Licit Magic - GlobalLit Working Papers 6. Nevāʾī's Meter of Meters. Introduction & Partial Translation

Are you tripping over your own feet, incapable of advancing even a single metre, when it comes to understanding the technicalities of the feet and metres of pre-modern Islamicate poetry? Then you should probably not consult Nevāʾī’s Meter of Meters, since you are better off with the works of a Wheeler Thackston or a Finn Thiesen... If, however, you want to see for yourself just how sophisticated a toolbox Islamicate rhetoricians had developed to discuss poetic meter, then you are well off with Nevāʾī’s Meter of Meters. While the name of Nevāʾī might well not ring a bell with many of us, across vast swaths of the Islamic world it resonates deeply. Indeed, ever since the late 15th century, both professional poets and aficionados have marvelled at his countless verses, and this they did well beyond the poet’s homeland in present-day Uzbekistan: in the Balkans and in Sinkiang, and pretty much everywhere in between. Introduced and partially translated here is not one of his celebrated divans or versified romances, but a didactic work that focuses squarely on the technicalities of the meter of classical Islamicate poetry. While his work, contrary to his own statement, is not the oldest of its kind in Turkic, it is still by far the best-known one, celebrated by Ottomans, Mughals, and Qajars and Ottomans alike. Starting from the bare letter as poetry’s fundamental building blocks, Nevāʾī details how these letters combine into pillars, how these pillars combine into feet (both the basic ones and the ones derived thereof), and, eventually, how these feet combine into nineteen sound and plenty more derivative meters. His analysis is sprinkled with illustrative verses in Chaghatay Turkic, and topped with a succinct defence of poetry, the tricks of poetry scansion, an appraisal of his patron and brother-in-arms, the Timurid ruler Ḥusayn Bayqara, and a rare discussion of Turkic prosodic forms that stretches the limits of classical prosody

Al-Rāzī’s Discussion on the Meaning of Speech [Kalām] & its Origins: Introduction & Translation

Known as one of the Sultans of the theologians, Fakhr al-Dīn al-Rāzī (1150 – 1210) was a Persian Sunnī scholar of Arab origins who was renowned for mastering various disciplines including but not limited to exegesis [tafsīr], principles of Islamic jurisprudence [uṣūl al-fiqh], theology [kalām], logic [manṭiq], astronomy [falak], cosmology, physics, anatomy, and medicine. This paper delves into al-Rāzī’s discussion on semantics and its relation to the foundations of languages by examining multiple views on the composition of speech. By raising the rather simple question as to what constitutes speech (kalām) and what does not, al-Rāzī presents us with a terse, yet enlightening presentation of rhetoric's very building blocks, the word. His focus then moves to the vexed issue of whether the waḍʿ or establishment of speech is through divine revelation or human convention

AI-based preeclampsia detection and prediction with electrocardiogram data

IntroductionMore than 76,000 women die yearly from preeclampsia and hypertensive disorders of pregnancy. Early diagnosis and management of preeclampsia can improve outcomes for both mother and baby. In this study, we developed artificial intelligence models to detect and predict preeclampsia from electrocardiograms (ECGs) in point-of-care settings.MethodsTen-second 12-lead ECG data was obtained from two large health care settings: University of Tennessee Health Science Center (UTHSC) and Atrium Health Wake Forest Baptist (AHWFB). UTHSC data was split into 80% training and 20% holdout data. The model used a modified ResNet convolutional neural network, taking one-dimensional raw ECG signals comprising 12 channels as an input, to predict risk of preeclampsia. Sub-analyses were performed to assess the predictive accuracy for preeclampsia prediction within 30, 60, or 90 days before diagnosis.ResultsThe UTHSC cohort included 904 ECGs from 759 females (78.8% African American) with a mean ± sd age of 27.3 ± 5.0 years. The AHWFB cohort included 817 ECGs from 141 females (45.4 African American) with a mean ± sd age of 27.4 ± 5.9 years. The cross-validated ECG-AI model yielded an AUC (95% CI) of 0.85 (0.77-0.93) on UTHSC holdout data, and an AUC (95% CI) of 0.81 (0.77-0.84) on AHWFB data. The sub-analysis of different time windows before preeclampsia prediction resulted in AUCs (95% CI) of 0.92 (0.84-1.00), 0.89 (0.81-0.98) and 0.90 (0.81-0.98) when tested on ECGs 30 days, 60 days and 90 days, respectively, before diagnosis. When assessed on early onset preeclampsia (preeclampsia diagnosed at <34 weeks of pregnancy), the model's AUC (95% CI) was 0.98 (0.89-1.00).DiscussionWe conclude that preeclampsia can be identified with high accuracy via application of AI models to ECG data

Table1_AI-based preeclampsia detection and prediction with electrocardiogram data.docx

IntroductionMore than 76,000 women die yearly from preeclampsia and hypertensive disorders of pregnancy. Early diagnosis and management of preeclampsia can improve outcomes for both mother and baby. In this study, we developed artificial intelligence models to detect and predict preeclampsia from electrocardiograms (ECGs) in point-of-care settings.MethodsTen-second 12-lead ECG data was obtained from two large health care settings: University of Tennessee Health Science Center (UTHSC) and Atrium Health Wake Forest Baptist (AHWFB). UTHSC data was split into 80% training and 20% holdout data. The model used a modified ResNet convolutional neural network, taking one-dimensional raw ECG signals comprising 12 channels as an input, to predict risk of preeclampsia. Sub-analyses were performed to assess the predictive accuracy for preeclampsia prediction within 30, 60, or 90 days before diagnosis.ResultsThe UTHSC cohort included 904 ECGs from 759 females (78.8% African American) with a mean ± sd age of 27.3 ± 5.0 years. The AHWFB cohort included 817 ECGs from 141 females (45.4 African American) with a mean ± sd age of 27.4 ± 5.9 years. The cross-validated ECG-AI model yielded an AUC (95% CI) of 0.85 (0.77-0.93) on UTHSC holdout data, and an AUC (95% CI) of 0.81 (0.77-0.84) on AHWFB data. The sub-analysis of different time windows before preeclampsia prediction resulted in AUCs (95% CI) of 0.92 (0.84-1.00), 0.89 (0.81-0.98) and 0.90 (0.81-0.98) when tested on ECGs 30 days, 60 days and 90 days, respectively, before diagnosis. When assessed on early onset preeclampsia (preeclampsia diagnosed at DiscussionWe conclude that preeclampsia can be identified with high accuracy via application of AI models to ECG data.</p

Cross-Cultural Perspectives on the Role of Empathy during COVID-19's First Wave

The COVID-19 pandemic has spread throughout the world, and concerns about psychological, social, and economic consequences are growing rapidly. Individuals' empathy-based reactions towards others may be an important resilience factor in the face of COVID-19. Self-report data from 15,375 participants across 23 countries were collected from May to August 2020 during the early phases of the COVID-19 pandemic. In particular, this study examined different facets of empathy-Perspective-Taking, Empathic Concern, and Personal Distress, and their association with cross-cultural ratings on Individualism, Power Distance, The Human Development Index, Social Support Ranking, and the Infectious Disease Vulnerability Index, as well as the currently confirmed number of cases of COVID-19 at the time of data collection. The highest ratings on Perspective-Taking were obtained for USA, Brazil, Italy, Croatia, and Armenia (from maximum to minimum); on Empathetic Concern, for the USA, Brazil, Hungary, Italy, and Indonesia; and on Personal Distress, from Brazil, Turkey, Italy, Armenia, Indonesia. Results also present associations between demographic factors and empathy across countries. Limitations and future directions are presented

Predictors of Anxiety in the COVID-19 Pandemic from a Global Perspective: Data from 23 Countries

Prior and ongoing COVID-19 pandemic restrictions have resulted in substantial changes to everyday life. The pandemic and measures of its control affect mental health negatively. Self-reported data from 15,375 participants from 23 countries were collected from May to August 2020 during the early phases of the COVID-19 pandemic. Two questionnaires measuring anxiety level were used in this study—the Generalized Anxiety Disorder Scale (GAD-7), and the State Anxiety Inventory (SAI). The associations between a set of social indicators on anxiety during COVID-19 (e.g., sex, age, country, live alone) were tested as well. Self-reported anxiety during the first wave of the COVID-19 pandemic varied across countries, with the maximum levels reported for Brazil, Canada, Italy, Iraq and the USA. Sex differences of anxiety levels during COVID-19 were also examined, and results showed women reported higher levels of anxiety compared to men. Overall, our results demonstrated that the self-reported symptoms of anxiety were higher compared to those reported in general before pandemic. We conclude that such cultural dimensions as individualism/collectivism, power distance and looseness/tightness may function as protective adaptive mechanisms against the development of anxiety disorders in a pandemic situation