Detecting Variation Trends of Temperature and Precipitation for the Dadu River Basin, China

This study analyzes the variation trends of temperature and precipitation in the Dadu River Basin of China based on observed records from fourteen meteorological stations. The magnitude of trends was estimated using Sen’s linear method while its statistical significance was evaluated using Mann-Kendall’s test. The results of analysis depict increase change from northwest to southeast of annual temperature and precipitation in space. In temporal scale, the annual temperature showed significant increase trend and the annual precipitation showed increase trend. For extreme indices, the trends for temperature are more consistent in the region compared to precipitation. This paper has practical meanings for an effective management of climate risk and provides a foundation for further study of hydrological situation in this river basin

Combined Anterior Sclera Staphylectomy and Vitrectomy with Anterior Sclera Staphyloma and Vitreous Hemorrhage Occurring 38 Years after Cataract Surgery

Introduction. To report a case of anterior sclera staphyloma and vitreous hemorrhage occurring over 38 years after bilateral cataract surgery. Methods. A 58-year-old man presented with anterior sclera staphyloma and vitreous hemorrhage in the right eye, after bilateral cataract surgery, over 38 years ago. We performed combined anterior sclera staphylectomy and vitrectomy of right eye for anterior sclera staphyloma and vitreous hemorrhage. Results. Forty-eight months after the combined surgery, best-corrected visual acuity was 0.3 (+10.00/−4.50 × 60) with eutopic stitches of the corneoscleral junction on the superior nasal quadrant and a stable ocular surface. Conclusions. This is the first reported case of anterior sclera staphyloma with vitreous hemorrhage successfully managed by combined surgery

Metaverse Security and Privacy: An Overview

Metaverse is a living space and cyberspace that realizes the process of virtualizing and digitizing the real world. It integrates a plethora of existing technologies with the goal of being able to map the real world, even beyond the real world. Metaverse has a bright future and is expected to have many applications in various scenarios. The support of the Metaverse is based on numerous related technologies becoming mature. Hence, there is no doubt that the security risks of the development of the Metaverse may be more prominent and more complex. We present some Metaverse-related technologies and some potential security and privacy issues in the Metaverse. We present current solutions for Metaverse security and privacy derived from these technologies. In addition, we also raise some unresolved questions about the potential Metaverse. To summarize, this survey provides an in-depth review of the security and privacy issues raised by key technologies in Metaverse applications. We hope that this survey will provide insightful research directions and prospects for the Metaverse's development, particularly in terms of security and privacy protection in the Metaverse.Comment: IEEE BigData 2022. 10 pages, 2 figure

AI-Generated Content (AIGC): A Survey

To address the challenges of digital intelligence in the digital economy, artificial intelligence-generated content (AIGC) has emerged. AIGC uses artificial intelligence to assist or replace manual content generation by generating content based on user-inputted keywords or requirements. The development of large model algorithms has significantly strengthened the capabilities of AIGC, which makes AIGC products a promising generative tool and adds convenience to our lives. As an upstream technology, AIGC has unlimited potential to support different downstream applications. It is important to analyze AIGC's current capabilities and shortcomings to understand how it can be best utilized in future applications. Therefore, this paper provides an extensive overview of AIGC, covering its definition, essential conditions, cutting-edge capabilities, and advanced features. Moreover, it discusses the benefits of large-scale pre-trained models and the industrial chain of AIGC. Furthermore, the article explores the distinctions between auxiliary generation and automatic generation within AIGC, providing examples of text generation. The paper also examines the potential integration of AIGC with the Metaverse. Lastly, the article highlights existing issues and suggests some future directions for application.Comment: Preprint. 14 figures, 4 table

Large Language Models in Education: Vision and Opportunities

With the rapid development of artificial intelligence technology, large language models (LLMs) have become a hot research topic. Education plays an important role in human social development and progress. Traditional education faces challenges such as individual student differences, insufficient allocation of teaching resources, and assessment of teaching effectiveness. Therefore, the applications of LLMs in the field of digital/smart education have broad prospects. The research on educational large models (EduLLMs) is constantly evolving, providing new methods and approaches to achieve personalized learning, intelligent tutoring, and educational assessment goals, thereby improving the quality of education and the learning experience. This article aims to investigate and summarize the application of LLMs in smart education. It first introduces the research background and motivation of LLMs and explains the essence of LLMs. It then discusses the relationship between digital education and EduLLMs and summarizes the current research status of educational large models. The main contributions are the systematic summary and vision of the research background, motivation, and application of large models for education (LLM4Edu). By reviewing existing research, this article provides guidance and insights for educators, researchers, and policy-makers to gain a deep understanding of the potential and challenges of LLM4Edu. It further provides guidance for further advancing the development and application of LLM4Edu, while still facing technical, ethical, and practical challenges requiring further research and exploration.Comment: IEEE BigData 2023. 10 page

Federated Learning Attacks and Defenses: A Survey

In terms of artificial intelligence, there are several security and privacy deficiencies in the traditional centralized training methods of machine learning models by a server. To address this limitation, federated learning (FL) has been proposed and is known for breaking down ``data silos" and protecting the privacy of users. However, FL has not yet gained popularity in the industry, mainly due to its security, privacy, and high cost of communication. For the purpose of advancing the research in this field, building a robust FL system, and realizing the wide application of FL, this paper sorts out the possible attacks and corresponding defenses of the current FL system systematically. Firstly, this paper briefly introduces the basic workflow of FL and related knowledge of attacks and defenses. It reviews a great deal of research about privacy theft and malicious attacks that have been studied in recent years. Most importantly, in view of the current three classification criteria, namely the three stages of machine learning, the three different roles in federated learning, and the CIA (Confidentiality, Integrity, and Availability) guidelines on privacy protection, we divide attack approaches into two categories according to the training stage and the prediction stage in machine learning. Furthermore, we also identify the CIA property violated for each attack method and potential attack role. Various defense mechanisms are then analyzed separately from the level of privacy and security. Finally, we summarize the possible challenges in the application of FL from the aspect of attacks and defenses and discuss the future development direction of FL systems. In this way, the designed FL system has the ability to resist different attacks and is more secure and stable.Comment: IEEE BigData. 10 pages, 2 figures, 2 table

Multimodal Large Language Models: A Survey

The exploration of multimodal language models integrates multiple data types, such as images, text, language, audio, and other heterogeneity. While the latest large language models excel in text-based tasks, they often struggle to understand and process other data types. Multimodal models address this limitation by combining various modalities, enabling a more comprehensive understanding of diverse data. This paper begins by defining the concept of multimodal and examining the historical development of multimodal algorithms. Furthermore, we introduce a range of multimodal products, focusing on the efforts of major technology companies. A practical guide is provided, offering insights into the technical aspects of multimodal models. Moreover, we present a compilation of the latest algorithms and commonly used datasets, providing researchers with valuable resources for experimentation and evaluation. Lastly, we explore the applications of multimodal models and discuss the challenges associated with their development. By addressing these aspects, this paper aims to facilitate a deeper understanding of multimodal models and their potential in various domains.Comment: IEEE BigData 2023. 10 page

Assessing the Therapeutic Effect of 630 nm Light-emitting Diodes Irradiation on the Recovery of Exercise-induced Hand Muscle Fatigue with Surface Electromyogram

This paper aims to investigate the effect of light emitting diode therapy (LEDT) on exercise-induced hand muscle fatigue by measuring the surface electromyography (sEMG) of flexor digitorum superficialis. Ten healthy volunteers were randomly placed in the equal sized LEDT group and control group. All subjects performed a sustained fatiguing isometric contraction with the combination of four fingertips except thumb at 30% of maximal voluntary contraction (MVC) until exhaustion. The active LEDT or an identical passive rest therapy was then applied to flexor digitorum superficialis. Each subject was required to perform a re-fatigue task immediately after therapy which was the same as the pre-fatigue task. Average rectified value (ARV) and fractal dimension (FD) of sEMG were calculated. ARV and FD were significantly different between active LEDT and passive rest groups at 20%–50%, 70%–80%, and 100% of normalized contraction time (P \u3c 0.05 ). Compared to passive rest, active LEDT induced significantly smaller increase in ARV values and decrease in FD values, which shows that LEDT is effective on the recovery of muscle fatigue. Our preliminary results also suggest that ARV and FD are potential replacements of biochemical markers to assess the effects of LEDT on muscle fatigue

Validation of internal control for gene expression study in soybean by quantitative real-time PCR

<p>Abstract</p> <p>Background</p> <p>Normalizing to housekeeping gene (HKG) can make results from quantitative real-time PCR (qRT-PCR) more reliable. Recent studies have shown that no single HKG is universal for all experiments. Thus, a suitable HKG should be selected before its use. Only a few studies on HKGs have been done in plants, and none in soybean, an economically important crop. Therefore, the present study was conducted to identify suitable HKG(s) for normalization of gene expression in soybean.</p> <p>Results</p> <p>All ten HKGs displayed a wide range of Ct values in 21 sample pools, confirming that they were variably expressed. GeNorm was used to determine the expression stability of the HGKs in seven series sets. For all the sample pools analyzed, the stability rank was <it>ELF1B</it>, <it>CYP2 </it>> <it>ACT11 </it>> <it>TUA </it>> <it>ELF1A </it>> <it>UBC2 </it>> <it>ACT2/7 </it>> <it>TUB </it>> <it>G6PD </it>> <it>UBQ10</it>. For different tissues under the same developmental stage, the rank was <it>ELF1B</it>, <it>CYP2 </it>> <it>ACT2/7 </it>> <it>UBC2 </it>> <it>TUA </it>> <it>ELF1A </it>> <it>ACT11 </it>> <it>TUB </it>> <it>G6PD </it>> <it>UBQ10</it>. For the developmental stage series, the stability rank was <it>ACT2/7</it>, <it>TUA </it>> <it>ELF1A </it>> <it>UBC2 </it>> <it>ELF1B </it>> <it>TUB </it>> <it>CYP2 </it>> <it>ACT11 </it>> <it>G6PD </it>> <it>UBQ10</it>. For photoperiodic treatments, the rank was <it>ACT11</it>, <it>ELF1B </it>> <it>CYP2 </it>> <it>TUA </it>> <it>ELF1A </it>> <it>UBC2 </it>> <it>ACT2/7 </it>> <it>TUB </it>> <it>G6PD </it>> <it>UBQ10</it>. For different times of the day, the rank was <it>ELF1A</it>, <it>TUA </it>> <it>ELF1B </it>> <it>G6PD </it>> <it>CYP2 </it>> <it>ACT11 </it>> <it>ACT2/7 </it>> <it>TUB </it>> <it>UBC2 </it>> <it>UBQ10</it>. For different cultivars and leaves on different nodes of the main stem, the ten HKGs' stability did not differ significantly. ΔCt approach and 'Stability index' were also used to analyze the expression stability in all 21 sample pools. Results from ΔCt approach and geNorm indicated that <it>ELF1B </it>and <it>CYP2 </it>were the most stable HKGs, and <it>UBQ10 </it>and <it>G6PD </it>the most variable ones. Results from 'Stability index' analysis were different, with <it>ACT11 </it>and <it>CYP2 </it>being the most stable HKGs, and <it>ELF1A </it>and <it>TUA </it>the most variable ones.</p> <p>Conclusion</p> <p>Our data suggests that HKGs are expressed variably in soybean. Based on the results from geNorm and ΔCt analysis, <it>ELF1B </it>and <it>CYP2 </it>could be used as internal controls to normalize gene expression in soybean, while <it>UBQ10 </it>and <it>G6PD </it>should be avoided. To achieve accurate results, some conditions may require more than one HKG to be used for normalization.</p