With the widespread application of blockchain technology across various industries, detecting and analyzing performance bottlenecks is crucial for evaluating and optimizing blockchain system performance. However, current research needs general performance metrics for detecting and analyzing bottlenecks. Only some studies focus on this aspect within blockchain systems. To address this, this paper first proposes 18 fine-grained performance metrics to evaluate performance across various layers of blockchain systems comprehensively. Subsequently, we introduce a generalized loosely coupled performance measurement framework to capture these metrics and construct the causal relationship between them, i.e., the mesoscopic performance structure. This approach allows for the detection and analysis of performance bottlenecks. Finally, numerous experimental results demonstrate that the causality between the relevant performance metrics disappears when the system reaches a performance bottleneck. Additionally, the framework has a performance impact of less than 15% on ChainMaker.

Existing studies tend tofocus onmodel modifications and integration with higher accuracy, which improve performance but also carry huge computational costs, resulting in longer detection times. Inmedical imaging, the use of time is extremely sensitive. And at present most of the semantic segmentation models have encoder-decoder structure or double branch structure. Their several times of the pooling use with high-level semantic information extraction operation cause information loss although there si a reverse pooling or other similar action to restore information loss of pooling operation. In addition, we notice that visual attention mechanism has superior performance on a variety of tasks. Given this, this paper proposes non-pooling network(NPNet), non-pooling commendably reduces the loss of information and attention enhancement m o d u l e ( A M ) effectively increases the weight of useful information. The method greatly reduces the number of parametersand computation costs by the shallow neural network structure. We evaluate the semantic segmentation model of our NPNet on three benchmark datasets comparing w i t h multiple current state-of-the-art(SOTA) models, and the implementation results show thatour NPNetachieves SOTA performance, with an excellent balance between accuracyand speed.

Graph anomaly detection (GAD) aims to identify nodes that significantly deviate from typical structural or attribute patterns. While autoencoder-based methods detect anomalies via reconstruction errors, they frequently suffer from the identity mapping problem and over-smoothing, failing to maintain a sharp discriminative boundary. Although recent advances in Diffusion Models (DMs) provide powerful generative priors for manifold restoration, existing diffusion-based GAD approaches rely on a static, monolithic denoising depth. This ``one-size-fits-all'' strategy ignores the inherent heterogeneity in anomaly severity, forcing a suboptimal compromise between under-denoising camouflaged anomalies and over-smoothing benign nodes. To resolve this important trade-off, we propose ARLDM, an Autoencoder Reconstruction-based Latent Diffusion Model. Breaking from fixed-step paradigms, ARLDM introduces a Reconstruction-based Adaptive Step Controller that dynamically calibrates the optimal denoising depth at the instance level by monitoring trajectory consistency. Furthermore, we devise a consistency-aware bidirectional feature fusion mechanism. Acting as an adversarial amplifier, it gates the semantic flow between the forward noisy and reverse denoised representations, explicitly maximizing the dual-view reconstruction discrepancy for anomalous nodes. Extensive experiments on six benchmark datasets demonstrate that ARLDM achieves competitive or superior performance, consistently outperforming 14 baselines in both detection accuracy and robustness. Our code is available at https://anonymous.4open.science/r/ARLDM-F5D7.