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Mitochondria undergo dynamic fusion/fission, biogenesis and mitophagy in response to stimuli or stresses. Disruption of mitochondrial homeostasis could lead to cell senescence, although the underlying mechanism remains unclear. We show that deletion of mitochondrial phosphatase PGAM5 leads to accelerated retinal pigment epithelial (RPE) senescence in vitro and in vivo. Mechanistically, PGAM5 is required for mitochondrial fission through dephosphorylating DRP1. PGAM5 deletion leads to increased mitochondrial fusion and decreased mitochondrial turnover. As results, cellular ATP and reactive oxygen species (ROS) levels are elevated, mTOR and IRF/IFN-β signaling pathways are enhanced, leading to cellular senescence. Overexpression of Drp1 K38A or S637A mutant phenocopies or rescues mTOR activation and senescence in PGAM5 − /− cells, respectively. Young but not aging Pgam5 −/− mice are resistant to sodium iodate-induced RPE cell death. Our studies establish a link between defective mitochondrial fission, cellular senescence and age-dependent oxidative stress response, which have implications in age-related diseases. Mitochondria are a hub that can direct cellular outcomes in response to stress. Here, the authors show that mitochondrial phosphatase PGAM5 has a role in mitochondrial turnover and regulation of cellular senescence by altering organellar dynamics via fission regulator Drp1.

Fault diagnosis methods based on deep learning and big data have achieved good results on rotating machinery. However, the conventional deep learning method of bearing fault diagnosis is mostly based on laboratory artificial simulation data, and there is an error with actual fault data, which will reduce the generalization performance of the deep learning method. In addition, labeled data are very precious in real industrial environment. Due to expensive equipment and personnel safety issues, it is difficult to obtain a large amount of high-quality fault labeling data. Therefore, in this paper, we propose a metric-based meta-learning method named Reinforce Relation Network (RRN) for diagnosing bearing faults with few-shot samples. In the proposed method, a 1D convolution neural network is used to extract fault features, and a metric learner is used to predict the similarity between samples under different transfer conditions. Label smoothing and the Adabound algorithm are utilized to further improve the performance of network classification. The performance of the proposed method is verified on a dataset which contains artificial damage and natural damage data. The comparison studies with other methods demonstrate the superiority of the proposed method in the few-shot scenario.

Feature selection is a fundamental pre‐processing step in machine learning that aims to reduce the dimensionality of a dataset by selecting the most effective features from the original features. This process is regarded as a combinatorial optimization problem, and the grey wolf optimizer (GWO), a novel meta‐heuristic algorithm, has gained popularity in feature selection due to its fast convergence speed and easy implementation. In this paper, an improved binary GWO algorithm incorporating a novel Population Adaptation strategy called PA‐BGWO is proposed. The PA‐BGWO takes into account the characteristics of the feature selection problem and designs three strategies. The proposed strategy includes an adaptive individual update procedure to enhance the exploitation ability and accelerate convergence speed, a head wolf fine‐tuned mechanism to exert the impact on each independent feature of the objective function, and a filter‐based method ReliefF for calculating feature weights with dynamically adjusted mutation probabilities based on the ranking features to effectively escape from local optima. Experimental comparisons with several state‐of‐the‐art feature selection methods on 15 classification problems demonstrate that the proposed approach can select a small feature subset with higher classification accuracy in most cases.

The performance of sensorless control in a permanent magnet synchronous machine (PMSM) highly depends on the accuracy of rotor position estimation. Owing to its strong robustness, phase-locked loop (PLL) is widely used in rotor position estimation. However, due to the influence of harmonics existing in back electromotive force (EMF), estimation error occurs by using PLL. In this paper, a hybrid filtering stage-based PLL is proposed to improve the rotor position estimation. Adaptive notch filters and moving average filters are integrated together to eliminate harmonic EMF. To make the method effective under varying speed conditions, adaptive parameters design guidelines are provided, considering dynamic performance under a wide operating range. The proposed method can accurately detect rotor position even under harmonic EMF disturbances. It can also adjust the frequency adaptively based on the rotating speed of the rotor, which means the estimation performance is not deteriorated under rotating speed changing conditions. The simulation results verify the effectiveness of the proposed method.

Electrohydrodynamic (EHD) printing technique, which deposits micro/nanostructures through high electric force, has recently attracted significant research interest owing to their fascinating characteristics in high resolution (<1 μm), wide material applicability (ink viscosity 1–10 000 cps), tunable printing modes (electrospray, electrospinning, and EHD jet printing), and compatibility with flexible/wearable applications. Since the laboratory level of the EHD printed electronics' resolution and efficiency is gradually approaching the commercial application level, an urgent need for developing EHD technique from laboratory into industrialization have been put forward. Herein, we first discuss the EHD printing technique, including the ink design, droplet formation, and key technologies for promoting printing efficiency/accuracy. Then we summarize the recent progress of EHD printing in fabrication of displays, organic field‐effect transistors (OFETs), transparent electrodes, and sensors and actuators. Finally, a brief summary and the outlook for future research effort are presented. Electrohydrodynamic (EHD) printing presents unique advantages of ultra‐high resolution (<1 μm), wide ink compatibility (e.g., perovskite, quantum dots, 2D materials), and powerful multi‐mode‐tunability. To promote its practical applications in new materials patterning and industrial manufacturing, this review highlights the EHD based ink design, droplet formation, key technologies for promoting efficiency/accuracy, and wide applications in electronics fabrication.

To address the multi-peak characteristics of the P-U output characteristic curve when the photovoltaic array is partially shaded, a Maximum Power Point Tracking (MPPT) method combining Harris Hawk optimization, and a variable step conductance increment method is proposed in this article. Although the Harris Hawk Optimization algorithm has advantages in optimizing multi-modal P-U curves, when the lighting conditions suddenly change, the output characteristic curve will undergo drastic changes, causing the algorithm to oscillate repeatedly at the maximum power point. Moreover, due to its inherent limitations, the Harris Hawk algorithm has a low convergence accuracy, slow tracking speed, and is prone to getting trapped in a local optimum. In order to compensate for the shortcomings of the Harris Hawk Optimization algorithm, the variable step conductance increment method is switched for local exploration to improve the algorithm’s ability to escape from the local optimal solution and reduce power oscillation. Finally, the MPPT control of the proposed strategy was simulated and verified on the MATLAB/Simulink simulation (2021) platform. In the MPPT test experiment, the proposed composite algorithm was applied to simulate and verify uneven irradiance conditions.

With the rise of smart robots in the field of industrial automation, the motion control theory of the robot servo controller has become a research hotspot. The parameter mismatch of the controller will reduce the efficiency of the equipment and damage the equipment in serious cases. Compared to other parameters of servo controllers, the moment of inertia and friction viscous coefficient have a significant effect on the dynamic performance in motion control; furthermore, accurate real-time identification is essential for servo controller design. An improved integration method is proposed that increases the sampling period by redefining the update condition in this paper; it then expands the applied range of the classical method that is more suitable for the working characteristics of a robot servo controller and reducesthe speed quantization error generated by the encoder. Then, an optimization approach using the incremental probabilistic neural network with improved Gravitational Search Algorithm (IGSA-IPNN) is proposed to filter the speed error by a nonlinear process and provide more precise input for parameter identification. The identified inertia and friction coefficient areused for the PI parameter self-tuning of the speed loop. The experiments prove that the validity of the proposed method and, compared to the classical method, it is more accurate, stable and suitable for the robot servo controller.

The quality of aerial remote sensing imaging is heavily impacted by the thermal distortions in optical cameras caused by temperature fluctuations. This paper introduces a lumped parameter thermal network (LPTN) model for the optical system of aerial cameras, aiming to serve as a guideline for their thermal design. By optimizing the thermal resistances associated with convection and radiation while considering the camera’s unique internal architecture, this model endeavors to improve the accuracy of temperature predictions. Additionally, the proposed LPTN framework enables the establishment of a heat leakage network, which offers a detailed examination of heat leakage paths and rates. This analysis offers valuable insights into the thermal performance of the camera, thereby guiding the refinement of heating zones and the development of effective active control strategies. Operating at a total power consumption of 26 W, the thermal system adheres to the low-power limit. Experimental data from thermal tests indicate that the temperatures within the optical system are maintained consistently between 19 °C and 22 °C throughout the flight, with temperature gradients remaining below 3 °C, satisfying the temperature requirements. The proposed LPTN model exhibits swiftness and efficacy in determining thermal characteristics, significantly facilitating the thermal design process and ensuring optimal power allocation for aerial cameras.

Large area and highly aligned polymer semiconductor sub-microwires were fabricated using the coaxial focused electrohydrodynamic jet printing technology. As indicated by the results, the sub-microwire arrays have smooth morphology, well reproducibility and controllable with a width of ~110 nm. Analysis shows that the molecular chains inside the sub-microwires mainly exhibited edge-on arrangement and the π-stacking direction (010) of the majority of crystals is parallel to the long axis of the sub-microwires. Sub-microwires based organic field effect transistors showed high mobility with an average of 1.9 cm 2 V −1 s −1 , approximately 5 times higher than that of thin film based organic field effect transistors. In addition, the number of sub-microwires can be conveniently controlled by the printing technique, which can subsequently concisely control the performance of organic field effect transistors. This work demonstrates that sub-microwires fabricated by the coaxial focused electrohydrodynamic jet printing technology create an alternative path for the applications of high-performance organic flexible device. Here, the authors fabricate large area and highly aligned polymer semiconductor sub-microwires arrays via coaxial focused electrohydrodynamic jet printing technology, achieving high on/off ratio and average mobility that is 5x higher than that of thin film based organic field effect transistors.

In response to the non-linear and underactuated characteristics of quadrotor UAV suspension transportation system, this paper proposes a novel control strategy aimed at achieving precise position control, attitude control, and anti-swing capabilities. Firstly, a dynamical model required for controller design is established through the Newton-Euler method. In the controller design process, the paper employs the energy method and barrier Lyapunov function to design a double-closed-loop nonlinear controller. This controller is capable of not only accurately controlling the position and attitude angles of the quadrotor UAV suspension transportation system but also effectively suppressing the swing of the payload. Building on this, considering the elastic deformation of the lifting cable, and by analyzing the forces in the Newton-Euler equations, this paper proposes an adaptive control design for the case where the length of the cable connecting the UAV and the payload is unknown. To validate the effectiveness of the proposed control scheme, comparative experiments were conducted in the MATLAB simulation environment, and the results indicate that the method proposed in this paper exhibits superior control performance compared to traditional controllers.