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Traditional helicopters usually adopt single response type, which is difficult to meet demands for pilot control. Several experience of test flight has proved that multi-response type switching control can significantly improve the flight performance and reduce pilot’s control load. In this paper, based on the Aeronautical Design Standard-33F (ADS-33F), a new switching control strategy based on six response types is studied on a manned helicopter. Simulation results on each control strategy show that the corresponding controlled state variable can follow the command signal according to the specification requirements. Simulation results on the application scenario combined by the control strate-gies show that the designed control system can significantly reduce the manipulated workload, so as to verify the conclusions: the designed helicopter multi-response type switching control meets the response requirements specified by ADS-33F, which can significantly improve flight performance and pilot’s feeling, and the response converges rapidly to guarantee the flight safety and control accuracy.

In this work, Er5Si3B0.5 compound with the Mn5Si3-type hexagonal structure was synthesized, and the structure, magnetic properties, and the magnetocaloric effect were investigated theoretically and experimentally. The magnetic measurement results show a complex successive magnetic transition below T N. However, the magnetization of the Er5Si3B0.5 compound below T N is saturated under lower magnetic field relative to the Er5Si3 compound. Theoretical calculation indicates that this was attributed to the enhanced inter-orbital exchange interaction after doping B element. The complicated successive magnetic transitions contribute to the table-like magnetocaloric effect observed in the Er5Si3B0.5 compound with a wide temperature region. The maximum magnetic entropy change and the temperature averaged entropy change (30) are 10.1 and 9.02 J/kg K for the Er5Si3B0.5 compound under varying magnetic fields from 0 to 5 T, respectively. The temperature averaged entropy change (30) is reduced by just 11% compared to the maximum magnetic entropy change. While presenting an ideal magnetic refrigeration material with a large table-like magnetocaloric effect for hydrogen liquefaction, our work also demonstrates the feasibility of regulating magnetic behavior through enhanced orbital exchange interactions to develop magnetic refrigeration materials with outstanding performance. [ABSTRACT FROM AUTHOR]

Conventional data-driven soft sensors commonly rely on the assumption that processes are operating at steady states. As chemical processes involve evident dynamics, conventional soft sensors may suffer from transient inaccuracy and poor robustness. In addition, the control performance is unsatisfactory when the outputs of soft sensors serve as the feedback signals for quality control. This brief develops a dynamic soft-sensing model combining finite impulse response and support vector machine to describe dynamic and nonlinear static relationships. The model parameters are then estimated within a Bayesian framework. The results from both the simulated and the industrial case show its superiority to conventional static models in terms of dynamic accuracy and practical applicability. [ABSTRACT FROM AUTHOR]

Mostdata-driven soft sensors assume that the processes operatein a steady state, which may be improper because of the essentialdynamics in the process industries. Because of commonly existing irregularquality samples, establishing dynamic soft sensor models is a difficulttask. To cope with this problem, a nonlinear dynamic soft sensor modelwith a Wiener structure is proposed in this paper. Such a structureconsists of two parts: (i) finite impulse responses of first-ordertransfer functions with dead time are introduced to approximate thedynamic properties and (ii) a nonlinear network is utilized to describethe nonlinearity. An iterative two-level optimization method is appliedto establish this dynamic soft sensor model. The computational costis reduced, and convergence can be guaranteed. The proposed dynamicsoft sensor approach is validated through simulation and industrialcase studies. [ABSTRACT FROM AUTHOR]

Oxidative stress plays a crucial role in the development of osteoporosis. In this study, it was observed that donkey bone collagen (DC) at a concentration of 500 μg/mL scavenged 17.89 % of 1,1-Diphenyl-2-picrylhydrazyl (DPPH) free radicals, indicating its antioxidant properties. Additionally, when an oxidative damage osteoblast model was created using H 2 O 2 , 100 μg/mL DC demonstrated the ability to enhance cell survival by 27.31 %. Furthermore, 50 μg/mL DC increased the intracellular differentiation marker alkaline phosphatase (ALP) level by 62.65 %. Additionally, the study revealed that DC significantly increased the expression of osteoporosis-related factors in serum and effectively restored the abnormal structure of spongy bone in mice osteoporosis model. Peptides (GGWFL, ANLGPA, and GWFK) isolated from DC through gastrointestinal digestion and subsequent enzymatic purification in vitro demonstrated the ability to safeguard osteoblasts from H 2 O 2 -induced damage by reducing intracellular reactive oxygen species (ROS). This protection resulted in enhanced cell survival and promoted osteoblast differentiation. This investigation underscores that DC can shield oxidative damage osteoblast model from oxidative stress, ameliorate osteoporosis, and enhance bone density in mice osteoporosis model. These findings suggest various DC applications in the food and medicine industries., Competing Interests: The authors declare the following financial interests/personal relationships which may be considered as potential competing interests:Haining Tan, Feng Sun, Jie Wang, Huiwen Hou, Yan Li, Wen Tang, Zengmei Liu, Didi Gao has patent #202210885147.8 issued to Shandong University. If there are other authors, they declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (© 2023 The Authors. Published by Elsevier Ltd.)