This paper establishes a comprehensive statistical framework for analyzing quantum teleportation protocols under realistic noisy conditions. We develop novel mathematical tools to characterize the complete statistical distribution of teleportation fi...
Background/Objectives: Synthetic data generated from aggregate statistics may support preliminary clinical research when patient-level data access is restricted. However, statistical similarity does not establish clinical validity, analytical utility...
Background/Objectives: Statistical shape modeling (SSM) is used to describe transtibial residual-limb morphology for prosthetic socket design, simulation, and future structural testing. However, conventional intrinsic metrics such as compactness, gen...
Probabilistic models are used to describe random processes and quantify prediction uncertainties in a principled way. Examples include geotechnical and geological investigations that seek to model subsurface hydrostratigraphic properties or mineral d...
The rapid advancement of data generation techniques has spurred innovation across multiple domains. This comprehensive review delves into the realm of data generation methodologies, with a keen focus on statistical and machine learning-based approach...
In the dispersed flow film boiling regime (DFFB), which exists under post-LOCA (loss-of-coolant accident) conditions in pressurized water reactors (PWRs), there is a complex interplay between droplet dynamics and turbulence in the surrounding steam....
The Monte Carlo simulation is a popular statistical method to estimate the effect of uncertainties on the solutions of nonlinear partial differential equations, but it requires a huge computational cost of the deterministic model, and the convergence...
Modern engineering systems may require multidisciplinary design optimization (MDO) to account for the interactions between coupled physical phenomena. When uncertainties affect model parameters or design variables, these analyses must be extended to...
Deadband algorithms are implemented inside industrial gateways to reduce the volume of data sent across different networks. By tuning the deadband sampling resolution by a preset interval
Class imbalance in tabular datasets poses a challenge for machine learning classification tasks, often leading to biased models that underperform in predicting minority class instances. This study presents a comparative analysis of synthetic data gen...
Considering loads when optimizing wind-farm layouts or designing farm-control strategies is important, but the computational cost of using high-fidelity wake models in the loop can be prohibitively high. Using simpler models that consider only the sp...
Surrogate-based algorithms are indispensable in the aerospace engineering field for reducing the computational cost of optimization and uncertainty quantification analyses, particularly those involving computationally intensive solvers. This paper pr...
Background: This study evaluated the impact of high-fidelity simulation versus simulation with standardized patients on the development of reflective practice among medical students. Methods: A randomized controlled trial design with both pre- and po...
Corporate social responsibility (CSR) and business ethics are perceived as distinct constructs by the consumer, although research from this perspective is scarce. Thus, the objective of this study is to investigate the impact of CSR and business ethi...
An adaptive method for quantum state fidelity estimation in bipartite higher dimensional systems is established. This method employs state verifier operators which are constructed by local POVM operators and adapted to the measurement statistics in t...
3 March 2022
Nursing graduates are required to have both excellent theoretical and practical skills that should be used during stressful emergency interventions. Since the received knowledge should be practiced to gain skills and trained to achieve competences, s...
Generating realistic road scenes is crucial for advanced driving systems, particularly for training deep learning methods and validation. Numerous efforts aim to create larger and more realistic synthetic datasets using graphics engines or synthetic-...
Introduction: Robotic surgery training requires effective simulation methods to ensure proficiency. Virtual reality (VR) simulators and physical models offer different approaches. Methods: A study was conducted with 30 surgical residents, divided int...
Touch plays a fundamental role in our daily interactions, allowing us to interact with and perceive objects and their spatial properties. Despite its importance in the real-world, touch is often ignored in virtual environments. However, accurately si...
We review the application of level dynamics to spectra of quantally chaotic systems. We show that the statistical mechanics approach gives us predictions about level statistics intermediate between integrable and chaotic dynamics. Then we discuss in...
Carbon black nanocomposites are complex systems that show potential for engineering applications. Understanding the influence of preparation methods on the engineering properties of these materials is critical for widespread deployment. In this study...
In X-ray tomography image reconstruction, one of the most successful approaches involves a statistical approach with
Efficient Robust Design Optimization (RDO) strategies coupling a parsimonious uncertainty quantification (UQ) method with a surrogate-based multi-objective genetic algorithm (SMOGA) are investigated for a test problem in computational fluid dynamics...
The rapid proliferation of open-access vector building footprint datasets has created substantial opportunities for large-scale urban analysis, yet their reliability remains difficult to assess effectively and consistently. This challenge arises prim...
Background: African American (AA)/Black and Latinx communities have limited access to evidence-based fall prevention programs such as Tai Chi Prime (TCP). Community-led interventions that incorporate peer support are cost-effective and sustainable. U...
Low-fidelity engineering wake models are often combined with linear superposition laws to predict wake velocities across wind farms under steady atmospheric conditions. While convenient for wind farm planning and long-term performance evaluation, suc...
The purpose of this study was to investigate student satisfaction and perceived clinical reasoning and learning using a computer-based simulation platform that incorporates case-based learning principles. The simulation was used to replace a previous...
To validate the validity of a driving simulation platform, this study constructs a dual dataset using field data and driving simulation experiments on an urban arterial road. Comprehensive validation is conducted from both subjective perception and o...
This work presents a Fourier-domain encryption scheme for multiplexed image databases that integrates virtual-optical multiplexing with chaotic diffusion. By combining chaotic encryption with spectral-domain symmetry reduction, the proposed approach...
Wave energy conversion is a renewable energy technology with a promising potential. Although it has been developed for more than 200 years, the technology is still far from mature. The survivability in extreme weather conditions is a key parameter ha...
Aim/Objective: To evaluate undergraduate nursing students’ perceptions of the educational design, debriefing quality, satisfaction, and self-confidence associated with a leadership-focused high-fidelity simulation scenario involving the managem...
To estimate the degree of quantum entanglement of random pure states, it is crucial to understand the statistical behavior of entanglement indicators such as the von Neumann entropy, quantum purity, and entanglement capacity. These entanglement metri...
We show that the minimum entropy production in near-reversible quantum state transport along a path is a simple function of the path length measured according to the Fisher–KMB metrics. Hence, for the sharp values of path lengths, also called s...
The development of high-fidelity digital twins for scintillation spectrometer detectors demands precise experimental characterization of timing parameters. This work presents a comprehensive solution comprising a digitizer-based time-correlated singl...
This study presents a texture-aware image synthesis framework designed to generate material-consistent façades using adversarial learning. The proposed architecture incorporates a mask-guided channel-wise attention mechanism that adaptively me...
Low-power NPUs enable on-device LLM inference through efficient integer and fixed-point algebra, yet their lack of native exponential support makes Transformer softmax a critical performance bottleneck. Existing NPU kernels approximate
Text-to-image generation aims to produce images that match the semantic content of a text prompt. In style transfer tasks, the model must further integrate reference styles while preserving prompt semantics. However, balancing semantic consistency an...
Closed droplet contours provide the geometric basis for area estimation in vision-based droplet observation. In OLED inkjet printing, droplets are deposited into pixel wells with predefined geometry; projected area is therefore a primary geometric qu...
Vision Transformers (ViTs) have achieved remarkable success; however, their internal decision-making mechanisms remain largely opaque. The Dynamic Accumulated Attention Map (DAAM) has emerged as a prominent interpretability tool that visualizes model...
Existing post hoc XAI (Explainable Artificial Intelligence) methods produce numerical attributions without symbolic structure (SHAP, LIME), low-coverage local rules (Anchors), or crisp tree surrogates without an interpretable rule-level uncertainty p...
This study systematically optimised extrusion-printing parameters for polyhydroxybutyrate (PHB) using a Design of Experiment (DoE) approach to improve printability and construct fidelity. A five-factor DoE was conducted to evaluate the individual and...
A probability-based model (PBM) is developed to predict the evolution of a population of impinging droplets on a solid substrate and the eventual transition between dropwise and filmwise regimes. A dedicated heat transfer model is designed, in order...
Educational Data Mining (EDM) increasingly depends on large, high-quality datasets to drive predictive and adaptive learning systems. However, data scarcity, privacy restrictions, and limited accessibility severely hinder research reproducibility and...
The exponential growth of unstructured medical data, particularly clinical notes and diagnostic reports, presents mounting challenges for healthcare knowledge extraction and utilization. This study introduces the Digital Medical Catalog (DMC), a fram...
In the present work, a reduced-order modeling (ROM) framework based on a recurrent neuro-fuzzy model (NFM) that is serial connected with a multilayer perceptron (MLP) neural network is applied for the computation of transonic aileron buzz. The traini...
Critically ill patients with rapidly deteriorating clinical status secondary to respiratory and cardio-vascular compromise are at risk for immediate collapse if the underlying pathology is not recognized and treated. Rapid diagnosis is of utmost impo...
The quality of digital 3D models of fossils is important from the perspective of their further usage, either for scientific or didactical purposes. However, fidelity evaluation has rarely been attempted for digitized fossil objects. In the present re...
The generation of synthetic medical data has become a focal point for researchers, driven by the increasing demand for privacy-preserving solutions. While existing generative methods heavily rely on real datasets for training, access to such data is...
Photonic quantum communication enables secure and high-fidelity information transfer beyond classical limits, with direct relevance to emerging quantum networks operating in free-space environments. While physical-layer models of depolarizing noise,...
The emerging technology of wearable inertial sensors has shown its advantages in collecting continuous longitudinal gait data outside laboratories. This freedom also presents challenges in collecting high-fidelity gait data. In the free-living enviro...
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