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Stretchable micropower sources with high energy density and stability under repeated tensile deformation are key components of flexible/wearable microelectronics. Herein, through the combination of strain engineering and modulation of the interlayer spacing, freestanding and lightweight MXene/bacterial cellulose (BC) composite papers with excellent mechanical stability and a high electrochemical performance are first designed and prepared via a facile all‐solution‐based paper‐making process. Following a simple laser‐cutting kirigami patterning process, bendable, twistable, and stretchable all‐solid‐state micro‐supercapacitor arrays (MSCAs) are further fabricated. As expected, benefiting from the high‐performance MXene/BC composite electrodes and rational sectional structural design, the resulting kirigami MSCAs exhibit a high areal capacitance of 111.5 mF cm−2, and are stable upon stretching of up to 100% elongation, and in bent or twisted states. The demonstrated combination of an all‐solution‐based MXene/BC composite paper‐making method and an easily manipulated laser‐cutting kirigami patterning technique enables the fabrication of MXene‐based deformable all‐solid‐state planar MSCAs in a simple and efficient manner while achieving excellent areal performance metrics and high stretchability, making them promising micropower sources that are compatible with flexible/wearable microelectronics. The demonstrated combination of an all‐solution‐based MXene/bacterial cellulose composite paper‐making method and easily manipulated laser‐cutting kirigami patterning technique enables the fabrication of MXene‐based all‐solid‐state stretchable micro‐supercapacitor arrays in a simple and efficient manner while achieving both a high areal capacitance of 111.5 mF cm−2 and a high elongation of 100%, making them promising compatible micropower sources for flexible/wearable microelectronics.

Micro‐supercapacitors are notorious for their low energy densities compared to micro‐batteries. While MXenes have been identified as promising capacitor‐type electrode materials for alternative zinc‐ion hybrid micro‐supercapacitors (ZHMSCs) with higher energy density, their tightly spaced layered structure renders multivalent zinc‐ions with large radii intercalation inefficient. Herein, through insertion of 1D core‐shell conductive BC@PPy nanofibers between MXene nanosheets, an interlayer structure engineering technique for MXene/BC@PPy capacitor‐type electrodes towards ZHMSCs is presented. Owing to simultaneously achieving two objectives: (i) widening the interlayer space and (ii) providing conductive connections between the loose MXene layers, enabled by the conductive BC@PPy nanospacer, the approach effectively enhances both ion and electron transport within the layered MXene structure, significantly increasing the areal capacitance of the MXene/BC@PPy film electrode to 388 mF cm−2, which is a 10‐fold improvement from the pure MXene film electrode. Pairing with CNTs/MnO2 battery‐type electrodes, the obtained ZHMSCs exhibit an areal energy density up to 145.4 μWh cm−2 with an outstanding 95.8% capacity retention after 25000 cycles, which is the highest among recently reported MXene‐based MSCs and approaches the level of micro‐batteries. The interlayer structure engineering demonstrated in the MXene‐based capacitor‐type electrode provides a rational means to achieve battery‐levelenergy density in the ZHMSCs. The demonstrated interlayer structure engineering synchronously realized the facilitated zinc‐ion and electron transfer kinetics between loose MXene nanosheets, resulting in enhanced charge storage capacity of MXene‐based capacitor‐type electrode toward hybrid micro‐supercapacitor with battery‐level energy density.

Vehicle detection is one of the important applications of object detection in intelligent transportation systems. It aims to extract specific vehicle-type information from pictures or videos containing vehicles. To solve the problems of existing vehicle detection, such as the lack of vehicle-type recognition, low detection accuracy, and slow speed, a new vehicle detection model YOLOv2_Vehicle based on YOLOv2 is proposed in this paper. The k-means++ clustering algorithm was used to cluster the vehicle bounding boxes on the training dataset, and six anchor boxes with different sizes were selected. Considering that the different scales of the vehicles may influence the vehicle detection model, normalization was applied to improve the loss calculation method for length and width of bounding boxes. To improve the feature extraction ability of the network, the multi-layer feature fusion strategy was adopted, and the repeated convolution layers in high layers were removed. The experimental results on the Beijing Institute of Technology (BIT)-Vehicle validation dataset demonstrated that the mean Average Precision (mAP) could reach 94.78%. The proposed model also showed excellent generalization ability on the CompCars test dataset, where the “vehicle face” is quite different from the training dataset. With the comparison experiments, it was proven that the proposed method is effective for vehicle detection. In addition, with network visualization, the proposed model showed excellent feature extraction ability.

Lung cancer is one of the major causes of cancer-related deaths due to its aggressive nature and delayed detections at advanced stages. Early detection of lung cancer is very important for the survival of an individual, and is a significant challenging problem. Generally, chest radiographs (X-ray) and computed tomography (CT) scans are used initially for the diagnosis of the malignant nodules; however, the possible existence of benign nodules leads to erroneous decisions. At early stages, the benign and the malignant nodules show very close resemblance to each other. In this paper, a novel deep learning-based model with multiple strategies is proposed for the precise diagnosis of the malignant nodules. Due to the recent achievements of deep convolutional neural networks (CNN) in image analysis, we have used two deep three-dimensional (3D) customized mixed link network (CMixNet) architectures for lung nodule detection and classification, respectively. Nodule detections were performed through faster R-CNN on efficiently-learned features from CMixNet and U-Net like encoder–decoder architecture. Classification of the nodules was performed through a gradient boosting machine (GBM) on the learned features from the designed 3D CMixNet structure. To reduce false positives and misdiagnosis results due to different types of errors, the final decision was performed in connection with physiological symptoms and clinical biomarkers. With the advent of the internet of things (IoT) and electro-medical technology, wireless body area networks (WBANs) provide continuous monitoring of patients, which helps in diagnosis of chronic diseases—especially metastatic cancers. The deep learning model for nodules’ detection and classification, combined with clinical factors, helps in the reduction of misdiagnosis and false positive (FP) results in early-stage lung cancer diagnosis. The proposed system was evaluated on LIDC-IDRI datasets in the form of sensitivity (94%) and specificity (91%), and better results were obatined compared to the existing methods.

The zonal oscillation of the western Pacific subtropical high (WPSH) significantly influences the weather and climate over East Asia. This study investigates characteristics and mechanisms of the zonal variability of the WPSH on subseasonal time scales during summer by using a subseasonal WPSH (Sub-WPSH) index. Accompanied with the Sub-WPSH index, strong anticyclonic (cyclonic) anomalies are found over East Asia and coastal region south of 30°N on both 850 hPa and 500 hPa. During the positive period of the Sub-WPSH index, the WPSH extends more westward with enhanced precipitation over the Yangtze–Huaihe river basin and suppressed precipitation over the south of the Yangtze River in China. These precipitation anomalies can last for at least 1 week. While the subseasonal zonal variability of the WPSH is found to be closely associated with atmospheric teleconnections and local air- sea interaction, the mechanisms of the variability are different before and after mid-July (early and late summer). In both early and late summer, the East Asia/Pacific (EAP) wave train pattern affects the zonal shift of the WPSH by inducing a low-level anomalous anticyclonic/cyclonic circulation over the subtropical western Pacific, and this mechanism is stronger in late summer. In constrast, the influence of the Silk-Road pattern wave train is more important in the early summer. Meanwhile, in late summer, a stronger SST forcing on the atmosphere and a faster cycle of subseasonal variations of the WPSH are observed before the westward stretch of the WPSH, which could be related to the colder local SST anomalies. The westward stretch of the WPSH is accompanied by stronger anticyclonic anomalies in late summer.

Drought is one of the most serious abiotic stress factors limiting crop yields. Although sunflower is considered a moderate drought-tolerant plant, drought stress still has a negative impact on sunflower yield as cultivation expands into arid regions. The extent of drought stress is varieties and time-dependent, however, the molecular response mechanisms of drought tolerance in sunflower with different varieties are still unclear. Here, we performed comparative physiological and transcriptome analyses on two sunflower inbred lines with different drought tolerance at the seedling stage. The analysis of nine physiological and biochemical indicators showed that the leaf surface area, leaf relative water content, and cell membrane integrity of drought tolerance inbred line were higher than those of drought-sensitive inbred line under drought stress, indicating that DT had stronger drought resistance. Transcriptome analyses identified 24,234 differentially expressed genes (DEGs). Gene ontology (GO) analysis showed the up-regulated genes were mainly enriched in gibberellin metabolism and rRNA processing, while the down-regulated genes were mainly enriched in cell-wall, photosynthesis, and terpene metabolism. Kyoto Encyclopedia of Genes and Genomes(KEGG) pathway analysis showed genes related to GABAergic synapse, ribosome biogenesis were up-regulated, while genes related with amino sugar and nucleotide sugar metabolism, starch and sucrose metabolism, photosynthesis were down-regulated. Mapman analysis revealed differences in plant hormone-signaling genes over time and between samples. A total of 1,311 unique putative transcription factors (TFs) were identified from all DEGs by iTAK, among which the high abundance of transcription factor families include bHLH, AP2/ERF, MYB, C2H2, etc. Weighted gene co-expression network analysis (WGCNA) revealed a total of 2,251 genes belonging to two modules(blue 4, lightslateblue), respectively, which were significantly associated with six traits. GO and KEGG enrichment analysis of these genes was performed, followed by visualization with Cytoscape software, and the top 20 Hub genes were screened using the CytoHubba plugin.

Decentralized Federated Learning improves data privacy and eliminates single points of failure by removing reliance on centralized storage and model aggregation in distributed computing systems. Ensuring the integrity of computations during local model training is a significant challenge, especially before sharing gradient updates from each local client. Current methods for ensuring computation integrity often involve patching local models to implement cryptographic techniques, such as Zero-Knowledge Proofs. However, this approach becomes highly complex and sometimes impractical for large-scale models that use techniques such as random dropouts to improve training convergence. These random dropouts create non-deterministic behavior, making it challenging to verify model updates under deterministic protocols. We propose ProxyZKP, a novel framework combining Zero-Knowledge Proofs with polynomial proxy models to provide computation integrity in local training to address this issue. Each local node combines a private model for online deep learning applications and a proxy model that mediates decentralized model training by exchanging gradient updates. The multivariate polynomial nature of proxy models facilitates the application of Zero-Knowledge Proofs. These proofs verify the computation integrity of updates from each node without disclosing private data. Experimental results indicate that ProxyZKP significantly reduces computational load. Specifically, ProxyZKP achieves proof generation times that are 30–50% faster compared to established methods like zk-SNARKs and Bulletproofs. This improvement is largely due to the high parallelization potential of the univariate polynomial decomposition approach. Additionally, integrating Differential Privacy into the ProxyZKP framework reduces the risk of Gradient Inversion attacks by adding calibrated noise to the gradients, while maintaining competitive model accuracy. The results demonstrate that ProxyZKP is a scalable and efficient solution for ensuring training integrity in decentralized federated learning environments, particularly in scenarios with frequent model updates and the need for strong model scalability.

One long-standing issue in the paleoclimate records is whether East Asian Summer Monsoon peaked in the early Holocene or mid-Holocene. Here, combining a set of transient earth system model simulations with proxy records, we propose that, over northern China, monsoon rainfall peaked in the early Holocene, while soil moisture and tree cover peaked in the mid-Holocene. The delayed ecosystem (soil moisture and tree cover) response to rainfall is caused by the vegetation response to winter warming and the subsequent feedback with soil moisture. Our study provides a mechanism for reconciling different evolution behaviors of monsoon proxy records; it sheds light on the driving mechanism of the monsoon evolution and monsoon-ecosystem feedback over northern China, with implications to climate changes in other high climate sensitivity regions over the globe. How the East Asian Summer Monsoon has changed over the Holocene has been debated, as some proxy records disagree with each other. Here, the authors suggest that monsoonal rainfall peaked in the early Holocene, while ecosystem responses peaked in the mid-Holocene, explaining the differences between records.

The application of steel strut force servo systems in deep excavation engineering is not widespread, and there is a notable scarcity of in-situ measured datasets. This presents a significant research gap in the field. Addressing this, our study introduces a valuable dataset and application scenarios, serving as a reference point for future research. The main objective of this study is to use machine learning (ML) methods for accurately predicting strut forces in steel supporting structures, a crucial aspect for the safety and stability of deep excavation projects. We employed five different ML methods: radial basis function neural network (RBFNN), back propagation neural network (BPNN), K-Nearest Neighbor (KNN), support vector machine (SVM), and random forest (RF), utilizing a dataset of 2208 measured points. These points included one output parameter (strut forces) and seven input parameters (vertical position of strut, plane position of strut, time, temperature, unit weight, cohesion, and internal frictional angle). The effectiveness of these methods was assessed using root mean square error (RMSE), correlation coefficient (R), and mean absolute error (MAE). Our findings indicate that the BPNN method outperforms others, with RMSE, R, and MAE values of 72.1 kN, 0.9931, and 57.4 kN, respectively, on the testing dataset. This study underscores the potential of ML methods in precisely predicting strut forces in deep excavation engineering, contributing to enhanced safety measures and project planning.

Wearable sensors-based human activity recognition introduces many useful applications and services in health care, rehabilitation training, elderly monitoring and many other areas of human interaction. Existing works in this field mainly focus on recognizing activities by using traditional features extracted from Fourier transform (FT) or wavelet transform (WT). However, these signal processing approaches are suitable for a linear signal but not for a nonlinear signal. In this paper, we investigate the characteristics of the Hilbert-Huang transform (HHT) for dealing with activity data with properties such as nonlinearity and non-stationarity. A multi-features extraction method based on HHT is then proposed to improve the effect of activity recognition. The extracted multi-features include instantaneous amplitude (IA) and instantaneous frequency (IF) by means of empirical mode decomposition (EMD), as well as instantaneous energy density (IE) and marginal spectrum (MS) derived from Hilbert spectral analysis. Experimental studies are performed to verify the proposed approach by using the PAMAP2 dataset from the University of California, Irvine for wearable sensors-based activity recognition. Moreover, the effect of combining multi-features vs. a single-feature are investigated and discussed in the scenario of a dependent subject. The experimental results show that multi-features combination can further improve the performance measures. Finally, we test the effect of multi-features combination in the scenario of an independent subject. Our experimental results show that we achieve four performance indexes: recall, precision, F-measure, and accuracy to 0.9337, 0.9417, 0.9353, and 0.9377 respectively, which are all better than the achievements of related works.