Urea is an added value chemical with wide applications in the industry and agriculture. The release of urea waste to the environment affects ecosystem health despite its low toxicity. Online monitoring of urea for industrial applications and environmental health is an unaddressed challenge. Electroanalytical techniques can be a smart integrated solution for online monitoring if sensors can overcome the major barrier associated with long-term stability. Mixed metal oxides have shown excellent stability in environmental conditions with long lasting operational lives. However, these materials have been barely explored for sensing applications. This work presents a proof of concept that demonstrates the applicability of an indirect electroanalytical quantification method of urea. The use of Ti/RuO2-TiO2-SnO2 dimensional stable anode (DSA®) can provide accurate and sensitive quantification of urea in aqueous samples exploiting the excellent catalytic properties of DSA® on the electrogeneration of active chlorine species. The cathodic reduction of accumulated HClO/ClO− from anodic electrogeneration presented a direct relationship with urea concentration. This novel method can allow urea quantification with a competitive LOD of 1.83 × 10−6 mol L−1 within a linear range of 6.66 × 10−6 to 3.33 × 10−4 mol L−1 of urea concentration.

Major urban centers are warming due to a combination of global and local phenomena. City governments are increasingly adopting strategies to mitigate the causes and impacts of extreme heat on their populations. Among these strategies are high solar-reflectance (cool) surfaces installed on building roofs and walls. Use of cool surfaces is a cost-effective and simple strategy that replaces conventional darker surfaces with surfaces that have a high reflectance to shortwave (solar) energy.

This report reviews the recent history of cool-surface deployment efforts. This includes peer-reviewed literature, conference proceedings, and grey literature to identify challenges and barriers to wide-scale deployment of cool surfaces. We have also researched heat action plans and programs from cities and different codes and standards, as well as available incentive and rebate programs.

The review identifies challenges, barriers, and opportunities associated with large-scale deployment of cool surfaces and categorizes them broadly as being related to product development & performance or policies & mandates. It provides a foundation upon which we intend to build a roadmap for rapidly accelerating future deployments of cool surfaces. This roadmap will address identified challenges and incorporate lessons learned from historical efforts to generate a practical and actionable plan.

Recently the domestic aviation industry has been influenced by rapidly growing ultra low-cost carriers (ULCCs). The pattern of airport markets served by ULCCs is incongruous with legacy carriers and low-cost airlines alike. Existing literature, however, is limited for North American ULCCs: research has only recently begun to identify them separately from mainstream low-cost carriers. This study sought to understand the market factors that influence ULCC service decisions. The relationship between ULCC operations and airport market factors was analyzed using three methods: mapping 2019 flight data for four ULCCs combined, two regression analyses to evaluate variables, and three case studies examining distinct scenarios through interviews with airport managers. Enplanement data were assembled for every domestic airport offering scheduled service in 2019. Independent variables were collected for each Part 139 airport. The first model estimated an ordinary least squares regression model to analyze ULCC enplanements. The second model estimated a binary logistic equation for presence of ULCC service. Case studies for Bellingham, Waco, and Lincoln were selected using compelling airport factors and relevant ULCC experience. Maps of ULCC enplanements revealed concentrations of operations on the East Coast. Both regression analyses showed strong relationships between population and non-ULCC enplanements (two measures of airport market size) and ULCC operations. A significant relationship also existed between tourism and enplanements. In the logit model, distance and competition variables were associated with ULCC presence. Case studies emphasized the importance of airport fees and competition in ULCC preferences, although aeronautical costs were generally not significant in the regressions.

Chapter from the ACRL book Toxic Dynamics: Disrupting, Dismantling, and Transforming Academic Library Culture. This chapter provides a bit of background on punk and libraries, identifies some toxic library cultures, and then suggests concrete ways for library workers to disrupt these cultures while maintaining boundaries that are regularly violated in library work.

The curse of dimensionality poses a significant challenge to modern multilayer perceptron-based architectures, often causing performance stagnation and scalability issues. Addressing this limitation typically requires vast amounts of data. In contrast, Kolmogorov-Arnold Networks have gained attention in the machine learning community for their bold claim of being unaffected by the curse of dimensionality. This paper explores the Kolmogorov-Arnold representation theorem and the mathematical principles underlying Kolmogorov-Arnold Networks, which enable their scalability and high performance in high-dimensional spaces. We begin with an introduction to foundational concepts necessary to understand Kolmogorov-Arnold Networks, including interpolation methods and Basis-splines, which form their mathematical backbone. This is followed by an overview of perceptron architectures and the Universal approximation theorem, a key principle guiding modern machine learning. This is followed by an overview of the Kolmogorov-Arnold representation theorem, including its mathematical formulation and implications for overcoming dimensionality challenges. Next, we review the architecture and error-scaling properties of Kolmogorov-Arnold Networks, demonstrating how these networks achieve true freedom from the curse of dimensionality. Finally, we discuss the practical viability of Kolmogorov-Arnold Networks, highlighting scenarios where their unique capabilities position them to excel in real-world applications. This review aims to offer insights into Kolmogorov-Arnold Networks' potential to redefine scalability and performance in high-dimensional learning tasks.