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    Data on Artificial Intelligence Discussed by Researchers at University of Verona (Artificial intelligence in the pre-analytical phase: State-of-the art and future perspectives)

    10-11页
    查看更多>>摘要:Research findings on artificial intelligence are discussed in a new report. According to news originating from Verona, Italy, by NewsRx correspondents, research stated, “The use of artificial intelligence (AI) has become widespread in many areas of science and medicine, including laboratory medicine.” The news reporters obtained a quote from the research from University of Verona: “Although it seems obvious that the analytical and post-analytical phases could be the most important fields of application in laboratory medicine, a kaleidoscope of new opportunities has emerged to extend the benefits of AI to many manual labor-intensive activities belonging to the pre-analytical phase, which are inherently characterized by enhanced vulnerability and higher risk of errors. These potential applications involve increasing the appropriateness of test prescription (with computerized physician order entry or demand management tools), improved specimen collection (using active patient recognition, automated specimen labeling, vein recognition and blood collection assistance, along with automated blood drawing), more efficient sample transportation (facilitated by the use of pneumatic transport systems or drones, and monitored with smart blood tubes or data loggers), systematic evaluation of sample quality (by measuring serum indices, fill volume or for detecting sample clotting), as well as error detection and analysis.”

    New Findings from McGill University Describe Advances in Artificial Intelligence (Who Are the Publics Engaging In Ai?)

    11-12页
    查看更多>>摘要:Researchers detail new data in Artificial Intelligence. According to news reporting out of Montreal, Canada, by NewsRx editors, research stated, “Given the importance of public engagement in governments’ adoption of artificial intelligence systems, artificial intelligence researchers and practitioners spend little time reflecting on who those publics are. Classifying publics affects assumptions and affordances attributed to the publics’ ability to contribute to policy or knowledge production.” Financial support for this research came from Social Sciences and Humanities Research Council of Canada (SSHRC). Our news journalists obtained a quote from the research from McGill University, “Further complicating definitions are the publics’ role in artificial intelligence production and optimization. Our structured analysis of the corpus used a mixed method, where algorithmic generation of search terms allowed us to examine approximately 2500 articles and provided the foundation to conduct an extensive systematic literature review of approximately 100 documents. Results show the multiplicity of ways publics are framed, by examining and revealing the different semantic nuances, affordances, political and expertise lenses, and, finally, a lack of definitions.”

    Reports Outline Machine Translation Study Results from University of Alacant (Non-fluent Synthetic Target-language Data Improve Neural Machine Translation)

    12-12页
    查看更多>>摘要:Investigators publish new report on Machine Translation. According to news reporting originating from Valencia, Spain, by NewsRx correspondents, research stated, “When the amount of parallel sentences available to train a neural machine translation is scarce, a common practice is to generate new synthetic training samples from them. A number of approaches have been proposed to produce synthetic parallel sentences that are similar to those in the parallel data available.” Financial support for this research came from Ministerio de Ciencia e Innovacin. Our news editors obtained a quote from the research from the University of Alacant, “These approaches work under the assumption that non-fluent target-side synthetic training samples can be harmful and may deteriorate translation performance. Even so, in this paper we demonstrate that synthetic training samples with non-fluent target sentences can improve translation performance if they are used in a multilingual machine translation framework as if they were sentences in another language. We conducted experiments on ten low-resource and four high-resource translation tasks and found out that this simple approach consistently improves translation performance as compared to state-of-the-art methods for generating synthetic training samples similar to those found in corpora.”

    Study Results from University of Jinan in the Area of Machine Learning Reported (Ozone Concentration Estimation and Meteorological Impact Quantification In the Beijing-tianjin-hebei Region Based On Machine Learning Models)

    13-14页
    查看更多>>摘要:Fresh data on Machine Learning are presented in a new report. According to news reporting originating from Guangzhou, People’s Republic of China, by NewsRx correspondents, research stated, “Accurate estimation of ozone (O3) concentrations and quantitative meteorological contribution are crucial for effective control of O3 pollution. In recent years, there has been a growing interest in leveraging machine learning for O3 pollution research due to its advantages, such as high accuracy, strong generalization, and ease of use.” Financial supporters for this research include National Natural Science Foundation of China, Guangzhou Municipal Science and Technology Project, Special Fund Project for Science and Technology Innovation Strategy of Guangdong Province, Guangdong Provincial Introduction of Innovative Research and Development Team. Our news editors obtained a quote from the research from the University of Jinan, “In this study, we utilized meteorological parameters obtained from european center for medium-range weather forecasts (EMCWF) Reanalysis v5 data as input and employed five distinct machine learning methods to estimate values of maximum daily 8-hr average (MDA8) O3 concentrations and analyze meteorological contributions. To improve the accuracy and generalization capabilities of the estimation, we employed Grid SearchCV techniques to select optimal parameters and mitigate the risk of overfitting. Additionally, we incorporated meteorological normalization and the SHAP model to quantify the influence of various parameters. Among the models evaluated, the Extreme Gradient Boost model exhibited exceptional performance from 2015 to 2022, yielding determination coefficients of 0.85 and 0.80 for the training and test data sets, respectively. The outcomes of meteorological normalization revealed that meteorological parameters accounted for 87.7% of the impacts in 2018, while emission-related factors constituted 80.8% of the impacts in 2021. Over the period spanning 2015-2022, 2 m temperature emerged as the most influential parameter affecting daily MDA8 O3 concentration, with an average contribution of 9.4 mu g m-3.”

    Findings from Federal University Mato Grosso do Sul Yields New Findings on Machine Learning (Machine Learning Models for Dry Matter and Biomass Estimates On Cattle Grazing Systems)

    14-15页
    查看更多>>摘要:Researchers detail new data in Machine Learning. According to news reporting fromCampo Grande, Brazil, by NewsRx journalists, research stated, “Monitoring pasture conditions contributes to the animals’ decision-making process, avoiding supplementation losses, and improving cattle performance. Environmental parameters and herd characteristics can influence pasture quantity (biomass and dry matter) and controlling these parameters is a challenge nowadays.” Funders for this research include Fundacao de Apoio ao Desenvolvimento do Ensino Ciencia e Tecnologia do Estado de Mato Grosso do Sul (FUNDECT MS), Conselho Nacional de Desenvolvimento Cientifico e Tecnologico (CNPQ), UFMS, Coordenacao de Aperfeicoamento de Pessoal de Nivel Superior (CAPES), Ministry of Agriculture, Livestock and Food Supply (MAPA).

    Shanghai Maritime University Researcher Reveals New Findings on Robotics (Distributed Fixed-Time Formation Tracking Control for the Multi-Agent System and an Application in Wheeled Mobile Robots)

    14-14页
    查看更多>>摘要:Research findings on robotics are discussed in a new report. According to news originating from Shanghai, People’s Republic of China, by NewsRx correspondents, research stated, “This work addresses the issue of multi-agent system (MAS) formation control under external disturbances and a directed communication topology.” Financial supporters for this research include National Natural Science Foundation of China; Natural Science Foundation of Shanghai. The news journalists obtained a quote from the research from Shanghai Maritime University: “Firstly, a new disturbance observer is proposed to effectively reconstruct and compensate for external disturbances within a short period of time. Then, the integral terminal sliding mode technology is introduced to devise a novel distributed formation control protocol, ultimately realizing the stability of the MAS within a fixed time. Moreover, by means of rigorous Lyapunov theory analyses, a faster formation convergence rate and more accurate consensus accuracies are achieved in the proposed fixed-time strategy with variable exponent form.”

    University of California Reports Findings in Machine Learning (Highly Accurate Prediction of NMR Chemical Shifts from Low- Level Quantum Mechanics Calculations Using Machine Learning)

    16-16页
    查看更多>>摘要:New research on Machine Learning is the subject of a report. According to news reporting out of Berkeley, California, by NewsRx editors, research stated, “Theoretical predictions of NMR chemical shifts from first-principles can greatly facilitate experimental interpretation and structure identification of molecules in gas, solution, and solid-state phases. However, accurate prediction of chemical shifts using the gold-standard coupled cluster with singles, doubles, and perturbative triple excitations [CCSD(T)] method with a complete basis set (CBS) can be prohibitively expensive.” Our news journalists obtained a quote from the research from the University of California, “By contrast, machine learning (ML) methods offer inexpensive alternatives for chemical shift predictions but are hampered by generalization to molecules outside the original training set. Here, we propose several new ideas in ML of the chemical shift prediction for H, C, N, and O that first introduce a novel feature representation, based on the atomic chemical shielding tensors within a molecular environment using an inexpensive quantum mechanics (QM) method, and train it to predict NMR chemical shieldings of a high-level composite theory that approaches the accuracy of CCSD(T)/CBS. In addition, we train the ML model through a new progressive active learning workflow that reduces the total number of expensive high-level composite calculations required while allowing the model to continuously improve on unseen data. Furthermore, the algorithm provides an error estimation, signaling potential unreliability in predictions if the error is large. Finally, we introduce a novel approach to keep the rotational invariance of the features using tensor environment vectors (TEVs) that yields a ML model with the highest accuracy compared to a similar model using data augmentation.”

    New Findings from Ohio State University in the Area of Machine Learning Reported (Machine Learning Reveals That Climate, Geography, and Cultural Drift All Predict Bird Song Variation In Coastal zonotrichia Leucophrys)

    17-18页
    查看更多>>摘要:Fresh data on Machine Learning are presented in a new report. According to news originating from Columbus, Ohio, by NewsRx correspondents, research stated, “Previous work has demonstrated that there is extensive variation in the songs of White-crowned Sparrow (Zonotrichia leucophrys) throughout the species range, including between neighboring (and genetically distinct) subspecies Z. l. nuttalli and Z. l. pugetensis. Using a machine learning approach to bioacoustic analysis, we demonstrate that variation in song is correlated with year of recording (representing cultural drift), geographic distance, and climatic differences, but the response is subspecies- and season-specific.” Financial supporters for this research include National Science Foundation (NSF), Undergraduate Diversity at Evolution program.

    Reports on Robotics and Automation Findings from Technical University of Denmark (DTU) Provide New Insights (Zoom In On the Plant: Fine-grained Analysis of Leaf, Stem, and Vein Instances)

    17-17页
    查看更多>>摘要:Researchers detail new data in Robotics - Robotics and Automation. According to news reporting originating from Lyngby, Denmark, by NewsRx correspondents, research stated, “Robot perception is far from what humans are capable of. Humans do not only have a complex semantic scene understanding but also extract fine-grained intra-object properties for the salient ones.” Financial support for this research came from European Commission and European GNSS Agency. Our news editors obtained a quote from the research from the Technical University of Denmark (DTU), “When humans look at plants, they naturally perceive the plant architecture with its individual leaves and branching system. In this work, we want to advance the granularity in plant understanding for agricultural precision robots. We develop a model to extract fine-grained phenotypic information, such as leaf-, stem-, and vein instances. The underlying dataset RumexLeaves is made publicly available and is the first of its kind with keypoint-guided polyline annotations leading along the line from the lowest stem point along the leaf basal to the leaf apex. Furthermore, we introduce an adapted metric POKS complying with the concept of keypoint-guided polylines.”

    Affiliated Cancer Hospital and Institute of Guangzhou Medical University Reports Findings in Cervical Cancer (Machine Learning- Based Multiparametric Magnetic Resonance Imaging Radiomics Model for Preoperative Predicting the Deep Stromal ...)

    18-19页
    查看更多>>摘要:New research on Oncology - Cervical Cancer is the subject of a report. According to news reporting out of Guangzhou, People’s Republic of China, by NewsRx editors, research stated, “Deep stromal invasion is an important pathological factor associated with the treatments and prognosis of cervical cancer patients. Accurate determination of deep stromal invasion before radical hysterectomy (RH) is of great value for early clinical treatment decision-making and improving the prognosis of these patients.” Financial support for this research came from National Natural Science Foundation of China. Our news journalists obtained a quote from the research from the Affiliated Cancer Hospital and Institute of Guangzhou Medical University, “Machine learning is gradually applied in the construction of clinical models to improve the accuracy of clinical diagnosis or prediction, but whether machine learning can improve the preoperative diagnosis accuracy of deep stromal invasion in patients with cervical cancer was still unclear. This cross-sectional study was to construct three preoperative diagnostic models for deep stromal invasion in patients with early cervical cancer based on clinical, radiomics, and clinical combined radiomics data using the machine learning method. We enrolled 229 patients with early cervical cancer receiving RH combined with pelvic lymph node dissection (PLND). The least absolute shrinkage and selection operator (LASSO) and the fivefold cross-validation were applied to screen out radiomics features. Univariate and multivariate logistic regression analyses were applied to identify clinical predictors. All subjects were divided into the training set (n = 160) and testing set (n = 69) at a ratio of 7:3. Three light gradient boosting machine (LightGBM) models were constructed in the training set and verified in the testing set. The radiomics features were statistically different between deep stromal invasion <1/3 group and deep stromal invasion 1/3 group. In the training set, the area under the curve (AUC) of the prediction model based on radiomics features was 0.951 (95% confidence interval (CI) 0.922-0.980), the AUC of the prediction model based on clinical predictors was 0.769 (95% CI 0.703-0.835), and the AUC of the prediction model based on radiomics features and clinical predictors was 0.969 (95% CI 0.947-0.990). The AUC of the prediction model based on radiomics features and clinical predictors was 0.914 (95% CI 0.848-0.980) in the testing set.”