Degenerate lower dimensional tori in reversible systems

AbstractIn this paper we prove the persistence of lower dimensional invariant tori with prescribed frequencies and singular normal matrices in reversible systems. The normal variable is two-dimensional and the unperturbed nonlinear terms in the differential equation for this variable have a special structure

An invariance principle for the law of the iterated logarithm for vector-valued additive functionals of Markov chains

In this note, we prove the Strassen\u27s strong invariance principle for vector-valued additive functionals of a Markov chain via the martingale argument and the theory of fractional coboundaries

Protective Effects of Lycium barbarum

To observe the effects of Lycium barbarum polysaccharides (LBP) on testis spermatogenic injuries induced by Bisphenol A (BPA) in mice. BPA was subcutaneously injected into mice at a dose of 20 mg/kg body weight (BW) for 7 consecutive days. LBP was administered simultaneously with BPA by gavage daily at the dose of 50, 100, and 200 mg/kg BW for 7 days. After treatment, the weight and the histopathology changes of testis and epididymis were examined; the contents of T, LH, GnRH, antioxidant enzyme, and malondialdehyde (MDA) in serum were detected; proapoptotic protein Bax and antiapoptotic protein Bcl-2 were also detected by immunohistochemical method. Results showed that the weights of testis and epididymis were all increased after supplement with different dosages of LBP compared with BPA group, and the activities of SOD and GSH-Px were significantly increased in LBP groups, while MDA contents were gradually decreased. Moreover, the levels of T, LH, and GnRH were significantly elevated in serum treated with 100 mg/kg LBP. LBP also shows significant positive effects on the expression of Bcl-2/Bax in BPA treated mice. It is concluded that LBP may be one of the potential ingredients protecting the adult male animals from BPA induced reproductive damage

A survey-based analysis of the public's willingness for disaster relief in China

Meteorological disasters frequently occur in China and around the world. These natural hazards can cause huge economic losses and threaten the personal safety of citizens. The public’s willingness to engage with disaster relief efforts and the degree of participation is critical to reduce the impact of such disasters. This study conducted a survey with 62,903 respondents from China. The study utilized statistical analysis and correlation analysis in order to understand the differences and similarities of the public’s willingness to take part in disaster relief across gender and age. The study found that: (1) the public’s awareness of insurance and willingness to make donations during climate disasters is low, and that more than half of the public are only willing to insure for very less money; (2) although the public has very high enthusiasm to participate in disaster relief, they are less willing to learn the basic skills of reducing disasters and for participating in training for disaster reduction as volunteers. This was especially the case for elderly citizens and females; (3) the willingness of the public to prevent and reduce disasters is high, and this was the case across various gender and age groups. Finally, the study puts forward several measures to improve the uptake of disaster relief and disaster prevention among citizens

A New Study on the Parameter Relationships of Planetary Roller Screws

As a more powerful transmission device, planetary roller screws (PRSs) recently have received more attention, compared to conventional ball screws. However, due to the complicated and unclear relationships among the PRS components’ parameters, it is difficult to design high-quality PRSs. To facilitate the PRS design, a new study on the parameter relationships of PRS is conducted in this work. New models of the axial stiffness and the frictional moment of PRS are developed, and the relationships of the axial stiffness and the frictional moment in terms of contact angle, helical angle, and tooth number of the roller thread are investigated. This study could contribute to the research of PRS to improve its transmission performance, especially to increase its positioning accuracy

A cost-sensitive attention temporal convolutional network based on adaptive top-k differential evolution for imbalanced time-series classification

Imbalanced time-series classification (ITSC) is ubiquitous in many real-world applications. In this study, a novel cost-sensitive deep learning framework, namely ACS-ATCN, is proposed for ITSC. With the framework of ACS-ATCN, first, weighted class costs are optimized jointly with the hyperparameters of an attention temporal convolutional network (ATCN). Second, an improved evolutionary algorithm, termed adaptive top-k differential evolution (ATDE), is presented for optimizing class costs as well as the network's hyperparameter. Experiments on five data sets demonstrate that ACS-ATCN achieves a higher average G-mean than other cost-sensitive learning and oversampling algorithms while using much less computational time. Comparison between different deep learning frameworks also confirms its advantages over other existing benchmarking methods in ITSC. Experimental results also reveal that ATDE provides more accurate classification than the vanilla DE algorithm, and saves as high as 41.53% of average computational expense for convergence.This work was supported in part by National Natural Science Foundation of China (grant number 62101611) and Natural Science Foundation of Guangdong Province (grant number 2022A1515011375)

Kernel-based feature aggregation framework in point cloud networks

Various effective deep networks have been developed for analysis of 3D point clouds. One key step in these networks is to aggregate the features of orderless points into a compact representation for the cloud. As a typical order-invariant aggregation method, max-pooling has been widely applied. However, while enjoying simplicity and high efficiency, max-pooling does not fully exploit the feature information since it not only ignores the non-maximum elements in each feature dimension but also neglects the interactions between different dimensions. These drawbacks of max-pooling motivate us to explore advanced feature aggregation methods for 3D point cloud analysis. The desired advanced method should be capable of modeling richer information between the point features than max-pooling, and, at the same time, it can readily replace max-pooling without the need to modify other parts of the existing network architecture. To this end, this paper proposes a novel kernel-based feature aggregation framework for 3D point cloud analysis for the first time. The proposed method effectively considers all the elements in each dimension and models the nonlinear interactions between feature dimensions as complementary information to max-pooling. In addition, it is a plug-in module that can be integrated to many common networks as a replacement of max-pooling. Comprehensive experiments are conducted to demonstrate the consistently superior performance and high generality of the proposed method over max-pooling. Specifically, the proposed kernel-based feature aggregation framework consistently improves the max-pooling with three representative backbones of PointNet, DGCNN and PCT across four 3D point cloud based analysis tasks, including supervised 3D object classification, 3D part segmentation, indoor semantic segmentation and one additional unsupervised place retrieval task. Especially, it shows remarkable performance improvement over max-pooling in the unsupervised retrieval task, demonstrating its advantage in forming 3D point cloud representation

Three-Dimensional Geophysical Characterization of Deeply Buried Paleokarst System in the Tahe Oilfield, Tarim Basin, China

Paleokarst reservoirs are the major type of the Ordovician carbonate reservoirs in the Tahe Oilfield. Due to the strong heterogeneity in distribution, it is a real challenge to detect the spatial distribution of paleokarst reservoirs, especially those deeply buried more than 5500 m in the Tahe area. Based on the abundant core samples, this paper first described the structure of paleocaves drilled by well. Second, after time−depth conversions, the results from drilled wells were tied to three-dimensional (3D) seismic datasets, and then the threshold of host rocks and caves in wave impedance were identified. Third, the seismic-scale mapping and visualization of the paleokarst reservoirs were achieved by tracing the distribution of paleocaves. This approach was applied in the well T403 area, and the structure of the paleokarst, especially the runoff zone, was interpreted. 3D structure and spatial distribution of the paleokarst system was demonstrated by plane, vertical, and 3D models. Additionally, according to the hydrology genetic relationships, the paleocaves in the runoff zone were divided into sinkholes, main channel, and branch channel. The approach of a 3D geophysical characterization of a deeply buried paleokarst system can be applicable to Tahe and other similar paleokarst oilfields, which will guide hydrocarbon exploration in paleokarst reservoirs

Blockchain-Powered Incentive System for JIT Arrival Operations and Decarbonization in Maritime Shipping

Efficiency and sustainability are undisputedly the most critical objectives for modern ports. Current exercises for port services still lack performance profiling for arriving vessels regarding their arrival punctuality and compliance with port resource schedule for Just-in-time (JIT) service, as well as their efforts contributing towards less emission through reduced turnaround time within port. As a result, a performance-based incentive is missing. Bringing in the incentive component may facilitate the objectives of achieving both port efficiency and sustainability. Blockchain technology, owning to its intrinsic features like immutability, traceability, governance and provenance, and in-built tokens (for most public chain platforms), allow for the establishment of system solutions to record key performance indicators (KPIs) and distribute incentives to good performers. This paper is the first to propose a blockchain-based system to incentivize JIT and green operations in ports. The platform system design and operating mechanisms are elaborated in detail, and a prototype system has been implemented based on the Solana blockchain to demonstrate the core features. The current system’s potential is substantial, considering the industry’s increasing awareness about its environmental footprint. Continuous developments can be facilitated by connecting to market-based measures such as carbon pricing and emission trading in the maritime sector