To address prevalent industrial challenges, including the high cost of fabricating microstructures via photolithography and 3D printing, impurity residues easily generated by conventional physical/chemical pore-forming techniques, and the limited sensitivity of regular capacitive sensors, this paper innovatively proposes an integrated low-temperature in situ gas foaming strategy using ammonium bicarbonate for the fabrication of porous TPU-based ionic gels. Relying on the complete gaseous decomposition property of ammonium bicarbonate upon heating, a three-dimensionally interconnected continuous porous network is spontaneously constructed inside the polymer matrix. Thermoplastic polyurethane (TPU) is selected as the continuous polymer phase, and [EMIM][TFSI] imidazolium ionic liquid is blended as the ion source to synthesize composite ionic gel substrates. A PDMS composite slurry filled with graphene is employed to prepare flexible substrates, followed by low-temperature oxygen plasma surface modification to introduce polar functional groups such as hydroxyl and carboxyl onto electrode surfaces. A standard sandwich-structured ionic pressure sensor with the configuration of “top modified electrode—porous ionic gel dielectric layer—bottom modified electrode” is finally assembled. The porous framework and modified electrodes constitute a dual synergistic enhancement system: the porous structure markedly reduces the equivalent elastic modulus of the gel and improves its compressive deformation capacity; polar-modified electrodes optimize the interfacial compatibility between electrodes and gels, shorten ion migration paths and lower interfacial contact resistance. Systematic calibration of multiple batches of parallel samples reveals that the as-fabricated sensor achieves a high sensitivity of 25.3 kPa⁻¹ across the full measuring range from 0 to 1000 kPa with a linear fitting coefficient R² = 0.992. The loading response time and unloading recovery time of the device are 60 ms and 80 ms respectively, with a performance degradation of less than 3% after 1000 consecutive loading–unloading cycles, featuring low hysteresis error and excellent signal repeatability. Multi-scenario in vivo wearable tests on human subjects verify that the device can precisely capture subtle fluctuations of radial artery pulse and periodic laryngeal deformation during swallowing, distinguish characteristic waveform patterns of various English words according to differences in vocal cord vibration, and accurately detect bending motions when attached to finger joints. The entire fabrication process adopts common chemical raw materials and standard laboratory equipment without expensive micro-nano processing facilities, featuring convenient raw material procurement and high process fault tolerance, which enables large-area coating-based mass production. This work delivers a novel technical route for the low-cost large-scale production of high-performance ionic flexible sensors and bears significant industrialization reference value for applications in wearable medical monitoring, bionic robotic electronic skin, flexible human–machine interactive touch panels and other related fields.

The combustion temperature field is a key indicator of combustion stability, equipment integrity, and abnormal operating conditions. This study addresses short-horizon forecasting of laboratory-scale apparent infrared temperature fields using a Convolutional Long Short-Term Memory (ConvLSTM) network. A chronological 70/15/15 split is applied to 1800 available sequences, with the first 10 frames of each sample treated as observed inputs. Proper orthogonal decomposition (POD) is applied exclusively to the input frames at a 95% cumulative energy retention threshold, and predictions are evaluated on subsequent frames. POD is therefore used as an input-side low-rank modal preprocessing method to extract dominant coherent structures and suppress redundant variations, rather than as a reduction in the ConvLSTM network architecture itself. Performance is assessed via normalized domain PSNR/SSIM and Kelvin domain errors. The first three forecast steps maintain appreciable image similarity, while the fourth and fifth steps exhibit marked degradation. Therefore, conclusions are restricted to short-horizon forecasting, with no assertions regarding closed loop combustion control or cross-setup generalization. Simultaneously, ablation experiments with the same architecture, including and excluding the POD preprocessing step, quantified the individual contribution of the POD preprocessing step.

Despite the aim of meeting the demand for long-term online monitoring of structural cracks in fields such as infrastructure, rail transit, aerospace and others, traditional detection methods fail to realize passive wireless, flexible conformal and non-contact measurement. This paper proposes a flexible wireless passive crack sensor based on resonant rings. Taking polyimide (PI) as the substrate, the sensor integrates a sensitive interdigital resonant ring structure. Variations in crack width disturb the electromagnetic field, which further leads to a resonant frequency shift to realize crack width detection. The sensing mechanism is elaborated based on microwave resonance and equivalent circuit theories. Structural optimization and crack width sensitivity analysis are carried out via electromagnetic simulation. Samples are fabricated by flexible printing technology, and a test platform is established. Experiments reveal that the sensor achieves excellent linearity within the crack width range of 0~2.5 mm, with the resonant frequency decreasing monotonically as crack width increases, and a sensitivity of 67.02 MHz/mm. It can operate stably under varying distances, installation angles and bending conditions, demonstrating outstanding flexible conformability. Featuring no power supply requirement, a chip-free design, a simple structure and strong anti-interference capability, the sensor is suitable for long-term crack monitoring of metal structures. Compared with existing studies, the proposed sensor exhibits prominent advantages in flexible adaptability, wireless passive performance and engineering practicability and can provide a novel wireless passive solution for structural health monitoring.

To address the challenges of complex operation, high server deployment costs, and insufficient automated identification capabilities in community-based centralized electrocardiogram (ECG) screening, a novel arrhythmia screening system based on handheld ECG leads and email transmission is proposed. The system is operated in a zero-interaction mode: ECG acquisition is initiated automatically upon skin contact with the electrodes, and upon completion, the ECG signal is automatically analyzed and the email transmission function is triggered—no user intervention being required. First, noise in the ECG signal is effectively suppressed by cascading a zero-phase high-pass filter with a sliding window and a zero-crossing-rate (ZCR) guided adaptive wavelet thresholding technique. Subsequently, RR interval sequences are extracted from the denoised signals and fed into a lightweight bidirectional long short-term memory (BiLSTM) network for automatic arrhythmia detection. In the final step, a 30 s standard ECG, screening status, and acquired image are automatically delivered to clinicians via standard IMAP/SMTP email protocols—eliminating the need for dedicated mobile applications or cloud platforms. Experimental results demonstrated that the relative signal-to-noise ratio (SNR_ECG) was improved by 2.36 dB. On the independent test set, a sensitivity of 97.98%, a specificity of 98.21%, and an AUC of 0.994 were achieved. Furthermore, an end-to-end email transmission latency of less than 7.68 s was recorded. These findings confirm the potential of the proposed system as a low-cost, easily deployable, and elderly-friendly remote ECG solution for primary healthcare settings. Finally, in a pilot screening involving 10 volunteers, one case of arrhythmia was successfully identified, which validated the feasibility of the system.

To address the challenges of data processing caused by uncertain emissivity in multispectral radiation thermometry, this paper proposes a temperature retrieval method based on the Chaotic Artificial Hummingbird Algorithm (CAHA). Without relying on an assumed emissivity model, the method can automatically identify the emissivity distribution and selects the optimal output through multiple iterations to enhance accuracy. Simulations and offline tests conducted on rocket nozzles demonstrate that CAHA maintains high accuracy both in noise-free conditions and under 5% noise, with a single execution time of approximately 0.15 s. Furthermore, the method is validated through experiments on blackbody sources and candle flames: the relative error in retrieved temperature for blackbody sources remains below 0.93%, while the retrieved outer flame temperature of candle flames shows a relative error of 0.66% compared with thermocouple measurements. Combining high precision with rapid computation, this method is suitable for practical applications in radiation thermometry.