Chattanooga State Community College (Chattanooga State or, informally, Chatt State) is a public community college in Chattanooga, Tennessee. The college is a member of the Tennessee Board of Regents System and is accredited by the Southern Association of Colleges and Schools (SACS). Athletically, Chattanooga State is a member of Region VII of the NJCAA.Chattanooga State offers a variety of programs and degrees including 50 career programs; three university parallel degrees (Associate of Science, Associate of Art, and Associate of Science in Teaching) with areas of emphasis in the arts, humanities, mathematics, and natural sciences; 20 technical certificate programs; corporate training; continuing education; adult education, including GED preparation; Collegiate High at Chattanooga State (formerly Middle College High School); Early College (dual enrollment); and community service programs.Chattanooga State is the only community college in Tennessee that has a Tennessee College of Applied Technology (TCAT) as an integral part of its organization. The TCAT offers 21 diploma programs and 7 certificate programs with a combined annual enrollment of over 2,300 students and has 1151 employees.Total fall 2012 headcount enrollment, including the non-college credit providing TCAT, was 11,357.Chattanooga State functions as an open-entry postsecondary institution for students residing in six counties in Southeast Tennessee, as well as seven bordering counties of North Georgia and Northeast Alabama, including Bledsoe, Grundy, Hamilton, Marion, Rhea, and Sequatchie in Tennessee; Catoosa, Dade, Fannin, Murray, Walker, and Whitfield in Georgia; and Jackson in Alabama.Students who want to transfer to a four-year institution can go through the Tennessee Transfer Pathways program to transfer to other Tennessee Board of Regents institutions, the University of Tennessee (UT), and other Tennessee public universities. Students can also enter the University of Tennessee at Chattanooga (UTC) as juniors or get guaranteed acceptance to Middle Tennessee State University (MTSU) under special agreements signed between Chattanooga State, UTC, and MTSU.The college offers instruction in a variety of modes including traditional classroom and laboratory instruction; asynchronous online instruction (more than 100 courses entirely online as well as many hybrid courses); synchronous instruction engaging students simultaneously at multiple sites; and one-to-one tutoring.
Generative artificial intelligence (GenAI) models have become central to modern Artificial Intelligence (AI) systems and analysis in high-dimensional, low-sample-size (HDLSS) biological domains. This article presents a critical review and comparative analysis of major Gen-AI families—including Generative Adversarial Networks (GANs), Variational Autoencoders (VAEs), diffusion-based probabilis-tic models, and energy-based models—with emphasis on their behavior, stability, and generative fidelity under data-scarce conditions. We survey the literature from 2015–2025, organize the field through a taxonomy of current generative paradigms, and analyze how these models are adapted to omics settings such as 1 scRNA-seq, bulk RNA-seq, spatial transcriptomics, metabolomics, and related molecular data. A recurring finding across the literature is that most models rely on aggressive preprocessing, such as highly variable gene selection, principal component analysis, or the latent space structure of an encoder-decoder network , effectively replacing the original HDLSS problem with a lower-dimensional proxy. These models are typically applied to moderate to medium-sized data sets and currently lack validation in biological applications where sample size is significantly small, e.g., fewer than 10 replicates. We further identify the absence of embedded sparsity-aware feature selection as a major methodological gap. To complement the survey, we experimentally compare representative VAE-, GAN-, and diffusion-based models on benchmark biological datasets in the HDLSS regime. The results show that VAEs remain comparatively stable in low-sample settings, whereas GANs and diffusion models improve substantially as sample size increases. Overall, this review provides a structured perspective on genera-tive modeling under HDLSS biological constraints and highlights open directions in data-efficient, interpretable, and reproducible Gen-AI for biological discovery.
As electric vehicles experience an all-time peak growth, Internal Combustion Engine (ICE) vehicles continue to make a strong case in range and performance, particularly in extreme low and high weather conditions. Their main drawbacks include their greenhouse effect and their low thermal efficiencies which are in the range $30-43 \%$. Research suggests that electric vehicles excel in the compact and short-range vehicle classes even without government subsidies. Their performance, however, decreases as vehicle size class and range increases, making them underperform ICE vehicles even with government subsidies in the large vehicle classes. The main challenge that faces electric vehicles is the extremely low specific energy of current battery technologies compared to that in hydrocarbon fuels, for instance current batteries can store up to 2.5% of the energy stored in gasoline of similar weight. Under a scenario where research achieves ICE efficiencies in the 60% range, these engines could potentially remain a significant force in global transportation for several decades. However, eventually for them to continue dominance, a revolutionary change in internal combustion engines that is not based on the Carnot cycle is needed, in order to advance their efficiencies to beyond the 67% limit.
We examined how college students perceived and discussed an intimate partner stalking case during mock jury deliberations. Specifically, undergraduates constituting 22 mixed-gender mock juries provided individual pre- and postdeliberation verdicts and deliberated in mixed-gender groups. We used Pathfinder analyses to derive semantic networks from their written pre- and postdeliberation verdict reasoning and the transcribed jury deliberations. During deliberations, women primarily made pro-alleged victim and antidefendant comments, considering the alleged victim’s fear and the defendant’s capability of harm. Men mainly made prodefendant comments, such as romanticizing his behavior. After deliberations, women became more prodefendant in their verdict reasons, while men remained similarly prodefendant and sometimes anti-alleged victim pre- and postdeliberations. Results provide evidence for gendered perceptions of criminal stalking, particularly pertaining to defendant threat and alleged victim fear. We consider implications for stalking legislation and education, and legal decision-making.
