Thermal Analysis of Potted Litz Wire for High-Power-Density Aerospace Electric Machines

Increasing the power density and efficiency of electric machines (motors and generators) is integral to bringing Electrified Aircraft (EA) to commercial realization. To that end an effort to create a High Efficiency Megawatt Motor (HEMM) with a goal of exceeding 98% efficiency and 1.46 MW of power has been undertaken at the NASA Glenn Research Center. Of the motor components the resistive losses in the stator windings are by far the largest contributor (34%) to total motor loss. The challenge is the linear relationship between resistivity and temperature, making machine operation sensitive to temperature increases. In order to accurately predict the thermal behavior of the stator the thermal conductivity of the Litz wire-potting-electrical insulation system must be known. Unfortunately, this multi material system has a wide range of thermal conductivities (0.1 W/m-K 400 W/m-K) and a high anisotropy (axial vs transverse) making the prediction of the transverse thermal conductivity an in turn the hot spot temperatures in the windings is difficult. In order to do this a device that simulates the thermal environment found in the HEMM stator was designed. This device is not unlike the motorettes (little motors) that are described in IEEE standards for testing electrical insulation lifetimes or other electric motor testing. However, because the HEMM motor design includes significant rotor electrical and thermal considerations the term motorette was not deemed appropriate. Instead statorette (or little stator) was adopted as the term for this test device. This paper discussed the design, thermal heat conjugate analysis (thermal model), manufacturing and testing of HEMM's statorette. Analysis of the results is done by thermal resistance network model and micro thermal model and is compared to analytical predictions of thermal conductivity of the insulated and potted Litz wire system

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Word embeddings reveal growing moral concern for people, animals, and the environment

The Enlightenment idea of historical moral progress asserts that civil societies become more moral over time. This is often understood as an expanding moral circle and is argued to be tightly linked with language use, with some suggesting that shifts in how we express concern for others can be considered an important indicator of moral progress. Our research explores these notions by examining historical trends in natural language use during the 19th and 20th centuries. We found that the associations between words denoting moral concern and words referring to people, animals, and the environment grew stronger over time. The findings support widely-held views about the nature of moral progress by showing that language has changed in a way that reflects greater concern for others

To what extent can behaviour change techniques be identified within an adaptable implementation package for primary care? A prospective directed content analysis

Interpreting evaluations of complex interventions can be difficult without sufficient description of key intervention content. We aimed to develop an implementation package for primary care which could be delivered using typically available resources and could be adapted to target determinants of behaviour for each of four quality indicators: diabetes control, blood pressure control, anticoagulation for atrial fibrillation and risky prescribing. We describe the development and prospective verification of behaviour change techniques (BCTs) embedded within the adaptable implementation packages

Predictors of starting and stopping chemsex in men who have sex with men in England: findings from the AURAH2 prospective study

BACKGROUND: Chemsex (the use of psychoactive drugs in sexual contexts) has been associated with HIV acquisition and other STIs, so there is benefit in identifying those most likely to start chemsex to offer risk reduction interventions such as pre-exposure prophylaxis (PrEP). To date, there have been no data from a longitudinal study analysing factors most associated with starting and stopping chemsex. METHODS: The prospective cohort study, Attitudes to and Understanding Risk of Acquisition of HIV over Time (AURAH2), collected 4 monthly and annual online questionnaire data from men who have sex with men (MSM) from 2015 to 2018. We investigate the association of sociodemographic factors, sexual behaviours and drug use with starting and stopping chemsex among 622 men who completed at least one follow-up questionnaire. Poisson models with generalised estimating equations were used to produce risk ratios (RRs) accounting for multiple starting or stopping episodes from the same individual. Multivariable analysis was adjusted for age group, ethnicity, sexual identity and university education. FINDINGS: In the multivariable analysis, the under 40 age group was significantly more likely to start chemsex by the next assessment (RR 1.79, 95% CI 1.12 to 2.86). Other factors which showed significant association with starting chemsex were unemployment (RR 2.10, 95% CI 1.02 to 4.35), smoking (RR 2.49, 95% CI 1.63 to 3.79), recent condomless sex (CLS), recent STI and postexposure prophylaxis (PEP) use in the past year (RR 2.10, 95% CI 1.33 to 3.30). Age over 40 (RR 0.71, 95% CI 0.51 to 0.99), CLS, and use of PEP (RR 0.64, 95% CI 0.47 to 0.86) and PrEP (RR 0.47, 95% CI 0.29 to 0.78) were associated with lower likelihood of stopping chemsex by the next assessment. INTERPRETATION: Knowledge of these results allows us to identify men most likely to start chemsex, thus providing an opportunity for sexual health services to intervene with a package of risk mitigation measures, especially PrEP use

Reconstructing Global Daily CO2 Emissions via Machine Learning

High temporal resolution CO2 emission data are crucial for understanding the drivers of emission changes, however, current emission dataset is only available on a yearly basis. Here, we extended a global daily CO2 emissions dataset backwards in time to 1970 using machine learning algorithm, which was trained to predict historical daily emissions on national scales based on relationships between daily emission variations and predictors established for the period since 2019. Variation in daily CO2 emissions far exceeded the smoothed seasonal variations. For example, the range of daily CO2 emissions equivalent to 31% of the year average daily emissions in China and 46% of that in India in 2022, respectively. We identified the critical emission-climate temperature (Tc) is 16.5 degree celsius for global average (18.7 degree celsius for China, 14.9 degree celsius for U.S., and 18.4 degree celsius for Japan), in which negative correlation observed between daily CO2 emission and ambient temperature below Tc and a positive correlation above it, demonstrating increased emissions associated with higher ambient temperature. The long-term time series spanning over fifty years of global daily CO2 emissions reveals an increasing trend in emissions due to extreme temperature events, driven by the rising frequency of these occurrences. This work suggests that, due to climate change, greater efforts may be needed to reduce CO2 emissions

Deep-learning-based clustering of OCT images for biomarker discovery in age-related macular degeneration (Pinnacle study report 4)

Diseases are currently managed by grading systems, where patients are stratified by grading systems into stages that indicate patient risk and guide clinical management. However, these broad categories typically lack prognostic value, and proposals for new biomarkers are currently limited to anecdotal observations. In this work, we introduce a deep-learning-based biomarker proposal system for the purpose of accelerating biomarker discovery in age-related macular degeneration (AMD). It works by first training a neural network using self-supervised contrastive learning to discover, without any clinical annotations, features relating to both known and unknown AMD biomarkers present in 46,496 retinal optical coherence tomography (OCT) images. To interpret the discovered biomarkers, we partition the images into 30 subsets, termed clusters, that contain similar features. We then conduct two parallel 1.5-hour semi-structured interviews with two independent teams of retinal specialists that describe each cluster in clinical language. Overall, both teams independently identified clearly distinct characteristics in 27 of 30 clusters, of which 23 were related to AMD. Seven were recognised as known biomarkers already used in established grading systems and 16 depicted biomarker combinations or subtypes that are either not yet used in grading systems, were only recently proposed, or were unknown. Clusters separated incomplete from complete retinal atrophy, intraretinal from subretinal fluid and thick from thin choroids, and in simulation outperformed clinically-used grading systems in prognostic value. Overall, contrastive learning enabled the automatic proposal of AMD biomarkers that go beyond the set used by clinically established grading systems. Ultimately, we envision that equipping clinicians with discovery-oriented deep-learning tools can accelerate discovery of novel prognostic biomarkers

Compound risks and complex emergencies require new approaches to preparedness

Increasingly, we face compounding and interrelated environmental, socioeconomic, and political crises. Yet our approaches to these problems are often siloed, fragmented, and inadequate. The current pandemic, for instance, continues to collide with a number of other threats to human life and livelihoods. These include violent conflicts, displacement, insect swarms, droughts, heat waves, and structural inequality in the form of racism and gender discrimination. We believe we are at a critical juncture, faced with a need and responsibility to redesign institutions to be proactive, agile, and socially just when confronted with increasingly likely compound risks

The blood-sucking tick Ixodes hexagonus reveals dietary stable isotope signatures of mammalian hosts

Ticks are obligate haematophagous ('blood-sucking') ectoparasites that are capable of retaining host dietary traces post-moult, providing an opportunity to investigate parasite-host interactions and explore their potential as non-invasive subsampling techniques. However, research on the preservation of biochemical host signatures within whole engorged parasites remains limited. Here, we examine stable isotope ratios of nitrogen (δ15N) and carbon (δ13C) across different tick tissues (exoskeleton vs. blood meal) and between whole ticks and one of their hosts, the European polecat Mustela putorius. Additionally, carbon and nitrogen weight percentages (wt%) are assessed to explore potential biochemical changes linked to blood meal digestion. Our findings showed that the isotopic composition of tick exoskeleton and blood meal differed significantly, with exoskeletons potentially reflecting a previous host. Whole engorged ticks showed a close δ15N relationship to their host, consistent with that of trophic enrichment, while the observed δ13C values were more variable. These findings enhance our understanding of how haematophagous parasites preserve host dietary signatures and, with further research, could support their use as a valuable alternative to invasive sampling methods, particularly when destructive sampling is not feasible