Click on this link to download the full high-definition PDF for AJUR Volume 23 Issue 2 (June 2026). The Crossref link for this issue is https://doi.org/10.33697/ajur.2026.171
Links to individual manuscripts, abstracts, and keywords are provided below.
p.3. Exploring the Link Between Mental Health Stigma and Help-Seeking Behavior in Law Enforcement Officers
Taylor Radtke & Cindy Prins
https://doi.org/10.33697/ajur.2026.172
ABSTRACT: Law enforcement officers work in high-stress situations with frequent exposure to traumatic events. This increases their likelihood of experiencing an impact to their mental health, yet many officers hesitate to seek professional support. Stigma persists as an important factor affecting help-seeking behaviors in many individuals. The present study surveyed 46 law enforcement officers using an anonymous Qualtrics questionnaire to assess how perceived stigma relates to willingness to seek support. Most (87.0%) reported mental health challenges as common in their field, yet few felt comfortable discussing such issues with their supervisors or therapists affiliated with their work. Conversely, 45.7% were comfortable discussing these topics with coworkers, and 52.1% with a therapist outside of their work. Additional barriers cited by officers included concerns of job security (47.8%), confidentiality (28.3%), and judgment from peers (30.4%). 52.2% of officers reported personally witnessing or experiencing stigma at their workplace, and 54.3% believed reaching out for help leads to receiving negative perceptions from peers. Spearman’s rank correlation analyses found that officers who experienced stigma were less comfortable seeking help from internal resources. Additionally, qualitative data highlighted the need for confidential support resources, organizational support, and a culture change in their organization. These findings align with current research that stigma harms help-seeking behaviors and highlights the need for targeted interventions, such as anti-stigma training and peer programs. KEY WORDS: Mental health; Stigma; Law enforcement; Help-seeking behavior; Stigma reduction, Mental illness, Perceived stigma; Workplace culture
p.13. Clustering in High-Dimension – Tools and Challenges
Lucy Liu & Haim Bar
https://doi.org/10.33697/ajur.2026.173
ABSTRACT: Dimensionality reduction methods such as Multidimensional scaling (MDS) or t-Distributed Stochastic Neighbor Embedding ( t-SNE) are often followed by clustering in the reduced plot. To examine whether or to what extent these methods affect clustering, we simulate several data structures and apply clustering methods. We first perform clustering using the data in the original space, where we know the true clusters, then perform MDS and t-SNE to scale the data down to two dimensions, cluster on this projected data, and compare differences in the results. We find that MDS and t-SNE can either increase or decrease clustering performance, and are unable to correctly represent data structures with certain shape structures, or in the presence of noise. We examine several clustering methods and show that their performance depends to a large extent on the structure of the data, original dimension, and the noise level, even before we perform dimensionality reduction via MDS and t-SNE. No method among the ones considered here dominates the others in terms of clustering accuracy. KEYWORDS: K-means; Hierarchical clustering; Ward linkage; Complete linkage; Average linkage; Single linkage; Persistence Diagram; Simulation
p.27. Framing the News: How an Article’s Headline and Content Valence Shape Readers’ Understanding and Impressions
Kyle Smoak, Kenneth Barideaux Jr., & Christa Christ
https://doi.org/10.33697/ajur.2026.174
ABSTRACT: Media outlets often use “clickbait” or misleading headlines to increase engagement, but these strategies may distort reader understanding. Although prior research shows that negative information is more likely to capture attention and spread online, most studies have focused on the dissemination of news on social media rather than on readers’ cognitive processing. Fewer studies have examined how headline features, such as emotional valence and congruence with article content, influence judgments, comprehension, and behavioral intentions. Across two experiments, we investigated how headline valence and headline–article congruence influenced readers’ impressions and comprehension. In Study 1, college students read an article with a positive, negative, or neutral headline and rated their impressions of the article. In Study 2, participants read articles written in a positive or negative tone, paired with either congruent or incongruent headlines, and completed the same impression measures plus comprehension questions. Study 2 also included a replication of Study 1 as well as an independent replication sample for the unique Study 2 analysis using a national sample obtained using Prolific. Across both studies, headline valence did not significantly affect participants’ impressions. However, congruency between the headline and article did influence comprehension in one sample. Together, these findings suggest that the relationship between headline framing and news comprehension is complex and may depend on headline–article alignment as well as contextual and individual factors. KEYWORDS: Headlines; News Articles; Valence; Comprehension; Negativity Bias; Clickbait; Reader Impressions; Article Tone
p.41. Physics-Informed Neural Network Frameworks for the Analysis of Engineering and Biological Dynamical Systems Governed by Ordinary Differential Equations
Andrew Particka, Tyrus Whitman, Christopher Diers, Ian Griffin, Charuka Wickramasinghe, & Pradeep Ranaweera
https://doi.org/10.33697/ajur.2026.175
ABSTRACT: This study presents and validates the predictive capability of the Physics-Informed Neural Networks (PINNs) methodology for solving a variety of engineering and biological dynamical systems governed by ordinary differential equations (ODEs). While traditional numerical methods are effective for many ODEs, they often struggle to achieve convergence in problems involving high stiffness, shocks, irregular domains, singular perturbations, high dimensions, or boundary discontinuities. Alternatively, PINNs offer a powerful approach for handling challenging numerical scenarios. In this study, classical ODE problems are employed as controlled testbeds to systematically evaluate the accuracy, training efficiency, and generalization capability of the PINNs framework under controlled conditions. Although not a universal solution, PINNs can achieve superior results by embedding physical laws directly into the learning process. The existence and uniqueness properties of several benchmark problems are first analyzed, and the PINNs methodology is subsequently validated on each model system. The results demonstrate that, for complex problems to converge to correct solutions, the loss function components data loss, initial condition loss, and residual loss must be appropriately balanced through careful weighting. It was further established that systematic tuning of hyperparameters including network depth, layer width, activation functions, learning rate, optimization algorithms, weight initialization schemes, and collocation point sampling plays a crucial role in achieving accurate solutions. Additionally, embedding prior knowledge and imposing hard constraints on the network architecture, without losing the generality of the ODE system, significantly enhances the predictive capability of PINNs. KEYWORDS: Physics-Informed Neural Networks; Ordinary Differential Equations; DeepXDE; Dynamical Systems; Activation functions; Adams Algorithm; Picard–Lindelöf Theorem; Grönwall’s Inequality