Purdue University System

This paper is a contribution to a special session focused on the analysis and optimization of a vehicle inspired by the Stratolaunch Talon–A vehicle, the baseline case of which we will refer to as Talon-P. Mach 6.0 flow over a generic boost glide vehicle was investigated through laminar CFD simulations. Comparisons were made between simulated results and experimental data collected in Purdue University’s BAM6QT hypersonic tunnel. Similar surface heating patterns, such as streaks on the lower and upper surfaces of the vehicle, were observed both in CFD simulations and experimental infrared imaging. Six different nose geometries were investigated, and overall good agreement between the experimental and simulated results was observed. Two configurations of the Talon-P experienced substantial disagreement between simulated predictions and experimental results; possible reasons for these discrepancies are discussed. The effect of nose geometry on downstream flow behavior is also investigated, with special emphasis on the vortical structures which form near the nose on the bottom surface of the geometry. The CFD results presented in this paper are part of a larger design, downselection, and analysis process.

PurposeTechnological innovations are rapidly transforming service frontlines, resulting in increasingly complex service touchpoints. These touchpoints place greater demands on frontline employees (FLEs) to deliver a positive customer experience. Despite the considerable extant body of knowledge on FLE competencies, the literature on frameworks for managing the complexity of contemporary frontlines from the FLE’s perspective is sparse. This paper aims to fill this critical gap by developing a framework that enables FLEs to deliver positive moments of truth (MOTs) while ensuring the well-being of all actors involved.Design/methodology/approachThis paper uses a conceptual approach rooted in the organizational mindfulness and individual mindfulness literature as the theoretical lens. This is complemented by a comprehensive review of the FLE skills literature supported by marketplace examples to illustrate the optimal use of the said skills.FindingsThis paper proposes a conceptual framework of mindfulness orientation which delineates how FLE competencies underpinned by a set of key skills can deliver positive MOTs and actor well-being.Research limitations/implicationsThe research is conceptual in nature and does not contain validation through empirical data.Practical implicationsThis comprehensive skill set provides a clear roadmap for firms in both recruitment and developing training for their FLEs, thus contributing to practice.Originality/valueFirstly, we present a conceptual framework of mindfulness, combining organizational mindfulness and individual mindfulness that will enable employees to help facilitate the creation of positive MOTs. Secondly, we develop a comprehensive set of employee skills that underpin the mindfulness orientation framework.

Visual detection of stromata (brown-black, elevated fungal fruiting bodies) is the primary method for quantifying tar spot early in the season because these structures are definitive signs of the disease and essential for effective disease monitoring and management. Here, we present the Stromata Contour Detection Algorithm version 2 (SCDA v2), which addresses the limitations of the previously developed SCDA version 1 (SCDA v1), without the need to empirically search for optimal decision-making input parameters (DMIPs) while achieving higher and consistent accuracy in tar spot stromata detection. SCDA v2 operates in two components: (i) SCDA v1 producing tar spot-like region proposals for a given input corn leaf Red-Green-Blue (RGB) image and (ii) a pretrained convolutional neural network (CNN) classifier identifying true tar spot stromata from the region proposals. To demonstrate the enhanced performance of the SCDA v2, we used datasets of RGB images of corn leaves from field (low, middle, and upper canopies) and glasshouse conditions under variable environments, exhibiting different tar spot severities at various corn developmental stages. Various accuracy analyses (F1 score, linear regression, and Lin's concordance correlation) showed that SCDA v2 had a greater agreement with the reference data (human visual annotation) than SCDA v1. SCDA v2 achieved 73.7% mean Dice values (overall accuracy) compared with 30.8% for SCDA v1. The enhanced F1 score primarily resulted from eliminating overestimation cases using the CNN classifier. Our findings indicate the promising potential of SCDA v2 for glasshouse and field-scale applications, including tar spot phenotyping and surveillance projects.[Formula: see text] Copyright © 2024 The Author(s). This is an open access article distributed under the CC BY-NC-ND 4.0 International license.

Epidemiological studies to better understand wheat blast (WB) spatial and temporal patterns were conducted in three field environments in Bolivia between 2019 and 2020. The temporal dynamics of wheat leaf blast (WLB) and spike blast (WSB) were best described by the logistic model compared with the Gompertz and exponential models. The nonlinear logistic infection rates were higher under defined inoculation in experiments two and three than under undefined inoculation in experiment one, and they were also higher for WSB than for WLB. The onset of WLB began with a spatial clustering pattern according to autocorrelation analysis and Moran's index values, with higher severity and earlier onset for defined than for undefined inoculation until the last sampling time. The WSB onset did not start with a spatial clustering pattern; instead, it was detected later until the last sampling date across experiments, with higher severity and earlier onset for defined than for undefined inoculation. Maximum severity (Kmax) was 1.0 for WSB and less than 1.0 for WLB. Aggregation of WLB and WSB was higher for defined than for undefined inoculation. The directionality of hotspot development was similar for both WLB and WSB, mainly occurring concentrically for defined inoculation. Our results show no evidence of synchronized development but suggest a temporal and spatial progression of disease symptoms on wheat leaves and spikes. Thus, we recommend that monitoring and management of WB should be considered during early growth stages of wheat planted in areas of high risk.

This study investigates the electrochemical behavior of molybdenum disulfide (MoS2) as an anode in Li-ion batteries, focusing on the extra capacity phenomenon. Employing advanced characterization methods such as in situ and ex situ X-ray diffraction, Raman spectroscopy, X-ray photoelectron spectroscopy, and transmission electron microscopy, the research unravels the complex structural and chemical evolution of MoS2 throughout its cycling. A key discovery is the identification of a unique Li intercalation mechanism in MoS2, leading to the formation of reversible LixMoS2 phases that contribute to the extra capacity of the MoS2 electrode. Density function theory calculations suggest the potential for overlithiation in MoS2, predicting Li5MoS2 as the most energetically favorable phase within the lithiation-delithiation process. Additionally, the formation of a Li-rich phase on the surface of Li4MoS2 is considered energetically advantageous. After the first discharge, the battery system engages in two main reactions. One involves operation as a Li-sulfur battery within the carbonate electrolyte, and the other is the reversible intercalation and deintercalation of Li in LixMoS2. The latter reaction contributes to the extra capacity of the battery. The incorporation of reduced graphene oxide as a conductive additive in MoS2 electrodes notably improves their rate capability and cycling stability.

Combinatorial optimization is anticipated to be one of the primary use cases for quantum computation in the coming years. The Quantum Approximate Optimization Algorithm and Quantum Annealing can potentially demonstrate significant run-time performance benefits over current state-of-the-art solutions. Inspired by existing methods to characterize classical optimization algorithms, we analyze the solution quality obtained by solving Max-cut problems using gate-model quantum devices and a quantum annealing device. This is used to guide the development of an advanced benchmarking framework for quantum computers designed to evaluate the trade-off between run-time execution performance and the solution quality for iterative hybrid quantum-classical applications. The framework generates performance profiles through compelling visualizations that show performance progression as a function of time for various problem sizes and illustrates algorithm limitations uncovered by the benchmarking approach. As an illustration, we explore the factors that influence quantum computing system throughput, using results obtained through execution on various quantum simulators and quantum hardware systems.

Analysis of factors that lead to the functionality of transcriptional activation domains remains a crucial and yet challenging task owing to the significant diversity in their sequences and their intrinsically disordered nature. Almost all existing methods that have aimed to predict activation domains have involved traditional machine learning approaches, such as logistic regression, that are unable to capture complex patterns in data or plain convolutional neural networks and have been limited in exploration of structural features. However, there is a tremendous potential in the inspection of the structural properties of activation domains, and an opportunity to investigate complex relationships between features of residues in the sequence. To address these, we have utilized the power of graph neural networks which can represent structural data in the form of nodes and edges, allowing nodes to exchange information among themselves. We have experimented with two kinds of graph formulations, one involving residues as nodes and the other assigning atoms to be the nodes. A logistic regression model was also developed to analyze feature importance. For all the models, several feature combinations were experimented with. The residue-level GNN model with amino acid type, residue position, acidic/basic/aromatic property and secondary structure feature combination gave the best performing model with accuracy, F1 score and AUROC of 97.9%, 71% and 97.1% respectively which outperformed other existing methods in the literature when applied on the dataset we used. Among the other structure-based features that were analyzed, the amphipathic property of helices also proved to be an important feature for classification. Logistic regression results showed that the most dominant feature that makes a sequence functional is the frequency of different types of amino acids in the sequence. Our results consistent have shown that functional sequences have more acidic and aromatic residues whereas basic residues are seen more in non-functional sequences.

Design and technology practitioners are increasingly aware of the ethical impact of their work practices, desiring tools to support their ethical awareness across a range of contexts. In this paper, we report on findings from a series of six co-creation workshops with 26 technology and design practitioners that supported their creation of a bespoke ethics-focused action plan. Using a qualitative content analysis and thematic analysis approach, we identified a range of roles and process moves that practitioners and design students with professional experience employed and illustrate the interplay of these elements that impacted the creation of their action plan and revealed aspects of their ethical design complexity. We conclude with implications for supporting ethics in socio-technical practice and opportunities for the further development of methods that support ethical engagement and are resonant with the realities of practice.

The agricultural, food, and natural resources (AFNR) system provides a rich context for solving complex and authentic problems. Agricultural educators have been teaching science, technology, and mathematics for many decades. Yet, the integrated STEM approach is an emergent pedagogy to intentionally teach academics in the context of AFNR. The authors argue that school-based agricultural education (SBAE) plays a unique and important role in serving as an integrator for STEM. AFNR can drive integrated STEM. The authors introduce the history of STEM and how it evolved in SBAE. They provide a rationale why integrated STEM should be taught in AFNR and SBAE and how epistemological thinking in AFNR provides a unique and complementary approach to solving complex problems using STEM. Integrated STEM in AFNR is defined and with characteristics. A review of research studies is summarized, and the authors propose a research agenda for future studies. This chapter on integrated STEM in AFNR has raised more questions, philosophically, conceptually, pedagogically, and empirically, which they hope launches scholarly discussions.