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In the context of the vigorous development of the global digital economy, e-commerce platforms, as the core hub connecting production and consumption, have increasingly prominent energy consumption issues in their supply chains. According to statistics, the energy consumption of China’s e-commerce logistics and warehousing sector accounts for 3.2% of the total energy consumption of the whole society, with an average annual growth rate of 8.5%. The traditional extensive management model has been difficult to adapt to the requirements of the \"double carbon\" strategy. In view of the multi-link, strong coupling, and dynamic characteristics of e-commerce platform supply chain, this study proposes an energy efficiency optimization framework that integrates deep reinforcement learning and digital twin technology. By constructing a three-dimensional simulation model including order allocation, warehousing temperature control and transportation route, the equipment power, environmental parameters and order data are collected in real time, the dynamic load is predicted by LSTM network, and the improved DDPG algorithm is used to dynamically adjust the equipment running state. Experiments show that this method can improve the comprehensive energy efficiency of the supply chain by 19.7%, reduce the carbon emission intensity by 14.3%, and increase the inventory turnover efficiency by 12% in the pilot application of a leading e-commerce platform. The research results provide theoretical support and technical paths for the intelligent low-carbon transformation of complex supply chain systems.

Layered lithium cobalt oxide (LiCoO 2 , LCO) is the most successful commercial cathode material in lithium-ion batteries. However, its notable structural instability at potentials higher than 4.35 V (versus Li/Li + ) constitutes the major barrier to accessing its theoretical capacity of 274 mAh g −1 . Although a few high-voltage LCO (H-LCO) materials have been discovered and commercialized, the structural origin of their stability has remained difficult to identify. Here, using a three-dimensional continuous rotation electron diffraction method assisted by auxiliary high-resolution transmission electron microscopy, we investigate the structural differences at the atomistic level between two commercial LCO materials: a normal LCO (N-LCO) and a H-LCO. These powerful tools reveal that the curvature of the cobalt oxide layers occurring near the surface dictates the structural stability of the material at high potentials and, in turn, the electrochemical performances. Backed up by theoretical calculations, this atomistic understanding of the structure–performance relationship for layered LCO materials provides useful guidelines for future design of new cathode materials with superior structural stability at high voltages. A three-dimensional continuous rotation electron diffraction method allows atomistic characterization of the chemistry of curved layered cathode materials.

Fish are indicative species with a relatively balanced ecosystem. Underwater target fish detection is of great significance to fishery resource investigations. Traditional investigation methods cannot meet the increasing requirements of environmental protection and investigation, and the existing target detection technology has few studies on the dynamic identification of underwater fish and small targets. To reduce environmental disturbances and solve the problems of many fish, dense, mutual occlusion and difficult detection of small targets, an improved CME-YOLOv5 network is proposed to detect fish in dense groups and small targets. First, the coordinate attention (CA) mechanism and cross-stage partial networks with 3 convolutions (C3) structure are fused into the C3CA module to replace the C3 module of the backbone in you only look once (YOLOv5) to improve the extraction of target feature information and detection accuracy. Second, the three detection layers are expanded to four, which enhances the model’s ability to capture information in different dimensions and improves detection performance. Finally, the efficient intersection over union (EIOU) loss function is used instead of the generalized intersection over union (GIOU) loss function to optimize the convergence rate and location accuracy. Based on the actual image data and a small number of datasets obtained online, the experimental results showed that the mean average precision (mAP@0.50) of the proposed algorithm reached 94.9%, which is 4.4 percentage points higher than that of the YOLOv5 algorithm, and the number of fish and small target detection performances was 24.6% higher. The results show that our proposed algorithm exhibits good detection performance when applied to densely spaced fish and small targets and can be used as an alternative or supplemental method for fishery resource investigation.

The timely identification and detection of surface cracks in concrete dams, an important public safety infrastructure, is of great significance in predicting engineering hazards and ensuring dam safety. Due to their low efficiency and accuracy, manual detection methods are gradually being replaced by computer vision techniques, and deep learning semantic segmentation methods have higher accuracy and robustness than traditional image methods. However, the lack of data images and insufficient detection performance remain challenges in concrete dam surface crack detection scenarios. Therefore, this paper proposes an intelligent detection method for concrete dam surface cracks based on two-stage transfer learning. First, relevant domain knowledge is transferred to the target domain using two-stage transfer learning, cross-domain and intradomain learning, allowing the model to be fully trained with a small dataset. Second, the segmentation capability is enhanced by using residual network 50 (ResNet50) as a UNet model feature extraction network to enhance crack feature information extraction. Finally, multilayer parallel residual attention (MPR) is integrated into its jump connection path to improve the focus on critical information for clearer fracture edge segmentation. The results show that the proposed method achieves optimal mIoU and mPA of 88.3% and 92.7%, respectively, among many advanced semantic segmentation models. Compared with the benchmark UNet model, the proposed method improves mIoU and mPA by 4.6% and 3.2%, respectively, reduces FLOPs by 36.7%, improves inference speed by 48.9%, verifies its better segmentation performance on dam face crack images with a low fine crack miss detection rate and clear crack edge segmentation, and achieves an accuracy of over 85.7% in crack area prediction. In summary, the proposed method has higher efficiency and accuracy in concrete dam face crack detection, with greater robustness, and can provide a better alternative or complementary approach to dam safety inspections than the benchmark UNet model.

The impairment of sperm maturation is one of the major pathogenic factors in male subfertility, a serious medical and social problem affecting millions of global couples. Regrettably, the existing research on sperm maturation is slow, limited, and fragmented, largely attributable to the lack of a global molecular view. To fill the data gap, we newly established a database, namely the Sperm Maturation Database (SperMD, http://bio-add.org/SperMD ). SperMD integrates heterogeneous multi-omics data (170 transcriptomes, 91 proteomes, and five human metabolomes) to illustrate the transcriptional, translational, and metabolic manifestations during the entire lifespan of sperm maturation. These data involve almost all crucial scenarios related to sperm maturation, including the tissue components of the epididymal microenvironment, cell constituents of tissues, different pathological states, and so on. To the best of our knowledge, SperMD could be one of the limited repositories that provide focused and comprehensive information on sperm maturation. Easy-to-use web services are also implemented to enhance the experience of data retrieval and molecular comparison between humans and mice. Furthermore, the manuscript illustrates an example application demonstrated to systematically characterize novel gene functions in sperm maturation. Nevertheless, SperMD undertakes the endeavor to integrate the islanding omics data, offering a panoramic molecular view of how the spermatozoa gain full reproductive abilities. It will serve as a valuable resource for the systematic exploration of sperm maturation and for prioritizing the biomarkers and targets for precise diagnosis and therapy of male subfertility.

Synthetic aperture radar (SAR) is a high-resolution imaging radar. With the deteriorating electromagnetic environment, SAR systems are susceptible to various forms of electromagnetic interference, including deception jamming. This jamming notably impacts SAR signal processing and subsequently worsens the quality of acquired images. Chaotic frequency modulation (CFM) signals could effectively counteract deception jamming. Nevertheless, due to the inadequate band-limited performance of CFM signals, higher oversampling factors are needed for achieving optimal low sidelobe levels, leading to increased system costs and excessively high data rates. Additionally, not all chaotic sentences meet the CFM signal requirements. In response, this paper proposes a novel signal modulation method called chaotic-nonlinear frequency modulation (C-NLFM) that enhances band-limited performance. The optimum parameters for C-NLFM signals are selected using the particle swarm optimization (PSO) algorithm. In this way, C-NLFM signals attain ideal low sidelobe levels even when faced with reduced oversampling factors. Meanwhile, this chaotic coding mode retains its capability to effectively suppress deception jamming. Moreover, C-NLFM signals demonstrate versatility in adapting to various chaotic sequences, thereby enhancing their flexibility to modify the signals as required. Through comprehensive simulations, data analysis, and a semi-physical experiment, the effectiveness and superiority of this proposed method have been confirmed.

In this paper, based on the improved DeepSORT algorithm, four target species of passing fish ( Schizothorax o’connori , Schizothorax waltoni , Oxygymnocypris stewartii and Schizopygopsis younghusbandi ) from a fishway project in the middle reaches of the Y River were used to achieve dynamic identification and automatic counting of passing fish species using fishways monitoring video. This method used the YOLOv5 model as the target detection model. In view of the large deformation by fish body twisting, the network structure of the re-identification (ReID) model was deepened to strengthen the feature extraction ability of the model. It was proposed to identify and track fish that cross the line by setting a virtual baseline to achieve the dynamic identification of fish species passing and the automatic counting of upward and downward quantities. The results showed that 1) among the five models, YOLOv5n, YOLOv5s, YOLOv5m, YOLOv5l, and YOLOv5x, the highest value of mean average precision (mAP) was 92.8% achieved by the YOLOv5x model. Specifically, recognition accuracies of 96.95%, 94.95%, 88.79%, and 91.93% were recorded for Schizothorax o’connori , S. waltoni , S. younghusbandi and O. stewartii , respectively. 2) The error rate of the improved ReID model was 20.3%, which was 20% lower than that before the improvement, making it easier for the model to obtain target features. 3) The average accuracy of the improved DeepSORT algorithm for counting four target fishes was 75.5%, among which the accuracy of Schizothorax o’connori , S. waltoni , S. younghusbandi and O. stewartii were 83.6%, 71.1%, 68.1%, and 79.3%, respectively. Meanwhile, the running speed was 44.6 fps, which met the real-time monitoring. This method is the first to implement intelligent identification of the target passing fish in fishways projects, which can accumulate long series monitoring data for fishways operation and management and provide a technical solution and reference for the work related to the realization of intelligent and informative passing fish monitoring.

Wheat leaf rust, caused by Puccinia triticina Eriks. ( Pt ), is a global wheat disease threatening wheat production. Dissecting how Pt effector proteins interact with wheat has great significance in understanding the pathogenicity mechanisms of Pt . In the study, the cDNA of Pt 13-5-72 interacting with susceptible cultivar Thatcher was used as template to amplify Pt13024 gene. The expression pattern and structure of Pt13024 were analyzed by qRT-PCR and online softwares. The secretion function of Pt13024 signal peptide was verified by the yeast system. Subcellular localization of Pt13024 was analyzed using transient expression on Nicotiana benthamiana . The verification that Pt13024 inhibited programmed cell death (PCD) was conducted on N. benthamiana and wheat. The deletion mutation of Pt13024 was used to identify the virulence function motif. The transient transformation of wheat mediated by the type III secretion system (TTSS) was used to analyze the activity of regulating the host defense response of Pt13024. Pt13024 gene silencing was performed by host-induced gene silencing (HIGS). The results showed that Pt13024 was identified as an effector and localized in the cytoplasm and nucleus on the N. benthamiana . It can inhibit PCD induced by the Bcl-2-associated X protein (BAX) from mice and infestans 1 (INF1) from Phytophthora infestans on N. benthamiana , and it can also inhibit PCD induced by DC3000 on wheat. The amino acids 22 to 41 at N-terminal of the Pt13024 are essential for the inhibition of programmed cell death (PCD) induced by BAX. The accumulation of reactive oxygen species and deposition of callose in near-isogenic line TcLr30, which is in Thatcher background with Lr30 , induced by Pt13024 was higher than that in 41 wheat leaf rust-resistant near-isogenic lines (monogenic lines) with different resistance genes and Thatcher. Silencing of Pt13024 reduced the leaf rust resistance of Lr30 during the interaction between Pt and TcLr30. We can conclude that Pt13024 is avirulent to TcLr30 when Pt interacts with TcLr30. These findings lay the foundation for further investigations into the role of Pt effector proteins in pathogenesis and their regulatory mechanisms.

Changes in fish phenotypes during aquaculture must be monitored to improve the quality of fishery resources. Therefore, a method for segmenting and measuring phenotypes rapidly and accurately without harming the fish is essential. This study proposes an intelligent fish phenotype segmentation method based on the residual network, ResNet50, and atrous spatial pyramid pooling (ASPP). Methods: A sufficient number of fish phenotypic segmentation datasets rich in experimental research was constructed, and diverse semantic segmentation datasets were developed. ResNet50 was then built as the backbone feature extraction network to prevent the loss of fish phenotypic feature information and improve the precision of fish phenotypic segmentation. Finally, an ASPP module was designed to improve the phenotypic segmentation accuracy of different parts of fish. Results: The test algorithm based on the collected fish phenotype segmentation datasets showed that the proposed algorithm (RA-UNet) yielded the best results among several advanced semantic segmentation models. The mean intersection over union (mIoU) and mean pixel accuracy (mPA) were 87.8% and 92.3%, respectively. Discussion: Compared with the benchmark UNet algorithm, RA-UNet demonstrated improvements in the mIoU and mPA by 5.0 and 1.8 percentage points, respectively. Additionally, RA-UNet exhibited superior fish phenotype segmentation performance, with a low false detection rate and clear and complete edge segmentation. Conclusively, the RA-UNet proposed in this study has high accuracy and edge segmentation ability and can, therefore, directly improve the efficiency of phenotypic monitoring in fish farming.

Interface reaction between lithium (Li) and materials at the anode is not well understood in an all-solid environment. This paper unveils a new phenomenon of constriction susceptibility for materials at such an interface, the utilization of which helps facilitate the design of an active three-dimensional scaffold to host rapid plating and stripping of a significant amount of a thick Li metal layer. Here we focus on the well-known anode material silicon (Si) to demonstrate that, rather than strong Li–Si alloying at the conventional solid–liquid interface, the lithiation reaction of micrometre-sized Si can be significantly constricted at the solid–solid interface so that it occurs only at thin surface sites of Si particles due to a reaction-induced, diffusion-limiting process. The dynamic interaction between surface lithiation and Li plating of a family of anode materials, as predicted by our constrained ensemble computational approach and represented by Si, silver (Ag) and alloys of magnesium (Mg), can thus more homogeneously distribute current densities for the rapid cycling of Li metal at high areal capacity, which is important in regard to solid-state battery application. Interfacial reactions between lithium and anodes are not well understood in an all-solid environment. For the silicon anode we now demonstrate that, rather than strong Li–Si alloying at the conventional solid–liquid interface, the lithiation reaction of micrometre-sized Si can be greatly constricted at the solid–solid interface.