Unveiling the Potential of Knowledge-Prompted ChatGPT for Enhancing Drug Trafficking Detection on Social Media

Social media platforms such as Instagram and Twitter have emerged as critical channels for drug marketing and illegal sale. Detecting and labeling online illicit drug trafficking activities becomes important in addressing this issue. However, the effectiveness of conventional supervised learning methods in detecting drug trafficking heavily relies on having access to substantial amounts of labeled data, while data annotation is time-consuming and resource-intensive. Furthermore, these models often face challenges in accurately identifying trafficking activities when drug dealers use deceptive language and euphemisms to avoid detection. To overcome this limitation, we conduct the first systematic study on leveraging large language models (LLMs), such as ChatGPT, to detect illicit drug trafficking activities on social media. We propose an analytical framework to compose \emph{knowledge-informed prompts}, which serve as the interface that humans can interact with and use LLMs to perform the detection task. Additionally, we design a Monte Carlo dropout based prompt optimization method to further to improve performance and interpretability. Our experimental findings demonstrate that the proposed framework outperforms other baseline language models in terms of drug trafficking detection accuracy, showing a remarkable improvement of nearly 12\%. By integrating prior knowledge and the proposed prompts, ChatGPT can effectively identify and label drug trafficking activities on social networks, even in the presence of deceptive language and euphemisms used by drug dealers to evade detection. The implications of our research extend to social networks, emphasizing the importance of incorporating prior knowledge and scenario-based prompts into analytical tools to improve online security and public safety

Offline and Online Optical Flow Enhancement for Deep Video Compression

Video compression relies heavily on exploiting the temporal redundancy between video frames, which is usually achieved by estimating and using the motion information. The motion information is represented as optical flows in most of the existing deep video compression networks. Indeed, these networks often adopt pre-trained optical flow estimation networks for motion estimation. The optical flows, however, may be less suitable for video compression due to the following two factors. First, the optical flow estimation networks were trained to perform inter-frame prediction as accurately as possible, but the optical flows themselves may cost too many bits to encode. Second, the optical flow estimation networks were trained on synthetic data, and may not generalize well enough to real-world videos. We address the twofold limitations by enhancing the optical flows in two stages: offline and online. In the offline stage, we fine-tune a trained optical flow estimation network with the motion information provided by a traditional (non-deep) video compression scheme, e.g. H.266/VVC, as we believe the motion information of H.266/VVC achieves a better rate-distortion trade-off. In the online stage, we further optimize the latent features of the optical flows with a gradient descent-based algorithm for the video to be compressed, so as to enhance the adaptivity of the optical flows. We conduct experiments on a state-of-the-art deep video compression scheme, DCVC. Experimental results demonstrate that the proposed offline and online enhancement together achieves on average 12.8% bitrate saving on the tested videos, without increasing the model or computational complexity of the decoder side.Comment: 9 pages, 6 figure

Effect of Liuweibuqi capsule, a Chinese patent medicine, on the JAK1/STAT3 pathway and MMP9/TIMP1 in a chronic obstructive pulmonary disease rat model

AbstractObjectiveTo observe effect of Liuweibuqi Capsule, a Traditional Chinese Medicine (TCM), on the janus kinase (JAK)/signal transducer and activator of transcription (STAT) pathway and matrix metalloproteinases (MMPs) in a chronic obstructive pulmonary disease (COPD) rat model with lung deficiency in terms of TCM's pattern differentiation.MethodsRats were randomly divided into a normal group, model group, Liuweibuqi group, Jinshuibao group, and spleen aminopeptidase group (n= 10). Aside from the normal group, all rats were exposed to smoke plus lipopolysaccharide tracheal instillation to establish the COPD model with lung deficiency. Models were established after 28 days and then the normal and model groups were given normal saline (0.09 g/kg), Liuweibuqi group was given Liuweibuqi capsule (0.35 g/kg), Jinshuibao group was given Jinshuibao capsules (0.495 g/kg), and the spleen group was given spleen aminopeptidase (0.33 mg/kg), once a day for 30 days. Changes in symptoms, signs, and lung histology were observed. Lung function was measured with a spirometer. Serum cytokines were detected using enzyme-linked immunosorbent assay, and changes in the JAK/STAT pathway, MMP-9, and MMPs inhibitor 1 (TIMP1) were detected by immunohistochemistry, RT-PCR, and western blotting, respectively.ResultsCompared with the normal group, lung tissue was damaged, and lung function was reduced in the model control group. Additionally, the levels of interleukin (IL)-1β, γ interferon (IFN-γ), and IL-6 were higher, while IL-4 and IL-10 were lower in the model control group than those in the normal group. The expressions of JAK1, STAT3, p-STAT3, and MMP-9 mRNA and protein in lung tissue were higher, and TIMP1 mRNA and protein was lower in the model group compared with the normal group. After treatment, compared with the model group, the expression of inflammatory cytokines was lower in each treatment group, and expressions of JAK/STAT pathway, MMPs were lower. Compared with the positive control groups, the Jinshuibao and spleen aminopeptidase groups, lung function was better, and JAK1, STAT3, and p-STAT3 protein were lower and TIMP1 was higher in the Liuweibuqi group.ConclusionLiuweibuqi capsules can improve the symptoms of COPD possibly by regulating the expression of the JAK1/STAT3 pathway and MMP9/TIMP1

Nonsurgical molding of congenital auricular deformities and analysis of the correction outcomes: A single-center, retrospective study in east China

ObjectiveOur research was carried out to provide a clinical reference for the application of nonsurgical therapy in newborns with congenital auricular deformities in east China.MethodsA retrospective study of consecutive newborns using noninvasive ear molding was conducted in Hangzhou in east China's Zhejiang Province. The demographic and clinical information and photographs of the ear before and after treatment were taken. The diagnosis of each auricular deformity was identified, and the treatment outcome was evaluated.ResultsA total of 224 patients including 356 congenital ear anomalies received noninvasive ear molding. The median age of infants to initiate treatment was 39.5 days. The median treatment duration was 42.5 days. The median follow-up time was 137.0 days. The overall treatment effective rate of all infants with nonoperative ear molding was 92.1%, and mild skin irritation and ulceration occurred in 34 ear deformities (9.6%). It confirmed that the treatment efficiency was satisfactory and the complication rate was still acceptable despite the late initiation treatment of neonates in east China. Further analysis of treatment outcomes among three subgroups of infants (the ages to initiate the ear molding were respectively less than or equal to 28, 29–56, and more than 57 days) revealed that initiation treatment was significantly related to the treatment results and the earlier the initiation treatment, the higher the effective rate and the lower the complication incidence.ConclusionOur study hints that newborns in east China may have a longer period for correction. What is more, although our study affirmed a longer period for noninvasive molding, early diagnosis and treatment are still recommended to improve therapy efficiency and reduce treatment duration and complications

Spatiotemporal correlation analysis of hydraulic fracturing and stroke in the United States

Hydraulic fracturing or fracking has led to a rapid growth of oil and gas production in the United States, but the impact of fracking on public health is an important but underresearched topic. We designed a methodology to study spatiotemporal correlations between the risk of fracking and stroke mortality. An annualized loss expectancy (ALE) model is applied to quantify the risk of fracking. The geographically and temporally weighted regression (GTWR) model is used to analyze spatiotemporal correlations of stroke mortality, fracking ALE, and nine other socioeconomic- and health-related factors. The analysis shows that fracking ALE is moderately correlated with stroke mortality at ages over 65 in most states of fracking, in addition to cardiovascular disease and drug overdose being positively correlated with stroke mortality. Furthermore, the correlations between fracking ALE and stroke mortality in men appear to be higher than in women near the Marcellus Shale, including Ohio, Pennsylvania, West Virginia, and Virginia, while stroke mortality among women is concentrated in the Great Plains, including Montana, Wyoming, New Mexico, and Oklahoma. Lastly, within two kilometers of the fracking mining activity, the level of benzene in the air was found to be significantly correlated with the fracking activity in Colorado

A coupled photo-piezo-catalytic effect in a BST-PDMS porous foam for enhanced dye wastewater degradation

Over the last decade, ferro-/piezo-electric materials have provided new directions to improve catalysis. However, current challenges that must be solved include secondary pollution by the piezoelectric particulates and a limited potential for reuse and recyclability. Here, we report an efficient approach of using a piezoceramic-polymer porous foam to package barium strontium titanate (BST) particulates and prevent secondary pollution, while being able to maintain a high photo-piezo-catalytic performance after 10 cycles of repeated catalytic reactions. The photo-piezo-catalysis achieves a 97.8% dye degradation and an enhanced performance of 275% when compared to individual photocatalysis by light irradiation or periodic low-frequency mechanical squeezing alone. It is suggested the photo-piezo-catalytic coupling effect combines the advantages of increased generated electron-hole pairs and the induced piezoelectric electric field leads to a higher degree of electron-hole separation. The BST-PDMS porous foam for photo-piezo-catalysis offers a potential approach in wastewater degradation via utilizing both solar energy and environmental mechanical sources.</p