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Using photovoltaic (PV) energy to produce hydrogen through water electrolysis is an environmentally friendly approach that results in no contamination, making hydrogen a completely clean energy source. Alkaline water electrolysis (AWE) is an excellent method of hydrogen production due to its long service life, low cost, and high reliability. However, the fast fluctuations of photovoltaic power cannot integrate well with alkaline water electrolyzers. As a solution to the issues caused by the fluctuating power, a hydrogen production system comprising a photovoltaic array, a battery, and an alkaline electrolyzer, along with an electrical control strategy and energy management strategy is proposed. The energy management strategy takes into account the predicted PV power for the upcoming hour and determines the power flow accordingly. By analyzing the characteristics of PV panels and alkaline water electrolyzers and imposing the proposed strategy, this system offers an effective means of producing hydrogen while minimizing energy consumption and reducing damage to the electrolyzer. The proposed strategy has been validated under various scenarios through simulations. In addition, the system’s robustness was demonstrated by its ability to perform well despite inaccuracies in the predicted PV power.

Cementitious composites are heterogeneous porous materials whose pore structure plays a critical role in resistance to chloride-ion penetration. A waterborne nano-silicate-based densifier (CF-S5) was used to examine its influence on the pore structure and resistance to the chloride ion penetration of mortar. We investigated the resistance to the chloride ion penetration of mortar with added CF-S5 admixture through the Rapid Chloride Permeability Test (RCPT). We investigated the pore structure characteristics of mortar by mercury intrusion porosimetry (MIP) coupled with fractal theory and investigated the degree of hydration of the cement paste by thermogravimetric analysis (TG). Ultimately, the degree of correlation between multifractal parameters and the chloride ion migration coefficient of mortar was examined using gray relational analysis (GRA). Results indicate that the CF-S5 admixture reduces mortar porosity and the content of harmful pores while increasing pore tortuosity, thus improving the resistance to the chloride ion penetration of mortar. Multifractal analysis indicated that the CF-S5 admixture decreased the connectivity and increased the complexity of the mortar pore structure. The CF-S5 admixture did not reduce the hydration degree of cement paste at 28 d. Additionally, the multifractal parameters show a high gray relational degree with the chloride migration coefficient; therefore, they may serve as potential indicators to reflect the resistance to the chloride ion penetration of mortar.

The vacuum degree is the key parameter reflecting the quality and performance of vacuum glass. This investigation proposed a novel method, based on digital holography, to detect the vacuum degree of vacuum glass. The detection system was composed of an optical pressure sensor, a Mach–Zehnder interferometer and software. The results showed that the deformation of monocrystalline silicon film in an optical pressure sensor could respond to the attenuation of the vacuum degree of vacuum glass. Using 239 groups of experimental data, pressure differences were shown to have a good linear relationship with the optical pressure sensor’s deformations; pressure differences were linearly fitted to obtain the numerical relationship between pressure difference and deformation and to calculate the vacuum degree of the vacuum glass. Measuring the vacuum degree of vacuum glass under three different conditions proved that the digital holographic detection system could measure the vacuum degree of vacuum glass quickly and accurately. The optical pressure sensor’s deformation measuring range was less than 4.5 μm, the measuring range of the corresponding pressure difference was less than 2600 pa, and the measuring accuracy’s order of magnitude was 10 pa. This method has potential market applications.

To address the problems of low coverage rate and low detection accuracy in UAV-based aircraft skin defect detection under complex real-world conditions, this paper proposes a method combining a Greedy-based Breadth-First Search Coverage Path Planning (GB-CPP) approach with an improved YOLOv11 architecture (INN-YOLO). GB-CPP generates collision-free, near-optimal flight paths on the 3D aircraft surface using a discrete grid map. INN-YOLO enhances detection capability by reconstructing the neck with the BiFPN (Bidirectional Feature Pyramid Network) for better feature fusion, integrating the SimAM (Simple Attention Mechanism) with convolution for efficient small-target extraction, as well as employing RepVGG within the C3k2 layer to improve feature learning and speed. The model is deployed on a Jetson Nano for real-time edge inference. Results show that GB-CPP achieves 100% surface coverage with a redundancy rate not exceeding 6.74%. INN-YOLO was experimentally validated on three public datasets (10,937 images) and a self-collected dataset (1559 images), achieving mAP@0.5 scores of 42.30%, 84.10%, 56.40%, and 80.30%, representing improvements of 10.70%, 2.50%, 3.20%, and 6.70% over the baseline models, respectively. The proposed GB-CPP and INN-YOLO framework enables efficient, high-precision, and real-time UAV-based aircraft skin defect detection.

In recent years, deep learning-based detection methods have been applied to pavement crack detection. In practical applications, surface cracks are divided into inner and edge regions for pavements with rough surfaces and complex environments. This creates difficulties in the image detection task. This paper is inspired by the U-Net semantic segmentation network and holistically nested edge detection network. A side-output part is added to the U-Net decoder that performs edge extraction and deep supervision. A network model combining two tasks that can output the semantic segmentation results of the crack image and the edge detection results of different scales is proposed. The model can be used for other tasks that need both semantic segmentation and edge detection. Finally, the segmentation and edge images are fused using different methods to improve the crack detection accuracy. The experimental results show that mean intersection over union reaches 69.32 on our dataset and 61.05 on another pavement dataset group that did not participate in training. Our model is better than other detection methods based on deep learning. The proposed method can increase the MIoU value by up to 5.55 and increase the MPA value by up to 10.41 when compared to previous semantic segmentation models.

The compound lift and thrust Vertical Take-Off and Landing (VTOL) fixed-wing Unmanned Aerial Vehicle (UAV) has generated considerable interest in configuration research due to its unique application advantages. This investigation examines the aerodynamic phenomena between the rotors and the main wings, as well as canards, during the transition phase through numerical simulations, thereby advancing the understanding of canard configurations in such UAVs. Based on a systems engineering approach, a 6 kg canard-configured compound lift and thrust VTOL fixed-wing UAV was preliminarily designed for evaluation. Computational Fluid Dynamics (CFD) methods were employed to study the aerodynamic interference under various freestream velocities and rotor speeds during the transition phase. The reliability of the CFD methodology was validated through rotor thrust experiments. Simulations were conducted with freestream velocities ranging from 3 m/s to 15 m/s and rotor speeds from 4000 to 10,000 RPM. The results indicate that the interference of the rotating rotor during the transition phase initially reduces lift, then increases lift, and finally reduces lift again for the wing, while it increases lift for the canard. This phenomenon results from the coupled influence of freestream velocity and rotor-induced flow effects.

The intrusion of foreign objects on airport runways during aircraft takeoff and landing poses a significant safety threat to air transportation. Small-scale Foreign Object Debris (FOD) cannot be ruled out on time by traditional manual inspection, and there is also a potential risk of secondary foreign body intrusion. A deep-learning-based intelligent detection method is proposed to solve the problem of low accuracy and low efficiency of small-scale FOD detection. Firstly, a dual light camera system is utilized for the collection of FOD data. It generates a dual light FOD dataset containing both infrared and visible light images. Subsequently, a multi-attention mechanism and a bidirectional feature pyramid are integrated into the baseline network YOLOv5. This integration prioritizes the extraction of foreign object features and boosts the network’s ability to distinguish FOD from complex backgrounds. Additionally, it enhances the fusion of higher-level features to improve the representation of multi-scale objects. To ensure fast and accurate localization and recognition of targets, the Complete-IoU (CIoU) loss function is used to optimize the bounding boxes’ positions. The experimental results indicate that the proposed model achieves a detection speed of 36.3 frame/s, satisfying real-time detection requirements. The model also attains an average accuracy of 91.1%, which is 7.4% higher than the baseline network. Consequently, this paper verifies the effectiveness and practical utility of our algorithm for the detection of small-scale FOD targets.

A cement-based piezoelectric composite, modified by graphene oxide (GO), was prepared to study piezoresistive capacity. The testing confirms that GO is more effective than other carbon nanomaterials at improving piezoresistive sensitivity of cement-based composites, because the content of GO in cement paste was much lower than other carbon nanomaterials used in previously published research. Further investigation indicates that the addition of GO significantly improved the stability and repeatability for piezoresistive capacity of cement paste under cycle loads. Based on experiment results, the piezoresistive sensitivity of this composite depended on GO content, water-to-cement weight ratio (w/c) and water-loss rate, since the highest piezoresistive gauge factor value (GF = 35) was obtained when GO content was 0.05 wt.%, w/c was 0.35 and water-loss rate was 3%. Finally, microstructure analysis confirmed that conductivity and piezoresistivity were achieved through a tunneling effect and by contacting conduction that caused deformation of GO networks in the cement matrix.

This study aims to enhance pavement crack detection methods by integrating unmanned aerial vehicles (UAVs) with deep learning techniques. Current methods encounter challenges such as low accuracy, limited efficiency, and constrained application scenarios. We introduce an innovative approach that employs a UAV equipped with a binocular camera for identifying pavement surface cracks. This method is augmented by a binocular ranging algorithm combined with edge detection and skeleton extraction algorithms, enabling the quantification of crack widths without necessitating a preset shooting distance—a notable limitation in existing UAV crack detection applications. We developed an optimized model to enhance detection accuracy, incorporating the YOLOv5s network with an Efficient Channel Attention (ECA) mechanism. This model features a decoupled head structure, replacing the original coupled head structure to optimize detection performance, and utilizes a Generalized Intersection over Union (GIoU) loss function for refined bounding box predictions. Post identification, images within the bounding boxes are segmented by the Unet++ network to accurately quantify cracks. The efficacy of the proposed method was validated on roads in complex environments, achieving a mean Average Precision (mAP) of 86.32% for crack identification and localization with the improved model. This represents a 5.30% increase in the mAP and a 6.25% increase in recall compared to the baseline network. Quantitative results indicate that the measurement error margin for crack widths was 10%, fulfilling the practical requirements for pavement crack quantification.

Due to its ability to improve mechanical properties when incorporated into cement, graphene oxide (GO) has received extensive attention from scholars. Graphene oxide is also a filler that improves the self-sensing properties of cement composites (CCs). However, existing studies have not focused sufficient attention on the electric conductivity of cement composites filled with graphene oxide (GO/CCs) and their mechanisms, especially polarization. This study examines the effects of water content and temperature on the electrical conductivity of GO/CCs. GO/CC polarization phenomena are analyzed to reveal the conductive mechanism. The results show that water has a significant influence on the electrical conductivity of GO/CCs. With increasing water loss, the electrical resistivity of GO/CCs increases by four orders of magnitude. For the same water content, a 0.1% GO concentration significantly decreases the resistivity of GO/CCs. Temperature can significantly enhance the current intensity of GO/CCs; furthermore, there is a quadratic relationship between current intensity and temperature. The conductive mechanism of GO/CCs is attributed to the interaction between ionic conductivity and electronic conductivity.