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    Study Data from Russian Academy of Sciences Provide New Insights into Machine Learning (Smap Sea Surface Salinity Improvement In the Arctic Region Using Machine Learning Approaches)

    19-20页
    查看更多>>摘要：Investigators discuss new findings in Machine Learning. According to news originating from Moscow, Russia, by NewsRx correspondents, research stated, “Sea surface salinity (SSS) is a key physicochemical characteristic of the ocean that plays a significant role in describing the climate. Routine SSS retrieval algorithms exploiting remote sensing data have been developed and validated with high precision for typical regions of the World Ocean.” Funders for this research include Moscow Institute of Physics and Technology Development Program (Priority-2030), Russian Science Foundation (RSF). Our news journalists obtained a quote from the research from the Russian Academy of Sciences, “Their effectiveness is worse in the Arctic though. To address this limitation, in this study, we employ machine learning (ML) techniques to enhance the quality of standard algorithms. We evaluate a few ML models, ranging from classical methods that process vector features, provided by standard Soil Moisture Active Passive (SMAP) satellite salinity algorithms, to deep artificial neural networks that combine vector features with two-dimensional fields extracted from the ERA5 reanalysis. We validate these models using in situ the data collected by the Shirshov Institute of Oceanology RAS during the expeditions to the Barents, Kara, Laptev, and East Siberian seas from 2015 to 2021. The results of the study indicate that the SMAP sea surface salinity standard product is improved in these regions.”
      原文链接:
    • NETL
    • NSTL
    • Pleiades Publishing Inc

    Reports from Tianjin University Provide New Insights into Robotics (An Angle-sensitive Microcolumn-based Capacitive Shear Force Sensor for Robot Grasping)

    20-21页
    查看更多>>摘要：Investigators discuss new findings in Robotics. According to news originating from Tianjin, People’s Republic of China, by NewsRx correspondents, research stated, “Shear force sensors play an indispensable role in tactile perception for robot manipulation tasks. However, recent advancements in shear force sensors have been hindered by issues such as direction sensitivity and integration limitations.” Funders for this research include National Natural Science Foundation of China (NSFC), China Association for Science and Technology, China Instrument and Control Society. Our news journalists obtained a quote from the research from Tianjin University, “This paper proposes a microcolumn array dielectric layer produced using photolithography technology that enables tunability of sensor sensitivity and detection range by adjusting the aspect ratio and interval of the microstructures. Meanwhile, the impact of five constant normal force couplings on the sensitivity of shear force perception is investigated. The structure array with a 1:2 aspect ratio and 600 mu m interval demonstrates an ultrahigh sensitivity of 6.189 N-1 and outstanding linearity (R2 = 0.9873) within the range up to 0.1 N. The sensor exhibits low hysteresis and robust stability over 3000 cycles. Additionally, it exhibits remarkable anisotropic direction sensitivity, enabling accurate positioning within a quarter-circle angle. An intentionally designed orthogonal array is employed to extend the shear angle range up to 360 degrees. Owing to the high performance of the sensor, it is further integrated onto a gripper to facilitate the grasping operation and effectively capture delicate movements. The experimental outcomes highlight that the designed sensor holds promise for applications in robotic applications and electronic skin domains. A capacitive shear force sensor based on microcolumns dielectric layer with a 1:2 aspect ratio and 600 mu m interval realizes ultrahigh anisotropic sensitivity to shear forces from different directions. The single sensor achieves a shear range of 90 degrees, and the range can be further expanded to 360 degrees via orthogonal sensor array.”
      原文链接:
    • NETL
    • NSTL
    • Wiley

    Southeast University Reports Findings in Machine Learning (Examining the rationality of Giant Panda National Park’s zoning designations and management measures for habitat conservation: Insights from interpretable machine learning methods)

    21-22页
    查看更多>>摘要：New research on Machine Learning is the subject of a report. According to news reporting originating from Nanjing, People’s Republic of China, by NewsRx correspondents, research stated, “The examination of the rationality of zoning and management measures in the initial establishment of national parks in China is of great significance for supporting decision-making regarding habitat conservation. There exists a research gap in exploring the threshold effects of both environmental and human-related factors on habitats in the context of national parks.” Our news editors obtained a quote from the research from Southeast University, “However, it may be a challenge because of the limited species distribution data. Using the Sichuan region of the Giant Panda National Park (GPNP) as an example, this study made use of accessible remote sensing and big data to predict the distribution of giant panda habitat (GPH) in 2020 by constructing a species distribution model based on the random forest algorithm. Interpretable machine learning methods, namely Partial dependence plots (PDPs) and SHapley Additive exPlanations (SHAP), were utilized to uncover the underlying mechanisms of environmental and anthropogenic factors influencing the GPH distribution in Sichuan province. Through GIS overlay analysis, areas where conflicts between human settlements, transportation infrastructure, and GPH exist were identified. Our findings indicated a potential 28.44 % decrease in GPH from 2014 to 2020. Environmental factors such as temperature, topography, and vegetation type, as well as anthropogenic factors including distance to built-up areas and transportation infrastructure, notably distance to national roads, provincial roads and city arterial roads, influenced the GPH distribution with threshold effects significantly. The overlay analysis revealed escalated conflicts between human settlements, transportation infrastructure, and GPH in 2020 compared to 2014. Currently, the Sichuan region of the GPNP implements two zones: a core protection zone and a general control zone, covering 63.71 % of the GPH, while 36.29 % remains outside the management scope.”
      原文链接:
    • NETL
    • NSTL

    Report Summarizes Machine Learning Study Findings from University of Connecticut (Machine Learning Methods for Endocrine Disrupting Potential Identification Based On Single-cell Data)

    22-23页
    查看更多>>摘要：Current study results on Machine Learning have been published. According to news reporting out of Storrs, Connecticut, by NewsRx editors, research stated, “Humans are continuously exposed to a variety of toxicants and chemicals which is exacerbated during and after environmental catastrophes such as floods, earthquakes, and hurricanes. The hazardous chemical mixtures generated during these events threaten the health and safety of humans and other living organisms.” Funders for this research include National Institutes of Health (NIH) - USA, University of Connecticut, University of Connecticut Storrs High Performance Computing facility, Integrated Microscopy Core at Baylor College of Medicine, National Institutes of Health (NIH) - USA, Cancer Prevention & Research Institute of Texas. Our news journalists obtained a quote from the research from the University of Connecticut, “This necessitates the development of rapid decision-making tools to facilitate mitigating the adverse effects of exposure on the key modulators of the endocrine system, such as the estrogen receptor alpha (ER alpha), for example. The mechanistic stages of the estrogenic transcriptional activity can be measured with high content/high throughput microscopy-based biosensor assays at the single-cell level, which generates millions of object-based minable data points. By combining computational modeling and experimental analysis, we built a highly accurate data-driven classification framework to assess the endocrine disrupting potential of environmental compounds. The effects of these compounds on the ER alpha pathway are predicted as being receptor agonists or antagonists using the principal component analysis (PCA) projections of high throughput, high content image analysis descriptors. The framework also combines rigorous preprocessing steps and nonlinear machine learning algorithms, such as the Support Vector Machines and Random Forest classifiers, to develop highly accurate mathematical representations of the separation between ER alpha agonists and antagonists.”
      原文链接:
    • NETL
    • NSTL
    • Elsevier

    Medical College of Yangzhou University Reports Findings in Stroke (Machine learning-based clinical prediction models for acute ischemic stroke based on serum xanthine oxidase levels)

    23-24页
    查看更多>>摘要：New research on Cerebrovascular Diseases and Conditions - Stroke is the subject of a report. According to news reporting from Yangzhou, People’s Republic of China, by NewsRx journalists, research stated, “Early prediction of the onset, progression and prognosis of acute ischemic stroke (AIS) is helpful for treatment decision-making and proactive management. Although several biomarkers have been found to predict the progression and prognosis of AIS, these biomarkers have not been widely used in routine clinical practice.” The news correspondents obtained a quote from the research from the Medical College of Yangzhou University, “Xanthine oxidase (XO) is a form of xanthine oxidoreductase (XOR), which is widespread in various organs of the human body and plays an important role in redox reactions and ischemia-reperfusion injury. Our previous studies have shown that serum XO levels on admission have certain clinical predictive value for AIS. The purpose of this study was to utilize serum XO levels and clinical data to establish machine learning models for predicting the onset, progression and prognosis of AIS. We enrolled 328 consecutive patients with AIS and 107 healthy controls from October 2020 to September 2021. Serum XO levels and stroke-related clinical data were collected. We established five machine learning modelsthe logistic regression (LR), support vector machine (SVM), decision tree, random forest, and K-nearest neighbor (KNN) models-to predict the onset, progression and prognosis of AIS. The area under the receiver operating characteristic curve (AUROC), accuracy, sensitivity, specificity, negative predictive value and positive predictive value were used to evaluate the predictive performance of each model. Among the five machine learning models predicting AIS onset, the AUROC values of four prediction models were over 0.7, while that of the KNN model was lower (AUROC=0.6708, 95% CI 0.576-0.765). The LR model showed the best AUROC value (AUROC=0.9586, 95% CI 0.927-0.991). Although the five machine learning models showed relatively poor predictive value for the progression of AIS (all AUROCs ＜0.7), the LR model still showed the highest AUROC value (AUROC=0.6543, 95% CI 0.453-0.856). We compared the value of five machine learning models in predicting the prognosis of AIS, and the LR model showed the best predictive value (AUROC=0.8124, 95% CI 0.715-0.910). The tested machine learning models based on serum levels of XO could predict the onset and prognosis of AIS. Among the five machine learning models, we found that the LR model showed the best predictive performance.”
      原文链接:
    • NETL
    • NSTL

    Central South University Reports Findings in Pancreatic Cancer (Explainable cancer factors discovery: Shapley additive explanation for machine learning models demonstrates the best practices in the case of pancreatic cancer)

    25-25页
    查看更多>>摘要：New research on Oncology - Pancreatic Cancer is the subject of a report. According to news reporting from Changsha, People’s Republic of China, by NewsRx journalists, research stated, “Pancreatic cancer is one of digestive tract cancers with high mortality rate. Despite the wide range of available treatments and improvements in surgery, chemotherapy, and radiation therapy, the five-year prognosis for individuals diagnosed pancreatic cancer remains poor.” The news correspondents obtained a quote from the research from Central South University, “There is still research to be done to see if immunotherapy may be used to treat pancreatic cancer. The goals of our research were to comprehend the tumor microenvironment of pancreatic cancer, found a useful biomarker to assess the prognosis of patients, and investigated its biological relevance. In this paper, machine learning methods such as random forest were fused with weighted gene co-expression networks for screening hub immune-related genes (hub-IRGs). LASSO regression model was used to further work. Thus, we got eight hub-IRGs. Based on hub-IRGs, we created a prognosis risk prediction model for PAAD that can stratify accurately and produce a prognostic risk score (IRG_Score) for each patient. In the raw data set and the validation data set, the five-year area under the curve (AUC) for this model was 0.9 and 0.7, respectively. And shapley additive explanation (SHAP) portrayed the importance of prognostic risk prediction influencing factors from a machine learning perspective to obtain the most influential certain gene (or clinical factor). The five most important factors were TRIM67, CORT, PSPN, SCAMP5, RFXAP, all of which are genes. In summary, the eight hub-IRGs had accurate risk prediction performance and biological significance, which was validated in other cancers.”
      原文链接:
    • NETL
    • NSTL

    Studies from Ohio University in the Area of Machine Learning Reported (Comparison of Machine Learning Methodologies for Predicting Kinetics of Hydrothermal Carbonization of Selective Biomass)

    26-26页
    查看更多>>摘要：Fresh data on Machine Learning are presented in a new report. According to news reporting originating from Athens, Ohio, by NewsRx correspondents, research stated, “We have examined performance of various machine learning (ML) methods (artificial neural network, random forest, support vector-machine regression, and K nearest neighbors) in predicting the kinetics of hydrothermal carbonization (HTC) of cellulose, poplar, and wheat straw performed under two different conditions: first, isothermal conditions at 200, 230, and 260 degrees C, and second, with a linear temperature ramp of 2 degrees C/min from 160 to 260 degrees C. The focus of this study was to determine the predictability of the ML methods when the biomass type is not known or there is a mixture of biomass types, which is often the case in commercial operations. In addition, we have examined the performance of ML methods in interpolating kinetics results when experimental data is available for only a handful of time-points, as well as their performance in extrapolating the kinetics when the experimental data from only a few initial time-points is available.” Financial supporters for this research include United States Department of Agriculture (USDA), NSF XSEDE grant.
      原文链接:
    • NETL
    • NSTL
    • Springer Nature

    New Findings on Machine Learning from Hebei University Summarized (Damage Recognition of Acoustic Emission and Micro-ct Characterization of Bi-adhesive Repaired Composites Based On the Machine Learning Method)

    27-27页
    查看更多>>摘要：Investigators publish new report on Machine Learning. According to news reporting from Baoding, People’s Republic of China, by NewsRx journalists, research stated, “Bi-adhesive repair method is one of several repair technologies that use the adhesive bonding approach for patch-repaired composites. However, these repairs are subject to matrix-cracking and interface debonding damage.” Financial supporters for this research include National Natural Science Foundation of China (NSFC), National Natural Science Foundation of China (NSFC), Innovation Team of Nondestructive Testing Technology, Hebei University. The news correspondents obtained a quote from the research from Hebei University, “Furthermore, a change in the length ratio (the length of the rigid adhesive region divided by the length of the overall repaired region) also produces a change in the damage modes, which has a significant impact on the repair performance. Hence, this study aims to evaluate the effects of four different length ratios (0, 0.2, 0.5, 1) on the behavior of damage evolution in bi-adhesive repaired composites. The acoustic emission damage identification and micro-CT characterization are carried out based on the machine learning method. A simple prediction method is employed to distinguish damage modes in bi-adhesive repaired composites, achieving a prediction accuracy over 90%. The results demonstrated that the length ratio has a substantial effect on matrix-cracking, fiber-matrix debonding, and their interaction in bi-adhesive repaired composites. These acquired characteristics information of acoustic emission signals provide insights into the impact of length ratio on the progression of damage evolution. Additionally, the visualization of interior damage offers insights into the variations in failure characteristics within distinct bi-adhesive repaired composites, thereby supporting the conclusions gained from acoustic emission studies.”
      原文链接:
    • NETL
    • NSTL
    • Springer Nature

    Findings from Nanjing Tech University Provides New Data on Machine Learning (Interpretable Machine Learning-assisted Screening of Perovskite Oxides)

    28-28页
    查看更多>>摘要：A new study on Machine Learning is now available. According to news reporting from Jiangsu, People’s Republic of China, by NewsRx journalists, research stated, “Perovskite oxides are extensively utilized in energy storage and conversion. However, they are conventionally screened via timeconsuming and cost-intensive experimental approaches and density functional theory.” Financial support for this research came from Natural Science Foundation of Jiangsu Province. The news correspondents obtained a quote from the research from Nanjing Tech University, “Herein, interpretable machine learning is applied to identify perovskite oxides from virtual perovskite-type combinations by constructing classification and regression models to predict their thermodynamic stability and energy above the convex hull (Eh), respectively, and interpreting the models using SHapley Additive exPlanations. The highest occupied molecular orbital energy and the elastic modulus of the B-site elements of perovskite oxides are the top two features for stability prediction, whereas the Stability Label and features involving the elastic modulus and ionic radius are crucial for Eh regression. A classification model, which displays an accuracy of 0.919, precision of 0.937, F1-score of 0.932, and recall of 0.935, screens 682 143 stable perovskite oxides from 1 126 668 virtual perovskite-type combinations. The Eh values of the predicted stable perovskites are forecasted by a regression model with a coefficient of determination of 0.916, and root mean square error of 24.2 meV atom-1. Good agreement is observed between the regression model predicted and density functional theory-calculated Eh values.”
      原文链接:
    • NETL
    • NSTL
    • Royal Soc Chemistry

    Studies from East China Jiaotong University Yield New Data on Intelligent Systems (A Multi-scale Residual Graph Convolution Network With Hierarchical Attention for Predicting Traffic Flow In Urban Mobility)

    29-29页
    查看更多>>摘要：New research on Machine Learning - Intelligent Systems is the subject of a report. According to news reporting from Jiangxi, People’s Republic of China, by NewsRx journalists, research stated, “Accurate prediction of traffic flow is essential for optimizing transportation resource allocation and enhancing urban mobility efficiency. However, traffic data generated daily are vast and complex, involving dynamic and intricate changes in the traffic road network and traffic flow.” Funders for this research include National Natural Science Foundation of China (NSFC), National Natural Science Foundation of China (NSFC), Natural Science Foundation of Jiangxi Province, Jiangxi Province Graduate Innovation Special Fund. The news correspondents obtained a quote from the research from East China Jiaotong University, “Therefore, real-time and accurate prediction of traffic flow is a challenging task that requires modeling the intricate spatial-temporal dynamics of traffic data. In this paper, we propose a novel approach for traffic flow prediction, based on a Multi-Scale Residual Graph Convolution Network with hierarchical attention. First, we design a novel encoder-decoder with multi-independent channels to capture traffic flow information from different time scales and diverse temporal dependencies. Second, we employ a coupled graph convolution network with residual graph attention to dynamically learn the varying spatial features among and within traffic stations. Third, we utilize channel attention to fuse the multi-scale spatial-temporal dependencies and accurately predict traffic flow.”
      原文链接:
    • NETL
    • NSTL
    • Springer Nature