Ultra-high-performance concrete (UHPC) is characterized by exceptional compressive strength; however, its structural performance is primarily governed by tensile behavior, fracture resistance, and energy dissipation. This study presents a comprehensive mechanical characterization of a steel fiber-reinforced UHPC incorporating a slag–limestone powder-based binder system with a low water-to-binder ratio of 0.15 and steam curing at 90 °C for 48 h. The experimental program comprised compressive strength, flexural behavior, split and direct tensile response, impact energy absorption, ultrasonic pulse velocity, and an assessment of specimen size and geometry effects. The UHPC achieved mean compressive strengths of approximately 209 and 218 MPa at 7 and 28 days, respectively, in 75 × 150 mm cylindrical specimens, indicating only modest strength development after the initial steam-curing period. Smaller cube specimens exhibited higher nominal compressive strengths, reaching approximately 221 and 227 MPa at 7 and 28 days, respectively, demonstrating a measurable but limited specimen-size effect. Flexural testing produced an average strength of 33.1 MPa and a stable post-peak response, although no strain hardening in bending was observed. Split tensile strength reached approximately 16.1 MPa, exceeding that of conventional normal-strength concrete by more than four times. Direct tensile tests demonstrated an intrinsically ductile response, with tensile strengths above 10.9 MPa and strain capacities of 0.25–0.30%, including a pronounced strain-hardening regime. Under drop-weight impact loading, specimens absorbed more than 40 J of energy without catastrophic fragmentation. Ultrasonic pulse velocity averaged 5344 m/s, indicating a dense and well-integrated microstructure. Overall, the results confirm that the investigated UHPC functions as a fracture-resistant structural composite in which tensile capacity, fiber-controlled crack bridging, and energy dissipation govern performance across multiple loading modes.

Quantum sensing utilizing nitrogen-vacancy (NV) centers in diamond has emerged as a transformative technology for probing magnetic phase transition1-4, evidencing Meissner effect of superconductors1,5-9, and visualizing stress distribution3,9 under extreme conditions. Recent development in NV configurations and hydrostatic environments have raised the operational pressures of NV centers to 140 GPa2,6,10,11, but substantial challenges remain in extending sensing capabilities into multi-megabar range, critical for research in hydrogen-rich superconductors like La-Sc-H (T_c of 271-298 K at 195-266 GPa)12 and evolution of minerals near Earth's core13. Here we report the fabrication of shallow NV centers through ion implantation followed by high-pressure and high-temperature (HPHT) annealing, leading to increased density, improved coherence, and mitigated internal stresses, a pre-requisite for reducing their degradation under compression. This NV magnetometry enable breakthrough of pressure capabilities exceeding 240 GPa, constrained by structural integrity of the 50 um diamond anvils, suggesting that the untapped pressure limit may enable further advancements with smaller cutlets or more robust diamonds. We present compelling evidence of the Meissner effect and trapped flux at record-high pressure of 180 GPa for superconducting transition in elemental titanium (Ti) as benchmark, establishing a solid foundation for high-pressure magnetometry in exploring complex quantum phenomena at previously unreachable pressures.

As the first discovered p-type transparent conductive material, copper(I) iodide (CuI) is considered the most competitive p-type candidate in the field of transparent electronics. Herein, we introduced a low-temperature buffer-layer-assisted strategy to grow γ-CuI with significantly improved structural quality and electrical transport properties by pulsed laser deposition. By adjusting the growth temperature, we can manipulate the rotation domain structure, control the hole concentration Nh from 1014 to 1019 cm−3, and achieve mobility μh = 25 cm2 V−1 s−1 being similar to that of bulk CuI. Based on the temperature-dependent Hall-effect measurement, the ionization energy of a shallow acceptor of EI,S = 137 ± 8 meV and that of a deeper acceptor of EI,D = 262 ± 23 meV were determined. This grown strategy not only enables high-quality CuI film preparation, but also to tailor their electrical properties for integration with n-type semiconductors in transparent electronic circuits.

In this work, polycrystalline cuprous oxide (Cu2O) films deposited by magnetron sputtering were annealed in situ in N2 atmospheres. Room-temperature and temperature-dependent Hall-effect measurements were performed on as-deposited and N2-annealed Cu2O films to probe the electrical transport mechanisms. The results show that the hole mobility of 0.8 Pa N2-annealed Cu2O film is enhanced compared with its as-deposited counterpart. It is shown that at low temperatures, the hole mobilities of Cu2O films appear to be limited by grain boundary scattering, while acoustic-phonon scattering comes into play at elevated temperatures. Moreover, the grain boundary potential barrier height which inhibits the hole transport is found to be reduced by N2-annealing at 0.8 Pa, thereby leading to the observed improvement in the hole mobility. It is thought that N2 is mainly concentrated at the grain boundaries in polycrystalline Cu2O films, thus passivating the grain boundary defect trap states. The results reported in this work suggest that N2-annealing induced grain boundary passivation could be an effective method to improve the photoelectric properties of polycrystalline Cu2O films.

UAVs need to use a large number of sensors to complete autonomous navigation tasks in indoor environments. The evaluation of multi-sensors ranging capabilities in different application scenarios is an important research direction at present. This paper analyzes three main factors (light intensity, distance, target material) affecting the ranging capability of three sensors (depth camera D435i, binocular camera T265, and lidar Rplidar A1). To quantify the ranging capability of sensors in different application scenarios, the concept of dynamic ranging capability index (DRCI) is proposed in this study, which mainly depends on ranging accuracy. DRCI is a fuzzy evaluation index. The higher DRCI value, the stronger ranging capability. Through the analysis of a large number of experimental data, we establish the DRCI calculation method. This paper uses three sensors for a ranging task while calculating DRCI by the method in Airsim simulation scenarios and real scenarios. Finally, the real-time changes in the ranging capability of multi-sensors can be effectively evaluated by the method, and the results in simulation scenarios are validated in real scenarios.

Unmanned aerial vehicles (UAVs) have advantages of rapidity and efficiency in inspection assignments. However, UAVs suffer from high costs and the difficulty of repairing faults. Thus, it is important to reduce the quantity of the UAVs used in tasks. To address the above challenges, this paper first establishes a correlation model to maximize the mission coverage rate as well as to minimize the quantity of UAVs. Then, a three-step method is designed by combining a genetic algorithm with the greedy algorithm to optimize the UAVs quantity and their paths. Finally, the UAVs without failure will be assigned to perform the unfinished tasks of the abnormal UAVs. Simulation results show that the method proposed in this paper can not only improve the mission coverage, but also minimize the quantity of the UAVs effectively.

With the continuous expansion of the application field of the Internet of Things (IoT), mobile edge computing (MEC) is regarded as a promising technique to reduce the time-delay and energy-consumption of application. However, the conventional MEC infrastructure is lack of flexibility, and failed to meet the different requires of mobile device. UAVs have the distinct features of high scalability and mobility for communications, which can act as the complement of conventional MEC infrastructure. This paper investigates the issues of multi-objective cooperative computation offloading for MEC in UAVs hybrid Networks. The proposed UAVs hybrid MEC system enables edge-cloud and UAVs cooperation to address the flexible limitations of conventional MEC infrastructure and the efficient computation offloading of computation task. To support good-quality services in a cost-effective manner, we model the computation offloading problem as a multi-objective optimization process, and propose an intelligent computation offloading algorithms based on integrated optimization framework, including mixed integer transformation solving framework, improved multi-adaptive MOEA/D-DE(MOEA/D-MSDE) and Grey Relational Projection (GRP). Evaluation results show that the proposed algorithms outperform in solving multi-objective cooperative computation offloading problem in terms of service time-delay, energy-consumption and server-cost.

This article studies the problems of finite-time and fixed-time bipartite containment control for cooperative-antagonistic networks with multiple leaders over arbitrary weakly connected signed digraphs, in which leaders can be stationary or evolving dynamically through interacting with other leaders in their neighborhood. To this end, a unified distributed nonlinear control scheme is proposed using the nearest neighbor rule. It is shown that under the proposed control scheme, all followers can be guaranteed to converge towards the convex hull formed by each leader's trajectory and its symmetrical one within a finite time or fixed time so long as the underlying weakly connected signed digraph has at least one structurally balanced closed strong component. The efficacy of the proposed control scheme is illustrated via simulations.

Multi-robot systems have great advantages and wide applications in the fields of ground reconnaissance, environmental monitoring, and key area patrols. However, in the field of multi-robot collaboration, it is difficult to take into account the patrol efficiency and path privacy simultaneously, especially when any intelligent intruders happen. To conquer the above difficulty, path randomness is proposed as a criterion to establish a multi-robot cooperative path planning model based on idle time dispersion, so as the path information privacy and security in the patrol process can be protected. In order to get the solution of the model, the index of idle time dispersion is introduced to get a tailored ant colony algorithm. Finally, patrol paths of multi mobile robots with high security are obtained. Several simulations in the simple road network and complex road network are conducted to verify the feasibility and effectiveness of the proposed method.

In aerospace applications, multiple safety regulations were introduced to address associated with pyrolysis. Predictive modeling of pyrolysis is a challenging task since multiple thermo-chemo-mechanical laws need to be concurrently solved at each time step. So far, classical modeling approaches were mostly focused on defining the basic chemical processes (pyrolysis and ignite) at micro-scale by decoupling them from thermal solution at the micro-scale and then validating them using meso-scale experimental results. The advent of Machine Learning (ML) and AI in recent years has provided an opportunity to construct quick surrogate ML models to replace high fidelity multi-physics models, which have a high computational cost and may not be applicable for high nonlinear equations. This serves as the motivation for the introduction of innovative Physics informed neural networks (PINNs) to simulate multiple stiff, and semi-stiff ODEs that govern Pyrolysis and Ablation. Our Engine is particularly developed to calculate the char formation and degree of burning in the course of pyrolysis of crosslinked polymeric systems. A multi-task learning approach is hired to assure the best fitting to the training data. The proposed Hybrid-PINN (HPINN) solver was bench-marked against finite element high fidelity solutions on different examples. We developed PINN architectures using collocation training to forecast temperature distributions and the degree of burning in the course of pyrolysis in multiple one- and two-dimensional examples. By decoupling thermal and mechanical equations, we can predict the loss of performance in the system by predicting the char formation pattern and localized degree of burning at each continuum.

This paper introduces a novel physics-informed multi-agents constitutive model to propose prediction in quasi-static constitutive behavior of cross-linked elastomer and the loss of mechanical performance during environmental aging. The presented model is used to simulate the effect of single-mechanism chemical aging (i.e. thermal-inducedor hydrolytic aging) on the behavior of the material in this hybrid framework. Those environmental single-mechanism damages change the polymer matrix over time due to massive chain scission, chain formations, and changing the arrangement of molecules in the polymer matrix. We propose a data-driven super-constrained machine-learned engine to represent damage in the polymer matrix and capture the changes in material behavior, including its inelastic features such as Mullins effect and permanent set in the course of aging. We have simplified the 3D stress–strain tensor mapping problem into a small number of super-constrained 1D mapping problems by means of a sequential order reduction. An assembly of multiple replicated conditional neural-network learning-agents (L-agents) is trained to systematically simplify the high-dimensional mapping problem into multiple 1D problems, each represented by a different type of agent. Our hybrid framework is designed to capture the effect of deformation history, aging time, and aging temperature. The model is validated with respect to a comprehensive set of experiments specifically designed to benchmark model capabilities and also against available data in the literature. Thermodynamic consistency and frame independency have been verified. Besides acceptable predictive abilities, a significant reduction of computational cost to predict behavior at multiple states of deformation is the most significant feature of this model.

Ferroelectric domain is an elusive feature on bulk photovoltaic effect and can be tuned by the growth conditions. Here, in (111)‐oriented BiFe1−xCoxO3 epitaxial films, the periodic ferroelectric domains structure is manipulated by Co‐doped concentration. The intrinsic 109° domain structure is turned into 71° domain structure due to reduction of crystal cell volume. The experimental results show that ferroelectric domain and ferroelectric domain wall (DW) play completely different roles in bulk photovoltaic effect. In ferroelectric domain structure, the domain provides a depolarization field for the separation of photogenerated carriers and DW is carriers transport channel. The 71° ferroelectric domain creates a larger depolarization field than the 109° domain, which leads to open‐circuit voltage exceeding 1.7 V. Besides, the 71° domain wall has a higher photoconductivity than the 109° domain wall, which makes short‐circuit current density reaching 0.42 mA cm−2. The optimization of domain structure will effectively enhance bulk photovoltaic effect and also provides a new method to regulate the domain structure for bulk photovoltaic devices.

In order to handle the path planning problem in complex environments, a novel method combining probabilistic roadmap, ant colony optimization, and third order Bezier curve has been developed in this study. There are three steps in the suggested procedure. Using a probabilistic roadmap approach, a random map is first created based on the complexity of the surrounding area. This could be accomplished by selecting $\boldsymbol{N}$ nodes randomly in complex static environments, then establishing connections between these nodes according to specific criteria or conditions. The created roadmap offers a significant number of potential path segments that could link the start and final point. The second stage entails choosing a path within the already-built roadmap. The final path between the start point and the goal point will be searched by using ant colony optimization. Then, the third stage employs a Bezier curve to smooth out and shorten the obtained path. Several simulations in different environments demonstrate that the proposed approach guarantee to obtain a short, smooth, and safe path between the start point and the goal point.

As devices with plasticity similar to biological synapses, photovoltaic memristors based on the bulk photovoltaic effect exhibit impressive abilities for autonomous learning and memory.

Power consumption makes next-generation large-scale photodetection challenging. In this work, the source-gated transistor (SGT) is adopted first as a photodetector, demonstrating the expected low power consumption and high photodetection performance. The SGT is constructed by the functional sulfur-rich shelled GeS nanowire (NW) and low-function metal, displaying a low saturated voltage of 0.61 V ± 0.29 V and an extremely low power consumption of 7.06 pW. When the as-constructed NW SGT is used as a photodetector, the maximum value of the power consumption is as low as 11.96 nW, which is far below that of the reported phototransistors working in the saturated region. Furthermore, benefiting from the adopted SGT device, the photodetector shows a high photovoltage of 6.6 × 10-1 V, a responsivity of 7.86 × 1012 V W-1, and a detectivity of 5.87 × 1013 Jones. Obviously, the low power consumption and excellent responsivity and detectivity enabled by NW SGT promise a new approach to next-generation, high-performance photodetection technology.

High-quality epitaxial LaNiO3 films have been grown on (0 0 1)-oriented SrTiO3 substrate with Sr3Al2O6 buffer layer by pulsed laser deposition. Different from traditional LaNiO3 metallic behavior, LaNiO3/Sr3Al2O6 films exhibit semi-conductor behavior. In-plane lattice parameters of the LaNiO3 films are changed by in-plane strain from Sr3Al2O6. The valence state of Ni in the film is related to in-plane strain. Ni3+ is the dominant factor for LaNiO3 films to maintain metallic behavior. Meanwhile, with the thickness of LaNiO3 increasing, the resistivity of LaNiO3/Sr3Al2O6 films can be adjusted. Conductive mechanism of LaNiO3 and all LaNiO3/Sr3Al2O6 films can be attributed to different Ni3+/Ni2+ ratios.

Elastomers are now commonly used in a number of industries, including aerospace, structure, transportation, shipbuilding, and automotive, due to their excellent workability, formability, and flexibility. During their activity, elastomers are subjected to harsh environmental conditions, which decreases their resilience. False predictions made early in their lives can have major financial and environmental implications. Elastomers’ performance and properties, such as strength, durability, and density, are influenced by chemical changes in these materials, known as degradation, which occurs over time. This process can alter the morphology of a polymer matrix as well as cause chain scission and cross-linking, resulting in different behaviors than that of the unaged material. To demonstrate the effect of thermaloxidative aging on the mechanical behavior of elastomers, several experimental and theoretical models have been proposed. In view of the large volume of experimental data available on micro-structural evolution in the course of aging, we propose a physics-based data-driven approach to overcome the shortcomings of both phenomenological and micro-mechanical models. This work presents a novel thermodynamically consistent, multiagent machine-learned model for predicting the constitutive behavior of cross-linked elastomers during environmental aging, such as thermo-oxidative and hydrolytic aging for various states of deformation. Single mechanism degradation changes the polymer matrix over time where it is causing chain scission, reduction of cross-links, and morphology change. To capture the idealized Mullins effect and permanent set due to the effect of single aging mechanisms on nonlinear mechanical responses of elastomers, we propose a data-driven model for simulating inelastic elements in a polymer matrix. By using a sequential order reduction, we were able to reduce the 3D stress-strain tensor mapping problem to a small number of super-constrained 1D mapping problems. To systematically classify such mapping problems into a few categories, an assembly of multiple replicated conditional neural network learning agents (L-agents) is used based on our recent work. Each category is represented by a different type of agent. The effect of deformation history, aging time, and aging temperature is captured by this model. The model is validated using a broad collection of data, ranging from our experimental results to data from the literature. In addition, thermodynamic consistency and frame independence are investigated. The most significant achievements of this model are its precision, simplicity, and prediction of inelasticity under various states of deformation. The model’s accuracy and simplicity make it a good option for commercial and industrial applications. Conveniently, due to the model modular nature, it can be expanded in the future to include viscoelasticity and non-isotropic formation for better precision.

A robust control approach based on Gaussian process learning is proposed to solve the problem of trajectory tracking for a quadrotor UAV with parameter uncertainty and wind disturbance. First, Inverse dynamics control (IDC) is used for converting the nonlinear dynamics of the attitude subsystem to a group of double integrators. Then, the upper bound of the model error caused by parameter uncertainty and wind disturbance is estimated by the Gaussian process learning method, and the estimated upper bound is used to compensate the PD controller of the IDC to ensure system stability. The robust control method is also used for position control to achieve high-accuracy tracking. Finally, the simulation results show that the proposed method has better control performance than the MPC-ADRC method.

High-quality epitaxial LaMnO 3 films have been grown on (001)-oriented LaAlO 3 substrates at different substrate temperatures by pulsed laser deposition. The layer-by-layer growth is indicated by oscillations of reflection high-energy electron diffraction. Raman spectra together with in-plane resistivity measurements reveal that the degree of Jahn–Teller (JT) distortion can be well controlled by the substrate temperature during film deposition. The JT distortion-related/induced electron localization is studied by fitting temperature-dependent resistivity with a three-dimensional variable range hopping model. It is found that the larger JT distortion leads to a stronger localization of electrons. This study might pave the way for further study of JT interaction and highly correlated electronic states in perovskites.

Person re-identification (ReID) is an important task in video surveillance application. To address the issue that various low-resolutions and scale mismatching always exist in the real world, a multi-domain image-to-image translation network, termed Multi-Domain image Super-Resolution Generative Adversarial Network (MSRGAN), is proposed to learn the mapping relationship between the various low-resolution domains and the high-resolution domain. MSRGAN can ensure that the transferred image has a similar resolution as in the target domain. It is also able to keep the identity information of images from low-resolution domain during the translation. In addition, a novel ReID model, termed CSA-ReID in which channel attention and spatial attention module are introduced, is designed to learn resolution-invariant deep representations. The proposed method achieves 90.7% rank-1 accuracy and 96.4% rank-5 accuracy on multiple low-resolutions Market-1501 dataset. The experimental results prove that the proposed method achieves promising generalization ability and accuracy compared with the state-of-the-art methods.