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    Peking University First Hospital Reports Findings in Prostate Cancer (Development and Validation of Interpretable Machine Learning Models for Clinically Significant Prostate Cancer Diagnosis in Patients With Lesions of PI-RADS v2.1 Score 3)

    38-39页
    查看更多>>摘要：New research on Oncology - Prostate Cancer is the subject of a report. According to news reporting out of Beijing, People’s Republic of China, by NewsRx editors, research stated, “For patients with PI-RADS v2.1 3, prostate biopsy is strongly recommended. Due to the unsatisfactory positive rate of biopsy, improvements in clinically significant prostate cancer (csPCa) risk assessments are required.” Our news journalists obtained a quote from the research from Peking University First Hospital, “To develop and validate machine learning (ML) models based on clinical and imaging parameters for csPCa detection in patients with PI-RADS v2.1 3. Retrospective. One thousand eighty-three patients with PIRADS v2.1 3, randomly split into training (70%, N = 759) and validation (30%, N = 324) datasets, and 147 patients enrolled prospectively for testing. 3.0 T scanners/T2-weighted fast spin echo sequence and DWI with diffusion-weighted single-shot gradient echo planar imaging sequence. The factors evaluated for csPCa detection were age, prostate specific antigen, prostate volume, and the diameter and location of the index lesion, PI-RADSv2.1. Five ML models for csPCa detection were developed: logistic regression (LR), extreme gradient boosting, random forest (RF), decision tree, and support vector machines. The csPCa was defined as Gleason grade 2. Univariable and multivariable LR analyses to identify parameters associated with csPCa. Area under the receiver operating characteristic curve (AUC), Brier score, and DeLong test were used to assess and compare the csPCa diagnostic performance with the LR model. The significance level was defined as 0.05. The RF model exhibited the highest AUC (0.880-0.904) and lowest Brier score (0.125-0.133) among the ML models in the validation and testing cohorts, however, there was no difference when compared to the LR model (P = 0.453 and 0.548). The sensitivity and negative predictive values in the validation and testing cohorts were 93.8%-97.6% and 82.7%-95.1%, respectively, at a threshold of 0.450 (99% sensitivity of the RF model).”
      原文链接:
    • NETL
    • NSTL
    • Wiley

    Findings from West Virginia University Yields New Data on Machine Learning (Simultaneous Well Spacing and Completion Optimization Using an Automated Machine Learning Approach. a Case Study of the Marcellus Shale Reservoir, Northeastern United ...)

    39-40页
    查看更多>>摘要：Fresh data on Machine Learning are presented in a new report. According to news reporting from Morgantown, West Virginia, by NewsRx journalists, research stated, “Optimizing unconventional field development requires simultaneous optimization of well spacing and completion design. However, the conventional practice of using cross plots and sensitivity analysis via Monte Carlo simulations for independent optimization of well spacing and completion design has proved inadequate for unconventional reservoirs.” The news correspondents obtained a quote from the research from West Virginia University, “This is due to the inability of cross plots to capture non-linear cross-correlations between parameters affecting hydrocarbon production, and the computational expense and difficulty of Monte Carlo simulations. Recently, automated machine learning (AutoML) workflows have been used to tackle complex problems. However, applying AutoML workflows to engineering problems presents unique challenges, as achieving high accuracy in forecasting the physics of the problem is crucial. To address this issue, a new physics-informed AutoML workflow based on the TPOT open-source tool developed that guarantees the physical plausibility of the optimum model while minimizing human bias and uncertainty. The workflow has been implemented in a Marcellus Shale reservoir with over 1500 wells to determine the optimal well spacing and completion design parameters for both the field and each well. The results show that using a shorter stage length and a higher sand-to-water ratio is preferable for this field, as it can increase cumulative gas production by up to 8%.”
      原文链接:
    • NETL
    • NSTL
    • Geological Soc Publ House

    New Machine Learning Study Findings Recently Were Reported by Researchers at Chinese Academy of Sciences (Quantifying and Predicting Air Quality On Different Road Types In Urban Environments Using Mobile Monitoring and Automated Machine Learning)

    40-41页
    查看更多>>摘要：Researchers detail new data in Machine Learning. According to news reporting out of Shenyang, People’s Republic of China, by NewsRx editors, research stated, “Traffic emissions are a primary source of air pollution in urban areas, with air quality being influenced by different types of roads characterized by varying traffic volumes and speeds. Comprehending the distribution of air pollutants and the factors influencing it across different road types holds immense significance in endeavors to enhance air quality within urbanized regions.” Financial support for this research came from National Natural Science Foundation of China (NSFC). Our news journalists obtained a quote from the research from the Chinese Academy of Sciences, “This study recorded concentrations of PM, SO2, NO2, CO, and O3 on different road types in Shenyang, China, using mobile monitoring. The impacts of road type and microclimatic factors on air quality were quantified using automated machine learning. Among the six road types, the suburban highway exhibited the highest PM, SO2, and NO2 pollution. On the other hand, secondary roads experienced the highest levels of CO and O3 pollution. The automated machine learning models provided accurate predictions for PM2.5, PM10, SO2, NO2, and O3 concentrations (R2 = 0.91, 0.83, 0.82, 0.83, 0.79, respectively). Relative humidity played the most significant role in PM2.5 and PM10 concentrations (55.93% and 59.39%, respectively), followed by air temperature (15.36% and 17.73%) and road types (14.28% and 8.74%). Road types contributed 24.33%, 20.60%, 16.61%, and 11.90% to SO2, CO, O3, and NO2 concentrations, respectively.”
      原文链接:
    • NETL
    • NSTL
    • Turkish Natl Committee Air Pollution Res & Control-Tuncap

    Studies from Beijing Institute of Technology Further Understanding of Robotics (Exploring the Attractiveness of Service Robots In the Hospitality Industry: Analysis of Online Reviews)

    41-42页
    查看更多>>摘要：Data detailed on Robotics have been presented. According to news reporting out of Beijing, People’s Republic of China, by NewsRx editors, research stated, “As the hospitality industry has begun adopting service robots to replace frontline human services, service robots’ attractiveness becomes a salient factor in their design and implementation. However, it is unclear what consist of service robots’ attractiveness and how they affect customer responses.” Financial supporters for this research include Foreign High-Talent Subsidy Program-Beijing Municipal Government, National Natural Science Foundation of China (NSFC). 41 Our news journalists obtained a quote from the research from the Beijing Institute of Technology, “This study examines the effects of multiple dimensions of service robots’ attractiveness on customers’ emotions using a text mining approach. For the data analysis, we collected 50,629 online reviews on 59 hotels and restaurants using service robots from the largest social commerce platform in China. Using the Linguistic Inquiry and Word Count (LIWC) method, we analyzed 7570 online reviews that are directly related to service robots. With the LIWC outcomes, the relationships between the attractiveness dimensions and customer emotions were investigated. Based on our findings, finally, we provide propositions for understanding the attractiveness of service robots.”
      原文链接:
    • NETL
    • NSTL
    • Springer Nature

    Findings on Machine Translation Reported by Investigators at National Institute of Technology (A Comprehensive Survey On Various Fully Automatic Machine Translation Evaluation Metrics)

    42-43页
    查看更多>>摘要：Fresh data on Machine Translation are presented in a new report. According to news originating from Himachal Prades, India, by NewsRx correspondents, research stated, “The fast advancement in machine translation models necessitates the development of accurate evaluation metrics that would allow researchers to track the progress in text languages. The evaluation of machine translation models is crucial since its results are exploited for improvements of translation models.” Our news journalists obtained a quote from the research from the National Institute of Technology, “However fully automatically evaluating the machine translation models in itself is a huge challenge for the researchers as human evaluation is very expensive, time-consuming, unreproducible. This paper presents a detailed classification and comprehensive survey on various fully automated evaluation metrics, which are used to assess the performance or quality of machine translated output. Various fully automatic evaluation metrics are classified into five categories that are lexical, character, semantic, syntactic, and semantic & syntactic evaluation metrics for better understanding purpose. Taking account of the challenges posed in the field of machine translation evaluation by Statistical Machine Translation and Neural Machine 42 Translation, along with a discussion on the advantages, disadvantages, and gaps for each fully automatic machine translation evaluation metric has been provided.”
      原文链接:
    • NETL
    • NSTL
    • Springer Nature

    Studies from University of Southern California Keck School of Medicine in the Area of Artificial Intelligence Described (Radiology As a Specialty In the Era of Artificial Intelligence: a Systematic Review and Meta-analysis On Medical Students, ...)

    43-44页
    查看更多>>摘要：Researchers detail new data in Artificial Intelligence. According to news reporting originating from Los Angeles, California, by NewsRx correspondents, research stated, “Artificial intelligence (AI) is changing radiology by automating tasks and assisting in abnormality detection and understanding perceptions of medical students, radiology trainees, and radiologists is vital for preparing them for AI integration in radiology. A systematic review and meta-analysis were conducted following established guidelines.” Our news editors obtained a quote from the research from the University of Southern California Keck School of Medicine, “PubMed, Scopus, and Web of Science were searched up to March 5, 2023. Eligible studies reporting outcomes of interest were included, and relevant data were extracted and analyzed using STATA software version 17.0. A meta-analysis of 21 studies revealed that 22.36% of individuals were less likely to choose radiology as a career due to concerns about advances in AI. Medical students showed higher rates of concern (31.94%) compared to radiology trainees and radiologists (9.16%) (P <.01). Radiology trainees and radiologists also demonstrated higher basic AI knowledge (71.84% vs 35.38%). Medical students had higher rates of belief that AI poses a threat to the radiology job market (42.66% vs 6.25%, P<.02). The pooled rate of respondents who believed that ‘AI will revolutionize radiology in the future’ was 79.48%, with no significant differences based on participants’ positions. The pooled rate of responders who 43 believed in the integration of AI in medical curricula was 81.75% among radiology trainees and radiologists and 70.23% among medical students.”
      原文链接:
    • NETL
    • NSTL
    • Elsevier

    Researcher from University of Kansas Describes Findings in Machine Learning (Optimizing Multidimensional Pooling for Variational Quantum Algorithms)

    44-45页
    查看更多>>摘要：New research on artificial intelligence is the subject of a new report. According to news reporting from Lawrence, Kansas, by NewsRx journalists, research stated, “Convolutional neural networks (CNNs) have proven to be a very efficient class of machine learning (ML) architectures for handling multidimensional data by maintaining data locality, especially in the field of computer vision.” The news journalists obtained a quote from the research from University of Kansas: “Data pooling, a major component of CNNs, plays a crucial role in extracting important features of the input data and downsampling its dimensionality. Multidimensional pooling, however, is not efficiently implemented in existing ML algorithms. In particular, quantum machine learning (QML) algorithms have a tendency to ignore data locality for higher dimensions by representing/flattening multidimensional data as simple one-dimensional data. In this work, we propose using the quantum Haar transform (QHT) and quantum partial measurement for performing generalized pooling operations on multidimensional data. We present the corresponding decoherence-optimized quantum circuits for the proposed techniques along with their theoretical circuit depth analysis. Our experimental work was conducted using multidimensional data, ranging from 1-D audio data to 2-D image data to 3-D hyperspectral data, to demonstrate the scalability of the proposed methods.”
      原文链接:
    • NETL
    • NSTL
    • Mdpi

    Findings from Lanzhou University Broaden Understanding of Machine Learning (Simulations and Prediction of Historical and Future Maximum Freeze Depth In the Northern Hemisphere)

    45-46页
    查看更多>>摘要：A new study on Machine Learning is now available. According to news originating from Lanzhou, People’s Republic of China, by NewsRx correspondents, research stated, “The maximum annual freeze depth (MFD) is a primary indicator of the thermal state of frozen ground, affecting ecosystems, hydrological processes, vegetation growth, infrastructure, and human activities in cold regions. It is thus important to quantify the past, present, and future spatial and temporal variability of MFD at the hemispheric scale.” Funders for this research include National Natural Science Foundation of China (NSFC), Fundamental Research Funds for the Central Universities. Our news journalists obtained a quote from the research from Lanzhou University, “We develop a datadriven MFD simulation method within a machine learning framework, integrating MFD observations from meteorological stations and several environmental predictors, to analyze past and future scenarios in the Northern Hemisphere (NH). Based on ERA5 reanalysis estimates and historical to future CMIP6 scenarios, the NH MFD averaged 133 cm (ERA5) and 131 cm (CMIP6) during 1981-2010, and will vary 81-112 cm during 2015-2100 depending on the emission scenario. During 1950-2013, MFD decreased by 0.37 cm/a (ERA5) versus 0.22 cm/a (CMIP6), and is projected to decrease 0.16-0.69 cm/a by 2100. During 1981-2010, MFD decreased by an average of 19.1% (ERA5) and 13.9% (CMIP6), with a net change of -17 cm (ERA5) and -13 cm (CMIP6). Depending on the emission scenario, MFD will decrease 11% (-12 cm) to 42% (-19 cm) between 2015 and 2099 relative to the 1981-2010. Warming, increased moisture, warmer cold seasons, warmer warm seasons, shallower snow depths, and increased vegetation cover all lead to a reduction in MFD. The results from this novel machine learning approach provide useful insights regarding the fate of future frozen ground changes. Seasonally frozen ground covers approximately half of the exposed ground surface in the Northern Hemisphere and is found in areas of intense human activity. There, the moisture retention and occurrence of freezing and thawing significantly impact agricultural production and infrastructure. Maximum freeze depth is a key indicator of the status of seasonally frozen ground. We simulate and predict the spatial distribution of maximum freeze depth at the hemispheric scale and quantify the variability of maximum freeze depth over past and future periods. Depending on the choice of future emission scenario, average maximum freeze depth in the Northern Hemisphere will decrease by 11% (-12 cm) to 42% (-19 cm) between 2015 and 2099, relative to the base period (1981-2010).”
      原文链接:
    • NETL
    • NSTL
    • Amer Geophysical Union

    Sisli Hamidiye Etfal Education and Research Hospital Reports Findings in Artificial Intelligence [Quantitative evaluation of Saliency-Based Explainable artificial intelligence (XAI) methods in Deep Learning-Based mammogram analysis]

    46-47页
    查看更多>>摘要：New research on Artificial Intelligence is the subject of a report. According to news reporting originating in Istanbul, Turkey, by NewsRx journalists, research stated, “Explainable Artificial Intelligence (XAI) is prominent in the diagnostics of opaque deep learning (DL) models, especially in medical imaging. Saliency methods are commonly used, yet there’s a lack of quantitative evidence regarding their performance.” The news reporters obtained a quote from the research from Sisli Hamidiye Etfal Education and Research Hospital, “To quantitatively evaluate the performance of widely utilized saliency XAI methods in the task of breast cancer detection on mammograms. Three radiologists drew ground-truth boxes on a balanced mammogram dataset of women (n = 1496 cancer-positive and negative scans) from three centers. A modified, pre-trained DL model was employed for breast cancer detection, using MLO and CC images. Saliency XAI methods, including Gradient-weighted Class Activation Mapping (Grad-CAM), Grad-CAM++, and Eigen-CAM, were evaluated. We utilized the Pointing Game to assess these methods, determining if the maximum value of a saliency map aligned with the bounding boxes, representing the ratio of correctly identified lesions among all cancer patients, with a value ranging from 0 to 1. The development sample included 2,244 women (75%), with the remaining 748 women (25%) in the testing set for unbiased XAI evaluation. The model’s recall, precision, accuracy, and F1-Score in identifying cancer in the testing set were 69%, 88%, 80%, and 0.77, respectively. The Pointing Game Scores for Grad-CAM, Grad-CAM++, and Eigen-CAM were 0.41, 0.30, and 0.35 in women with cancer and marginally increased to 0.41, 0.31, and 0.36 when considering only true-positive samples.”
      原文链接:
    • NETL
    • NSTL

    New Robotics Study Findings Recently Were Reported by Researchers at University of Science and Technology China (Optimal Reconfiguration Planning of a 3-dof Point-mass Cable-driven Parallel Robot)

    47-48页
    查看更多>>摘要：Research findings on Robotics are discussed in a new report. According to news reporting from Hefei, People’s Republic of China, by NewsRx journalists, research stated, “Cable-driven parallel robots (CDPRs) have attracted much attention due to their advantages, such as large workspace and excellent load capacity. However, their adaptability to different tasks has been limited because of fixed configurations.” Funders for this research include National Natural Science Foundation of China (NSFC), Natural Science Foundation of Anhui Province, Anhui Science Fund for Distinguished Young Scholars, China Postdoctoral Science Foundation, State Key Laboratory of Robotics and Systems. The news correspondents obtained a quote from the research from the University of Science and Technology China, “To improve this, a novel three-DOF point-mass reconfigurable CDPR (RCDPR) has been 47 designed, and its configuration can be changed by adjusting the positions of multiple cable anchors. Since wrench feasible workspace (WFW) is an essential criterion that describes the configuration characteristics, an optimal reconfiguration planning method is proposed to schedule the sequence and number of all movable cable anchors for adjusting the WFW range. Based on a two-level optimization process, the method can realize static reconfiguration (SR) or dynamic reconfiguration (DR) of the RCDPR. If SR cannot provide the required WFW by finding a static optimal configuration, the WFW range will be dynamically adjusted by DR. Besides, the number of movable cable anchors is minimized in DR by applying L-1-norm optimization to the anchor velocity sequences.”
      原文链接:
    • NETL
    • NSTL