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After a major debt crisis occurs in a listed company, the stock prices of related enterprises may also fluctuate sharply, resulting in the spread of debt risk to more enterprises. Taking the stocks of listed companies as network nodes, we constructed a complex stock market network over three periods of time through the logarithmic return rate of stocks for the three periods of prophase, metaphase, and anaphase of the debt crisis. We studied the topological characteristics of the network and destructiveness over the three periods. Finally, the minimum spanning tree was used to construct a network and the community structure of the network. The empirical analysis took the debt crisis of the China Evergrande Company as an example to analyze the impact of its major debt crisis on the Chinese stock market. The research findings were as follows: First, the debt crisis increased the inter-industry connections within the network, that is, the correlations between enterprises in different industries was enhanced. Second, the closeness of the cross-industry connections increased the connectivity efficiency of the network, but compared with the other two periods, the debt crisis in the metaphase was less stable. Third, community research showed that in the metaphase of the debt crisis, the enterprises became the core nodes of the network. [ABSTRACT FROM AUTHOR]

Achieving the target of carbon neutrality has been an important approach for China to mitigate global climate change. Enterprises are major carbon emitters, and a well-designed early warning system is needed to ensure that their emissions align with carbon neutrality goals. Therefore, this study utilized electricity big data to construct an early warning model for enterprise carbon emissions based on carbon quota allocation. Taking key carbon-emitting enterprises in Guangxi as a case study, we aim to provide insights to support China's dual carbon goals. Firstly, we established the Carbon Quota Allocation System, enabling carbon quota allocation at the enterprise levels. Secondly, we developed the Enterprise Carbon Neutrality Index, facilitating dynamic warnings for carbon emissions among enterprises. The main conclusions are as follows: (1) In 2020, Guangdong received the highest carbon quota of 606 million tons, representing 5.72% of the national total, while Guangxi only received 2.63 billion tons. (2) Only 39.34% of enterprises in Guangxi are able to meet the carbon neutrality target, indicating significant emission reduction pressure faced by enterprises in the region. (3) Over 90% of enterprises in Guangxi receive Commendation and Encouragement warning levels, suggesting that enterprises in Guangxi are demonstrating a promising trend in emission reduction efforts. [ABSTRACT FROM AUTHOR]

Leaf morphology is a crucial aspect of plant architecture, yet the molecular mechanisms underlying leaf development remain incompletely understood. In this study, a narrow leaf mutant, m625, was identified in rice (Oryza sativa L.), exhibiting pleiotropic developmental defects. Pigment measurement revealed reduced levels of photochromic pigments in m625. Cytological analysis demonstrated that the m625 gene affected vascular patterns and cell division. Specifically, the narrowing of the leaf was attributed to a decrease in small vein number, shorter vein spacing, and an abnormal V-shaped arrangement of bulliform cells, while the thickening was caused by longer leaf veins, thicker mesophyll cells, and an increased number of parenchyma cell layers. The dwarf stature and thickened internode were primarily due to shortened internodes and an increase in cell layers, respectively. Positional cloning and complementation assays indicated that the m625 gene is a novel allele of NAL1. In the m625 mutant, a nucleotide deletion at position 1103 in the coding sequence of NAL1 led to premature termination of protein translation. Further RNA-Seq and qRT-PCR analyses revealed that the m625 gene significantly impacted regulatory pathways related to IAA and ABA signal transduction, photosynthesis, and lignin biosynthesis. Moreover, the m625 mutant displayed thinner sclerenchyma and cell walls in both the leaf and stem, particularly showing reduced lignified cell walls in the midrib of the leaf. In conclusion, our study suggests that NAL1, in addition to its known roles in IAA transport and leaf photosynthesis, may also participate in ABA signal transduction, as well as regulate secondary cell wall formation and sclerenchyma thickness through lignification. [ABSTRACT FROM AUTHOR]

In the traditional control scheme of a 12/8-pole bearingless switched reluctance motor (BSRM), radial force and torque are usually controlled as a compromise due to the conflict between their effective output areas. Additionally, each phase requires individual power circuits and is excited in turn to produce a continuous levitation force, resulting in high power device requirements and high controller costs. This paper discusses a 12/8-pole single-winding hybrid-rotor bearingless switched reluctance motor (HBSRM) with a hybrid rotor consisting of cylindrical and salient-pole lamination segments. The asymmetric rotor of the HBSRM slightly increases the complexity of its structure and magnetic circuit, but makes it possible to generate the desired radial force at any rotor angular position. A control scheme for the HBSRM is developed to utilize the independent excitation of the four windings in one phase to generate the desired levitation force at any rotor angular position, and it requires only half the number of power circuits used in the conventional control scheme of a 12/8-pole single-winding BSRM. Different from the average torque chosen to be controlled in traditional methods, this scheme directly regulates the instantaneous total torque produced by all excited phases together and presents a current algorithm to optimize the torque contribution of each phase so as to reduce torque pulsation, and the improved performance of this bearingless motor is finally validated by simulation analysis. [ABSTRACT FROM AUTHOR]

Federated learning is a privacy-preserving machine learning framework where multiple data owners collaborate to train a global model under the orchestra of a central server. The local training results from trainers should be submitted to the central server for model aggregation and update. Busy central server and malicious trainers can introduce the issues of a single point of failure and model poisoning attacks. To address the above issues, the trusty decentralized federated learning (called TrustDFL) framework has been proposed in this paper based on the zero-knowledge proof scheme, blockchain, and smart contracts, which provides enhanced security and higher efficiency for model aggregation. Specifically, Groth 16 is applied to generate the proof for the local model training, including the forward and backward propagation processes. The proofs are attached as the payloads to the transactions, which are broadcast into the blockchain network and executed by the miners. With the support of smart contracts, the contributions of the trainers could be verified automatically under the economic incentive, where the blockchain records all exchanged data as the trust anchor in multi-party scenarios. In addition, IPFS (InterPlanetary File System) is introduced to alleviate the storage and communication overhead brought by local and global models. The theoretical analysis and estimation results show that the TrustDFL efficiently avoids model poisoning attacks without leaking the local secrets, ensuring the global model's accuracy to be trained. [ABSTRACT FROM AUTHOR]

With the increasing demand for person re-identification (Re-ID) tasks, the need for all-day retrieval has become an inevitable trend. Nevertheless, single-modal Re-ID is no longer sufficient to meet this requirement, making Multi-Modal Data crucial in Re-ID. Consequently, a Visible-Infrared Person Re-Identification (VI Re-ID) task is proposed, which aims to match pairs of person images from the visible and infrared modalities. The significant modality discrepancy between the modalities poses a major challenge. Existing VI Re-ID methods focus on cross-modal feature learning and modal transformation to alleviate the discrepancy but overlook the impact of person contour information. Contours exhibit modality invariance, which is vital for learning effective identity representations and cross-modal matching. In addition, due to the low intra-modal diversity in the visible modality, it is difficult to distinguish the boundaries between some hard samples. To address these issues, we propose the Graph Sampling-based Multi-stream Enhancement Network (GSMEN). Firstly, the Contour Expansion Module (CEM) incorporates the contour information of a person into the original samples, further reducing the modality discrepancy and leading to improved matching stability between image pairs of different modalities. Additionally, to better distinguish cross-modal hard sample pairs during the training process, an innovative Cross-modality Graph Sampler (CGS) is designed for sample selection before training. The CGS calculates the feature distance between samples from different modalities and groups similar samples into the same batch during the training process, effectively exploring the boundary relationships between hard classes in the cross-modal setting. Some experiments conducted on the SYSU-MM01 and RegDB datasets demonstrate the superiority of our proposed method. Specifically, in the VIS→IR task, the experimental results on the RegDB dataset achieve 93.69% for Rank-1 and 92.56% for mAP. [ABSTRACT FROM AUTHOR]

Automaticidentification and prevention of leaf diseases and insect pests on fruit crops represent a key trend in the development of smart agriculture. In order to address the limitations of existing models with low identification rates of apple leaf diseases and insect pests, a novel identification model based on an improved ResNet-50 architecture was proposed, which incorporated the coordinate attention (CA) module and weight-adaptive multi-scale feature fusion (WAMFF) to enhance the ResNet-50's image feature extraction capabilities. Transfer learning and online data enhancement are employed to boost the model's generalization ability. The proposed model achieved a top-1 accuracy rate of 98.32% on the basis of AppleLeaf9 datasets, which is 4.58% higher than the value from the original model, and the improved model can effectively improve the localization of lesion features. Furthermore, compared with mainstream deep networks, such as AlexNet, VGG16, DenseNet, MNASNet, and GoogLeNet on the same dataset, the top-1 accuracy rate increased by 7.3%, 3.19%, 4.98%, 6.04% and 3.87%, respectively. The experimental results demonstrate that the improved model is effective in improving the identification accuracy of apple leaf diseases and insect pests and enhancing the model's effective feature extraction capabilities. [ABSTRACT FROM AUTHOR]

In recent years, perovskite solar cells have achieved high efficiency in small areas, but the industrialization of perovskite solar cells is still hampered by the efficiency loss of cells during area scaling. Series modules are currently the most widely used and effective modular process for perovskite solar cells, a process that requires the patterning of the layer stack in individual cells. This interconnection scheme consists of three lines, P1–P3, which are ablated using a pulsed laser beam. In this work, we developed an efficient perovskite module with a high geometric fill factor (GFF) based on a two-step spin-coating method by optimizing the laser ablation step. We constructed the entire device structure by means of a picosecond green pulsed laser, meanwhile, the dead area is greatly reduced by optimizing the parameters and process, and finally achieving an ultra-high GFF of over 99%. In addition, we investigated the effect of poor P2 and P3 ablation on the perovskite solar modules and compared the performance of the module before and after ablation optimization. We finally obtained a maximum photoelectric conversion efficiency of 22.79% with an aperture area of 12.6 cm2. Concurrently, we conducted electrocatalytic hydrogen production experiments on the prepared perovskite solar modules, and the experimental results have also shown excellent performance and good prospects. [ABSTRACT FROM AUTHOR]

With the aim of increasing the model accuracy of lithium-ion batteries (LIBs), this paper presents a complex-order beetle swarm optimization (CBSO) method, which employs complex-order (CO) operator concepts and mutation into the traditional beetle swarm optimization (BSO). Firstly, a fractional-order equivalent circuit model of LIBs is established based on electrochemical impedance spectroscopy (EIS). Secondly, the CBSO is used for model parameters' identification, and the model accuracy is verified by simulation experiments. The root-mean-square error (RMSE) and maximum absolute error (MAE) optimization metrics show that the model accuracy with CBSO is superior when compared with the fractional-order BSO. [ABSTRACT FROM AUTHOR]

This paper proposes a model-based systems engineering (MBSE) methodology to design a first-stage separation system for a launch vehicle. It focuses on the whole process of system modeling, such as modeling the requirements analysis, logical architecture design, physical architecture design, and system verification and validation. Finally, the component requirements are obtained as the baseline for the component design. Requirements analysis is carried out by identifying stakeholders with the cycle modeling for this system and the use of case modeling to ensure that the requirements are comprehensive and correct. Additionally, the standard system requirements are obtained and baselined. Based on system requirements, the trade-off analysis of hierarchical functional architecture and key indicators was mainly carried out to design the logical architecture. Once the logical architecture was decided, the logical architecture was allocated to the physical architecture to be implemented. Several physical architectures are analyzed hierarchically to seek the optimal architectures. Then, other CAE analysis tools were integrated to verify the physical architecture design. All these processes are modeled and integrated as the authority system model, which benefits the system engineer for managing the requirement changes easier and rapidly provides multi-views for different roles. [ABSTRACT FROM AUTHOR]

With the continuous evolution of bacteria and the constant use of traditional antibiotics, the emergence of drug-resistant bacteria and super viruses has attracted worldwide attention. Antimicrobial therapy has become the most popular and important research field at present. Coordination Polymer (CP) and/or metal-organic framework (MOF) platforms have the advantages of a high biocompatibility, biodegradability, and non-toxicity, have a great antibacterial potential and have been widely used in antibacterial treatment. This paper reviewed the mechanism and antibacterial effect of three typical MOFs (pure Ag-MOFs, hybrid Ag-MOFs, and Ag-containing-polymer @MOFs) in silver-based coordination polymers. At the same time, the existing shortcomings and future views are briefly discussed. The study on the antibacterial efficacy and mechanism of Ag-MOFs can provide a better basis for its clinical application and, meanwhile, open up a novel strategy for the preparation of more advanced Ag-contained materials with antibacterial characteristics. [ABSTRACT FROM AUTHOR]

A precursor film with a Cu gradient was prepared in order to improve the quality of the absorber film produced by sputtering CIGS targets when using glass substrates. Two ceramic quaternary targets with different copper content were used for alternatively sputtering to get a bi-layer precursor film with a Cu gradient; meanwhile, the crystallization property and cell performance were studied. This was done in order to study the activities of the Cu element in the precursor layer before and after selenization. The film states of the temperature-rise period and high temperature selenization period were investigated. The appropriate structure of the precursor film was the Cu-rich layer underneath the Cu-poor layer. The Cu–Se phase, which is important for the crystalline property, can be produced in the Cu-rich layer under the heating period. The Cu-poor layer on the top reacts with the Cu-Se compound in the annealing process at a high temperature, and the big grain size of the absorber layer can be obtained due to Cu diffusion promoted by the gradient in the precursor film, as well as better conversion efficiency. This result shows that constructing the precursor film with a Cu gradient by sputtering different quaternary CIGS targets is a very promising fabrication method to obtain high-performance solar-cell devices with a good crystallization property under an annealing temperature of 550 °C and is suitable for further industrialized application. [ABSTRACT FROM AUTHOR]

The occurrence of rainfall is the result of a combination of various meteorological factors. Traditional rainfall early warning models solely use Global Navigation Satellite System (GNSS)-derived Zenith Total Delay (ZTD) or Precipitable Water Vapor (PWV) to forecast rainfall, resulting in a low true detected rate. While non-linear rainfall early warning models based on the Back-Propagation Neural Network (BP-NN) algorithm consider the influences of various meteorological factors, the forecasts often exhibit a high false rate. To further improve the prediction of rainfall, a short-term rainfall early warning model based on the GNSS and BP-NN algorithms is proposed in this study. The method uses the traditional rainfall forecasting model and utilizes the BP-NN algorithm to combine various meteorological factors for rainfall early warning. The results of GNSS and BP-NN together improve the precision of rainfall early warning. Observation data from eight GNSS stations, the fifth-generation reanalysis of European Centre for Medium-Range Weather Forecast (ECMWF ERA5), and temperature, pressure, and rainfall data from corresponding meteorological stations in Ningbo, China were utilized to verify the rainfall early warning model proposed in this study. The results show that the proposed model can complement the advantages of the traditional linear and non-linear rainfall early warning methods. The model can maintain a high True Detected Rate (TDR) of rainfall early warning while simultaneously reducing the False Forecasted Rate (FFR). The average TDR of the eight GNSS stations is 100% and the FFR is 20.75%, which are both better than those of existing traditional linear and non-linear rainfall early warning models. [ABSTRACT FROM AUTHOR]

Licorice has previously been shown to affect gene expression in cells; however, the underlying mechanisms remain to be clarified. We analyzed the microRNA expression profile of serum from mice treated by gavage with licorice decoction, and obtained 11 differentially expressed microRNAs (DEmiRNAs). We also screened differentially expressed genes (DEgenes) based on RNA-Seq data, and 271 common genes were identified by intersection analysis of the predicted target genes of 11 DEmiRNAs and the DEgenes. The miRNA–gene network showed that most of the hub genes were immune-related. KEGG enrichment analysis of the 271 genes identified three significant pathways, and the 21 genes involved in these three pathways, and the 11 DEmiRNAs, were constructed into a miRNA pathway–target gene network, in which mmu-miR-27a-3p stood out. Compared to ImmPort, there were 13 immune genes within the above group of 21 genes, and three intersected with the mmu-miR-27a-3p predicted target genes, Cd28, Grap2 and Cxcl12, of which the expression of Cd28 changed most significantly. We confirmed the regulation of Cd28 by mmu-miR-27a-3p using a dual-luciferase assay, and further confirmed that overexpression of mmu-miR-27a-3p could significantly downregulate the expression of Cd28 in lymphocytes. These results indicate that mmu-miR-27a-3p could be involved in the licorice-mediated regulation of the expression of Cd28 in mice. [ABSTRACT FROM AUTHOR]

In this work, Al-1Zn-0.1In-0.1Sn-0.5Mg-xMn (x = 0, 0.1, 0.2, 0.3) alloys are prepared and used as the anode of an Al-air battery (AAB). We use scanning electron microscopy (SEM) with energy-dispersive X-ray spectroscopy (EDS) and optical microscopy (OM) to analyze the microstructures of the alloys. The hydrogen evolution rate, electrochemical performance (including polarization curves), electrochemical impedance spectroscopy (EIS), and battery performance of the samples are examined in the 4 M NaOH electrolyte. The experimental data display that the average grain size is significantly refined after adding manganese into the Al-1Zn-0.1In-0.1Sn-0.5Mg alloy, with a decrease in grain size from over 100 μm to about 10 μm. The improved activity of the aluminum anode in the AAB can be attributed to the introduction of manganese. The Al-1Zn-0.1In-0.1Sn-0.5Mg-0.1Mn alloy possesses the optimal overall performance with a lower self-corrosion rate (0.128 mL∙cm−2∙min−1), the highest working potential (1.630 V) and energy density (2415 mWh·g−1), a higher capacity (1481 mAh·g−1) and anodic utilization (49.75%). [ABSTRACT FROM AUTHOR]

A novel fowl adenovirus 4 (FAdV-4) has caused significant economic losses to the poultry industry in China since 2015. We established an easy-to-use reverse genetics system for modification of the whole right and partial left ends of the novel FAdV-4 genome, which worked through cell-free reactions of restriction digestion and Gibson assembly. Three recombinant viruses were constructed to test the assumption that species-specific viral genes of ORF4 and ORF19A might be responsible for the enhanced virulence: viral genes of ORF1, ORF1b and ORF2 were replaced with GFP to generate FAdV4-GFP, ORF4 was replaced with mCherry in FAdV4-GFP to generate FAdV4-GX4C, and ORF19A was deleted in FAdV4-GFP to generate FAdV4-CX19A. Deletion of ORF4 made FAdV4-GX4C form smaller plaques while ORF19A deletion made FAdV4-CX19A form larger ones on chicken LMH cells. Coding sequence (CDS) replacement with reporter mCherry demonstrated that ORF4 had a weak promoter. Survival analysis showed that FAdV4-CX19A-infected chicken embryos survived one more day than FAdV4-GFP- or FAdV4-GX4C-infected ones. The results illustrated that ORF4 and ORF19A were non-essential genes for FAdV-4 replication although deletion of either gene influenced virus growth. This work would help function study of genes on the right end of FAdV-4 genome and facilitate development of attenuated vaccines. [ABSTRACT FROM AUTHOR]

Junction temperature is a key parameter that influences both the performance and the reliability of the insulated gate bipolar transistor (IGBT) module, while solder fatigue has a significant effect on the accuracy of junction temperature estimates using the electro-thermal model. In this paper, an improved electro-thermal model, which is independent of solder fatigue, is proposed to accurately estimate the junction temperature of IGBT module. Firstly, solder fatigue conditions are monitored in real time with the information of the case temperatures. Secondly, when solder fatigue is found, the update process of the electro-thermal model parameters is performed to match the model parameters with the fatigue device. With the above two-step process, the influence of solder fatigue on the accuracy of temperature estimates can be removed in good time. Experimental results are provided to validate the effectiveness of the proposed method. [ABSTRACT FROM AUTHOR]

The diagnosis method for a weak-fault model with only normal behaviors of each component has evolved over decades. However, many systems now demand a strong-fault models, the fault modes of which have specific behaviors as well. It is difficult to diagnose a strong-fault model due to its non-monotonicity. Currently, diagnosis methods usually employ conflicts to isolate possible fault and the process can be expedited when some observed output is consistent with the model's prediction where the consistency indicates probably normal components. This paper solves the problem of efficiently diagnosing a strong-fault model by proposing a novel Logic-based Truth Maintenance System (LTMS) with two search approaches based on conflict and consistency. At the beginning, the original a strong-fault model is encoded by Boolean variables and converted into Conjunctive Normal Form (CNF). Then the proposed LTMS is employed to reason over CNF and find multiple minimal conflicts and maximal consistencies when there exists fault. The search approaches offer the best candidate efficiency based on the reasoning result until the diagnosis results are obtained. The completeness, coverage, correctness and complexity of the proposals are analyzed theoretically to show their strength and weakness. Finally, the proposed approaches are demonstrated by applying them to a real-world domain-the heat control unit of a spacecraft-where the proposed methods are significantly better than best first and conflict directly with A* search methods., Competing Interests: The authors declare no conflict of interest.