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Current research focuses on how to efficiently extract and crawl network information because, with the growth of the Internet, network information is becoming more and more diverse. To address the problem of incorrect data extraction and topic judgment of web crawlers, this study proposes a novel approach based on a file inverse frequency algorithm and Word2Vec feature extraction. The new method improves the retrieval capability of web crawlers by using the file inverse frequency algorithm and uses Word2Vec to extract data features, which improves the data extraction capability of current crawlers. The results showed that the F1 values of the research use model were 25.8% and 26.2% higher than those of the digital filtering algorithm, respectively. The total number of localization resources for the research use strategy was 2800 and the network coverage was 81%, which was 12% higher than the optimal strategy. The research use strategy had a shorter retrieval time and the model could recognize the vocabulary of the keywords. Finally, the model used by the research also had a good model processing capability when compared to other models. In summary, the new model built by the research can improve the data retrieval ability and data extraction ability of the web crawler, which provides new research ideas for future web information extraction.

Benzothiazoles have played an important role in the field of biochemistry and medicinal chemistry due to their highly pharmaceutical and biological activity. The development of synthetic processes is undoubtedly one of the most significant problems facing researchers. In this review paper, we provided recent advances in the synthesis of benzothiazole compounds related to green chemistry from condensation of 2-aminobenzenethiol with aldehydes/ketones/acids/acyl chlorides and the cyclization of thioamide or carbon dioxide (CO2) as raw materials, and the future development trend and prospect of the synthesis of benzothiazoles were anticipated.

Background. Androgenetic alopecia (AGA) represents the most frequent clinical complaint encountered by dermatologists and is characterized by a progressive miniaturization of the hair follicle. However, the efficacy and safety of current medical treatment remain limited, and more personalized therapeutic approaches for AGA are needed. Therefore, the present study is aimed at investigating the efficacy and safety of botulinum toxin type A (BTA) in patients with AGA. Methods. 63 patients with AGA meeting the inclusion criteria were included in this study and treated with BTA injection or BTA injection combined with oral finasteride (FNS). In the scalp, 30 sites were injected with 100 U of BTA in each site and patients received BTA after every 3 months for a total of 4 times. Hair counts, head photographs, evaluation scores, and self-assessment were assessed in patients with AGA. Results. Hair counts in both groups at all time points were significantly higher as compared with those before treatment. After 4 times of treatment, hair counts in the BTA+FNS group were higher than those in the BTA group. Hair growth and density were significantly augmented, and the area of hair loss was attenuated after each treatment as revealed by head photographs. The effective rates of BTA and BTA+FNS groups were 73.3% and 84.8%, respectively, following 4 times treatment. Conclusion. BTA is a safe and effective therapeutic strategy for the treatment of AGA without adverse effects, and BTA combined with FNS exhibited a superior therapeutic effect than BTA alone.

•Pain empathy varies from individual to individual and is related to pain perception.•Electroencephalography showed that both delta and alpha bands were involved in pain empathy regulation.•The average power in the delta band of Fz reflects individual differences in pain empathy.•The phase-locking values of the alpha band between Fz and P3 reflect individual differences in pain sensitivity. Pain empathy is a complex form of psychological inference that enables us to understand how others feel in the context of pain. Since pain empathy may be grounded in our own pain experiences, it exhibits huge inter-individual variability. However, the neural mechanisms behind the individual differences in pain empathy and its association with pain perception are still poorly understood. In this study, we aimed to characterize brain mechanisms associated with individual differences in pain empathy in adult participants (n = 24). The 32-channel electroencephalography (EEG) was recorded at rest and during a pain empathy task, and participants viewed static visual stimuli of the limbs submitted to painful and nonpainful stimulation to solicit empathy. The pain sensitivity of each participant was measured using a series of direct current stimulations. In our results, the N2 of Fz and the LPP of P3 and P4 were affected by painful pictures. We found that both delta and alpha bands in the frontal and parietal cortex were involved in the regulation of pain empathy. For the delta band, a close relationship was found between average power, either in the resting or task state, and individual differences in pain empathy. It suggested that the spectral power in Fz's delta band may reflect subjective pain empathy across individuals. For the alpha band, the functional connectivity between Fz and P3 under painful picture stimulation was correlated to individuals' pain sensitivity. It indicated that the alpha band may reflect individual differences in pain sensitivity and be involved in pain empathy processing. Our results suggested the distinct role of the delta and alpha bands of EEG signals in pain empathy processing and may deepen our understanding of the neural mechanisms underpinning pain empathy.

•We propose a novel seed-based fiber streamline (sFS) analysis method.•Our method produces fiber clusters with consistent geometric structures.•Our method enables tract analysis along fiber clusters of a seed-based fiber network.•Our method could predict pain empathy in participants with chronic pain. Pain empathy, defined as the ability of one person to understand another person's pain, shows large individual variations. The anterior insula is the core region of the pain empathy network. However, the relationship between white matter (WM) properties of the fiber tracts connecting the anterior insula with other cortical regions and an individual's ability to modulate pain empathy remains largely unclear. In this study, we outline an automatic seed-based fiber streamline (sFS) analysis method and multivariate pattern analysis (MVPA) to predict the levels of pain empathy in healthy women and women with primary dysmenorrhoea (PDM). Using the sFS method, the anterior insula-based fiber tract network was divided into five fiber cluster groups. In healthy women, interindividual differences in pain empathy were predicted only by the WM properties of the five fiber cluster groups, suggesting that interindividual differences in pain empathy may rely on the connectivity of the anterior insula-based fiber tract network. In women with PDM, pain empathy could be predicted by a single cluster group. The mean WM properties along the anterior insular–rostroventral area of the inferior parietal lobule further mediated the effect of pain on empathy in patients with PDM. Our results suggest that chronic periodic pain may lead to maladaptive plastic changes, which could further impair empathy by making women with PDM feel more pain when they see other people experiencing pain. Our study also addresses an important gap in the analysis of the microstructural characteristics of seed-based fiber tract network.

The focus of this paper is to apply an improved machine learning algorithm to realize the efficient and reliable identification and classification of network communication encrypted traffic, and to solve the challenges faced by traditional algorithms in analyzing encrypted traffic after adding encryption protocols. In this study, an enhanced random forest (ERF) algorithm is introduced to optimize the accuracy and efficiency of the identification and classification of encrypted network traffic. Compared with traditional methods, it aims to improve the identification ability of encrypted traffic and fill the knowledge gap in this field. Using the publicly available datasets and preprocessing the original PCAP format packets, the optimal combination of the relevant parameters of the tree was determined by grid search cross-validation, and the experimental results were evaluated in terms of performance using accuracy, precision, recall and F1 score, which showed that the average precision was more than 98 %, and that compared with the traditional algorithm, the error rate of the traffic test set was reduced, and the data of each performance evaluation index were better, which It shows that the advantages of the improved algorithm are obvious. In the experiment, the enhanced random forest and traditional random forest models were trained and tested on a series of data sets and the corresponding test errors were listed as the basis for judging the model quality. The experimental results show that the enhanced algorithm has good competitiveness. These findings have implications for cybersecurity professionals, researchers, and organizations, providing a practical solution to enhance threat detection and data privacy in the face of evolving encryption technologies. This study provides valuable insights for practitioners and decision-makers in the cybersecurity field

Carbon nanotubes could accumulate in organism and have a negative impact on the structure and function of the ecosystem when they were discharged into environment. Furthermore, it will affect the migration and fate of pollutants in the water body. The study is mainly to explore the adsorption behavior and mechanism of beta-blocker on multi-walled carbon nanotubes (MWCNTs). Propranolol (PRO) was selected as the representative of beta-blocker. The effects of different environmental factors such as pH, ionic strength and humic acid (HA) on the adsorption process were investigated. The adsorption results were characterized by Zeta potential. At the same time, the effects of different types of drugs on the adsorption process were explored and the possible adsorption mechanisms were analyzed. The experimental results showed that the adsorption behavior was significantly different under different pH conditions. π-π EDA interaction, hydrophobic interaction and hydrogen bonding were speculated to be the main adsorption mechanisms for PRO adsorption on MWCNTs.

Forest parks are important for ecological conservation, recreation, and the health and well-being of the people who use them. However, forest parks located in urban areas often face the problem of crowding. To better understand perceived crowding in urban forest parks and to improve tourists’ recreation experiences and satisfaction, we constructed a conceptual model of the relationships between perceived crowding, emotion, and satisfaction with mechanisms of adjustment based on survey data from Dafu Mountain Forest Park in China. The results indicate that, in urban forest parks, perceived crowding significantly and negatively affects tourists’ satisfaction, but there is no significant difference in satisfaction between different activity types. Both positive and negative emotions have partially mediating effects on the relationship between perceived crowding and satisfaction. Crucially, our modeled mechanisms of adjustment play a moderating role in the effect of crowding on tourist satisfaction, and the choice of adjustment behaviors varies according to the activity type. This work enriches the research related to perceived crowding, mechanisms of adjustment, and satisfaction in tourist destinations and provides a theoretical basis for the future management of urban forest parks.

Dust pollutants generated from the coal transfer process in a high-rise building of the mine hoisting system not only undermine the operating environment but also reduce the surrounding air quality. Therefore, this study aimed to determine the spatiotemporal distribution of coal dust in the high-rise buildings using field measurement and numerical simulation. Based on the discrete phase model (DPM), the dust migration process under the hybrid ventilation system was investigated in detail. Then, the feasibility of the established model to predict the spatiotemporal distribution of dust pollutants was proven through the measurements of both the airflow and the dust concentration. The present study showed that dust distribution is not uniform in time and space, which also differs for different floors. The dust concentration of the 3 rd floor is relatively larger when compared with those of other floors. The dust concentration increases for the upper floors when the upward air velocity increases, while those of the lower floors are not always low due to the backflows, particularly for the 2 nd floor. PM 2.5 takes up more than 20% of all discharged particles.

This study aimed to explore the mechanism of fucoidan in chronic kidney disease (CKD)-triggered cognitive dysfunction. The adenine-induced ICR strain CKD mice model was applied, and RNA-Seq was performed for differential gene analysis between aged-CKD and normal mice. As a result, fucoidan (100 and 200 mg kg−1) significantly reversed adenine-induced high expression of urea, uric acid in urine, and creatinine in serum, as well as the novel object recognition memory and spatial memory deficits. RNA sequencing analysis indicated that oxidative and inflammatory signaling were involved in adenine-induced kidney injury and cognitive dysfunction; furthermore, fucoidan inhibited oxidative stress via GSK3β-Nrf2-HO-1 signaling and ameliorated inflammatory response through regulation of microglia/macrophage polarization in the kidney and hippocampus of CKD mice. Additionally, we clarified six hallmarks in the hippocampus and four in the kidney, which were correlated with CKD-triggered cognitive dysfunction. This study provides a theoretical basis for the application of fucoidan in the treatment of CKD-triggered memory deficits.