The impinging–freezing of supercooled water droplets (SLDs) is the root cause of aircraft icing. This work presented an experimental investigation of a millimeter-sized supercooled droplet (−10 $^\circ {\rm{C}}$) impact onto cold surfaces. For the majority of the current research on freezing behaviour, the quantitative analysis of impingement contributions was neglected. The present study established prediction models for the frozen area ratio, initial freezing height and solidification time by changing Weber number and Stefan number. The results showed that with the decrease in surface temperatures, the maximum spreading factor and the peak height factor were unchanged; however, the receding velocity of the liquid film reduced. Besides, regardless of the three freezing modes (quasi-static, instantaneous and delayed), the frozen area ratio consistently increased with decreasing Weber number. For the Stefan number exceeded 0.12, the frozen area ratio increased with decreasing surface temperature; otherwise, it was independent of the surface temperature. In addition, the initial height of asymmetrical frozen droplets was characterised using the ‘two-ellipse’ method, revealing an inverse proportionality to the square root of the frozen area ratio. Furthermore, the solidification time of the hemisphere and pancake frozen droplets shortened with the decrease in the initial height and surface temperature. This fundamental study provides valuable insights for understanding aircraft icing and optimising anti-icing systems.

This paper proposes a cooperative midcourse guidance law with target changing and topology switching for multiple interceptors intercepting targets in the case of target loss and communication topology switching. Firstly, a three-dimensional guidance model is established and a cooperative trajectory shaping guidance law is given. Secondly, the average position consistency protocol of virtual interception points is designed for communication topology switching, and the convergence of the average position of virtual interception points under communication topology switching is proved by Lyapunov stability theory. Then, in the case of the target changing, the target handover law and the handover phase guidance law are designed to ensure the acceleration smoothing, at last, the whole cooperative midcourse guidance law is given based on the combination of the above guidance laws. Finally, numerical simulation results show the effectiveness and the superiority of the proposed cooperative midcourse guidance law.

We investigate the dynamics of a columnar Taylor–Green vortex array under strong stratification, focusing on Froude numbers $0.125\leq Fr \leq 1.0$, with the aim of identifying and understanding the primary instabilities that lead to the vortices’ breakdown. Linear stability analysis reveals that the fastest-growing vertical wavenumber scales with $Fr^{-1}$, while the dimensionless growth rate remains approximately constant. The most unstable eigenmode, identified as the mixed hyperbolic mode by Hattori et al. (J. Fluid Mech., vol. 909, 2021, A4), bears significant similarities to the zigzag instability, first discovered by Billant & Chomaz (J. Fluid Mech., vol. 418, 2000, pp. 167–188). Direct numerical simulations further confirm that the zigzag instability is crucial in amplifying initial random perturbations to finite amplitude, with the flow structure and modal growth rate consistent with the linear stability analysis. In particular, the characteristic vertical length scale of turbulence matches that of the fastest-growing linear mode. These findings underscore the broader relevance of the zigzag instability mechanism beyond its initial discovery in vortex pairs, demonstrating its role in facilitating direct energy transfer from vertically uniform vortical motions to a characteristic vertical length scale proportional to $Fr$ in strongly stratified flows.

Background: Neurosurgical conditions impose a significant burden on the Canadian healthcare system. This study quantifies the economic impact and explores predictive models for postoperative length of stay. Methods: We analyzed data from the Canadian Institute for Health Information National Health Expenditure Trends database for 2015-2019, focusing on case volumes, healthcare costs, and lengths of stay (LOS) across age groups and conditions. Decision tree models were created to predict total LOS from patient age and average acute LOS. Results: There was a modest increase in case volumes from 6,220 ± 3,103 in 2015 to 6,492 ± 3,240 in 2018, with a slight decrease in 2019. The total estimated hospital costs ranged from 2.27 ± 0.38 million CAD in 2015 to 2.23 ± 0.44 million CAD in 2019. The highest costs were seen in the 18-59 age group, at 2.53 ± 0.43 million CAD. Decision tree models showed high accuracy for predicting LOS in cases like spinal injury (F1-score: 0.98) but were less accurate for interventions with trauma or complications (F1-scores from 0.66 to 0.97). Conclusions: The study delineates the financial demands of neurosurgery in Canada and suggests decision tree models as useful tools for predicting hospital stay, with variable accuracy depending on the case complexity.

Background: The formation of pial-pial collateral network aneurysms due to carotid occlusion is a rare neurological phenomenon. This case details a 69-year-old male who developed a pial-pial collateral network aneurysm secondary to left internal carotid artery occlusion, leading to intracranial hemorrhage. Methods: The patient presented with altered consciousness due to left temporal intracerebral hemorrhage, subdural hematoma, and intraventricular hemorrhage. Cerebral angiography revealed an occluded left internal carotid artery, with superficial temporal artery (STA) and superior orbital artery anastomosis, and extensive pial-pial collaterals from the posterior temporal artery. A 4 mm aneurysm arising from this collateral network was identified. Surgical intervention involved a left temporal craniectomy and excision of the aneurysm, prioritizing the preservation of the STA. N.B., Informed patient consent was obtained in this study. Results: Successful aneurysm removal and preservation of collateral pathways were confirmed by postoperative imaging. The patient exhibited rapid neurological improvement; by postoperative day (POD) one, the patient showed limited response to stimuli. He was extubated by POD4 and discharged on POD27, where he conversed well, was independently ambulatory, and needed minimal to no assistance in activities of daily living. Conclusions: This case highlights the need for careful preoperative planning and intraoperative precision, especially in preserving vital collateral vascular pathways.

Background: The integration of Artificial Intelligence (AI) in medical education is an area of growing importance. While AI models have been evaluated extensively in multiple-choice question formats, their proficiency in written exams remains to be explored. Methods: Four AI models—GPT-4 (OpenAI), Claude-2.1 (Anthropic), Gemini Pro (Google), and Perplexity 70B (Perplexity)—were tested using the Canadian Royal College Sample Neurosurgery Exam. The written exam covered diagnostic reasoning, knowledge of neurosurgical conditions, and understanding of radiographic imaging techniques. Results: GPT-4 and Perplexity 70B both achieved a score of 68.42%, followed by Claude-2.1 with 60.53%, and Gemini Pro with 57.89%. The models showed proficiency in answering questions that required factual knowledge, such as identifying pathogens in spinal epidural abscess. However, they struggled with more complex diagnostic reasoning tasks, particularly in explaining the pathophysiology behind a sudden rise in blood pressure during surgery and interpreting radiographic characteristics of intracranial abscesses on MRI. Conclusions: The findings indicate that while AI models like GPT-4 and Perplexity 70B are adept at handling factual neurosurgical questions, their performance in complex diagnostic reasoning in a written format is less consistent. This underscores the need for more advanced and specialized AI training, particularly in the nuances of medical diagnostics and decision-making.

Background: Chordomas are rare, malignant bone tumors that present significant challenges in management and treatment due to their complex anatomical locations and propensity for recurrence. Advancements in artificial intelligence (AI) and machine learning (ML) show promise in improving chordoma management. Methods: A comprehensive literature search was conducted following PRISMA guidelines across multiple databases, including MEDLINE, Cochrane, Embase, Scopus, and Web of Science. The search targeted articles related to AI and ML applications in clinical tasks associated with chordoma management. The selection process involved systematic screening, data extraction, and assessment of inter-rater variability. Results: The search yielded 1,006 records, with 18 included for analysis. Convolutional neural networks (CNNs) excelled in tumor volume estimation, with the state-of-the-art model achieving a Dice similarity score of 74.2%, sensitivity of 79.4%, and positive predictive value of 74.3%. Clustering algorithms were effective in prognostic evaluations. Bayesian models and logistic regression demonstrated robustness in diagnostics. Support vector machines (SVMs) were noted for their diagnostic precision. Conclusions: AI and ML algorithms, particularly CNNs, clustering algorithms, Bayesian models, logistic regression, and SVMs, show promise in improving chordoma management through enhanced imaging, diagnostics, and prognostics. Future research should focus on larger, externally validated datasets and explore underutilized techniques like multi-modal data integration.

Background: Sex is associated with differences in early outcomes with preterm males at greater risk for mortality and morbidity. The objective of this study was to examine preterm sex differences in neurodevelopmental outcomes and brain development from early-life to 8-years. Methods: A prospective cohort of preterm infants born 24-32 weeks gestation were followed to 8-years with standardized measures. MRI scans were performed after birth, term-equivalent age and 8-years. Associations between sex, risk factors, brain volumes, white matter fractional anisotropy (FA) and outcomes were assessed using generalized estimating equations. Results: Preterm males (N=83) and females (N=72) had similar risk factors, brain injury and pain exposure. Sex was a predictor of cognitive scores (P=0.02) and motor impairment (P=0.03), with males having lower cognitive scores and higher motor impairment over time. There was a sex effect for FA (P=0.04), with males having lower FA over time. There were significant sex-brain injury and sex-pain interactions for cognitive and motor outcomes. Conclusions: In this longitudinal study, preterm males had lower cognitive scores and greater motor impairment, which may relate to differences in white matter maturation. Effects of brain injury and pain on outcomes is moderated by sex, indicating a differential response to early-life adversity in preterm males and females.

Background: OSCE-GPT (https://learnmedicine.ca/) is an AI-based app that integrates history, physical exam, and relevant components for case guidance across medical disciplines to help trainees improve clinical skills. With global users across 60+ countries, this preliminary quality improvement study gathers user feedback on neurology and otolaryngology cases. Methods: A survey was distributed to users at the University of Ottawa and Cumming School of Medicine. Participants provided insights on the app’s use, perceived benefits, and suggested improvements. Results: Using 5-point Likert scales, 13 respondents, 9 of which evaluated an otolaryngology case, rated the overall usefulness of the learning tool 4.57± 0.51 (1=very poor, 5=very good), with a score of 4.00±0.65 relative to other teaching methods, such as didactic lectures or grand rounds (1=much worse, 5=much better). Users noted realistic interactions and self-paced learning as beneficial factors. Areas for improvement included a more fluid transition between physical exams and history, geographic variations in cases, and the addition of elements such as non-verbal patient cues or emotional. Conclusions: This study demonstrates utility of OSCE-GPT for medical trainees, particularly for otolaryngology and neurology cases. As cases continue to be added, feedback will be implemented to further improve user experience.

With the advent of deep, all-sky radio surveys, the need for ancillary data to make the most of the new, high-quality radio data from surveys like the Evolutionary Map of the Universe (EMU), GaLactic and Extragalactic All-sky Murchison Widefield Array survey eXtended, Very Large Array Sky Survey, and LOFAR Two-metre Sky Survey is growing rapidly. Radio surveys produce significant numbers of Active Galactic Nuclei (AGNs) and have a significantly higher average redshift when compared with optical and infrared all-sky surveys. Thus, traditional methods of estimating redshift are challenged, with spectroscopic surveys not reaching the redshift depth of radio surveys, and AGNs making it difficult for template fitting methods to accurately model the source. Machine Learning (ML) methods have been used, but efforts have typically been directed towards optically selected samples, or samples at significantly lower redshift than expected from upcoming radio surveys. This work compiles and homogenises a radio-selected dataset from both the northern hemisphere (making use of Sloan Digital Sky Survey optical photometry) and southern hemisphere (making use of Dark Energy Survey optical photometry). We then test commonly used ML algorithms such as k-Nearest Neighbours (kNN), Random Forest, ANNz, and GPz on this monolithic radio-selected sample. We show that kNN has the lowest percentage of catastrophic outliers, providing the best match for the majority of science cases in the EMU survey. We note that the wider redshift range of the combined dataset used allows for estimation of sources up to $z = 3$ before random scatter begins to dominate. When binning the data into redshift bins and treating the problem as a classification problem, we are able to correctly identify $\approx$76% of the highest redshift sources—sources at redshift $z > 2.51$—as being in either the highest bin ($z > 2.51$) or second highest ($z = 2.25$).

The strongly nonlinear Miyata–Choi–Camassa model under the rigid lid approximation (MCC-RL model) can describe accurately the dynamics of large-amplitude internal waves in a two-layer fluid system for shallow configurations. In this paper, we apply the MCC-RL model to study the internal waves generated by a moving body on the bottom. For the case of the moving body speed $U=1.1c_{0}$, where ${c_0}$ is the linear long-wave speed, the accuracy of the MCC-RL results is assessed by comparing with Euler's solutions, and very good agreement is observed. It is found that when the moving body speed increases from $U=0.8c_{0}$ to $U=1.241c_{0}$, the amplitudes of the generated internal solitary waves in front of the moving body become larger. However, a critical moving body speed is found between $U=1.241c_{0}$ and $U=1.242c_{0}$. After exceeding this critical speed, only one internal wave right above the body is generated. When the moving body speed increases from $U=1.242c_{0}$ to $U=1.5c_{0}$, the amplitudes of the internal waves become smaller.

Background: A phase 3 trial, ADVANCE (NCT03777059), demonstrated that atogepant, an oral, CGRP receptor antagonist dosed once daily, results in clinically meaningful reductions in mean monthly migraine days. This open-label extension for ADVANCE trial completers evaluated long-term safety and tolerability of atogepant over 40-weeks. Methods: Participants in this trial (NCT03939312), rolled over from the ADVANCE trial, were treated with atogepant 60mg once daily for 40-weeks, with a 4-week safety follow-up. Only safety data were collected. Results: 685 participants took at least one dose of study drug, 74.6% completed the 40-week treatment period; mean age of 41.8 years, 88.2% female, 84.4% white, and mean BMI of 30.58 kg/m2. Mean (SD) treatment duration was 233.6 (89.32) days. 62.5% of participants experienced a treatment-emergent adverse event (TEAE), with 8.8% considered treatment-related by the investigator; serious adverse events (SAEs) occurred in 3.4% of participants, none were treatment-related. The most frequent AEs leading to discontinuation was nausea (0.4%, n=3); the most frequent TEAEs observed included upper respiratory tract infection (5.5%, n=38) and urinary tract infection (5.3%, n=36). No deaths or hepatic safety issues were observed. Conclusions: Safety results are consistent with known safety profile of atogepant and support long-term safety and tolerability of once daily dosing of atogepant 60mg.

The commercial Computational Fluid Dynamics (CFD) software STAR-CCM+ was used to simulate the flow and breakup characteristics of a Liquid Jet Injected into the gaseous Crossflow (LJIC) under real engine operating conditions. The reasonable calculation domain geometry and flow boundary conditions were obtained based on a civil aviation engine performance model similar to the Leap-1B engine which was developed using the GasTurb software and the preliminary design results of its low-emission combustor. The Volume of Fluid (VOF) model was applied to simulate the breakup feature of the near field of LJIC. The numerical method was validated and calibrated through comparison with the public test data at atmospheric conditions. The results showed that the numerical method can capture most of the jet breakup structure and predict the jet trajectory with an error not exceeding ±5%. The verified numerical method was applied to simulate the breakup of LJIC at the real engine operating condition. The breakup mode of LJIC was shown to be surface shear breakup at elevated condition. The trajectory of the liquid jet showed good agreement with Ragucci’s empirical correlation.

The full-wing solar-powered UAV has a large aspect ratio, special configuration, and excellent aerodynamic performance. This UAV converts solar energy into electrical energy for level flight and storage to improve endurance performance. The UAV only uses a differential throttle for lateral control, and the insufficient control capability during crosswind landing results in a large lateral distance bias and leads to multiple landing failures. This paper analyzes 11 landing failures and finds that a large lateral distance bias at the beginning of the approach and the coupling of base and differential throttle control is the main reason for multiple landing failures. To improve the landing performance, a heading angle-based vector field (VF) method is applied to the straight-line and orbit paths following and two novel 3D Dubins landing paths are proposed to reduce the initial lateral control bias. The results show that the straight-line path simulation exhibits similar phenomenon with the practical failure; the single helical path has the highest lateral control accuracy; the left-arc to left-arc (L-L) path avoids the saturation of the differential throttle; and both paths effectively improve the probability of successful landing.

Three-dimensional (3-D) measurements of flame stretch are experimentally challenging. In this paper, two-dimensional (2-D) and 3-D measurements of flame stretch and turbulence–flame interactions were examined using direct numerical simulation (DNS) data of turbulent premixed flames, and models to estimate 3-D statistics of flame stretch-related quantities by correcting 2-D measurements were developed. A variety of DNS cases were simulated, including three freely propagating planar flames without a mean shear and a slot-jet flame with a mean shear. The main findings are summarized as follows. First, the mean shear mainly influences the flame orientations. However, it does not change the flame stretch and turbulence–flame interactions qualitatively. The distributions of out-of-plane angle of all cases are nearly isotropic. Second, models were proposed to approximate the 3-D statistics of flame stretch-related quantities using 2-D measurements, the performance of which was verified by comparing modelled and actual 3-D surface averages and probability density functions of tangential strain rate, curvature and displacement velocity. Third, 2-D measurements of flame stretch capture properly the trends of the 3-D results, with flame surface area being produced in low curvature regions and destroyed in highly curved regions. However, the magnitude of flame stretch was under-estimated in 2-D measurements. Finally, 2-D and 3-D turbulence–flame interactions were examined. The flame normal vector is aligned with the most compressive strain rate in both 2-D and 3-D measurements. Meanwhile, the flame normal vector is misaligned (weakly aligned) with the most extensive strain rate in 3-D (2-D) measurements, highlighting the difference in 2-D and 3-D results of turbulence–flame interactions.

Gas-fluidized beds of flexible fibres, which have been rarely studied before, are investigated in this work using the coupled approach of the discrete element method and computational fluid dynamics. In the present numerical method, gas–fibre interaction is modelled by calculating the interaction force for each constituent element in the fibre, and the composition of the interaction forces on the constituent elements generates a resultant hydrodynamic force and a resultant hydrodynamic torque on the fibre. Pressure drops and fibre orientation results from the present simulations with various fibre aspect ratios are in good agreement with previous experimental and simulation results. Some novel results are obtained for the effects of fibre flexibility. Larger hydrodynamic forces on fibres (before the bed is fluidized) and smaller minimum fluidization velocities (MFVs) are observed for more flexible fibre beds due to the smaller porosities, while smaller hydrodynamic forces are obtained for the more flexible fibres when the beds are fluidized with significant fibre motion. By scaling the superficial gas velocity using the MFVs, the data of pressure drop can collapse onto the Ergun correlation for stiff fibres of various aspect ratios; however, the pressure drop curves deviate from the Ergun correlation for very flexible fibres, due to the significant fibre bed expansion before the MFV is reached. The fibre aspect ratio and flexibility both have an impact on the solids mixing rate, and it is found that the solids mixing rates are essentially determined by the ratio of the superficial gas velocity to MFV.

Jeankempite, Ca5(AsO4)2(AsO3OH)2(H2O)7, is a new mineral species (IMA2018-090) discovered amongst coatings of arsenate minerals on oxidised copper arsenides from the Mohawk No. 2 mine, Mohawk, Keweenaw County, Michigan, USA. The new mineral occurs as lamellar bundles of colourless to white plates up to 1 mm wide and is visually indistinguishable from guérinite, with which it forms intergrowths. Jeankempite is transparent to translucent with a waxy lustre and white streak, is non-fluorescent under longwave and shortwave ultraviolet illumination, has a Mohs hardness of ~1.5 and brittle tenacity with uneven fracture. Crystals are flattened on {01$\bar{1}$} and exhibit perfect cleavage on {01$\bar{1}$}. Optically, jeankempite is biaxial (+), α = 1.601(2), β = 1.607(2), γ = 1.619(2) (white light); 2Vmeas. = 72(2)° and 2Vcalc. = 71.0°. The empirical formula is (Ca4.97Na0.013Mg0.017)(As3.99S0.01)4O23H16, based on 23 O and 16 H atoms per formula unit. Thermogravimetric analysis indicates that jeankempite undergoes four weight losses totalling 16.82%, close to the expected loss of 16.30%, corresponding to eight H2O. Jeankempite is triclinic, P$\bar{1}$, a = 6.710(6), b = 14.901(14), c = 15.940(15) Å, α = 73.583(12)°, β = 81.984(12)°, γ = 82.754(12)°, V = 1507(2) Å3 and Z = 3. The final structure was refined to R1 = 0.0591 for 2781 reflections with Iobs > 3σI. The crystal structure of jeankempite is built from a network of edge- and vertex-sharing CaO6, CaO7 and AsO4 polyhedra, and we hypothesise that the new mineral has formed due to a topotactic reaction brought on by dehydration of preexisting guérinite.