Iontronic neuromorphic computing has emerged as a rapidly expanding paradigm. The arrival of angstrom-confined iontronic devices enables ultra-low power consumption with dynamics and memory timescales that intrinsically align well with signals of natural origin, a challenging combination for conventional (solid-state) neuromorphic materials. However, comparisons to earlier conventional substrates and evaluations of concrete application domains remain a challenge for iontronics. Here we propose a pathway toward iontronic circuits that can address established time series benchmark tasks, enabling performance comparisons and highlighting possible application domains for efficient real-time time series processing. We model a Kirchhoff-governed circuit with iontronic memristors as edges, while the dynamic internal voltages serve as output vector for a linear readout function, during which energy consumption is also logged. All these aspects are integrated into the open-source pyontronics package. Without requiring input encoding or virtual timing mechanisms, our simulations demonstrate prediction performance comparable to various earlier solid-state reservoirs, notably with an exceptionally low energy consumption of over 5 orders of magnitude lower. These results suggest a pathway of iontronic technologies for ultra-low-power real-time neuromorphic computation.

The mcstas_gisans framework is a collection of Python scripts and modules to facilitate the simulation of grazing-incidence small-angle neutron scattering (GISANS) experiments. This approach combines McStas instrument simulation with BornAgain sample modeling capabilities. The Monte Carlo Particle Lists format for particle trajectory allows exchange between simulations that enables seamless transition from instrument modeling to sample scattering analysis. The Python-based processing utilities handle data transformation, scaling to virtual experiment times for absolute intensities, and visualization. The required software environment is managed through Conda, ensuring reproducible deployments across platforms. This integrated approach facilitates accurate simulation and analysis and enables the comparison of the GISANS capability of different neutron scattering instruments.

PurposeTo evaluate the agreement between a virtual clinic model based on ultra-widefield imaging (UWFI) and spectral-domain optical coherence tomography (SD-OCT) and conventional face-to-face (F2F) slit-lamp fundus examination with SD-OCT for treatment decision-making in patients receiving intravitreal injections.MethodsIn this retrospective masked paired comparative study, consecutive patients receiving intravitreal injections underwent F2F evaluation by a retina specialist using slit-lamp biomicroscopy and SD-OCT. F2F examination was predefined as the reference standard. During the same clinical encounter, UWFI was obtained. A second retina specialist, masked to the clinical findings and decisions, independently reviewed the SD-OCT and UWFI images in a virtual setting and made management decisions.ResultsA total of 426 eyes from 304 patients were included. Of these, 217 eyes (50.94%) had neovascular age-related macular degeneration (NVAMD), 151 (35.45%) diabetic macular edema (DME), and 56 (13.15%) retinal vein occlusion (RVO; 36 branch RVO, 14 central RVO, and 6 hemi-retinal RVO). One eye (0.23%) had myopic choroidal neovascularization (CNV), and one (0.23%) had Sorsby macular dystrophy with CNV.The virtual assessment demonstrated 98.12% agreement with the F2F examination for treatment decisions (Cohen's κ = 0.90; 95% CI, 0.83-0.97). Recognition of NVAMD-associated macular hemorrhages was comparable between modalities. UWFI identified additional cases of neovascularization of the disc (NVD) and neovascularization elsewhere (NVE), all of which were subsequently confirmed on slit-lamp examination. The mean virtual review time was more than twofold shorter than the F2F evaluation (P < 0.001).ConclusionsVirtual assessment using ultra-widefield imaging demonstrated high agreement with F2F slit-lamp examination for treatment decision-making in patients receiving intravitreal injections. This approach may represent an efficient alternative for selected follow-up visits in which anterior segment evaluation is not required.

This study aimed to investigate whether structured simulation-based training in a virtual environment improves procedural efficiency among medical students, and whether skills acquired in one virtual interventional radiology scenario can be transferred to a different scenario through cross-training. Gender-related differences in performance and confidence were examined exploratively. Twenty medical students were randomized into 2 groups (n = 10 each) and trained on an endovascular simulator for interventional radiology. After 5 sessions on their primary case, participants switched cases for a sixth cross-training session. Performance was assessed through virtual fluoroscopy time. Pre- and post-training questions evaluated skill development, confidence, and career perceptions. Virtual fluoroscopy times decreased significantly over 5 sessions, with a mean reduction of 72% (31.35-8.88 minutes, P = .0023), indicating improved procedural efficiency. Cross-training demonstrated effective skill transfer, with virtual fluoroscopy times 50% shorter than initial training of the alternate group (P = .029, n = 10; P = .035, n = 8). Female participants (n = 11) reported lower confidence levels in self-assessed manual dexterity (P = .019) but showed comparable or superior performance improvements to males (n = 9) by the end of training. Simulation effectively enhances procedural expertise and skill adaptability in a virtual environment. Exploratory analyses suggest that simulation training may help address confidence gaps and objective performance was comparable between genders. Cross-training enhances skill transferability across different interventional radiology procedures. Despite gender-specific differences in perceptions of manual dexterity, performance between genders remains comparable.

Choosing difficulty: Self-determined versus assigned tasks in motor sequence learning.

Deterministic, microsecond-level timing reproduction in full-system virtualization remains a key challenge for hardware-in-the-loop simulation of timing-sensitive communication buses. This paper presents a virtual time-driven approach that models protocol timing semantics as discrete events on a deterministic virtual timeline, and validates it using MIL-STD-1553B, a representative aerospace bus with strict microsecond-level requirements, as a case study. The MIL-STD-1553B data bus is widely used in aerospace and high-reliability embedded systems, where communication correctness depends not only on message formats but also critically on microsecond-level timing semantics such as message intervals, frame periods, response timeouts, and automatic retries. However, existing Quick Emulator (QEMU)-based virtualization solutions typically rely on host scheduling for timing, making it difficult to maintain determinism under varying loads, which may lead to missed detections or false alarms in timeout/retry behaviors. This paper implements a configurable BU-64843 device model supporting bus controller (BC), remote terminal (RT), and monitor terminal (MT) multi-role switching under a unified framework and completes behavioral modeling of both legacy and enhanced bus controllers covering message scheduling, execution, and exception handling paths. We propose a virtual time-driven precise timing modeling method that explicitly models key timing semantics as discrete events on a virtual timeline. Extensive experiments across 10 timing scenarios demonstrate that our method reduces timing deviation from an average of 8 µs to 65–124 ns (99.1% improvement), achieving deterministic simulation decoupled from host execution speed while meeting the 1 µs minimum resolution requirement. While demonstrated on 1553B, the virtual time-driven method is applicable to other timing-sensitive bus protocols in QEMU-based simulation environments, offering a low-cost, reproducible, and high-precision simulation environment for protocol compliance verification, driver development, and system integration.

Virtual mass simulation is one of the recent topics in the field of haptic devices (HDs), which can alter the apparent mass of the HD. Simulating negative values of virtual mass leads to a decrease in the apparent effective mass, improving transparency but weakening stability. Positive virtual mass rendering increases the apparent mass, reduces transparency, and enhances stability. This paper analyzes the stability of a haptic device while simulating a virtual environment consisting of a mass, spring, and damper in the presence of a constant time delay. The results are closed-form equations that can predict the stability boundary for small and even large values of virtual damping and time delay. These closed-form equations demonstrate that the maximum renderable virtual mass is twice the physical mass of the HD, and the minimum value equals its negative; both occur in the case of zero time delay. Increasing the time delay reduces both the minimum and maximum values of the renderable virtual mass. The study also shows that using virtual mass can improve the maximum value of a renderable virtual spring. The equations show that, in the absence of delay, properly tuning the virtual mass and virtual damping can enlarge the maximum renderable stiffness by up to 5.8 times in theory. In the experiments under time delay, the stiffness increased by a factor of 3.5, compared to the theoretical prediction of 4.1 times. The results further reveal situations where a nonzero minimum stiffness is required for stability. All findings are validated via simulations and experiments on a dedicated test bed.

Dynamic Graph Convolutional Network (DGCN) can represent temporal evolutionary features. Its compatibility with the spectral-dimensional characteristics of hyperspectral images (HSI), such as continuous gradients and local abrupt changes in spectral signatures, makes it valuable in this field. This study introduces dynamic graph modeling into HSI analysis. By mapping spectral dimensions onto virtual time series, we propose the Dynamic Adaptive Graph Convolutional Network (DAGCN). The core idea is to use dynamic graph evolution to simulate continuous variations and local abrupt changes in spectral sequences, thereby capturing subtle spectral features of the Earth’s surface. The framework includes three components: The Dynamic Graph Sequence Construction (DGS) strategy uses superpixel segmentation and spatio-temporal graph modeling to construct slice graphs for each spectral band. The Multi-Scale Adaptive Graph Convolution (MAGC) module dynamically generates multi-scale adjacency matrices through adaptive convolutions, learning pixel features within homogeneous regions while preserving multi-scale context. The Spatio-Temporal Graph Collaborative Fusion (STGC) strategy includes a Dynamic Segmentation-based Graph Update (DSG Update) module and a Multi-Spectral Channel Attention (MSCA) mechanism. DSG Update dynamically optimizes the graph structure of adjacent temporal phases using MAGC’s adjacency matrix, extending independent multi-graph learning to spatio-temporally coupled multi-graph learning for modeling spectral-spatial evolution. MSCA assigns self-attention weights to each temporal phase to enhance discriminative band selection. Experiments on public HSI datasets show that DAGCN outperforms state-of-the-art methods in classification accuracy, especially in capturing subtle spectral variations, confirming its effectiveness and superiority.

Schedulers play a vital role for GPU cluster serving model training jobs, and an ideal scheduler shall behave well in both fairness and efficiency. However, existing clusters mostly focus on only one aspect and fall short in the other. To solve that problem, given that the resource demand of a model training job can often be approximated a priori, our insight is to preferentially service jobs that complete earlier under instantaneous fair sharing, which can emulate shortest job first while avoiding starvation. Following that insight, in this article we propose Ares, an efficient and also fair scheduler for deep learning jobs. Ares leverages the conception of virtual finish time in network fair queuing methods, which supports efficient estimation of job completion order at job arrival time. For the jobs with earlier virtual finish times, we allow it to use more resources than it originally demands to attain fast completion—so that those resources can also be released sooner and no job is actually hurt. We keep the global batch size unchanged to ensure accuracy validity, and also ensure that the degradation of resource utilization caused by scaling-out is bounded. We call such scheduling method elastic fair queuing, which can provide theoretical fairness guarantee. We evaluate Ares performance with both testbed experiments and large-scale simulations. The results show that Ares can reduce the average job completion time by over 20% and also reduce the number of unfairly-served jobs by over 40%.

Considering that multi-band interference often leads to a significant increase in the bit error rate at the system receiver end in actual underwater acoustic communication environments, this paper proposes a subcarrier silence anti-interference technology scheme based on filter bank multi-carrier (FBMC) with index modulation (IM). First, it is analyzed that, under three different underwater acoustic channels and without added interference, the underwater acoustic filter bank multi-carrier with index modulation (FBMC-IM) communication system outperforms traditional FBMC systems in terms of bit error rate performance. Subsequently, targeting the frequency distribution characteristics of multi-band interference, this paper designs an adaptive subcarrier silence mechanism. Through notch detection, interference band information is fed back to the transmitter, and subcarriers within the communication band that overlap with the interference signal spectrum are silenced, while unaffected subcarriers continue to carry communication information, thereby achieving multi-band partitioning to avoid interference effects. Additionally, to further enhance system performance, the paper integrates Virtual Time Reversal Mirror (VTRM) channel equalization technology, which leverages the time-focusing characteristics of multipath signals to effectively suppress multipath interference and delay spread in the acoustic channel. Simulation and field test results demonstrate that the proposed subcarrier-silence-based FBMC-IM anti-interference scheme significantly improves system reliability under multi-narrowband interference conditions. In the simulated underwater acoustic channel, the BER is reduced by approximately 65–80% at a signal-to-noise ratio of 0 dB; in the 5 km test channel in the Bohai Sea, the BER is reduced by 70–85% compared to the traditional FBMC system; in the test channel near Dalian with strong multipath spread, the BER is improved by more than one order of magnitude at a signal-to-noise ratio of 30 dB, with a BER reduction exceeding 90% under the configuration of Q = 4, k = 1. These results fully validate the superior anti-interference capability and communication robustness of the proposed scheme in interfering underwater acoustic environments.

This work presents a time-domain approach for characterizing the Ground Reaction Forces (GRFs) exerted by a pedestrian during running. It is focused on the vertical component, but the methodology is adaptable to other components or activities. The approach is developed from a statistical perspective. It relies on experimentally measured force-time series obtained from a healthy male pedestrian at eight step frequencies ranging from 130 to 200 steps/min. These data are subsequently used to build a stochastic data-driven model. The model is composed of multivariate normal distributions which represent the step patterns of each foot independently, capturing potential disparities between them. Additional univariate normal distributions represent the step scaling and the aerial phase, the latter with both feet off the ground. A dimensionality reduction procedure is also implemented to retain the essential geometric features of the steps using a sufficient set of random variables. This approach accounts for the intrinsic variability of running gait by assuming normality in the variables, validated through state-of-the-art statistical tests (Henze-Zirkler and Shapiro-Wilk) and the Box-Cox transformation. It enables the generation of virtual GRFs using pseudo-random numbers from the normal distributions. Results demonstrate strong agreement between virtual and experimental data. The virtual time signals reproduce the stochastic behavior, and their frequency content is also captured with deviations below 4.5%, most of them below 2%. This confirms that the method effectively models the inherent stochastic nature of running human gait.

Abstract Background Several scoring systems (e.g., MIRACLE2, OHCA, CAHP, TTM) have been developed to predict neurological outcomes in out-of-hospital cardiac arrest (OHCA) by incorporating post-hospitalization data. In contrast, the R-EDByUS score is a prehospital-only prognostic tool that evaluates both patient-related factors (e.g., age and initial shockable rhythm) and elements of the underlying emergency system infrastructure (e.g., time to return of spontaneous circulation [ROSC] or transport, witnessed arrest, and bystander cardiopulmonary resuscitation [CPR]). Our institution’s Rapid Response Vehicle (Dr Car) system enables earlier first medical contact—effectively advancing the virtual hospital arrival time—and provides prompt physician intervention that may modify these factors. This study examines whether the predictive accuracy of the R-EDByUS score is maintained in the Dr Car setting, where early intervention is available. Methods We retrospectively analyzed 793 adult OHCA cases transported to our institution between 2015 and 2021. Patients were categorized into two cohorts based on their status at first medical contact: those achieving ROSC and those requiring ongoing CPR. The Dr Car group was compared with the standard emergency medical service (EMS) group. The primary outcome was an unfavorable neurological status (CPC 3–5) at 30 days. To assess whether the R-EDByUS score’s predictive accuracy is preserved under early intervention conditions, we performed propensity score matching using the score’s components. Results Among 306 ROSC cases (EMS: 208; Dr Car: 98), the R-EDByUS score demonstrated a C-statistic of 0.8771. Neurological outcomes were significantly better in the Dr Car group (p &amp;lt; 0.001), despite longer transport times. After propensity score matching, no significant difference in outcomes was observed, indicating that the R-EDByUS score remains valid even with early physician intervention. Among 487 CPR cases (EMS: 228; Dr Car: 259), the score yielded a C-statistic of 0.7818. Neurological outcomes were significantly better in the Dr Car group (p = 0.017), and after propensity score matching—even with longer transport times—the outcomes remained comparable, further supporting the reliability of the R-EDByUS score under early intervention conditions. Conclusions Early first medical contact via the Dr Car system—effectively advancing the virtual hospital arrival time—contributes to improved neurological outcomes in OHCA patients. Although the scope of care provided by Dr Car is limited compared to full in-hospital treatment, this early intervention yields outcomes comparable to those achieved by standard EMS care. The maintained predictive accuracy of the R-EDByUS score underscores its value in guiding prehospital decision-making across diverse emergency response models. Further studies are warranted to refine Dr Car dispatch protocols and assess its cost-effectiveness in clinical practice.

Corrigendum to ‘The persuasive potential of virtual time travel using augmented reality: Focusing on the role of temporal presence’

With the rapid development of renewable energy and the continuous pursuit of efficient energy utilization, distributed photovoltaic power generation has been widely used in village-level microgrids. As a key platform connecting distributed photovoltaics with users, energy storage systems play an important role in alleviating the imbalance between supply and demand in VMG. However, current energy storage systems rely heavily on lithium batteries, and their frequent charging and discharging processes lead to rapid lifespan decay. To solve this problem, this study proposes a hybrid energy storage system combining supercapacitors and lithium batteries for VMG, and designs a hybrid energy storage scheduling strategy to coordinate the “source–load–storage” resources in the microgrid, effectively cope with power supply fluctuations and slow down the life degradation of lithium batteries. In order to give full play to the active support ability of supercapacitors in suppressing grid voltage and frequency fluctuations, the scheduling optimization goal is set to maximize the sum of the virtual inertia time constants of the supercapacitor. In addition, in order to efficiently solve the high-complexity model, the reason for choosing the snow goose algorithm is that compared with the traditional mathematical programming methods, which are difficult to deal with large-scale uncertain systems, particle swarm optimization, and other meta-heuristic algorithms have insufficient convergence stability in complex nonlinear problems, SGA can balance global exploration and local development capabilities by simulating the migration behavior of snow geese. By improving the convergence effect of SGA and constructing a multi-objective SGA, the effectiveness of the new algorithm, strategy and model is finally verified through three cases, and the loss is reduced by 58.09%, VMG carbon emissions are reduced by 45.56%, and the loss of lithium battery is reduced by 40.49% after active support optimization, and the virtual energy inertia obtained by VMG from supercapacitors during the scheduling cycle reaches a total of 0.1931 s.

Underwater acoustical (UWA) channels exhibit severe multipath propagation and long delay spreads, resulting in serious intersymbol interference in communications. In this article, we propose a virtual time compressed mirror (VTCM), a sparse adaptive equalizer, and its reinforcement learning (RL)-based version, RLVTCM. VTCM can compress the time-spread channel with a virtual mirror based on sparse estimation, while RLVTCM enhances this approach by exploiting RL to adjust VTCM’s parameter-setting according to the UWA environment adaptively. In addition, we introduce a main path to side path ratio (MSR) criterion to evaluate the equalization performance in multipath channels before demodulation. Simulations and experiments demonstrate that both VTCM and RLVTCM significantly improve communication performance. MSR consistently reflects symbol error rate performance.

Advancements in communication technologies have enabled individuals to transcend the physical environment, and there is sizable research exploring how users interact with the mediated environments, particularly with respect to perceived shifts in spatial and social presence. However, despite the ability of new communication technologies to facilitate a change in perceived time via virtual time travel, there is scant empirical work testing the predictors and psychological and persuasive outcomes of temporal presence. To fill this gap, this study employed a single-factor experiment using an augmented reality (AR) filter with two conditions (virtual time travel with AR [ n = 63] vs without AR [ n = 77]) in the context of anti-aging sunscreen advertising. Results showed that temporal presence can be evoked by virtual time travel using AR through perceived interactivity and vividness. Temporal presence subsequently increased positive attitude toward the ad and brand, as well as purchase intentions through different psychological factors.

Data fragmentation operates to build pieces based on the available virtual machines (Vm) of financial cloud security. The replication process tends to enhance the security of data from one network to another network with fewer replications. An advanced financial cloud security system that utilizes AI to divide and copy data improves incursion security by distributing information over several locations and employing complex algorithms to handle and preserve data distribution. The disadvantage of this strategy is an overreliance on AI, which can result in flaws, raising the danger of data breaches and undermining system security and integrity. To overcome this issue, this study proposes an improved butterfly optimized finite elliptic curve cryptography (IBO-FECC) for autonomous technique and advanced encryption method that safeguard valuable financial information across cloud settings which strengthens data security and privacy. The system architecture employs networks to conduct cloud-based tests with successful results. The system efficiency is determined using task frequency and Vm capability. The findings are evaluated according to the unit count and virtual machine configurations, response time and throughput fluctuate with data size, but Vm=15 consistently outperformed Vm=5. Memory utilization is developed as the data becomes more complicated. Data fragmentation increases the speed of processing at a higher velocity. The study concluded that the suggested IBO-FECC identifies the optimal network pathway in financial cloud safety systems regarding system resiliency and security.

The integration of large-scale wind power clusters significantly reduces the inertia level of the power system, increasing the risk of frequency instability. Accurately assessing the equivalent virtual inertia of wind farms is critical for grid stability. Addressing the dual bottlenecks in existing inertia assessment methods, where physics-based modeling requires full control transparency and data-driven approaches lack interpretability for inertia response analysis, thus failing to reconcile commercial confidentiality constraints with analytical needs, this paper proposes a symbolic regression framework for inertia evaluation in doubly fed wind farms with limited control information constraints. First, a dynamic model for the inertia response of DFIG wind farms is established, and a mathematical expression for the equivalent virtual inertia time constant under different control strategies is derived. Based on this, a nonlinear function library reflecting frequency-active power dynamic is constructed, and a symbolic regression model representing the system’s inertia response characteristics is established by correlating operational data. Then, sparse relaxation optimization is applied to identify unknown parameters, allowing for the quantification of the wind farm’s equivalent virtual inertia. Finally, the effectiveness of the proposed method is validated in an IEEE three-machine nine-bus system containing a doubly fed wind power cluster. Case studies show that the proposed method can fully utilize prior model knowledge and operational data to accurately assess the system’s inertia level with low computational complexity.

Floquet multiplier (FM) is a commonly used metric for evaluating gait orbital stability in biomechanics. However, variability of human gait and noise from various sources can induce significant bias and variance in the estimation of FM. Furthermore, FM is employed in gait analysis without standardized protocols, leading to highly case-dependent outcomes. To address these challenges, we quantify the effects of sampling conditions on the accuracy and consistency of FM estimations. We recruited 20 healthy participants and conducted five trials of 10 minutes of walking per participant. Using individualized Jacobian matrices calculated from the walking experiments, we synthesized multiple sets of virtual time series with varying lengths and trial counts. Using stochastic linear models, we simulated the error dynamics depending on the sampling methods. The bias and variance of FM estimates decreased as the time series lengthened, achieving a strong correlation with the true value after 140 strides for 14-dimensional state vector. Our results further suggest that partitioning a long time series into appropriately sized segments can yield more reliable FM estimates, reducing both bias and variance in FM estimations.

Background With the rapid development of digital twinning technology, the compatibility of digital twinning technology to other technologies is continuously enhanced. It is because of this that the application of image recognition technology in digital twinning technology becomes a reality. However, the key technology of digital twin, the virtual-actual mutual control technology, is not mature enough, and the image recognition technology applied in digital twin technology also has the problem of coordinate system transformation, which becomes an important link of image recognition technology in digital twin. Methods Based on the above two problems, we take the tangram splicing project as an example to realize a virtual-actual mutual control method, so that the digital twinning technology can be well presented. Furthermore, we implement an image recognition applied to the conversion of digital twinning technology, so that digital twinning technology and image recognition have a seamless connection, allowing the application range of digital twinning technology to be further expanded. Results In this paper, image recognition technology is successfully applied to digital twin technology by adopting the conversion between different coordinate systems and the real and virtual real time control, which makes the applicability of digital twin technology in high-tech fields such as smart factories and smart manufacturing to a higher level. Conclusions Finally, through the tangram splicing project, the motion trajectories of the robotic arm and the tangram are consistent with the virtual robotic arm and the tangram in the computer. It is proved that our method can well combine digital twinning technology and image recognition technology.