Guided by the Technology-Organization-Environment (TOE) framework, this study explores how firefighting gear manufacturers perceive 3D apparel visualization software and how these perceptions shape their intentions to adopt the technology. Semi-structured interviews were conducted with 12 experts from six companies producing wildland and structural turnout gear, including pattern makers, designers, technical designers, and a business manager. Content analysis revealed that manufacturers viewed 3D apparel visualization software as a useful tool for pattern optimization, visualizing air gaps, and improving communication with municipal customers. However, participants also highlighted significant technical limitations, particularly in simulating composite materials, multilayer garment structures, and substantial ease and air gap behavior in structural turnout gear. They also noted constraints related to organizational slack, including financial and human resource demands and disruptions to established workflows. Adoption decisions were further shaped by key industry characteristics, such as stringent National Fire Protection Association (NFPA) standards, unchanging garment style requirements, conservative industry norms, and varying levels of business partner readiness. The findings extend TOE research to a specialized functional clothing context by highlighting sector-specific constraints and the importance of distinguishing between current capabilities and perceived future value. Practical implications are offered for software developers, regulators, and manufacturers seeking to align digital design investments with the technological and regulatory realities of firefighting gear innovation.
The genus Leotia (Leotiaceae) is globally distributed with approximately 45 named species and several subspecies, varieties and forms. Persoon first proposed Leotia in 1794. Most of the taxonomic work has been done in the last 100 years with the most recent species added in 1982 by Otani. Here we describe L. chiricana, a new species associated with tropical monodominant forests of Oreomunnea mexicana (Fagales, Juglandaceae) in Panama and Trigonobalanus excelsa (Fagales, Fagaceae) in Colombia. This new species is easily recognized by its distinctive vivid dark greenish blue color as compared to other species in the genus. The structure of the apothecia also differs in having only one gelatinous layer instead of two in the stipe. Phylogenetic analyses using ITS ribosomal DNA and a variable region of the RNA polymerase II (RPB2) gene strongly support a monophyletic L. chiricana clade that is sister to L. lubrica group I sensu Zhong and Pfister. Our field data showed that L. chiricana was more abundant in habitats with soils of high nitrogen availability. Illumina sequence data (ITS2) and stable isotope analysis suggest that this species forms mycorrhizal associations with O. mexicana. The discovery of this Leotia species from Panama and Colombia contributes to the knowledge of the understudied and diverse communities of ectomycorrhizal fungi within the Neotropics.
Advanced Persistent Threats (APTs) have caused significant losses across a wide range of sectors, including the theft of sensitive data and harm to system integrity. As attack techniques grow increasingly sophisticated and stealthy, the arms race between cyber defenders and attackers continues to intensify. The revolutionary impact of Large Language Models (LLMs) has opened up numerous opportunities in various fields, including cybersecurity. An intriguing question arises: can the extensive knowledge embedded in LLMs be harnessed for provenance analysis and play a positive role in identifying previously unknown malicious events? To seek a deeper understanding of this issue, we propose a new strategy for taking advantage of LLMs in provenance-based threat detection. In our design, the state-of-the-art LLM offers additional details in provenance data interpretation, leveraging their knowledge of system calls, software identity, and high-level understanding of application execution context. The advanced contextualized embedding capability is further utilized to capture the rich semantics of event descriptions. We comprehensively examine the quality of the resulting embeddings, and it turns out that they offer promising avenues. Subsequently, machine learning models built upon these embeddings demonstrated outstanding performance on real-world data. In our evaluation, supervised threat detection achieves a precision of 99.0 detection attains a precision of 96.9
Mounting causal evidence shows particulate matter pollution reduces real-time cognitive function and increases aggressive behavior by reducing neural connectivity through oxidative stress and neuro-inflammation. We investigate a setting in which reduced cognition can generate significant private and external costs: driving. Using exogenous variation in wind direction, we show that a one-unit increase in PM2.5 exposure results in just under a one-percent increase in fatal car crashes and fatalities. Further, these effects are limited to same-day pollution exposure, highlighting the immediate negative effects of high-pollution days. An across-the-board one standard deviation reduction in fine particulate matter pollution would have averted nearly 2000 motor vehicle fatalities in 2019.
