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The shear pin structure is widely used in aeronautics and astronautics structures to deal with emergency structure separation problems. The shear pin design has a strict restriction on the precise failure load and definite failure mode. Previous research has conducted shear fracture tests and simulations of solid shear pins while there is a lack of detailed research on the shear fracture of hollow shear pins with large diameters. In this research, a 3-dimensional finite element model was built based on the actual shear pin installed on the aircraft engine pylon and the model was validated by the experiment. The influences of the inner diameter of hollow shear pins on the shear fracture process were investigated by conducting finite element simulations. The structural deformation, energy dissipation in the fracture process, and failure load of shear pins were evaluated. It is found that as the inner diameter increases, the failure mode of shear pins changed and would result in difficulties on the structure separation. To solve this problem, a new configuration of hollow shear pin was proposed for the purpose of obtaining both desired failure load and failure mode. The new configuration was verified by the fracture simulation and it is found that the new configuration is effective and can be used to improve the shear fracture performance.

Low-altitude flight in mountainous terrains is a difficult flight task applied in both military and civilian fields. The helicopter has to maintain low altitude to realize search and rescue, reconnaissance, penetration, and strike operations. It contains complex environment perception, multilevel decision making, and multi-objective flight control; thus, flight is currently mainly conducted by human pilots. In this work, a control framework is implemented to realize autonomous flight for unmanned helicopter operations in an unknown mountainous environment. The identification of targets and threats is introduced using a deep neural network. A 3D vector field histogram method is adopted for local terrain avoidance based on airborne Lidar sensors. In particular, we propose an intuitive direct-viewing method to judge and change the visibilities of the helicopter. On this basis, a finite state machine is built for decision making of the autonomous flight. A highly realistic simulation environment is established to verify the proposed control framework. The simulation results demonstrate that the helicopter can autonomously complete flight missions including a fast approach, threat avoidance, cover concealment, and circuitous flight operations similar to human pilots. The proposed control framework provides an effective solution for complex flight tasks and expands the flight control technologies for high-level unmanned helicopter operations.

An 8-week growth trial was conducted to evaluate the effects of dietary arginine (Arg) levels on growth, gut morphology, oxidation resistance and immunity of hybrid grouper (Epinephelus fuscoguttatus♀×Epinephelus lanceolatus♂) juveniles. Seven isoenergetic (1465 kJ (350 kcal)/100-g DM), isoproteic (53·5 % of DM) and isolipidic (7 % of DM) experimental diets were formulated to contain graded Arg levels ranging from 1·9 to 4·7 % (dry weight) at approximately 0·5 % increments. Each diet was randomly assigned to triplicate groups of 16 juvenile fish (average initial body weight: 11·7 (sd 0·1) g) and was administered twice daily (08.00 and 16.00 hours). After the growth trial, all remaining fish were fed their prescribed diets for 2 d and then exposed to 4·5 mg Cu2+/l water for 36 h. Results showed that growth performance and feed utilisation of experimental fish were significantly affected by different dietary Arg levels. Weight gain % (WG%) of fish was increased as dietary Arg increased, reaching a peak value at 3·8 % dietary Arg level, and when dietary Arg level increased to 4·7 % WG% was reduced. Fish fed 1·9 and 2·2 % dietary Arg levels had higher daily feed intake compared with fish fed other dietary Arg levels. Feed conversion ratios in fish fed 1·9, 2·2, 2·7 and 4·7 % dietary Arg levels were higher than those in fish fed 3·1, 3·8 and 4·1 % dietary Arg levels. Protein efficiency ratio and protein productive value (PPV) increased with an increase in dietary Arg, up to a peak value at 3·8 % dietary Arg level, above which these parameters declined. On the basis of quadratic regression analysis of weight gain % (WG%) or PPV against dietary Arg levels, the optimal dietary Arg requirement for hybrid grouper was estimated to be 3·65 %. Fish fed 3·8 % dietary Arg had higher whole-body and muscle protein contents compared with fish fed other dietary Arg levels. Fish fed 3·8 and 4·1 % dietary Arg levels had higher levels of mRNA for insulin-like growth factor-I and target of rapamycin in the liver compared with fish fed other dietary Arg levels. Hepatic S6 kinase 1 mRNA expression in fish fed 3·8 % dietary Arg level was higher than that in fish fed any of the other dietary Arg levels. Gut morphology, hepatic antioxidant indices and immune indices in serum and head kidney were significantly influenced by dietary Arg levels. In conclusion, the optimal dietary Arg requirement for hybrid grouper was estimated to be 3·65 %, and suitable dietary Arg supplementations improved gut morphology and oxidation resistance of hybrid grouper.

Purpose This paper aims to investigate the rebound process and the secondary-impact process of the fuselage section that occurs in the actual crash events. Design/methodology/approach A full-scale three-dimensional finite element model of the fuselage section was developed to carry out the dynamic simulations. The rebound process was simulated by removing the impact surface at a certain point, while the secondary-impact process was simulated by striking the impact surface against the fuselage bottom after the first impact. Findings For the rebound process, the fuselage structure restores deformation due to the springback of the fuselage bottom, and it results in structural vibration of the fuselage section. For the secondary-impact process, the fuselage deformation is similar with that of the single impact process, indicating that the intermittent impact loading has little influence on the overall deformation of the fuselage section. The strut failure is the determining factor to the acceleration responses for both the rebound process and the secondary-impact process. Practical implications The rebound process and the secondary-impact process, which is difficult to study by experiments, was investigated by finite element simulations. The structure deformations and acceleration responses were obtained, and they can provide guidance for the crashworthy design of fuselage structures. Originality/value This research first investigated the rebound process and the secondary-impact process of the fuselage section. The absence of the ground load and the secondary-impact was simulated by controlling the impact surface, which is a new simulating method and has not been used in the previous research.

The requirements for the quantity and quality of pilots are getting more and more demanding. The helicopter robot pilot was put forward as a new autonomous driving scheme which can achieve fast switch between pilot driving and autonomous driving without modifying helicopters. The overall design of the robot was determined and the actuators of the robot was demonstrated in detail according to the handling characteristics of helicopter manipulation mechanism. Based on the detailed design scheme, the robot pilot was processed and assembled. In order to test the control performance of the robot pilot, the robot was installed in the flight simulator to carry out helicopter flight simulations. The test results illustrated that the robot pilot is able to accomplish basic flight control of helicopter.

Background Recent studies have reported that Integrin alpha 2 (ITGA2) plays an essential role in tumor cell proliferation, invasion, metastasis, and angiogenesis. An abnormally expressed ITGA2 correlates with unfavorable prognoses in multiple types of cancer. However, the specific mechanism of how ITGA2 contributes to tumorigenesis remains unclear. Methods The GEPIA web tool was used to find the clinical relevance of ITGA2 in cancer, and this significance was verified using Western blotting analysis of paired patient tissues and immunohistochemistry of the pancreatic cancer tissue. Functional assays, such as the MTS assay, colony formation assay, and transwell assay, were used to determine the biological role of ITGA2 in human cancer. The relationship between ITGA2 and programmed death-ligand 1 (PD-L1) was examined using Western blot analysis, RT-qPCR assay, and immunohistochemistry. The protein-protein interaction between ITGA2 and STAT3 was detected via co-immunoprecipitation. Results Our study showed that ITGA2 was markedly overexpressed in several malignant tumor cells and clinical tissues. Blocking ITGA2 inhibited the proliferation and invasion ability of cancer cells significantly, whereas overexpressed ITGA2 increased the degree of those processes considerably. Additionally, the RNA-seq assay indicated that ITGA2 transcriptionally regulated the expression of PD-L1 in pancreatic cancer. We also demonstrated that ITGA2 interacted with STAT3 and up-regulated the phosphorylation of STAT3; this interaction might involve the mechanism of ITGA2 inducing PD-L1 expression in cancer cells. Our results suggest that ITGA2 plays a critical role in cancer cell progression and the regulation of PD-L1 by activating the STAT3 pathway. Conclusions We identified a novel mechanism by which ITGA2 plays a critical role in modulating cancer immune response by transcriptionally increasing the expression of PD-L1 in cancer cells. Thus, targeting ITGA2 is an effective method to enhance the efficacy of checkpoint immunotherapy against cancer.

With the development of high‐performance computing and advanced experimental methods, data‐driven machine learning, especially neural network technology, has shown great potential in fluid mechanics research and has become a fourth paradigm research tool. In particular, remarkable achievements have been made in turbulence modeling, near‐wall flow prediction, and combustion dynamic evolution. Researchers use neural network model to assist turbulence control, improve Reynolds average turbulence model, and harnesses the deep learning method to solve the problem of complex flow phenomenon prediction driven by large‐scale data, which effectively improves the accuracy and efficiency of internal flow and wall effect simulation of supersonic combustion ramjet (scramjet) engine. These studies not only promote the development of fluid mechanics but also provide strong support for the design optimization of scramjet engines.

Active disturbance rejection control is implemented in a LC-filtered surface-mounted permanent magnet synchronous motor (SPMSM) drive system to enhance current control dynamics. However, the combined effects of computation one-beat delay and the pulse-width modulation zero-order hold (ZOH) effect significantly degrade system stability and dynamic performance. To address these limitations, an improved predictive extended state observer (ESO) with an accurate ZOH discretization method is proposed to ensure fast and robust dynamic performance. The predictive ESO predicts one beat to compensate for the delay effect, while the ZOH discretization yields a more precise discrete dynamic model of the system. These combined improvements substantially enhance the system’s phase and gain margins, leading to superior dynamic performance. Furthermore, a discrete-domain transfer function of the control system is analytically derived, with the control parameters systematically designed using frequency-domain analysis to guarantee robust performance. Experimental validation on a LC-filtered SPMSM drive system demonstrates remarkable enhancement in current control dynamics while maintaining sufficient robustness.

s Background Pancreatic ductal adenocarcinoma is one of the leading causes of cancer-related death worldwide. Immune checkpoint blockade therapy, including anti-PD-1 and anti-PD-L1, is a new therapeutic strategy for cancer treatment but the monotherapy with PD-L1 inhibitors for pancreatic cancer is almost ineffective for pancreatic cancer. Thus, exploring the regulatory mechanism of PD-L1 in cancer cells, especially in pancreatic cancer cells, is one of the key strategies to improving cancer patient response to PD-L1 blockade therapy. Histone acetyltransferase 1(HAT1) is a classic type B histone acetyltransferase and the biological role of HAT1 in pancreatic cancer is unclear. Methods The clinical relevance of HAT1 was examined by the GEPIA web tool, Western blotting and immunohistochemistry of pancreatic cancer tissue microarray slides. Tumor cell motility was investigated by MTS assay, colony formation assay and xenografts. The relationship between HAT1 and PD-L1 was examined by Western blot analysis, RT-qPCR and immunohistochemistry. Results HAT1 was upregulated in PDAC and associated with poor prognosis in PDAC patients. The knockdown of HAT1 decreased the proliferation of pancreatic cancer cells in vivo and in vitro. Strikingly, we showed that HAT1 transcriptionally regulated PD-L1, and this process was mainly mediated by BRD4 in pancreatic cancer. The knockdown of HAT1 improved the efficacy of immune checkpoint blockade by decreasing the PD-L1. Conclusions The recognition of HAT1 in regulating tumor cell proliferation and cancer immunity indicated that HAT1 might be employed as a new diagnostic and prognostic marker and a predictive marker for pancreatic cancer therapy, especially in immune checkpoint blockade therapy. Targeting HAT1 highlights a novel therapeutic approach to overcome immune evasion by tumor cells.

Wearable silent speech systems hold significant potential for restoring communication in patients with speech impairments. However, seamless, coherent speech remains elusive, and clinical efficacy is still unproven. Here, we present an AI-driven intelligent throat (IT) system that integrates throat muscle vibrations and carotid pulse signal sensors with large language model (LLM) processing to enable fluent, emotionally expressive communication. The system utilizes ultrasensitive textile strain sensors to capture high-quality signals from the neck area and supports token-level processing for real-time, continuous speech decoding, enabling seamless, delay-free communication. In tests with five stroke patients with dysarthria, IT’s LLM agents intelligently corrected token errors and enriched sentence-level emotional and logical coherence, achieving low error rates (4.2% word error rate, 2.9% sentence error rate) and a 55% increase in user satisfaction. This work establishes a portable, intuitive communication platform for patients with dysarthria with the potential to be applied broadly across different neurological conditions and in multi-language support systems. Wearable silent speech systems hold potential for restoring communication in patients suffering from speech impairments. Tang et al. report an AI-driven silent speech system for dysarthria patients, which enables zero-time-delay expression and context-aware emotion decoding-based sentence expansion.