Rolling bearings are pivotal components within rotating mechanical systems, and accurately predicting their remaining service life holds significant practical importance. This paper addresses issues prevalent in common deep learning methods for predicting remaining useful life (RUL), notably inadequate feature extraction and low prediction accuracy resulting from reliance solely on short-term or long-term dependent features.In this paper, we introduce a residual useful life prediction method for bearings, named TcLstmNet-CBAM. Compared to conventional deep learning-based bearing life prediction methods, the proposed approach leverages a temporal convolutional network (TCN) to extract long-term temporal dependencies and higher-level spatial features from historical data, while employing a long short-term memory (LSTM) network to capture short-term temporal dependencies and sequence relationships. Additionally, a Convolutional Block Attention Module (CBAM) is incorporated to perform multi-dimensional weighting on the extracted features, assigning greater importance to critical features. Consequently, this method enables more comprehensive feature extraction and enhances RUL prediction accuracy by emphasizing key features. Finally, to validate the effectiveness of the proposed approach, we conducted experiments on the PHM2012 and XJTU-SY rolling bearing datasets, comparing its performance against several other prevalent deep learning prediction methods. Experimental results demonstrate that the proposed TcLstmNet-CBAM method can effectively predict the remaining useful life (RUL) of bearings, achieving a mean absolute error (MAE) of 2.287 and a root mean square error (RMSE) of 3.123. These results strongly validate the effectiveness and superiority of the proposed method.

Rice spikelet size, spikelet length and spikelet width, are very important traits directly related to a rice crop’s yield. The accurate measurement of these parameters is quite significant in research such as breeding, yield evaluation and variety improvement for rice crops. Traditional measurement methods still mainly rely on manual labor, which is time-consuming, labor-intensive and error-prone. In this study, a novel method, dubbed the “SSM-Method”, based on convolutional neural network and traditional image processing technology has been developed for the efficient and precise measurement of rice spikelet size parameters on rice panicle structures. Firstly, primary branch images of rice panicles were collected at the same height to build an image database. The spikelet detection model using convolutional neural network was then established for spikelet recognition and localization. Subsequently, the calibration value was obtained through traditional image processing technology. Finally, the “SSM-Method” integrated with a spikelet detection model and calibration value was developed for the automatic measurement of spikelet sizes. The performance of the developed SSM-Method was evaluated through testing 60 primary branch images. The test results showed that the root mean square error (RMSE) of spikelet length for two rice varieties (Huahang15 and Qingyang) were 0.26 mm and 0.30 mm, respectively, while the corresponding RMSE of spikelet width was 0.27 mm and 0.31 mm, respectively. The proposed algorithm can provide an effective, convenient and low-cost tool for yield evaluation and breeding research.

The recent discovery of spherical trihedral metallo-borospherenes Ln₃B₁₈⁻ (Ln = La, Tb) represents the onset of an unprecedented class of boron-metal binary nanomaterials, where heterogeneous La or Tb atoms, alongside B atoms, constitute integral components of the outer cage surface. Here, C_3v A₁-Sc₃B₁₆⁺ is theoretically predicted as the global minimum by the particle swarm optimization (PSO) algorithm, and its B₁₆-framework comprises unequal B₆ and B₇ triangles, connected by three B atoms between the vertexes of the B₆ triangle and the interval edges of the B₇ triangle. This arrangement forms three equivalent η⁷-B₇ rings, each centered with a Sc atom. The cage surface of the second low-lying A₂ isomer features an asymmetric triangle of B₃ and B₇, along with three B₂-linked units. This configuration gives rise to three equivalent octacoordinated Sc atoms, each centered within η⁸-B₈ rings. These two cationic isomers have been proven to be very stable in kinetics and thermodynamics. Our findings regarding unequal boron triangles and smaller odd-coordinated rings in the B-framework enrich the geometric patterns of trihedral metallo-borospherenes.

Rolling bearings represent one of the core functional components of rotating machinery, with their application scope continuously expanding into various sectors of modern social production and life, making the research on fault diagnosis of rolling bearings increasingly significant. Effective vibration feature extraction and improved classification models are crucial to achieving accurate and automated fault diagnosis of rolling bearings. We proposed a fault diagnosis approach based on a Swin Transformer–Improved ResNet module. In the data preprocessing stage, the frequency-domain features and time-domain multi-scale features of fault signals are extracted using FFT and VMD methods, respectively. And then, dual-channel feature extraction is employed using both the Swin Transformer and Improved ResNet module, followed by feature fusion through an ECA module, thereby enhancing diagnostic accuracy and model robustness. The architecture retains shallow-level feature details while incorporating global contextual information, improving feature representation and detection precision. Extensive experiments were carried out on data collected from an SEU bearing dataset, including model validation, ablation analysis, comparative evaluation and simulated noise testing. An average classification accuracy of 99.41% was achieved by the proposed model under uniform experimental conditions, as evidenced by the obtained experimental results, outperforming other models by at least 0.96%. Even under severe noise interference with a signal-to-noise ratio of −4, the model maintained an average accuracy of 91.92%, exceeding that of noise-resistant counterparts. Moreover, generalization experiments on the CWRU bearing dataset under varying load conditions revealed an average fault diagnosis accuracy exceeding 98%, confirming the model’s strong cross-domain adaptability.

In recent years, the application scenarios of Unmanned Aerial Vehicles (UAVs) have become increasingly widespread. As core components of UAVs, rotor blades’ health status is directly related to flight safety. Aiming at issues such as insufficient feature extraction, weak noise resistance, and low diagnostic accuracy in the crack fault diagnosis of UAV rotor blades, this study proposes a one-dimensional deep network integrating multi-scale convolution, a multi-path residual module, BiLSTM, and a self-attention mechanism, referred to as 1dMC-MPR-SABinet. Taking the triaxial (X, Y, Z) vibration signals of rotor blades as input, the method integrates a multi-scale convolution module and a multi-path residual module, models the bidirectional temporal dependencies of signals through Bi-LSTM, and is combined with a self-attention mechanism to enhance the capture of subtle fault features. Meanwhile, it adopts the Northern Goshawk Optimization algorithm to optimize hyperparameters, thereby improving stability in noisy environments. Experiments are validated based on a self-collected fault vibration dataset, with precision, recall, and F1-score as evaluation metrics. The results show that the proposed model achieves a diagnostic accuracy of 99.37% under noise-free conditions without NGO-based hyperparameter optimization, representing a maximum improvement of 6.50% over the comparative models. Under a strong noisy condition with SNR = 1, the base model achieves 91.95% accuracy, while after NGO-based hyperparameter optimization, the model performance is further improved, with the precision, recall, and F1-score reaching 97.64%, 97.78%, and 97.01%, respectively. Ablation experiments and generalization experiments further verify the rationality and effectiveness of the proposed architecture.

The Flexible Job Shop Scheduling Problem (FJSP) is central to smart manufacturing, yet standard algorithms often prioritize productivity (makespan) at the expense of cost and reliability. This paper introduces t-MOHHO, a collaborative optimization framework designed to equilibrate machine load, processing costs, and delivery timeliness alongside throughput. By incorporating an adaptive Student’s t-distribution mutation operator and a non-linear energy escape mechanism, t-MOHHO effectively navigates high-dimensional search spaces. Extensive validation on 10 MK benchmark instances reveals that t-MOHHO demonstrates significant advantages over classic HHO, MOPSO, and MOEA/D across most metrics. Notably, in comparison to the state-of-the-art NSGA-III, t-MOHHO executes a clear trade-off: it trades marginal makespan efficiency for substantial reductions in cost and tardiness. Specifically, on the large-scale MK10 instance, t-MOHHO reduces total tardiness by 56.2% and lowers processing costs by 3.4% compared to NSGA-III. These results demonstrate that t-MOHHO can strategically sacrifice maximum speed to secure superior punctuality and cost-efficiency, making it a robust decision-support tool for Just-in-Time (JIT) production environments.