Background: Schizencephaly is a congenital brain malformation involving a cleft in the cerebral hemisphere lined with abnormal gray matter with an estimated incidence of 1.5 per 100,000 live births. Methods: This study aims to characterize the radiological, etiological, and clinical features of schizencephaly, identifying factors predictive of patient outcomes. A retrospective cohort of 94 individuals, both adult and pediatric, was analyzed across four tertiary care centers. A neuroradiologist systematically reviewed imaging, while charts were reviewed for clinical features. Results: Several perinatal risk factors were identified, including young maternal age and prenatal infections. However, genetic testing yielded only one pathogenic COL4A1 mutation. MRI findings showed frequent additional malformations, including those in the pituitary, corpus callosum, and fornix. Clinical characteristics included neurodevelopmental delay (71.6%), seizures (50.0%), and motor impairments (53.3%). Outcomes were heterogeneous, with bilateral and open-lip clefts associated with more severe developmental delays, while seizure rates were comparable across subtypes. Conclusions: The complexity of schizencephaly is highlighted in the largest cohort reported with high rates of seizures, neurodevelopmental delays, and motor impairments, but outcomes varied widely based on imaging features, underscoring the importance of individualized management. The low yield of genetic findings emphasizes prenatal environmental risk factors as etiological contributors.

The First Large Absorption Survey in H i (FLASH) is a large-area radio survey for neutral hydrogen in and around galaxies in the intermediate redshift range $0.4\lt z\lt1.0$, using the 21-cm H i absorption line as a probe of cold neutral gas. The survey uses the ASKAP radio telescope and will cover 24,000 deg$^2$ of sky over the next five years. FLASH breaks new ground in two ways – it is the first large H i absorption survey to be carried out without any optical preselection of targets, and we use an automated Bayesian line-finding tool to search through large datasets and assign a statistical significance to potential line detections. Two Pilot Surveys, covering around 3000 deg$^2$ of sky, were carried out in 2019-22 to test and verify the strategy for the full FLASH survey. The processed data products from these Pilot Surveys (spectral-line cubes, continuum images, and catalogues) are public and available online. In this paper, we describe the FLASH spectral-line and continuum data products and discuss the quality of the H i spectra and the completeness of our automated line search. Finally, we present a set of 30 new H i absorption lines that were robustly detected in the Pilot Surveys, almost doubling the number of known H i absorption systems at $0.4\lt z\lt1$. The detected lines span a wide range in H i optical depth, including three lines with a peak optical depth $\tau\gt1$, and appear to be a mixture of intervening and associated systems. Interestingly, around two-thirds of the lines found in this untargeted sample are detected against sources with a peaked-spectrum radio continuum, which are only a minor (5–20%) fraction of the overall radio-source population. The detection rate for H i absorption lines in the Pilot Surveys (0.3 to 0.5 lines per 40 deg$^2$ ASKAP field) is a factor of two below the expected value. One possible reason for this is the presence of a range of spectral-line artefacts in the Pilot Survey data that have now been mitigated and are not expected to recur in the full FLASH survey. A future paper in this series will discuss the host galaxies of the H i absorption systems identified here.

This investigation examines the dynamic response of an accelerating turbulent pipe flow using direct numerical simulation data sets. A low/high-pass Fourier filter is used to investigate the contribution and time dependence of the large-scale motions (LSM) and the small-scale motions (SSM) into the transient Reynolds shear stress. Additionally, it analyses how the LSM and SSM influence the mean wall shear stress using the Fukagata–Iwamoto–Kasagi identity. The results reveal that turbulence is frozen during the early flow excursion. During the pretransition stage, energy growth of the LSM and a subtle decay in the SSM is observed, suggesting a laminarescent trend of SSM. The transition period exhibits rapid energy growth in the SSM energy spectrum at the near-wall region, implying a shift in the dominant contribution from LSM to SSM to the frictional drag. The core-relaxation stage shows a quasisteady behaviour in large- and small-scale turbulence at the near-wall region and progressive growth of small- and large-scale turbulence within the wake region. The wall-normal gradient of the Reynolds shear stress premultiplied energy cospectra was analysed to understand how LSM and SSM influence the mean momentum balance across the different transient stages. A relevant observation is the creation of a momentum sink produced at the buffer region in large- and very large-scale (VLSM) wavelengths during the pretransition. This sink region annihilates a momentum source located in the VLSM spectrum and at the onset of the logarithmic region of the net-force spectra. This region is a source term in steady wall-bounded turbulence.

Optimal transport tasks naturally arise in gas networks, which include a variety of constraints such as physical plausibility of the transport and the avoidance of extreme pressure fluctuations. To define feasible optimal transport plans, we utilize a $p$-Wasserstein metric and similar dynamic formulations minimizing the kinetic energy necessary for moving gas through the network, which we combine with suitable versions of Kirchhoff’s law as the coupling condition at nodes. In contrast to existing literature, we especially focus on the non-standard case $p \neq 2$ to derive an overdamped isothermal model for gases through $p$-Wasserstein gradient flows in order to uncover and analyze underlying dynamics. We introduce different options for modelling the gas network as an oriented graph including the possibility to store gas at interior vertices and to put in or take out gas at boundary vertices.

In this paper, we present experimental evidence on the effect adverse selection has on coverage choices and pricing in corporate insurance markets. Two sets of experimental data, each generated by experiments utilizing a specific parameterization of a corporate insurance decision, are presented to gauge these effects. In the first, subject behavior conforms to a unique equilibrium in which high risk firms choose higher coverage and contracts are priced accordingly. Insurers act competitively and convergence to equilibrium behavior is marked. In the second set, there is little evidence that subject behavior is consistent with either of the two equilibrium outcomes supported by the experimental setting—pooling by fully insuring losses and pooling by self insuring.

This study compares the design practices and performance of ChatGPT 4.0, a large language model (LLM), against graduate engineering students in a 48-h prototyping hackathon, based on a dataset comprising more than 100 prototypes. The LLM participated by instructing two participants who executed its instructions and provided objective feedback, generated ideas autonomously and made all design decisions without human intervention. The LLM exhibited similar prototyping practices to human participants and finished second among six teams, successfully designing and providing building instructions for functional prototypes. The LLM’s concept generation capabilities were particularly strong. However, the LLM prematurely abandoned promising concepts when facing minor difficulties, added unnecessary complexity to designs, and experienced design fixation. Communication between the LLM and participants was challenging due to vague or unclear descriptions, and the LLM had difficulty maintaining continuity and relevance in answers. Based on these findings, six recommendations for implementing an LLM like ChatGPT in the design process are proposed, including leveraging it for ideation, ensuring human oversight for key decisions, implementing iterative feedback loops, prompting it to consider alternatives, and assigning specific and manageable tasks at a subsystem level.