Search

As one of the main problems in the processing of carbon fiber reinforced polymer (CFRP), tool wear affects the quality and efficiency of hole making. As a new CFRP hole making process, the suction-type internal chip removal method can effectively slow down the tool wear rate. In order to further clarify the reasons for this method to slow down the tool wear, this study investigates the wear mechanism of the internal chip removal tool. The main contents include: first, the experimental parameters such as drilling axial force, drilling temperature, and tool wear are obtained through the experimental method, and the drawing of the drilling temperature change curve of internal chip drilling is completed; second, the polar analysis method is used to study the influence of the drill parameters on the drilling axial force and the tool wear, and the basis for the selection of the structure of the internal chip drill is given; finally, the method of comparative experiments of the internal chip drilling process and the non-internal chip drilling process is used to conclude that the use of the internal chip drilling process can effectively slow down the tool wear rate. Finally, the method of comparing the internal chip removal and non-internal chip removal processes is used to conclude that the use of internal chip removal processing method can effectively reduce the axial force, drilling temperature, and slow down the tool wear.

The Leaf Area Index (LAI) is a critical parameter that sheds light on the composition and function of forest ecosystems. Its efficient and rapid measurement is essential for simulating and estimating ecological activities such as vegetation productivity, water cycle, and carbon balance. In this study, we propose to combine high-resolution GF-6 2 m satellite images with the LESS three-dimensional RTM and employ different machine learning algorithms, including Random Forest, BP Neural Network, and XGBoost, to achieve LAI inversion for forest stands. By reconstructing real forest stand scenarios in the LESS model, we simulated reflectance data in blue, green, red, and near-infrared bands, as well as LAI data, and fused some real data as inputs to train the machine learning models. Subsequently, we used the remaining measured LAI data for validation and prediction to achieve LAI inversion. Among the three machine learning algorithms, Random Forest gave the highest performance, with an R2 of 0.6164 and an RMSE of 0.4109, while the BP Neural Network performed inefficiently (R2 = 0.4022, RMSE = 0.5407). Therefore, we ultimately employed the Random Forest algorithm to perform LAI inversion and generated LAI inversion spatial distribution maps, achieving an innovative, efficient, and reliable method for forest stand LAI inversion. [ABSTRACT FROM AUTHOR]

At present, the challenge of high-performance flexible piezoresistive pressure sensors remains the combination of high sensitivity and a wide monitoring range. The micro-morphological engineering of sensing materials will greatly contribute to the design and development of flexible piezoresistive sensors. Therefore, a flexible piezoresistive sensor based on a multi-layer interlocking "sandwich" structure was developed. The structure is assembled from polydimethylsiloxane (PDMS)/carbon nanotube (CNT) films with microdome arrays and thermoplastic polyurethane (TPU)/Ti3C2Tx MXene nanofiber films. The multi-layer interlocking structure not only significantly increases the contact area and sensitivity between layers, but also greatly improves mechanical and electrical stability. The results show that the PDMS/CNT–TPU/MXene pressure sensor has a high sensitivity of 82.17 kPa−1, a wide pressure detection range of 0–25 kPa, an extremely short response and recovery time of 100 ms, and an excellent dynamic cycling stability of more than 2000 cycles. In addition, the sensor has been used to perform comprehensive human motion monitoring, such as joint flexion, gait monitoring, and gesture recognition, demonstrating its potential for applications in health monitoring, wearable devices, and human–computer interaction. [ABSTRACT FROM AUTHOR]

Carbon fiber reinforced polymer (CFRP) has been widely used in the aviation, space and automobile structural parts manufacturing due to its excellent physical and mechanical properties. However, the properties of CFRP materials with inhomogeneity and anisotropy are vastly different from metals. In the process of drilling, it is easy to produce delamination, tearing, burr and other defects, which seriously affect the quality of drilling. Therefore, drilling technology of CFRP-metal stacks has become a difficult problem in aircraft assembly process. This paper summarizes the researches of drilling mechanism of CFRP in recent years. The relationship between chip formation mechanism and fiber orientation angle is generalized. Meanwhile, the relationship between axial drilling force, drilling temperature and tool shape, workpiece material, process parameters is summarized. The causes, detection, evaluation methods and restraining measures of typical machining damage during CFRP drilling processes, such as delamination, burr and tearing are analyzed. The latest advances in material, geometric structure of CFRP drilling tools, and development of simulation technology of CFRP drilling process are expounded. It is also proposed that the establishment of accurate and reliable constitutive model is the key to the research of drilling simulation technology. The development trend of CFRP drilling technology is prospected by introducing new CFRP drilling technologies, such as variable process parameters drilling, robotic automatic drilling and suction-type internal chip removal drilling.

Despite growing recognition of nature-based solutions (NBS), there remains a research gap in understanding their implementation in urban areas, which poses a significant challenge for urban forest development. Therefore, our paper aims to explore the intersection of NBS with urban forests (UF), identify current barriers, propose strategies to maximize the potential of urban forests as nature-based solutions (UF-NBS) in effectively improving the resilience of urban forests, and enhance the service capacity of urban forest ecosystems. To achieve our objective, we conducted a comprehensive analysis that included a bibliometric review to summarize the evolution of the UF-NBS literature and classify UF-NBS types for the first time. Subsequently, we identified and organized current challenges faced by UF-NBS. Additionally, we proposed an original technological framework system for urban forest development based on NBS principles. The results show the significance of UF-NBS for enhancing urban resilience and human wellbeing, with multiple successful implementations in both China and Europe, validating their effectiveness. However, the implementation of UF-NBS faces several challenges, including inadequate financing, the gap between scientific knowledge and practical implementation, the absence of region-specific information, and the need for interdisciplinary collaboration. This study contributes to establishing a scientific theoretical basis for integrating UF and NBS and provides a systematic approach for decision-makers in urban forest management. Future research should focus on exploring the integration of UF within the NBS framework and prioritize knowledge sharing, international cooperation, and education initiatives to promote the global adoption of UF-NBS and address pressing urban challenges. [ABSTRACT FROM AUTHOR]

Dioecious plants are often used in landscaping because of sex differences in individual appearance, resistance and ornamental value. Although a large number of studies have investigated the overall differences in resource allocation between different sexes in dioecious plants, the effects of environmental factors such as temperature and precipitation on resource allocation in sex-dimorphic plants are not fully understood. In order to explore the influencing factors, 39 works of literature on dioecious plant resource allocation published in English databases from 1992 to 2023 were selected, and the biomass data of dioecious plant stems, leaves, flowers and fruits from these pieces of literature were extracted. A total of 545 independent experimental groups were obtained, which were divided into four groups for analysis, and the data were analyzed using METAWIN 2.1 software. Four sets of data were used to quantitatively study the effects of different temperatures, precipitations and life forms on the resource allocation of dioecious plants of different sexes in large-scale space. The results showed that female plants invested more resources in reproductive growth and less resources in vegetative growth. In terms of total biomass, the average biomass of female plants was 3.09% higher than that of male plants, indicating that female plants reduced nutrient investment to compensate for reproductive investment in the process of resource allocation. Temperature and precipitation significantly affect the adaptability of male and female plants to environmental changes and the cooperative relationships among the stressed components. The vegetative biomass investment of female plants showed a positive correlation with the increase in temperature, while the reproductive biomass showed a negative correlation with the decrease. The average annual precipitation had little effect on the vegetative biomass of dioecious plants, but had a significant effect on reproductive biomass. The study of this trade-off relationship is helpful in revealing the relationship between vegetative growth and reproductive growth of plants, exploring the countermeasures of plant life history, and providing a scientific basis for urban landscaping and urban forest management. [ABSTRACT FROM AUTHOR]