Bradley University

Previous research has empirically demonstrated that personal resilience and organizational level interventions are important considerations when studying workplace adversity such as burnout and excessive workloads in the context of a pandemic. To explore interest in participating in a wellness initiative program designed specifically for advanced practice registered nurses (APRNs) and physician associates (PAs), with a voluntary option for participation in research. An anonymous, brief, online survey with Likert-response type items and one free-text question was delivered with passive informed consent via email to all APRNs and PAs (N = 312 participants) within one health care system. A positive correlation between likelihood of seeking well-being care and having sufficient time (0.36, p < .01) was found, which indicates that APRNs or PAs who felt they had sufficient time to get counseling were actually more likely to seek counseling services. Results from the path model show that the comfort-level of seeking counseling completely mediates the relationship between fear of negative perceptions of receiving counseling and the likelihood of participating in a wellness program. Well-being strategies are important for organizations to engage in to support their employees; however, confidentiality concerns and competing priorities for time may influence engagement. Interest among APRNs and PAs for participation in research exists if enough information is provided and anonymity can be assured. Organizations should focus on finding innovative ways to integrate wellness into APRN and PA practice that mitigates the belief around the burden of time commitment and seeks ways to provide psychological safety for its employees.

ABSTRACT: The influx of Fiber-to-the-x (FTTx) networks all over the world has amplified the necessity to have efficient, precise, and scalable network design approaches. The planning of fiber networks traditionally has been based on the computer-Aided Design (AutoCAD) tools that are mostly manual, geometry-based and subject to human error. Although useful in small scale project development, these approaches cannot meet the growing complexity and data demands of the contemporary broadband deployment. The result is a paradigm shift in the industry toward design frameworks that are based on Geographic Information System (GIS) and data-driven design frameworks. Although the recent developments have been made, there is a considerable gap in research in the systematic comparison of the conventional CAD based work flows and intelligent, attribute-based GIS methods, especially on the efficiency of automation, splice loss modeling, and accuracy of capacity planning. This paper will seek to fill this gap by critically assessing the performances and results of the two design paradigms. The study uses comparative methodology of analysis, which incorporates literature synthesis, workflow benchmarking and simulation-based assessment. The significance of smart, data-driven design models is measured by the key performance indicators, such as design accuracy, design planning time, error rates, splice loss margins, and network scalability. The results prove that GIS-based and attribute-driven planning is much better than traditional CAD methods regarding automation, accuracy, and scalability. The intelligent design systems lower the planning time, provide less splice loss uncertainties, and allow proactive capacity prediction via data analytics and optimization algorithms. In practice, the research offers viable information to the network operators and planners who want to streamline their design procedures. Its findings help in strategic decision making to move towards smart planning platforms that ultimately would result in a high level of deployment efficiency, lower operation costs and the long run sustainability of a network.

ABSTRACT Despite high costs and failure rates, expatriate assignments are a primary tool for multinational enterprises to open new operations and execute strategies in international markets. To investigate this seemingly counterintuitive phenomenon, we utilize a case study approach to examine expatriate assignments through the lens of host country managers (HCM). Most expatriate assignment literature has incorporated perspectives from both expatriate managers and MNE leadership; our work extends existing knowledge by incorporating the voice of HCMs in the expatriate experience—from selection through repatriation. Specifically, this research proposes a holistic expatriate model that highlights HCM value‐adding activities at each stage of the expatriate journey. This research, guided by accepted theories in expatriate assignment literature, also expands existing frameworks to assess expatriate assignments and suggests managerial alternatives to improve expatriate success.

The construction industry significantly contributes to global sustainability challenges, producing 30–40 percent of global carbon dioxide emissions and consuming large amounts of natural resources. Pervious concrete has emerged as a sustainable alternative to conventional pavements due to its ability to promote stormwater infiltration and groundwater recharge. However, the absence of fine aggregates creates a highly porous structure that results in reduced compressive strength, limiting its broader structural use. Determining compressive strength traditionally requires destructive laboratory testing of concrete specimens, which demands considerable material, energy, and curing time, often up to 28 days—before results can be obtained. This makes iterative mix design and optimization both slow and resource intensive. To address this practical limitation, this study applies Machine Learning (ML) as a rapid, preliminary estimation tool capable of providing early predictions of compressive strength based on mix composition and curing parameters. Rather than replacing laboratory testing, the developed ML models serve as supportive decision-making tools, enabling engineers to assess potential strength outcomes before casting and curing physical specimens. This can reduce the number of trial batches produced, lower material consumption, and minimize the environmental footprint associated with repeated destructive testing. Multiple ML algorithms were trained and evaluated using data from existing literature and validated through laboratory testing. The results indicate that ML can provide reliable preliminary strength estimates, offering a faster and more resource-efficient approach to guiding mix design adjustments. By reducing the reliance on repeated 28-day test cycles, the integration of ML into previous concrete research supports more sustainable, cost-effective, and time-efficient material development practices.

Accurate prediction of communication link quality metrics is essential for vehicle-to-infrastructure (V2I) systems, enabling smooth handovers, efficient beam management, and reliable low-latency communication. The increasing availability of sensor data from modern vehicles motivates the use of multimodal large language models (MLLMs) because of their adaptability across tasks and reasoning capabilities. However, MLLMs inherently lack three-dimensional spatial understanding. To overcome this limitation, a lightweight, plug-and-play bird’s-eye view (BEV) injection connector is proposed. In this framework, a BEV of the environment is constructed by collecting sensing data from neighboring vehicles. This BEV representation is then fused with the ego vehicle’s input to provide spatial context for the large language model. To support realistic multimodal learning, a co-simulation environment combining CARLA simulator and MATLAB-based ray tracing is developed to generate RGB, LiDAR, GPS, and wireless signal data across varied scenarios. Instructions and ground-truth responses are programmatically extracted from the ray-tracing outputs. Extensive experiments are conducted across three V2I link prediction tasks: line-of-sight (LoS) versus non-line-of-sight (NLoS) classification, link availability, and blockage prediction. Simulation results show that the proposed BEV injection framework consistently improved performance across all tasks. The results indicate that, compared to an ego-only baseline, the proposed approach improves the macro-average of the accuracy metrics by up to 13.9%. The results also show that this performance gain increases by up to 32.7% under challenging rainy and nighttime conditions, confirming the robustness of the framework in adverse settings.