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Evidence for similar chemical characteristics around low- and high-mass protostars has been found: in particular, a variety of carbon-chain species and complex organic molecules (COMs) are formed around them. On the other hand, the chemical compositions around intermediate-mass (IM; $2 M_{\odot} < m_* <8 M_{\odot}$) protostars have not been studied with large samples. In particular, it is unclear the extent to which carbon-chain species are formed around them. We aim to obtain the chemical compositions, particularly focusing on carbon-chain species, towards a sample of IM protostars. We have conducted Q-band (31.5-50 GHz) line survey observations towards eleven mainly intermediate-mass protostars with the Yebes 40 m radio telescope. The target protostars were selected from a sub-sample of the source list of the SOFIA Massive (SOMA) Star Formation project. Nine carbon-chain species (HC$_3$N, HC$_5$N, C$_3$H, C$_4$H, $linear-$H$_2$CCC, $cyclic-$C$_3$H$_2$, CCS, C$_3$S, and CH$_3$CCH), three COMs (CH$_3$OH, CH$_3$CHO, and CH$_3$CN), H$_2$CCO, HNCO, and four simple sulfur (S)-bearing species ($^{13}$CS, C$^{34}$S, HCS$^+$, H$_2$CS) have been detected. The rotational temperatures of HC$_5$N are derived to be $\sim20-30$ K in three IM protostars and they are very similar compared to those around low- and high-mass protostars. These results indicate that carbon-chain molecules are formed in lukewarm ($\sim20-30$ K) gas around the IM protostars by the Warm Carbon-Chain Chemistry (WCCC) process. Carbon-chain formation occurs ubiquitously in the warm gas around protostars across a wide range of stellar masses. Carbon-chain molecules and COMs coexist around most of the target IM protostars, which is similar to the situation in low- and high-mass protostars. The chemical characteristics around protostars are common in the low-, intermediate- and high-mass regimes., Comment: Accepted for publication in the Astronomy and Astrophysics (A&A)

Potassium cations play many important roles in living organisms, especially in electro-physiology, since they are involved in neurotransmission and muscle contractions. We report the synthesis of a ratiometric fluorescent microsensor for potassium (K+) detection, based on the fluorescent probe ION potassium green 4. Potassium-sensitive fluorescent microparticles were obtained by using silica as core material. We obtained silica-based microsensors with a size in the micrometer range, spherical shape, good monodispersity, optimal selectivity and a sensitivity range between 0 to 40 mM. The microsensors also proved to be non-toxic in cell cultures as well as suitable for fluorescent imaging, offering new possibilities for non-invasive optical detection, quantification and in situ monitoring of K+ variations in cell culture systems.

AI-based weather and climate models are emerging as accurate and computationally efficient tools. Beyond weather forecasting, they also show promise to accelerate storyline analyses. We rigorously evaluate NeuralGCM's ability to reproduce an extreme heatwave against physics-based climate model. NeuralGCM accurately replicates the targeted event, and generates stable and realistic mid-century projections. However, due to the absence of land feedbacks, NeuralGCM underestimates the projected warming amplitude compared to physics-based model benchmarks.

We consider the problem of scheduling elective surgeries in a Children's Hospital, where disruptions due to emergencies and no-shows may arise. We account for two features that occur in many pediatric settings: i) that it is not uncommon for pediatric patients to fall ill on the very day of their operation and, consequentially, to be unable to undergo surgery and ii) that operating rooms normally reserved for elective surgeries can be used to treat emergency cases. Elective surgeries are scheduled taking into account the time spent on the waiting list and the patient's priority, which considers the severity of their condition and their surgical deadline, generating a nominal schedule. This schedule is optimized in conjunction with a series of back-up schedules: in fact, back-up schedules shall be available in advance so as to guarantee that the operating rooms activity immediately recovers in case of a disruption. We propose an Integer Linear Programming-based approach for the problem. As there is no consolidated data on the features of both emergencies and no show, we enumerate a representative subset of the possible emergency and no-show scenarios and for each of them a back-up plan is designed. The approach reschedules patients in a way that minimizes disruption with respect to the nominal schedule and applies an as-soon-as-possible policy in case of emergencies to ensure that all patients receive timely care. The approach shows to be effective in managing disruptions, ensuring that the waiting list is managed properly, with a balanced mix of urgent and less urgent patients. Therefore, the approach provides an effective solution for scheduling patients in a pediatric hospital, taking into account the unique features of such facilities.

We employ molecular dynamics simulations to investigate ion and water transport driven by an electric field through quasi-two-dimensional nanoslits with a tapered geometry. Despite the absence of surface charge on the channel walls and the associated electric double layer, we do observe a robust ion selectivity. This selectivity favors the transport of cations from base to tip when the electric field is directed from base to tip, and anions from base to tip when the field direction is reversed. Additionally, we observe a corresponding electro-osmotic water flow from base to tip, regardless of the electric field direction. Intriguingly, ion selectivity and electro-osmotic flow are conventionally associated with surface charge and electric double layers. However, in uncharged tapered nanoslits, we uncover a novel mechanism for these phenomena, where ion selectivity arises from the divergence of the heterogeneous water polarization., Comment: 5 pages, 4 figures + supplementary material (8 pages, 11 figures)

Disruptions to personnel rosters caused by absenteeism often necessitate last-minute adjustments to the employees' working hours. A common strategy to mitigate the impact of such changes is to assign employees to reserve shifts: special on-call duties during which an employee can be called in to cover for an absent employee. To maximize roster robustness, we assume a predict-then-optimize approach that uses absence predictions from a machine learning model to schedule an adequate number of reserve shifts. In this paper we propose a methodology to evaluate the robustness of rosters generated by the predict-then-optimize approach, assuming the machine learning model will make predictions at a predetermined prediction performance level. Instead of training and testing machine learning models, our methodology simulates the predictions based on a characterization of model performance. We show how this methodology can be applied to identify the minimum performance level needed for the model to outperform simple non-data-driven robust rostering policies. In a computational study on a nurse rostering problem, we demonstrate how the predict-then-optimize approach outperforms non-data-driven policies under reasonable performance requirements, particularly when employees possess interchangeable skills.

Let (W,S) be a Coxeter system of affine type D, and let TL(W) the corresponding generalized Temperley-Lieb algebra. In this extended abstract we define an infinite dimensional associative algebra made of decorated diagrams which is isomorphic to TL(W). Moreover, we describe an explicit basis for such an algebra of diagrams which is in bijective correspondence with the classical monomial basis of TL(W), indexed by the fully commutative elements of W., Comment: In Proceedings GASCom 2024, arXiv:2406.14588

Oxygen (O2)-sensing matrices are promising tools for the live monitoring of extracellular O2 consumption levels in long-term cell cultures. In this study, ratiometric O2-sensing membranes were prepared by electrospinning, an easy, low-cost, scalable, and robust method for fabricating nanofibers. Poly({\epsilon}-caprolactone) and poly(dimethyl)siloxane polymers were blended with tris(4,7-diphenyl-1,10-phenanthroline) ruthenium(II) dichloride, which was used as the O2-sensing probe, and rhodamine B isothiocyanate, which was used as the reference dye. The functionalized scaffolds were morphologically characterized by scanning electron microscopy, and their physicochemical profiles were obtained by Fourier transform infrared spectroscopy, thermogravimetric analysis, and water contact angle measurement. The sensing capabilities were investigated by confocal laser scanning microscopy, performing photobleaching, reversibility, and calibration curve studies toward different dissolved O2 (DO) concentrations. Electrospun sensing nanofibers showed a high response to changes in DO concentrations in the physiological-pathological range from 0.5 to 20% and good stability under ratiometric imaging. In addition, the sensing systems were highly biocompatible for cell growth promoting adhesiveness and growth of three cancer cell lines, namely metastatic melanoma cell line SK-MEL2, breast cancer cell line MCF-7, and pancreatic ductal adenocarcinoma cell line Panc-1, thus recreating a suitable biological environment in vitro. These O2-sensing biomaterials can potentially measure alterations in cell metabolism caused by changes in ambient O2 content during drug testing/validation and tissue regeneration processes., Comment: 15 pages, 4 figures

Stellar mergers and accretion events have been crucial in shaping the evolution of the Milky Way (MW). These events have been dynamically identified and chemically characterised using red giants and main-sequence stars. RR Lyrae (RRL) variables can play a crucial role in tracing the early formation of the MW since they are ubiquitous, old (t$\ge$10 Gyr) low-mass stars and accurate distance indicators. We exploited Data Release 3 of the GALAH survey to identify 78 field RRLs suitable for chemical analysis. Using synthetic spectra calculations, we determined atmospheric parameters and abundances of Fe, Mg, Ca, Y, and Ba. Most of our stars exhibit halo-like chemical compositions, with an iron peak around [Fe/H]$\approx -$1.40, and enhanced Ca and Mg content. Notably, we discovered a metal-rich tail, with [Fe/H] values ranging from $-$1 to approximately solar metallicity. This sub-group includes almost ~1/4 of the sample, it is characterised by thin disc kinematics and displays sub-solar $\alpha$-element abundances, marginally consistent with the majority of the MW stars. Surprisingly, they differ distinctly from typical MW disc stars in terms of the s-process elements Y and Ba. We took advantage of similar data available in the literature and built a total sample of 535 field RRLs for which we estimated kinematical and dynamical properties. We found that metal-rich RRLs (1/3 of the sample) likely represent an old component of the MW thin disc. We also detected RRLs with retrograde orbits and provided preliminary associations with the Gaia-Sausage-Enceladus, Helmi, Sequoia, Sagittarius, and Thamnos stellar streams., Comment: Accepted for publication in MNRAS. 29 pages, 20 figures

Polymer composite materials require softening to reduce their glass transition temperature and improve processability. To this end, plasticizers, which are small organic molecules, are added to the polymer matrix. The miscibility of these plasticizers has a large impact on their effectiveness and therefore their interactions with the polymer matrix must be carefully considered. Many plasticizer characteristics, including their size, topology and flexibility, can impact their miscibility and, because of the exponentially large numbers of plasticizers, the current trial-and-error approach is very ineffective. In this work we show that using molecular simulations of a small dataset of 48 plasticizers, it is possible to identify topological and thermodynamic descriptors that are proxy for their miscibility. Using ad-hoc molecular dynamics simulation set-ups that are relatively computationally inexpensive, we establish correlations between the plasticizers' topology, internal flexibility, thermodynamics of aggregation and their degree of miscibility and use these descriptors to classify the molecules as miscible or immiscible. With all available data we also construct a decision tree model which achieves a F1 score of 0.86 +/- 0.01 with repeated, stratified 5-fold cross-validation, indicating that this machine learning method is a promising route to fully automate the screening. By evaluating the individual performance of the descriptors, we show this procedure enables a 10-fold reduction of the test space and provides the basis for the development of workflows which can efficiently screen thousands of plasticizers with a variety of features., Comment: 49 pages, 16 figures

The rapid launch of hundreds of thousands of satellites into Low Earth Orbit will significantly alter our view of the sky and raise concerns about the sustainability of Earth's orbital space. A new framework for sustainable space development must balance technological advancement, protection of space environments, and our capacity to explore the Universe.

Machine-learning interatomic potentials have revolutionized materials modeling at the atomic scale. Thanks to these, it is now indeed possible to perform simulations of \abinitio quality over very large time and length scales. More recently, various universal machine-learning models have been proposed as an out-of-box approach avoiding the need to train and validate specific potentials for each particular material of interest. In this paper, we review and evaluate five different universal machine-learning interatomic potentials (uMLIPs), all based on graph neural network architectures which have demonstrated transferability from one chemical system to another. The evaluation procedure relies on data both from a recent verification study of density-functional-theory implementations and from the Materials Project. Through this comprehensive evaluation, we aim to provide guidance to materials scientists in selecting suitable models for their specific research problems, offer recommendations for model selection and optimization, and stimulate discussion on potential areas for improvement in current machine-learning methodologies in materials science., Comment: 7 figures

The Wide-field Spectroscopic Telescope (WST) is proposed as a new facility dedicated to the efficient delivery of spectroscopic surveys. This white paper summarises the initial concept as well as the corresponding science cases. WST will feature simultaneous operation of a large field-of-view (3 sq. degree), a high multiplex (20,000) multi-object spectrograph (MOS) and a giant 3x3 sq. arcmin integral field spectrograph (IFS). In scientific capability these requirements place WST far ahead of existing and planned facilities. Given the current investment in deep imaging surveys and noting the diagnostic power of spectroscopy, WST will fill a crucial gap in astronomical capability and work synergistically with future ground and space-based facilities. This white paper shows that WST can address outstanding scientific questions in the areas of cosmology; galaxy assembly, evolution, and enrichment, including our own Milky Way; origin of stars and planets; time domain and multi-messenger astrophysics. WST's uniquely rich dataset will deliver unforeseen discoveries in many of these areas. The WST Science Team (already including more than 500 scientists worldwide) is open to the all astronomical community. To register in the WST Science Team please visit https://www.wstelescope.com/for-scientists/participate, Comment: 194 pages, 66 figures. Comments are welcome (wstelescope@gmail.com)

The origin of the stellar Initial Mass Function (IMF) and how it may vary with galactic environment is a matter of debate. Certain star formation theories involve a close connection between the IMF and the Core Mass Function (CMF) and so it is important to measure this CMF in a range of locations in the Milky Way. Here we study the CMF of three Galactic Center clouds: G0.253+0.016 ("The Brick"), Sgr B2 (Deep South field) and Sgr C. We use ALMA 1 mm continuum images and identify cores as peaks in thermal dust emission via the dendrogram algorithm. We develop a completeness correction method via synthetic core insertion, where a realistic mass-dependent size distribution is used for the synthetic cores. After corrections, a power law of the form $\text{d}N/\text{d}\log M \propto M^{-\alpha}$ is fit to the CMFs above 2 M$_\odot$. The three regions show disparate CMFs, with the Brick showing a Salpeter-like power law index $\alpha=1.21\pm0.11$ and the other two regions showing shallower indices ($\alpha=0.92\pm0.09$ for Sgr C and $\alpha=0.66\pm0.05$ for Sgr B2-DS). Furthermore, we analyze the spatial distribution and mass segregation of cores in each region. Sgr C and Sgr B2-DS show signs of mass segregation, but the Brick does not. We compare our results to several other CMFs from different Galactic regions derived with the same methods. Finally, we discuss how these results may help define an evolutionary sequence of star cluster formation and can be used to test star formation theories., Comment: Submitted to ApJ, comments welcome. 26 pages, 14 figures

The tumor microenvironment (TME) features distinct hallmarks, including acidosis, hypoxia, reactive oxygen species (ROS) generation, and altered ion fluxes, which are crucial targets for early cancer biomarker detection, tumor diagnosis, and therapeutic strategies. A variety of imaging and sensing techniques have been developed and employed in both research and clinical settings to visualize and monitor cellular and TME dynamics. Among these, ratiometric fluorescence-based sensors have emerged as powerful analytical tools, providing precise and sensitive insights into the TME and enabling real-time detection and tracking of dynamic changes. In this comprehensive review, we discuss the latest advancements in ratiometric fluorescent probes designed for optical mapping of pH, oxygen, ROS, ions, and biomarkers within the TME. We elucidate their structural designs and sensing mechanisms, as well as their applications in in vitro and in vivo detection. Furthermore, we explore integrated sensing platforms that reveal the spatiotemporal behavior of complex tumor cultures, highlighting the potential of high-resolution imaging techniques combined with computational methods. This review aims to provide a solid foundation for understanding the current state of the art and the future potential of fluorescent nano- and microparticles in the field of cellular microenvironment sensing.

Magnetic fields may play a crucial role in setting the initial conditions of massive star and star cluster formation. To investigate this, we report SOFIA-HAWC+ $214\:\mu$m observations of polarized thermal dust emission and high-resolution GBT-Argus C$^{18}$O(1-0) observations toward the massive Infrared Dark Cloud (IRDC) G28.37+0.07. Considering the local dispersion of $B$-field orientations, we produce a map of $B$-field strength of the IRDC, which exhibits values between $\sim0.03 - 1\:$mG based on a refined Davis-Chandrasekhar-Fermi (r-DCF) method proposed by Skalidis \& Tassis. Comparing to a map of inferred density, the IRDC exhibits a $B-n$ relation with a power law index of $0.51\pm0.02$, which is consistent with a scenario of magnetically-regulated anisotropic collapse. Consideration of the mass-to-flux ratio map indicates that magnetic fields are dynamically important in most regions of the IRDC. A virial analysis of a sample of massive, dense cores in the IRDC, including evaluation of magnetic and kinetic internal and surface terms, indicates consistency with virial equilibrium, sub-Alfv\'enic conditions and a dominant role for $B-$fields in regulating collapse. A clear alignment of magnetic field morphology with direction of steepest column density gradient is also detected. However, there is no preferred orientation of protostellar outflow directions with the $B-$field. Overall, these results indicate that magnetic fields play a crucial role in regulating massive star and star cluster formation and so need to be accounted for in theoretical models of these processes., Comment: Submitted to ApJ, comments welcome

Study Objectives: Polysomnography (PSG) currently serves as the benchmark for evaluating sleep disorders. Its discomfort makes long-term monitoring unfeasible, leading to bias in sleep quality assessment. Hence, less invasive, cost-effective, and portable alternatives need to be explored. One promising contender is the in-ear-EEG sensor. This study aims to establish a methodology to assess the similarity between the single-channel in-ear-EEG and standard PSG derivations. Methods: The study involves four-hour signals recorded from ten healthy subjects aged 18 to 60 years. Recordings are analyzed following two complementary approaches: (i) a hypnogram-based analysis aimed at assessing the agreement between PSG and in-ear-EEG-derived hypnograms; and (ii) a feature-based analysis based on time- and frequency- domain feature extraction, unsupervised feature selection, and definition of Feature-based Similarity Index via Jensen-Shannon Divergence (JSD-FSI). Results: We find large variability between PSG and in-ear-EEG hypnograms scored by the same sleep expert according to Cohen's kappa metric, with significantly greater agreements for PSG scorers than for in-ear-EEG scorers (p < 0.001) based on Fleiss' kappa metric. On average, we demonstrate a high similarity between PSG and in-ear-EEG signals in terms of JSD-FSI (0.79 +/- 0.06 -awake, 0.77 +/- 0.07 -NREM, and 0.67 +/- 0.10 -REM) and in line with the similarity values computed independently on standard PSG-channel-combinations. Conclusions: In-ear-EEG is a valuable solution for home-based sleep monitoring, however further studies with a larger and more heterogeneous dataset are needed., Comment: 20 figures, 6 tables

Purpose: This study aims to enhance the clinical use of automated sleep-scoring algorithms by incorporating an uncertainty estimation approach to efficiently assist clinicians in the manual review of predicted hypnograms, a necessity due to the notable inter-scorer variability inherent in polysomnography (PSG) databases. Our efforts target the extent of review required to achieve predefined agreement levels, examining both in-domain and out-of-domain data, and considering subjects diagnoses. Patients and methods: Total of 19578 PSGs from 13 open-access databases were used to train U-Sleep, a state-of-the-art sleep-scoring algorithm. We leveraged a comprehensive clinical database of additional 8832 PSGs, covering a full spectrum of ages and sleep-disorders, to refine the U-Sleep, and to evaluate different uncertainty-quantification approaches, including our novel confidence network. The ID data consisted of PSGs scored by over 50 physicians, and the two OOD sets comprised recordings each scored by a unique senior physician. Results: U-Sleep demonstrated robust performance, with Cohen's kappa (K) at 76.2% on ID and 73.8-78.8% on OOD data. The confidence network excelled at identifying uncertain predictions, achieving AUROC scores of 85.7% on ID and 82.5-85.6% on OOD data. Independently of sleep-disorder status, statistical evaluations revealed significant differences in confidence scores between aligning vs discording predictions, and significant correlations of confidence scores with classification performance metrics. To achieve K of at least 90% with physician intervention, examining less than 29.0% of uncertain epochs was required, substantially reducing physicians workload, and facilitating near-perfect agreement.