The University of St. Francis is a private Franciscan university with its main campus in Joliet, Illinois. It enrolls more than 3,900 students at locations throughout the country with about 1,300 students at its main campus.The University of St.
The existing research on travel constraints for tourists has predominantly concentrated on the lack of infrastructure, safety, and the impact of natural disasters or war, with limited attention to individual disabilities, particularly chronic illnesses like rheumatoid arthritis. This study addresses this gap by examining the interrelationships amongst health constraints, negotiation strategies, and the travel intentions of tourists with rheumatoid arthritis using the Constraint-Effects-Mitigation (CEM) model. The findings indicate that tourists with rheumatoid arthritis' travel intentions are influenced by both tourism involvement and intrapersonal negotiation strategies. Moreover, the study reveals a significant mediating role for intrapersonal negotiation strategies in the relationships between travel constraints and travel intentions, as well as between tourism involvement and travel intentions. This research contributes to existing knowledge regarding this particular market segment, comprising tourists with rheumatoid arthritis, and provides implications for guiding the development of effective marketing and product development strategies in this context.
A valuable goal for correctional settings is the application of workforce development education for incarcerated individuals as they prepare for the reentry process. Exploring the transitional bridge from prison to society signifies the need for "employment skills" that empower the formerly incarcerated to achieve employment success and avoid recidivism. The U-Work Program student volunteers teach reentry classes focused on workplace employment skills, including financial literacy, communications, resume writing, interviewing, and technology. This article showcases the themes that emerged from incarcerees' responses in an exploratory study of the U-Work Program at a low-security federal facility in the Northeast, illustrating the importance of workforce development education in the reentry process.
In this article, a numerical technique is applied by designing a specialized neural network based on Fibonacci polynomials to solve a complex mathematical problem viz., nonlinear time fractional order reaction-advection-diffusion equation (RADE), which plays a crucial role in physics and engineering. In the proposed approach, Fibonacci polynomials of varying degrees have been used as activation functions in the hidden layer of the network. The output of the Fibonacci neural network (FNN) is assigned as a solution to the nonlinear time fractional RADE under consideration. The nonlinear time fractional RAD problem, along with its initial and boundary conditions, is reformulated as an unconstrained optimization problem, represented as a cost function. This cost function is then minimized using a suitable optimization Marquardt’s method. The effectiveness of the proposed approach as compared to other existing methods is demonstrated through two numerical examples. Then the verified algorithm is applied to the concerned problem, and the solution profile is analyzed under different variations of parameters to assess their impact on the system behavior. The present work illustrates the power of FNN approach in tackling complicated mathematical models that often elude traditional methods, making it a magnificent contribution to the field.
Background: Early identification of Autism Spectrum Disorder (ASD) is critical for optimizing developmental outcomes, yet community-based screening remains underutilized in Tanzania due to systemic, cultural, and logistical barriers. Purpose: This study explores stakeholder perceptions of the acceptability, feasibility, and potential implementation strategies for culturally adapted autism screening tools in community-based early childhood settings in the Kilimanjaro region, Tanzania. Methods: A phenomenological qualitative study guided by the Socioecological Model (SEM) was conducted across preschools, daycare centers, and rehabilitation facilities in Moshi Municipality and Hai District. Multi-stakeholder participants included daycare center directors, kindergarten teachers, healthcare providers, and parents and guardians of children with ASD and policymakers (10 in-depth interviews, 3 focus group discussions). Data were collected through usability assessments, cognitive debriefing, and semi-structured discussions, and were thematically analyzed using NVivo 15 aligned with SEM domains. Results: Participants reported high perceived acceptability of utilizing the tools, driven by the tools Swahili adaptation, perceived clinical usefulness, and cultural relevance. Key anticipated feasibility enablers included private administration of the tools to mitigate stigma, trained non-specialist screeners, and structured caregiver psychoeducation. Primary barriers included questionnaire length, caregiver stress, and initial resistance. Multi-level implementation strategies emerged: standardized training, consistent home school communication, multidisciplinary care coordination, task-shifting to Community Health Workers, engagement of local and faith leaders, and integration into digital and policy frameworks. Conclusion: Culturally adapted ASD screening tools are feasible and acceptable in Tanzanian community settings when deployed with attention to linguistic accessibility, confidentiality, staff capacity, and cross-sectoral partnerships. Embedding screening within existing early childhood and community health infrastructure, supported by policy alignment and digital outreach, offers a scalable model for improving early ASD identification in low-resource contexts
Background Generative AI is increasingly used in evidence synthesis, introducing a subtle but largely undetected threat we term evidence recycling : the AI-driven paraphrasing of existing research findings while preserving core conclusions, thereby creating an illusion of novel evidence without new primary data. Unlike traditional plagiarism or duplication bias, evidence recycling operates through semantic rewriting that bypasses current plagiarism detection tools. Left unaddressed, it risks inflating false-positive rates in meta-analyses that guide global health policy, particularly in low- and middle-income countries (LMICs) where verification capacity is most limited. Methods We conducted a conceptual framework development informed by a structured narrative synthesis. We searched PubMed, arXiv and Retraction Watch (January 2020 – March 2025), identifying 47 records and selecting 10 primary empirical sources documenting AI-related citation fabrication, retraction propagation and automation bias. We mapped documented failure modes to existing editorial safeguards, iteratively developed an eight-domain framework and conducted a structured face-validity assessment with two independent methodological experts. As a proof-of-concept pilot, we applied the framework's dependency matrix to three recently published open-access global health meta-analyses to evaluate feasibility. The framework was pre-registered on OSF prior to drafting (DOI:10.17605/OSF.IO/D86BK ). Results The resulting EVIDENCE Framework comprises eight domains: Evaluation of originality, Verification of sources, Integrity in synthesis, Diversity of data, Ethical AI use, Novel contribution, Contextual relevance and Editorial responsibility. Its operational core is a dependency matrix a structured table mapping every extracted effect size to its unique source DOI or PMID. Pilot application of the dependency matrix to three published meta-analyses identified source-tracing gaps in all three, with one meta-analysis unable to provide a unique DOI for two of its twelve effect sizes. For resource-constrained settings, a lightweight alternative the Two Reviewer Rule requires only a 20% random-sample trace-back. Conclusions Evidence recycling is an emergent, scalable threat to research integrity enabled by generative AI and amplified by current editorial blind spots. The EVIDENCE Framework provides a practical, low-cost and context-sensitive methodology tool. We invite five major journals to conduct a six-month field test to validate its real-world effectiveness and quantify the prevalence of evidence recycling in the current literature.
