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Students in the foster care system tend to have lower educational outcomes than their peers, including more frequent disciplinary events. However, few studies have explored how transitions into and out of foster care placements are associated with educational outcomes. Using longitudinal data from four California school districts, this study investigated the dynamics of entering versus exiting foster care to predict school discipline and how this relationship ultimately influences absenteeism. Our findings suggest that students in foster care are more likely than their peers to face disciplinary action, especially exclusionary discipline, particularly when entering foster care. We also find suggestive evidence that disciplinary actions upon entry increase student absenteeism for students in foster care.

Miniature bioelectronic implants promise revolutionary therapies for cardiovascular and neurological disorders. Wireless power transfer (WPT) is a significant method for miniaturization, eliminating the need for bulky batteries in devices. Despite successful demonstrations of millimetric battery free implants in animal models, the robustness and efficiency of WPT are known to degrade significantly under misalignment incurred by body movements, respiration, heart beating, and limited control of implant orientation during surgery. This article presents an omnidirectional WPT platform for millimetric bioelectronic implants, employing the emerging magnetoelectric (ME) WPT modality, and magnetic field steering technique based on multiple transmitter (TX) coils. To accurately sense the weak coupling in a miniature implant and adaptively control the multicoil TX array in a closed loop, we develop an active echo (AE) scheme using a tiny coil on the implant. Our prototype comprises a fully integrated 14.2 mm3 implantable stimulator embedding a custom low power system on chip (SoC) powered by an ME film, a TX with a custom three channel AE RX chip, and a multicoil TX array with mutual inductance cancellation. The AE RX achieves negative 161 dBm per Hz input referred noise with 64 dB gain tuning range to reliably sense the AE signal, and offers fast polarity detection for driver control. AE simultaneously enhances the robustness, efficiency, and charging range of ME WPT. Under 90 degree rotation from the ideal position, our omnidirectional WPT system achieves 6.8x higher power transfer efficiency (PTE) than a single coil baseline. The tracking error of AE negligibly degrades the PTE by less than 2 percent from using ideal control., Comment: 13 pages, 27 figures

Atom probe tomography (APT) enables near atomic scale three dimensional elemental mapping through the controlled field evaporation of surface atoms triggered by the combined application of a DC voltage and either voltage or laser pulses. As the selected laser wavelength for the atom probes transitioned from the near-infrared (1050 nm) to shorter wavelengths e.g., green (532 nm) and near ultraviolet, the quality of data improved and the range of materials amenable for analysis broadened. A new commercial laser atom probe with a wavelength of 257.5 nm, referred to as deep ultraviolet (DUV), has been recently launched. However, the effects of DUV lasers on different classes of materials have not yet been systematically investigated. In this study, a range of materials, including metals, semiconductor, and oxides, have been examined using commercial atom probes with different laser wavelengths but in principle comparable particle detection system. The quality of the NUV and DUV laser atom probe data is evaluated based on four key metrics: background, detection events, ion detection histogram, and mass-resolving power. Furthermore, the application of a thin coating to the finished APT specimens enhances the data quality for both laser wavelengths.

This paper describes the R package fdesigns that implements a methodology for identifying Bayesian optimal experimental designs for models whose factor settings are functions, known as profile factors. This type of experiments which involve factors that vary dynamically over time, presenting unique challenges in both estimation and design due to the infinite-dimensional nature of functions. The package fdesigns implements a dimension reduction method leveraging basis functions of the B-spline basis system. The package fdesigns contains functions that effectively reduce the design problem to the optimisation of basis coefficients for functional linear functional generalised linear models, and it accommodates various options. Applications of the fdesigns package are demonstrated through a series of examples that showcase its capabilities in identifying optimal designs for functional linear and generalised linear models. The examples highlight how the package's functions can be used to efficiently design experiments involving both profile and scalar factors, including interactions and polynomial effects.

Background: Transitioning from hospital to home presents significant challenges, particularly for patients aged 75 and above. The 'Your Care Needs You' (YCNY) intervention, evaluated through a cluster randomised controlled trial (cRCT), aims to empower these patients by providing essential information and skills to prepare them for being at home. This study evaluates the cost-effectiveness of the YCNY intervention compared to standard care within the UK National Health Service (NHS) over the trial's duration (90 days post-discharge). Methods: The economic evaluation utilised individual patient-level data from the YCNY cRCT. The analysis was conducted from the perspective of the NHS and Personal Social Services. The analysis calculated the adjusted mean differences in costs and Quality-Adjusted Life Years (QALYs) between the YCNY intervention and control groups. In our base case, Multilevel Mixed-Effects Generalised Linear Models (MME-GLM) were employed to account for the hierarchical data structure. Additional alternative scenarios and a probabilistic sensitivity analysis were conducted to assess the robustness of the findings. Results: The base case results indicated the YCNY intervention reduced costs ({\pounds}269) and improved outcomes (QALY gain of 0.0057). The net health benefit (NHB) of the YCNY intervention using a cost-effectiveness threshold of {\pounds}15.000/QALY was 0.0246. These findings suggest the YCNY intervention dominates usual care, with a high probability (89%) of the YCNY intervention being cost-effective at a {\pounds}15,000 per QALY threshold. The results remained robust across several alternative scenario analyses. Discussion: The YCNY intervention could represent a promising and potentially cost-effective approach for enhancing the safety and experience of older adults transitioning from hospital to home in the UK.

Scientific workflows have become highly heterogenous, leveraging distributed facilities such as High Performance Computing (HPC), Artificial Intelligence (AI), Machine Learning (ML), scientific instruments (data-driven pipelines) and edge computing. As a result, Identity and Access Management (IAM) and Cybersecurity challenges across the diverse hardware and software stacks are growing. Nevertheless, scientific productivity relies on lowering access barriers via seamless, single sign-on (SSO) and federated login while ensuring access controls and compliance. We present an implementation of a federated IAM solution, which is coupled with multiple layers of security controls, multi-factor authentication, cloud-native protocols, and time-limited role-based access controls (RBAC) that has been co-designed and deployed for the Isambard-AI and HPC supercomputing Digital Research Infrastructures (DRIs) in the UK. Isambard DRIs as a national research resource are expected to comply with regulatory frameworks. Implementation details for monitoring, alerting and controls are outlined in the paper alongside selected user stories for demonstrating IAM workflows for different roles., Comment: 9 pages, 2 figures

Isambard-AI is a new, leadership-class supercomputer, designed to support AI-related research. Based on the HPE Cray EX4000 system, and housed in a new, energy efficient Modular Data Centre in Bristol, UK, Isambard-AI employs 5,448 NVIDIA Grace-Hopper GPUs to deliver over 21 ExaFLOP/s of 8-bit floating point performance for LLM training, and over 250 PetaFLOP/s of 64-bit performance, for under 5MW. Isambard-AI integrates two, all-flash storage systems: a 20 PiByte Cray ClusterStor and a 3.5 PiByte VAST solution. Combined these give Isambard-AI flexibility for training, inference and secure data accesses and sharing. But it is the software stack where Isambard-AI will be most different from traditional HPC systems. Isambard-AI is designed to support users who may have been using GPUs in the cloud, and so access will more typically be via Jupyter notebooks, MLOps, or other web-based, interactive interfaces, rather than the approach used on traditional supercomputers of sshing into a system before submitting jobs to a batch scheduler. Its stack is designed to be quickly and regularly upgraded to keep pace with the rapid evolution of AI software, with full support for containers. Phase 1 of Isambard-AI is due online in May/June 2024, with the full system expected in production by the end of the year., Comment: 11 pages, 11 figures

Achieving deterministic control over the properties of low-dimensional materials with nanoscale precision is a long-sought goal. Mastering this capability has a transformative impact on the design of multifunctional electrical and optical devices. Here, we present an ion-assisted synthetic technique that enables precise control over the material composition and energy landscape of two-dimensional (2D) atomic crystals. Our method transforms binary transition metal dichalcogenides (TMDs), like MoSe$_2$, into ternary MoS$_{2\alpha}$Se$_{2(1-{\alpha}})$ alloys with systematically adjustable compositions, ${\alpha}$. By piecewise assembly of the lateral, compositionally modulated MoS$_{2\alpha}$Se$_{2(1-{\alpha})}$ segments within 2D atomic layers, we present a synthetic pathway towards the realization of multi-compositional designer materials. Our technique enables the fabrication of complex structures with arbitrary boundaries, dimensions as small as 30 nm, and fully customizable energy landscapes. Our optical characterizations further showcase the potential for implementing tailored optoelectronics in these engineered 2D crystals., Comment: 22 pages, 5 Figures

State of the art reinforcement learning methods sometimes encounter unsafe situations. Identifying when these situations occur is of interest both for post-hoc analysis and during deployment, where it might be advantageous to call out to a human overseer for help. Efforts to gauge the criticality of different points in time have been developed, but their accuracy is not well established due to a lack of ground truth, and they are not designed to be easily interpretable by end users. Therefore, we seek to define a criticality framework with both a quantifiable ground truth and a clear significance to users. We introduce true criticality as the expected drop in reward when an agent deviates from its policy for n consecutive random actions. We also introduce the concept of proxy criticality, a low-overhead metric that has a statistically monotonic relationship to true criticality. Safety margins make these interpretable, when defined as the number of random actions for which performance loss will not exceed some tolerance with high confidence. We demonstrate this approach in several environment-agent combinations; for an A3C agent in an Atari Beamrider environment, the lowest 5% of safety margins contain 47% of agent losses; i.e., supervising only 5% of decisions could potentially prevent roughly half of an agent's errors. This criticality framework measures the potential impacts of bad decisions, even before those decisions are made, allowing for more effective debugging and oversight of autonomous agents., Comment: 17 pages, 10 figures. This work has been submitted to the IEEE for possible publication

Here we publish three years of data for the UCNtau experiment performed at the Los Alamos Ultra Cold Neutron Facility at the Los Alamos Neutron Science Center. These data are in addition to our previously published data. Our goals in this paper are to better understand and quantify systematic uncertainties and to improve the lifetime statistical precision. We report a measured value for these runs from 2020-2022 for the neutron lifetime of 877.94+/-0.37 s; when all the data from UCNtau are averaged we report an updated value for the lifetime of 877.82+/-0.22 (statistical)+0.20-0.17 (systematic) s. We utilized improved monitor detectors, reduced our correction due to UCN upscattering on ambient gas, and employed four different main UCN detector geometries both to reduce the correction required for rate dependence and explore potential contributions due to phase space evolution.

The $^{246}$Cm(n,$\gamma$) and $^{248}$Cm(n,$\gamma$) cross-sections have been measured at the Experimental Area 2 (EAR2) of the n_TOF facility at CERN with three C$_6$D$_6$ detectors. This measurement is part of a collective effort to improve the capture cross-section data for Minor Actinides (MAs), which are required to estimate the production and transmutation rates of these isotopes in light water reactors and innovative reactor systems. In particular, the neutron capture in $^{246}$Cm and $^{248}$Cm open the path for the formation of other Cm isotopes and heavier elements such as Bk and Cf and the knowledge of (n,$\gamma$) cross-sections of these Cm isotopes plays an important role in the transport, transmutation and storage of the spent nuclear fuel. The reactions $^{246}$Cm(n,$\gamma$) and $^{248}$Cm(n,$\gamma$) have been the two first capture measurements analyzed at n_TOF EAR2. Until this experiment and two recent measurements performed at J-PARC, there was only one set of data of the capture cross-sections of $^{246}$Cm and $^{248}$Cm, that was obtained in 1969 in an underground nuclear explosion experiment. In the measurement at n_TOF a total of 13 resonances of $^{246}$Cm between 4 and 400 eV and 5 of $^{248}$Cm between 7 and 100 eV have been identified and fitted. The radiative kernels obtained for $^{246}$Cm are compatible with JENDL-5, but some of them are not with JENDL-4, which has been adopted by JEFF-3.3 and ENDF/B-VIII.0. The radiative kernels obtained for the first three $^{248}$Cm resonances are compatible with JENDL-5, however, the other two are not compatible with any other evaluation and are 20% and 60% larger than JENDL-5.

The strong coupling between electromagnetic field and lattice oscillation in piezoelectric materials gives rise to phonon polariton excitations. Such quasiparticles open up new directions in modulating the ubiquitous Casimir force. Here by utilizing the generalized Born-Huang hydrodynamics model, three types of phonons in piezoelectrics are studied: longitudinal optical phonon, transverse optical phonon and phonon polariton. The phonon-electromagnetic coupling results in a complex set of Fresnel reflection matrices which prevents the utilization of the standard Lifshitz approach for calculating Casimir forces in the imaginary frequency domain. Our calculations are based on an approach within real frequency and finite temperatures, through which various regimes of the Casimir interaction are examined. Our study shows that piezoelectrics emerge as a set of materials where this ubiquitous force can be controlled via phonon properties for the first time. The Casimir interaction appears as a suitable means to distinguish between different types of surface phonon polaritons associated with different structural piezoelectric polytypes., Comment: 8 pages, 4 figures, supplementary information with 10 pages, 4 figures; submitted

We define and explore the bounded skew power series ring $R^+[[x;\sigma,\delta]]$ defined over a complete, filtered, Noetherian prime ring $R$ with a commuting skew derivation $(\sigma,\delta)$. We establish precise criteria for when this ring is well-defined, and for an appropriate completion $Q$ of $Q(R)$, we prove that if $Q$ has characteristic $p$, $\delta$ is an inner $\sigma$-derivation and no positive power of $\sigma$ is inner as an automorphism of $Q$, then $Q^+[[x;\sigma,\delta]]$ is often prime, and even simple under certain mild restrictions on $\delta$. It follows from this result that $R^+[[x;\sigma,\delta]]$ is itself prime.

In recent years, there has been increasing interest in the understanding and application of nanoparticle assemblies driven by external fields. Although these systems can exhibit marked transitions in behavior compared to non-interacting counterparts, it has often proven challenging to connect their dynamics with underlying physical mechanisms or even to verifiably establish their structure under realistic experimental conditions. We have studied colloidal iron oxide nanoparticles that assemble into ordered, few-particle linear chains under the influence of oscillating and pulsed magnetic fields. Cryo-EM has been used to flash freeze and image the structures formed by oscillatory drive fields, and magnetic relaxometry has been used to extract the multiple time constants associated with magnetic switching of the short chains. Armed with the physical structure from cryo-EM and the field-dependent switching times from magnetic measurements, we have conducted extensive micromagnetic simulations, revealing probable mechanisms for each time constant regime spanning $10^{9}$ in time and how switching develops from individual particles to entire chains. These types of magnetic nanomaterials have great potential for biomedical technologies, particularly magnetic particle imaging and hyperthermia, and rigorous elucidation of their physics will hasten their optimization.

Magnetic particle imaging (MPI) is an emerging imaging technique with many applications and a very active field of research. This app provides users with the opportunity to develop some intuition about the inner workings of MPI as it is being researched through NIST's Thermal MagIC project in an interactive and fun way. Users can vary different experimental and post-processing parameters to see how the image quality and particle reconstruction changes for different measurement conditions., Comment: 7 pages, 6 figures

The Space Industry Responsive Intelligent Thermal (SpIRIT) 6U CubeSat is a mission led by The University of Melbourne in cooperation with the Italian Space Agency. Launched in a 510 km Polar Sun Synchronous Orbit in December 2023, SpIRIT carries multiple subsystems for scientific and technology demonstration. The main payload is the HERMES instrument for detection of high-energy astrophysics transients (Gamma Ray Bursts), and for studies of their variability at scales below 1 ms. The satellite includes a novel thermal management system for its class, based on a Stirling-cycle cooler and deployable thermal radiator, designed to cool HERMES to reduce instrumental background noise. A low-latency communication subsystem based on a sat-phone network is supporting rapid transmission of time-critical data and telecommands. SpIRIT is also equipped with a set of RGB and thermal IR cameras, connected to an on-board image processing unit with artificial intelligence capabilities for autonomous feature recognition. To effectively manage all interfaces between different subsystems and mission stakeholders, the University of Melbourne developed an instrument control unit (PMS) which operates all payloads. PMS also provides backup uninterruptible power to the HERMES instrument through a supercapacitor-based UPS for safe instrument shutdown in case of platform power interruptions. This paper first presents a mission and payload overview, and early in-orbit results, along with lessons learned throughout the mission. This work not only sheds light on the novelty of some of the on-board technologies onboard and on their potential impact to enable greater utilization of CubeSats for scientific missions, but also offers insights into the practical challenges and accomplishments related to developing and operating a multi-organization CubeSat with a complex array of instruments and systems., Comment: 12 pages, 38th Annual Small Satellite Conference

The Thermal Management Integrated System (TheMIS) is a key element of the Australia-Italy Space Industry Responsive Intelligent Thermal (SpIRIT) mission, launched in a 510km Polar Sun-Synchronous orbit in December 2023. SpIRIT is a 6U CubeSat led by The University of Melbourne in cooperation with ASI, with support from ASA and with contributions from Australian space industry and international research organizations. The TheMIS subsystem actively cools and controls the temperature of sensitive instruments, increasing the potential range of payloads supported on small spacecraft systems. TheMIS core functionality is based on a commercial Stirling Cycle Cryocooler in-principle capable of reaching cold-tip temperatures below T=100K. The cooler is operated by customized control electronics and is connected to deployable radiators through pyrolytic graphite sheet thermal straps, all developed by the University of Melbourne. Until now, this level of thermal control has been relatively uncommon in nanosatellites. TheMIS aims to validate the design and performance by controlling the thermal environment of SpIRIT's HERMES payload, an X-ray instrument provided by ASI which has a noise background strongly sensitive to temperature. Beyond SpIRIT, TheMIS has the potential to support a broad range of applications, including holding infrared focal plane arrays at cryogenic temperatures, and increasing resilience of electronics to space weather. This paper provides an overview of TheMIS's design, implementation, and operational performance, detailing the commissioning phase and the early results obtained from its operations in orbit, with comparison to the thermal model developed during the mission environmental testing campaign. Finally, the paper discusses ongoing challenges for thermal management of payloads in small satellite systems and potential future strategies for continuous improvement, Comment: Smallsats 2024

The information-encoding molecules RNA and DNA form a combinatorially large set of secondary structures through nucleic acid base pairing. Thermodynamic prediction algorithms predict favoured, or minimum free energy (MFE), secondary structures, and can assign an equilibrium probability to any structure via the partition function: a Boltzman-weighted sum over the set of secondary structures. MFE is NP-hard in the presence pseudoknots, base pairings that violate a restricted planarity condition. However, unpseudoknotted structures are amenable to dynamic programming: for a single DNA/RNA strand there are polynomial time algorithms for MFE and partition function. For multiple strands, the problem is more complicated due to entropic penalties. Dirks et al [SICOMP Review; 2007] showed that for O(1) strands, with N bases, there is a polynomial time in N partition function algorithm, however their technique did not generalise to MFE which they left open. We give the first polynomial time (O(N^4)) algorithm for unpseudoknotted multiple (O(1)) strand MFE, answering the open problem from Dirks et al. The challenge lies in considering rotational symmetry of secondary structures, a feature not immediately amenable to dynamic programming algorithms. Our proof has two main technical contributions: First, a polynomial upper bound on the number of symmetric secondary structures to be considered when computing rotational symmetry penalties. Second, that bound is leveraged by a backtracking algorithm to find the MFE in an exponential space of contenders. Our MFE algorithm has the same asymptotic run time as Dirks et al's partition function algorithm, suggesting efficient handling of rotational symmetry, although higher space complexity. It also seems reasonably tight in the number of strands since Codon, Hajiaghayi & Thachuk [DNA27, 2021] have shown that unpseudoknotted MFE is NP-hard for O(N) strands., Comment: 35 pages, 4 figures, 3 appendices

The development of sustainable transportation and communication systems requires an increase in both energy density and capacity retention of Li-batteries. Using substrates forming a solid solution with body centered cubic Li enhances the cycle stability of anode-less batteries. However, it remains unclear how the substrate microstructure affects the lithiation behavior. Here, we deploy a correlative, near-atomic scale probing approach through combined ion- and electron-microscopy to examine the distribution of Li in Li-Ag diffusion couples as model system. We reveal that Li regions with over 93.8% at.% nucleate within Ag at random high angle grain boundaries, whereas grain interiors are not lithiated. We evidence the role of kinetics and mechanical constraint from the microstructure over equilibrium thermodynamics in dictating the lithiation process. The findings suggest that grain size and grain boundary character are critical to enhance the electrochemical performance of interlayers/electrodes, particularly for improving lithiation kinetics and hence reducing dendrite formation.

AI-enabled capabilities are reaching the requisite level of maturity to be deployed in the real world, yet do not always make correct or safe decisions. One way of addressing these concerns is to leverage AI control systems alongside and in support of human decisions, relying on the AI control system in safe situations while calling on a human co-decider for critical situations. We extend a methodology for adversarial explanations (AE) to state-of-the-art reinforcement learning frameworks, including MuZero. Multiple improvements to the base agent architecture are proposed. We demonstrate how this technology has two applications: for intelligent decision tools and to enhance training / learning frameworks. In a decision support context, adversarial explanations help a user make the correct decision by highlighting those contextual factors that would need to change for a different AI-recommended decision. As another benefit of adversarial explanations, we show that the learned AI control system demonstrates robustness against adversarial tampering. Additionally, we supplement AE by introducing strategically similar autoencoders (SSAs) to help users identify and understand all salient factors being considered by the AI system. In a training / learning framework, this technology can improve both the AI's decisions and explanations through human interaction. Finally, to identify when AI decisions would most benefit from human oversight, we tie this combined system to our prior art on statistically verified analyses of the criticality of decisions at any point in time., Comment: 19 pages, 12 figures

A new scheme for the computation of dispersive interactions from first principles is presented. This cost-effective approach relies on a Wannier function representation compatible with density function theory descriptions. This is an electronic-based many-body method that captures the full electronic and optical response properties of the materials. It provides the foundation to discern van der Waals and induction energies as well as the role of anisotropy and different stacking patterns when computing dispersive interactions in systems. Calculated results for binding energies in layered materials, such as graphite, hBN, and MoS$_2$ give encouraging comparisons with available experimental data. Strategies for broadened computational descriptions of dispersive interactions are also discussed. Our investigation aims at stimulating new experimental studies to measure van der Waals energies in a wider range of materials, especially in layered systems., Comment: 24 pages, 1 abstract figure, 4 figures, source codes: 10.6084/m9.figshare.26023024.v2 or https://github.com/Diem-TX-Dang/vdW-MBDWan-code/tree/main, submitted

The past two decades have yielded several new measurements and reanalysis of older measurements of the neutron lifetime. These have led to a 4.4 standard deviation discrepancy between the most precise measurements of the neutron decay rate producing protons in cold neutron beams and the most precise lifetime measured in neutron storage experiments. Here we publish an analysis of the recently published UCN aimed a searching for an explanation of this difference using the model proposed by Koch and Hummel.

Reinforcement learning (RL) has recently shown promise in predicting Alzheimer's disease (AD) progression due to its unique ability to model domain knowledge. However, it is not clear which RL algorithms are well-suited for this task. Furthermore, these methods are not inherently explainable, limiting their applicability in real-world clinical scenarios. Our work addresses these two important questions. Using a causal, interpretable model of AD, we first compare the performance of four contemporary RL algorithms in predicting brain cognition over 10 years using only baseline (year 0) data. We then apply SHAP (SHapley Additive exPlanations) to explain the decisions made by each algorithm in the model. Our approach combines interpretability with explainability to provide insights into the key factors influencing AD progression, offering both global and individual, patient-level analysis. Our findings show that only one of the RL methods is able to satisfactorily model disease progression, but the post-hoc explanations indicate that all methods fail to properly capture the importance of amyloid accumulation, one of the pathological hallmarks of Alzheimer's disease. Our work aims to merge predictive accuracy with transparency, assisting clinicians and researchers in enhancing disease progression modeling for informed healthcare decisions. Code is available at https://github.com/rfali/xrlad., Comment: Previous versions accepted to NeurIPS 2023's XAIA and AAAI 2024's XAI4DRL workshops

We report on the observation of previously-unknown isotope $^{21}$Al, the first unbound aluminum isotope located beyond the proton dripline. The $^{21}$Al nucleus decays by one-proton (1p) emission, and its in-flight decays were detected by tracking trajectories of all decay products with micro-strip silicon detectors. The 1p-emission processes were studied by analyses of the measured angular correlations of decay products $^{20}$Mg+p. The 1p-decay energies of ground and low-lying excited states of $^{21}$Al, its mass excess and proton separation energy value $S_p$=$-1.1(1)$ MeV were determined., Comment: 6 pages, 7 figures

TikTok, a popular short video sharing application, emerged as the dominant social media platform for young people, with a pronounced influence on how young women and people of color interact online. The application has become a global space for youth to connect with each other, offering not only entertainment but also opportunities to engage with artificial intelligence/machine learning (AI/ML)-driven recommendations and create content using AI/M-powered tools, such as generative AI filters. This provides opportunities for youth to explore and question the inner workings of these systems, their implications, and even use them to advocate for causes they are passionate about. We present different perspectives on how youth may learn in personally meaningful ways when engaging with TikTok. We discuss how youth investigate how TikTok works (considering data and algorithms), take into account issues of ethics and algorithmic justice and use their understanding of the platform to advocate for change.

The weathering crust is a layer of porous ice that can form at the surface of an ice sheet. It grows and decays in response changing weather and climate conditions, affecting the albedo, the melt rate, and the transport of meltwater across the surface. To understand this behaviour, we seek time-dependent solutions to a continuum, thermodynamic model for the porosity, temperature and thickness of the weathering crust, and the internal and surface melt rates. We find solutions using a numerical enthalpy method, presented in this study. We use idealised `switching' and sinusoidal forcings to explore the different dynamics exhibited during growth and decay, the timescales involved, and the impact of diurnal vs. annual variations. The results demonstrate qualitative agreement with observations, and provide insight into the relative importance of different surface heat fluxes during the growth and decay of the crust. The model therefore provides a useful tool for exploring the response of the weathering crust to climate change., Comment: 36 pages, 11 figures

Travel to academic conferences -- where international flights are the norm -- is responsible for a sizeable fraction of the greenhouse gas (GHG) emissions associated with academic work. In order to provide a benchmark for comparison with other fields, as well as for future reduction strategies and assessments, we estimate the CO2-equivalent emissions for conference travel in the field of astronomy for the prepandemic year 2019. The GHG emission of the international astronomical community's 362 conferences and schools in 2019 amounted to 42,500 tCO2e, assuming a radiative-forcing index factor of 1.95 for air travel. This equates to an average of 1.0 $\pm$ 0.6 tCO2e per participant per meeting. The total travel distance adds up to roughly 1.5 Astronomical Units, that is, 1.5 times the distance between the Earth and the Sun. We present scenarios for the reduction of this value, for instance with virtual conferencing or hub models, while still prioritizing the benefits conferences bring to the scientific community., Comment: Supplementary material is available at PNAS Nexus online: https://academic.oup.com/pnasnexus/article/3/5/pgae143/7659884

Security conferences are important venues at which academics and practitioners share knowledge about new attacks and state-of-the-art defenses. Despite this, researchers have not studied who shares information and about which security topics. To address this, our study characterizes the speakers, sponsors, and topics presented at the most prestigious academic and industry conferences. We collect a longitudinal data set that contains 9,728 abstracts and 1,686 sponsors across 4 academic and 6 industry conferences. There is limited knowledge sharing between industry and academia. Conferences vary significantly in the equality of how talks/authorship is distributed across individuals. The topics of academic and industry abstracts display consistent coverage of techniques within the MITRE ATT&CK framework. Top tier academic conferences, as well as DEFCON and Black Hat, inconsistently address the governance, response and recovery functions of the NIST Cybersecurity Framework. Commercial InfoSec and insurance conferences (RSA, Gartner, Advisen and NetDillgience) cover the framework more consistently. Prevention and detection remain the most common topic of talks, with no clear temporal trend., Comment: Code and data can be found in the repository: https://git.uibk.ac.at/csaw9252/master_thesis

The understanding of protein structure, folding, and interaction with other proteins remains one of the grand challenges of modern biology. Tremendous progress has been made thanks to X-ray- or electron-based techniques that have provided atomic configurations of proteins, and their solvation shell. These techniques though require a large number of similar molecules to provide an average view, and lack detailed compositional information that might play a major role in the biochemical activity of these macromolecules. Based on its intrinsic performance and recent impact in materials science, atom probe tomography (APT) has been touted as a potential novel tool to analyse biological materials, including proteins. However, analysis of biomolecules in their native, hydrated state by APT have not yet been routinely achieved, and the technique's true capabilities remain to be demonstrated. Here, we present and discuss systematic analyses of individual amino-acids in frozen aqueous solutions on two different nanoporous metal supports across a wide range of analysis conditions. Using a ratio of the molecular ions of water as a descriptor for the conditions of electrostatic field, we study the fragmentation and behavior of those amino acids. We discuss the importance sample support, specimen preparation route, acquisition conditions and data analysis, to pave the way towards establishing guidelines for cryo-APT analysis of biomolecules.