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The growing interest in the flexibility and operational capabilities of soft manipulators in confined spaces emphasizes the need for precise modeling and accurate motion control. Conventional control methods encounter difficulties in modeling and involve intricate computations. This work introduces a novel deep reinforcement learning (DRL) control algorithm based on neural network modeling. Using the Whale Optimization Algorithm, an approximate dynamic model for the soft manipulator is established. The twin delayed deterministic policy gradient is employed for DRL control. Domain randomization is applied during pretraining in a simulated environment. The algorithm addresses issues related to dependency on measurement data quality and redundant mappings, outperforming other methods by 8–15 mm in control accuracy. The trained DRL controller achieves precise trajectory tracking within the soft manipulator's task space, enabling successful grasping tasks in various complex environments, including pipelines and other narrow spaces. Experimental results confirm the autonomy of our controller in performing these tasks without human intervention.

Abstract Background Mood swings have been observed in patients with intracranial aneurysm (IA), but it is still unknown whether mood swings can affect IA. Aim To explore the causal association between mood swings or experiencing mood swings and IA through a two‐sample Mendelian randomization (MR) study. Methods Summary‐level statistics of mood swings, experiencing mood swings, IA, aneurysm‐associated subarachnoid hemorrhage (aSAH), and non‐ruptured IA (uIA) were collected from the genome‐wide association study. Two‐sample MR and various sensitivity analyses were employed to explore the causal association between mood swings or experiencing mood swings and IA, or aSAH, or uIA. The inverse‐variance weighted method was used as the primary method. Results Genetically determined mood swings (odds ratio [OR] = 5.23, 95% confidence interval (95%CI): 1.65–16.64, p = .005) and experiencing mood swings (OR = 2.50, 95%CI: 1.37–4.57, p = .003) were causally associated with an increased risk of IA. Mood swings (OR = 5.67, 95%CI: 1.40–23.04, p = .015) and experiencing mood swings were causally associated with the risk of aSAH (OR = 2.91, 95%CI: 1.47–5.75, p = .002). Neither mood swings (OR = 1.95, 95%CI: .31–12.29, p = .478) nor experiencing mood swings (OR = 1.20, 95%CI: .48–3.03, p = .693) were associated with uIA. Conclusions Mood swings and experiencing mood swings increased the risk of IA and aSAH incidence. These results suggest that alleviating mood swings may reduce IA rupture incidence and aSAH incidence.

The effects of temperature and water content on the electrical conductivity of cement mortar with different sizes of carbon nanotubes were studied, and the effect of the size of carbon nanotubes on the electrical conductivity of cement mortar was revealed. The results show that the small diameter carbon nanotubes have the best enhancement effect on the electrical conductivity of cement mortar. The electrical conductivity of cement mortar with different diameters of carbon nanotubes is positively correlated with water content, and with the decrease of the diameter of carbon nanotubes in the sample, the effect of water content on the electrical conductivity of carbon nanotube cement mortar becomes smaller and smaller. With the increase of temperature, the electrical conductivity of cement mortar containing carbon nanotubes of different diameters increases in varying degrees, but the increase of samples with small diameter carbon nanotubes is the smallest, indicating that the electrical conductivity of cement mortars with small diameter carbon nanotubes is less affected by temperature.

A large number of numerical simulations are required to design an energy absorption scheme for train crashworthiness, leading to low design efficiency in the early stage. Based on train collision dynamics theory and the finite element method, a dynamic finite element model of longitudinal train collision is established. According to the model, we studied the acceleration time-history characteristics during the train collision process, obtained the mean-peak ratio coefficient, and determined the calculation formula for the maximum mean acceleration of a longitudinal train collision. Through characteristic analysis of the vehicle acceleration, interface force, and other parameters during a longitudinal train collision, the calculation method of the mean acceleration was improved. The analysis shows that the maximum mean acceleration depends on two stages in the collision process: (1) the coupler action of the head vehicle: the mean-peak ratio coefficient of the head vehicle is 0.7 in this stage, and the mean-peak ratio coefficient of other vehicles is 0.43; (2) the coupler of the collision interface is cut off, and the energy absorption devices of the head vehicle or intermediate vehicle absorb energy; the mean-peak ratio coefficient of the vehicle is 0.93 in this stage. On this basis, a mathematical function is established describing the mean acceleration of the vehicle and the average crushing force of the coupler collapse tube and the energy-absorbing device. The calculation formula is obtained for the maximum mean acceleration of the longitudinal train collision, and the results are compared with the mean acceleration obtained by numerical simulation. The Kruskal–Wallis ANOVA multisample independent nonparametric test was conducted to verify the reliability of the calculation results in the 95% confidence interval. The calculation formula can be used to calculate the maximum mean acceleration in the energy allocation stage of train crashworthiness design to effectively improve the efficiency of train collision energy allocation.

TEEG (3β,16β,23-trihydroxy-13,28-epoxyurs-11-ene-3-O-β-D-glucopyranoside) is derived from the chloroform extract of the Chinese medicine formula Shenqi San (CE-SS). In the present study, we aimed to elucidate the anticancer effect and possible molecular mechanism underlying the action of TEEG against the human non-small cell lung cancer (NSCLC) cell line A549 in vitro. A549 cells were incubated with different concentrations of TEEG. Cell proliferation was assessed by MTT assay. Autophagy was evaluated by immunofluorescence staining. Autophagy-associated proteins were examined by Western blot analysis. TEEG markedly inhibited A549 cell proliferation in a concentration-dependent manner. Immunofluorescence staining showed that TEEG induced autophagy in A549 cells. The LC3-II : LC3-I conversion ratio and the expression of Beclin-1, Atg5, Atg7, and Atg12 increased with the concentration of TEEG. In addition, increased TEEG concentration enhanced the expression of Class III p-PI3K and reduced the expression of Class I p-PI3K, p-AKT, p-mTOR, and p-P70S6K. These results indicate that TEEG induces autophagy of A549 cells through regulation of the PI3K/AKT/mTOR signaling pathway.

Diabetes mellitus (DM) is one of the most familiar chronic diseases threatening human health. Recent studies have shown that the development of diabetes is closely related to an imbalance of the gut microbiota. Accordingly, there is increasing interest in how changes in the gut microbiota affect diabetes and its underlying mechanisms. Immunomodulatory cells play important roles in maintaining the normal functioning of the human immune system and in maintaining homeostasis. Intestinal immunomodulatory cells (IICs) are located in the intestinal mucosa and are regarded as an intermediary by which the gut microbiota affects physiological and pathological properties. Diabetes can be regulated by IICs, which act as a bridge linking the gut microbiota and DM. Understanding this bridge role of IICs may clarify the mechanisms by which the gut microbiota contributes to DM. Based on recent research, we summarize this process, thereby providing a basis for further studies of diabetes and other similar immune-related diseases.

For the nonminimum phase behavior of the air-breathing hypersonic vehicle model caused by elevator-to-lift coupling, a nonlinear adaptive equivalent control method based on interconnection subsystems is proposed. In the altitude loop, the backstepping strategy is applied, where the virtual control inputs about flight-path angle and attack angle are designed step by step. In order to avoid the inaccurately direct cancelation of elevator-to-lift coupling when aerodynamic parameters are uncertain, the real control inputs, that is, elevator deflection and canard deflection, are linearly converted into the equivalent control inputs which are designed independently. The reformulation of the altitude-flight-path angle dynamics and the attack angle-pitch rate dynamics is constructed into interconnection subsystems with input-to-state stability via small-gain theorem. For the velocity loop, the dynamic inversion controller is designed. The adaptive approach is used to identify the uncertain aerodynamic parameters. Simulation of the flexible hypersonic vehicle demonstrates effectiveness of the proposed method.