An LSTM-Based Predictive Monitoring Method for Data with Time-varying Variability

The recurrent neural network and its variants have shown great success in processing sequences in recent years. However, this deep neural network has not aroused much attention in anomaly detection through predictively process monitoring. Furthermore, the traditional statistic models work on assumptions and hypothesis tests, while neural network (NN) models do not need that many assumptions. This flexibility enables NN models to work efficiently on data with time-varying variability, a common inherent aspect of data in practice. This paper explores the ability of the recurrent neural network structure to monitor processes and proposes a control chart based on long short-term memory (LSTM) prediction intervals for data with time-varying variability. The simulation studies provide empirical evidence that the proposed model outperforms other NN-based predictive monitoring methods for mean shift detection. The proposed method is also applied to time series sensor data, which confirms that the proposed method is an effective technique for detecting abnormalities.Comment: 19 pages, 9 figures, 6 table

Evidence linking exposure of fish primary macrophages to antibiotics activates the NF-kB pathway.

Low doses of antibiotics are ubiquitous in the marine environment and may exert negative effects on non-target aquatic organisms. Using primary macrophages of common carp, we investigated the mechanisms of action following exposure to several common antibiotics; cefotaxime, enrofloxacin, tetracycline, sulfamonomethoxine, and their mixtures, and explored the immunomodulatory effects associated with the nuclear factor-κB (NF-κB) signaling pathway. A KEGG pathway analysis was conducted using the sixty-six differentially expressed genes found in all treatments, and showed that exposure to 100 μg/L of antibiotics could affect regulation of the NF-κB signaling pathway, suggesting that activation of NF-κB is a common response in all four classes of antibiotics. In addition, the four antibiotics induced nf-κb and NF-κB-associated cytokines expression, as verified by qPCR, however, these induction responses by four antibiotics were minor when compared to the same concentration of LPS treatment (100 μg/L). Antagonists of NF-κB blocked many of the immune effects of the antibiotics, providing evidence that NF-κB pathways mediate the actions of all four antibiotics. Moreover, exposure to environmentally relevant, low levels (0.01-100 μg/L) of antibiotics induced a NF-κB-mediated immune response, including endogenous generation of ROS, activity of antioxidant enzymes, as well as expression of cytokine and apoptosis. Moreover, exposure to mixtures of antibiotics presented greater effects on most tested immunological parameters than exposure to a single antibiotic, suggesting additive effects from multiple antibiotics in the environment. This study demonstrates that exposure of fish primary macrophages to low doses of antibiotics activates the NF-kB pathway

The influence of labor education participation on the subjective well-being of college students: chain mediation effect of self-efficacy and healthy lifestyle

BackgroundIn the process of modernization, along with economic development, intensified social competition, and increasing mental health problems such as anxiety and depression, the issue of subjective well-being has received widespread attention. The level of subjective well-being of college students also affects whether society can achieve sustainable development. In philosophy, political science, economics, sociology and other disciplines, labor is regarded as an important factor affecting subjective well-being. Labor education is an educational activity carried out by Chinese universities in recent years. This further inspires the author to think, for the college students, will the labor education received on campus have an impact on the subjective well-being? What characteristics will its impact mechanism present? What are the characteristics of the influence on subjective well-being?.MethodsThis research adopts a cross-sectional design, specifically employing a random sampling approach. In this study, the questionnaire was distributed to the college’s students of 14 universities in China through the Internet. A total of 2100 questionnaires were collected.ResultsThis paper mainly used questionnaires to collect data, and on this basis, examined the relationship between labor education participation, self-efficacy, healthy lifestyle and subjective well-being of college students. The results showed that: (1) Labor education participation positively affected college students’ subjective well-being. (2) Self-efficacy partially mediated the relationship between labor education participation and college students’ subjective well-being. (3) Healthy lifestyle partially mediated the relationship between labor education participation and college students’ subjective well-being. (4) Self-efficacy and healthy lifestyle played a chain mediating role between labor education participation and college students’ subjective well-being

WanJuan: A Comprehensive Multimodal Dataset for Advancing English and Chinese Large Models

The rise in popularity of ChatGPT and GPT-4 has significantly accelerated the development of large models, leading to the creation of numerous impressive large language models(LLMs) and multimodal large language models (MLLMs). These cutting-edge models owe their remarkable performance to high-quality data. However, the details of the training data used in leading paradigms are often kept confidential. This lack of transparency, coupled with the scarcity of open-source data, impedes further developments within the community. As a response, this paper presents "Wan Juan", a large-scale multimodal dataset composed of both Chinese and English data, collected from a wide range of web sources. The dataset incorporates text, image-text, and video modalities, with a total volume exceeding 2TB. It was utilized in the training of InternLM, a model that demonstrated significant advantages in multi-dimensional evaluations when compared to models of a similar scale. All data can be accessed at https://opendatalab.org.cn/WanJuan1.0.Comment: Technical Repor

IconQA: A New Benchmark for Abstract Diagram Understanding and Visual Language Reasoning

Current visual question answering (VQA) tasks mainly consider answering human-annotated questions for natural images. However, aside from natural images, abstract diagrams with semantic richness are still understudied in visual understanding and reasoning research. In this work, we introduce a new challenge of Icon Question Answering (IconQA) with the goal of answering a question in an icon image context. We release IconQA, a large-scale dataset that consists of 107,439 questions and three sub-tasks: multi-image-choice, multi-text-choice, and filling-in-the-blank. The IconQA dataset is inspired by real-world diagram word problems that highlight the importance of abstract diagram understanding and comprehensive cognitive reasoning. Thus, IconQA requires not only perception skills like object recognition and text understanding, but also diverse cognitive reasoning skills, such as geometric reasoning, commonsense reasoning, and arithmetic reasoning. To facilitate potential IconQA models to learn semantic representations for icon images, we further release an icon dataset Icon645 which contains 645,687 colored icons on 377 classes. We conduct extensive user studies and blind experiments and reproduce a wide range of advanced VQA methods to benchmark the IconQA task. Also, we develop a strong IconQA baseline Patch-TRM that applies a pyramid cross-modal Transformer with input diagram embeddings pre-trained on the icon dataset. IconQA and Icon645 are available at https://iconqa.github.io.Comment: Corrected typos. Accepted to NeurIPS 2021, 27 pages, 18 figures. Data and code are available at https://iconqa.github.i

Remaining Useful Life Modelling with an Escalator Health Condition Analytic System

The refurbishment of an escalator is usually linked with its design life as recommended by the manufacturer. However, the actual useful life of an escalator should be determined by its operating condition which is affected by the runtime, workload, maintenance quality, vibration, etc., rather than age only. The objective of this project is to develop a comprehensive health condition analytic system for escalators to support refurbishment decisions. The analytic system consists of four parts: 1) online data gathering and processing; 2) a dashboard for condition monitoring; 3) a health index model; and 4) remaining useful life prediction. The results can be used for a) predicting the remaining useful life of the escalators, in order to support asset replacement planning and b) monitoring the real-time condition of escalators; including alerts when vibration exceeds the threshold and signal diagnosis, giving an indication of possible root cause (components) of the alert signal.Comment: 14 pages, 12 figures, 7 table