Multimodal graph-based recommendation has shown strong potential by jointly modeling user–item interactions and rich multimodal item content. However, existing methods often suffer from noisy features, severe popularity bias, and uniform message propagation that fails to adapt to heterogeneous item popularity and varying signal reliability. To address these issues, we propose PAMS-GNN, a Popularity-Aware Multimodal Semantic Graph Neural Network that formulates recommendation as conditional message passing over collaborative and semantic graphs. The core idea is to adapt both propagation behaviors and channel reliability according to item popularity and local structural conditions. PAMS-GNN introduces a compact prototype-based framework with popularity-aware routing, enabling items from different popularity regimes to adopt distinct multi-hop propagation behaviors with minimal overhead. In addition, a variance-guided fusion mechanism adaptively balances collaborative and semantic signals based on local reliability, while lightweight projection and gating are used to purify multimodal representations and stabilize semantic graph construction. Extensive experiments on multiple benchmarks demonstrate that PAMS-GNN consistently outperforms state-of-the-art methods, effectively alleviating popularity bias and improving long-tail recommendation performance with favorable efficiency.

Traumatic brain injury (TBI) assessment is crucial for protecting the health of casualties involved in sudden head-impact incidents. However, traditional methods for assessing TBI are primarily based on bulky imaging equipment, which suffers from time lag, leading to misjudgment of the injury and missing the golden opportunity for treatment. Moreover, most existing Internet of Things (IoT)-aided sensor-based head impact detection studies monitor only a single biomechanical parameter, either the external head impact force or the head center-of-mass acceleration, thus offering an incomplete injury profile. This study proposed a method for rapidly acquiring on-site time-series data to support TBI assessment, which employed flexible piezoelectric sensors to detect head impact, whose electromechanical coupling was characterized by an equivalent-impedance model identified with the genetic algorithm (GA). The voltage signal from the sensor was transmitted to mobile devices via Bluetooth to estimate head-impact biomechanics. Specifically, the impact force on the wearer’s head was converted from the voltage using the sequential quadratic programming algorithm (SQP). Based on the impact force data, the acceleration of the center of mass of the head was predicted with a neural network containing the long short-term memory (LSTM) layer. The experiments were carried out on a head-neck dummy subjected to different levels of impact. The resulting models achieved coefficients of determination (R2) of 0.935 for impact force (amplitude of 0–3 kN) and 0.848 for acceleration of the center of mass (amplitude of 0–120 g). The fabricated wireless flexible wearable sensor was installed on the dummy for performance validation, successfully demonstrating collision detection, impact force and acceleration estimation for TBI assessment, which can be conveniently deployed in helmets used in construction, sports, and emergency rescue, leveraging IoT technology for real-time dual-parameter head-impact estimation and medical-data synchronization to safeguard the golden treatment window and hopefully promote the efficient allocation of medical resources.

Recently, the detection of volatile organic compounds (VOCs) has received extensive attention in the field of industrial pollutants detection. As a microgravity sensor based on microelectromechanical system (MEMS), film bulk acoustic resonator (FBAR) plays an important role in the detection of micro-mass and composition. Combined with metal-organic framework (MOF), a type of porous crystal material, FBAR has the ability to adsorb micro-VOC vapor with high sensitivity. Herein, we proposed a MIL-101(Cr) MOF-based FBAR sensors with high resonant frequency and Q factor for five VOCs detection (acetone, ethanol, isopropanol, acetonitrile, and methanol), realizing high sensitivity and stable sensing performance. The experimental data shows the highest sensitivity is 35.15 Hz/ppm for acetone with the limit of detection (LOD) of 322 ppm, which can provide support in ensuring industrial production safety and VOC real-time in-situ detection.

Graph active learning algorithms can reduce the amount of labeling and improve the applicability of graph neural networks. However, existing graph active learning algorithms are mainly performed at the node granularity. Those setting does not hold to datasets that are sensitive to edge attributes. To solve this problem, we propose a graph active learning algorithm at subgraph granularity. The algorithm tackles two critical challenges: how to estimate the expected labeling value of subgraphs and how to search for high-value subgraphs in the whole graph efficiently. For the first challenge, we evaluate the expected labeling values of subgraphs based on heuristic metrics, including uncertainty, representativeness, centrality, and diversity. Among them, uncertainty cannot be measured directly. Therefore, we measure subgraph inner cohesion by GNN attention weights and estimate uncertainty based on it. For subgraph search, we propose an efficient subgraph search algorithm. The proposed algorithm includes a simulated annealing search algorithm for a single subgraph and beam search with subgraph-effective reception field algorithms for multiple subgraphs. Experiments demonstrate that the subgraph granularity active learning algorithm proposed in this paper can achieve great results on edge-sensitive datasets.

The well-functioning of disc-pad brake subsystems concerns the safe operation of both automobiles and trains. However, there is a lack of effective constant condition monitoring methods for the vital disc-pad brake pairs in such brake subsystems of driving vehicles. Therefore, we propose a brake pair misalignment detection scheme based on a battery-free electromagnetic-based gap sensor, to deal with several common brake pair misalignments including distance changes, translational deviations, and rotary deflections. The alternating-polarity magnet array in the proposed sensor on the brake disc generates a unique distribution of the effective magnetic flux density within a relatively moving microfabricated planar coil sheet along with the brake pad, which is sensitive to the varied gap if misalignments occur between the disc-pad brake pair. Thus, discriminative voltage performance is induced by the inductive planar coils in response to different misalignment types and degrees. Comprehensive finite element simulation analysis and experiments are conducted to elaborate the misalignment detection capability of the proposed sensor. The proposed scheme has the potential to enhance the self-perception of intelligent vehicle systems, without adding the power consumption burden due to the battery-free nature of the electromagnetic-based sensor.

Pressure sensors are essential in precise robotic operations because they can provide force feedback information. In this paper, we propose a soft piezoresistive pressure sensor consisting of a top porous conductive polymer composite (PCPC) layer and a bottom interdigital electrode (IDE) layer, demonstrating a high sensitivity up to 58.60%$\text{kPa}^{-1}$and a hysteresis error as low as 3.18%. Devices with different pore sizes and IDE geometric parameters are fabricated and experimentally characterized. Results show the dependence of the sensor performance on porosity and IDE configuration, which is consistent with theoretical analysis. The proposed technique enables a customized design of soft piezoresistive pressure sensors for different sensing ranges. As a practical demonstration, the proposed pressure sensor is integrated with a soft robotic gripper to perform delicate manipulation tasks.

A unity feedback length-extend multistage noise-shaping (MASH) delta-sigma modulator (DSM) is presented in this paper. The proposed length extension technique adds a feedback path, like HK-MASH, and fixes the feedback factor to unity. In this way, only one additional register is needed, which decreases the operating time and the hardware cost of DSM, and achieves maximum sequence length of M-1 ($M = {2^{{n_0}}}$, n0is input word length). Using this structure, MASH DSMs are optimized with maximum sequence length extending to (M-1)l( l is the order of the MASH DSM) and the minimum extending to N(M-1)l-1( N is the smallest prime number of M-1). This paper proves that the output sequence length is exponentially increased, regardless of the input value. Compared with classical structure, the proposed MASH 1-1-1 structure shows a spur-free performance at the expense of limited hardware cost.