With aggressive scaling and multilevel cell technology, the reliability of NAND flash continuously degrades. The lifetime of NAND flash is highly restricted by the bit error rate (BER), and error-correcting codes (ECCs) can provide only limited error correction capability to tolerate increasing bit errors. To cope with this issue, a novel page endurance variance aware (PEVA) strategy is proposed to extend the lifetime of NAND flash based on the experimental observations from our hardware-software codesigned experimental platform. The experimental observations indicate that the BER distribution of retention error shows distinct variances in different pages. The key purpose of PEVA is to exploit the lifetime potency of every page in a block by introducing fine-grained bad page management instead of coarse-grained bad block management (BBM). The experimental results show that the PEVA can extend the lifetime of 2×-nm NAND flash by 9.8× compared with the conventional BBM and that there is at most an 8.7% degradation in writing speed compared with the traditional sector mapping technology. In addition, the maximum writing response time increased by at most 5.9% during the operation of the PEVA strategy.

The detection of leaks in water distribution systems (WDS) has always been a major concern for urban water supply companies. However, the performance of traditional leak detection classifiers highly depends on the effectiveness of handcrafted features. An alternative method is to use a convolutional neural network (CNN) to process raw signals directly to obtain deep representations that may ignore prior information about the leakage. The study proposes a novel approach to leak detection in WDS using ground acoustic signals, and demonstrates the effectiveness of combining handcrafted features and deep representations using a pseudo-siamese convolutional neural network (PCNN) model. Mel frequency cepstral coefficient (MFCCs) are selected as additional handcrafted features to traditional time- and frequency-domain (TFD) features. Based on the results of the model performance evaluation, the optimized PCNN model performs better than other methods, with an accuracy of 99.70%. A quantitative analysis of the PCNN demonstrates the effectiveness of handcrafted features and deep representations. Model visualization and interpretation analysis show that feature fusion occurs in the feedforward of the PCNN, hence improving the model’s performance. The present work can effectively support the development of novel intelligent leak detection equipment for WDS.

In recent years, climate change has increased the frequency of extreme rainfall events, significantly impacting surface water quality (SWQ). This study focuses on Hangzhou, utilizing rainfall data from June 2021 to May 2024 to calculate a series of rainfall extreme indices (REIs). It explores the spatiotemporal variations in these REIs alongside SWQ parameters, including water temperature (WT), dissolved oxygen (DO), pH, total phosphorus (TP), total nitrogen (TN), and turbidity. This research also analyzes the correlations between SWQ parameters and REIs for the first time. The results show that extreme rainfall events primarily occur in July, with increases in both intensity and frequency during the study period. Influenced by human activities, natural conditions, and environmental policies, SWQ parameters in Hangzhou exhibit notable spatiotemporal variability. Correlation analyses reveal significant positive relationships between TP, TN, and turbidity in most areas with REIs. However, the correlations between pH, WT, and turbidity with REIs differ between the eastern and western regions, resulting from variations in land use. These findings will provide a theoretical basis for developing models to predict changes in SWQ based on REIs, contributing to the safeguarding of surface water quality.