Thermal and mechanical performance of 3D printing functionally graded concrete:The role of SAC on the rheology and phase evolution of 3DPC

In order to address the dual objectives of enhancing the mechanical and thermal performance of 3D printed concrete, this paper presents a 3D printing approach to design and prepare functional graded concrete for energy-saving sandwich structures with both thermal insulation and load-bearing functions. The 3D printing functionally graded concrete (3DPFGC) with a sandwich structure, consists of an expanded polystyrene concrete inner layer and a 3D printed concrete outer layer. In addition to the three-dimensional compressive strength and double-shearing tests, the thermal conductivity of 3DPFGC was also measured by steady-state method and compared with the transient method and verified by theoretical formulas. The influence of sulphoaluminate cement on the printability of the load-bearing layer was also comprehensively investigated by calorimetry test, rheological test, XRD and TG analysis. The addition of SAC has a significant impact on the early fresh properties, including accelerating the setting time and optimizing the rheological properties, directly improving the printing performance. 3DPFGC exhibits significantly higher compressive strength compared with other lightweight insulating concrete with similar thermal conductivities. The outcome of this research provides valuable guidance for the application of 3DPFGC in building engineering, contributing to the development of energy-efficient and structural construction materials.</p

Quench Dynamics in Holographic First-Order Phase Transition

In this work, we study the real-time dynamics of heating a phase-separated state in the holographic model of first-order phase transition. Although the response to the positive quench demonstrates a distinct behavior from that to the negative one, they share three characteristic stages in common. In the first stage, each of the two separated phases is driven to a new higher energy state, with a group of energy peaks or valleys excited around the domain walls, depending on whether the quench is positive or negative. In the second stage, such excitations propagate towards the interior of the high energy phase, meet and pass each other with the amplitude decaying gradually. Finally in the third stage, the system settles down to a new phase-separated state through the long wave hydrodynamics. The effect of the quench strength on the final state of the system is also investigated, where the two critical values of the quench strength are found to distinguish three different classes of final states. For a small quench strength, the final state is still a phase-separated state but with higher energy. For a moderate quench strength, the final state can be a metastable state below the phase transition temperature. While for a large quench strength, the final state can be a thermodynamically preferred state at a higher temperature

Many Episode Learning in a Modular Embodied Agent via End-to-End Interaction

In this work we give a case study of an embodied machine-learning (ML) powered agent that improves itself via interactions with crowd-workers. The agent consists of a set of modules, some of which are learned, and others heuristic. While the agent is not "end-to-end" in the ML sense, end-to-end interaction is a vital part of the agent's learning mechanism. We describe how the design of the agent works together with the design of multiple annotation interfaces to allow crowd-workers to assign credit to module errors from end-to-end interactions, and to label data for individual modules. Over multiple automated human-agent interaction, credit assignment, data annotation, and model re-training and re-deployment, rounds we demonstrate agent improvement

Hairy black holes induced by nonlinear superadiant instability

We unveil a new insight: beyond linear superradiant instability, a nonlinear mechanism can transform linearly stable bald black holes into hairy ones in asymptotically flat spacetime without necessitating artificial mirrors. As an illustrative example, by perturbing Reissner-Nordstr\"om (RN) black holes with a complex scalar field featuring a physical Q-ball type potential, we discover that significant perturbations can destroy the stability of RN black holes, resulting in the formation of Q-hairy black holes through an innovative nonlinear superradiant instability. For the first time in a black hole system, we observe the scalar hair displaying rhythmic radial expansion and contraction, indicating a novel type of bosenova. We find critical solutions at transition thresholds between bald and hairy black holes, inducing distinctive dynamical critical behaviors stemming from an intricate linear instability inherent in the critical solution.Comment: 8 pages, 5 figure

Adapting Large Language Model with Speech for Fully Formatted End-to-End Speech Recognition

Most end-to-end (E2E) speech recognition models are composed of encoder and decoder blocks that perform acoustic and language modeling functions. Pretrained large language models (LLMs) have the potential to improve the performance of E2E ASR. However, integrating a pretrained language model into an E2E speech recognition model has shown limited benefits due to the mismatches between text-based LLMs and those used in E2E ASR. In this paper, we explore an alternative approach by adapting a pretrained LLMs to speech. Our experiments on fully-formatted E2E ASR transcription tasks across various domains demonstrate that our approach can effectively leverage the strengths of pretrained LLMs to produce more readable ASR transcriptions. Our model, which is based on the pretrained large language models with either an encoder-decoder or decoder-only structure, surpasses strong ASR models such as Whisper, in terms of recognition error rate, considering formats like punctuation and capitalization as well

Holographic Einstein Ring of a Charged AdS Black Hole

Taking into account that the real quantum materials are engineered generically at a finite chemical potential, we investigate the Einstein ring structure for the lensed response of the complex scalar field as a probe wave on the charged AdS black hole in the context of AdS/CFT. On the one hand, we find that the resulting Einstein ring radius has no variation with the chemical potential, which is similar to the behavior for the weakly interacting quantum system. On the other hand, not only can such a ring exist well within the screen, but also the temperature dependence of its radius exhibits a distinct feature in the sense that it displays an appreciable increase at low temperatures while the ring keeps unchanged right at the edge of the screen for the weakly interacting system. Note that such a Einstein ring emerges in the large frequencies and can be well captured by the photon sphere away from the black hole horizon in the geometric optics approximation, thus such a distinct feature may be regarded as a universal behavior associated with the high energy modes of the strongly coupled system which has a gravity dual.Comment: totally new version with the authors added and perspectives sharpened, 11 figures, to appear in JHE

Rheological behavior of 3D printed concrete:Influential factors and printability prediction scheme

The rheological properties of cementitious materials play a crucial role in determining the printability for extrusion-based 3D concrete printing. This study develops data-driven machine learning (ML) models to predict two key rheological parameters - plastic viscosity (PV) and yield stress (YS) of 3D printable cementitious composites based on the mixture composition and time after water addition. A systematic experimental study is conducted by varying the contents of cement, fly ash, silica fume, sulfoaluminate cement, superplasticizer, and water-to-binder ratio, and time after water addition. The measured rheological data is used to construct a database for training predictive models including linear regression, support vector regression, random forest, extreme gradient boosting, and multi-layer perceptron neural network. The extreme gradient boosting model achieves the highest prediction accuracy with low root mean square error and all coefficients of determination exceeding 0.9 for both plastic viscosity and yield stress. Importance analysis identifies the most influential parameters affecting the rheological properties. A printability classification scheme is proposed using the model predictions by defining a printable zone of PV and YS. The data-driven framework is validated to effectively predict printability of new mixtures without trial-and-error. This study demonstrates the potential of ML models to accelerate the design and optimization of 3D printable cementitious materials.</p