There are challenges that must be overcome to make recommender systems useful in healthcare settings. The reasons are varied: the lack of publicly available clinical data, the difficulty that users may have in understanding the reasons why a recommendation was made, the risks that may be involved in following that recommendation, and the uncertainty about its effectiveness. In this work, we address these challenges with a recommendation model that leverages the structure of psychometric data to provide visual explanations that are faithful to the model and interpretable by care professionals. We focus on a narrow healthcare niche, gerontological primary care, to show that the proposed recommendation model can assist the attending professional in the creation of personalised care plans. We report results of a comparative offline performance evaluation of the proposed model on healthcare datasets that were collected by research partners in Brazil, as well as the results of a user study that evaluates the interpretability of the visual explanations the model generates. The results suggest that the proposed model can advance the application of recommender systems in this healthcare nic
We construct four-dimensional de Sitter space as an excited state, rather than as a vacuum configuration, in type IIB, heterotic SO(32), and heterotic E_8 \times E_8 string theories. This framework provides a mechanism to evade vacuum-based no-go theorems for de Sitter solutions in string theory. Starting from a generic M-theory configuration, we obtain de Sitter isometry in the dual string theories through appropriate dynamical duality sequences in the late-time limit. The excited state, identified as a Glauber-Sudarshan state, is constructed as the expectation value of the metric operator in M-theory using path-integral techniques. We further analyze the conditions required for the existence of a well-defined effective field theory description and show that these conditions are equivalent to the null energy condition for a (3+1)-dimensional FLRW cosmology. Finally, we investigate constraints arising from axionic cosmology and demonstrate how the time-dependent solutions are modified when experimental bounds on the axionic coupling constant are taken into account. This article serves as a computational companion to sections 3 and 4 of the paper arXiv:2511.03798 [hep-th].
This review underscores the vital role of interoperability in digital health, advocating for a standardized framework. It focuses on implementing a Fast Healthcare Interoperability Resources (FHIR) server, addressing technical, semantic, and process challenges. FHIR's adaptability ensures uniformity within Primary Care Health Information Systems, fostering interoperability. Patient data management complexities highlight the pivotal role of semantic interoperability in seamless patient care. FHIR standards enhance these efforts, offering multiple pathways for data search. The ADR-guided FHIR server implementation systematically addresses challenges related to patient identity, biometrics, and data security. The detailed development phases emphasize architecture, API integration, and security. The concluding stages incorporate forward-looking approaches, including HHIMS Synthetic Dataset testing. Envisioning FHIR integration as transformative, it anticipates a responsive healthcare environment aligned with the evolving digital health landscape, ensuring comprehensive, dynamic, and interconnected systems for efficient data exchange and access.
In this article, we present a galactic gravitational model of three degrees of freedom (3D), in order to study and reveal the character of the orbits of the stars, in a binary stellar system composed of a primary quiet or active galaxy and a small satellite companion galaxy. Our main dynamical analysis will be focused on the behavior of the primary galaxy. We investigate in detail the regular or chaotic nature of motion, in two different cases: (i) the time-independent model in both 2D and 3D dynamical systems and (ii) the time-evolving 3D model. Our numerical calculations indicate that the percentage of the chaotic orbits increases when the primary galaxy has a dense and massive nucleus. The presence of the dense galactic core also increases the stellar velocities near the center of the galaxy. Moreover, for small values of the distance R between the two bodies, low-energy stars display chaotic motion, near the central region of the galaxy, while for larger values of the distance R, the motion in active galaxies is entirely regular for low-energy stars. Our simulations suggest that in galaxies with a satellite companion, the chaotic nature of motion is not only a result of the gal
Demand for health care is constantly increasing due to the ongoing demographic change, while at the same time health service providers face difficulties in finding skilled personnel. This creates pressure on health care systems around the world, such that the efficient, nationwide provision of primary health care has become one of society's greatest challenges. Due to the complexity of health care systems, unforeseen future events, and a frequent lack of data, analyzing and optimizing the performance of health care systems means tackling a wicked problem. To support this task for primary care, this paper introduces the hybrid agent-based simulation model SiM-Care. SiM-Care models the interactions of patients and primary care physicians on an individual level. By tracking agent interactions, it enables modelers to assess multiple key indicators such as patient waiting times and physician utilization. Based on these indicators, primary care systems can be assessed and compared. Moreover, changes in the infrastructure, patient behavior, and service design can be directly evaluated. To showcase the opportunities offered by SiM-Care and aid model validation, we present a case study for
High-contrast long-slit spectrographs can be used to characterize exoplanets. High-contrast long-slit spectroscopic data are however corrupted by stellar leakages which largely dominate other signals and make the process of extracting the companion spectrum very challenging. This paper presents a complete method to calibrate the spectrograph and extract the signal of interest. The proposed method is based on a flexible direct model of the high-contrast long-slit spectroscopic data. This model explicitly accounts for the instrumental response and for the contributions of both the star and the companion. The contributions of these two components and the calibration parameters are jointly estimated by solving a regularized inverse problem. This problem having no closed-form solution, we propose an alternating minimization strategy to effectively find the solution. We have tested our method on empirical long-slit spectroscopic data and by injecting synthetic companion signals in these data. The proposed initialization and the alternating strategy effectively avoid the self-subtraction bias, even for companions observed very close to the coronagraphic mask. Careful modeling and calibrat
Millions of people now use non-clinical Large Language Model (LLM) tools like ChatGPT for mental well-being support. This paper investigates what it means to design such tools responsibly, and how to operationalize that responsibility in their design and evaluation. By interviewing experts and analyzing related regulations, we found that designing an LLM tool responsibly involves: (1) Articulating the specific benefits it guarantees and for whom. Does it guarantee specific, proven relief, like an over-the-counter drug, or offer minimal guarantees, like a nutritional supplement? (2) Specifying the LLM tool's "active ingredients" for improving well-being and whether it guarantees their effective delivery (like a primary care provider) or not (like a yoga instructor). These specifications outline an LLM tool's pertinent risks, appropriate evaluation metrics, and the respective responsibilities of LLM developers, tool designers, and users. These analogies - LLM tools as supplements, drugs, yoga instructors, and primary care providers - can scaffold further conversations about their responsible design.
We find ourselves on the ever-shifting cusp of an AI revolution -- with potentially metamorphic implications for the future practice of healthcare. For many, such innovations cannot come quickly enough; as healthcare systems worldwide struggle to keep up with the ever-changing needs of our populations. And yet, the potential of AI tools and systems to shape healthcare is as often approached with great trepidation as celebrated by health professionals and patients alike. These fears alight not only in the form of privacy and security concerns but for the potential of AI tools to reduce patients to datapoints and professionals to aggregators -- to make healthcare, in short, less caring. This infixated concern, we - as designers, developers and researchers of AI systems - believe it essential we tackle head on; if we are not only to overcome the AI implementation gap, but realise the potential of AI systems to truly augment human-centred practices of care. This, we argue we might yet achieve by realising newly-accessible practices of AI healthcare innovation, engaging providers, recipients and affected communities of care in the inclusive design of AI tools we may yet enthusiastically
Large blood vessels entering the CNS are surrounded by perivascular spaces that communicate with the cerebrospinal fluid and, at their termini, with the interstitial space. Solutes and particles can translocate along these perivascular conduits, reportedly in both directions. Recently, this prompted a renewed interest in the intrathecal therapy delivery route for CNS-targeted therapeutics. However, the extent of the CNS coverage by the perivascular system is unknown, making the outcome of drug administration to the CSF uncertain. We traced the translocation of model macromolecules from the CSF into the CNS of rats and non-human primates. Conduits transporting macromolecules were found to extend throughout the parenchyma from both external and internal (fissures) CNS boundaries, excluding ventricles, in large numbers, on average ca. 40 channels per mm2 in rats and non-human primates. The high density and depth of extension of the perivascular channels suggest that the perivascular route can be suitable for delivery of therapeutics to parenchymal targets throughout the CNS.
Robot-Assisted Therapy (RAT) has successfully been used in Human Robot Interaction (HRI) research by including social robots in health-care interventions by virtue of their ability to engage human users in both social and emotional dimensions. Robots used for these tasks must be designed with several user groups in mind, including both individuals receiving therapy and care professionals responsible for the treatment. These robots must also be able to perceive their context of use, recognize human actions and intentions, and follow the therapeutic goals to perform meaningful and personalized treatment. Effective interactions require for robots to be capable of coordinated, timely behavior in response to social cues. This means being able to estimate and predict levels of engagement, attention, intentionality and emotional state during human-robot interactions. An additional challenge for social robots in therapy and care is the wide range of needs and conditions the different users can have during their interventions, even if they may share the same pathologies their current requirements and the objectives of their therapies can varied extensively. Therefore, it becomes crucial for
Graphs are very effective tools in visualizing information and are used in many fields including the medical field. In most developing countries primary care, graphs are used to monitor child growth. These measures are therefore often displayed using line graphs, basing it on three indicators (stunting, underweight and wasting) based on the WHO 2006 Child Growth Standard. Most literature on information visualization of electronic health record data focuses on aggregate data visualization tools. This research therefore, was set out to provide such an overview of requirements for computerized graphs for individual patient data, implemented in a way that all kinds of medical graphs showing the development of medical measures over time can be displayed. This research was interpretive, using a user-centric approach for data collection where interviews and web search was used to ensure that the graphs developed are fit the user requirements. This followed prototype development using one of the three free, open source software libraries for Android that were evaluated. The prototype was then used to refine the user requirements. The health workers interpreted the graphs developed flawless
Recently, two independent analyses have asserted that the cause of the Long Secondary Period (LSP) observed in the variability spectrum of our nearest red supergiant, Betelgeuse ($α$ Ori), is an as-yet undetected, low-mass binary companion dubbed $α$ Ori B. In this paper, we present the results of a far-UV observational campaign using the STIS echelle spectrograph on the Hubble Space Telescope aimed at detecting spectral signatures of the companion. The four-quadrant tiling pattern and timing of the observations were optimized to isolate the companion, with observations taking place during a period of maximum angular and velocity separation between Betelgeuse and the putative companion. Spectral differencing between quadrants recovers no spectral features at the companion's velocity in excess of the background or Betelgeuse's chromosphere, i.e. a non-detection. Having determined that $α$ Ori B is most likely a Young Stellar Object (YSO) thanks to constraints from a complementary X-ray campaign with the Chandra X-ray Observatory in a companion paper, comparison of our data against canonical spectra from YSOs in the ULLYSES database allows us to confidently exclude masses above $\gtr
We present updated results from our near-infrared long-baseline interferometry (LBI) survey to constrain the multiplicity properties of intermediate-mass A-type stars within 80 pc. Previous adaptive optics surveys of A-type stars are incomplete at separations $<$ 20au. Therefore, a LBI survey allows us to explore separations previously unexplored. Our sample consists of 54 A-type primaries with estimated masses between 1.44-2.93 M$_{\odot}$ and ages 10-790 Myr, which we observed with the MIRC-X and MYSTIC instruments at the CHARA Array. We use the open source software CANDID to detect two new companions, seven in total, and we performed a Bayesian demographic analysis to characterize the companion population. We find the separation distribution consistent with being flat, and we estimate a power-law fit to the mass ratio distribution with index -0.13$^{+0.92}_{-0.95}$ and a companion frequency of 0.25$^{+0.17}_{-0.11}$ over mass ratios 0.1-1.0 and projected separations 0.01-27.54au. We find a posterior probability of 0.53 and 0.04 that our results are consistent with extrapolations based on previous models of the solar-type and B-type companion population, respectively. Our resu
Depression is underdiagnosed in primary care, yet timely identification remains critical. Recorded clinical encounters, increasingly common with digital scribing technologies, present an opportunity to detect depression from naturalistic dialogue. We investigated automated depression detection from 1,108 audio-recorded primary care encounters in the Establishing Focus study, with depression defined by PHQ-9 (n=253 depressed, n=855 non-depressed). We compared three supervised approaches, Sentence-BERT + Logistic Regression (LR), LIWC+LR and ModernBERT, against a zero-shot GPT-OSS. GPT-OSS achieved the strongest performance (AUPRC=0.510, AUROC=0.774), with LIWC+LR competitive among supervised models (AUPRC=0.500, AUROC=0.742). Combined dyadic transcripts outperformed single-speaker configurations, with providers linguistically mirroring patients in depression encounters, an additive signal not captured by either speaker alone. Meaningful detection is achievable from the first 128 patient tokens (AUPRC=0.356, AUROC=0.675), supporting in-the-moment clinical decision support. These findings argue for passively collected clinical audio as a low-burden complement to existing screening wor
If the primordial fluctuations are non-Gaussian, then this non-Gaussianity will be apparent in the cosmic microwave background (CMB) sky. With their sensitive all-sky observation, MAP and Planck satellites should be able to detect weak non-Gaussianity in the CMB sky. On large angular scale, there is a simple relationship between the CMB temperature and the primordial curvature perturbation. On smaller scales; however, the radiation transfer function becomes more complex. In this paper, we present the angular bispectrum of the primary CMB anisotropy that uses the full transfer function. We find that the bispectrum has a series of acoustic peaks that change a sign, and a period of acoustic oscillations is twice as long as that of the angular power spectrum. Using a single non-linear coupling parameter to characterize the amplitude of the bispectrum, we estimate the expected signal-to-noise ratio for COBE, MAP, and Planck experiments. We find that the detection of the primary bispectrum by any kind of experiments should be problematic for the simple slow-roll inflationary scenarios. We compare the sensitivity of the primary bispectrum to the primary skewness and conclude that when we
Accurate classification of self-care problems in children who suffer from physical and motor affliction is an important problem in the healthcare industry. This is a difficult and a time consumming process and it needs the expertise of occupational therapists. In recent years, healthcare professionals have opened up to the idea of using expert systems and artificial intelligence in the diagnosis and classification of self care problems. In this study, we propose a new deep learning based approach named Care2Vec for solving these kind of problems and use a real world self care activities dataset that is based on a conceptual framework designed by the World Health Organization (WHO). Care2Vec is a mix of unsupervised and supervised learning where we use Autoencoders and Deep neural networks as a two step modeling process. We found that Care2Vec has a better prediction accuracy than some of the traditional methods reported in the literature for solving the self care classification problem viz. Decision trees and Artificial neural networks.
We evaluate the impact of large language model-based clinical decision support in live care. In partnership with Penda Health, a network of primary care clinics in Nairobi, Kenya, we studied AI Consult, a tool that serves as a safety net for clinicians by identifying potential documentation and clinical decision-making errors. AI Consult integrates into clinician workflows, activating only when needed and preserving clinician autonomy. We conducted a quality improvement study, comparing outcomes for 39,849 patient visits performed by clinicians with or without access to AI Consult across 15 clinics. Visits were rated by independent physicians to identify clinical errors. Clinicians with access to AI Consult made relatively fewer errors: 16% fewer diagnostic errors and 13% fewer treatment errors. In absolute terms, the introduction of AI Consult would avert diagnostic errors in 22,000 visits and treatment errors in 29,000 visits annually at Penda alone. In a survey of clinicians with AI Consult, all clinicians said that AI Consult improved the quality of care they delivered, with 75% saying the effect was "substantial". These results required a clinical workflow-aligned AI Consult i
Large Language Models(LLMs) hold promise for improving healthcare access in low-resource settings, but their effectiveness in African primary care remains underexplored. We present a methodology for creating a benchmark dataset and evaluation framework focused on Kenyan Level 2 and 3 clinical care. Our approach uses retrieval augmented generation (RAG) to ground clinical questions in Kenya's national guidelines, ensuring alignment with local standards. These guidelines were digitized, chunked, and indexed for semantic retrieval. Gemini Flash 2.0 Lite was then prompted with guideline excerpts to generate realistic clinical scenarios, multiple-choice questions, and rationale based answers in English and Swahili. Kenyan physicians co-created and refined the dataset, and a blinded expert review process ensured clinical accuracy, clarity, and cultural appropriateness. The resulting Alama Health QA dataset includes thousands of regulator-aligned question answer pairs across common outpatient conditions. Beyond accuracy, we introduce evaluation metrics that test clinical reasoning, safety, and adaptability such as rare case detection (Needle in the Haystack), stepwise logic (Decision Poin
Unlike most primary headaches, secondary headaches need specialized care and can have devastating consequences if not treated promptly. Clinical guidelines highlight several 'red flag' features, such as thunderclap onset, meningismus, papilledema, focal neurologic deficits, signs of temporal arteritis, systemic illness, and the 'worst headache of their life' presentation. Despite these guidelines, determining which patients require urgent evaluation remains challenging in primary care settings. Clinicians often work with limited time, incomplete information, and diverse symptom presentations, which can lead to under-recognition and inappropriate care. We present a large language model (LLM)-based multi-agent clinical decision support system built on an orchestrator-specialist architecture, designed to perform explicit and interpretable secondary headache diagnosis from free-text clinical vignettes. The multi-agent system decomposes diagnosis into seven domain-specialized agents, each producing a structured and evidence-grounded rationale, while a central orchestrator performs task decomposition and coordinates agent routing. We evaluated the multi-agent system using 90 expert-valid
The star V766 Cen (=HR 5171A) was originally classified as a yellow hypergiant but lately found to more likely be a 27-36 Msun red supergiant (RSG). Recent observations indicated a close eclipsing companion in the contact or common-envelope phase. Here, we aim at imaging observations of V766 Cen to confirm the presence of the close companion. We used near-infrared H -band aperture synthesis imaging at three epochs in 2014, 2016, and 2017, employing the PIONIER instrument at the Very Large Telescope Interferometer (VLTI). The visibility data indicate a mean Rosseland angular diameter of 4.1+/-0.8 mas, corresponding to a radius of 1575+/-400 Rsun. The data show an extended shell (MOLsphere) of about 2.5 times the Rosseland diameter, which contributes about 30% of the H-band flux. The reconstructed images at the 2014 epoch show a complex elongated structure within the photospheric disk with a contrast of about 10%. The second and third epochs show qualitatively and quantitatively different structures with a single very bright and narrow feature and high contrasts of 20-30%. This feature is located toward the south-western limb of the photospheric stellar disk. We estimate an angular s