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This comprehensive study delves into the nuanced causes and repercussions surrounding the failure of Silicon Valley Banks(SVB), scrutinizing both external and internal factors. Internally, the demise is attributed to mismanagement of assets and liabilities, coupled with flaws in the business model. Externally, the Federal Reserve’s interest rate hike and relaxed financial regulations during the Trump era are identified as pivotal triggers. Post-bankruptcy, the rapid acquisition and sale of SVB, First Total Bank, and Signature Bank temporarily alleviate risks within the US banking sector. Regulatory authorities respond by fortifying rules, enhancing industry resilience but simultaneously tightening loan conditions, potentially intensifying economic downturn pressures. Presently, market confidence is on the mend due to regulatory influence, limiting systemic risks. However, the looming 2023 interest rate hike poses a threat, particularly with tight monetary policies. Small and medium-sized US banks, heavily invested in commercial real estate, face heightened vulnerability, potentially precipitating a downward spiral in property prices. Drawing lessons from Lehman Brothers, the study advocates for a balanced approach to monetary policy, emphasizing financial stability, improved risk management by regulators, and ongoing efforts to avert and mitigate future financial crises.

In recent years, with the rise of digital twin technology in the field of artificial intelligence and the continuous advancement of hardware imaging equipment, significant progress has been made in the detection of structural damage in buildings and sculptures. Structural damage to cultural heritage buildings poses a major threat to their integrity, making accurate detection of such damage crucial for cultural heritage preservation. However, existing deep learning-based object detection technologies face limitations in achieving full coverage of architectural sculptures and enabling multi-angle, free observation, while also exhibiting substantial detection errors. To address these challenges, this paper proposes a detection method that integrates digital modeling with an improved YOLO algorithm. By scanning architectural scenes to generate digital twin models, this method enables full-angle and multi-seasonal scene transformations. Specifically, the Nanjing Sheli pagoda is selected as the research subject, where drone-based panoramic scanning is employed to create a digitalized full-scene model. The improved YOLO algorithm is then used to evaluate detection performance under varying weather and lighting conditions. Finally, evaluation metrics are utilized to automatically analyze detection accuracy and the extent of damage. Compared to traditional on-site manual measurement methods, the proposed YOLO-based automatic detection technology in digitalized scenarios significantly reduces labor costs while improving detection accuracy and efficiency. This approach provides a highly effective and reliable technical solution for assessing the extent of damage in historical buildings.

Compared to traditional single-polarization radar, dual-polarization radar has a number of advantages for quantitative precipitation estimation because more information about the drop size distribution and hydrometeor type can be gleaned. In this paper, an improved dual-polarization rainfall methodology is proposed, which is driven by a region-based hydrometeor classification mechanism. The objective of this study is to incorporate the spatial coherence and self-aggregation of dual-polarization observables in hydrometeor classification and to produce robust rainfall estimates for operational applications. The S-band dual-polarization data collected from the NASA Polarimetric (NPOL) radar during the GPM Iowa Flood Studies (IFloodS) ground validation field campaign are used to demonstrate and evaluate the proposed rainfall algorithm. Results show that the improved rainfall method provides better performance than a few single- and dual-polarization algorithms in previous studies. This paper also investigates the impact of radar beam broadening on various rainfall algorithms. It is found that the radar-based rainfall products are less correlated with ground disdrometer measurements as the distance from the radar increases.

Machine learning‐based approaches demonstrate a significant potential in radar quantitative precipitation estimation (QPE) applications. In contrast to conventional methods that depend on local raindrop size distributions, deep learning (DL) can establish an effective mapping from three‐dimensional radar observations to ground rain rates. However, the lack of transparency in DL models poses challenges toward understanding the underlying physical mechanisms that drive their outcomes. This study aims to develop a DL‐based QPE system and provide a physical explanation of radar precipitation estimation process. This research is designed by employing a deep neural network consisting of two modules. The first module is a quantitative precipitation estimation network that has the capability to learn precipitation patterns and spatial distribution from multidimensional polarimetric radar observations. The second module introduces a quantitative precipitation estimation shapley additive explanations method to quantify the influence of each radar observable on the model estimate across various precipitation intensities. Plain Language Summary Ground radars can provide continuous spatial observations over large areas with high spatiotemporal resolutions, so they form the infrastructure for precipitation monitoring and observation in many countries. Recently, deep learning (DL) techniques have shown great potential for use in polarimetric radar‐based precipitation estimates. Nevertheless, the black‐box and turn‐key characteristics of DL models make it difficult for researchers to understand the model decision‐making process and cast doubt on the reliability of the model results. This study introduces a physically explainable polarization radar‐based quantitative precipitation estimation (QPE) system built on DL technology that can explain the causes of the precipitation estimates provided by deep learning models under different rainfall amounts. An experiment indicates that our model achieves better estimates than the conventional methods. Furthermore, the explainability methodology allows for visualization of the microphysical precipitation information. Being the initial attempt to apply explainability learning in the QPE domain, the explainability results may offer valuable guidance for rainfall estimation. Key Points A polarimetric radar‐based rainfall estimation system is developed using deep neural networks The deep learning‐based rainfall estimates generally outperform products derived from traditional parametric relations The proposed deep learning interpretation method can provide physical and statistical explanations of the model decision‐making process

As the road traffic situation becomes complex, the task of traffic management takes on an increasingly heavy load. The air-to-ground traffic administration network of drones has become an important tool to promote the high quality of traffic police work in many places. Drones can be used instead of a large number of human beings to perform daily tasks, as: traffic offense detection, daily crowd detection, etc. Drones are aerial operations and shoot small targets. So the detection accuracy of drones is less. To address the problem of low accuracy of Unmanned Aerial Vehicles (UAVs) in detecting small targets, we designed a more suitable algorithm for UAV detection and called GBS-YOLOv5. It was an improvement on the original YOLOv5 model. Firstly, in the default model, there was a problem of serious loss of small target information and insufficient utilization of shallow feature information as the depth of the feature extraction network deepened. We designed the efficient spatio-temporal interaction module to replace the residual network structure in the original network. The role of this module was to increase the network depth for feature extraction. Then, we added the spatial pyramid convolution module on top of YOLOv5. Its function was to mine small target information and act as a detection head for small size targets. Finally, to better preserve the detailed information of small targets in the shallow features, we proposed the shallow bottleneck. And the introduction of recursive gated convolution in the feature fusion section enabled better interaction of higher-order spatial semantic information. The GBS-YOLOv5 algorithm conducted experiments showing that the value of mAP@0.5 was 35.3 % and the mAP@0.5:0.95 was 20.0 % . Compared to the default YOLOv5 algorithm was boosted by 4.0 % and 3.5 % , respectively.

The difficulty in transforming old industrial areas constitutes a significant factor contributing to regional development imbalances. Can regional tax incentives, as a crucial component of regional policies, polish the “rust belt” regions? This study leverages the inaugural Value-Added Tax (VAT) reform in China as an opportunity to explore the potential of regional tax incentives in achieving sustainable development in traditional industrial areas. Drawing upon a comprehensive industrial enterprise database, we employ a Propensity Score Matching-Difference in Differences (PSM-DID) approach to examine the efficacy of these tax incentives. Our findings reveal that: (1) Regional tax incentives primarily enhance firms productivity by stimulating investment in enterprises, yet they do not contribute to improved investment efficiency or spur innovation within firms. (2) Regional tax incentives have alleviated financing constraints for enterprises in old industrial bases, significantly enhancing the Total Factor Productivity (TFP) of firms with higher financing constraints. This policy has had an even stronger impact on improving the TFP of state-owned and monopolistic enterprises. (3) Regional tax incentives have impeded productivity growth by preventing the exit of low-efficiency firms and the entry of high-efficiency ones. These incentives also increased the likelihood of “zombie firms” forming and failed to promote endogenous economic growth in the Northeast region. Additionally, they have distorted the allocation of resources towards capital and technology-intensive industries in that area. In China’s old industrial bases, regional tax incentives should be coordinated with market-oriented reforms; these regional tax incentive policies should also be further enriched.

Background Since the first case of a coronavirus disease 2019 (COVID-19) infection pneumonia was detected in Wuhan, China, a series of confirmed cases of the COVID-19 were found in Southwest China. The aim of this study was to describe the imaging manifestations of hospitalized patients with confirmed COVID-19 infection in southwest China. Methods In this retrospective study, data were collected from 131 patients with confirmed coronavirus disease 2019 (COVID-19) from 3 Chinese hospitals. Their common clinical manifestations, as well as characteristics and evolvement features of chest CT images, were analyzed. Results A total of 100 (76%) patients had a history of close contact with people living in Wuhan, Hubei. The clinical manifestations of COVID-19 included cough, fever. Most of the lesions identified in chest CT images were multiple lesions of bilateral lungs, lesions were more localized in the peripheral lung, 109 (83%) patients had more than two lobes involved, 20 (15%) patients presented with patchy ground glass opacities, patchy ground glass opacities and consolidation of lesions co-existing in 61 (47%) cases. Complications such as pleural thickening, hydrothorax, pericardial effusion, and enlarged mediastinal lymph nodes were detected but only in rare cases. For the follow-up chest CT examinations (91 cases), We found 66 (73%) cases changed very quickly, with an average of 3.5 days, 25 cases (27%) presented absorbed lesions, progression was observed in 41 cases (46%), 25 (27%) cases showed no significant changes. Conclusion Chest CT plays an important role in diagnosing COVID-19. The imaging pattern of multifocal peripheral ground glass or mixed consolidation is highly suspicious of COVID-19, that can quickly change over a short period of time.

YOLOv5 is one of the most popular object detection algorithms, which is divided into multiple series according to the control of network depth and width. To realize the deployment of mobile devices or embedded devices, the paper proposes a lightweight aerial image object detection algorithm (LAI-YOLOv5s) based on the improvement of YOLOv5s with a relatively small amount of calculation and parameter and relatively fast reasoning speed. Firstly, to better detect small objects, the paper replaces the minimum detection head with the maximum detection head and proposes a new feature fusion method, DFM-CPFN(Deep Feature Map Cross Path Fusion Network), to enrich the semantic information of deep features. Secondly, the paper designs a new module based on VoVNet to improve the feature extraction ability of the backbone network. Finally, based on the idea of ShuffleNetV2, the paper makes the network more lightweight without affecting detection accuracy. Based on the VisDrone2019 dataset, the detection accuracy of LAI-YOLOv5s on the mAP@0.5 index is 8.3% higher than that of the original algorithm. Compared with other series of YOLOv5 and YOLOv3 algorithms, LAI-YOLOv5s has the advantages of low computational cost and high detection accuracy.

In recent years, highway accidents occur frequently, the main reason is that there is always foreign body invasion on the highway, which makes people unable to respond to emergencies in time. In order to reduce the occurrence of highway incidents, an object detection algorithm for highway intrusion was proposed in this paper. Firstly, a new feature extraction module was proposed to better preserve the main information. Secondly, a new feature fusion method was proposed to improve the accuracy of object detection. Finally, a lightweight method was proposed to reduce the computational complexity. We compare the algorithm in this paper with existing algorithms, the experimental results showed that: On the Visdrone dataset (small size targets), (a) the CS-YOLO was 3.6% more accurate than the YOLO v8. (b) The CS-YOLO was 1.2% more accurate than the YOLO v8 on the Tinypersons dataset (minimal size targets). (c) CS-YOLO was 1.4% more accurate than YOLO v8 on VOC2007 data set (normal size).

This study proposes a design method for near-slope roads along multi-level slopes that integrates excavation requirements and post-construction ecological restoration through sprayed eco-protection. Firstly, the design principles and procedural steps for near-slope roads are established. The planar layouts of multi-level slopes are categorized, including mixing areas, turnaround areas, berms, and access ramps. Critical technical parameters, such as curve radii and widths of berms and ramps, as well as dimensional specifications for turnaround areas, are systematically formulated with corresponding design formulas. The methodology is applied to the ecological restoration project of multi-level slopes in the Huamahu mountainous area, and a comparative technical-economic analysis is conducted between the proposed design and the original scheme. Results demonstrate that the optimized design reduces additional maintenance costs caused by near-slope roads by 6.5–8.0% during the curing period. This research advances the technical framework for multi-level slope governance and enhances the ecological design standards for slope protection engineering.