The Advanced Technology College (ATC) is a four-year technical college located in Daytona Beach, Florida in the United States. This technical college carries courses such as computer technology, construction, manufacturing, engineering, and automotive services.The ATC is involved in a joint-partnership program with Volusia County and Flagler County school districts. The Advanced Technology Center provides high school students in their junior and senior years the ability to dual-enroll and receive college credits. There is no longer transportation to and from the home high schools to the ATC. The ATC also allows for adult students from Daytona State College to take courses at the ATC.
Hot stars born as rapid rotators are expected to induce meridional currents that mix hydrogen from the envelope into the core and return CNO-cycle processed material to the envelope, which should enhance the N at the surface at the expense of C and possibly also O depending on the ambient conditions. But the photospheric C and N abundances could also be influenced by mass transfer in a close binary system which spins up the mass gainer and deposits either processed or unprocessed material to its surface depending on just how much material has been peeled off the mass donor. We focus on the chemical composition of Be star photospheres to infer the present and past evolution of rapidly rotating early B stars. To mitigate the effects of gravity darkening and photospheric line blending on the abundances, we chose 8 Be stars with low vsin i that have good high-resolution FUV spectra in the IUE archive. We carried out a conventional NLTE abundance analysis of selected N iii, N i, and C iii lines in the FUV spectral region. We find clear evidence that the C iii 1176 Å multiplet is weak in the core region in most program stars, suggesting CNO processing. However, in all cases we infer a N abundance that is solar or less, raising a conundrum as to what happened to the “missing C.” Since a similar pattern of weak C yet normal N is also found in the mass gainer in some Algol binaries, there appears to be an emerging challenge to explain this apparent abundance anomaly. We speculate that the excess N from CNO processing might be converted into O (and perhaps on to Ne) by fusion with He in the hot but low-density regions either in the trail of ashes just outside the receding carbon-fusing core, or in He-shell flash regions, of a highly evolved mass loser in its final stage of mass transfer.
We propose the joint graph attention neural network (GAT), clustering with adaptive neighbors (CAN) and probabilistic graphical model for dynamic power flow analysis and fault characteristics. In fact, computational efficiency is the main focus to enhance, whilst we ensure the performance accuracy at the accepted level. Note that Machine Learning (ML) based schemes have a requirement of sufficient labeled data during training, which is not easily satisfied in practical applications. Also, there are unknown data due to new arrived measurements or incompatible smart devices in complex smart grid systems. These problems would be resolved by our proposed GAT based framework, which models the label dependency between the network data and learns object representations such that it could achieve the semi-supervised fault diagnosis. To create the joint label dependency, we develop the graph construction from the raw acquired signals by using CAN. Next, we develop the probabilistic graphical model of Markov random field for graph representation, which supports for the GAT based framework. We then evaluate the proposed framework in the use-case application in smart grid and make a fair comparison to the existing methods.
Abstract A high-velocity fireball was detected over the South Atlantic (41.9°S, 54.7°W) on 2026 April 1 at 02:13:14 UTC by U.S. Government (USG) sensors, with peak brightness at 90.5 km altitude. The event was well-observed from geostationary orbit by two civilian lightning imagers with near-orthogonal geometry, the Geostationary Operational Environmental Satellite-East Geostationary Lightning Mapper (GLM) and the Exploitation of Meteorological Satellites Meteosat Third Generation Imager 1 Lightning Imager, and it produced low-frequency acoustic signatures. The GLM measured a total radiated energy of 2.4 × 10 10 J, corresponding to a calculated impact energy of 0.086 kt TNT equivalent. Stereoscopic triangulation of the imager tracks yields an independent pre-atmospheric velocity of ~57 km s −1 , some 18% below the USG-reported value. Because orbital provenance is acutely sensitive to the entry velocity, whose reported uncertainty could be substantial, this discrepancy is notable. We document the multi-sensor record and identify the analysis required to assess provenance, which a subsequent study will present.
This systematic literature review explores the convergence of design thinking (DT) and artificial intelligence (AI) as transformative strategies in Technical and Vocational Education and Training (TVET). Amidst accelerating technological shifts, TVET systems face growing pressure to equip learners not only with technical skills but also with creativity, adaptability, and systems thinking. DT and AI, while individually valuable, remain under-integrated in vocational curricula, often limited to isolated pilot initiatives without strategic institutional anchoring. This review synthesizes findings from 12 empirical studies published between 2021 and 2025, analyzing their methodologies, impacts, and thematic contributions. Three overarching themes emerged: (i) design thinking as a driver of pedagogical transformation in vocational settings; (ii) leveraging emerging technologies to personalize and humanize learning in TVET; and (iii) integrating design thinking with institutional strategy for workforce readiness in TVET. While these studies showcase innovative practices, critical gaps persist particularly in long-term scalability, ethical AI integration, and systemic policy frameworks. Notably, few studies operationalize the combined use of DT and AI within holistic institutional strategies. This review argues for a paradigm shift: repositioning TVET as a human-centered engine for digital innovation. It calls for co-created, cross-sector frameworks that embed AI-powered DT models into educational policy and institutional planning. The findings highlight an urgent need for sustainable, evidence-based models to make vocational education responsive, inclusive, and future-ready.
Large-scale Vision-Language Models like CLIP have demonstrated impressive open-set localization capabilities at the image level. However, adapting this capability to pixel-level dense prediction poses challenges due to global feature biases. In this paper, we introduce CLIPix, a simple yet effective framework that repurposes CLIP to perform pixel-level localization. By tracing back CLIP's classification process, CLIPix identifies object-specific attentive regions and repurposes them as pixel-level localization cues. To address noise introduced by global biases, we propose a Noise-Resistant Correction strategy, refining these cues for more precise segmentation. Additionally, we introduce a Localization Embedding strategy to integrate both localization and enriched detail information, enabling accurate, high-resolution segmentation. Our approach preserves CLIP's generalization strength and unlocks its potential for segmenting arbitrary objects. Extensive experiments on the PASCAL and COCO datasets demonstrate that CLIPix achieves state-of-the-art performance, underscoring its effectiveness.
