Cal Poly Corporation

The California Prescribed Fire Monitoring Program dataset (2019–2024) provides comprehensive ecological monitoring data from prescribed fire treatments across California’s diverse forest ecosystems. This dataset encompasses forest structure and cover, fuel loads, and post-fire recovery metrics, collected using a standardized protocol, from over 36 disparate sites (114 burn units, 972 plots, and 1,838 total surveys). Data collected during pre-fire, immediate, and multi-year post-fire sampling episodes allow for robust analysis of prescribed fire effects across variable environmental conditions. The monitoring framework captures key ecological indicators, including tree mortality, fuel consumption, understory vegetation response, species composition, and regeneration. This dataset can address critical knowledge gaps regarding prescribed fire effectiveness for ecological restoration, hazardous fuel reduction, and ecosystem resilience objectives. These data can support evidence-based fire management decisions, validate fire effects models, and establish baseline reference conditions for future prescribed fire implementation throughout California’s fire-prone landscapes.

Purpose Roy (1952) proposed the conceptually simple concept of Safety-First (SF) as a behavioral basis for decisions made under risk and uncertainty. SF is based on the simple and appealing principle that decision-makers will make choices to minimize the probability of a disastrous outcome occurring. Agricultural economics literature has mostly ignored SF preferences as a factor that may influence these decisions. In this paper we demonstrate in practice the importance of the SF rule in decision-making under risk in a grain marketing environment. Design/methodology/approach Using technology in the form of a context-rich grain marketing game, we track individual grain marketing decisions within multiple marketing years. Experimental economics methods are incorporated into the grain marketing simulation game, enabling us to elicit individual risk preferences in a controlled environment and track how those preferences affect grain marketing decisions under diverse market conditions represented by different grain price paths. Our approach incorporates the dynamic characteristic of realistic price variation at each decision point without introducing considerable cognitive load on the experimental subject. Findings Our results within and across marketing years indicate that SF risk preferences significantly impact grain marketing decisions. Specifically, individuals displaying SF risk preferences sell an additional 8.45% of harvest in the first month of the marketing year over those not displaying these preferences. Such upfront high grain sales are observed across all market conditions. This outcome represents the fact that selling early in the marketing cycle guarantees a profit in each pricing scenario and reduces the probability of financial disaster if prices fall during the marketing season. Research limitations/implications Producers would be ideal subjects for this study; however, recruiting them to participate in experiments is often extremely challenging due to high demands on their time, concerns about confidentiality of responses, complexity of the decision-making process and other privacy issues. Student subjects were used to study human behavior in the current context involving dynamic decision-making and learning, which is hard to isolate with primary data of any other kind (Rosch et al., 2021). We are hopeful this work will motivate future research to tackle the issues presented by studying producers’ grain marketing decisions. Originality/value We investigate a conceptually simple, yet not well understood, concept of Safety-First (SF) framework for decisions under risk and uncertainty. Our approach also merges technology, experimental economics methods and a risk management tool of forward contracting, allowing us to gather primary data and match individuals' risk preferences with a type of decision that is otherwise particularly challenging to be tracked over time.

A recent United Nations Environment Programme report revealed that greenhouse gas emissions from the building and construction sector continue to rise. (UNEP 2024a) To contend with architects’ and engineers’ responsibilities in climate change, professionals must radically restructure their working methods to integrate early and consistent collaboration to effectively address the collective project of decarbonization. Diverse voices enable the innovation required to rethink entrenched practices, making collaboration an ethical imperative from the very start of any complex project. The authors interviewed dozens of practitioners over many years of research and developed guidelines to restructure traditional architect-engineer partnerships. These guidelines are examined with special attention to climate urgency and the benefits of collaboration in working towards ethical building designs. Critically today, as architects and engineers upend harmful carbon-intensive industrialized systems, collaboration is foundational to climate- and equity-informed decision making, concluding that collaborative practice is fundamental to ethical practice.

The last five years have seen an increasing emphasis placed on equitable pedagogy, striving to support a more diverse body of learners. As part of this movement, we have seen the creation of guidelines on inclusive teaching (Launch Years Initiative, 2021; Being Human in STEM, 2022). Although these documents provide general inclusivity guidelines, it can remain difficult to see exactly how to implement these guidelines in our individual statistics or data science classrooms. In this paper, we continue these conversations on equitable teaching by outlining a framework that can be used to evaluate the inclusivity of pedagogical practices. We demonstrate how to implement this framework with an example familiar to statistics and data science educators—designing a data analysis project—evaluating the inclusivity of a variety of design decisions with which educators are faced.

Abstract. As the northern high latitude permafrost zone experiences accelerated warming, permafrost has become vulnerable to widespread thaw. Simultaneously, wildfire activity across northern boreal forest and Arctic/subarctic tundra regions impact permafrost stability through the combustion of insulating organic matter, vegetation and post-fire changes in albedo. Efforts to synthesise the impacts of wildfire on permafrost are limited and are typically reliant on antecedent pre-fire conditions. To address this, we created the FireALT dataset by soliciting data contributions that included thaw depth measurements, site conditions, and fire event details with paired measurements at environmentally comparable burned and unburned sites. The solicitation resulted in 52,466 thaw depth measurements from 18 contributors across North America and Russia. Because thaw depths were taken at various times throughout the thawing season, we also estimated end of season active layer thickness (ALT) for each measurement using a modified version of the Stefan equation. Here, we describe our methods for collecting and quality checking the data, estimating ALT, the data structure, strengths and limitations, and future research opportunities. The final dataset includes 47,952 ALT estimates (27,747 burned, 20,205 unburned) with 32 attributes. There are 193 unique paired burned/unburned sites spread across 12 ecozones that span Canada, Russia, and the United States. The data span fire events from 1900 to 2022. Time since fire ranges from zero to 114 years. The FireALT dataset addresses a key challenge: the ability to assess impacts of wildfire on ALT when measurements are taken at various times throughout the thaw season depending on the time of field campaigns (typically June through August) by estimating ALT at the end of season maximum. This dataset can be used to address understudied research areas particularly algorithm development, calibration, and validation for evolving process-based models as well as extrapolating across space and time, which could elucidate permafrost-wildfire interactions under accelerated warming across the high northern latitude permafrost zone. The FireALT dataset is available through the Arctic Data Center.

We propose a digitally assisted analog computing circuit for real-time model predictive control (MPC) of a DCDC buck converter. The computing circuit comprises analog elements for speed and digital components for programmability. It implements gradient-flow dynamics for a penalty-based reformulation of the quadratic optimization problem corresponding to the original MPC formulation. The MPC problem is set up to regulate output voltage while explicitly enforcing constraints on the buck converter’s inductor current and duty cycle. Simulation results in a closed-loop configuration demonstrate superior dynamic response with lower settling time and overshoot compared to the Type-III controller and linear quadratic regulator (LQR). The proposed approach achieves accuracy similar to the numerically computed optimal solution from the interior-point-convex algorithm of MATLAB’s quadprog solver while offering real-time implementation capability.

Programming is widespread in multiple domains and is being integrated into various discipline-specific university courses where, like students in a typical introductory computing course, students from other disciplines face challenges with learning to program. We offer a case study in which we study undergraduate students majoring in either chemistry or biochemistry as they learn programming in a physical chemistry course sequence. Using surveys and think-aloud sessions with students, we conducted a thematic content analysis to explain the challenges they face in this endeavor. We found that students struggled to transfer their programming knowledge to new representations and problems, and they did not have strategies in place for solving problems with programming. These facts combine to lower students' confidence in their programming abilities, making it less likely that they will reach for computing to help solve domain-specific problems. We recommend that students in end-user programming contexts be explicitly taught the skills of abstraction, decomposition, and metacognitive awareness as they pertain to programming.

Many high school students these days do not quickly recognize or identify Industrial Engineering (IE) procedures, practices, and products.Recruitment of qualified students into IE programs requires continuous planning, publicizing, and networking.Traditional recruitment efforts have included high school visits, brochures & flyers, and university-sponsored workshops.However, these techniques have only achieved mediocre success.Currently, a need exists for new promotional materials that utilize visual tools and provide a "hands-on" approach.We have developed several exercises that successfully introduce IE.Two such exercises are explained in this paper.The first exercise demonstrates the differences between Assembly line and Cellular Manufacturing by engaging the students in various stages of a production line.The students are then asked to study the effects of different strategies as they manufacture a simple product.The second exercise introduces Mechatronics.Students are asked to build a model car that responds to a source of controlled light.In limited trials, these exercises have proven successful.

Presented here is select observations of the seismic performance of earth dams and port facilities from the 2023 Turkiye Earthquakes. The 2023 Februrary 6th events included a M7.8 and M7.7 within 9 hours of each other on separate but adjacent faults. Ground shaking was recorded throughout the region with median values at roughly one half gravity. Seventeen earth dams experienced some type of geotechnical damage due to liquefaction, fault rupture, or seismic compression. Several ports facilities were also impacted by liquefaction and/or lateral spreading. A synopsis of key geotechnical earthquake damage patterns is presented.