Large Systems Research Articles

Development of a large diameter invitro flow loop thrombogenicity test system.

To accommodate a wider range of medical device sizes, a larger invitro flow loop thrombogenicity test system using 9.5 -mm inner diameter (ID) tubing was developed and evaluated based on our previously established 6.4 -mm ID tubing system. Four cardiopulmonary bypass roller pumps were used concurrently to drive four flow loops during testing. To ensure that each pump produced a consistent thrombogenic response for the same material under the same test conditions, a novel dynamic roller occlusion setting method was applied. Five materials with varying thrombogenic potentials were tested: polytetrafluoroethylene (PTFE), silicone, 3D-printed nylon, latex, and nitrile rubber (BUNA). Day-old bovine blood was heparinized to a donor-specific concentration and recirculated through the flow loops containing test materials at 20 rpm for 1 h at room temperature. Material thrombogenicity was characterized by measuring the thrombus surface coverage, thrombus weight, and platelet (PLT) count reduction. The larger tubing system can differentiate thrombogenic materials (latex, BUNA) from the thromboresistant PTFE material. Additionally, silicone and the 3D-printed nylon exhibited an intermediate thrombogenic response with significantly less thrombus surface coverage and PLT count reduction than latex and BUNA but more thrombus surface coverage than PTFE (p < 0.05). The 9.5 -mm ID test system can effectively differentiate materials of varying thrombogenic potentials when appropriate pump occlusion settings and donor-specific anticoagulation are used. This system is being assessed in an interlaboratory study to develop standardized best practices for performing invitro dynamic thrombogenicity testing of medical devices and materials.

E(n)-Equivariant cartesian tensor message passing interatomic potential.

Machine learning potential (MLP) has been a popular topic in recent years for its capability to replace expensive first-principles calculations in some large systems. Meanwhile, message passing networks have gained significant attention due to their remarkable accuracy, and a wave of message passing networks based on Cartesian coordinates has emerged. However, the information of the node in these models is usually limited to scalars, and vectors. In this work, we propose High-order Tensor message Passing interatomic Potential (HotPP), an E(n) equivariant message passing neural network that extends the node embedding and message to an arbitrary order tensor. By performing some basic equivariant operations, high order tensors can be coupled very simply and thus the model can make direct predictions of high-order tensors such as dipole moments and polarizabilities without any modifications. The tests in several datasets show that HotPP not only achieves high accuracy in predicting target properties, but also successfully performs tasks such as calculating phonon spectra, infrared spectra, and Raman spectra, demonstrating its potential as a tool for future research.

A particle swarm optimization inspired global and local stability driven predictive load balancing strategy

In distributed systems and parallel computing, optimal load balancing is difficult. These abstract addresses load balancing in distributed situations, highlighting current solutions' flaws and emphasizing the need for new ones. Load balancing research includes centralized and distributed algorithms, heuristics, and predictive models. Despite various successful methods, workload adaptability, overhead reduction, and scaling to large systems remain unresolved. This study proposes a particle swarm optimization (PSO) load balancing method that considers global and local stability considerations. The proposed method uses PSO principles to balance exploration and exploitation and allocate resources among distributed nodes. Predictive components improve preventative load management by predicting workload changes. Global and local load balancing stability criteria distinguish this study. The recommended method considers global system-wide performance indicators, local node-level characteristics, and micro-level stability to maximize system efficiency. A dual-focus technique distinguishes the proposed load balancing strategy from others, solving dynamic distributed system challenges. The study examines load balancing system advances and suggests improvements and further research. More accurate prediction modeling, stability measures, and application-specific enhancements may be studied in the future. Experimental validation and real-world implementation of the recommended approach are necessary to determine its practicality and ability to handle modern distributed computing systems.

Beyond the Basics: Partnerships and Innovations for Empowerment to Address Workplace Violence, a Call to Action.

Workplace violence (WPV) has become a crisis for health care workers, with a significant increase in violent events taking place in health care settings across the nation. Health care organizations should develop a comprehensive WPV program that includes recognition, management, and reporting of all types of WPV. A large health care system developed strategies and a program to address WPV. The health care system believes that a safe and respectful work environment is foundational to the goal of zero preventable harm. The "Safety in the Workplace" initiative was designed to raise awareness of concerning behaviors and to offer tools and resources for identifying, responding to, and reporting incidents of violence, aggression, or disruptive behaviors of WPV. During a 6-month pilot of a WPV bundle, there was a 44% reduction in physical violence reports and a 44% reduction in reported incidents of WPV. From June 2020 to June 2021, there was a 10% decrease in WPV overall.

Thermal Annealing Effects on Long-lived Fluorenol Room Temperature Phosphorescence for Styrene Detection.

Fluorene derivatives have been widely developed in OLEDs because of its efficient fluorescence quantum efficiency, but for which unique rigid biphenyl planar structure and large conjugated system, we hypothesize that they have a great potential for room temperature phosphorescence (RTP) applications, and confirmed this conjecture by subjecting polyvinyl alcohol (PVA) and phosphors to thermal annealing. The cross-linked structure formed during thermal annealing judiciously modulates the phosphorescence emission characteristics of the fluorenol with the synergistic interaction between PVA and fluorenol. Specifically, the lifetime exhibited a substantial increase from 1352.2 ms to 2874.1 ms, accompanied by a quantum yield augmentation from 4.8% to 11.3%, which substantiate that cross-linked induced by thermal annealing effectively amplifies the phosphorescent intensity and stability of the phosphors, facilitating ultralong phosphorescent emission at ambient conditions. Furthermore, an effective probe based on this film is developed for its highly sensitive, quantitative and immediate detection of volatile organic compounds. This investigation not only proffers a novel paradigm for the development of advanced RTP materials but also imparts insightful considerations for optimizing the performance of polymers in conjunction with functional materials, encompassing bioimaging, sensing, and optoelectronic devices.

System-level trends in ischemic stroke admissions after adding endovascular stroke capabilities in community hospitals

BackgroundThere is substantial interest in adding endovascular stroke therapy (EST) capabilities in community hospitals. Here, we assess the effect of transitioning to an EST-performing hospital (EPH) on acute ischemic stroke...

Melting and freezing of a skyrmion lattice

We report comprehensive Monte-Carlo studies of the melting of skyrmion lattices (SkL) in systems of small, medium, and large sizes with the number of skyrmions ranging from 103to over 105. Large systems exhibit hysteresis similar to that observed in real experiments on the melting of SkLs. For sufficiently small systems which achieve thermal equilibrium, a fully reversible sharp solid-liquid transition on temperature with no intermediate hexatic phase is observed. A similar behavior is found on changing the magnetic field that provides the control of pressure in the SkL. We find that on heating the melting transition occurs via a formation of grains with different orientations of hexagonal axes. On cooling, the fluctuating grains coalesce into larger clusters until a uniform orientation of hexagonal axes is slowly established. The observed scenario is caused by collective effects involving defects and is more complex than a simple picture of a transition driven by the unbinding and annihilation of dislocation and disclination pairs.

Breaking the size limitation of nonadiabatic molecular dynamics in condensed matter systems with local descriptor machine learning

Nonadiabatic molecular dynamics (NA-MD) is a powerful tool to model far-from-equilibrium processes, such as photochemical reactions and charge transport. NA-MD application to condensed phase has drawn tremendous attention recently for development of next-generation energy and optoelectronic materials. Studies of condensed matter allow one to employ efficient computational tools, such as density functional theory (DFT) and classical path approximation (CPA). Still, system size and simulation timescale are strongly limited by costly ab initio calculations of electronic energies, forces, and NA couplings. We resolve the limitations by developing a fully machine learning (ML) approach in which all the above properties are obtained using neural networks based on local descriptors. The ML models correlate the target properties for NA-MD, implemented with DFT and CPA, directly to the system structure. Trained on small systems, the neural networks are applied to large systems and long timescales, extending NA-MD capabilities by orders of magnitude. We demonstrate the approach with dependence of charge trapping and recombination on defect concentration in MoS2. Defects provide the main mechanism of charge losses, resulting in performance degradation. Charge trapping slows with decreasing defect concentration; however, recombination exhibits complex dependence, conditional on whether it occurs between free or trapped charges, and relative concentrations of carriers and defects. Delocalized shallow traps can become localized with increasing temperature, changing trapping and recombination behavior. Completely based on ML, the approach bridges the gap between theoretical models and realistic experimental conditions and enables NA-MD on thousand-atom systems and many nanoseconds.

Enhancing Reduced Density Matrix Functional Theory Calculations by Coupling Orbital and Occupation Optimizations.

Reduced density matrix functional theory (RDMFT) calculations are usually implemented in a decoupled manner, where the orbital and occupation optimizations are repeated alternately. Typically, orbital updates are performed using the unitary optimization method, while occupations are optimized through the explicit-by-implicit (EBI) method. The EBI method addresses explicit constraints by incorporating implicit functions, effectively transforming constrained optimization scenarios into unconstrained minimizations. Although the unitary and EBI methods individually achieve robust performance in optimizing orbitals and occupations, respectively, the decoupled optimization methods often suffer from slow convergence and require dozens of alternations between the orbital and occupation optimizations. To address this issue, this work proposes a coupled optimization method that combines unitary and EBI optimizations to update orbitals and occupations simultaneously at each step. To achieve favorable convergence in coupled optimization using a simple first-order algorithm, an effective and efficient preconditioner and line search are further introduced. The superiority of the new method is demonstrated through numerous tests on different molecules, random initial guesses, different basis sets, and different functionals. It outperforms all decoupled optimization methods in terms of convergence speed, convergence results, and convergence stability. Even a large system like C60 can converge to 10-8 au in 154 iterations, which shows that the coupled optimization method can make RDMFT more practical and facilitate its wider application and further development.

Small wetland‐fringed estuaries deliver disproportionately large carbon loads to the ocean

AbstractPrevious estimates of dissolved carbon export from estuaries focused on larger systems in the Northern Hemisphere, with little data for smaller tropical estuaries often fringed by intertidal wetlands. We investigated lateral export (outwelling) and transformation rates of dissolved organic carbon (DOC), dissolved inorganic carbon (DIC), and total alkalinity (TA) as well as CO2 emissions from 18 diverse Australian estuaries. Most estuaries acted as net sources for DOC (72%), DIC (83%), and TA (50%). On average, estuaries exported 120 ± 55 and 344 ± 150 mmol m−2 catchment yr−1 DOC and DIC, respectively. Estuarine CO2 emissions (33 ± 20 mmol m−2 estuary d−1) equalled 13% ± 16% of the dissolved lateral carbon export. Carbon export positively correlated with runoff, rain, and intertidal wetland cover, and negatively correlated with estuary and catchment area. Mangroves and saltmarshes cover &lt; 1% of all catchments but can contribute 46% ± 11% of the DOC and 67% ± 13% of the DIC exported to the ocean. Upscaling our observations, Australian estuaries export 2.8 ± 2.2 TgC yr−1 DOC, 8.1 ± 6.2 TgC yr−1 DIC, and 0.7 ± 0.6 Tmol yr−1 TA. Small catchments (&lt; 10 ha) making up 70% of all estuaries and accounting for 18% of the total freshwater flow provided 27% to the total dissolved carbon export. Overall, small tropical estuaries fringed by highly productive intertidal wetlands are hotspots of carbon exports and should be considered in marine carbon budgets.

Association of COVID-19 Vaccination with Risk of Medically-Attended Post-Acute Sequelae of COVID-19 During the Ancestral, Alpha, Delta, and Omicron Variant Eras

Structured Abstract Background Uncertainty exists regarding the effectiveness of COVID-19 vaccine to prevent Post-Acute Sequelae of COVID-19 (PASC) following a breakthrough infection. While most studies using symptom surveys found an association between preinfection vaccination status and PASC symptoms, studies of medically attended PASC are less common and have reported conflicting findings. Methods In this retrospective cohort of patients with an initial SARS-CoV-2 infection, who were continually empaneled for primary care in a large US health system, the electronic health record was queried for pre-infection vaccination status, demographics, comorbidity index, and diagnosed conditions. Multivariable logistic regression was used to model the outcome of a medically-attended PASC diagnosis within 6 months of SARS-CoV-2 infection. Likelihood ratio tests were used to assess the interaction between vaccination status and prevalent variant at the time of infection, and between vaccination status and hospitalization for the SARS-CoV-2 infection. Results During the observation period, 6.9% of patients experienced medically-attended and diagnosed PASC. A diagnosis of PASC was associated with older age, female sex, hospitalization for the initial infection, and an increased severity-weighted comorbidity index, and was inversely associated with infection during the Omicron period. No difference in the development of diagnosed PASC was observed between unvaccinated patients, those vaccinated with 2 doses of an mRNA vaccine, and those with &amp;gt;2 doses. Conclusions We found no association between vaccination status at time of infection and subsequent development of medically diagnosed PASC. Vaccine remains an important measure to prevent SARS-CoV-2 infection and severity. Further research is needed to identify effective measures to prevent and treat PASC.

A stochastic Schrödinger equation and matrix product state approach to carrier transport in organic semiconductors with nonlocal electron-phonon interaction.

Evaluation of the charge transport property of organic semiconductors requires exact quantum dynamics simulation of large systems. We present a numerically nearly exact approach to investigate carrier transport dynamics in organic semiconductors by extending the non-Markovian stochastic Schrödinger equation with complex frequency modes to a forward-backward scheme and by solving it using the matrix product state (MPS) approach. By utilizing the forward-backward formalism for noise generation, the bath correlation function can be effectively treated as a temperature-independent imaginary part, enabling a more accurate decomposition with fewer complex frequency modes. Using this approach, we study the carrier transport and mobility in the one-dimensional Peierls model, where the nonlocal electron-phonon interaction is taken into account. The reliability of this approach was validated by comparing carrier diffusion motion with those obtained from the hierarchical equations of motion method across various parameter regimes of the phonon bath. The efficiency was demonstrated by the modest virtual bond dimensions of MPS and the low scaling of the computational time with the system size.

PhotoSolver: A bidirectional photonic solver for systems of linear equations

Solving systems of linear equations is one of the most fundamental challenges in computing, with ubiquitous applications in science and engineering. Solving large-scale linear equations is particularly challenging, requiring tremendous computing expenses. However, conventional solvers in electronic digital computers face inevitable physical bottlenecks that hinder further advancements in electronic chips. Unlike the electronic architectures, optical computing harnesses the inherent advantages of lights, such as ultimately high speed, negligible energy consumption, and high parallelism, making optical architectures attractive sources for ultra-high speed analog computing. Especially, recent advances in integrated photonic circuits bring exciting new possibilities for high performance computing based on photonic chips. In this work, we propose a bidirectional PhotoSolver using the propagation of optical signals in different directions on a single integrated photonic chip to perform multiple operations in solving systems of linear equations, scalable to solve large-scale systems. Specifically, we propose an in situ approach to iteratively update solutions by performing the forward and backward propagations within a single photonic chip. Furthermore, we propose a partitioning approach to solve large-scale systems using arrays of photonic chips. Numerical experiments demonstrate these capabilities with representative systems of linear equations. By utilizing lights propagating in integrated photonic chips, our PhotoSolvers provide a promising computing architecture to solve large systems of linear equations, with potentials in wide computational applications.

Global solutions for a hyperbolic conservation system of three equations

In this short paper, we are concerned with a mathematical model, which is the variant of non-isentropic system of polytropic gas. The main contribution is to prove the a-priori L∞ estimates for the viscosity approximation solutions and to obtain a new application, on a large hyperbolic system of three equations, of the global existence framework of weak solutions in the paper “Existence of Global Entropy Solutions to a Nonstrictly Hyperbolic System” (Lu, 2005).

Improved Fast-Response Consensus Algorithm Based on HotStuff.

Recent Byzantine Fault-Tolerant (BFT) State Machine Replication (SMR) protocols increasingly focus on scalability and security to meet the growing demand for Distributed Ledger Technology (DLT) applications across various domains. Current BFT consensus algorithms typically require a single leader node to receive and validate votes from the majority process and broadcast the results, a design challenging to scale in large systems. We propose a fast-response consensus algorithm based on improvements to HotStuff, aimed at enhancing transaction ordering speed and overall performance of distributed systems, even in the presence of faulty copies. The algorithm introduces an optimistic response assumption, employs a message aggregation tree to collect and validate votes, and uses a dynamically adjusted threshold mechanism to reduce communication delay and improve message delivery reliability. Additionally, a dynamic channel mechanism and an asynchronous leader multi-round mechanism are introduced to address multiple points of failure in the message aggregation tree structure, minimizing dependence on a single leader. This adaptation can be flexibly applied to real-world system conditions to improve performance and responsiveness. We conduct experimental evaluations to verify the algorithm's effectiveness and superiority. Compared to the traditional HotStuff algorithm, the improved algorithm demonstrates higher efficiency and faster response times in handling faulty copies and transaction ordering.

How a Robust Community Clinical Practice Arm Can Support an Academic Health System’s Tripartite Mission

Natural tension exists between the community clinical practice arm and the academic clinical practice arm within a large academic health system (AHS). Disparate salary structures, overlapping geography, competition for patients, and inequitable application of standards and resources can all lead to suboptimal system performance, as each clinical practice works to maximize individual outcomes. These tensions can lead to unnecessary duplication of resources and wasting time and energy focusing on internal politics and competition rather than on addressing external competitive threats, such as the influence of venture capital, virtual services, and retail entrants into the clinical space. At the same time, the health care landscape continues to shift, as physician ownership of practices has fallen below 50%, creating both challenges and opportunities for health systems. This article describes how, over the last 15 years, UNC Health has built a robust community practice arm called UNC Physicians Network (UNCPN) alongside the academic department structure that fully supports UNC Health’s tripartite mission. The authors describe how a community arm within an AHS can expand its clinical footprint and harmonize research and teaching efforts to support the AHS mission in a community setting. To accomplish this harmonization, UNCPN identified three high-priority focus areas for intentional investment in collaboration between the community and academic arms: (1) partnering to increase community-based research; (2) growing internal capacity for learners; and (3) scaling care delivery quality and population health initiatives across a wide geography. These intentional efforts have allowed UNC Health to improve the performance of all AHS missions: research, education, and population-based clinical care. Specifically, the number of community clinics that were actively participating in research more than doubled, from 23 practices in 2019 to 49 in 2023. In association with the UNC Schools of Medicine and Nursing, for the last few years, approximately 65–70 preceptors have been committing as many as 23,000 hours to teaching about 95 students each academic year. In addition, during that same period, the percentage of School of Medicine student learners precepted nearly doubled in UNCPN adult clinics (Family Medicine and Internal Medicine) and increased by 50% in UNCPN pediatric clinics. As the number of UNCPN clinicians increased from 44 in 2010 to 697 in 2024, the number of primary care–empaneled patients grew from 200,000 in 2019 to more than 350,000 in 2024, with currently more than 540,000 patients in total, inclusive of primary and specialty care, which has allowed scaling of high-quality care, leading to top-tier performance in value-based contracts.

Identifying the dominant operating variables to evaluate the cascading failure potential in the power system by theory of mutual information

In this study, the potential of cascading failure as a result of transmission line outages through large power systems is evaluated. Transmission lines are more dispose of the outage resulting from different fault occurrences and extend the uncontrolled failures compared to other power system devices. In this case, power system dynamic security must evaluate the possibility of cascading failures using a power system control center (PSCC) in order to limit the spread of outages caused by line failures. For this issue, using the PSCC online information consisting of power system operating variables, it is possible to present a variables-based scheme to estimate power system cascading failures concerning fault events. In order to cover the issue, based on developing a set of dominant operating variables (DOVs), this paper presents an online scheme of identifying cascading failures caused by line outages in the presence of line operational diversities and communication restrictions. In this scenario, during each time interval, by evaluating the proposed DOVs, the line information consisting of dynamic securities and sensitivities to cascading failure are investigated which the lines with the potential of cascading failures are identified online. Proper DOVs are determined for this issue by studying a variety of mathematical formulations according to theory of mutual information (MI) and measuring the entropy among the variables. In a real-time environment, by using the proposed DOVs and substituting them through online boundary equations based on the Least Square Error (LSE) method, the potentials of power system cascading failures without acting any line outage are provided. The effectiveness of the suggested technique is evaluated using a typical IEEE-39 bus and IEEE-118 bus, and proper results with high accuracy are achieved by evaluating the system potential concerning large cascading failures.

A Kaczmarz-inspired approach to accelerate the optimization of neural network wavefunctions

Neural network wavefunctions optimized using the variational Monte Carlo method have been shown to produce highly accurate results for the electronic structure of atoms and small molecules, but the high cost of optimizing such wavefunctions prevents their application to larger systems. We propose the Subsampled Projected-Increment Natural Gradient Descent (SPRING) optimizer to reduce this bottleneck. SPRING combines ideas from the recently introduced minimum-step stochastic reconfiguration optimizer (MinSR) and the classical randomized Kaczmarz method for solving linear least-squares problems. We demonstrate that SPRING outperforms both MinSR and the popular Kronecker-Factored Approximate Curvature method (KFAC) across a number of small atoms and molecules, given that the learning rates of all methods are optimally tuned. For example, on the oxygen atom, SPRING attains chemical accuracy after forty thousand training iterations, whereas both MinSR and KFAC fail to do so even after one hundred thousand iterations.

Factors Associated with Completeness of Sex and Gender Fields in Electronic Health Records.

Purpose: Our purpose was to understand the completeness of sex and gender fields in electronic health record (EHR) data and patient-level factors associated with completeness of those fields. In doing so, we aimed to inform approaches to EHR sex and gender data collection. Methods: This was a retrospective observational study using 2016-2021 deidentified EHR data from a large health care system. Our sample included adults who had an encounter at any of three hospitals within the health care system or were enrolled in the health care system's Accountable Care Organization. The sex and gender fields of interest were gender identity, sex assigned at birth (SAB), and legal sex. Patient characteristics included demographics, clinical features, and health care utilization. Results: In the final study sample (N = 3,473,123), gender identity, SAB, and legal sex (required for system registration) were missing for 75.4%, 75.8%, and 0.1% of individuals, respectively. Several demographic and clinical factors were associated with having complete gender identity and SAB. Notably, the odds of having complete gender identity and SAB were greater among individuals with an activated patient portal (odds ratio [OR] = 2.68; 95% confidence interval [CI] = 2.66-2.70) and with more outpatient visits (OR = 4.34; 95% CI = 4.29-4.38 for 5+ visits); odds of completeness were lower among those with any urgent care visits (OR = 0.80; 95% CI = 0.78-0.82). Conclusions: Missingness of sex and gender data in the EHR was high and associated with a range of patient factors. Key features associated with completeness highlight multiple opportunities for intervention with a focus on patient portal use, primary care provider reporting, and urgent care settings.

Ultrafast response of diketo-pyrrolo-pyrrole dyes with large two-photon absorption enhancement as well as AIE property

To study the effect of terminal groups substituent on the two-photon absorption (TPA) properties of [Formula: see text]-[Formula: see text]-[Formula: see text]-[Formula: see text]-[Formula: see text] structural dyes with diketo-pyrrolo-pyrrole (DPP) as central electron acceptor group, linear/transient absorption spectroscopy, one/two-photon fluorescence spectra, open-aperture [Formula: see text]-scan, and excited states quantum chemical calculations have been performed to systematically study the intermediate product electron acceptor J0 as well as three [Formula: see text]-[Formula: see text]-[Formula: see text]-[Formula: see text]-[Formula: see text] structural dyes using benzene, triphenylamine and 2,3-methoxy-triphenylamine as terminal electron donors, named as J1, J2, and J3, respectively. The intermediate product electron acceptor itself exhibits a relatively large TPA response, which indicates the DPP group has a large conjugated system. J2, with strong electron-donating ability groups triphenylamine as terminal groups, shows a large TPA cross-section of 534[Formula: see text]GM, which is approximately 10 times larger than that of J1 with weak electron pushing/pulling abilities groups benzene as terminal groups (51[Formula: see text]GM). The result indicates that the electron-donating abilities of terminal groups do play a key role in TPA response. Furthermore, methoxy substituent on the terminal triphenylamine groups can further effectively increase the TPA response, J3exhibits a 15-fold enhancement compared with that of J1. Transient absorption spectra and quantum chemical calculation results are well accorded with the experimental results. Terminal substituents with strong electron donor groups are beneficial for the formation of intramolecular charge transfer (ICT) process. Besides, methoxy substituent in the terminal of triphenylamine groups can further enhance the degree of ICT and induce AIE characteristics in the molecule.