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A great deal of attention has been directed to the toxicity, enrichment, and accumulation of urban river sediment pollution. To understand the spatial-temporal variation, ecological risk and source of nutrients, and heavy metals in sediments from the Weihe River, the concentrations of total nitrogen (TN), total phosphorus (TP), organic matter (OM), and 10 heavy metals (Cd, Sb, As, Co, Cu, Pb, Ni, Cr, Zn, and Mn) in sediments at 14 sampling sites along the river were investigated. The results showed that nutrients and heavy metals had an interannual decreasing trend, and that the high-value regions were concentrated in urban locations within the study area. Ecological risk assessment results showed that TN was between the security level (no toxic effect) and the lowest level (tolerable for organisms), TP was at the lowest level, and OM was within the security level, all mainly from external sources. The geoaccumulation index ( I geo ) and enrichment factor (EF) of 10 heavy metals were all within the unpolluted level, while the pollution load index (PLI) of 12 sampling sites had reached the moderate pollution level. The results of Pearson correlation, principal component analysis, and cluster analysis showed that heavy metals originated mainly from industrial and domestic sources, geochemical environments, and agricultural activities, indicating that heavy metals in the Weihe River sediments were influenced significantly by anthropogenic activities. The results are expected to provide a scientific basis for the development and utilization of the Weihe River water resources. Graphical abstract

Anthracnose, a significant disease of aloe with characteristics of contact transmission, poses a considerable threat to the economic viability of aloe cultivation. To address the challenges of accurately detecting and classifying crop diseases in complex environments, this study proposes an enhanced algorithm, YOLOv11-seg-DEDB, based on the improved YOLOv11-seg model. This approach integrates multi-scale feature enhancement and a dynamic attention mechanism, aiming to achieve precise segmentation of aloe anthracnose lesions and effective disease level discrimination in complex scenarios. Specifically, a novel Disease Enhance attention mechanism is introduced, combining spatial attention and max pooling to improve the accuracy of lesion segmentation. Additionally, the DCNv2 is incorporated into the network neck to enhance the model’s ability to extract multi-scale features from targets in challenging environments. Furthermore, the Bidirectional Feature Pyramid Network structure, which includes an additional p2 detection head, replaces the original PANet network. A more lightweight detection head structure is designed, utilizing grouped convolutions and structural simplifications to reduce both the parameter count and computational load, thereby enhancing the model’s inference capability, particularly for small lesions. Experiments were conducted using a self-collected dataset of aloe anthracnose infected leaves. The results demonstrate that, compared to the original model, the improved YOLOv11-seg-DEDB model improves segmentation accuracy and mAP@50 for infected lesions by 5.3% and 3.4%, respectively. Moreover, the model size is reduced from 6.0 MB to 4.6 MB, and the number of parameters is decreased by 27.9%. YOLOv11-seg-DEDB outperforms other mainstream segmentation models, providing a more accurate solution for aloe disease segmentation and grading, thereby offering farmers and professionals more reliable disease detection outcomes.

Model updating is a challenging task with regard to maintaining the performance of non-destructive detection models while using hyperspectral imaging techniques for detecting the internal quality of fresh fruits like blueberries. Different sample batches and differences in hyperspectral image acquisition environments may lead to a significant decline in the performance of hyperspectral detection models. This study investigated the transferability of a hyperspectral model for the quantitating soluble solid content of blueberries across different batches for two harvest years. Hyperspectral images and SSC values of blueberries were collected from two batches, including 364 samples from 2024 and 175 samples from 2025. The differences between SSC measurements and spectral data across these two batches were analyzed. Based on the sample dataset of the year 2024, a high-performance quantitative model for detecting SSC values was established by combining it with partial least squares regression (PLSR) and competitive adaptive reweighted sampling (CARS). This high-performance model could achieve a high determination coefficient (RP2) of 0.8965 and a low root mean square error of prediction (RMSEP) of 0.3707 °Brix. Using the sample dataset for the year 2025, the hyperspectral model was updated by the semi-supervised parameter-free calibration enhancement (SS-PFCE) algorithm. The updated model performed better than those established using individual datasets from 2024 and 2025, and obtained an RP2 of 0.8347 and an RMSEP of 0.4930 °Brix. This indicates that the calibration transfer strategy is superior in improving hyperspectral model performance. This study demonstrated that the SS-PFCE algorithm, as a calibration transfer strategy, could effectively improve the transferability of the established model for detecting the SSC of blueberries across different sample batches.

Sweetpotato virus disease (SPVD) is widespread and causes significant economic losses. Current diagnostic methods are either costly or labor-intensive, limiting both efficiency and scalability. The segmentation algorithm proposed in this study can rapidly and accurately identify SPVD lesions from field-captured photos of sweetpotato leaves. Two custom datasets, DS-1 and DS-2, are utilized, containing meticulously annotated images of sweetpotato leaves affected by SPVD. DS-1 is used for training, validation, and testing the model, while DS-2 is exclusively employed to validate the model's reliability. This study employs a deep learning-based semantic segmentation network, DeepLabV3+, integrated with an Attention Pyramid Fusion (APF) module. The APF module combines a channel attention mechanism with multi-scale feature fusion to enhance the model's performance in disease pixel segmentation. Additionally, a novel data augmentation technique is utilized to improve recognition accuracy in the edge background areas of real large images, addressing issues of poor segmentation precision in these regions. Transfer learning is applied to enhance the model's generalization capabilities. The experimental results indicate that the model, with 62.57M parameters and 253.92 Giga Floating Point Operations Per Second (GFLOPs), achieves a mean Intersection over Union (mIoU) of 94.63% and a mean accuracy (mAcc) of 96.99% on the DS-1 test set, and an mIoU of 78.59% and an mAcc of 79.47% on the DS-2 dataset. Ablation studies confirm the effectiveness of the proposed data augmentation and APF methods, while comparative experiments demonstrate the model's superiority across various metrics. The proposed method also exhibits excellent detection results in simulated scenarios. In summary, this study successfully deploys a deep learning framework to segment SPVD lesions from field images of sweetpotato foliage, which will contribute to the rapid and intelligent detection of sweetpotato diseases.

A triple modular redundancy SRAM was designed as the embedded high-speed memory for a radiation-tolerant ARM processor with ST Microelectronics 28-nm FDSOI technology. The single event upset (SEU) cross-section of the SRAM was tested by using heavy ions with the linear energy transfer of 15.0 meV.cm2.mg−1 in both non-TMR and TMR modes with different accumulated fluence. The SRAM cell was also simulated by using Cogenda TCAD simulation suite and the cross section was calculated by using analytic method. The results showed the cross-section is around 2E-10 cm2/bit in non-TMR mode, and in TMR mode it varied from one to several orders lower than the non-TMR mode according to the specific accumulated fluence. As a scrubbing circuit was designed to reduce the accumulated number of SEUs in the SRAM, the Failure In Time (FIT) rate at sea level in New York City could be as low as 8E-11, which is robust enough for the whole circuit.

AimsWith the wide application of 7Be (Beryllium-7) in soil erosion investigations, retention and interception of 7Be by vegetation plays an important role in documenting soil 7Be redistribution, with a large impact on the interpretation of 7Be measurements. However, the dynamic and temporal changes in plants and the relationship with soil 7Be concentration remain unclear, and the significance of dead plants in 7Be interception is under-researched.MethodsThe samples of single plants (6 different species), compositive plants (including living and dead plants), along with soil reference on the Loess Plateau were collected individually to analyze the variations of 7Be concentration during the growth period from 2010 to 2012.ResultsThe accumulation of 7Be per mass is significantly higher in leaves than stems. The 7Be activity per mass and per area in living plants with seasonal trends ranged from 173.9 to 703.1 Bq kg–1 and 21.5 to 190.1 Bq m–2, respectively, and in dead plants ranged from 381.8 to 964.5 Bq kg–1 and 30.4 to 285.7 Bq m–2. Precipitation accounted for the largest contribution to the accumulation of 7Be in plants, followed by plant growth, species and parts. Plants accounted for 7Be interception on slope up to 66% (living plants accounted for 7% ~ 31% and dead plants accounted for 6% ~ 44%). The interception of living plants is low at first, then increases with the accumulation of rainfall and biomass together.ConclusionsOur results highlight that 7Be in plants (especially for the dead plants) is of great significance for 7Be in soil on the slope, and is controlled by precipitation, growth status and plant characteristics. The reference information obtained in this work will contribute to improving the accuracy of 7Be tracing technology, and broadening its scope.

Single-event transient (SET)-induced soft errors are becoming a more significant threat to the reliability of electronic systems in space, especially for advanced technologies. The SET pulse width, which is vulnerable to SET propagation, is a critical parameter for developing SET mitigation techniques. This paper investigates the pulse-broadening effect in the process of SET propagation in logic circuits and the SET-sensitive region distribution in the layout using the pulsed-laser mapping technique in logic circuits implemented with 28 nm Ultra-Thin Body and BOX (UTBB) FDSOI technology. The experiments were carried out at the Naval Research Laboratory (NRL) to measure the SET-induced errors and map the SET-sensitive region distribution at various clock frequencies and laser energy levels. The results illustrate that the number of errors increases with the clock frequency and energy for combinational logic circuits and that the flip-flop SEU rate is less sensitive to clock frequency. The SET pulse-broadening effect was also observed using SET mapping for an OR gate chain at different laser energy levels. In addition, the simulation results revealed the mechanism of the SET pulse-broadening effect in an OR gate chain.

This paper presents an SEU-resilient 12 T SRAM bitcell. Simulation results demonstrate that it has higher critical charge than the traditional 6 T cell. Alpha and proton testing results validate that it has a lower soft error rate compared to the reference designs for all data patterns and supply voltage levels. The improvement in SEU tolerance is achieved at the expense of 2X area penalty.