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    Data on Artificial Intelligence Reported by Eric Lucas and Colleagues (Artificia l intelligence strengthenes cervical cancer screening -present and future)

    59-59页
    查看更多>>摘要:New research on Artificial Intelligenc e is the subject of a report. According to news reporting out of Lyon, France, b y NewsRx editors, research stated, "Cervical cancer is a severe threat to women' s health. The majority of cervical cancer cases occur in developing countries." Our news journalists obtained a quote from the research, "The WHO has proposed s creening 70% of women with high-performance tests between 35 and 4 5 years of age by 2030 to accelerate the elimination of cervical cancer. Due to an inadequate health infrastructure and organized screening strategy, most low-and middle-income countries are still far from achieving this goal. As part of t he efforts to increase performance of cervical cancer screening, it is necessary to investigate the most accurate, efficient, and effective methods and strategi es. Artificial intelligence (AI) is rapidly expanding its application in cancer screening and diagnosis and deep learning algorithms have offered human-like int erpretation capabilities on various medical images. AI will soon have a more sig nificant role in improving the implementation of cervical cancer screening, mana gement, and follow-up. This review aims to report the state of AI with respect t o cervical cancer screening."

    Research on Artificial Intelligence Reported by a Researcher at Silesian Univers ity of Technology (Digital Transformation of Grocery In-Store Shopping-Scanners, Artificial Intelligence, Augmented Reality and Beyond: A Review)

    60-60页
    查看更多>>摘要:Researchers detail new data in artific ial intelligence. According to news reporting out of Gliwice, Poland, by NewsRx editors, research stated, "This paper reviews the digital transformation of groc ery shopping, focusing on the technological innovations that have redefined cons umer experiences over the past decades." Funders for this research include Silesian University of Technology. Our news correspondents obtained a quote from the research from Silesian Univers ity of Technology: "By analyzing both academic literature and up-to-date informa tion from websites, the study provides a review of the evolution of grocery shop ping from traditional methods to modern, technology-driven approaches. The revie w categorizes developments into two primary areas: in-store and online grocery s hopping. In-store shopping has progressed from traditional interactions to the i mplementation of selfservice checkouts, handheld scanners, mobile apps, and AI-based solutions, including augmented reality (AR) and facial recognition."

    New Machine Learning Data Have Been Reported by Investigators at Kansas State Un iversity (Machine Learning and Fluorosensing for Estimation of Maize Nitrogen St atus At Early Growth-stages)

    61-62页
    查看更多>>摘要:Current study results on Machine Learn ing have been published. According to news reporting out of Manhattan, Kansas, b y NewsRx editors, research stated, "Potential of mobile fluorescence sensor meas urements have been in focus for quantifying plant nitrogen (N) variability early in the crop growing season. Real time estimation of such N status indicators at field scale would enable precision management of N fertilizers." Our news journalists obtained a quote from the research from Kansas State Univer sity, "In standard practice, linear regression analysis involves the use of seve ral fluorescence channels and indices as predictive variables for estimating pla nt nitrogen content. Considering the multi-collinearity between these predictor variables, the conventional regression analysis (multiple linear regression) oft en fails to deliver a good range of prediction accuracies. Hence, machine learni ng regression techniques are utilized in this study to estimate N status indicat ors i.e., %N, above ground biomass, and N uptake at V6 and V9 growt h stages of maize across three site-years in 2012 and 2013 crop growing seasons. The Multiplex ®3 (FORCE-A) portable active fluorescence system was used to cap ture fluorescence information. Derived indices including four N balance indices (NBI_R, NBI_B, NBI_B, and NBI1), two chlo rophyll indices (CHL and CHL1), and one flavonoid index (FLAV) were used as pred ictors. The independent site data were first utilized in a Support Vector Regres sion (SVR) model to assess the training and test accuracies in estimation of N s tatus indicators considering a comparative analysis between V6 and V9 growth sta ges. The current research also involved assessing how well the machine learning-trained model could be applied to a different dataset and validated its performa nce in a cross-site experimental setting. Subsequently, cross-site comparisons o f nitrogen status estimates were conducted to recommend the selection of machine learning strategies. These strategies include (1) Partial Least Square Regressi on, (2) Support Vector Regression, (3) Gaussian Process Regression, (4) Random F orest Regression, and (5) Artificial Neural Network. The comparative investigati on demonstrated promising accuracy in estimating plant nitrogen content, above-g round biomass, and nitrogen uptake at the V6 stages of maize, with correlation c oefficients in the moderate range (r = 0.72 +/-0.03) and Root Mean Square Error . Superior prediction accuracies were obtained at V9 growth stages than at V6."

    Reports from Kunming University Highlight Recent Findings in Machine Learning (P rediction of the Degradation of Organic Pollutants By Metal-activated Peracetic Acid Using Machine Learning)

    62-62页
    查看更多>>摘要:Investigators discuss new findings in Machine Learning. According to news reporting out of Kunming, People's Republic of China, by NewsRx editors, research stated, "This study utilized machine learn ing (ML) to predict the kinetic constant (k) of pollutant degradation by using m etal-activated peracetic acid (PAA) and evaluated the performance of three diffe rent ML models. The Random Forest (RF) model obtained by parameter optimization had the best prediction performance." Financial support for this research came from Natural Science Foundation of Yunn an Province. Our news journalists obtained a quote from the research from Kunming University, "The study delved into the significance of various characteristics, and three c atalogues (reaction conditions, properties of metal and pollutants) were conside red. The results revealed that the density (rho) and polar surface area (PSA) of pollutants, pH, humic acid (HA), metal ionization energy and atomic radius of m etal (r) exhibited the most pronounced characteristic effects. Among them, rho, PSA and r positively affected k. While HA, pH and ionization energy had negative effects. Based on the results, the metals met ionization energy <710 KJ/mol and r> 0.165 nm (e.g., Ru, Mo, Ti and Tc) we re suggested to be the catalyst to activate PAA. And pH <8 was conducive to the reaction. Furthermore, the metal-activated PAA would be ef ficient to eliminate organic pollutants with the pollutant density > 1.6 g/cm3 and PSA > 180 (e.g., tetracycline, macrocycli c lipids and antibiotics)."

    Investigators at Polytechnic University Milan Describe Findings in Machine Learn ing (Comparative Study of Machine Learning Techniques for the State of Health Es timation of Li-ion Batteries)

    63-64页
    查看更多>>摘要:Current study results on Machine Learn ing have been published. According to news reporting originating from Milan, Ita ly, by NewsRx correspondents, research stated, "Lithium-Ion batteries play a cru cial role in vehicle electrification to meet the goals of reducing fossil fuels. However, they deteriorate over time and it is thus needed to predict their rate of decay since, after a certain threshold, they are not suitable anymore for th eir designed application." Financial support for this research came from European Union (EU).

    Study Findings from Nagoya University Broaden Understanding of Neural Computatio n (Latent Space Bayesian Optimization With Latent Data Augmentation for Enhanced Exploration)

    63-63页
    查看更多>>摘要:Investigators publish new report on ne ural computation. According to news reporting out of Nagoya University by NewsRx editors, research stated, "Latent space Bayesian optimization (LSBO) combines g enerative models, typically variational autoencoders (VAE), with Bayesian optimi zation (BO), to generate de novo objects of interest." The news editors obtained a quote from the research from Nagoya University: "How ever, LSBO faces challenges due to the mismatch between the objectives of BO and VAE, resulting in poor exploration capabilities. In this article, we propose no vel contributions to enhance LSBO efficiency and overcome this challenge. We fir st introduce the concept of latent consistency/inconsistency as a crucial proble m in LSBO, arising from the VAE-BO mismatch. To address this, we propose the lat ent consistent aware-acquisition function (LCA-AF) that leverages consistent poi nts in LSBO. Additionally, we present LCA-VAE, a novel VAE method that creates a latent space with increased consistent points through data augmentation in late nt space and penalization of latent inconsistencies. Combining LCA-VAE and LCA-A F, we develop LCA-LSBO."

    Studies from Colegio de Postgraduados Have Provided New Data on Machine Learning [Leaf Morphometric Analysis of the Mexican Species of forest iera (Oleaceae) Using Machine Learning]

    64-65页
    查看更多>>摘要:Current study results on Machine Learn ing have been published. According to news reporting originating from Texcoco, M exico, by NewsRx correspondents, research stated, "Forestiera includes woody pla nts distributed from the United States to Ecuador, with a wide distribution in M exico. However, basic knowledge about its biology and taxonomy is still limited. " Financial support for this research came from CONAHCYT. Our news editors obtained a quote from the research from Colegio de Postgraduado s, "In the present study, a leaf morphometric analysis of the 14 Mexican Foresti era species, including F. neomexicana, was carried out to determine if leaf char acters allow classifying the species and to evaluate if the inclusion of qualita tive features in statistical analyses improves the fit of used methods. Four qua ntitative and four qualitative variables from herbarium specimens, were evaluate d. A matrix was generated which was submitted to multivariate analysis, includin g Linear Discriminant Analysis and Random Forest. Results revealed that the spec ies are correctly classified, with Random Forest being the method with the best classifying capacity. The most important variables to differentiate the species are the shape of the base, width and margin of the leaves."

    New Data from Tecnalia Research & Innovation Illuminate Findings i n Robotics (An Overactuated Aerial Robot Based On Cooperative Quadrotors Attache d Through Passive Universal Joints: Modeling, Control and 6-dof Trajectory Track ing)

    65-66页
    查看更多>>摘要:Current study results on Robotics have been published. According to news originating from San Sebastian, Spain, by New sRx correspondents, research stated, "This article discusses a novel aerial robo t architecture that overcomes the underactuation of conventional multirotor syst ems without adding dedicated rotor tilting actuators. The proposed system is bas ed on four quadrotors cooperatively carrying a central body to which they are at tached through passive universal joints." Financial support for this research came from ELKARTEK 2022 program of the Basqu e Government, Spain. Our news journalists obtained a quote from the research from Tecnalia Research & Innovation, "While conventional parallel axis multirotors are underactuated, the proposed mechanism makes the system overactuated, enabling independent position and orientation control of the main body. This implies that the payload can be carried in the minimum drag orientation, it enables take-off and landing on incl ined surfaces and it provides thrust-vectoring capabilities to the system, leadi ng to high control authority. A detailed dynamic model is derived making use of Lagrangian formalism and a hierarchical control law based on such model is propo sed to stabilize the system. This control law is designed to ensure good trackin g while minimizing power consumption."

    Findings on Robotics Detailed by Investigators at Shanghai Jiao Tong University (Precise Control of Soft Robots Amidst Uncertain Environmental Contacts and Forc es)

    66-67页
    查看更多>>摘要:Investigators publish new report on Ro botics. According to news reporting originating in Shanghai, People's Republic o f China, by NewsRx journalists, research stated, "Recent studies have reported o n the remarkable ability of bioinspired soft robots to exhibit dexterous and con tact-friendly motions. However, for these robots with deformable bodies, it is e xtremely challenging to achieve precise and robust control when undergoing uncer tain forces and contact in the environment." Financial supporters for this research include National Natural Science Foundati on of China (NSFC), Science & Technology Commission of Shanghai Mu nicipality (STCSM), State Key Laboratory of Mechanical System and Vibration.

    Researchers from Harbin Institute of Technology Describe Findings in Robotics (A Novel Collision Detection Method Based On Current Residuals for Robots Without Joint Torque Sensors: a Case Study On Ur10 Robot)

    67-68页
    查看更多>>摘要:Investigators discuss new findings in Robotics. According to news originating from Harbin, People's Republic of China, by NewsRx correspondents, research stated, "Existing model -based collision det ection methods rely on accurate torque dynamic parameters identified using measu red joint torques. However, for robots lacking joint torque sensors, only joint currents can be measured, and joint torques must be estimated through the linear relationship between joint currents and joint torque constants." Financial supporters for this research include National Key R&D Pro gram of China, National Natural Science Foundation of China (NSFC). Our news journalists obtained a quote from the research from the Harbin Institut e of Technology, "This way can lead to cumulative identification errors in torqu e dynamic parameters, thereby diminishing the performance of model -based collis ion detection algorithms. To tackle this challenge, this article proposes an inn ovative collision detection method based on current residuals, which represent t he disparities between measured joint currents and predicted joint currents comp uted by current dynamic parameters. Then, a dynamic threshold method for current residuals is designed to mitigate the impact of modeling errors at zero -speed direction changes on collision detection performance. Additionally, a suppressio n strategy based on online load identification and compensation is introduced to reduce the interference of noncollision load factors on collision detection sig nals. The proposed method mitigates the accumulation of errors on torque dynamic identification resulting from inaccuracies in joint torque constants, ultimatel y enhancing collision detection performance for robots without joint torque sens ors."