The widespread adoption of wearable devices necessitates accurate multi-class arrhythmia detection from photoplethysmography (PPG) signals for early cardiovascular intervention. While existing methods excel in single-arrhythmia classification, they face significant challenges in multi-class scenarios, particularly in distinguishing clinically confusable types. To address these challenges, we propose MSCTA-Net-a novel network architecture integrating multi-scale CNN, Transformer, and channel attention fusion mechanism. The multi-scale CNN branch employs parallel convolutions with four kernel sizes to extract local features across multiple frequency bands, while the Transformer module captures global temporal dependencies. A channel attention fusion mechanism dynamically integrates both feature types. Evaluated on a clinical dataset of 46,827 PPG segments covering six rhythm types (SR, PVC, PAC, VT, SVT, AF), MSCTA-Net achieves state-of-the-art performance with 92.88% accuracy, 87.79% mean F1-score, and mean precision, recall, specificity of 88.03%, 87.57%, 98.56%, respectively.

ECG-based biometric identification faces significant challenges in cross-state scenarios where physiological states differ between enrollment and authentication. Existing methods typically assume identical states, leading to substantial performance degradation when applied to real-world dual-state conditions. We propose CrossStateECG-Lite, an adaptive thresholding attention network specifically designed for robust cross-state ECG identification. The network integrates multi-scale feature learning, deep residual connections, and efficient attention mechanisms to capture state-invariant cardiac patterns. To address the inherent uncertainty in cross-state identification, we develop a personalized decision-making mechanism using Bayesian adaptive thresholding, which customizes authentication boundaries based on individual physiological variability. Evaluated on 45 subjects with paired rest-exercise recordings, CrossStateECG- Lite achieves balanced accuracies of 95.26 % (rest-to-post-exercise) and 96.05% (post-exercise-to-rest). The lightweight architecture with only 797K parameters demonstrates that efficient design combined with personalized decision-making provides an effective solution for real-world ECG biometrics where physiological states cannot be controlled. Comprehensive ablation studies confirm the synergistic contributions of the architectural components, with deep convolutional layers being particularly effective in learning state-invariant representations.

Talent is an important force for national construction and a fundamental guarantee for rural revitalization. College students’ returning home to start their own businesses will inject new vitality into the integrated development of urban and rural areas under the background of the rural revitalization strategy. Based on the background of big data and based on clarifying the connotation of college students’ returning entrepreneurial ability, this paper conducted a questionnaire survey on students with entrepreneurial intention and students who have started businesses in ten colleges and universities in Henan Province and used fuzzy comprehensive evaluation method to construct an evaluation model of college students’ returning entrepreneurial ability. And the empirical analysis aims to find out the factors affecting the entrepreneurial ability of rural college students returning home, and propose corresponding countermeasures and suggestions based on big data technology. At the same time, this paper gives the breakthrough and realization path of the university innovation and entrepreneurship education docking with the rural revitalization strategy.

In multi-FPGA systems, Time-Division Multiplexing (TDM) is a widely used method for transferring multiple signals over a common wire. The circuit performance will be significantly influenced by this inter-FPGA delay. Some inter-FPGA nets are driven by different clocks, in which case they cannot share the same wire. In this paper, to minimize the maximum delay of inter-FPGA nets, we propose a two-step framework. First, a TDM-aware partitioning algorithm is adopted to minimize the maximum cut size between an FPGA-pair. A TDM ratio assignment method is then applied to assign TDM ratio for each inter-FPGA net optimally. Experimental results show that our algorithm can reduce the maximum TDM ratio significantly within reasonable runtime.

In multi-FPGA systems, time-division multiplexing (TDM) is a widely used technique to transfer signals between FPGAs. While TDM can greatly increase logic utilization, the inter-FPGA delay will also become longer. A good time-multiplexing scheme for inter-FPGA signals is very important for optimizing the system performance. In this work, we propose a fast algorithm to generate high quality time-multiplexed routing results for multiple FPGA systems. A hybrid routing algorithm is proposed to route the nets between FPGAs, by maze routing and by a fast minimum terminal spanning tree method. After obtaining a routing topology, a two-step method is applied to perform TDM assignment to optimize timing, which includes an initial assignment and a competitive-based refinement. Experiments show that our system-level routing and TDM assignment algorithm can outperform both the top winner of the ICCAD 2019 Contest and the state-of-the-art methods. Moreover, compared to the state-of-the-art works [17, 22], our approach has better run time by more than 2x with better or comparable TDM performance.

As the complexity of circuit designs continues growing, multi-FPGA systems are becoming more and more popular for logic emulation and rapid prototyping. In a multi-FPGA system, different FPGAs are connected by limited physical wires, in other words, one FPGA usually has direct connections with only a few FPGAs. During the circuit partitioning stage, assigning two directly connected nodes to two FPGAs without physical links would significantly increase the delay and degrade the overall performance. However, some well-known partitioners, like hMETIS and PaToH, mainly focus on cut size minimization without considering such topology constraints of FPGAs, which limits their practical usage. In this paper, we propose a multi-level topology-driven partitioning framework, named as TopoPart, to deal with topology constraints in a multi-FPGA system. In particular, we firstly devise a candidate FPGA propagation algorithm in the coarsening phase to guarantee the later stages free of topology violations. In the last refinement phase, cut size is iteratively optimized maintaining both topology and resource constraints. Compared with the proposed baseline, our partitioning algorithm achieves zero topology violation while giving less cut size.

The continuous development of modern VLSI technology has brought new challenges for on-chip interconnections. Different from classic net-by-net routing, bus routing requires all the nets (bits) in the same bus to share similar or even the same topology, besides considering wire length, via count, and other design rules. In this paper, we present MARCH, an efficient maze routing method under a concurrent and hierarchical scheme for buses. In MARCH, to achieve the same topology, all the bits in a bus are routed concurrently like marching in a path. For efficiency, our method is hierarchical, consisting of a coarse-grained topology-aware path planning and a fine-grained track assignment for bits. Additionally, an effective rip-up and reroute scheme is applied to further improve the solution quality. In experimental results, MARCH significantly outperforms the first place at 2018 IC/CAD Contest in both quality and runtime.

As of December 2, 2016, Henan has 74 listed companies, which are an important part of the capital market and contribute greatly to the economic and social development of Henan Province. This paper analyzes the problems existing in the development of Listed Companies in Henan Province from the regional and industrial distribution, market capitalization and capital structure, management level, etc, and offers recommendations for improvement.

The problem ofintercell scheduling considering transportation capacity with the objective of minimizing total weighted tardiness is addressed in this paper, which in nature is the coordination of production and transportation. Since it is a practical decision-making problem with high complexity and large problem instances, a hybrid evolutionary hyper-heuristic (HEH) approach, which combines heuristic generation and heuristic selection, is developed in this paper. In order to increase the diversity and effectiveness of heuristic rules, genetic programming is used to automatically generate new rules based on the attributes of parts, machines, and vehicles. The new rules are added to the candidate rule set, and a rule selection genetic algorithm is developed to choose appropriate rules for machines and vehicles. Finally, scheduling solutions are obtained using the selected rules. A comparative evaluation is conducted, with some state-of-the-art hyper-heuristic approaches which lack some of the strategies proposed in HEH, with a meta-heuristic approach that is suitable for large scale scheduling problems, and with adaptations of some well-known heuristic rules. Computational results show that the new rules generated in HEH have similarities to the best-performing human-made rules, but are more effective due to the evolutionary processes in HEH. Moreover, the HEH approach has advantages over other approaches in both computational efficiency and solution quality, and is especially suitable for problems with large instance sizes. Note to Practitioners-Our survey of the equipment manufacturing industry in China indicates that, for complex products like synthetic transmission devices, intercell transfers occur in the processing routes of more than 51% of parts. More than 47% of tardy parts are caused by inefficient intercell cooperation. Therefore, intercell transfers are inevitable and it is worth an effort to find out an effective approach to intercell scheduling. To solve intercell scheduling problems, two characteristics in industrial environments of complex products cannot be neglected. The first one is the large problem sizes, which involve up to hundreds of parts and thousands of operations; and the second one is the importance of transportation to intercell scheduling, which involves allocation and utilization of vehicles. However, sufficient transportation capacity is taken as a common assumption in most of research with respect to intercell scheduling, which shields the transportation dimension and hinders the application of these intercell scheduling approaches. Therefore, intercell scheduling with limited transportation capacity is considered, and a hybrid evolutionary hyper-heuristic is proposed in this paper. The advantages of this approach lie in that, (i) as a hyper-heuristic, it provides high computational efficiency, which is suitable for industrial environments with large problem sizes; and (ii) genetic programming is employed to generate problem-specific heuristic rules, which enhances the learning and searching ability of the approach. We compare the proposed approach with the man-made heuristic rules that are widely used in practice. Experimental results indicate that, for hundreds of parts and thousands of operations, given the same running time, our approach outperforms man-made rules with an average gap of 60.6% in minimizing total weighted tardiness. Therefore, our approach is advantageous in both computational efficiency and solution quality, and is especially suitable for the intercell scheduling problems in practice.

The problem of a k-stage hybrid flow shop (HFS) with one stage composed of non-identical batch processing machines and the others consisting of non-identical single processing machines is analyzed in the context of the equipment manufacturing industry. Due to the complexity of the addressed problem, a hyper-heuristic which combines heuristic generation and heuristic search is proposed to solve the problem. For each sub-problem, i.e., part assignment, part sequencing and batch formation, heuristic rules are first generated by genetic programming (GP) offline and then selected by ant colony optimization (ACO) correspondingly. Finally, the scheduling solutions are obtained through the above generated combinatorial heuristic rules. Aiming at minimizing the total weighted tardiness of parts, a comparison experiment with the other hyper-heuristic for the same HFS problem is conducted. The result has shown that the proposed algorithm has advantages over the other method with respect to the total weighted tardiness.