The Influence of the Pressure Head on the Indonesian Seas Circulation

A high resolution, regional, non-linear, barotropic ocean model (2D POM) was used to show that a pressure difference between the Pacific and Indian Ocean does not significantly influence the total transport of the Indonesian throughflow

Discovery of high-entropy ceramics via machine learning

AbstractAlthough high-entropy materials are attracting considerable interest due to a combination of useful properties and promising applications, predicting their formation remains a hindrance for rational discovery of new systems. Experimental approaches are based on physical intuition and/or expensive trial and error strategies. Most computational methods rely on the availability of sufficient experimental data and computational power. Machine learning (ML) applied to materials science can accelerate development and reduce costs. In this study, we propose an ML method, leveraging thermodynamic and compositional attributes of a given material for predicting the synthesizability (i.e., entropy-forming ability) of disordered metal carbides. The relative importance of the thermodynamic and compositional features for the predictions are then explored. The approach’s suitability is demonstrated by comparing values calculated with density functional theory to ML predictions. Finally, the model is employed to predict the entropy-forming ability of 70 new compositions; several predictions are validated by additional density functional theory calculations and experimental synthesis, corroborating the effectiveness in exploring vast compositional spaces in a high-throughput manner. Importantly, seven compositions are selected specifically, because they contain all three of the Group VI elements (Cr, Mo, and W), which do not form room temperature-stable rock-salt monocarbides. Incorporating the Group VI elements into the rock-salt structure provides further opportunity for tuning the electronic structure and potentially material performance

Dynamical Balance in the Indonesian Seas Circulation

A high resolution, four-open port, non-linear, barotropic ocean model (2D POM) is used to analyze the Indonesian Seas circulation. Both local and overall momentum balances are studied. It is shown that geostrophy holds over most of the area and that the Pacific-Indian Ocean pressure difference is essentially balanced by the resultant of pressure forces acting on the bottom

Fabrication of high-entropy nitrides and carbonitrides

In high-entropy alloys, the use of multiple principle alloying elements is known to entropically stabilize the material. Refractory nitrides and carbides of transition metals are widely known for their ultra high-temperature stability and their high hardness, properties that make them valuable materials for extreme environments, such as coating the exterior of hypersonic flight vehicles and the interior of nuclear reactors. By creating entropy-stabilized complex solid solutions of nitrides and carbides, one can take advantage of the inherent favorable properties of these materials, as well as increased thermal stability and solid solution strengthening. Five-metal systems are chosen using first-principles calculations to describe the energetic distribution of possible atomic configurations, in order to identify systems that are likely to form an entropy-stabilized solid solution. Bulk samples of equiatomic, hexanery (5-metal), high-entropy refractory nitrides and carbonitrides were then fabricated to demonstrate this concept, by using a combination of high-energy ball milling, spark plasma sintering, and hot pressing. The uniformity of the microstructures is characterized, and single-phase solid solutions are achieved, thus demonstrating the ability to entropically stabilize multi-component random mixtures of refractory carbides and nitrides. This work is supported by the U.S. Office of Naval Research MURI program (Grant No. N00014-15- 1-2863

Ongoing burden and recent trends in severe hospitalised hypoglycaemia events in people with type 1 and type 2 diabetes in Scotland:A nationwide cohort study 2016–2022

Aims: We examined severe hospitalised hypoglycaemia (SHH) rates in people with type 1 and type 2 diabetes in Scotland during 2016–2022, stratifying by sociodemographics. Methods: Using the Scottish National diabetes register (SCI-Diabetes), we identified people with type 1 and type 2 diabetes alive anytime during 2016–2022. SHH events were determined through linkage to hospital admission and death registry data. We calculated annual SHH rates overall and by age, sex, and socioeconomic status. Summary estimates of time and stratum effects were obtained by fitting adjusted generalised additive models using R package mgcv. Results: Rates for those under 20 with type 1 diabetes reached their minimum at the 2020–2021 transition, 30% below the study period average. A gradual decline over time also occurred among 20–49-year-olds with type 1 diabetes. Overall, females had 15% higher rates than males with type 2 diabetes (rate ratio 1.15, 95% CI 1.08–1.22). People in the most versus least deprived quintile experienced 2.58 times higher rates (95% CI 2.27–2.93) in type 1 diabetes and 2.33 times higher (95% CI 2.08–2.62) in type 2 diabetes. Conclusions: Despite advances in care, SHH remains a significant problem in diabetes. Future efforts must address the large socioeconomic disparities in SHH risks.</p

Radiative falloff in Schwarzschild-de Sitter spacetime

We consider the time evolution of a scalar field propagating in Schwarzschild-de Sitter spacetime. At early times, the field behaves as if it were in pure Schwarzschild spacetime; the structure of spacetime far from the black hole has no influence on the evolution. In this early epoch, the field's initial outburst is followed by quasi-normal oscillations, and then by an inverse power-law decay. At intermediate times, the power-law behavior gives way to a faster, exponential decay. At late times, the field behaves as if it were in pure de Sitter spacetime; the structure of spacetime near the black hole no longer influences the evolution in a significant way. In this late epoch, the field's behavior depends on the value of the curvature-coupling constant xi. If xi is less than a critical value 3/16, the field decays exponentially, with a decay constant that increases with increasing xi. If xi > 3/16, the field oscillates with a frequency that increases with increasing xi; the amplitude of the field still decays exponentially, but the decay constant is independent of xi.Comment: 10 pages, ReVTeX, 5 figures, references updated, and new section adde

Ongoing burden and recent trends in severe hospitalised hypoglycaemia events in people with type 1 and type 2 diabetes in Scotland:A nationwide cohort study 2016–2022

Aims: We examined severe hospitalised hypoglycaemia (SHH) rates in people with type 1 and type 2 diabetes in Scotland during 2016–2022, stratifying by sociodemographics. Methods: Using the Scottish National diabetes register (SCI-Diabetes), we identified people with type 1 and type 2 diabetes alive anytime during 2016–2022. SHH events were determined through linkage to hospital admission and death registry data. We calculated annual SHH rates overall and by age, sex, and socioeconomic status. Summary estimates of time and stratum effects were obtained by fitting adjusted generalised additive models using R package mgcv. Results: Rates for those under 20 with type 1 diabetes reached their minimum at the 2020–2021 transition, 30% below the study period average. A gradual decline over time also occurred among 20–49-year-olds with type 1 diabetes. Overall, females had 15% higher rates than males with type 2 diabetes (rate ratio 1.15, 95% CI 1.08–1.22). People in the most versus least deprived quintile experienced 2.58 times higher rates (95% CI 2.27–2.93) in type 1 diabetes and 2.33 times higher (95% CI 2.08–2.62) in type 2 diabetes. Conclusions: Despite advances in care, SHH remains a significant problem in diabetes. Future efforts must address the large socioeconomic disparities in SHH risks.</p

Risk factors and prediction of hypoglycaemia using the Hypo-RESOLVE cohort:a secondary analysis of pooled data from insulin clinical trials

AIMS/HYPOTHESIS: The objective of the Hypoglycaemia REdefining SOLutions for better liVES (Hypo-RESOLVE) project is to use a dataset of pooled clinical trials across pharmaceutical and device companies in people with type 1 or type 2 diabetes to examine factors associated with incident hypoglycaemia events and to quantify the prediction of these events.METHODS: Data from 90 trials with 46,254 participants were pooled. Analyses were done for type 1 and type 2 diabetes separately. Poisson mixed models, adjusted for age, sex, diabetes duration and trial identifier were fitted to assess the association of clinical variables with hypoglycaemia event counts. Tree-based gradient-boosting algorithms (XGBoost) were fitted using training data and their predictive performance in terms of area under the receiver operating characteristic curve (AUC) evaluated on test data. Baseline models including age, sex and diabetes duration were compared with models that further included a score of hypoglycaemia in the first 6 weeks from study entry, and full models that included further clinical variables. The relative predictive importance of each covariate was assessed using XGBoost's importance procedure. Prediction across the entire trial duration for each trial (mean of 34.8 weeks for type 1 diabetes and 25.3 weeks for type 2 diabetes) was assessed.RESULTS: For both type 1 and type 2 diabetes, variables associated with more frequent hypoglycaemia included female sex, white ethnicity, longer diabetes duration, treatment with human as opposed to analogue-only insulin, higher glucose variability, higher score for hypoglycaemia across the 6 week baseline period, lower BP, lower lipid levels and treatment with psychoactive drugs. Prediction of any hypoglycaemia event of any severity was greater than prediction of hypoglycaemia requiring assistance (level 3 hypoglycaemia), for which events were sparser. For prediction of level 1 or worse hypoglycaemia during the whole follow-up period, the AUC was 0.835 (95% CI 0.826, 0.844) in type 1 diabetes and 0.840 (95% CI 0.831, 0.848) in type 2 diabetes. For level 3 hypoglycaemia, the AUC was lower at 0.689 (95% CI 0.667, 0.712) for type 1 diabetes and 0.705 (95% CI 0.662, 0.748) for type 2 diabetes. Compared with the baseline models, almost all the improvement in prediction could be captured by the individual's hypoglycaemia history, glucose variability and blood glucose over a 6 week baseline period.CONCLUSIONS/INTERPRETATION: Although hypoglycaemia rates show large variation according to sociodemographic and clinical characteristics and treatment history, looking at a 6 week period of hypoglycaemia events and glucose measurements predicts future hypoglycaemia risk.</p

Risk factors and prediction of hypoglycaemia using the Hypo-RESOLVE cohort:a secondary analysis of pooled data from insulin clinical trials

AIMS/HYPOTHESIS: The objective of the Hypoglycaemia REdefining SOLutions for better liVES (Hypo-RESOLVE) project is to use a dataset of pooled clinical trials across pharmaceutical and device companies in people with type 1 or type 2 diabetes to examine factors associated with incident hypoglycaemia events and to quantify the prediction of these events.METHODS: Data from 90 trials with 46,254 participants were pooled. Analyses were done for type 1 and type 2 diabetes separately. Poisson mixed models, adjusted for age, sex, diabetes duration and trial identifier were fitted to assess the association of clinical variables with hypoglycaemia event counts. Tree-based gradient-boosting algorithms (XGBoost) were fitted using training data and their predictive performance in terms of area under the receiver operating characteristic curve (AUC) evaluated on test data. Baseline models including age, sex and diabetes duration were compared with models that further included a score of hypoglycaemia in the first 6 weeks from study entry, and full models that included further clinical variables. The relative predictive importance of each covariate was assessed using XGBoost's importance procedure. Prediction across the entire trial duration for each trial (mean of 34.8 weeks for type 1 diabetes and 25.3 weeks for type 2 diabetes) was assessed.RESULTS: For both type 1 and type 2 diabetes, variables associated with more frequent hypoglycaemia included female sex, white ethnicity, longer diabetes duration, treatment with human as opposed to analogue-only insulin, higher glucose variability, higher score for hypoglycaemia across the 6 week baseline period, lower BP, lower lipid levels and treatment with psychoactive drugs. Prediction of any hypoglycaemia event of any severity was greater than prediction of hypoglycaemia requiring assistance (level 3 hypoglycaemia), for which events were sparser. For prediction of level 1 or worse hypoglycaemia during the whole follow-up period, the AUC was 0.835 (95% CI 0.826, 0.844) in type 1 diabetes and 0.840 (95% CI 0.831, 0.848) in type 2 diabetes. For level 3 hypoglycaemia, the AUC was lower at 0.689 (95% CI 0.667, 0.712) for type 1 diabetes and 0.705 (95% CI 0.662, 0.748) for type 2 diabetes. Compared with the baseline models, almost all the improvement in prediction could be captured by the individual's hypoglycaemia history, glucose variability and blood glucose over a 6 week baseline period.CONCLUSIONS/INTERPRETATION: Although hypoglycaemia rates show large variation according to sociodemographic and clinical characteristics and treatment history, looking at a 6 week period of hypoglycaemia events and glucose measurements predicts future hypoglycaemia risk.</p