Suzhou Vocational University

Modular arithmetic enjoys a broad spectrum of applications. In recent years, the advancement of post-quantum cryptography (PQC) has imposed growing demands on the flexibility and scalability of domain-specific accelerators. This article presents a fast modular reduction algorithm based on generalized Mersenne (GM) primes, which employs approximate scaling and iterative compression approaches to rapidly converge the quotient value while reducing the precomputation complexity to rely solely on the modulus itself. Building upon this algorithm, we have designed a reconfigurable modular reduction array using multiplier units with smaller word length. Operating at 1GHz, the proposed array achieves an area reduction of 45.14% compared to Barrett-based structures, and 13.93% compared to Montgomery-based structures. The array has been integrated into a complete number-theoretic transform (NTT) acceleration architecture. The resulting reconfigurable GM/general modular reduction domain-specific architecture elevates the chip frequency to <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$0.42\sim 1$</tex-math> </inline-formula> GHz under the same process technology. Under identical test conditions, it improves area efficiency by 10.6% and reduces energy consumption by 17.0% compared to the state-of-the-art ASIC design. When compared to the latest field-programmable gate array (FPGA) implementations, it achieves a reduction in area-time product (ATP) by 7.2%~18.4% for Kyber and by 13.4% for Dilithium. These results strongly demonstrate the notable advantages of the hardware-friendly GM algorithm.

This paper deals with the following parabolic-parabolic-parabolic chemotaxis system with indirect signal production and nonlinear sensitivity:$ \begin{equation} \begin{cases} u_t = \nabla\cdot((u+1)^\alpha\nabla u)-\chi\nabla\cdot(uv^k\nabla v)+u(a-bu), &amp; t &gt;0, \; x\in\Omega, \\ v_t = \Delta v-v+w, &amp; t &gt;0, \; x\in\Omega, \\ w_t = \Delta w-w+u, &amp; t &gt;0, \; x\in\Omega, \\ \frac{\partial u}{\partial\nu} = \frac{\partial v}{\partial\nu} = \frac{\partial w}{\partial\nu} = 0, &amp; t &gt;0, \; x\in\partial\Omega, \\ u(0, x) = u_0(x), \; \; v(0, x) = v_0(x), \; \; w(0, x) = w_0(x), &amp; x \in\Omega, \end{cases} \end{equation} $where $ \Omega\subset\mathbb{R}^2 $ is a bounded domain with smooth boundary, $ \alpha, k, a, b&gt;0, $ and $ \chi\in \mathbb{R}. $ The key to controlling the singular chemotaxis term $ -\chi\nabla\cdot(uv^k\nabla v) $ for proving the global existence of classical solutions to (1) is to ensure the boundedness of $ \|v\|_{L^\infty} $. To this end, we prove the boundedness of $ \|v\|_{L^\infty} $ by constructing the energy functional $ \int_ \Omega u\ln u. $ Combining this with Gagliardo-Nirenberg inequality, the boundedness of $ \|u\|_{L^2} $ is obtained. With the help of these estimates, we prove system (1) has a global bounded classical solution. Finally, if $ \chi&gt;0, $ and $ b $ is appropriately large, it is shown that the solution exponentially converges to the constant stationary solution $ (\frac{a}{b}, \frac{a}{b}, \frac{a}{b}) $ as $ t\rightarrow \infty. $

The rapid evolution of unmanned aerial vehicle (UAV) technology and low-altitude economic development have propelled drone applications in critical infrastructure monitoring, particularly in intelligent transportation systems where real-time aerial image processing has emerged as a pressing requirement. We address the pivotal challenge of highway lane extraction from low-altitude UAV perspectives by applying a novel three-stage framework. This framework consists of (1) a deep learning-based semantic segmentation module that employs an enhanced STDC network with boundary-aware loss for precise detection of roads and lane markings; (2) an optimized polynomial fitting algorithm incorporating iterative classification and adaptive error thresholds to achieve robust lane marking consolidation; and (3) a global optimization module designed for context-aware lane generation. Our methodology demonstrates superior performance with 94.11% F1-score and 93.84% IoU, effectively bridging the technical gap in UAV-based lane extraction while establishing a reliable foundation for advanced traffic monitoring applications.

Permanent magnet synchronous motors (PMSMs) have been widespread used in high-performance automation due to their superior control precision and energy efficiency. With the development of digital processors, deadbeat predictive control (DPC) has promising control strategy for PMSM drive systems. However, the performance of DPC is highly sensitive to inaccuracies in system parameters, while the system delay and total harmonic distortion can also significantly affect its control effectiveness. This paper presents a comprehensive review of the advantages of DPC strategies applied to PMSMs, as well as their limitations in practical applications. Several improvement approaches are also discussed, including parameter identification methods, robust control methods, and delay compensation methods. Finally, the paper explores future research trends in this field, highlighting potential directions for the development of predictive control in PMSM drive systems.

BackgroundProtein-protein interactions (PPIs) play a critical role in essential biological processes such as signal transduction, enzyme activity regulation, cytoskeletal structure, immune responses, and gene regulation. However, current methods mainly focus on extracting features from protein sequences and using graph neural network (GNN) to acquire interaction information from the PPI network graph. This limits the model’s ability to learn richer and more effective interaction information, thereby affecting prediction performance.ResultsIn this study, we propose a novel deep learning method, MESM, for effectively predicting PPI. The datasets used for the PPI prediction task were primarily constructed from the STRING database, including two Homo sapiens PPI datasets, SHS27k and SHS148k, and two Saccharomyces cerevisiae PPI datasets, SYS30k and SYS60k. MESM consists of three key modules, as follows: First, MESM extracts multimodal representations from protein sequence information, protein structure information, and point cloud features through Sequence Variational Autoencoder (SVAE), Variational Graph Autoencoder (VGAE), and PointNet Autoencoder (PAE). Then, Fusion Autoencoder (FAE) is used to integrate these multimodal features, generating rich and balanced protein representations. Next, MESM leverages GraphGPS to learn structural information from the PPI network graph structure and combines Graph Attention Network (GAT) to further capture protein interaction information. Finally, MESM uses Graph Convolutional Network (GCN) and SubgraphGCN to extract global and local features from the perspective of the overall graph and subgraphs. Moreover, we build seven independent graphs from the overall PPI network graph to specifically learn the features of each PPI type, thereby enhancing the model’s learning ability for different types of interactions.ConclusionsCompared to the state-of-the-art methods, MESM achieved improvements of 8.77%, 4.98%, 7.48%, and 6.08% on SHS27k, SHS148k, SYS30k, and SYS60k, respectively. The experimental results demonstrate that MESM exhibits significant improvements in PPI prediction performance.Supplementary InformationThe online version contains supplementary material available at 10.1186/s12915-025-02356-y.

This paper reviews research on vehicle driving state estimation research. Based on the discussion of the importance, development history, and application fields of this topic of research, it focuses on analyzing vehicle state estimation techniques from different perspectives, namely (1) from the perspective of the estimation objects, including vehicle attitude and driving state estimations, chassis component key dynamic parameter estimations, and vehicle driving environment state estimations; (2) from the perspective of vehicle characteristics, including vehicle dynamics coupling characteristics, vehicle multi-source information redundancy characteristics, and vehicle state transition characteristics; (3) from the perspective of key estimation algorithms, including model-based Kalman filtering algorithms, data-driven machine learning algorithms, and optimization estimation algorithms combining mechanism-based and data-driven approaches. This manuscript helps interested readers to comprehensively understand the research progress, technical features, and future trends of vehicle state estimation technology from the perspective of overall architecture and subdomains.

Processing errors can result in an asymmetric stiffness distribution within a microelectromechanical system (MEMS) disk resonator gyroscope (DRG) and thereby cause a mode mismatch and reduce the mechanical sensitivity and closed-loop scale factor stability. This paper proposes an automatic mode-matching method that utilizes mode reversal to obtain the true resonant frequency of the operating state of a gyroscope for high-precision matching. This method constructs a gyroscope control system that contains a drive closed loop, sense force-to-rebalance (FTR) closed loop, and quadrature error correction closed loop. After the gyroscope was powered on and started up, the x- and y-axes were quickly switched to obtain the resonant frequencies of the two axes through a phase-locked loop (PLL), and the x-axis tuning voltage was automatically adjusted to match the two-axis frequency. The experimental results show that the method takes only 5 s to execute, the frequency matching accuracy reaches 0.01 Hz, the matching state can be maintained in the temperature range of −20 to 60 °C, and the fluctuation of the frequency split does not exceed 0.005 Hz.

To rapidly and accurately establish the model of switched reluctance motors (SRMs) and enhance torque control performance, this article proposes a model predictive torque control (MPTC) strategy based on the optimized voltage vector. First, a fourth-order Fourier series is used to calculate the flux and torque models, and a complete nonlinear model of the SRM is constructed using the Kriging model. Second, the torque characteristics are used to divide the sectors, thereby reducing the number of candidate voltage vectors (CVVs) and effectively decreasing the computational burden of predictive control. Finally, an adaptive adjustment algorithm for the sector boundary angle is proposed, where the position of the sectors is determined based on variations in speed and load. The proposed method reduces torque ripple and enhances dynamic response capability. The effectiveness of this approach is validated through experiments on a 12/10 pole six-phase SRM prototype.