Nova Research Company (United States)

The scaling of visual Artificial Intelligence (AI) for applications like smart cities and retail analytics is increasingly constrained by data pipeline inefficiencies rather than model computation. Conventional video codecs necessitate full-resolution decoding and extensive data movement, resulting in unnecessary power expenditure and GPU under-utilization. This paper evaluates MPEG-5 Low Complexity Enhancement Video Coding (LCEVC) [1] as a hierarchical solution that enables "base-first" operation, allowing AI inference to process only the low-resolution base stream. Using Intel’s Deep Learning Streamer framework on an integrated GPU architecture (Intel Core Ultra 7 258V), we quantify LCEVC’s impact in a high-density, eight Ultra High Definition (UHD) parallel AI inference stream environment compared to native AVC. Results confirm LCEVC-enhanced AVC achieves up to 3.4x pipeline speed-up, 71% lower average power consumption per channel, and a crucial 83% reduction in memory bandwidth utilization. These findings position LCEVC as a practical enabler for energy-efficient, high-density edge AI systems.

ABSTRACT This study examines the synthetic and curing processes for bi‐ and trifunctional end‐capped phthalonitrile (PN) Schiff base resins derived from phenolic aldehydes. Characterization of the resins was conducted via proton ( 1 H) and carbon ( 13 C) nuclear magnetic resonance (NMR) spectroscopy, differential scanning calorimetry (DSC), thermogravimetric analysis (TGA), mechanical analysis (torsion), rheometry, in‐source fast pyrolysis mass spectrometry (FP‐MS), and solid‐state NMR. The processing window for each resin was determined using DSC, with both bifunctional resins exhibiting relatively low melting points (57°C and 62°C) and large processing windows greater than 200°C. Each resin was self‐curing due to the imine and PN curing groups present and displayed a range of viscosities from 546 to 10,800 cP at 175°C. The resins were degassed and cured into their respective polymers by post‐curing up to 380°C. The resulting polymers all exhibited excellent thermal stability with a T d5% above 500°C in both an inert (N 2 ) and air atmosphere as well as a 73%–75% char yield under N 2 . Excellent toughness was observed for three of the resins with an initial storage modulus ( G' at 50°C) above 1530 MPa and moderate modulus retention. This combination of properties suggests the multi‐functional resins are excellent materials for high‐temperature and mechanical applications.

Integrating artificial intelligence (AI) in cosmetic surgery presents both transformative opportunities and significant challenges. From preoperative planning to postoperative care, AI offers tools that enhance precision, efficiency, and personalization. However, concerns such as algorithmic bias, ethical implications, and the potential depersonalization of patient care must be carefully considered. This article explores AI’s impact on cosmetic surgery, addressing its benefits, risks, and ethical considerations while proposing solutions on evidence-based strategies for safe and effective AI integration into clinical practice. By evaluating AI-driven advancements in patient education, treatment planning, and postoperative management, we aim to equip surgeons with actionable insights for navigating the future of our field. To fully realize AI’s potential in aesthetic and cosmetic surgery, it is essential to approach its integration through a structured framework that balances innovation with responsibility. This includes identifying key risk areas, implementing mitigation strategies, and outlining best practices for clinical adoption. The following sections are organized around major challenges—such as bias, ethical concerns, and depersonalization—and we aim to offer specific solutions to guide surgeons in applying AI thoughtfully and safely within their own practices. A combination of keywords related to “cosmetic surgery” and “artificial intelligence,” such as “AI trends,” “AI risks,” and “AI in surgery,” was used for a literature search across PubMed and Web of Science. The search was focused on published articles in English, in the past 10 years, to point out the recent trends and risks arising due to the use of AI in cosmetic plastic surgery. The search showed a long list of potential risks and complications associated with the use of AI when not applied carefully. On the contrary, the search also showed its versatility and colossal potential in transforming the field of cosmetic plastic surgery. While AI offers promising advancements in cosmetic plastic surgery, it is essential to navigate its integration carefully. By addressing issues such as potential biases, ethical concerns, and the risk of depersonalization, the field can leverage AI to enhance patient care and outcomes. The future of facial cosmetic plastic surgery with AI holds great potential, but it must be grounded in principles that prioritize patient safety, inclusivity, and the preservation of the human touch.

A class of bio-based bifunctional phthalonitrile (PN) resins composed of cinnamaldehyde derivatives, aminophenol, and end-capped with a PN moiety is detailed. The cinnamic-imine PN (CIPN) resins were synthesized by utilizing environmentally friendly solvents methanol (MeOH) and acetone at ambient temperatures. Characterization of the resins was conducted via proton (1H) and carbon (13C) nuclear magnetic resonance (NMR) spectroscopy, differential scanning calorimetry (DSC), thermogravimetric analysis (TGA), infrared spectroscopy (IR), mechanical analysis, single crystal X-ray diffraction (XRD), and rheometry. The CIPN resins were self-curing due to the vinylic, imine, and PN moieties present and displayed a range of viscosities from 153–1546 cP at 225 °C. The resulting CIPN polymers were prepared by post-curing up to 380 °C and exhibited excellent thermal stability with Td5% above 510 °C up to an 80% char yield. Moderate brittleness ranging from an initial storage modulus (G′ at 25 °C) of 86–1295 MPa was observed among the CIPN resins. This combination of properties suggests that the bifunctional CIPN resins are excellent materials for high temperature thermosets in composite applications.

Results of the study showed that there was no significant difference between the means of the predicted ACR from urine dipstick and spot urine ACR among patients with stage 1 (27.40AE18.26vs 45.61AE37.83;p0.06), stage 2 (82.30AE167.53vs 269.27AE604.19;p0.190), stage 3A (325.95AE450.21vs 563.75AE713.17;p0.215), stage 3B (203.05AE298.39vs 433.34AE813.68;p0.242), stage 4 (641.85AE589.33vs 1122.93AE186.93; p0.275), and stage 5 (699.54AE541.01vs 3162.98AE1991.39;p0.071).The concordance rates of the two tests were as follows: 60% for stage 1, 80% for stage 2, 60% for stage 3A, 75% for stage 3B, 85% for stage 4, and 60% for stage 5 with a 74.17% overall concordance rate.Finally, urine dipstick conversion formula to ACR has a diagnostic accuracy of 90.00% (95% CI: 83.18% -94.73%), sensitivity of 85.11% (95% CI: 71.69% -93.80%), specificity of 93.15% (95% CI: 84.74% -97.74%), positive predictive value of 88.89% (95% CI: 77.29% -94.95%), negative predictive value of 90.67% (95% CI: 83.02% -95.07%), positive likelihood ratio of 12.43 (95% CI: 5.29 to 29.20), and negative likelihood ratio of 0.16 (95% CI: 0.08 to 0.32).Conclusions: The urine dipstick conversion formula by Sumida et al. for urine ACR can be used as a surrogate for spot urine ACR in providing cost-effective and real-time rapid assessment of albuminuria in CKD patients and can be highly useful in resource-limited settings.I have no potential conflict of interest to disclose.I did not use generative AI and AI-assisted technologies in the writing process.