12; San Antonio, TX. Philadelphia (PA): AACR; Clin Cancer Res 2026;32(4 Suppl):Abstract nr PS3-13-16.

When positive effort belief affects second-order theory of mind reasoning in 6- to 8-year-olds

Distributing cumulative spinal loads among a diverse workforce – using math modelling to explore workload equality vs MSD risk equity in workload assignment policies

There is a long-standing debate about the role of visual mental images in reasoning. Knauff and Johnson-Laird's (2002) Visual Imagery Impedance Effect (VIIE) suggests visual imagery can hinder abstract reasoning, as evidenced by slower responses to visual compared to spatial and control problems. Aphantasia, reduced or absent visual imagery, offers a unique opportunity to test this hypothesis. In an online version of the reasoning paradigm used in VIIE studies, aphantasics and typical imagers completed three problem types (visual, spatial, control), while reaction times and accuracy scores were measured. In addition, a second classification, based on the Object-Spatial Imagery and Verbal Questionnaire (OSIVQ), was employed to differentiate participants according to their cognitive style and explore possible performance differences between the visualiser, spatialiser and verbaliser clusters. While our study replicated the VIIE in typical imagers, demonstrating robust evidence of a slowdown for visual problems in comparison to spatial and control ones, the effect was inconclusive for aphantasics. Although inconclusive, our results suggest that the VIIE may be smaller in participants with aphantasia than in those without. Furthermore, when complete aphantasics are distinguished from hypophantasics, we observed that the former may have a smaller VIIE than the latter. Finally, our OSIVQ classification-based analyses revealed a meaningful slowdown in reasoning for the visualiser cluster, pointing to a possible influence of cognitive style on performance. Overall, our results demonstrated the importance of considering the influence of mental images and cognitive styles in theories of reasoning.

Suppositions can be classified as indicative versus subjunctive and full versus partial. We propose a causal account of suppositional reasoning that naturally unifies all four types of reasoning based on this classification, provides a justification of the rather heterogenous canonical update rules, and gives rise to a new update rule for the partial subjunctive case in terms of generalized imaging.

Empathy abounds in paediatrics and is deemed a valuable trait that enhances child and family care. Concurrently, research indicates there has been a decline in paediatric training applications, both medical and nursing, and there are challenges to workforce retention related to empathy exhaustion. While the cause is unclear and likely multifactorial, there may be a correlation with empathy levels, requiring analysis by policy makers and governing bodies. Empathy is a disposition, generally understood as cognitive or affective, and I propose here that clinician empathy exists on a continuum. At the affective extreme, there may be an intense emotional transference or pathological empathy response-I use the portmanteau 'empathological' to describe this. Further, that this response may be associated with negative sequelae, and compromise child and family care when complexity, uncertainty, and tragedy co-occur. Identifying the appropriate empathy dose and duration is therefore key to mitigate harm to all parties. Developing strategies to harness empathy by judiciously employing reason and moral theory could be protective. To help understand empathy bounds and balance, I outline the moral foundations of clinical empathy and weigh its benefits and burdens in clinical settings. I conclude that reasoned empathy, which draws on specific elements of Paul Bloom's analysis of rational compassion, allows for engaging empathetically with children and families without paralysing moral action by overly deeply relating to tragic circumstances. Attending to a form of reasoned empathy could ultimately inform healthcare staff selection and training to sustain a healthier paediatric workforce, and lead to better care for sick children.

Agriculture keeps feeding the world, but today’s farms face tougher soils, uneven nutrients, patchy care, and also spreading crop illnesses. Old ways of checking soil or plants rely on labs, chemicals, even specialists - costing too much, taking too long, and leaving out smaller growers. At the same time, missing live updates means spotting sickness late, which often slashes harvests. Now, thanks to smart algorithms, neural networks, plus visual recognition tech, cheap photo-driven tools can help fix these gaps fast. This study introduces Green Thumb - a smart tool running online that checks plant health and soil quality through phone pictures alone. Instead of needing lab tests, it spots crop diseases, judges how green the farm is, guesses nutrients in dirt, plus plans upkeep tasks on its own. Built with Next.js up front and powered by FastAPI behind the scenes, it runs CNNs, uses Mobile Net to pull image details, along with math models for guessing mineral levels. Rather than relying on single methods, it mixes feature pulls from different sources, gives live answers, and shows data clearly through charts you can click around. Tests show it nails pH and NPK estimates, sorts sick plants right most times, works well across crops like corn, tomatoes, or beans. Real growers tried it out, said they liked using it, found it helpful without being tricky. So this isn't just another flashy app; it's built cheap, scales easily, helps small farms stay sharp where tools and cash are tight.

Conceptual similarity as aggregation over feature sets in geometric spaces.

We currently understand the basic mechanisms of crystallization, but when applying them to the reality of crystals, a number of questions arise regarding crystal lattices, crystal growth by addition of the crystal structure itself, nucleation, and atomic and molecular growth and proportions within the crystal structure. All these points will be addressed systematically, and ultimately, we will arrive at a homogeneous and reasonable general theory of crystallization based on the use of molecules, rather than the atom, as the building blocks of the crystal. The system used will be the analysis of existing crystallization theories, selecting only those models capable of explaining the crystal in its entirety, not just in parts.The true importance of this study lies not in the use of concepts such as addition, bonding points, or crystal lattices, as these have long been employed in crystallography. The truly novel aspect is having determined the precise moment when the crystal nucleus and the crystal's geometric shape are formed. This occurs when the first molecules come together, creating a geometric shape. Instead of adding more identical geometric shapes, this shape is covered by a crystal lattice composed of molecules. Logic dictates this crystal growth, as we have observed that some crystals, due to the complexity of their form, cannot grow by addition. Once addition is ruled out, the only remaining method is the covering of crystal lattices.

ABSTRACT This study investigates how political partisanship and emotional responses influenced Taiwanese public trust in government during COVID‐19. Drawing on crisis management and partisan motivated reasoning theories, we conducted a two‐phase survey ( N = 2136) examining attitudes toward six significant pandemic events. Political affiliation was the predominant predictor of attitudes toward governmental pandemic responses, with substantial differences between pan‐Green and pan‐Blue supporters. Education and relative deprivation also emerged as consistent predictors, with higher education corresponding to more critical evaluations. Most notably, negative emotions—particularly anger—significantly moderated the relationship between political affiliation and government trust. Anger demonstrated stronger moderating effects than fear across all models. These findings contradict the “rally‐around‐the‐flag” effect, suggesting Taiwan's pre‐existing political cleavages remained salient and were amplified by emotional responses during the pandemic, advancing understanding of differential emotional influences on political attitudes during crises within competitive democratic contexts.

This research creates a simple math model to look at household water use in Peshawar. It uses information collected from 200 homes. The model looks at how family size, income, and the number of appliances that use water affect daily water use. The findings show that family size is the best predictor of water use. Each additional person in a household increases water use. Owning more appliances also increases water use by about 42 liters per appliance each day. This shows how technology affects home water demand. Income also matters, but not as much. It suggests that as people's living standards rise, they will use more water. The model correctly predicts water use 58% of the time. This is pretty good for a simple model. The research shows that simple models are helpful when there isn't much data. It gives practical ideas for policymakers to create conservation and water management plans.

Networking theories of quantitative reasoning and mathematical reasoning to explore students’ understanding of functions