Deep neural networks have made significant strides in medical image segmentation tasks, but their large-scale parameters and high computational complexity limit their applicability on resource-constrained edge devices. To address this challenge, this paper introduces a lightweight nuclear segmentation network called Attention-Enhanced U-Net (AttE-Unet) for cell segmentation. AttE-Unet enhances the network’s feature extraction capabilities through an attention mechanism and combines the strengths of deep learning with traditional image filtering algorithms, while substantially reducing computational and storage demands. Experimental results on the PanNuke dataset demonstrate that AttE-Unet, despite its significant reduction in model size—with the number of parameters and floating-point operations per second reduced to 1.57% and 0.1% of the original model, respectively—still maintains a high level of segmentation performance. Specifically, the F1 score and Intersection over Union (IoU) score are 91.7% and 89.3% of the original model’s scores. Furthermore, deployment on an MCU consumes only 2.09 MB of Flash and 1.38 MB of RAM, highlighting the model’s lightweight nature and its potential for practical deployment as a medical image segmentation solution on edge devices.

CRYSTALS-Kyber and CRYSTALS-Dilithium are representative lattice-based post-quantum cryptographic schemes, where number theoretic transform (NTT), inverse NTT (INTT), and point-wise multiplication (PWM) dominate polynomial arithmetic. Existing hardware accelerators are typically optimized for a single scheme or operation, resulting in limited scalability and redundant hardware resources. This paper presents a unified and scalable NTT/INTT/PWM architecture for both Kyber and Dilithium. A configurable two-dimensional processing-element (PE) array combined with an EVEN/ODD ping-pong memory organization enables conflict-free memory access and partial inter-stage pipelining. A unified K-RED-based modular multiplier supports either one Dilithium multiplication or two parallel Kyber multiplications using the same DSP resources, while PWM is mapped onto the existing PE array without requiring a separate complete PWM arithmetic array. Experimental results on a Xilinx Artix-7 FPGA show that the proposed modular multiplier reduces LUT utilization by 39.8% compared with the previous unified Kyber/Dilithium design. Under matched PE configurations, the proposed architecture achieves ATP reductions of up to 79.5% for Dilithium NTT/INTT and 73.9% for Kyber NTT/INTT, while the Kyber/Dilithium PWM mode achieves an ATP reduction of up to 78.3%. These results demonstrate that the proposed architecture provides an efficient, scalable, and highly reusable hardware solution for lattice-based post-quantum cryptographic accelerators.

Accurately predicting the operational status of mining drilling systems under complex coal and rock conditions remains challenging due to the intricate dynamic characteristics of drilling tools, highly variable operating conditions, and the pronounced long-term dependence associated with drill-jamming risk. To address these challenges, this study proposes a deep temporal prediction network for forecasting the operational status of mining drilling systems. Specifically, a multiscale dilated causal convolutional network is employed to extract temporal features at multiple scales, thereby capturing dependencies over different time horizons. Subsequently, a temporal convolutional network (TCN) integrated with an attention mechanism is used to model multidimensional temporal correlations among the extracted features and adaptively assign channel-wise weights, thereby emphasizing informative features while suppressing irrelevant information. This architecture enables the accurate prediction of the drill-jamming factor. Furthermore, the joint objective supplements pointwise prediction error with a global temporal-correlation preference. The experimental results demonstrate that the proposed method achieves satisfactory predictive performance and captures temporal variation within the evaluated drilling sequence.

Existing microseismic prediction methods commonly incorporate domain prior knowledge as ordinary input features, which makes it difficult to fully exploit its structural constraints and physical implications. To address this limitation, this study proposes a geological-structure-prior-guided method (SPG) for predicting the distribution of microseismic energy fields. In the study, a dataset was constructed from 11,081 original microseismic events, yielding 476 day-indexed microseismic energy maps. In SPG, historical microseismic energy maps are used to characterize the recent dynamic evolution of the energy field, whereas geological spatial fields, including coal-seam depth, coal-seam thickness, fault distance, fold distance, and goaf distance, are used to represent static structural priors. A decoder-side prior modulation mechanism is designed to introduce structural constraints into the reconstruction of the target-day energy field. The experimental results show that SPG achieves the best overall continuous-field prediction performance compared with a baseline U-Net and representative spatiotemporal prediction models, including PredRNN-V2, SwinLSTM, and Earthformer. In particular, SPG reduces the mean squared error (MSE) by 11.77% and improves the structural similarity index measure (SSIM) by 2.20% relative to the best-performing comparison model, while also decreasing the average structural gap by 21.29% compared with the baseline U-Net. Furthermore, supplementary high-energy-mask evaluation indicates that SPG maintains competitive capability in identifying high-energy regions. These results indicate that SPG can more effectively reconstruct continuous energy distributions, thereby providing a feasible methodological reference for spatially resolved rockburst early warning and domain-knowledge-informed predictive modeling.