ethicalabs/Research-Intent-Collab huggingface dataset:
The economic benefits of health information exchange interoperability for Australia

Objective: To estimate costs and benefits for Australia of implementing health information exchange interoperability among health care providers and other health care stakeholders. Design: A cost?benefit model considering four levels of interoperability (Level 1, paper based; Level 2, machine transportable; Level 3, machine readable; and Level 4, machine interpretable) was developed for Government-funded health services, then validated by expert review. Results: Roll-out costs for Level 3 and Level 4 interoperability were projected to be $21.5 billion and $14.2 billion, respectively, and steady-state costs, $1470 million and $933 million per annum, respectively. Level 3 interoperability would achieve steadystate savings of $1820 million, and Level 4 interoperability, $2990 million, comprising transactions of: laboratory $1180 million (39%); other providers, $893 million (30%); imaging centre, $680 million (23%); pharmacy, $213 million (7%) and public health, $27 million (1%). Net steady-state Level 4 benefits are projected to be $2050 million: $1710 million more than Level 3 benefits of $348 million, reflecting reduced interface costs for Level 4 interoperability due to standardisation of the semantic content of Level 4 messages. Conclusions: Benefits to both providers and society will accrue from the implementation of interoperability. Standards are needed for the semantic content of clinical messages, in addition to message exchange standards, for the full benefits of interoperability to be realised. An Australian Government policy position supporting such standards is recommended.
Large-scale prediction of patient-level antibiotic resistance : towards clinical decision support for improved antimicrobial stewardship

Antibiotics are critical to modern medicine. However, levels of resistance have been rising, exacerbated by over-prescription and misuse of antibiotics. One major reason for this inappropriate usage is that doctors often must decide treatment without the results of microbiologic testing, a setting known as the empiric treatment setting. Thus, this work aims to provide clinical decision support through patient-specific predictions of resistance at the point of care. Combining information from diagnoses, procedures, medications, clinicians' notes, and other modalities present in electronic medical records, various machine learning models such as logistic regression and decision trees are used to predict patients' probabilities of resistance to various antibiotics. The full dataset consists of electronic medical records from patients presenting to the Massachusetts General Hospital and the Brigham & Women's Hospital between 2007 and 2016. On samples from the urinary tract (UTIs), which comprise approximately 48% of microbiology samples, the models achieve test AUCs ranging from 0.665 to 0.955 (depending on the antibiotic). To evaluate the practical utility of these models, we extract the uncomplicated UTI cohort. Combining model predictions with well-defined treatment guidelines, a decision algorithm is constructed to recommend antibiotic treatments. For uncomplicated UTIs, the algorithm reduces test set prescriptions of broad-spectrum antibiotics by about 6.6%, while retaining similar levels of inappropriate antibiotic therapy.
Edge Computing and 5G URLLC Slicing for Industrial IoT: End-to-End Latency Decomposition across RAN, Transport, and Computer

The deployment of fifth-generation networks as the communication backbone for industrial Internet of Things applications requiring sub-millisecond closed-loop control demands a systems engineering approach to latency management that treats the radio access network, fronthaul transport, multi-access edge compute, and application processing as jointly constrained components of a single end-to-end latency budget rather than independently optimized subsystems. This paper proposes a comprehensive end-to-end latency decomposition framework for industrial IoT ultra-reliable low-latency communication applications, partitioning the sub-millisecond latency budget across four domains and deriving the optimization constraints that each domain must satisfy for the system-level URLLC requirement to be achievable. The framework characterizes eight latency components spanning user equipment processing, radio access network scheduling, URLLC slice admission control, fronthaul propagation, multi-access edge computing queuing and processing, application logic execution, and actuation delay. For the radio access network slice management component, we build on the latency-aware SDN-P4 network slicing architecture of Prakhar et al., which demonstrated that programmable data plane-based admission control and in-band telemetry can enforce URLLC latency service-level agreements at the slice level, providing the sub-millisecond RAN contribution to the end-to-end budget. Experimental evaluation across six industrial IoT deployment scenarios, including computer numerical control machine tool control, autonomous mobile robot collision avoidance, and power grid protection relay, demonstrates that joint optimization across all four latency domains achieves the URLLC targets that domain-isolated optimization cannot satisfy, with an average 37 percent reduction in end-to-end latency violation rate relative to baseline architectures.
A Review of Explainable Deep Learning for Intrusion Detection in SDN Balancing Accuracy and Interpretability in Software-Defined Networking Security

Abstract Software-Defined Networking (SDN) centralizes policy and programmability, while simultaneously concentrating risk in the controller. Deep Learning (DL) has advanced network intrusion detection systems (IDS) beyond rule-based and shallow methods; however, opaque model decisions limit adoption in high-stakes control planes. We review deep learning architectures, including convolutional, recurrent, Transformer, and hybrid models. We analyze performance on canonical datasets (CICIDS2017, UNSW-NB15, CSE-CICIDS2018, ToN_IoT), discuss controller-aware metrics (latency, throughput, rule-install delay), and synthesize deployment lessons from testbeds. We find that Transformers and hybrids often exceed 99% accuracy, while post-hoc XAI provides actionable transparency with modest overhead. “Key gaps identified are, offlineonly evaluation, missing controller latency reporting, and limited in-loop explanations. We outline a practical roadmap toward real-time, explainable SDN IDS: efficient attention, standardized SDN–XAI benchmarks, and human-centered validation. Keywords Software-Defined Networking, Intrusion Detection System, Deep Learning, Explainable AI, CNN, LSTM, Transformer, SHAP, LIME.
From European critical infrastructure protection to the resilience of European critical entities: what does it mean?

The article is a public policy analysis of the development of legislation on critical infrastructure in the European Union (EU), covering 27 developed countries. More precisely, it concerns the 2022 CER Directive “on the resilience of critical entities’. This directive replaced the 2008 ECI Directive ‘on the identification and designation of European critical infrastructure and the assessment of the need to improve their protection’. We ask what is at stake in this process of moving from one directive to another. Why has the concept of protection been replaced by the concept of resilience, and why has the concept of critical infrastructure been replaced by the newly invented euroconcept of ‘critical entity’? In the concluding section we discuss the European integration dimension of this new directive; what does this development in the CI domain tell us about the current dynamics of European integration, and how it could be explained?
Exploring the Relationship between Social Media Use and Adolescent Mental Health &amp; Well-being: A Review

Social media is believed to worsen mental health issues. This review paper is planned to systematically explore the intricate relationship between social media use and its impact on adolescents’ mental health. For this, fifty-one papers were identified from the PubMed database after applying inclusion and exclusion criteria and other filters. The findings of identified studies are mixed suggesting both positive and negative impacts of social media use ranged from poor to moderate level. This review aims to provide a comprehensive understanding of the current research landscape on the relationship between social media use and adolescent mental health. Furthermore, the present study also aims to identify gaps in the existing literature to guide future research in this emerging field.
Improving the Science of Adolescent Social Media and Mental Health:  Challenges and Opportunities of Smartphone-Based Mobile Sensing and Digital Phenotyping

Objective: The impact of social media (SM) use (‘screentime’) on adolescent mental health has been the focus of increasing concern, despite mixed findings from empirical research. Current methodological approaches rely on self-reported SM use, which has limited accuracy and obscure the dynamic interplay of SM use and mental health. Smartphone-based mobile sensing offers new opportunities to gain insights into adolescents’ SM use patterns and behaviors, particularly at an idiographic level. Considerations and challenges of smartphone sensing methods for capturing adolescents’ SM use patterns and behaviors in clinical psychological science are discussed in the context of a pilot study using smartphone-based sensing with adolescents. Method: The pilot study included 19 adolescents (Mean age=15.84; 68% boys; 79% White) who installed a passive monitoring application (AWARE) on their phones for 31 (SD=5.6) days. Results: Descriptive data of sensing acceptability and feasibility are presented based on participant ratings and data yield ratio of usable data (74.18%). Sensing yielded 10,038 hourly observations collected from the ‘application foreground’ sensor across all participants from social media apps, and a total of 645 applications used. Categorization of SM apps were coded (kappa &amp;gt;.90) into ‘social networking’ (N=20 apps) and ‘broader SM’ (N=41) and compared to both Play Store-defined SM apps (N=26) and popular SM apps based on Common Sense Media Survey (N=9). Descriptive data on extracted behavioral features (duration, checking) from SM use categories (binned hourly and daily) are presented. Discussion: Challenges, opportunities, and future directions of sensing methods for SM use are discussed to inform our understanding of its impacts on mental health and to improve the rigor of SM research in clinical psychological science.
Chess2vec: Learning Vector Representations for Chess

We conduct the first study of its kind to generate and evaluate vector representations for chess pieces. In particular, we uncover the latent structure of chess pieces and moves, as well as predict chess moves from chess positions. We share preliminary results which anticipate our ongoing work on a neural network architecture that learns these embeddings directly from supervised feedback.
EpiQAL: Benchmarking Large Language Models in Epidemiological Question Answering for Enhanced Alignment and Reasoning

27 pages, 5 figures, 20 tables
Using Datix Electronic Incident Reporting System to Develop a Palliative and End-of-Life Care Patient-Centered Auditing, Quality Assessment and Reporting System

The need for specialist palliative care services has never been higher as the ongoing coronavirus disease (COVID) pandemic has highlighted. Yet even in the absence of the pandemic, specialist palliative care services in hospitals, hospices and within communities were already stretched beyond their capacity. The current fiscal climate within which most healthcare systems operate are undergoing global economic constraints making it a necessity that healthcare systems explore new ways of working to develop efficient high-quality services, maximising resources whilst maintaining good clinical governance. We describe how a hospital palliative and end-of-life care (PEoLC) specialist team, can harness an existing electronic incident reporting system Datix, adapting it to automatically track the longitudinal performance of the patient-centred, end-of-life care (EoLC) services that they deliver, along national standards in keeping with the Care Quality Commission (CQC) and the National Institute for Health and Excellence (NICE) guidelines for end-of-life care in the UK. Such automated systems can inform the quality of PEoLC services, improve the use of time and resource within specialist palliative care teams, support the delivery of evidence-based clinical governance standards, whilst supporting benchmarking across organisations, strategy development, insight into local/regional variations, and the establishment of standardisations of care.
Ensemble downscaling in coupled solar wind‐magnetosphere modeling for space weather forecasting

AbstractAdvanced forecasting of space weather requires simulation of the whole Sun‐to‐Earth system, which necessitates driving magnetospheric models with the outputs from solar wind models. This presents a fundamental difficulty, as the magnetosphere is sensitive to both large‐scale solar wind structures, which can be captured by solar wind models, and small‐scale solar wind “noise,” which is far below typical solar wind model resolution and results primarily from stochastic processes. Following similar approaches in terrestrial climate modeling, we propose statistical “downscaling” of solar wind model results prior to their use as input to a magnetospheric model. As magnetospheric response can be highly nonlinear, this is preferable to downscaling the results of magnetospheric modeling. To demonstrate the benefit of this approach, we first approximate solar wind model output by smoothing solar wind observations with an 8 h filter, then add small‐scale structure back in through the addition of random noise with the observed spectral characteristics. Here we use a very simple parameterization of noise based upon the observed probability distribution functions of solar wind parameters, but more sophisticated methods will be developed in the future. An ensemble of results from the simple downscaling scheme are tested using a model‐independent method and shown to add value to the magnetospheric forecast, both improving the best estimate and quantifying the uncertainty. We suggest a number of features desirable in an operational solar wind downscaling scheme.
Germ Line Stem Cells: A Promising Alternative Source for Stem-Cell-Based Therapies in Regenerative Medicine

Spermatogonial stem cells (SSCs) are exceptional cells with potential clinical applications in fertility preservation and regenerative medicine. Like other tissue-specific stem cells, SSCs are defined by their ability to balance between self-renewal and differentiation. This balance regulates the stem cell pool and guarantees the daily production of gametes. The preservation and transplantation of SSCs may become a promising strategy to treat reproductive stem cell loss disorders. As a consequence, the banking of testicular tissue from prepubertal patients is being introduced in a clinical setting. Moreover, with the increasing reports about the plasticity of mouse and human SSCs, their potential application in regenerative medicine is highly interesting. In contrast to males, females are born with a finite population of primordial follicles. Although the existence of ovarian stem cells (OSCs) has been suggested recently, it is still a matter of controversy. This review provides an update on the role of germ line stem cells in fertility restoration after germ cell loss, and gives an overview of (future) clinical applications making use of these fascinating cells.
Bootstrapping Secure Sensor Networks in the Internet of Things

Sakernas internet har blivit en central del i dagens samhälle och fortsätter att utvecklas och integreras allt mer. Enheterna förväntas fungera i många typer av miljöer och förhållanden samtidigt som de ska skicka data säkert och vara billiga att producera. Trots att utvecklingen gått framåt, är säkerheten fortfarande väldigt rudimentär och i behov av ytterligare utveckling. För vanliga nätverk finns det många väletablerade protokoll för att säkra kommunikation, men dessa anses oftast vara för komplicerade för de resursbegränsade enheterna. Till följd av detta har forskning inriktats på att effektivisera existerande protokoll men även på att utveckla enklare varianter. Det som fortfarande kvarstår som ett problem och ofta inte diskuteras, är hur den initiala distributionen av kryptografiska nycklar ska genomföras. Att använda sig utav förinstallerade nycklar är en möjlighet, men det brukar oftast bli problematiskt utifrån säkerhet och administrering när sensornätverken växer i storlek. Genom att granska och utvärdera föreslagna metoder för initial konfiguration av sensornätverk, ämnar detta arbete att ge en översikt i vilka olika metoder som finns tillgängliga och deras lämplighet. Resultat från arbetet visar att tack vare framsteg inom elliptisk kurvkryptografi är publik nyckelkryptografi ett rimligt alternativ att använda, då en sessionsnyckel kan etableras inom loppet av en minut. Vid utvärdering av enheternas förmåga att generera slumptal visar initiala resultat däremot att A/D-omvandlaren inte är en lämplig källa för detta då dess entropi är låg och genererad slumpdata har en dålig fördelning och hög upprepning. Det går därför att dra slutsatsen att om någon nivå av kryptografisk säkerhet ska erhållas, så måste källor till entropi utvärderas noggrant. De resursbegränsade enheterna kan även ha ett behov av dedikerad hårdvara för att generera slumptal.
Robotic learning of motion using demonstrations and statistical models for surgical simulation

In robotic-assisted surgical training, the expertise of surgeons in maneuvering surgical instruments may be utilized to provide the motion trajectories for teaching. However, the motion primitives for trajectory planning are not known until the motion trajectory is generalized. We hypothesize that a generic model that encodes surgical skills using demonstrations and statistical models can be used by the surgical training robot to determine the motion primitive base on the motion trajectory.The generic model was developed from twenty-two sets of motion trajectories of soft tissue division with laparoscopic scissors collected from a robotic laparoscopic surgical training system. Adaptive mean shift method with initial bandwidth determined by the plug-in-rule method was used to identify the primitives in the motion trajectories. Gaussian Mixture Model was applied to model the underlying motion structure. Gaussian Mixture Regression was then applied to reconstruct a generic motion trajectory for the task.The generic model and proposed method were investigated in experiments. Motion trajectory of tissue division was model and reconstructed. The motion model which was trained based on primitives determined by adaptive mean shift method produced RMS error of 3.05° and 3.08° with respect to the demonstrated trajectories of left and right instruments, respectively. The RMS error was smaller than that of k-means method and fixed bandwidth mean shift method. The dexterous features in the demonstrations were also preserved.Surgical tasks can be modeled using Gaussian Mixture Model and motion primitives identified by adaptive mean shift method with minimum user intervention. Generic motion trajectory has been successfully reconstructed based on the motion model. Investigation on the effectiveness of this method and generic model for surgical training is ongoing.
Translation of the agency for health care research and quality nursing home survey on patient safety for Swedish conditions

Abstract                   Patient safety culture is a critical component of health care quality in nursing homes. Surveys on patient safety culture can be helpful tools to evaluate care. The aims of the study were to translate a survey on patient safety culture, namely, the Nursing Home Survey on Patient Safety Culture (NHSOPS 1.0) from English into Swedish and to adapt it to Swedish conditions and to validate the translation. The survey was translated into Swedish following the TRAPD translation process —translate, review, adjudicate, pre‐test, and document, as suggested by the provider of the original survey. The validity of the items was measured by Cronbach's alpha. Some changes in wording were made according to Swedish conditions. A pre‐test with staff working in nursing homes showed adequate results for the translation. In this pilot study, all items and questions were considered valuable for measuring patient safety and were therefore retained in the Swedish version. The translated survey may be a helpful tool for measuring patient safety in nursing homes and prompting staff to reflect on their workplaces.
Home Price Index: A Machine Learning Methodology

Estimating house prices is essential for homeowners and investors alike with both needing to understand the value of their asset, and to understand real estate assets as part of an overall portfolios. Commonly-used indices like the National Association of Realtors (NAR) median home price index, or the celebrated Case-Shiller Home Price Index are reported exclusively over a large geographic areas, i.e., a metropolitan, whereby home price dynamics are lost. In this paper, we propose a improved method to capture price dynamics over time at the most granular level possible — a single home. Using over 16 years of home sale data, from the year 2000 to 2016, we estimate home price index for each house. Once home price dynamics is captured, its possible to aggregate price dynamics to construct a price index over geographies of any kind, e.g., ZIP code. This particular index relies on a so-called ‘gradient boosted’ model, a methodology framework relying on multiple calibration parameters and heavily dependent on sampling techniques. We demonstrate that this approach offers several strengths compared to the commonly reported indices, the ‘median sale’ and ‘repeat sales’ indices.
Implications of Non-unitarity on $$\theta _{23}$$, Neutrino Mass Hierarchy and CP-Violation Discovery Reach in Neutrino Oscillation Experiments

We have studied the effect of \(\theta _{23}\) octant, neutrino mass hierarchy and CP violation discovery reach on the sensitivity of non-unitarity in short baseline (SBL) experiments using minimal unitarity violation (MUV) scheme. We find that the \(\theta _{23}\) octant has distinguishing implications towards the sensitivities of non-unitary parameter \(|\rho _{\mu \tau }|\) with normal and inverted mass hierarchies. Using CP-fraction formalism, we find the possibilities to distinguish the CP-violating effects due to unitary \((\delta )\) and non-unitary \((\omega _{\mu \tau })\) CP phases.