Tensile Behaviour of Ultra-High-Performance Steel Fiber Reinforced Concrete

Reinforcing bars are provided in reinforced concrete structures on account of conventional concretes negligible resistance to tension. However, corrosion of steel reinforcement inevitably occurs due to carbonation and chloride ingress, which significantly reduces the service life of structures. An alternative to this predicament is now feasible with the advent in cementitious material technologies, such as ultra-high-performance, self-consolidating, steel fiber reinforced concrete (UHP-SFRC). The keystone of safe and economically feasible designs with UHP-SFRC is dependant on its characterization in tension. Thus, in the present work, a detailed research study including both experimental and analytical components was conducted to investigate the tensile behaviour of UHP-SFRC: tensile strength was quantified and correlated through direct tension test (DTT), four-point bending test (FPBT), splitting tensile test, nonlinear finite element analysis and a calibrated empirical expression in relation to cylinder compressive strength. In addition, effects of important parameters on flexural strength including casting methodology, volumetric ratio of steel fibers, aspect ratio of bending prism and prism size were assessed. Moreover, the bilinear stress-strain and stress-crack mouth opening relationships of UHP-SFRC were derived according to the inverse analysis procedures proposed by Annex 8.1 of CSA-S6 (2018) and Annex U of CSA-A23.1 (2019). Furthermore, a nonlinear finite element analysis software, VecTor2, was employed to develop numerical models with the ability to match the response curves obtained from FPBT. Analytical results indicated that cracking strength of UHP-SFRC derived from the inverse analysis method was generally greater than those obtained from direct tension test, splitting tensile test, nonlinear finite element models and the calibrated empirical expression. Additionally, inverse analysis and finite element analysis results indicated that the majority of prisms exhibited tension hardening behaviour with a hardening ratio greater than 1.1 and an ultimate tensile strain greater than 0.1%. In addition to tension tests, a host of non-destructive tests were conducted to assess the physical properties and durability performance of UHP-SFRC

Monolithic crystalline cladding microstructures for efficient light guiding and beam manipulation in passive and active regimes

Miniature laser sources with on-demand beam features are desirable devices for a broad range of photonic applications. Lasing based on direct-pump of miniaturized waveguiding active structures offers a low-cost but intriguing solution for compact light-emitting devices. In this work, we demonstrate a novel family of three dimensional (3D) photonic microstructures monolithically integrated in a Nd:YAG laser crystal wafer. They are produced by the femtosecond laser writing, capable of simultaneous light waveguiding and beam manipulation. In these guiding systems, tailoring of laser modes by both passive/active beam splitting and ring-shaped transformation are achieved by an appropriate design of refractive index patterns. Integration of graphene thin-layer as saturable absorber in the 3D laser structures allows for efficient passive Q-switching of tailored laser radiations which may enable miniature waveguiding lasers for broader applications. Our results pave a way to construct complex integrated passive and active laser circuits in dielectric crystals by using femtosecond laser written monolithic photonic chipsThis work was supported by National Natural Science Foundation of China (No. 11274203), Specialized Research Fund for the Doctoral Program of Higher Education of China (No. 20130131130001), and Junta de Castillay León under project SA086A12-

Study on Semantic Contrast Evaluation Based on Vector and Raster Data Patch Generalization

We used buffer superposition, Delaunay triangulation skeleton line, and other methods to achieve the aggregation and amalgamation of the vector data, adopted the method of combining mathematical morphology and cellular automata to achieve the patch generalization of the raster data, and selected the two evaluation elements (namely, semantic consistency and semantic completeness) from the semantic perspective to conduct the contrast evaluation study on the generalization results from the two levels, respectively, namely, land type and map. The study results show that: (1) before and after the generalization, it is easier for the vector data to guarantee the area balance of the patch; the raster data’s aggregation of the small patch is more obvious. (2) Analyzing from the scale of the land type, most of the land use types of the two kinds of generalization result’s semantic consistency is above 0.6; the semantic completeness of all types of land use in raster data is relatively low. (3) Analyzing from the scale of map, the semantic consistency of the generalization results for the two kinds of data is close to 1, while, in the aspect of semantic completeness, the land type deletion situation of the raster data generalization result is more serious

Hybrid waveguiding structure in LiTaO3 crystal fabricated by direct femtosecond laser writing

A hybrid waveguiding structure has been fabricated in a z-cut lithium tantalate (LiTaO3) crystal wafer by direct femtosecond laser writing. Due to the laser-induced anisotropic modifications of the extraordinary and ordinary refractive indices (ne and no) of LiTaO3 crystal, the structure exhibits polarization-sensitive guiding features along vertical and horizontal orientations. Based on this feature, circularly-polarized light beam can be converted to vertically-/horizontally-polarized ones (i.e., TE and TM), with approximately 1:1 power splitting ratio. The well-guided performance of the polarization-sensitive structure shows the potential for integration with existing light signals to realize all-optical information processing.The work is supported by the National Natural Science Foundation of China (No. 11274203), Junta de Castilla y León under project SA086A12-2, and Ministerio de Economía y Competitividad (under project FIS2013-44174-P), Spain

Efficient lasing in continuous wave and graphene Q-switched regimes from Nd:YAG ridge waveguides produced by combination of swift heavy ion irradiation and femtosecond laser ablation

We report on the continuous wave and passively Q-switched lasers in Nd:YAG ridge waveguides fabricated by a combination of swift Kr ion irradiation and femtosecond laser ablation. Owing to the deep penetration length (~50 μm) of 670 MeV Kr8+ ions into the crystal, ridge waveguides with large-area cross section, supporting nearly symmetric guiding modes, were produced. Continuous wave lasers with maximum 182 mW output power at ~1064 nm have been realized at 808-nm optical pump. Using graphene as a saturable absorber, passively Q-switched waveguide laser operations were achieved. The pulsed laser produces 90 ns pulses, with a ~4.2 MHz repetition rate, 19% slope efficiency and 110 mW average output power, corresponding to single-pulse energy of 26.5 nJ.The work was supported by the National Natural Science Foundation of China (No. U1332121) and the 973 Project (No. 2010CB832906) of China, and Junta de Castilla y León under project SA086A12-2

GridFormer: Towards Accurate Table Structure Recognition via Grid Prediction

All tables can be represented as grids. Based on this observation, we propose GridFormer, a novel approach for interpreting unconstrained table structures by predicting the vertex and edge of a grid. First, we propose a flexible table representation in the form of an MXN grid. In this representation, the vertexes and edges of the grid store the localization and adjacency information of the table. Then, we introduce a DETR-style table structure recognizer to efficiently predict this multi-objective information of the grid in a single shot. Specifically, given a set of learned row and column queries, the recognizer directly outputs the vertexes and edges information of the corresponding rows and columns. Extensive experiments on five challenging benchmarks which include wired, wireless, multi-merge-cell, oriented, and distorted tables demonstrate the competitive performance of our model over other methods.Comment: ACMMM202

Optical Ranging Using Coherent Kerr Soliton Dual-microcombs with Extended Ambiguity Distance

Optical ranging is a key technology in metrology. Optical frequency combs are shown to provide several advantages in light ranging, offering high precision with high acquisition rate. However, performance of traditional ranging systems based on microcombs is limited by the short ambiguity distance and non-real-time processing. Here, we show that dual-comb ranging system using coherent Kerr soliton microcombs and optical switch realizes extended ambiguity distance and provides a route to real-time processing. The ambguity distance is extended to 3.28 m from about 1.5 mm and the uncertainty reaches about 1.05 times 10^-7, while the system is compatible with low-bandwidth detectors. Combining coherent microcomb ranging systems with special FPGA could enable comb-based real-time ranging systems for several applications such as industrial process monitoring.Comment: 9 pages, 5 figure

On-chip topological transport of optical frequency combs in silicon-based valley photonic crystals

The generation and control of optical frequency combs in integrated photonic systems enables complex, high-controllable, and large-scale devices. In parallel, harnessing topological physics in multipartite systems has allowed them with compelling features such as robustness against fabrication imperfections. Here we experimentally demonstrate on-chip topological transport for optical frequency combs at telecommunication wavelengths, both in classical and nonclassical domains. We access both the quantum frequency combs and dissipative Kerr soliton combs with a micro-resonator. The quantum frequency comb, that is, a coherent superposition of multiple frequency modes, is proven to be a frequency-entangled qudit state. We also show that dissipative Kerr soliton combs are highly coherent and mode-locked due to the collective coherence or self-organization of solitons. Moreover, the valley kink states allow both quantum frequency combs and dissipative Kerr soliton combs with robustness against sharp bends. Our topologically protected optical frequency combs could enable the inherent robustness in integrated complex photonic systems.Comment: 20 pages,12 figure

FinMem: A Performance-Enhanced LLM Trading Agent with Layered Memory and Character Design

Recent advancements in Large Language Models (LLMs) have exhibited notable efficacy in question-answering (QA) tasks across diverse domains. Their prowess in integrating extensive web knowledge has fueled interest in developing LLM-based autonomous agents. While LLMs are efficient in decoding human instructions and deriving solutions by holistically processing historical inputs, transitioning to purpose-driven agents requires a supplementary rational architecture to process multi-source information, establish reasoning chains, and prioritize critical tasks. Addressing this, we introduce \textsc{FinMem}, a novel LLM-based agent framework devised for financial decision-making. It encompasses three core modules: Profiling, to customize the agent's characteristics; Memory, with layered message processing, to aid the agent in assimilating hierarchical financial data; and Decision-making, to convert insights gained from memories into investment decisions. Notably, \textsc{FinMem}'s memory module aligns closely with the cognitive structure of human traders, offering robust interpretability and real-time tuning. Its adjustable cognitive span allows for the retention of critical information beyond human perceptual limits, thereby enhancing trading outcomes. This framework enables the agent to self-evolve its professional knowledge, react agilely to new investment cues, and continuously refine trading decisions in the volatile financial environment. We first compare \textsc{FinMem} with various algorithmic agents on a scalable real-world financial dataset, underscoring its leading trading performance in stocks. We then fine-tuned the agent's perceptual span and character setting to achieve a significantly enhanced trading performance. Collectively, \textsc{FinMem} presents a cutting-edge LLM agent framework for automated trading, boosting cumulative investment returns