VIoTGPT: Learning to Schedule Vision Tools towards Intelligent Video Internet of Things

Video Internet of Things (VIoT) has shown full potential in collecting an unprecedented volume of video data. Learning to schedule perceiving models and analyzing the collected videos intelligently will be potential sparks for VIoT. In this paper, to address the challenges posed by the fine-grained and interrelated vision tool usage of VIoT, we build VIoTGPT, the framework based on LLMs to correctly interact with humans, query knowledge videos, and invoke vision models to accomplish complicated tasks. To support VIoTGPT and related future works, we meticulously crafted the training dataset and established benchmarks involving 11 representative vision models across three categories based on semi-automatic annotations. To guide LLM to act as the intelligent agent towards intelligent VIoT, we resort to ReAct instruction tuning based on the collected VIoT dataset to learn the tool capability. Quantitative and qualitative experimental results and analyses demonstrate the effectiveness of VIoTGPT

SCEI: A Smart-Contract Driven Edge Intelligence Framework for IoT Systems

Federated learning (FL) utilizes edge computing devices to collaboratively train a shared model while each device can fully control its local data access. Generally, FL techniques focus on learning model on independent and identically distributed (iid) dataset and cannot achieve satisfiable performance on non-iid datasets (e.g. learning a multi-class classifier but each client only has a single class dataset). Some personalized approaches have been proposed to mitigate non-iid issues. However, such approaches cannot handle underlying data distribution shift, namely data distribution skew, which is quite common in real scenarios (e.g. recommendation systems learn user behaviors which change over time). In this work, we provide a solution to the challenge by leveraging smart-contract with federated learning to build optimized, personalized deep learning models. Specifically, our approach utilizes smart contract to reach consensus among distributed trainers on the optimal weights of personalized models. We conduct experiments across multiple models (CNN and MLP) and multiple datasets (MNIST and CIFAR-10). The experimental results demonstrate that our personalized learning models can achieve better accuracy and faster convergence compared to classic federated and personalized learning. Compared with the model given by baseline FedAvg algorithm, the average accuracy of our personalized learning models is improved by 2% to 20%, and the convergence rate is about 2$\times$ faster. Moreover, we also illustrate that our approach is secure against recent attack on distributed learning.Comment: 12 pages, 9 figure

Ferroptosis in inflammatory arthritis: A promising future

Ferroptosis is a kind of regulatory cell death (RCD) caused by iron accumulation and lipid peroxidation, which is characterized by mitochondrial morphological changes and has a complex regulatory network. Ferroptosis has been gradually emphasized in the pathogenesis of inflammatory arthritis. In this review, we summarized the relevant research on ferroptosis in various inflammatory arthritis including rheumatoid arthritis (RA), osteoarthritis, gout arthritis, and ankylosing spondylitis, and focused on the relationship between RA and ferroptosis. In patients with RA and animal models of RA, there was evidence of iron overload and lipid peroxidation, as well as mitochondrial dysfunction that may be associated with ferroptosis. Ferroptosis inducers have shown good application prospects in tumor therapy, and some anti-rheumatic drugs such as methotrexate and sulfasalazine have been shown to have ferroptosis modulating effects. These phenomena suggest that the role of ferroptosis in the pathogenesis of inflammatory arthritis will be worth further study. The development of therapeutic strategies targeting ferroptosis for patients with inflammatory arthritis may be a promising future

Mediating effects of social support and presenteeism on turnover intention and post-traumatic stress disorder among Chinese nurses in the post-pandemic era: a cross-sectional study

BackgroundThe shift in national policies for epidemic prevention and control in the post-pandemic era is undoubtedly a challenge for health care professionals. Nurses, as an important part of the health care professional population, have an even greater impact on their mental health and occupational safety. This may expose nurses to post-traumatic stress disorder (PTSD) and presenteeism, and ultimately lead to their turnover.ObjectiveThis study aimed to investigate the relationship between turnover intention and post-traumatic stress disorder among Chinese nurses during post-pandemic era, and the mediating role of social support and presenteeism.MethodsIn this study, a multicentre cross-sectional survey was conducted in April 2023 among nursing staff in several tertiary general hospitals in northern China, with online data collection using the Turnover intention Scale (PTSD), the Impact of Events Scale (IES), the Social Support Scale (SSS), and the Stanford presenteeism Scale (STAS) and the relationship between variables was analyzed using hierarchical multivariate regression, and Structural Equation Modeling (SEM) was used to analyze the relationship between post-traumatic stress disorder and the Turnover intention from the pathway between.ResultsData were collected from 2,513 nurses who met the inclusion criteria, in which general information such as age, department, specific department, Professional title, history of alcohol consumption, form of employment, Years of working, and Average working hours per day were statistically significant with the difference in the turnover intention. The results of the study showed a 32% high turnover intention among nurses in the post-pandemic era, which was lower than the turnover intention during the pandemic. The results of hierarchical multiple regression analysis showed that post-traumatic stress disorder, social support, and presenteeism were significant predictors of turnover intention. The total effect of post-traumatic stress disorder on turnover intention to work was 0.472 [bias modified 95% confidence interval (0.415–0.483), p < 0.001]. Social support and attendance played a partially intermediate role in post-traumatic stress disorder and propensity to leave (an indirect effect of 26% of the total effect).ConclusionTurnover intention and post-traumatic stress disorder levels are high and social support plays an important role in the tendency to leave the job and post-traumatic stress disorder, healthcare institution can be achieved by strengthening social support for nurses in the post-epidemic era and preventing the occurrence of presenteeism

The Measurement of rho‐independent Transcription Terminator Efficiency

The purpose of this RFC is to provide standard methodology for the measurement of the absolute strength of terminators in bacteria. Because we have characterized the performance of terminator in E. coli and used a simple equation model, it can be expressed in PoPS