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    Data from Beihang University Provide New Insights into Machine Learning (Pore-in duced Fatigue Failure: a Prior Progressive Fatigue Life Prediction Framework of Laser-directed Energy Deposition Ti-6al-4v Based On Machine Learning)

    49-49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 originating from Beijing, People's Republic o f China, by NewsRx correspondents, research stated, "Pores are major cause of fa tigue failure in laser-directed energy deposition (L-DED) titanium alloy. For th e safe application of L-DED titanium alloys, it is essential to establish a fati gue life prediction method based on poreinduced fatigue." Funders for this research include Special Research Project of Chinese Civil Airc raft, Aero- nautical Science Foundation of China. Our news journalists obtained a quote from the research from Beihang University, "This paper proposes a prior progressive fatigue life prediction framework base d on ridge classification and kernel ridge regression algorithms. The fatigue li fe prediction was carried out on L-DED Ti-6Al-4V alloy in three steps: critical pore identification, fine granular area existence prediction and final fatigue l ife prediction. The fatigue life prediction method adopted in the current study outperform the others with a correlation coefficient as high as 0.951, followed by a comparison with the results derived from different machine learning algorit hms. The results show that the proposed fatigue life prediction framework can pr edict the fatigue life of L-DED Ti-6Al-4V alloy based on computed tomography tes ts and microstructure features." According to the news editors, the research concluded: "Due to its strong genera lization ability and effectiveness, the proposed prediction method is expected t o be valuable for fatigueresistant design of L-DED Ti-6Al-4V alloy." This research has been peer-reviewed.
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    Chiang Mai University Reports Findings in Robotics (Accuracy of implant placemen t with computer-aided static, dynamic, and robotassisted surgery: a systematic review and meta-analysis of clinical trials)

    50-50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 subjec t of a report. According to news reporting out of Chiang Mai, Thailand, by NewsR x editors, research stated, "This systematic review explores the accuracy of com puterized guided implant placement including computer-aided static, dynamic, and robotassisted surgery. An electronic search up to February 28, 2023, was condu cted using the PubMed, Embase, and Scopus databases using the search terms ‘surg ery', ‘computer-assisted', ‘dynamic computer-assisted', ‘robotic surgical proced ures', and ‘dental implants'." Our news journalists obtained a quote from the research from Chiang Mai Universi ty, "The outcome variables were discrepancies including the implant's 3D-coronal , -apical and -angular deviations. Articles were selectively retrieved according to the inclusion and exclusion criteria, and the data were quantitatively meta- analysed to verify the study outcomes. Sixty-seven articles were finally identif ied and included for analysis. The accuracy comparison revealed an overall mean deviation at the entry point of 1.11 mm (95 % CI: 1.02-1.19), and 1 .40 mm (95% CI: 1.31-1.49) at the apex, and the angulation was 3.5 1 (95% CI: 3.27-3.75). Amongst computerized guided implant placeme nts, the robotic system tended to show the lowest deviation (0.81 mm in coronal deviation, 0.77 mm in apical deviation, and 1.71 in angular deviation). No signi ficant differences were found between the arch type and flap operation in cases of dynamic navigation. The fully-guided protocol demonstrated a significantly hi gher level of accuracy compared to the pilot-guided protocol, but did not show a ny significant difference when compared to the partially guided protocol. The us e of computerized technology clinically affirms that operators can accurately pl ace implants in three directions."
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    Study Findings on Machine Learning Discussed by a Researcher at Tongji Universit y (Assessment of Factors Affecting Pavement Rutting in Pakistan Using Finite Ele ment Method and Machine Learning Models)

    51-51页
    查看更多>>摘要：By a News Reporter-Staff News Editor at Robotics & Machine Learning Daily News Daily News-Fresh data on artificial intelligence are presented in a new report. According to news reporting out of Shanghai, Peop le's Republic of China, by NewsRx editors, research stated, "This study research es environmental factors, vehicle dynamics, and loading conditions on pavement s tructures, aiming to comprehend and predict their impact." Our news journalists obtained a quote from the research from Tongji University: "The susceptibility of asphalt pavement to temperature variations, vehicle speed , and loading cycles is explored, with a particular focus on the lateral distrib ution of wheel tracks in driving and passing lanes. Utilizing video analysis and finite element modelling (FEM) through ABAQUS 2022 software, multiple input fac tors, such as speed (60, 80 and 100 km/h), loading cycles (100,000 to 500,000), and temperature range (0 ℃ to 50 ℃), are applied to observe the maximum ruttin g (17.89 mm to 24.7 mm). It is observed that the rut depth exhibited is directly proportional to the loading cycles and temperature, but the opposite is true in the case of vehicle speed. Moreover, interpretable machine learning models, par ticularly the Bayesian-optimized light gradient boosting machine (LGBM) model, d emonstrate superior predictive performance in rut depth. Insights from SHAP inte rpretation highlight the significant roles of temperature and loading frequency in pavement deformation."
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    New Findings from Hindustan Institute of Technology and Science Update Understan ding of Machine Learning (Efficient Retinal Detachment Classification Using Hybr id Machine Learning With Levy Flight-based Optimization)

    52-52页
    查看更多>>摘要：By a News Reporter-Staff News Editor at Robotics & Machine Learning Daily News Daily News-A new study on Machine Learning is now available. According to news reporting out of Tamil Nadu, India, by NewsRx edit ors, research stated, "Decreased vision acuity and blindness due to retinal deta chment (RD) are significant concerns, emphasizing the importance of early diagno sis and identification. However, manual screening of RD is labor-intensive and t imeconsuming and faces challenges such as poor quality and low accuracy." Our news journalists obtained a quote from the research from the Hindustan Insti tute of Technology and Science, "A novel hybrid machine learning(ML) algorithm i ncorporating Levy flight-based atom search optimization (LFB-ASO) is proposed to solve the above challenges. The dataset utilized for the experiment is the Reti nal Fundus Multi-disease Image dataset (RFMiD). The data preprocessing pipeline involves image resizing, normalization, data augmentation, masking and segmentat ion. To ensure consistent dimensions, all retinal images are standardized throug h resizing. Performance and convergence are improved using normalization. The da ta augmentation technique enhances diversity, while segmentation focuses on the region of interest (ROI). Then the deep features are extracted from the preproce ssed retinal images using a pre-trained ResNet18 model. LFB-ASO is employed to s elect the most discriminative deep features for RD classification. To achieve su perior accuracy, hybrid ML algorithms, namely Support Vector Machine (SVM), Grad ient Boosting Machine (GBM) and Random Forest (RF) are employed. The proposed mo del achieves remarkable results with accuracy, recall, F1 score and precision of 98.75%, 96.70%, 97.01% and 97.62% ." According to the news editors, the research concluded: "These results outperform existing methods such as HOS-LSDA, LR, NB, PCA and RD-Light-Net." This research has been peer-reviewed.
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    University of Manchester Reports Findings in Machine Learning (An Unsupervised M achine Learning Approach for the Automatic Construction of Local Chemical Descri ptors)

    53-54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 th e subject of a report. According to news reporting originating in Manchester, Un ited Kingdom, by NewsRx journalists, research stated, "Condensing the many physi cal variables defining a chemical system into a fixed-size array poses a signifi cant challenge in the development of chemical Machine Learning (ML). Atom Center ed Symmetry Functions (ACSFs) offer an intuitive featurization approach by means of a tedious and labor-intensive selection of tunable parameters." The news reporters obtained a quote from the research from the University of Man chester, "In this work, we implement an unsupervised ML strategy relying on a Ga ussian Mixture Model (GMM) to automatically optimize the ACSF parameters. GMMs e ffortlessly decompose the vastness of the chemical and conformational spaces int o well-defined radial and angular clusters, which are then used to build tailormade ACSFs. The unsupervised exploration of the space has demonstrated general a pplicability across a diverse range of systems, spanning from various unimolecul ar landscapes to heterogeneous databases. The impact of the sampling technique a nd temperature on space exploration is also addressed, highlighting the particul arly advantageous role of high-temperature Molecular Dynamics (MD) simulations. The reliability of the resulting features is assessed through the estimation of the atomic charges of a prototypical capped amino acid and a heterogeneous colle ction of CHON molecules. The automatically constructed ACSFs serve as high-quali ty descriptors, consistently yielding typical prediction errors below 0.010 elec trons bound for the reported atomic charges. Altering the spatial distribution o f the functions with respect to the cluster highlights the critical role of symm etry rupture in achieving significantly improved features. More specifically, us ing two separate functions to describe the lower and upper tails of the cluster results in the best performing models with errors as low as 0.006 electrons. Fin ally, the effectiveness of finely tuned features was checked across different ar chitectures, unveiling the superior performance of Gaussian Process (GP) models over Feed Forward Neural Networks (FFNNs), particularly in low-data regimes, wit h nearly a 2-fold increase in prediction quality. Altogether, this approach pave s the way toward an easier construction of local chemical descriptors, while pro viding valuable insights into how radial and angular spaces should be mapped."
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    New Robotics Data Have Been Reported by Researchers at Zhejiang University (Biof ouling Recognition and Boundary Tracking Control for Underwater Cleaning Robots)

    54-55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 subjec t of a report. According to news reporting originating from Hangzhou, People's R epublic of China, by NewsRx correspondents, research stated, "Underwater climbin g robots have shown outstanding potential for cleaning underwater structures. Im provements in robotic intelligence can further facilitate their development in e ngineering applications."Financial supporters for this research include National Natural Science Foundati on of China (NSFC), Key Research and Development Program of Zhejiang Province, S cience and Technology Innovation 2025 Major Project of Ningbo.
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    Reports from Hohai University Describe Recent Advances in Robotics (GestureMoRo: an algorithm for autonomous mobile robot teleoperation based on gesture recogni tion)

    54-54页
    查看更多>>摘要：By a News Reporter-Staff News Editor at Robotics & Machine Learning Daily News-A new study on robotics is now available. Accordi ng to news reporting out of Hohai University by NewsRx editors, research stated, "Gestures are a common way people communicate." Our news editors obtained a quote from the research from Hohai University: "Gest ure-based teleoperation control systems tend to be simple to operate and suitabl e for most people's daily use. This paper employed a LeapMotion sensor to develo p a mobile robot control system based on gesture recognition, which mainly estab lished connections through a client/server structure. The principles of gesture recognition in the system were studied and the relevant self-investigated algori thms-GestureMoRo, for the association between gestures and mobile robots were de signed. Moreover, in order to avoid the unstably fluctuated movement of the mobi le robot caused by palm shaking, the Gaussian filter algorithm was used to smoot h and denoise the collected gesture data, which effectively improved the robustn ess and stability of the mobile robot's locomotion."
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    Fourth Military Medical University Reports Findings in Gastric Cancer (The infla mmation score predicts the prognosis of gastric cancer patients undergoing Da Vi nci robot surgery)

    55-56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 Oncology - Gastric Can cer is the subject of a report. According to news originating from Xi'an, People 's Republic of China, by NewsRx correspondents, research stated, "Neutrophil-to- lymphocyte ratio (NLR), calculated from peripheral blood immune-inflammatory cel l counts, is considered a predictor of survival in various cancers. Nevertheless , there is a lack of research into the predictive value of NLR specifically in g astric cancer patients following surgery using the Da Vinci robot."
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    Xiamen University Reports Findings in Laryngeal Cancer (Creation of a machine le arning-based prognostic prediction model for various subtypes of laryngeal cance r)

    56-57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 Oncology - Laryngeal C ancer is the subject of a report. According to news reporting from Xiamen, Peopl e's Republic of China, by NewsRx journalists, research stated, "Depending on the source of the blastophore, there are various subtypes of laryngeal cancer, each with a unique metastatic risk and prognosis. The forecasting of their prognosis is a pressing issue that needs to be resolved."Financial supporters for this research include Natural Science Foundation of Fuj ian Science and Technology Department, Key Medical and Health Project of Xiamen Science and Technology Bureau.
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    Researchers from Autonomous University Report on Findings in Robotics (Optimal C ontrol and Grasping for a Robotic Hand With a Non-linked Double Tendon Arrangeme nt)

    57-58页
    查看更多>>摘要：By a News Reporter-Staff News Editor at Robotics & Machine Learning Daily News Daily News-Researchers detail new data in Robotic s. According to news reporting originating from Hidalgo, Mexico, by NewsRx corre spondents, research stated, "After comparing different robotic hand projects, a problem is identified: when a finger has a degree of freedom, the hand is unable to grasp irregularly shaped objects. This article proposes a solution." Our news editors obtained a quote from the research from Autonomous University, "The use of a non-linked doubletendon arrangement in the fingers allows them to have free movement; coupled with the use of Inertial Measurement Units to determ ine its position, ensures that, despite having one degree of freedom per finger, the hand can effectively grasp irregular objects. Additionally, a web applicati on is developed to control hand movements through voice commands. Finally, due t o the necessity for these types of devices to be mobile, an optimal control law is used to minimize energy consumption, thereby increasing autonomy when the han d is powered by batteries."
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