Harnessing the power of diffusion models for plant disease image augmentation.

Introduction: The challenges associated with data availability, class imbalance, and the need for data augmentation are well-recognized in the field of plant disease detection. The collection of large-scale datasets for plant diseases is particularly demanding due to seasonal and geographical constraints, leading to significant cost and time investments. Traditional data augmentation techniques, such as cropping, resizing, and rotation, have been largely supplanted by more advanced methods. In particular, the utilization of Generative Adversarial Networks (GANs) for the creation of realistic synthetic images has become a focal point of contemporary research, addressing issues related to data scarcity and class imbalance in the training of deep learning models. Recently, the emergence of diffusion models has captivated the scientific community, offering superior and realistic output compared to GANs. Despite these advancements, the application of diffusion models in the domain of plant science remains an unexplored frontier, presenting an opportunity for groundbreaking contributions. Methods: In this study, we delve into the principles of diffusion technology, contrasting its methodology and performance with state-of-the-art GAN solutions, specifically examining the guided inference model of GANs, named InstaGAN, and a diffusion-based model, RePaint. Both models utilize segmentation masks to guide the generation process, albeit with distinct principles. For a fair comparison, a subset of the PlantVillage dataset is used, containing two disease classes of tomato leaves and three disease classes of grape leaf diseases, as results on these classes have been published in other publications. Results: Quantitatively, RePaint demonstrated superior performance over InstaGAN, with average Fréchet Inception Distance (FID) score of 138.28 and Kernel Inception Distance (KID) score of 0.089 ± (0.002), compared to InstaGAN's average FID and KID scores of 206.02 and 0.159 ± (0.004) respectively. Additionally, RePaint's FID scores for grape leaf diseases were 69.05, outperforming other published methods such as DCGAN (309.376), LeafGAN (178.256), and InstaGAN (114.28). For tomato leaf diseases, RePaint achieved an FID score of 161.35, surpassing other methods like WGAN (226.08), SAGAN (229.7233), and InstaGAN (236.61). Discussion: This study offers valuable insights into the potential of diffusion models for data augmentation in plant disease detection, paving the way for future research in this promising field.

A systematic review of progress on hepatocellular carcinoma research over the past 30 years: a machine-learning-based bibliometric analysis.

Introduction: Research on hepatocellular carcinoma (HCC) has grown significantly, and researchers cannot access the vast amount of literature. This study aimed to explore the research progress in studying HCC over the past 30 years using a machine learning-based bibliometric analysis and to suggest future research directions. Methods: Comprehensive research was conducted between 1991 and 2020 in the public version of the PubMed database using the MeSH term "hepatocellular carcinoma." The complete records of the collected results were downloaded in Extensible Markup Language format, and the metadata of each publication, such as the publication year, the type of research, the corresponding author's country, the title, the abstract, and the MeSH terms, were analyzed. We adopted a latent Dirichlet allocation topic modeling method on the Python platform to analyze the research topics of the scientific publications. Results: In the last 30 years, there has been significant and constant growth in the annual publications about HCC (annual percentage growth rate: 7.34%). Overall, 62,856 articles related to HCC from the past 30 years were searched and finally included in this study. Among the diagnosis-related terms, "Liver Cirrhosis" was the most studied. However, in the 2010s, "Biomarkers, Tumor" began to outpace "Liver Cirrhosis." Regarding the treatment-related MeSH terms, "Hepatectomy" was the most studied; however, recent studies related to "Antineoplastic Agents" showed a tendency to supersede hepatectomy. Regarding basic research, the study of "Cell Lines, Tumors,'' appeared after 2000 and has been the most studied among these terms. Conclusion: This was the first machine learning-based bibliometric study to analyze more than 60,000 publications about HCC over the past 30 years. Despite significant efforts in analyzing the literature on basic research, its connection with the clinical field is still lacking. Therefore, more efforts are needed to convert and apply basic research results to clinical treatment. Additionally, it was found that microRNAs have potential as diagnostic and therapeutic targets for HCC.

Research Topics and Trends in Interprofessional Education in Nursing: A Text Network Analysis.

To provide nurse-led interprofessional practices in a healthcare setting, carrying out effective research that identifies the trends and characteristics of interprofessional education is necessary. This study aimed to objectively ascertain trends in the field through text network analysis of different types of interprofessional education literature. Titles and thesis abstracts were examined for terms "interprofessional education" and "nursing" and were found in 3926 articles from 1970 to August 2018. Python and Gephi software were used to analyze the data and visualize the networks. Keyword ranking was based on the frequency, degree centrality, and betweenness centrality. The terms "interprofessional," "education," "student," "nursing," and "health" were ranked the highest. According to topic analysis, the methods, provided programs, and outcome measures differed according to the research field. These findings can help create nurse-led research and effective future directions for interprofessional education pathways and topic selection. This will emphasize the importance of expanding research on various education programs and accumulating evidence regarding the professional and interdisciplinary impact these programs have on undergraduate and graduate students.

A Network Analysis of Research Topics and Trends in End-of-Life Care and Nursing.

This study identified the trends in end-of-life care and nursing through text network analysis. About 18,935 articles published until September 2019 were selected through searches on PubMed, Embase, Cochrane, Web of Science, and Cumulative Index to Nursing and Allied Health Literature. For topic modeling, Latent Dirichlet Allocation (K = 8) was applied. Most of the top ranked topic words for the degree and betweenness centralities were consistent with the top 1% through the semantic network diagram. Among the important keywords examined every five years, "care" was unrivaled. When analyzing the two- and three-word combinations, there were many themes representing places, roles, and actions. As a result of performing topic modeling, eight topics were derived as ethical issues of decision-making for treatment withdrawal, symptom management to improve the quality of life, development of end-of-life knowledge education programs, life-sustaining care plan for elderly patients, home-based hospice, communication experience, patient symptom investigation, and an analysis of considering patient preferences. This study is meaningful as it analyzed a large amount of existing literature and considered the main trends of end-of-life care and nursing research based on the core subject control and semantic structure.

Identification of the Knowledge Structure of Cancer Survivors' Return to Work and Quality of Life: A Text Network Analysis.

This study aimed to understand the trends in research on the quality of life of returning to work (RTW) cancer survivors using text network analysis. Titles and abstracts of each article were examined to extract terms, including "cancer survivors", "return to work", and "quality of life", which were found in 219 articles published between 1990 and June 2020. Python and Gephi software were used to analyze the data and visualize the networks. Keyword ranking was based on the frequency, degree centrality, and betweenness centrality. The keywords commonly ranked at the top included "breast", "patients", "rehabilitation", "intervention", "treatment", and "employment". Clustering results by grouping nodes with high relevance in the network led to four clusters: "participants and method", "type of research and variables", "RTW and education in adolescent and young adult cancer survivors", and "rehabilitation program". This study provided a visualized overview of the research on cancer survivors' RTW and quality of life. These findings contribute to the understanding of the flow of the knowledge structure of the existing research and suggest directions for future research.

Energy Efficient Real-Time Scheduling Using DPM on Mobile Sensors with a Uniform Multi-Cores.

In wireless sensor networks (WSNs), sensor nodes are deployed for collecting and analyzing data. These nodes use limited energy batteries for easy deployment and low cost. The use of limited energy batteries is closely related to the lifetime of the sensor nodes when using wireless sensor networks. Efficient-energy management is important to extending the lifetime of the sensor nodes. Most effort for improving power efficiency in tiny sensor nodes has focused mainly on reducing the power consumed during data transmission. However, recent emergence of sensor nodes equipped with multi-cores strongly requires attention to be given to the problem of reducing power consumption in multi-cores. In this paper, we propose an energy efficient scheduling method for sensor nodes supporting a uniform multi-cores. We extend the proposed T-Ler plane based scheduling for global optimal scheduling of a uniform multi-cores and multi-processors to enable power management using dynamic power management. In the proposed approach, processor selection for a scheduling and mapping method between the tasks and processors is proposed to efficiently utilize dynamic power management. Experiments show the effectiveness of the proposed approach compared to other existing methods.

T-L Plane Abstraction-Based Energy-Efficient Real-Time Scheduling for Multi-Core Wireless Sensors.

Energy efficiency is considered as a critical requirement for wireless sensor networks. As more wireless sensor nodes are equipped with multi-cores, there are emerging needs for energy-efficient real-time scheduling algorithms. The T-L plane-based scheme is known to be an optimal global scheduling technique for periodic real-time tasks on multi-cores. Unfortunately, there has been a scarcity of studies on extending T-L plane-based scheduling algorithms to exploit energy-saving techniques. In this paper, we propose a new T-L plane-based algorithm enabling energy-efficient real-time scheduling on multi-core sensor nodes with dynamic power management (DPM). Our approach addresses the overhead of processor mode transitions and reduces fragmentations of the idle time, which are inherent in T-L plane-based algorithms. Our experimental results show the effectiveness of the proposed algorithm compared to other energy-aware scheduling methods on T-L plane abstraction.

Lazy approaches for interval timing correlation of sensor data streams.

We propose novel algorithms for the timing correlation of streaming sensor data. The sensor data are assumed to have interval timestamps so that they can represent temporal uncertainties. The proposed algorithms can support efficient timing correlation for various timing predicates such as deadline, delay, and within. In addition to the classical techniques, lazy evaluation and result cache are utilized to improve the algorithm performance. The proposed algorithms are implemented and compared under various workloads.