Characterizing the mechanical response of materials traditionally requires solving optimization problems in which model parameters are calibrated or trained to minimize the discrepancy between model predictions and experimental data. This process can be computationally expensive and time-consuming. To overcome this limitation, we propose two neural operator architectures that directly map experimentally measured data to the constitutive functions governing the mechanical response of the material: Physics-Augmented Neural Operators (PANO) and Constitutive Artificial Neural Operators (CANO). The proposed neural operators approximate the mapping between the infinite-dimensional input space of full-field displacement measurements and net reaction forces, and the infinite-dimensional output space of hyperelastic strain-energy density functions. The displacement fields are encoded through Laplacian eigenfunctions to obtain discretization-independent and noise-robust predictions. We constrain the output space to physically admissible material models that satisfy fundamental physical requirements by design. Here, we focus on isotropic, incompressible hyperelasticity and assume that the material properties are uniform across the specimen. The neural operators are trained on simulated data tuples of displacement fields and reaction force functions for a range of material models. Once trained, the neural operators enable near-instantaneous material characterization and require only a single forward pass to infer the strain-energy density function from a given experimental dataset. We test the predictive power of the neural operators for unseen data, noisy data, data with missing information, data from different spatial discretizations, and data from geometries of different sizes. We finally discuss the ill-posedness of the inverse material characterization problem and show that constraining the output function space of our neural operator framework sufficiently regularizes the problem. The trained neural operators enable rapid and robust discovery of polyconvex strain-energy density functions while avoiding the need to solve computationally expensive inverse problems.
GEMSS (GEnerator of Multi-Sphere Shapes) converts 3D surface meshes or voxel grids into multi-sphere representations of granular particles using the recently published MSS algorithm. It computes key physical properties required for discrete element method (DEM) and general multibody dynamics simulations, including particle volume, center of mass, and principal moments and axes of inertia. Implemented as a header-only C++ library, GEMSS is easily integrated into DEM and molecular dynamics frameworks. The library has been integrated into MercuryDPM, which enables on-the-fly generation of multi-sphere particles directly within the simulation loop.
Social entrepreneurship (SE) often seeks to empower marginalized beneficiaries by reshaping unequal power relations and promoting more enduring forms of social change. Yet efforts to empower others inherently involve the exercise of power and may therefore reproduce the very asymmetries they seek to overcome. While this “empowerment paradox” has primarily been conceptualized through discrete empowerer-empoweree relationships, considerably less is known about how it unfolds across broader relational systems, particularly in international social entrepreneurship (ISE), where organizations operate across institutional contexts characterized by Global South-North asymmetries. Drawing on a qualitative multi-case study of six ISE ventures, informed by 45 interviews and extensive archival data, we develop an integrative model explaining how the empowerment paradox is structured, experienced, and navigated in these settings. We show that empowerment is organized through multilevel relational chains spanning ecosystem, organizational, and operational levels, that the paradox is experienced differently depending on actors’ positions within these chains, and that organizations navigate persistent tensions through the recursive interplay of relational and structural mechanisms. Building on these findings, we introduce ethical ambidexterity, the capacity to integrate pragmatic and critical orientations toward the use of power, as a means of engaging with rather than resolving the empowerment paradox.
With the growth in the use of digital payment systems, restaurants have the opportunity to alter how information is visually presented to customers during the payment process. One such option is the directional presentation of tip options included during the transaction (i.e., ascending vs. descending percentage values). In a field study conducted in partnership with an independently owned coffee shop, this research examines how the direction of the tip display impacts the amount customers leave as gratuities. The results support the impact of left-to-right processing, in which customers tip more when tip percentage options are presented in descending order (vs. ascending). These findings provide actionable insights for restaurant operators and demonstrate how subtle changes in digital payment system design can benefit service employees.
Firms disclose information about complex internal activities to keep capital markets informed about their long-term value. We engage two rival predictions, the 'more-is-better' assumption in information risk theory and the 'more-is-noisy' assumption in technological fad perspectives, and ask what shape best characterizes the association between firms' digital transformation (DT) disclosure and the cost of equity capital. Using FTSE350 firms from 2010 to 2020 and adopting a question-driven, phenomenon-based approach, we document a non-linear association in which both traditions describe the same relationship over different ranges of disclosure. Where disclosure is sparse, capital markets cannot separate commitment from fashion and additional disclosure in this range is associated with a higher cost of equity capital; where disclosure becomes substantively informative, the information benefit dominates and the association reverses. The estimated sufficiency threshold, 0.0149% of annual report text, is reached by almost no firm in our sample. Prevailing disclosure practice therefore sits on the ascending portion of the association, while the descending portion is identified from a thin upper tail. The concavity of this relationship is robust to excluding the highest-disclosing firms, to winsorisation, to selection correction, and to leaving out any single firm. We discuss implications for theories of corporate disclosure of complex internal activity and for the design of DT disclosure policy.
