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The transition state is the most critical and dangerous state of the VTOL fixed wing aircraft in the whole flight process. In this paper, a transition corridor for a distributed propulsion VTOL fixed-wing aircraft is studied based on the lift characteristics of the wing and the power constraints of the power unit. Firstly, according to the sliding flow theory, the dynamic characteristics models of the lift fan system in the front part of the fuselage and the distributed duct system in the rear part of the fuselage were established by introducing the influence factor of the duct, and verified with the test data. Secondly, according to the lift characteristics of the wing, the transition curves of the aircraft at different angles of attack are calculated, in which the transition curves corresponding to the zero-lift attack angle and stall attack angle constitute the lift characteristics transition corridor of the distributed propulsion VTOL fixed-wing aircraft. Finally based on the dynamic performance of the power unit model, calculate the lift characteristic transition each state point in corridor corresponding power demand, according to the power limit of the lift fan system, distributed duct system and power unit total power limitation, get distributed to promote vertical take-off and landing a fixed wing aircraft power limitation in lifting features and power unit under the condition of complete transition corridor. The final results show that the minimum forward velocity is inversely proportional to the attack angle. The power required by the tail distributed duct system will exceed the limit when the aircraft is in low speed and small dip angle transition. For the power limit boundary required, the power limit condition of individual component is stricter than the total power limit condition. The research results of this paper can provide some reference for the transition corridor research of such VTOL fixed-wing aircraft, and on this basis, the subsequent work such as parameter sensitivity analysis and control system design of transition corridor can be carried out.

Due to the space limitation of the launching platform, the wings of the space-based gravity delivery UAV need to be folded before launching. In the process of wing folding and launching, the dynamics model of UAV will show the characteristics of multiple rigid bodies and multiple degrees of freedom, and the aerodynamic characteristics of UAV are also quite different from that of cruise stage, so the traditional aircraft dynamics modeling method is no longer applicable. In this paper, the multibody dynamics model of gravitational airdrop UAV is first built based on the Lagrange equation, and then according to the aerodynamic change on unmanned aerial vehicle in deploying process, the angle between the wing and the fuselage is introduced as a new variable, the UAV launch phase nonlinear aerodynamic model is established, and through comparison with software calculation results the reliability of the aerodynamic model is verified. Based on the aerodynamic model and dynamics model, the dynamic response of UAV deployment process is simulated, and the influence of UAV deployment process on UAV subsequent pulling stage under the same control instructions is compared, and the dynamics and kinematics characteristics of gravity launch are studied and explored. Finally, the simulation results are compared with the experimental data, indicating that the multi-body dynamic model established in this paper can accurately describe the dynamic response of UAV in the launching stage and the subsequent modification stage, which can provide reference for the technical personnel engaged in the design, test and use of related models.

Thermal-actuation and piezoresistive-detection effects have been employed to pump the effective quality factor of MEMS resonators, targeting simple self-oscillation and better sensing performance in the air. However, the ratio of the pumped effective quality factor to the inherent mechanical quality factor, crucial for characterizing the amplification, is hard to obtain. The main difficulty stems from hidden Lorentz peaks caused by feedthrough effects and the pump effect once the current is applied. In this paper, we demonstrated the presence of high-order harmonic components in the output of thermal-piezoresistive resonators when the oscillation amplitude is sufficiently large. By utilizing second-order harmonics, we achieved the improvement in signal-to-bias ratio of, 20.85 dB compared to that without feedthrough cancellation and 9.67 dB compared to that using a de-embedded method when the bias current is 6.20 mA. Furthermore, the inherent mechanical quality factor is extracted at a low current of 1.8 mA with a value of 5800 using the second-order harmonics, and a nearly two orders of magnitude enhancement in Q factor can be obtained before entering the self-oscillation regime. An amplitude bias instability as good as 55 ppm and a frequency bias instability as good as 10 ppb are realized in the nonlinear operation regime with a pumped effective quality factor of 576k. The paper contributes to the fundamental understanding of the Q pump effect together with harmonic analysis of the thermal-piezoresistive resonators and also pushes forward the development of low-power consumption self-oscillation resonant sensors. [ABSTRACT FROM AUTHOR]

Background: Methylmalonic acidaemia (MMA) is a genetic metabolic disorder caused by congenital defects that may result in multisystem damage. However, MMA complicated with acute myeloid leukemia (AML) is very rare. Case presentation: Here, we report a case of MMA with AML in a boy aged 7 years and 6 months. The boy was screened for MMA after birth and received long-term treatment with dietary control, vitamin B12, and L-carnitine. He was readmitted at the age of 7 years and 6 months with systemic bleeding spots and was diagnosed with AML by bone marrow cytology. When the diagnosis was clear, the patient received chemotherapy in addition to maintenance treatment for MMA. His blood routine, liver and kidney function, blood biochemistry, blood glucose, blood lipids, lactic acid and blood ammonia were monitored continuously. Bone marrow cytology one month after starting chemotherapy showed complete remission. Sorafenib was also added during chemotherapy and was discontinued 1 year after completion of chemotherapy. At the end of chemotherapy, he chose venetoclax for consolidation, and to date, the patient's condition is stable, with sustained CR and no relapse. Conclusion: MMA combined with AML is rare. The prevention of infection, comprehensive assessment of nutritional status, continuous monitoring of related indicators and overall situation should be prioritized and comprehensive and individualized treatment approaches are paramount. [ABSTRACT FROM AUTHOR]

The treatment of infected bone defects (IBDs) needs simultaneous elimination of infection and acceleration of bone regeneration. One mechanism that hinders the regeneration of IBDs is the iron competition between pathogens and host cells, leading to an iron deficient microenvironment that impairs the innate immune responses. In this work, an in situ modification strategy is proposed for printing iron‐active multifunctional scaffolds with iron homeostasis regulation ability for treating IBDs. As a proof‐of‐concept, ultralong hydroxyapatite (HA) nanowires are modified through in situ growth of a layer of iron gallate (FeGA) followed by incorporation in the poly(lactic‐co‐glycolic acid) (PLGA) matrix to print biomimetic PLGA based composite scaffolds containing FeGA modified HA nanowires (FeGA‐HA@PLGA). The photothermal effect of FeGA endows the scaffolds with excellent antibacterial activity. The released iron ions from the FeGA‐HA@PLGA help restore the iron homeostasis microenvironment, thereby promoting anti‐inflammatory, angiogenesis and osteogenic differentiation. The transcriptomic analysis shows that FeGA‐HA@PLGA scaffolds exert anti‐inflammatory and pro‐osteogenic differentiation by activating NF‐κB, MAPK and PI3K‐AKT signaling pathways. Animal experiments confirm the excellent bone repair performance of FeGA‐HA@PLGA scaffolds for IBDs, suggesting the promising prospect of iron homeostasis regulation therapy in future clinical applications. [ABSTRACT FROM AUTHOR]

Antibodies represent a crucial class of complex protein therapeutics and are essential in the treatment of a wide range of human diseases. Traditional antibody discovery methods, such as hybridoma and phage display technologies, suffer from limitations including inefficiency and a restricted exploration of the immense space of potential antibodies. To overcome these limitations, we propose a novel method for generating antibody sequences using deep learning algorithms called AbDPP (target‐oriented antibody design with pretraining and prior biological knowledge). AbDPP integrates a pretrained model for antibodies with biological region information, enabling the effective use of vast antibody sequence data and intricate biological system understanding to generate sequences. To target specific antigens, AbDPP incorporates an antibody property evaluation model, which is further optimized based on evaluation results to generate more focused sequences. The efficacy of AbDPP was assessed through multiple experiments, evaluating its ability to generate amino acids, improve neutralization and binding, maintain sequence consistency, and improve sequence diversity. Results demonstrated that AbDPP outperformed other methods in terms of the performance and quality of generated sequences, showcasing its potential to enhance antibody design and screening efficiency. In summary, this study contributes to the field by offering an innovative deep learning‐based method for antibody generation, addressing some limitations of traditional approaches, and underscoring the importance of integrating a specific antibody pretrained model and the biological properties of antibodies in generating novel sequences. The code and documentation underlying this article are freely available at https://github.com/zlfyj/AbDPP. [ABSTRACT FROM AUTHOR]

The presence of cracks reduces egg quality and safety, and can easily cause food safety hazards to consumers. Machine vision-based methods for cracked egg detection have achieved significant success on in-domain egg data. However, the performance of deep learning models usually decreases under practical industrial scenarios, such as the different egg varieties, origins, and environmental changes. Existing researches that rely on improving network structures or increasing training data volumes cannot effectively solve the problem of model performance decline on unknown egg testing data in practical egg production. To address these challenges, a novel and robust detection method is proposed to extract max domain-invariant features to enhance the model performance on unknown test egg data. Firstly, multi-domain egg data are built on different egg origins and acquisition devices. Then, a multi-domain trained strategy is established by using Maximum Mean Discrepancy with Normalized Squared Feature Estimation (NSFE-MMD) to obtain the optimal matching egg training domain. With the NSFE-MMD method, the original deep learning model can be applied without network structure improvements, which reduces the extremely complex tuning process and hyperparameter adjustments. Finally, robust cracked egg detection experiments are carried out on several unknown testing egg domains. The YOLOV5 (You Only Look Once v5) model trained by the proposed multi-domain training method with NSFE-MMD has a detection mAP of 86.6% on the unknown test Domain 4, and the YOLOV8 (You Only Look Once v8) model has a detection mAP of 88.8% on Domain 4, which is an increase of 8% and 4.4% compared to the best performance of models trained on a single domain, and an increase of 4.7% and 3.7% compared to models trained on all domains. In addition, the YOLOV5 model trained by the proposed multi-domain training method has a detection mAP of 87.9% on egg data of the unknown testing Domain 5. The experimental results demonstrate the robustness and effectiveness of the proposed multi-domain training method, which can be more suitable for the large quantity and variety of egg detection production. [ABSTRACT FROM AUTHOR]

There is limited research on robotic systems designed for Endoscopic Retrograde Cholangiopancreatography (ERCP) procedures using a side-view duodenoscope. The unique structure of the duodenoscope presents challenges to safely and precisely control the distal end pose. Control methods applied can reduce potential medical risks. We have redesigned the control section of the duodenoscope to facilitate its manipulation by a robotic system. An orthogonal compensator is employed to rectify the motion planes to standard planes. A hysteresis compensator based on the Prandtl-Ishlinskii model enables precise control of the distal pose of the duodenoscope. Furthermore, we utilize a contact force prediction model to prevent excessive contact force at the distal end. The performance of the modified duodenoscope is comparable to that of the standard duodenoscope. Following orthogonal compensation, the deviation angles of the motion planes is reduced by 32% to 98%. Post-hysteresis compensation, the root mean square error (RMSE) of the output angle of the distal end is decreased from 8.347° to 4.826°. The accuracy of distal end contact force prediction was approximately ±25% under conditions of high contact force. In conclusion, the modification and control strategy we proposed can achieve relatively safe and precise control of bending section, laying the foundation for the subsequent roboticization of duodenoscope systems for ERCP procedures. [ABSTRACT FROM AUTHOR]