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    Universitas Andalas Researchers Update Current Data on Machine Learning [Sonic Log Prediction Based on Extreme Gradient Boosting (XGBoost) Machine Learning Algorithm by Using Well Log Data]

    57-57页
    查看更多>>摘要:Investigators discuss new findings in artificial intelligence. According to news originating from the Universitas Andalas by NewsRx editors, the research stated, “Sonic log is an important aspect that provides a detailed description of the subsurface properties associated with oil and gas reservoirs.” The news correspondents obtained a quote from the research from Universitas Andalas: “The problem that frequently occurs is the unavailability of sonic log data for various reasons needs to be given an effective solution. The alternative approach proposed in this research is sonic log prediction based on Extreme Gradient Boosting (XGBoost) machine learning algorithm, using available log data to build a reliable sonic log prediction model. In this research, the predicted DT log type is the Differential Time Shear Slowness (DTSM) log, which is the velocity of shear waves propagating in a formation. Log features used for training include gamma ray (GR), density (RHOB), porosity (NPHI), resistivity (RS and RD) logs with DTSM log as the prediction target. To optimise the performance and generalisation of the XGBoost algorithm in predicting log DTSM, hyperparameter tuning was applied using grid search technique to obtain optimal parameters for the prediction model. Based on the experimental results, this research found that hyperparameter tuning using grid search technique improved the accuracy of sonic log (DTSM) model prediction based on XGBoost algorithm, as proven by the decrease of RMSE and MAPE values to 19.699 and 7.713%.”

    Data from Karlsruhe Institute of Technology (KIT) Advance Knowledge in Machine Learning (Machine learning for rapid discovery of laminar flow channel wall modifications that enhance heat transfer)

    58-58页
    查看更多>>摘要:Fresh data on artificial intelligence are presented in a new report. According to news reporting out of Karlsruhe, Germany, by NewsRx editors, research stated, “Numerical simulation of fluid flow plays an essential role in modeling many physical phenomena, which enables technological advancements, contributes to sustainable practices, and expands our understanding of various natural and engineered systems.” Financial supporters for this research include Deutsche Forschungsgemeinschaft; Ministerium Fur Wissenschaft, Forschung Und Kunst Baden-wurttemberg; Bundesministerium Fur Bildung Und Forschung. Our news journalists obtained a quote from the research from Karlsruhe Institute of Technology (KIT): “The calculation of heat transfer in fluid flow in simple flat channels is a relatively easy task for various simulation methods. However, once the channel geometry becomes more complex, numerical simulations become a bottleneck in optimizing wall geometries. We present a combination of accurate numerical simulations of arbitrary, flat, and non-flat channels as well as machine learning models trained on simulated data to predict the drag coefficient and Stanton number. We show that convolutional neural networks (CNNs) can accurately predict target properties at a fraction of the computational cost of numerical simulations. We use CNN models in a virtual high-throughput screening approach to explore a large number of possible, randomly generated wall architectures. Data augmentation techniques are incorporated to enforce physical invariances toward shifting and flipping, contributing to precise prediction for fluid flow and heat transfer characteristics.”

    Study Results from Huazhong University of Science and Technology Broaden Understanding of Robotics (Prescribed Performance Control of a Human-Following Surveillance Robot with Incomplete Observation)

    59-59页
    查看更多>>摘要:New research on robotics is the subject of a new report. According to news originating from Wuhan, People's Republic of China, by NewsRx correspondents, research stated, “For people with lower limb muscle weakness, effective and timely rehabilitation intervention is essential for assisting in daily walking and facilitating recovery.” Funders for this research include National Natural Science Foundation of China. The news journalists obtained a quote from the research from Huazhong University of Science and Technology: “Numerous studies have been conducted on rehabilitation robots; however, some critical issues in the field of human-following remain unaddressed. These include potential challenges related to the loss of sensory signals for intention recognition and the complexities associated with maintaining the relative pose of robots during the following process. A human-following surveillance robot is introduced as the basis of the research. To address potential interruptions in motion signals, such as data transmission blockages or body occlusion, we propose a human walking intention estimation algorithm based on setmembership filtering with incomplete observation. To ensure uninterrupted user walking and maintain an effective aid and detection range, we propose a human-following control algorithm based on prescribed performance. The experiment verifies the effectiveness of the proposed methods.”

    Studies from University of Cologne Provide New Data on Machine Learning (MiGIS: micromorphological soil and sediment thin section analysis using an open-source GIS and machine learning approach)

    60-60页
    查看更多>>摘要:Fresh data on artificial intelligence are presented in a new report. According to news reporting out of Cologne, Germany, by NewsRx editors, research stated, “Micromorphological analysis using a petrographic microscope is one of the conventional methods to characterise microfacies in rocks (sediments) and soils. This analysis of the composition and structure observed in thin sections (TSs) yields seminal, but primarily qualitative, insights into their formation.” Financial supporters for this research include Deutsche Forschungsgemeinschaft. The news reporters obtained a quote from the research from University of Cologne: “In this context, the following question arises: how can micromorphological features be measured, classified, and particularly quantified to enable comparisons beyond the micro scale? With the Micromorphological Geographic Information System (MiGIS), we have developed a Python-based toolbox for the open-source software QGIS 3, which offers a straightforward solution to digitally analyse micromorphological features in TSs. By using a flatbed scanner and (polarisation) film, high-resolution red-green-blue (RGB) images can be captured in transmitted light (TL), cross-polarised light (XPL), and reflected light (RL) mode. Merging these images in a multi-RGB raster, feature-specific image information (e.g. light refraction properties of minerals) can be combined in one data set. This provides the basis for image classification with MiGIS. The MiGIS classification module uses the random forest algorithm and facilitates a semi-supervised (based on training areas) classification of the feature-specific colour values (multi-RGB signatures). The resulting classification map shows the spatial distribution of thin section features and enables the quantification of groundmass, pore space, minerals, or pedofeatures, such nodules being dominated by iron oxide and clay coatings. We demonstrate the advantages and limitations of the method using TSs from a loess-palaeosol sequence in Rheindahlen (Germany), which was previously studied using conventional micromorphological techniques. Given the high colour variance within the feature classes, MiGIS appears well-suited for these samples, enabling the generation of accurate TS feature maps. Nevertheless, the classification accuracy can vary due to the TS quality and the academic training level, in micromorphology and in terms of the classification process, when creating the training data.”

    New Findings from Nanjing Agricultural University Describe Advances in Machine Learning (Automatic Sentence Segmentation for Classical Chinese: the Spring and Autumn Annals As an Example)

    61-61页
    查看更多>>摘要:Current study results on Machine Learning have been published. According to news reporting originating in Nanjing, People's Republic of China, by NewsRx journalists, research stated, “There exists no sentence boundary in most classical Chinese literature texts. Since it is difficult to read literature of this kind, experts in literature or linguistics would segment the sentence manually.” Financial support for this research came from National Office of Philosophy and Social Sciences. The news reporters obtained a quote from the research from Nanjing Agricultural University, “This article explores the effectiveness of classical Chinese sentence segmentation method so as to provide a reference for classical Chinese punctuation. On the basis of the machine learning methods, we chose three components of machine learning, namely models, tagging schemes, and features, to compare the learning results. The models include conditional random field (CRF) models, long short term memory (LSTM) models, BiLSTM-CRF models, and three Bidirectional Encoder Representation from Transformers (BERT) models. There are five tagging schemes in this article and three features including the statistical feature, Guangyun, and Fanqie. Finally, the performance of the combined feature template is evaluated by ten-fold cross-validation on four classical Chinese texts in different genres. The SikuBERT model is proved to be the most effective model for sentence segmentation at present. Different tagging schemes and various features are introduced. The results show that 5-tag-J tagging schemes can improve performance. Statistical feature, as an important clue for classical Chinese sentence segmentation, is useful in related tasks, but Guangyun and Fanqie have little impact.”

    Federal Scientific Agroengineering Center VIM Researchers Have Published New Study Findings on Robotics (Development of control system for robotic apple harvesting device)

    62-62页
    查看更多>>摘要:New study results on robotics have been published. According to news reporting from the Federal Scientific Agroengineering Center VIM by NewsRx journalists, research stated, “This article focuses on the control system of a robotic device designed for efficient apple harvesting with minimal fruit damage. The developed device is equipped with specialized mechanisms and sensors aimed at reducing negative impacts on fruits during harvesting.” The news reporters obtained a quote from the research from Federal Scientific Agroengineering Center VIM: “A control system for the robotic device was developed, incorporating various sensors and modules. A module for determining the position of the grip arms was designed, consisting of a polymeric magnetic strip attached to a spiral-shaped cutout. A module to track the position of the grip arms was also developed, capable of indicating the current position of the grip arms, fully open and closed grip, as well as intermediate values. A module to monitor the grip force was designed, used to control the gripping force applied to the fruit. A current sensor was connected to the winding of the linear actuator motor to measure the force. Increasing the force applied by the grip arms to the fruit results in higher current flowing through the linear actuator motor winding. By reading the current sensor data, the degree of compression on the fruit is determined. During testing of the control system modules and sensors, it was found that the grip arm angle position module has the highest sensitivity in the range of 0 to 90 degrees. The obtained data after calibration enable the control of the degree of grip arm opening. The grip arm position module allowed controlling and adjusting the grip arm position with an accuracy of up to 2 mm. A control system for a robotic grip for apple harvesting was developed.”

    Guangdong Medical University Reports Findings in Sepsis (Identification of RRM2 as a key ferroptosis-related gene in sepsis)

    62-63页
    查看更多>>摘要:New research on Blood Diseases and Conditions - Sepsis is the subject of a report. According to news reporting originating from Guangdong, People's Republic of China, by NewsRx corre- spondents, research stated, “Sepsis and sepsis-associated organ failure are devastating conditions for which there are no effective therapeutic agent. Several studies have demonstrated the significance of ferroptosis in sepsis.” Our news editors obtained a quote from the research from Guangdong Medical University, “The study aimed to identify ferroptosis-related genes (FRGs) in sepsis, providing potential therapeutic targets. The weighted gene co-expression network analysis (WGCNA) was utilized to screen sepsis-associated genes. Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment analyses were used to explore gene functions. Three machine learning methods were employed to identify sepsis-related hub genes. Survival and multivariate Cox regression analysis allowed further screening for the key gene RRM2 associated with prognosis. The immune infiltration analysis of the screened sepsis key genes was performed. Additionally, a cecum ligation and puncture (CLP)-induced mouse sepsis model was constructed to validate the expression of key gene in the sepsis. Six sepsis-associated differentially expressed FRGs (RRM2, RPL7A, HNRNPA1, PEBP1, MYL8B and TXNIP) were screened by WGCNA and three machine learning methods analysis. Survival analysis and multivariate Cox regression analysis showed that RRM2 was a key gene in sepsis and an independent prognostic factor associated with clinicopathological and molecular features of sepsis. Immune cell infiltration analysis demonstrated that RRM2 had a connection to various immune cells, such as CD4 T cells and neutrophils. Furthermore, animal experiment demonstrated that RRM2 was highly expressed in CLP-induced septic mice, and the use of Fer-1 significantly inhibited RRM2 expression, inhibited serum inflammatory factor TNF-a, IL-6 and IL-1b expression, ameliorated intestinal injury and improved survival in septic mice. RRM2 plays an important role in sepsis and may contribute to sepsis through the ferroptosis pathway.”

    Wageningen University and Research Reports Findings in Artificial Intelligence (Making food systems more resilient to food safety risks by including artificial intelligence, big data, and internet of things into food safety early warning and ...)

    64-64页
    查看更多>>摘要:New research on Artificial Intelligence is the subject of a report. According to news originating from Wageningen, Netherlands, by NewsRx correspondents, research stated, “To enhance the resilience of food systems to food safety risks, it is vitally important for national authorities and international organizations to be able to identify emerging food safety risks and to provide early warning signals in a timely manner. This review provides an overview of existing and experimental applications of artificial intelligence (AI), big data, and internet of things as part of early warning and emerging risk identification tools and methods in the food safety domain.” Our news journalists obtained a quote from the research from Wageningen University and Research, “There is an ongoing rapid development of systems fed by numerous, real-time, and diverse data with the aim of early warning and identification of emerging food safety risks. The suitability of big data and AI to support such systems is illustrated by two cases in which climate change drives the emergence of risks, namely, harmful algal blooms affecting seafood and fungal growth and mycotoxin formation in crops. Automation and machine learning are crucial for the development of future real-time food safety risk early warning systems. Although these developments increase the feasibility and effectiveness of prospective early warning and emerging risk identification tools, their implementation may prove challenging, particularly for low- and middle-income countries due to low connectivity and data availability.”

    Technical University Berlin (TU Berlin) Reports Findings in Artificial Intelligence [Spatio-temporal feature attribution of European summer wildfires with Explainable Artificial Intelligence (XAI)]

    65-65页
    查看更多>>摘要:New research on Artificial Intelligence is the subject of a report. According to news reporting from Berlin, Germany, by NewsRx journalists, research stated, “Wildfires are among the most destructive natural disasters globally. Understanding the drivers behind wildfires is a crucial aspect of preventing and managing them.” The news correspondents obtained a quote from the research from Technical University Berlin (TU Berlin), “Machine learning methods have gained popularity in wildfire modeling in recent years, but their algorithms are usually complex and challenging to interpret. In this study, we employed the SHapley Additive exPlanations (SHAP) value, an Explainable Artificial Intelligence method, to interpret the model and thus generate spatio-temporal feature attributions. Our research focuses on the forest, shrub and herbaceous vegetated areas of Europe during the summers from 2018 to 2022. Using burned areas, meteorology, vegetation, topography, and anthropogenic activity data, we established a wildfire occurrence model using random forest classification. The model was highly accurate, with an Area Under the Receiver Operating Characteristic Curve of 0.940. The SHAP results revealed six features that significantly influence wildfire occurrences: land surface temperature (LST), solar radiation (SR), Temperature Condition Index (TCI), Normalized Difference Vegetation Index (NDVI), precipitation (Prep), and soil moisture (SM). The tipping points for the positive or negative shifts in contributions are around 30 ℃ (LST), 2.20e J/m.2 (SR), 0.2 (TCI), 0.78 (NDVI), 2 mm/h (Prep), and 0.18 (SM). These predictors display strong spatial variability in their contribution levels. In Southern Europe, LST and SR emerge as the primary contributors to wildfires, making substantial impacts. Conversely, in regions at mid and high latitudes in Europe, NDVI, Prep, and SM assume a more prominent role in promoting wildfires, albeit with relatively smaller contributions. Furthermore, the disparities in SHAP values for TCI and SMCI across different years provide valuable insights into the effects of variation in regional meteorological conditions on wildfires.”

    Southeast University Reports Findings in Liver Cancer (G6PD and machine learning algorithms as prognostic and diagnostic indicators of liver hepatocellular carcinoma)

    66-66页
    查看更多>>摘要:New research on Oncology - Liver Cancer is the subject of a report. According to news reporting originating in Jiangsu, People's Republic of China, by NewsRx journalists, research stated, “Liver Hepatocellular carcinoma (LIHC) exhibits a high incidence of liver cancer with escalating mortality rates over time. Despite this, the underlying pathogenic mechanism of LIHC remains poorly understood.” Financial support for this research came from Jiangsu Provincial Key Medical Discipline. The news reporters obtained a quote from the research from Southeast University, “To address this gap, we conducted a comprehensive investigation into the role of G6PD in LIHC using a combination of bioinformatics analysis with database data and rigorous cell experiments. LIHC samples were obtained from TCGA, ICGC and GEO databases, and the differences in G6PD expression in different tissues were investigated by differential expression analysis, followed by the establishment of Nomogram to determine the percentage of G6PD in causing LIHC by examining the relationship between G6PD and clinical features, and the subsequent validation of the effect of G6PD on the activity, migration, and invasive ability of hepatocellular carcinoma cells by using the low expression of LI-7 and SNU-449. Additionally, we employed machine learning to validate and compare the predictive capacity of four algorithms for LIHC patient prognosis. Our findings revealed significantly elevated G6PD expression levels in liver cancer tissues as compared to normal tissues. Meanwhile, Nomogram and Adaboost, Catboost, and Gbdt Regression analyses showed that G6PD accounted for 46%, 31%, and 49% of the multiple factors leading to LIHC. Furthermore, we observed that G6PD knockdown in hepatocellular carcinoma cells led to reduced proliferation, migration, and invasion abilities. Remarkably, the Decision Tree C5.0 decision tree algorithm demonstrated superior discriminatory performance among the machine learning methods assessed.”