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In this paper, the novel design mode of human-aided design (HAD) is proposed to replace conventional computer-aided design (CAD). In HAD, computers can automatically complete the whole product design via a new isogeometric topology optimization (ITO), while humans just assist to slightly modify the design to meet requirements. An embedded domain ITO is presented to design complex models with irregular design domains, and editable geometric models of optimized results can be automatically generated based on layered ITO results. Three examples are tested to verify the proposed HAD mode, including a 3D cantilever beam with a regular design domain, an automotive part with an irregular design domain, and a Messerschmitt-Bölkow-Blohm (MBB) beam with a multiscale structure. The results demonstrate that the proposed HAD mode can automatically deliver high-quality optimized models; thus, it has great potential as a revolutionary technology to change the current design mode from CAD to HAD.

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.

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.

BackgroundArbuscular mycorrhizal fungi (AMF) are beneficial soil fungi which can effectively help plants with acquisition of mineral nutrients and water and promote their growth and development. The effects of indigenous and commercial isolates of arbuscular mycorrhizal fungi on pear (Pyrus betulaefolia) trees, however, remains unclear.MethodsTrifolium repens was used to propagate indigenous AMF to simulate spore propagation in natural soils in three ways: 1. the collected soil was mixed with fine roots (R), 2. fine roots were removed from the collected soil (S), and 3. the collected soil was sterilized with 50 kGy 60Co γ-radiation (CK). To study the effects of indigenous AMF on root growth and metabolism of pear trees, CK (sterilized soil from CK in T. repens mixed with sterilized standard soil), indigenous AMF (R, soil from R in T. repens mixed with sterilized standard soil; S, soil from S in T. repens mixed with sterilized standard soil), and two commercial AMF isolates (Rhizophagus intraradices(Ri) and Funneliformis mosseae (Fm)) inoculated in the media with pear roots. Effects on plant growth, root morphology, mineral nutrient accumulation, metabolite composition and abundance, and gene expression were analyzed.ResultsAMF treatment significantly increased growth performance, and altered root morphology and mineral nutrient accumulation in this study, with the S treatment displaying overall better performance. In addition, indigenous AMF and commercial AMF isolates displayed common and divergent responses on metabolite and gene expression in pear roots. Compared with CK, most types of flavones, isoflavones, and carbohydrates decreased in the AMF treatment, whereas most types of fatty acids, amino acids, glycerolipids, and glycerophospholipids increased in response to the AMF treatments. Further, the relative abundance of amino acids, flavonoids and carbohydrates displayed different trends between indigenous and commercial AMF isolates. The Fm and S treatments altered gene expression in relation to root metabolism resulting in enriched fructose and mannose metabolism (ko00051), fatty acid biosynthesis (ko00061) and flavonoid biosynthesis (ko00941).ConclusionsThis study demonstrates that indigenous AMF and commercial AMF isolates elicited different effects in pear plants through divergent responses from gene transcription to metabolite accumulation.

A feeding trial was conducted to investigate the effects of dietary yeast culture (YC) on health status in digestive tract of juvenile Pacific white shrimp Litopenaeus Vannamei. Shrimps (initial weight: 3.33 ± 0.06 g) were fed with graded levels of dietary YC (control, 0.3%, 0.5% and 1.0%). Results of the present study showed that villus height and the ratio between villus height and crypt depth in the digestive tract of juvenile shrimp was significantly increased by dietary 0.5% and 1.0%YC (P < 0.05). Besides, dietary 0.5% and 1.0%YC significantly activities of phenoloxidase (PO), lysozyme (LZ), acid phosphatase (ACP) and alkaline phosphatase (AKP) (P < 0.05), significantly up-regulated mRNA levels of prophenoloxidase (propo), lysozyme (lz), anti-lipopolysaccharide factor (alf), crustin and penaienadin (P < 0.05) and down-regulated mRNA levels of caspase-1, nuclear factor κB p65 (nf-κbp65) myeloid differentiation primary response protein (myd88) and toll like receptor (tlr) in the digestive tract of juvenile shrimp (P < 0.05). Compared with the control, dietary 0.5%YC increased Chao1 index in the digestive tract of juvenile shrimp. In addition, compared with the control, dietary 0.5% and 1.0%YC significantly increased relative abundance of Lactobacillus (P < 0.05). It can be concluded that dietary YC made positive contribution to health status in digestive tract of juvenile shrimp through improving morphology and microbiota, enhancing immune function, and inhibiting inflammation of digestive tract.

In this note, our purpose is to introduce the concept of n-polynomial convex stochastic processes and study some of their algebraic properties. We establish new refinements for integral version of Hölder and power mean inequality. Also, we are concerned to extend several Hermite-Hadamard type inequalities for n-polynomial convex stochastic processes by using Hölder, Hölder-İşcan, power mean and improved power mean integral inequalities. Moreover, we give comparison of obtained results.

In this study, a set of numerical simulation analysis methods for studying the dynamic response of runway under the action of aircraft taxiing load are presented. An aircraft-runway coupled vibration model was established, and the runway pavement roughness was taken as the vibration excitation source; then, the aircraft taxiing dynamic load was obtained. A three-dimensional finite element model of the runway was established, and the vertical dynamic displacement (VDD) response and its variation law of the runway under different void conditions were studied under the action of the dynamic load of a taxiing aircraft. In addition, using wavelet packet transform, the acceleration signals at different positions of the pavement slab under the sliding load were decomposed into three layers. The relationship among the wavelet packet energy ratio (WPER), the wavelet packet energy entropy (WPEE) of each frequency band, and the void under the pavement slab was obtained. The results show that the established model could quickly and accurately solve the aircraft taxiing dynamic load. In the case of a slight void, the VDDs in the runway center and under the main landing gears had negative exponential and logarithmic relationships with the reduction coefficient of the dynamic elastic modulus of the base layer, respectively. When there was a severe void, the pavement slab was separated from the base layer. The VDDs in the runway center and under the main landing gears were exponentially and linearly related to the slab’s void area, respectively. The vibration signals were extracted at three measuring points, and the wavelet packet energy characteristics of the signals were compared and analyzed. It was found that the WPER and WPEE of the vibration signals in the void area of the slab corner could better reflect the void state of the slab bottom.

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.

Intelligent manufacturing is a general concept that is under continuous development. It can be categorized into three basic paradigms: digital manufacturing, digital-networked manufacturing, and new-generation intelligent manufacturing. New-generation intelligent manufacturing represents an in-depth integration of new-generation artificial intelligence (AI) technology and advanced manufacturing technology. It runs through every link in the full life-cycle of design, production, product, and service. The concept also relates to the optimization and integration of corresponding systems; the continuous improvement of enterprises’ product quality, performance, and service levels; and reduction in resources consumption. New-generation intelligent manufacturing acts as the core driving force of the new industrial revolution and will continue to be the main pathway for the transformation and upgrading of the manufacturing industry in the decades to come. Human-cyber-physical systems (HCPSs) reveal the technological mechanisms of new-generation intelligent manufacturing and can effectively guide related theoretical research and engineering practice. Given the sequential development, cross interaction, and iterative upgrading characteristics of the three basic paradigms of intelligent manufacturing, a technology roadmap for “parallel promotion and integrated development” should be developed in order to drive forward the intelligent transformation of the manufacturing industry in China. Keywords: Advanced manufacturing, New-generation intelligent manufacturing, Human-cyber-physical system, New-generation AI, Basic paradigms, Parallel promotion, Integrated development

Traditional workflow models have obvious shortcomings in describing complex workflows. Such complexity is due not only to the hierarchical property of business process, but also to the complicated dependencies among tasks. In this paper, we first introduce a conceptual task model based on UML class graph. Then we apply colored Petri nets to model task interfaces and composite task versions which form Task State Nets (TSNs) and Task Version Nets (TVNs). The main advantage is that they explicitly express all task interfaces´ internal states and thus they can easily describe the complex control dependencies. Next we further combine TSN/TVN with place refining mechanism and bring forward our Hierarchical Task State Net (HTSN). It supports the description of workflow's hierarchical structure, multi-versions configuration of composite task as well as the analysis of structural properties. Finally, we give a case study.