Infrared cameras form images by capturing the thermal radiation emitted by objects in the infrared spectrum, making them complex sensors widely used in maritime surveillance. However, the broad spectral range of the infrared band makes it susceptible to environmental interference, which can reduce the contrast between the target and the background. As a result, detecting infrared targets in complex marine environments remains challenging. This paper presents a novel and enhanced detection model developed from the real-time detection transformer (RT-DETR), which is designated as MAFF-DETR. The model incorporates a novel backbone by integrating CSP and parallelized patch-aware attention to enhance sensitivity to infrared imagery. Additionally, a channel attention module is employed during feature selection, leveraging high-level features to filter low-level information and enabling efficient multi-level fusion. The model’s target detection performance on resource-constrained devices is further enhanced by incorporating advanced techniques such as group convolution and ShuffleNetV2. The experimental results show that, although the enhanced RT-DETR algorithm still experiences missed detections under severe object occlusion, it has significantly improved overall performance, including a 1.7% increase in mAP, a reduction in 4.3 M parameters, and a 5.8 GFLOPs decrease in computational complexity. It can be widely applied to tasks such as coastline monitoring and maritime search and rescue.

In the field of new environmental energy harvesting technology, wind energy and piezoelectric energy harvesting have become research hotspots. By utilizing wind energy into vibration energy, i.e., wind-induced vibration effect is a research hotspot in the field of environmental energy harvesting. This article comprehensively analyzes the research status and development trend of piezoelectric energy harvesters based on wind-induced vibration effects at home and abroad. It was found that the conversion process of wind energy to vibration energy is mainly based on flutter, galloping, and acoustic resonance. The conversion of vibration energy into electrical energy is mainly piezoelectric power generation, supplemented by magnetic induction power generation and friction power generation. For high power density and small volume application scenarios, resonant cavity type wind vibration energy collection is the main development trend.

The effects of Fe, Cr and thermal exposure on the microstructure and mechanical properties of Al-Si alloy were investigated in this work. The results indicated that the main phases of the Al-Si-Cu-Ni-Mg-(0.6–0.9%) Fe (wt. %) alloy were α-Al, Si, Al₅Cu₂Mg₈Si₆, Al₃CuNi, Al₇Cu₄Ni, Al₂Cu, and AlFeSi at room temperature. The size of the AlSiFe phase increased with increasing the weight fraction of Fe. The shape of Fe-rich phase changed from rod-like to star-like, followed by long needle-like with Fe varying from 0.6% to 0.9%. The mechanical properties of the studied alloys at elevated temperatures increased with Fe. The ultimate tensile strength of the three alloys at 350 °C was 111.2 MPa, 124 MPa, and 128.7 MPa, respectively. In addition, the ductility and strength of the studied alloys at room temperature decreased with increasing the Fe, due to the large size of the hard and brittle Fe-rich phase strictly cleaved the aluminum matrix. After thermal exposure, the properties of the alloy at room temperature and elevated temperature decreased obviously at the beginning of 0.5~8 h, and then tend to be stabilized during thermal exposure at 350 °C for approximately 32~64 h. Fe-rich was a thermal stable phase at 350 °C.

Autonomous UAV landing on dynamic unmanned surface vessel platforms is affected by deck motion and degraded visual observations, which may lead to unsafe final descent decisions. This paper proposes a fully decentralized reliability-enhanced predictive landing method that combines probabilistic perception, visual quality assessment, and model predictive control. Target posterior probability, perception uncertainty, and task-oriented image quality are fused into an online observation reliability index, which is used to adapt observation noise, constrain phase switching, and penalize unreliable descent opportunities. FFT-based dominant-mode identification and Kalman correction are also used to predict deck roll and pitch for landing-window selection. Simulation results show that the proposed method achieves a 90% small-angle landing success rate and keeps the touchdown attitude angle within 5°. Compared with standard MPC, landings within a 15° deck inclination increase from 24% to 82%, and the 80th-percentile touchdown inclination decreases by 9°. Compared with SHMPC, the average solution time decreases from 913 ms to approximately 104 ms per iteration. These results indicate that the proposed reliability-aware framework can reduce unsafe descent decisions and improve landing robustness while maintaining real-time feasibility under degraded maritime visual conditions.

Maintaining prescribed joint motion while allowing for compliant responses to changing interaction is challenging in passive lower limb rehabilitation. We propose interaction error-driven adaptive admittance control (IE-AAC), which subtracts the predicted nominal-interaction difference between reference and commanded states. A soft dead zone processes the corrected residual before bounded scheduling of virtual damping, stiffness, and interaction mapping gain. We evaluate the controller using a common reference reconstructed from simulated therapist demonstrations and a phenomenological interaction model. In 100 strictly paired Monte Carlo trials, IE-AAC reduced the equally weighted hip–knee–ankle mean reference deviation RMSE by 20.26%, 32.09%, and 28.23% relative to fixed admittance and two literature-derived variable admittance comparators (Zhang-2018 and Cao-2024), respectively. All four controllers in this comparison share mismatch compensation, so the reductions quantify differences in the implemented scheduling laws; a separate ablation evaluates compensation itself. The results include knee-specific trade-offs in tracking, smoothness, and interaction residual.