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Twinning is an important mode of plastic deformation in metallic nanopillars. When twinning occurs on multiple systems, it is possible that twins belonging to different twin systems interact and forms a complex twin-twin junctions. Revealing the atomistic mechanisms of how twin-twin interactions lead to different twin junctions is crucial for our understanding of mechanical behaviour of materials. In this paper, we report the atomistic mechanisms responsible for the formation of two different twin-twin interactions/junctions in Cu nanopillars using atomistic simulations. One junction contains two twin boundaries along with one $Σ$9 boundary, while the other contains five twin boundaries (five-fold twin). These junctions were observed during the tensile deformation of [100] and $[1\bar1 0]$ Cu nanopillars, respectively.

Existing Digital Twin (DT) approaches often lack semantic reasoning capabilities for effective cybersecurity modelling in Cyber-Physical Systems (CPS). This paper presents HySecTwin, a knowledge-driven digital twin architecture that places automated reasoning at the core of real-time threat detection. HySecTwin incorporates semantic modelling to transform heterogeneous CPS telemetry, device attributes, and operational relationships into machine-interpretable representations, combined with an embedded reasoning engine operating over contextualized system states. Unlike opaque detection methods, the framework integrates deterministic rule-based inference with hybrid fuzzy reasoning to generate explicit, interpretable, and auditable security assessments from live device telemetry. This enables context-aware monitoring of complex CPS environments while preserving transparency and trust. Experimental evaluation using a representative CPS testbed and MITRE ATT\&CK campaign-inspired attack scenarios demonstrates sub-millisecond twin synchronization latency and up to 21.5\% faster threat detection compared with deterministic reasoning alone. The results show that semantic modelling, se

Establishing digital twins is a non-trivial endeavour especially when users face significant challenges in creating them from scratch. Ready availability of reusable models, data and tool assets, can help with creation and use of digital twins. A number of digital twin frameworks exist to facilitate creation and use of digital twins. In this paper we propose a digital twin framework to author digital twin assets, create digital twins from reusable assets and make the digital twins available as a service to other users. The proposed framework automates the management of reusable assets, storage, provision of compute infrastructure, communication and monitoring tasks. The users operate at the level of digital twins and delegate rest of the work to the digital twin as a service framework.

While deformation twinning in hexagonal close-packed metals has been widely studied due to its substantial impact on mechanical properties, an understanding of the detailed atomic processes associated with twin embryo growth is still lacking. Conducting molecular dynamics simulations on Mg, we show that the propagation of twinning disconnections emitted by basal-prismatic interfaces controls the twin boundary motion and is the rate-limiting mechanism during the initial growth of the twin embryo. The time needed for disconnection propagation is related to the distance between the twin tips, with widely spaced twin tips requiring more time for a unit twin boundary migration event to be completed. Thus, a phenomenological model, which unifies the two processes of disconnection and twin tip propagation, is proposed here to provide a quantitative analysis of twin embryo growth. The model fits the simulation data well, with two key parameters (twin tip velocity and twinning disconnection velocity) being extracted. In addition, a linear relationship between the ratio of twinning disconnection velocity to twin tip velocity and the applied shear stress is observed. Using an example of twin

Modeling deformation twin nucleation in magnesium has proven to be a challenging task. In particular, the absence of a heterogeneous twin nucleation model which provides accurate energetic descriptions for twin-related structures belies a need to more deeply understand twin energetics. To address this problem, molecular dynamics simulations are performed to follow the energetic evolution of $\{10\overline{1}2\}$ tension twin embryos nucleating from an asymmetrically-tilted grain boundary. The line, surface and volumetric terms associated with twin nucleation are identified. A micromechanical model is proposed where the stress field around the twin nucleus is estimated using the Eshelby formalism, and the contributions of the various twin-related structures to the total energy of the twin are evaluated. The reduction in the grain boundary energy arising from the change in character of the prior grain boundary is found to be able to offset the energy costs of the other interfaces. The defect structures bounding the stacking faults that form inside the twin are also found to possibly have significant energetic contributions. These results suggest that both of these effects could be cr

Deformation twinning is an important deformation mechanism in a variety of materials, including metals and ceramics. This deformation mechanism is particularly important in low-symmetry hexagonal close-packed (hcp) metals such as Magnesium (Mg), Zirconium (Zr) and Titanium (Ti). Extension twins in Mg, Zr and Ti can accommodate considerable plastic deformation as they grow. Thus, the rate and the mode of twinning greatly influences the mechanical behavior including strength and ductility. Herein, we study deformation twinning in terms of nucleation, twinning mode and variant selection as a function of strain rate in Mg single crystal (considered as a model material). We show that twin variant selection is sensitive to the loading rate, with more twin variants nucleating at the dynamic strain rates. Low Schmid factor twin variants (one of them being a double extension twin variant) were also found at the dynamic strain rates. Further at high strain rates, the first twins generated do not thicken beyond a critical width. Instead, plasticity proceeds with nucleation of second generation twins from the primary twin boundaries. The rates of area/volume fraction evolution of both generati

Purpose: Digital twins are virtual interactive models of the real world, exhibiting identical behavior and properties. In surgical applications, computational analysis from digital twins can be used, for example, to enhance situational awareness. Methods: We present a digital twin framework for skull-base surgeries, named Twin-S, which can be integrated within various image-guided interventions seamlessly. Twin-S combines high-precision optical tracking and real-time simulation. We rely on rigorous calibration routines to ensure that the digital twin representation precisely mimics all real-world processes. Twin-S models and tracks the critical components of skull-base surgery, including the surgical tool, patient anatomy, and surgical camera. Significantly, Twin-S updates and reflects real-world drilling of the anatomical model in frame rate. Results: We extensively evaluate the accuracy of Twin-S, which achieves an average 1.39 mm error during the drilling process. We further illustrate how segmentation masks derived from the continuously updated digital twin can augment the surgical microscope view in a mixed reality setting, where bone requiring ablation is highlighted to provi

As a bridge from virtuality to reality, Digital Twin has increased in popularity since proposed. Ideas have been proposed theoretical and practical for digital twins. From theoretical perspective, digital twin is fusion of data mapping between modalities; from practical point of view, digital twin is scenario implementation based on the Internet of Things and models. From these two perspectives, we explore the researches from idea to realization of digital twins and discuss thoroughly.

Prediction of wireless channels and their statistics is a fundamental procedure for ensuring performance guarantees in wireless systems. Statistical radio maps powered by Gaussian processes (GPs) offer flexible, non-parametric frameworks, but their performance depends critically on the choice of mean and covariance functions. These are typically learned from dense measurements without exploiting environmental geometry. Digital twins (DTs) of wireless environments leverage computational power to incorporate geometric information; however, they require costly calibration to accurately capture material and propagation characteristics. This work introduces a hybrid channel prediction framework that leverages uncalibrated DTs derived from open-source maps to extract geometry-induced prior information for GP prediction. These structural priors are fused with a small number of channel measurements, enabling data-efficient prediction of channel statistics across the entire environment. By exploiting the uncertainty quantification inherent to GPs, the framework supports principled measurement selection by identifying informative probing locations under resource constraints. Through this int

We investigate a cogenesis mechanism within the twin Higgs setup which can naturally explain the nature of dark matter, the cosmic coincidence puzzle, little hierarchy problem, leptogenesis and the tiny neutrino masses. Three heavy Majorana neutrinos are introduced to the standard model sector and the twin sector respectively, which explain the tiny neutrino masses and generate the lepton asymmetry and the twin lepton asymmetry at the same time. The twin cogenesis mechanism applies to any viable twin Higgs model without an explicit $\mathbb{Z}_2$ breaking in the leptonic sector and evading the $ΔN_{\rm eff}$ constraint. We illustrate the twin cogenesis mechanism using the neutrino-philic twin two Higgs doublet model, a newly proposed model to lift the twin neutrino masses with spontaneous $\mathbb{Z}_2$ breaking. The dark photon with a Stueckelberg mass $\mathcal{O}(10)$ MeV ensures the energy in the twin sector as well as the symmetric component of twin sector particles can be depleted. The lightest twin baryons are the dark matter candidates with masses approximately 5.5 GeV, which explain naturally the amount of dark matter and visible matter in the Universe are of the same orde

Conjoint analysis is a cornerstone of market research for estimating consumer preferences; however, traditional methods face persistent challenges regarding time, cost, and respondent fatigue. To address these limitations, this study proposes a framework that utilizes large language model (LLM)-based "customer digital twins (CDT)" as virtual respondents. We identified active users within the Reddit community and aggregated their comprehensive review histories to construct individualized vector databases. By integrating retrieval-augmented generation (RAG) with prompt engineering, this study developed customer agents capable of dynamically retrieving and reasoning upon their specific past preferences and constraints. These customer agents, called CDTs, performed pairwise comparison tasks on product profiles generated via fractional factorial design, and the resulting choice data was analyzed to estimate part-worth utilities by logistic regression. Empirical validation demonstrates that these CDTs predict the preferences of actual users with 87.73% accuracy. Furthermore, a case study on the computer monitor category successfully quantified trade-offs between attributes such as panel

Accelerated materials discovery is critical for addressing global challenges. However, developing new laboratory workflows relies heavily on real-world experimental trials, and this can hinder scalability because of the need for numerous physical make-and-test iterations. Here we present MATTERIX, a multiscale, graphics processing unit-accelerated robotic simulation framework designed to create high-fidelity digital twins of chemistry laboratories, thus accelerating workflow development. This multiscale digital twin simulates robotic physical manipulation, powder and liquid dynamics, device functionalities, heat transfer and basic chemical reaction kinetics. This is enabled by integrating realistic physics simulation and photorealistic rendering with a modular graphics processing unit-accelerated semantics engine, which models logical states and continuous behaviors to simulate chemistry workflows across different levels of abstraction. MATTERIX streamlines the creation of digital twin environments through open-source asset libraries and interfaces, while enabling flexible workflow design via hierarchical plan definition and a modular skill library that incorporates learning-based

We study extensions of the Twin Higgs model that solve the Hierarchy problem and simultaneously address problems of the large- and small-scale structures of the Universe. Besides naturally providing dark matter (DM) candidates as the lightest charged twin fermions, the twin sector contains a light photon and neutrinos, which can modify structure formation relative to the prediction from the $Λ$CDM paradigm. We focus on two scenarios. First, we study a Fraternal Twin Higgs model in which the spin-3/2 baryon $\hatΩ\sim(\hat{b}\hat{b}\hat{b})$ and the lepton twin tau $\hatτ$ contribute to the dominant and subcomponent dark matter densities. A non-decoupled scattering between the twin tau and twin neutrino arising from a gauged twin lepton number symmetry provides a drag force that damps the density inhomogeneity of a dark matter subcomponent. Next, we consider the possibility of having the twin hydrogen atom $\hat{H}$ as the dominant DM component. After recombination, a small fraction of the twin protons and leptons remains ionized during structure formation, and their scattering to twin neutrinos through a gauged U$(1)_{B-L}$ force provides the mechanism that damps the density inhomo

Radio Environment Map (REM) is transitioning from 5G homogeneous environments to B5G/6G heterogeneous landscapes. However, standard Federated Learning (FL), a natural fit for this distributed task, struggles with performance degradation in accuracy and communication efficiency under the non-independent and identically distributed (Non-IID) data conditions inherent to these new environments. This paper proposes EPFL-REMNet, an efficient personalized federated framework for constructing a high-fidelity digital twin of the 6G heterogeneous radio environment. The proposed EPFL-REMNet employs a"shared backbone + lightweight personalized head" model, where only the compressed shared backbone is transmitted between the server and clients, while each client's personalized head is maintained locally. We tested EPFL-REMNet by constructing three distinct Non-IID scenarios (light, medium, and heavy) based on radio environment complexity, with data geographically partitioned across 90 clients. Experimental results demonstrate that EPFL-REMNet simultaneously achieves higher digital twin fidelity (accuracy) and lower uplink overhead across all Non-IID settings compared to standard FedAvg and rece

General-purpose large language models (LLMs), despite their broad capabilities accrued from open-world data, frequently exhibit suboptimal performance when confronted with the nuanced and specialized demands inherent in real-time telecommunications applications. This investigation addresses this critical limitation through the meticulous fine-tuning of TSLAM-Mini developed by NetoAI, a compact (3.8-billion parameter) causal language model architecturally derived from Phi-4 Mini Instruct 4B. The fine-tuning regimen leverages a bespoke dataset comprising 100,000 samples, strategically engineered to address 20 pivotal telecommunications use-cases, encompassing domains such as Network Fundamentals, IP Routing, MPLS, Network Security, Automation, OSS/BSS, RAN, Mobile Core, Satellite Communications, and Ethical AI. This dataset was curated utilizing NetoAI's DigiTwin platform, enriched with granular insights from venerated network Subject Matter Experts (SMEs) and authoritative RFC documents, thereby capturing high-fidelity representations of real-world network dynamics through simulations inspired by digital twin paradigms. Employing Quantized Low-Rank Adaptation (QLoRA), a state-of-the

This paper explores a novel research direction where a digital twin is leveraged to assist the beamforming design for an integrated sensing and communication (ISAC) system. In this setup, a base station designs joint communication and sensing beamforming to serve the communication user and detect the sensing target concurrently. Utilizing the electromagnetic (EM) 3D model of the environment and ray tracing, the digital twin can provide various information, e.g., propagation path parameters and wireless channels, to aid communication and sensing systems. More specifically, our digital twin-based beamforming design first leverages the environment EM 3D model and ray tracing to (i) predict the directions of the line-of-sight (LoS) and non-line-of-sight (NLoS) sensing channel paths and (ii) identify the dominant one among these sensing channel paths. Then, to optimize the joint sensing and communication beam, we maximize the sensing signal-to-noise ratio (SNR) on the dominant sensing channel component while satisfying a minimum communication signal-to-interference-plus-noise ratio (SINR) requirement. Simulation results show that the proposed digital twin-assisted beamforming design ach

As an emerging technology in the era of Industry 4.0, digital twin is gaining unprecedented attention because of its promise to further optimize process design, quality control, health monitoring, decision and policy making, and more, by comprehensively modeling the physical world as a group of interconnected digital models. In a two-part series of papers, we examine the fundamental role of different modeling techniques, twinning enabling technologies, and uncertainty quantification and optimization methods commonly used in digital twins. This first paper presents a thorough literature review of digital twin trends across many disciplines currently pursuing this area of research. Then, digital twin modeling and twinning enabling technologies are further analyzed by classifying them into two main categories: physical-to-virtual, and virtual-to-physical, based on the direction in which data flows. Finally, this paper provides perspectives on the trajectory of digital twin technology over the next decade, and introduces a few emerging areas of research which will likely be of great use in future digital twin research. In part two of this review, the role of uncertainty quantification

Digital twins are now a staple of wireless networks design and evolution. Creating an accurate digital copy of a real system offers numerous opportunities to study and analyze its performance and issues. It also allows designing and testing new solutions in a risk-free environment, and applying them back to the real system after validation. A candidate technology that will heavily rely on digital twins for design and deployment is 6G, which promises robust and ubiquitous networks for eXtended Reality (XR) and immersive communications solutions. In this paper, we present BostonTwin, a dataset that merges a high-fidelity 3D model of the city of Boston, MA, with the existing geospatial data on cellular base stations deployments, in a ray-tracing-ready format. Thus, BostonTwin enables not only the instantaneous rendering and programmatic access to the building models, but it also allows for an accurate representation of the electromagnetic propagation environment in the real-world city of Boston. The level of detail and accuracy of this characterization is crucial to designing 6G networks that can support the strict requirements of sensitive and high-bandwidth applications, such as XR

In Twin Higgs models which contain the minimal particle content required to address the little hierarchy problem (i.e. fraternal models), the twin tau has been identified as a promising candidate for dark matter. In this class of scenarios, however, the elastic scattering cross section of the twin tau with nuclei exceeds the bounds from XENON1T and other recent direct detection experiments. In this paper, we propose a modification to the Fraternal Twin Higgs scenario that we call $\mathbb{Z}_2$FTH, incorporating visible and twin hypercharged scalars (with $Y = 2$) which break twin electromagnetism. This leads to new mass terms for the twin tau that are unrelated to its Yukawa coupling, as well as additional annihilation channels via the massive twin photon. We show that these features make it possible for the right-handed twin tau to freeze out with an acceptable thermal relic abundance while scattering with nuclei at a rate that is well below existing constraints. Nonetheless, large portions of the currently viable parameter space in this model are within the reach of planned direct detection experiments. The prospects for indirect detection using gamma rays and cosmic-ray antipro