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    University of Montreal Hospital Center Reports Findings in Robotics (Analyzing the influence of expanding multispecialty adoption of robotic surgery on robotic urologic care: A decade-long assessment of two Canadian academic hospitals)

    86-87页
    查看更多>>摘要：By a News Reporter-Staff News Editor at Robotics & Machine Learning Daily News Daily News – New research on Robotics is the subject of a report. According to news originating from Montreal, Canada, by NewsRx correspondents, research stated, “Most robot-assisted surgery (RAS) systems in Canada are donor-funded, with constraints on implementation and access due to significant costs, among other factors. Herein, we evaluated the impact of the growing multispecialty use of RAS on urologic RAS access and outcomes in the past decade.” Our news journalists obtained a quote from the research from the University of Montreal Hospital Center, “We conducted a retrospective review of all RAS performed by different surgical specialties in two high-volume academic hospitals between 2010 and 2019 (prior to the COVID pandemic). The assessed outcomes included the effect of increased robot access over the years on annual robotic-assisted radical prostatectomy (RARP) volumes, surgical waiting times (SWT), and pathologically positive surgical margins (PSM). Data were collected and analyzed from the robotic system and hospital databases. In total, six specialties (urology, gynecology, general, cardiac, thoracic, and otorhinolaryngologic surgery) were included over the study period. RAS access by specialty doubled since 2010 (from three to six). The number of active robotic surgeons tripled from seven surgeons in 2010 to 20 surgeons in 2019. Moreover, there was a significant drop in average case volume, from a peak of 40 cases in 2014 to 25 cases in 2019 (p=0.02). RARP annual case volume followed a similar pattern, reaching a maximum of 166 cases in 2014, then declining to 137 cases in 2019. The mean SWT was substantially increased from 52 days in 2014 to 73 days in 2019; however, PSM rates were not affected by the reduction in surgical volumes (p <0.05). Over the last decade, RAS access by specialty has increased at two Canadian academic centers due to growing multispecialty use. As there was a fixed, single-robotic system at each of the hospital centers, there was a substantial reduction in the number of RAS performed per surgeon over time, as well as a gradual increase in the SWT.”
      原文链接:
    • NETL
    • NSTL

    School of Artificial Intelligence Reports Findings in Mathematics (A Robust TabNet-Based Multi-Classification Algorithm for Infrared Spectral Data of Chinese Herbal Medicine with High-Dimensional Small Samples)

    87-87页
    查看更多>>摘要：By a News Reporter-Staff News Editor at Robotics & Machine Learning Daily News – New research on Mathematics is the subject of a report. According to news reporting originating from Wenzhou, People’s Republic of China, by NewsRx correspondents, research stated, “Robust classifi- cation algorithms for high-dimensional, small-sample datasets are valuable in practical applications. Faced with the infrared spectroscopic dataset with 568 samples and 3448 wavelengths (features) to identify the origins of Chinese medicinal materials, this paper proposed a novel embedded multiclassification algorithm, ITabNet, derived from the framework of TabNet.” Our news editors obtained a quote from the research from the School of Artificial Intelligence, “Firstly, a refined data pre-processing (DP) mechanism was designed to efficiently find the best adaptive one among 50 DP methods with the help of Support Vector Machine (SVM). Following this, an innovative focal loss function was designed and joined with a cross-validation experiment strategy to mitigate the impact of sample imbalance on algorithm. Detailed investigations on ITabNet were conducted, including comparisons of ITabNet with SVM for the conditions of DP and Non-DP, GPU and CPU computer settings, as well as ITabNet against XGBT (Extreme Gradient Boosting). The numerical results demonstrate that ITabNet can significantly improve the effectiveness of prediction. The best accuracy score is 1.0000, and the best Area Under the Curve (AUC) score is 1.0000. Suggestions on how to use models effectively were given. Furthermore, ITabNet shows the potential to apply the analysis of medicinal efficacy and chemical composition of medicinal materials.”
      原文链接:
    • NETL
    • NSTL

    National Institute of the Republic of Serbia Reports Findings in Science (A comparative study of the predictive performance of different descriptor calculation tools: Molecular-based elution order modeling and interpretation of retention ...)

    88-88页
    查看更多>>摘要：By a News Reporter-Staff News Editor at Robotics & Machine Learning Daily News Daily News – New research on Science is the subject of a report. According to news originating from Belgrade, Serbia, by NewsRx correspondents, research stated, “In the pharmaceutical industry, the need for analytical standards is a bottleneck for comprehensive evaluation and quality control of intermediate and end products. These are complex mixtures containing structurally related molecules.” Our news journalists obtained a quote from the research from the National Institute of the Republic of Serbia, “In this regard, chromatographic peak annotation, especially for critical pairs of isomers and closest structural analogs, can be supported by using a Quantitative Structure Retention Relationship (QSRR) approach. In our study, we investigated the fundamental basis of the reversed-phase (RP) retention mech- anism for 1141 isomeric compounds from the METLIN SMRT dataset. Nine different descriptor calculation tools combined with different feature selection methods (genetic algorithm (GA), stepwise, Boruta) and machine learning (ML) approaches (support vector machine (SVM), multiple linear regression (MLR), ran- dom forest (RF), XGBoost) were applied to provide a reliable molecular structure-based interpretation of RP retention behaviour of the isomeric compounds. Strict internal and external validation metrics were used to select models with the best predictive capabilities (r >0.73, order of elution >60 %). For the developed models, mean absolute errors were in the range of 60 to 110 s. Stepwise and GA showed the most suitable performance as descriptor selection methods, while SVM and XGBoost modeling gave satisfactory predictive characteristics in most cases. Validation performed on the published experimental data for structurally related pharmaceutical compounds confirmed the best accuracy of MLR modeling in combination with GA feature selection of general physico-chemical properties.”
      原文链接:
    • NETL
    • NSTL

    New Robotics Data Have Been Reported by Researchers at Beijing Jiaotong University (Multi-robot Path Planning Using Learningbased Artificial Bee Colony Algorithm)

    89-89页
    查看更多>>摘要：By a News Reporter-Staff News Editor at Robotics & Machine Learning Daily News – Researchers detail new data in Robotics. According to news reporting out of Beijing, People’s Republic of China, by NewsRx editors, research stated, “Multi-robot path planning (MRPP) in continuous and known environment is studied in this paper via proposing a novel local path planning approach. To plan optimal collision-free paths for multiple robots simultaneously, a novel implementation method suitable for the meta-heuristic algorithms is devised, and an improved artificial bee colony (ABC) algorithm is developed.” Financial supporters for this research include Fundamental Research Funds for the Central Universities, National Natural Science Foundation of China (NSFC), China Scholarship Council. Our news journalists obtained a quote from the research from Beijing Jiaotong University, “Three en- hancements to the ABC algorithm are made in this context. Firstly, to better lead the search direction, the global best individual is involved in the search equations of employed bee phase and scout bee phase. Meanwhile, to boost exploitation capability, the learning method of teaching-learning based optimization (TLBO) algorithm is incorporated into the onlooker bee phase. The proposed learning-based ABC (ABCL) algorithm is used to determine the subsequent positions for all the robots based on their current coordi- nates considering the path length, safety and planning efficiency. The experimental studies on benchmark functions show that ABCL is outstanding in solving different types of optimization problems compared against seven effective meta-heuristic algorithms. More importantly, MRPP simulation results prove that ABCL outperforms its competitors in terms of generating optimal collision-free paths and running time. Compared with the original ABC, ABCL improves these two aspects on average for all tasks by 2.1% and 12.6%, respectively.”
      原文链接:
    • NETL
    • NSTL
    • Elsevier

    Research on Machine Learning Discussed by a Researcher at South China University of Technology (Utilizing Machine Learning Models with Molecular Fingerprints and Chemical Structures to Predict the Sulfate Radical Rate Constants of Water ...)

    90-91页
    查看更多>>摘要：By a News Reporter-Staff News Editor at Robotics & Machine Learning Daily News Daily News – Data detailed on artificial intelligence have been presented. According to news reporting out of Guangzhou, People’s Republic of China, by NewsRx editors, research stated, “Sulfate radicals are increasingly recognized for their potent oxidative capabilities, making them highly effective in degrading persistent organic pollutants (POPs) in aqueous environments. These radicals excel in breaking down complex organic molecules that are resistant to traditional treatment methods, addressing the challenges posed by POPs known for their persistence, bioaccumulation, and potential health impacts.” Financial supporters for this research include National Science Fund of China For Young Scholars; China Postdoctoral Science Foundation; Guangzhou Basic And Applied Basic Research Foundation. The news editors obtained a quote from the research from South China University of Technology: “The complexity of predicting interactions between sulfate radicals and diverse organic contaminants is a notable challenge in advancing water treatment technologies. This study bridges this gap by employing a range of machine learning (ML) models, including random forest (DF), decision tree (DT), support vector machine (SVM), XGBoost (XGB), gradient boosting (GB), and Bayesian ridge regression (BR) models. Predicting performances were evaluated using R2, RMSE, and MAE, with the residual plots presented. Performances varied in their ability to manage complex relationships and large datasets. The SVM model demonstrated the best predictive performance when utilizing the Morgan fingerprint as descriptors, achieving the highest R2 and the lowest MAE value in the test set. The GB model displayed optimal performance when chemical descriptors were utilized as features.”
      原文链接:
    • NETL
    • NSTL
    • Mdpi

    New Artificial Intelligence Data Has Been Reported by a Researcher at Yulin Normal University (A Study of the Assistive Nature of Artificial Intelligence Technology for Japanese Translation and Interpretation)

    90-90页
    查看更多>>摘要：By a News Reporter-Staff News Editor at Robotics & Machine Learning Daily News Daily News – New study results on artificial intelligence have been published. According to news reporting out of Guangxi, People’s Republic of China, by NewsRx editors, research stated, “Traditional Japanese translation methods have certain disadvantages, and the introduction of artificial intelligence technology into them can enhance the effect of Japanese interpretation and translation.” The news reporters obtained a quote from the research from Yulin Normal University: “In this paper, the Japanese language data of Twitter and Facebook are used as the basis to construct a Japanese language interpretation and translation corpus, and the GPT-2 model is constructed on the basis of Transformer for Japanese text translation. To optimize the Seq2Seq model for Japanese speech interpretation, the Attention mechanism is introduced to establish a Japanese speech translation model. A Japanese oral and written corpus was used to analyze the validity of the methods mentioned above. The results show that the class/form ratio in the Japanese oral/translated corpus fluctuates between [0.1231, 0.1448], but the survival rate of borrowed words under the scientific category reaches the highest of 54.14%, and the average number of occurrences of each word is between [4.35, 4.95].” According to the news editors, the research concluded: “Japanese verbal and translated texts had an average sentence length of 40 hours, and their translation accuracy was approximately 74.16%. The quality of translation can be effectively improved, and cultural exchange between China and Japan can be enhanced by integrating AI technology with Japanese interpretation and translation.”
      原文链接:
    • NETL
    • NSTL

    New Machine Learning Findings from Hunan University of Science and Engineering Outlined (Exploring the Resilience of Supplementary Cementitious Materials-based Concrete To Elevated Temperatures Via Modern Computing Techniques)

    91-92页
    查看更多>>摘要：By a News Reporter-Staff News Editor at Robotics & Machine Learning Daily News Daily News – Data detailed on Machine Learning have been presented. According to news reporting originating in Yongzhou, People’s Republic of China, by NewsRx journalists, research stated, “Researchers are focused on the production of sustainable materials in order to reduce the negative environmental impact of conventional concrete. Utilizing industrial by-products as alternative cementitious materials in concrete is an appropriate strategy for promoting sustainability in construction.” Funders for this research include Natural Science Foundation of Hunan Province, Hunan Provincial Trans-portation Technology Project. The news reporters obtained a quote from the research from the Hunan University of Science and Engineering, “This research employed supervised machine learning techniques, including gradient boosting (GB), random forest (RF), and extreme gradient boosting (X-GB), in order to predict the compressive strength of sustainable concrete when exposed to high temperatures. SHapley Additive exPlanation (SHAP) analysis provided input component relevance. The comparison of these methodologies indicated that X- GB performed remarkably well and had a superior R2 value of 0.94 when compared to GB and RF. The results were further supported by a Taylor diagram, which demonstrated that the X-GB model best fitted the data, surpassing both the GB and RF models. This study’s findings demonstrated the potential of machine learning, and more particularly X-GB, for making accurate predictions of compressive strength in high-temperature concrete applications.”
      原文链接:
    • NETL
    • NSTL
    • Elsevier

    Studies from Alan Turing Institute in the Area of Artificial Intelligence Described (Artificial Intelligence In Government: Concepts, Standards, and a Unified Framework)

    92-93页
    查看更多>>摘要：By a News Reporter-Staff News Editor at Robotics & Machine Learning Daily News Daily News – Research findings on Artificial Intelligence are discussed in a new report. According to news reporting originating from London, United Kingdom, by NewsRx correspondents, research stated, “Recent advances in artificial intelligence (AI), especially in generative language modelling, hold the promise of transforming government. Given the advanced capabilities of new AI systems, it is critical that these are embedded using standard operational procedures, clear epistemic criteria, and behave in alignment with the normative expectations of society.” Financial supporters for this research include Towards Turing 2.0 under the EPSRC, Alan Turing Insti- tute.Our news editors obtained a quote from the research from Alan Turing Institute, “Scholars in multiple domains have subsequently begun to conceptualize the different forms that AI applications may take, highlighting both their potential benefits and pitfalls. However, the literature remains fragmented, with researchers in social science disciplines like public administration and political science, and the fast-moving fields of AI, ML, and robotics, all developing concepts in relative isolation. Although there are calls to formalize the emerging study of AI in government, a balanced account that captures the full depth of theoretical perspectives needed to understand the consequences of embedding AI into a public sector context is lacking. Here, we unify efforts across social and technical disciplines by first conducting an integrative literature review to identify and cluster 69 key terms that frequently co-occur in the multidisciplinary study of AI. We then build on the results of this bibliometric analysis to propose three new multifaceted concepts for understanding and analysing AI-based systems for government (AI-GOV) in a more unified way: (1) operational fitness, (2) epistemic alignment, and (3) normative divergence.”
      原文链接:
    • NETL
    • NSTL
    • Elsevier

    Data on Machine Learning Reported by Researchers at California State University (Machine learning algorithms applied to wildfire data in California's central valley)

    93-94页
    查看更多>>摘要：By a News Reporter-Staff News Editor at Robotics & Machine Learning Daily News – Researchers detail new data in artificial intelligence. According to news reporting originating from Fresno, United States, by NewsRx correspondents, research stated, “This study focuses on using Machine Learning methods to predict wildfires within California’s Central Valley. The specific areas within the Central Valley were Yosemite Valley, Sequoias, and Kings Canyon since these areas can be considered wildfire hotspots. This topic is relevant since California has seen an increase in wildfires with an increase in annual forest burned areas to +172 % from 1996 to 2021 (ABC 2024).” The news reporters obtained a quote from the research from California State University: “The algo- rithms selected were based on previous research that conducted similar studies. From this research it is hypothesized that the best performing algorithm for predicting wildfires would be Random Forest. The novelty in this study stems from focusing on the specific areas mentioned above, which is where many wildfires have occurred throughout the years. The overall goal is to determine the best machine learning algorithm to predict wildfires in the Central Valley and take the results to improve upon wildfire prevention within these regions. The methods implemented included Decision Trees, Random Forest, Naive Bayes, and Neural Networks. The dataset was gathered from the following satellite data which include MERRA-2 and USGS Landsat 8 along with fire history from 2012 to 2023 within these regions. Utilizing the dataset in the following two variations were a random split and a chronological split of training and testing sets. The best-performing algorithm using this dataset was Decision Trees at 550 maximum splits with an F1-Score of 0.689. The F1-Score ranges between 0 and 1 with a score of 0.7 or higher being deemed a good model to be used for predictions. The conclusion that could be determined from this result is that the randomized data has better predicting power over a chronologically split dataset. This can be seen in the confusion matrices for the chronological split dataset having zero true positive values in all the methods except for Naive Bayes. Overall, the results show that Decision trees with a larger maximum split in the leaf nodes result in a more accurate prediction of whether a fire will occur within the given regions.”
      原文链接:
    • NETL
    • NSTL
    • Elsevier

    Recent Findings from Michigan Technological University Has Provided New Information about Machine Learning (Machine Learning Approaches for Identification of Heat Release Shapes In a Low Temperature Combustion Engine for Control Applications)

    94-95页
    查看更多>>摘要：By a News Reporter-Staff News Editor at Robotics & Machine Learning Daily News Daily News – New research on Machine Learning is the subject of a report. According to news reporting originating from Houghton, Michigan, by NewsRx correspondents, research stated, “This paper presents the application of machine learning classification algorithms to identify and classify different heat release rate (HRR) shapes to control the combustion for an optimal multi -mode low -temperature combustion (LTC) engine operation. Low -temperature combustion engine produces low nitrogen oxides (NOx) and soot emissions and offers high thermal efficiency.” Funders for this research include National Science Foundation (NSF), U.S. Department of state, Bureau of Educational and Cultural Affairs, Fulbright Program.Our news editors obtained a quote from the research from Michigan Technological University, “But high in -cylinder pressure rise rates limit the operating range of the LTC engine. Therefore, it is imperative to control combustion in the LTC engine for safe operation. To this end, the HRR traces for over six hun- dred engine operating conditions are classified using supervised (i.e., Decision Tree, K -Nearest Neighbors (KNN), and Support Vector Machines (SVM)) and unsupervised (i.e., Kmeans clustering) machine learning approaches to segregate different combustion regimes based on HRR shape. Kmeans clustering was not successful in classifying the HRR shapes. Among different supervised machine learning techniques, SVM has proved to be the best method, having an overall classifier prediction accuracy of 92.4% for identifying the distinct shapes using normalized HRR data. In addition, three classifiers have been trained based on the combustion parameters and control inputs. These classifiers are then used as scheduling variables to develop predictive models. A model predictive control (MPC) framework is developed to control multi -mode LTC engine on cycle -to -cycle basis.”
      原文链接:
    • NETL
    • NSTL
    • Elsevier