Gansu Institute of Political Science and Law

The increase in the viscosity of high-rubber-content asphalt modified with rubber powder at high temperatures leads to processing difficulties and drastic changes in physical properties, which have long been a challenge in the asphalt industry. Although viscosity reducers have shown great potential in addressing these issues, their mechanisms of action in high-rubber-content asphalt modified with rubber powder remain unclear. This study employs diphenyl disulfide (DD) as a viscosity reducer and elucidates its mechanism of action in high-rubber-content asphalt, which includes three stages: (1) dissolution and dispersion in the asphalt matrix; (2) impregnation into the crosslinked network of the rubber powder; and (3) de-crosslinking via active free radicals. By optimizing the pre-impregnation time (12 h), temperature (110 °C), and rubber powder particle size (160–180 µm), the dispersion of DD can be enhanced, thereby improving the processability of high-rubber-content asphalt modified with rubber powder. Compared to untreated asphalt, the optimized conditions result in a significant reduction in the crosslinking density of 50% and a substantial decrease in viscosity at 180 °C. This study provides new insights into the viscosity reduction of high-rubber-content asphalt modified with rubber powder and contributes to a deeper understanding of the mechanisms of viscosity reducers.

ABSTRACT As network throughput increases and security threats escalate in big‐data environments, shallow machine‐learning models are inadequate for handling large‐scale network traffic. Aiming at the common class imbalance and high dimension problem of intrusion detection data sets, this paper proposes SSA‐CL (Sparrow Search Algorithm‐CNN‐LSTM) model based on SSA (Sparrow Search Algorithm) and CNN‐LSTM (Convolutional Neural Network, Long Short‐Term Memory) model. SSA‐CL model uses CNN and LSTM to build a CNN‐LSTM hybrid model that can automatically extract features and effectively process sequence data. We apply SMOTE to mitigate class imbalance and employ SSA to optimize hyperparameters, significantly improving detection accuracy and efficiency. The experimental results show that the proposed method has significant advantages in key indicators such as accuracy and recall, with excellent multi‐classification effect. SSA‐CL combines a compact architecture, fast training, and strong detection performance, indicating practical value for network‐security applications.

BackgroundIn 2016, the World Health Organization classification system of testicular tumors included the new entity prepubertal-type testicular neuroendocrine tumor based on its morphological and molecular profile, and the realization that these tumors may occur in postpubertal men. For treatment and prognostic purposes, it is important to distinguish prepubertal-type testicular neuroendocrine tumor from the usual postpubertal-type testicular neuroendocrine tumor, because the former is benign unlike the latter. The distinction may be challenging. Therefore, we hope that ultrasound examination can play an important role in the differential diagnosis of testicular tumors.Case presentationsThese two cases involved postpubertal males, one is 41-years-old and the other is 62-years-old, they both are Chinese and of the Han ethnicity. Both ultimately underwent surgical treatment without subsequent chemotherapy or other interventions. Up to now, during follow—up, the patients have exhibited no other symptoms.ConclusionPrepubertal-type testicular neuroendocrine tumor is a rare neuroendocrine tumor, and accounts for about 0.23% of testicular tumors. Clinical manifestations and images of it are not specific, routine ultrasound has significant advantages for early detection of lesions and short-term follow-up, and when combined with relevant laboratory tests, it may aid in the diagnosis of the disease, and the treatment is mainly radical surgery, and the prognosis is good.Supplementary InformationThe online version contains supplementary material available at 10.1186/s13256-025-05579-3.