When we perceive language cues, they are processed with a high degree of automaticity and can thus guide the processing of subsequent perceptions. We investigated here how associated and categorically congruent prime words influence responses in a semantic picture categorization task. A hierarchical diffusion model is applied to disentangle the underlying cognitive processes. In the experiment, participants were asked to categorize target pictures as living or non-living. These target pictures were preceded by prime words, for which associations and semantic category match with targets were manipulated. Results indicate robust priming effects of category congruency for both response times (RT) and error rates (ER), whereas associations only show an effect on response times (RT). Diffusion model analysis revealed different cognitive processes for both types of prime-target relations: Specifically, associative priming maps to non-decision times, suggesting a head start in visuo-semantic picture processing, whereas categorical priming was found to affect drift rate, suggesting facilitation of the decision-making process. These results suggest that priming effects in picture classification differ from cognitive processes involved in word-word priming. The implications for theoretical models of priming are discussed.

The proliferation of massive antenna arrays and the consequent intensification of near-field effects with 6G necessitate addressing critical security challenges in near-field communication environments. This paper presents a novel artificial noise-aided spatial and directional modulation (SDMN-AN) framework, specifically tailored for secure near-field communications. The proposed system integrates legitimate receiver indices, modulation symbols, and artificial noise (AN) confined to the null space of legitimate channels, thereby enhancing both spectral efficiency and communication security. Two precoding strategies—maximum-ratio transmission (MRT) and zero-forcing (ZF)—are investigated, offering trade-offs between hardware complexity and detection overhead. Analytical derivations of bit error rate (BER) bounds, corroborated by simulation results, underscore the superiority of the SDMN-AN framework in mitigating eavesdropping threats while significantly improving spectral efficiency, positioning it as a compelling solution for next-generation secure wireless networks.

Statistical Power for Moderation in Three-Level Multisite Individual Randomized Trials and Consequences of Ignoring a Level of Nesting

Early rehabilitation in neurocritical care is often underutilized due to fragmented workflows, interdisciplinary coordination challenges, and the absence of structured digital decision support. Traditional clinical decision support systems (CDSS) often address single domains and lack adaptability to the dynamic, multiprofessional workflows of intensive care units (ICUs). To develop and evaluate the usability of the ERATbi App (Early Recovery After Traumatic Brain Injury App), a modular, tablet-based CDSS was designed to streamline early rehabilitation planning and strengthen interdisciplinary coordination for patients with moderate-to-severe traumatic brain injury (TBI) in intensive care settings. The ERATbi app integrates four functional modules-delirium risk management, precision nutrition, stepwise early mobilization, and respiratory care for rib fractures-into a unified interface. A simulation-based usability study was conducted with 18 ICU clinicians. Evaluation metrics included System Usability Scale (SUS) scores, task completion rates, error rates, and task durations. Additional user feedback was collected via a 5-point Likert satisfaction survey and semi-structured qualitative interviews. The app demonstrated high usability (mean SUS score 83.6, SD 7.4), a 100% (18/18 participants) task completion rate, and a low error rate (4.2%). Average module completion time was 6.5 minutes, and user satisfaction was high (mean 4.7, SD 0.5). Users highlighted the value of the app's visual logic, real-time alerts, adaptive thresholds, and modular workflow integration for enhancing team coordination and decision consistency. The ERATbi app demonstrated excellent usability, high user satisfaction, and clinical relevance in simulated ICU workflows. Its logic-driven, workflow- integrated design may support scalable, interdisciplinary implementation of early rehabilitation in neurocritical care settings.

Purpose: Banking institutions deal with extensive volumes of documentation, such as onboarding forms of customers, compliance records, transaction information, and loan applications, on a daily basis. Manual processing and hard-core automation systems delay, lead to high error rates, and high human work. Artificial intelligence entries into part of the banking processes are admitted, but the majority of the current systems do not possess the contextual understanding which restricts their scope of application in very complex and highly regulated settings. The paper suggests a superior AI-based Robotic Process Automation (RPA) system that will address these shortcomings by facilitating smart, flexible, and regulation conscious document processing. Materials and Methods: The proposed platform incorporates smart document intake, context-sensitive document cognition and a dual rule-AI decision model. Risk sensitive workflow orchestration is a dynamic route mechanism that documents are directed into routing strategies according to complexity, confidence, and regulatory risk, where routine documents may be fully automated, and the high-risk or ambiguous documents must be sent to humans. Human in the loop validation mechanism will make sure that expert attention is given where it is most effective. Moreover, the system uses the dual-loop learning architecture which is constantly enhanced by human feedback and system-wide analytics to provide flexibility to changing types of documents and regulatory policies. Findings: Experimental analysis shows significant improvements in processing time, reduction of errors and unwarranted human intervention, and regulatory transparency of explainable and auditable automation. Unique Contribution to Theory, Practice and Policy: These findings show that context-aware risk-sensitive AI-enhanced RPA is a viable and scalable solution to current bank document processes.

This paper investigates a RIS‑assisted 6G framework operating at 300GHz (THz sub‑band) to enhance end‑to‑end Quality of Service (QoS) for streaming data under ultra-reliable low-latency communication (URLLC) constraints, focusing on reliability, latency, and throughput across line-of-sight (LoS) and non-line-of-sight (NLoS) scenarios. By programmatically shaping the propagation environment, RIS elevates NLoS Signal-to-Noise Ratio (SNR) from approximately - 20 dB to above 45 dB, reduces Bit Error Rate (BER) from error‑prone levels to below 5.5 × 10⁻¹⁰, and shortens packet delay from roughly 16.5ms to near 2ms, while increasing throughput from ~ 1 Gbps to ~ 20 Gbps under matched assumptions, thereby meeting stringent URLLC targets for reliability and latency. The modeling consolidates noise power, path loss, RIS array gain, capacity, BER, and an SNR‑dependent latency relation with clearly stated assumptions and citations and introduces coding‑aware BER and an improved 300GHz channel model including atmospheric absorption for realism. Robustness is demonstrated via multi‑run statistics on SNR, BER, delay, and throughput, and a benchmarking subsection contrasts RIS (this work) with CF‑mMIMO and relay baselines under matched or normalized scenarios to support comparative verification claims.

Digital systems are becoming increasingly feature-rich and interaction-intensive, while users vary widely in their goals and expertise, cognitive styles, accessibility requirements, and device contexts. This diversity makes one-size-fits-all interfaces inefficient and, at times, frustrating, often increasing error rates, cognitive load, and user dissatisfaction. Existing personalization approaches, such as themes, fixed preferences, and rule-based customizations, offer limited flexibility and fail to adapt to evolving user behavior and contextual changes. Although AI-driven adaptive interfaces have shown improvements, most approaches remain system-centric and insufficiently address human-centered considerations. This often results in disruptive interface changes, a perceived loss of control, and diminished user trust. This paper proposes a Human-Centered Deep Reinforcement Learning (HC-DRL) framework for generating personalized user interfaces, in which UI adaptation is modeled as a constrained sequential decision-making process. The framework combines continuous user modeling with a structured representation of the user interface based on a design system. A DRL agent predicts viable adaptation policies using a UX-sensitive reward function that explicitly maximizes task success and efficiency while accounting for user satisfaction, cognitive load, trust, perceived control, and disruption penalties. Safety guardrails are incorporated to enforce accessibility and usability constraints and to enable rollback to stable interface states when risks or performance degradation are detected. An end-to-end implementation and evaluation pipeline, including comparisons with static and heuristic baselines, ablation studies to quantify component contributions, and user studies, was employed to validate the proposed approach. The results demonstrate that HC-DRL provides a practical and robust foundation for adaptive interfaces that enhance functionality without compromising stability, accessibility, or user confidence.

Against the backdrop of high-proportion renewable energy grid integration, modeling accuracy for substations incorporating wind and solar power is critical. Traditional modeling methods rely on theoretical parameters and lack sufficient accuracy. This study uses the 154 kV/23 kV Yeonggwang Substation in Jeollanam-do, South Korea (connected to three wind farms and three solar power plants, with 35 Micro-Phasor Measurement Unit (μPMU) measurement points deployed) as a case study. It investigates three-phase detailed modeling using Power System Computer Aided Design (PSCAD) and μPMU data-driven calibration. Based on substation topology and equipment parameters, a simulation model encompassing main transformers, transmission lines, renewable energy units, and loads was established. A hierarchical calibration system of “data preprocessing—parameter identification—iterative correction” was constructed, employing an iterative optimization strategy of “main grid layer—renewable energy layer—load layer.” A multi-objective optimization function centered on voltage, current, and power was developed. Verification results show that after calibration, the mean relative error rates (MRE) for voltage, current, active power and reactive power are 2.46%, 2.57%, 2.52% and 3.96% respectively, with mean error reduction rates (MERRs) of 80%, 82.75%, 81.33%, and 74.94% compared to pre-calibration values. The uniqueness of the calibration method proposed in this study lies in its use of actual μPMU measurement data to drive PSCAD model parameter calibration, achieving precise matching with the actual characteristics of the substation. This provides a reference method for modeling and digital twin construction of similar substations, demonstrating significant engineering application value.

Automatic speech recognition (ASR) systems for real-life scenarios are required to process audio streams of arbitrary length with stable accuracy under limited computational resources. While the joint connectionist temporal classification (CTC) and attention-based encoder-decoder (AED) model delivers high recognition quality, its vanilla form is unable to meet these requirements. This paper proposes an input-synchronous blockwise decoding algorithm for the joint CTC-AED model. The algorithm processes overlapping blocks of audio synchronously with the input frames, utilizing CTC alignment to determine the proper context from the overlapping part for the AED component. The fixed block length ensures predictable and limited resource consumption and avoids long-form speech generalization issues, while the overlap mitigates WER degradation caused by edge effects. Unlike existing methods, the proposed approach requires neither model architecture modifications nor a special training procedure, while also supporting block overlapping. The word error rate (WER) performance of the algorithm is studied with respect to block size and overlap size.

ABSTRACT Traditional orthogonal frequency division multiplexing (OFDM) systems face significant performance degradation under dynamic channel conditions due to fixed channel estimation and static transmission parameters. Existing autoencoder‐based OFDM models improve end‐to‐end learning but lack adaptive mechanisms to handle varying SNR and multipath fading effectively. In this manuscript, dynamic autoencoder‐based framework for performance enhancement in OFDM systems using CNN and attention mechanisms (DAF‐OFDM‐CNN) is proposed. This paper proposes a dynamic autoencoder‐based framework to enhance the performance and reliability of OFDM systems under fluctuating signal‐to‐noise ratio (SNR) conditions. The framework integrates convolutional neural networks (CNN) and an attention mechanism within autoencoder architecture to improve feature extraction, channel estimation, and adaptive transmission. The system dynamically prioritizes important signal features, enabling effective data recovery and robust communication in multipath fading environments. Simulation results demonstrate that the proposed model significantly improves error rates, throughput, and latency compared to conventional OFDM systems, confirming its potential for next‐generation wireless communication networks.

To study the effect of a behavioral intervention, it should be compared to a control or an existing treatment in an intervention study. There exist many guidelines in the literature about the design and analysis of intervention studies, including recommendations for a priori sample size determination. The vast majority of these guidelines are based on the framework of null hypothesis significance testing, where a p-value is compared to a user-selected type I error rate to determine whether an effect is significant or not. This approach has received severe criticism over the past decades as it has resulted in publication bias, sloppy science, and fraud. The Bayesian approach to hypothesis testing has been developed to overcome some of these drawbacks. The Bayes factor quantifies the relative support in the data for one hypothesis over another hypothesis. The hypotheses do not necessarily have to include a null hypothesis and can be formulated based on observations, findings in the literature, or an expert's opinion. Posterior Model Probabilities, which are a function of the Bayes Factor, can be used to compare a set of hypotheses to one another and select the one most supported by the data. In this paper, we summarize the shortcomings of null hypothesis significance testing, introduce the Bayes factor and Posterior Model Probabilities, explain how they are calculated, and how they are interpreted. We also focus on a priori sample size determination in the Bayesian hypothesis testing framework. We introduce a criterion for sample size determination and a procedure to find the required sample size. We illustrate our methodology using a cluster randomized trial on the effectiveness of an online training in improving primary care doctors' competency in brief tobacco interventions. All analyses are done in R, and we provide the dataset and R syntax for straightforward replication.

In a weighted logrank test, such as the Harrington-Fleming test and the Tarone-Ware test, predetermined weights are used to emphasize early, middle, or late differences in survival distributions to maximize the test's power. The optimal weight function under an alternative, which depends on the true hazard functions of the groups being compared, has been derived. However, that optimal weight function cannot be directly used to construct an optimal test since the resulting test does not properly control the type I error rate. We further show that the power of a weighted logrank test with proper type I error control has an upper bound that cannot be achieved. Based on the theory, we propose a weighted logrank test that self-adaptively determines an "optimal" weight function. The new test is more powerful than existing standard and weighted logrank tests while maintaining proper type I error rates by tuning a parameter. We demonstrate through extensive simulation studies that the proposed test is both powerful and highly robust in a wide range of scenarios. The method is illustrated with data from several clinical trials in lung cancer.

This study sought to determine the intrasession repeatability of the diffusion-weighted (DW) arterial spin labeling (ASL) sequence at different postlabel delays (PLDs). We first performed numerical simulations to study the accuracy of the two-compartment water exchange rate (Kw) fitting model with added Gaussian noise for DW PLDs at 1500, 1800, and 2100 ms. Ten young, healthy participants then underwent a structural T1 scan and two intrasession in vivo DW ASL scans at each PLD on a 3T MRI. The Kw, arterial transit time (ATT), and cerebral blood flow maps were linearly registered to the structural images, which were then segmented using FreeSurfer into masks with 35 bilateral gray-matter regions. Simulation results showed that the Kw fitting model performed at an error rate less than 10% at physiological ATTs and Kw values, but that error and bias increased at a PLD of 2100 ms and at ATT ranges where the overall blood signal fraction (A1) is low. In vivo analysis showed a significant positive correlation between intrasession measurements of regional Kw at a DW PLD of 1800 ms (β = 0.33, p < 0.001) only. Furthermore, a significant positive relationship between Kw and cerebral blood flow was seen at a DW PLD of 1500 ms (β = 0.26, p = 0.005) and DW PLD of 2100 ms (β = 0.39, p = 0.006). Overall, DW ASL provides the strongest intrasession repeatability at a PLD of 1800 ms in young, healthy subjects, and a simulation study shows accurate Kw fits at physiologic range of ATTs and Kw values.

Towards Automated FIGO Staging in Radiology: The Role of LLMs in Cervical and Endometrial Cancer.

Comparative analysis of colistin and polymyxin B minimal inhibitory concentrations in Enterobacterales, Acinetobacter baumannii, and Pseudomonas aeruginosa.

Recommendations for the forensic analysis and interpretation of glass from contemporary portable electronic devices by refractive index measurement and micro-X-ray fluorescence spectrometry.

Deceiving question-answering models: A hybrid word-level adversarial approach.

The impact of dorsolateral prefrontal cortex stimulation on attention networks and saccadic performance in adults with amblyopia.

Evaluation of the VITEK 2 second-generation cassette for colistin susceptibility testing in carbapenem-resistant gram-negative bacteria.

SE-PDS enhanced NIR spectral transfer learning: A machine learning approach for cross-instrument jet fuel property quantification.