University of Colorado Colorado Springs

• Multiscale chain-based FE framework for double-network gel mechanics • Coupled damage and viscoelasticity drive path-dependent anisotropy • Predicts hysteresis, damage localization, and crack initiation • Energy-based formulation captures fracture resistance evolution • Physics-informed framework for designing tough soft materials Double-network (DN) gels exhibit an exceptional combination of stretchability, toughness, and energy dissipation, making them promising materials for soft robotics, biomedical devices, and flexible electronics. These outstanding properties arise from the interplay between a brittle, sacrificial primary network and a ductile, extensible secondary network. However, capturing the complex, anisotropic, and path-dependent mechanical response of DN gels - especially under multiaxial and non-uniform loading - remains a major modeling challenge. This work introduces a three-dimensional multiscale constitutive model that integrates progressive chain scission, anisotropic damage, viscoelastic relaxation, and inter-network interactions. The primary network is modeled as an entropic assembly of chains with stochastic lengths undergoing irreversible damage, upscaled via microsphere integration to capture directionality. The secondary network follows a generalized Maxwell formulation to reproduce rate-dependent dissipation. Energy coupling between the two networks enables the model to reflect microstructural rearrangement, directional softening, and residual anisotropy. The model is implemented as a UMAT in ABAQUS/Standard and validated against experimental data under both homogeneous and non-uniform loading conditions. It accurately captures nonlinear stress-strain behavior, hysteresis, and anisotropic softening resulting from prior deformation. In pre-notched specimens under pure shear, simulations reproduce force-displacement response, stress localization, and damage evolution up to crack initiation. An energy-based analysis highlights the roles of reversible and dissipated energy, with predicted critical energy release rates closely matching experiments across pre-stretch levels. The model offers a predictive, physically grounded framework for understanding and engineering tough, damage-resistant DN gels.

Abstract This paper presents a novel application of control barrier functions (CBFs) for estimating state of power (SOP) during charging and discharging cycles of lithium-ion batteries. We define SOP as the maximum amount of power that can be maintained over a specified time period. The proposed algorithm predicts the maximum achievable power level within a constraint set defining the operational boundaries of the cell, namely, state of charge (SOC), voltage, and core temperature. To demonstrate the efficacy of this approach, we simulate battery performance under the Urban Dynamometer Driving Schedule (UDDS), a representative profile of city driving conditions. Comparisons with i) model predictive control (MPC) and ii) a conventional bisection-based approach illustrate the merits of the CBF-based method in terms of practicality for real-world automotive applications. The aim of this study is to advance efforts to create safer and more effective battery management solutions for electric vehicles and energy storage technologies.

The globalization of higher education has increased cross-cultural educational exchanges. As international students navigate their new environment, they face numerous challenges. Challenges during the transitional phase are especially critical as they diminish their sense of mattering and marginalize. This qualitative study explores the initial arrival experiences of eleven international students at a public university in the U.S. through the lens of the theory of marginality and mattering. This phenomenological study's findings reveal that international students’ transitional challenges included issues related to housing, food, and transportation. Their sense of mattering is not linear and is highly dependent on specific spaces, recognition from staff and educators, and relationships with peers. Overall, the findings indicate that the adjustment of international students is situational and requires sustained, institutionally tailored support. Lack of this support marginalizes them, diminishing their sense of worthiness and negatively impacting their academic experience in the U.S.

Purpose The purpose of this paper is to develop a quantitative framework that translates cyber attribution uncertainty into defensive control priorities. Organizations lack systematic methods to convert sector-specific attribution probabilities into differentiated security investments. This study introduces a probability-weighted tactic-scoring function that converts estimated public attribution status into ranked defensive priorities for both known and unknown attackers, providing a defensible basis for resource allocation decisions. Design/methodology/approach This study analyzes 14,096 cyber incidents (2016–2023) using a two-stage framework. Stage 1 uses logistic models with sector-by-attack-class interactions to estimate attribution probabilities. Stage 2 integrates these with MITRE ATT&CK behavioral data using Bayesian-smoothed frequencies, generating separate priority scores for known and unknown attackers. Bootstrap validation and sensitivity analyses confirm robustness. Findings The framework reveals a fundamental divide in defensive priorities based on public attribution status. Defense Evasion (TA0005) is the universal top priority. The decision value lies in secondary priorities, which diverge as a function of attribution likelihood: Persistence (TA0003) dominates when attribution is unlikely (<40%), while Discovery (TA0007) dominates when attribution is likely (>70%). This pattern, whether reflecting adversary strategy or differential observability, provides actionable guidance for resource allocation. Patterns remain stable under parameter variation and resampling. Practical implications Organizations can map predicted attribution to control priorities. Low-attribution sectors benefit from emphasizing persistence detection; high attribution sectors benefit from discovery-focused controls. This study illustrates how tactic priorities translate to NIST SP 800–53 control families. Originality/value The probability-weighted scoring functional generalizes to any context with estimable class distributions, behavioral frequencies and uncertainty gates. This framework bridges attribution research and defensive strategy by quantitatively linking empirical attribution patterns to sector-specific control priorities, moving organizations beyond generic best practices toward attribution-aware security investment.

Physical injury events can lead to significant distress, but early psychosocial intervention may promote coping and prevent the development of psychopathology. This study evaluates a pilot program built upon a partnership with a U.S. Level 1 trauma center to serve these purposes. It aimed to (a) identify demographic and clinical characteristics of patients who engaged in therapy as an indicator of who such a program can serve and (b) evaluate the program's feasibility by examining patients' reported mental health-related changes and feedback about the program. The program provided 4-6 weeks of flexible, trauma-informed, patient-tailored, cognitive behaviorally oriented psychotherapy that emphasized trauma psychoeducation, coping self-efficacy, and self-compassion to facilitate symptom reduction, psychological growth, and preparation for transitioning to longer term trauma-focused therapy when appropriate (i.e., stepped care). Of the 149 patients referred to the program, 38 engaged in therapy, and 16 completed pre- to postsurveys including open-ended items soliciting feedback about the program and validated quantitative measures. Patient feedback responses were subjected to thematic analyses related to facets of feasibility. Patients' feedback included suggestions and appreciation of the program's accessibility and flexibility as well as evidence supporting the program's feasibility, in terms of acceptability, demand, and limited-efficacy testing. Relatedly, substantial quantitative improvements in posttraumatic stress disorder symptoms, depressive symptoms, coping self-efficacy, self-compassion, and posttraumatic growth were observed. This work demonstrates the potential utility of brief stabilization therapy programs to meet postinjury psychosocial needs, and it highlights successes and challenges that may inform the development and implementation of future programs. (PsycInfo Database Record (c) 2026 APA, all rights reserved).

This narrative review highlights the growing population of older adults (OA) who use cannabis and health considerations of cannabis use especially relevant to aging. The majority of OA who endorse using cannabis also report choosing it for medical purposes, most commonly to target symptoms of pain, insomnia, anxiety, and depression. However, many OA who use cannabis also do not discuss their use with their medical providers, and additional controlled clinical trials are needed to delineate which symptoms cannabis can successfully alleviate and any conditions or circumstances for which it is contraindicated. Further, emerging research in younger populations has documented possible cardiovascular risks of heavy cannabis use, and this area has yet to be thoroughly investigated in aging adults despite risk for vascular and other forms of dementia. Preclinical models are beginning to offer insight into health effects of cannabis use with aging, including cognition and dementia risk. In particular, cannabis represents a possible treatment for cognitive decline and neurodegeneration in OA, but further research is needed to characterize effects of cannabis use in healthy aging versus dementia processes.