Recognizing facial expression has attracted much more attention due to its broad range of applications in human–computer interaction systems. Although facial representation is crucial to final recognition accuracy, traditional handcrafted representations only reflect shallow characteristics and it is uncertain whether the convolutional layer can extract better ones. In addition, the policy that weights are shared across a whole image is improper for structured face images. To overcome such limitations, a novel method based on patches of interest, the Patch Attention Layer (PAL) of embedding handcrafted features, is proposed to learn the local shallow facial features of each patch on face images. Firstly, a handcrafted feature, Gabor surface feature (GSF), is extracted by convolving the input face image with a set of predefined Gabor filters. Secondly, the generated feature is segmented as nonoverlapped patches that can capture local shallow features by the strategy of using different local patches with different filters. Then, the weighted shallow features are fed into the remaining convolutional layers to capture high-level features. Our method can be carried out directly on a static image without facial landmark information, and the preprocessing step is very simple. Experiments on four databases show that our method achieved very competitive performance (Extended Cohn–Kanade database (CK+): 98.93%; Oulu-CASIA: 97.57%; Japanese Female Facial Expressions database (JAFFE): 93.38%; and RAF-DB: 86.8%) compared to other state-of-the-art methods.

In addition to high compliance to unstructured environments, soft robots can be further improved to gain the advantages of rigid robots by increasing stiffness. Indeed, realizing the adjustable stiffness of soft continuum robots can provide safer interactions with objects and greatly expand their application range. To address the above situation, we propose a tubular stiffening segment based on layer jamming. It can temporarily increase the stiffness of the soft robot in a desired configuration. Furthermore, we also present a spine-inspired soft robot that can provide support in tubular segments to prevent buckling. Theoretical analysis was conducted to predict the stiffness variation of the robot at different vacuum levels. Finally, we integrated the spine-inspired soft robot and tubular stiffening segment to obtain the tuneable-stiffness soft continuum robot (TSCR). Experimental tests were performed to evaluate the robot’s shape control and stiffness tuning effectiveness. Experimental results showed that the bending stiffness of the initial TSCR increased by more than 15× at 0°, 30× at 90°, and 60× in compressive stiffness.

Since climbing robots mainly rely on adhesion actuators to achieve adhesion, robust adhesion actuators have always been the challenge of climbing robot design. A novel under-actuated soft adhesion actuator (USAA) proposed in this paper for climbing robots can generate adhesion through robot’s load applied to the actuator. The actuator is composed of a soft film/substrate structure with an annular groove on the substrate and a cavity on the soft film. To fabricate the actuator, we first study the influence of the geometric parameters of the USAA on the maximum adhesion of the actuator by analysis and experiments, and then combine these parameters and the boundary conditions of the static meniscus in the mold to design the mold. Moreover, we fabricate a climbing robot equipped with USAAs and evaluate its performance on horizontal and inclined surfaces with a wide range of characteristics. The USAA can generate strong and controllable adhesion to various smooth and semi-smooth surfaces. Furthermore, the fabricated robot performs well on various surfaces under a certain load (at least 500 g) and speed (369 mm/min) through experiments. It’s adaptability to a variety of surfaces enables a wide range of applications and pushes the boundaries of soft adhesion actuators.

Modeling and control methods for stiffness-tunable soft robots (STSRs) have received less attention compared to standard soft robots. A major challenge in controlling STSRs is their infinite degrees of freedom, similar to standard soft robots. In this paper, demonstrate a novel STSR by combing a soft-rigid hybrid spine-mimicking actuator with a stiffness-tunable module. Additionally, we introduce a new kinematic and dynamic modeling methodology for the proposed STSR. Based on the STSR characteristics, we model it as a series of PRP segments, each composed of two prismatic joints(P) and one revolute joint(R). This method is simpler, more generalizable, and more computationally efficient than existing approaches. We also design a multi-input multi-output (MIMO) controller that directly adjusts the pressure of the STSR’s three pneumatic chambers to precisely control its posture. Both the novel modeling methodology and MIMO control system are implemented and validated on the proposed STSR prototype.

Micro-sandblasting pretreatment was applied to AlTiSiN-coated WC–Co tools to enhance cutting performance in 316 L stainless steel milling. An L₉(3³) Taguchi orthogonal array varied passivation pressure (0.1, 0.2, and 0.3 MPa), gun traverse speed (60, 80, and 100 m/min), and tool rotation speed (20, 30, and 40 r/min). Coating thickness varied only from 0.93 to 1.19 μm, and surface roughness remained within 0.044–0.077 μm, confirming negligible thickness and roughness effects. Under optimized conditions, coating adhesion strength and nano-hardness both exhibited significant improvements. A weighted-scoring method balancing these two responses identified the optimal pretreatment parameters as 0.1 MPa, 80 m/min, and 20 r/min. Milling tests at 85 m/min—using flank wear VBₘₐₓ = 0.1 mm as the failure criterion—demonstrated a cutting distance increase from 4.25 m (untreated) to 12.75 m (pretreated), a 200% improvement. Wear progressed through three stages: rapid initial wear, extended steady wear due to Al₂O₃ protective-film formation and Si-induced oxygen-diffusion suppression, and accelerated wear. Micro-sandblasting further prolonged the steady-wear phase by removing residual cobalt binder, exposing WC grains, and offsetting tensile residual stresses. These findings establish a practical, cost-effective micro-sandblasting pretreatment strategy that significantly enhances coating adhesion, hardness, and tool life, providing actionable guidance for improving the durability and machining performance of coated carbide tools in difficult-to-cut applications.