Low-cost monitors for measuring airborne contaminants have gained popularity due to their affordability, portability, and ease of use. However, they often exhibit significant biases compared to high-cost reference instruments. For optimal accuracy, these monitors require calibration and validation in their specific environment using expensive reference instruments, which are often scarce and costly. This study proposes machine-learning calibration methods that utilize a single high-cost instrument as an active reference to improve the accuracy of large networks of low-cost monitors. Three machine learning models-linear regression, random forest, and Gradient Boosting Regression (GBR)-were employed. The proposed approach was tested in a controlled chamber under two conditions: environmental simulations with salt- and dust-based aerosols and occupational settings using three electronic cigarette (ECIG) brands. The study involved thirty low-cost GeoAir2 monitors, divided into ten groups of three. Initially, all groups were collocated with a high-cost monitor using Aerosol A to develop prediction and regression models. These models, along with intrinsic error measurements from one group, were then applied to improve data accuracy for the remaining groups using Aerosol B. The results demonstrated substantial improvements in accuracy, with r² values ranging from 0.91 to 1.00 and RMSE reductions of up to 88 %, depending on the model and aerosol type. GBR consistently provided the highest accuracy and performance, particularly for complex, nonlinear patterns, while linear regression offered a faster, computationally efficient alternative suitable for less demanding scenarios. Random forest models performed moderately well, balancing accuracy and complexity. These methods provide a scalable and cost-effective solution for deploying networked low-cost sensors. Further research is needed to validate these findings in outdoor environments with meteorological and spatial influences, and indoor occupational settings where humidity effects may play a role.

Past broadband adoption initiatives underscored common wisdom: “We cannot manage what we cannot accurately measure”, “Trust but verify”. Self-reported Federal Communications Commission data has been notoriously unreliable or lacking granularity and that led to an overoptimistic perception of the quality and coverage of broadband services. Data collected in the past was deficient in providing a complete view of the user experience by focusing exclusively on a simple metric, bandwidth availability. Moreover, data collected in the past typically provided a point-in-time perspective on the state of broadband services in each region. In this paper, we discuss a new set of metrics and measurement techniques needed to make broadband funding decisions efficient in terms of quality of services delivered and return on investment, and to track their progress. We propose composite metrics that capture the complete value of the service to the user and emphasize the feasibility of measuring and monitoring such a metric over time.

Background: Outdoor agricultural workers experience significant heat exposure, yet few studies have evaluated whether wearable sensors can reliably measure continuous physiological responses in real field conditions. This pilot study examined the feasibility and predictive utility of core temperature, hydration, heart rate, and movement data collected from commercially available wearables. Methods: Thirty farmworkers in eastern North Carolina wore three devices, the CALERA core temperature sensor, the Nix Hydration Biosensor, and the Garmin Vivoactive smartwatch, during one July work shift. 1 min, physiological and environmental data were aligned to compute the modified Physiological Strain Index, mPSI. Device reliability, physiological responses, and heat exposure were summarized. Multi‐linear regression and machine learning, Random Forest (RF) and Gradient Boosting Regression (GBR), models were evaluated to predict mPSI, using raw and smoothed predictors to estimate mPSI. Results: CALERA retrieved 100 percent of the data, Garmin retrieved 97 percent, and Nix retrieved 80 percent. WBGT remained consistently high, approximately 28 to 29 degrees Celsius, and 12.6 percent of core temperature measurements exceeded 38 degrees Celsius. mPSI ranged from minimal to high heat strain, with 8.3 percent of observations at or above 5. Heart rate showed the strongest association with mPSI. The RF model using 60‐min moving averages achieved the highest predictive accuracy with an R ² of 0.96 and a root mean squared error of 0.25. Wearables captured meaningful physiological patterns that corresponded with environmental heat exposure, and WBGT‐based risk levels consistently overestimated heat strain relative to mPSI. Conclusion: Multi‐sensor wearable systems are feasible for continuous heat strain monitoring and can support accurate prediction of heat‐related risk among agricultural workers. This study is the first to evaluate the CALERA, Nix, and Garmin devices combined for heat strain monitoring in outdoor agricultural workers. By combining all three wearables in a field‐based feasibility assessment, it addresses a key gap in validating consumer‐grade technology for occupational safety in this high‐risk workforce. The findings of the study support the use of wearable devices for real‐time heat strain alerts, individualized hydration and work‐rest guidance, and early warning systems to help prevent heat‐related illness in physically demanding outdoor occupations.

Internet of Things (IoT) related and IoT enabled education and research are becoming more prevalent in today's academic environment. One of the challenges faced by educators and researchers is the availability of resources to support lab based, hands on learning and research projects. Existing resources are often highly customized, balkanized, and difficult to manage. For new technology areas such as IoT, these environments rarely provide opportunities for resource sharing and interdisciplinary collaboration. In this paper, we describe the development of a multi‐tenant, IoT centric platform, the Environmental Sensing Data Network (ESDN) meant to support plug and play sensor deployment and the tools necessary for data management. The platform turns the University campus into an open platform that can be used to easily set up IoT based labs for education, research, campus infrastructure management and community applications innovation. The paper covers the development of a sensor augmentation module facilitating easy deployment and management. The operational tools and services integration are also described. We believe the platform is an important step in facilitating distance and in person education and research. ESDN is rapidly expanding beyond the multi‐site University campus supporting a growing number of research and community projects.

The alignment of higher education with the skill needs of employers and industry at large is a topic of active debate, particularly for engineering education. Formal and informal content developed by companies, technology‐specific associations, consulting firms, and individuals forms a body of knowledge that might fast‐track careers toward very specific opportunities. By contrast, academic courseware is known for its pedagogical rigor while being slow to adapt to the fast‐paced changes in technology, particularly information technologies, and reliance on expensive course materials that burden students. This paper explores the drivers for integrating industry‐developed courseware in an existing, higher‐ed course on Scripting for Information Technology in the Department of Technology Systems at East Carolina University and the implications of such integration. We hypothesize that industry courseware is a streamlined, cost‐effective way to provide students with the knowledge they need in the workforce. Students were exposed to the two types of content and their scholarly performance was analyzed. Student perception of the two types of content was assessed through a postclass survey. The student results and the feedback they provided suggest that a hybrid approach is more suitable to address the learning preferences and predispositions of students. This case study concludes that textbook and industry‐generated content can be integrated into existing courses and improving them in terms student outcomes and in terms of alignment with industry's hiring expectations.