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Yayın Small molecule desorption from a swelling polymeric glass in polymer solution: Energy transfer method(Elsevier Science Sa, 2005-07-15) Uğur, Şaziye; Pekcan, Mehmet ÖnderDirect energy transfer (DET) method conjunction with fast transient fluorescence (FTRF) technique were used to study small molecule desorption from a swelling poly(methyl methacrylate) (PMMA) glass in polymer solution, which is consist of naphthalene (N)-labeled PMMA chains in chlorofrom-heptane mixture. Desorption coefficients, D-d of pyrene (P) desorbing from the swelling PMMA glass prior to dissolution were measured and found to be 8.3 x 10(-8) and 1.3 x 10(-5) cm(2) s(-1) in short and long time regions, respectively.Yayın A novel approach to non-invasive intracranial pressure wave monitoring: a pilot healthy brain study(Multidisciplinary Digital Publishing Institute (MDPI), 2025-06-28) Karaliunas, Andrius; Bartusis, Laimonas; Krakauskaite, Solventa; Chaleckas, Edvinas; Deimantavicius, Mantas; Hamarat, Yasin; Petkus, Vytautas; Stulge, Toma; Ratkunas, Vytenis; Çelikkaya, Güven; Januleviciene, Ingrida; Ragauskas, ArminasIntracranial pressure (ICP) pulse wave morphology, including the ratios of the three characteristic peaks (P1, P2, and P3), offers valuable insights into intracranial dynamics and brain compliance. Traditional invasive methods for ICP pulse wave monitoring pose significant risks, highlighting the need for non-invasive alternatives. This pilot study investigates a novel non-invasive method for monitoring ICP pulse waves through closed eyelids, using a specially designed, liquid-filled, fully passive sensor system named ‘Archimedes 02’. To our knowledge, this is the first technological approach that enables the non-invasive monitoring of ICP pulse waveforms via closed eyelids. This study involved 10 healthy volunteers, aged 26–39 years, who underwent resting-state non-invasive ICP pulse wave monitoring sessions using the ‘Archimedes 02’ device while in the supine position. The recorded signals were processed to extract pulse waves and evaluate their morphological characteristics. The results indicated successful detection of pressure pulse waves, showing the expected three peaks (P1, P2, and P3) in all subjects. The calculated P2/P1 ratios were 0.762 (SD = ±0.229) for the left eye and 0.808 (SD = ±0.310) for the right eye, suggesting normal intracranial compliance across the cohort, despite variations observed in some individuals. Physiological tests—the Valsalva maneuver and the Queckenstedt test, both performed in the supine position—induced statistically significant increases in the P2/P1 and P3/P1 ratios, supporting the notion that non-invasively recorded pressure pulse waves, measured through closed eyelids, reflect intracranial volume and pressure dynamics. Additionally, a transient hypoemic/hyperemic response test performed in the upright position induced signal changes in pressure recordings from the ‘Archimedes 02’ sensor that were consistent with intact cerebral blood flow autoregulation, aligning with established physiological principles. These findings indicate that ICP pulse waves and their dynamic changes can be monitored non-invasively through closed eyelids, offering a potential method for brain monitoring in patients for whom invasive procedures are not feasible.Yayın Grammar or crammer? the role of morphology in distinguishing orthographically similar but semantically unrelated words(Institute of Electrical and Electronics Engineers Inc., 2025) Ercan, Gökhan; Yıldız, Olcay TanerWe show that n-gram-based distributional models fail to distinguish unrelated words due to the noise in semantic spaces. This issue remains hidden in conventional benchmarks but becomes more pronounced when orthographic similarity is high. To highlight this problem, we introduce OSimUnr, a dataset of nearly one million English and Turkish word-pairs that are orthographically similar but semantically unrelated (e.g., grammar - crammer). These pairs are generated through a graph-based WordNet approach and morphological resources. We define two evaluation tasks - unrelatedness identification and relatedness classification - to test semantic models. Our experiments reveal that FastText, with default n-gram segmentation, performs poorly (below 5% accuracy) in identifying unrelated words. However, morphological segmentation overcomes this issue, boosting accuracy to 68% (English) and 71% (Turkish) without compromising performance on standard benchmarks (RareWords, MTurk771, MEN, AnlamVer). Furthermore, our results suggest that even state-of-the-art LLMs, including Llama 3.3 and GPT-4o-mini, may exhibit noise in their semantic spaces, particularly in highly synthetic languages such as Turkish. To ensure dataset quality, we leverage WordNet, MorphoLex, and NLTK, covering fully derivational morphology supporting atomic roots (e.g., '-co_here+ance+y' for 'coherency'), with 405 affixes in Turkish and 467 in English.
