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    New Findings from Anhui University of Science and Technology Describe Advances in Robotics (A trajectory planning method for a casting sorting robotic arm based on a nature-inspired Genghis Khan shark optimized algorithm)

    48-49页
    查看更多>>摘要：By a News Reporter-Staff News Editor at Robotics & Machine Learning Daily News-Investigators publish new report on robotics.Acc ording to news reporting originating from Huainan,People's Republic of China,b y NewsRx correspondents,research stated,"In order to meet the efficiency and s mooth trajectory requirements of the casting sorting robotic arm,we propose a t imeoptimal trajectory planning method that combines a heuristic algorithm inspi red by the behavior of the Genghis Khan shark (GKS) and segmented interpolation polynomials.First,the basic model of the robotic arm was constructed based on the arm parameters,and the workspace is analyzed." The news editors obtained a quote from the research from Anhui University of Sci ence and Technology:"A matrix was formed by combining cubic and quintic polynom ials using a segmented approach to solve for 14 unknown parameters and plan the trajectory.To enhance the smoothness and efficiency of the trajectory in the jo int space,a dynamic nonlinear learning factor was introduced based on the tradi tional Particle Swarm Optimization (PSO) algorithm.Four different biological be haviors,inspired by GKS,were simulated.Within the premise of time optimality,a target function was set to effectively optimize within the feasible space.Si mulation and verification were performed after determining the working tasks of the casting sorting robotic arm.The results demonstrated that the optimized rob otic arm achieved a smooth and continuous trajectory velocity,while also optimi zing the overall runtime within the given constraints." According to the news editors,the research concluded:"A comparison was made be tween the traditional PSO algorithm and an improved PSO algorithm,revealing tha t the improved algorithm exhibited better convergence.Moreover,the planning ap proach based on GKS behavior showed a decreased likelihood of getting trapped in local optima,thereby confirming the effectiveness of the proposed algorithm."
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    University of Toronto Reports Findings in Artificial Intelligence (Accuracy of a n Artificial Intelligence Chatbot's Interpretation of Clinical Ophthalmic Images )

    49-50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 Artificial Intelligenc e is the subject of a report.According to news reporting out of Toronto,Canada,by NewsRx editors,research stated,"Ophthalmology is reliant on effective int erpretation of multimodal imaging to ensure diagnostic accuracy.The new ability of ChatGPT-4 (OpenAI) to interpret ophthalmic images has not yet been explored." Our news journalists obtained a quote from the research from the University of T oronto,"To evaluate the performance of the novel release of an artificial intel ligence chatbot that is capable of processing imaging data.This cross-sectional study used a publicly available dataset of ophthalmic cases from OCTCases,a me dical education platform based out of the Department of Ophthalmology and Vision Sciences at the University of Toronto,with accompanying clinical multimodal im aging and multiple-choice questions.Across 137 available cases,136 contained m ultiple-choice questions (99%).The chatbot answered questions requ iring multimodal input from October 16 to October 23,2023.The primary outcome was the accuracy of the chatbot in answering multiple-choice questions pertainin g to image recognition in ophthalmic cases,measured as the proportion of correc t responses.ch2 Tests were conducted to compare the proportion of correct respo nses across different ophthalmic subspecialties.A total of 429 multiplechoice questions from 136 ophthalmic cases and 448 images were included in the analysis .The chatbot answered 299 of multiple-choice questions correctly across all cas es (70%).The chatbot's performance was better on retina questions than neuro-ophthalmology questions (77% vs 58%; diffe rence = 18%; 95% CI,7.5%-29.4% ; ch21 = 11.4; P<.001).The chatbot achieved a better perf ormance on nonimage-based questions compared with image-based questions (82% vs 65%; difference = 17%; 95% CI,7.8% -25.1%; ch21 = 12.2; P<.001).The chatbot perf ormed best on questions in the retina category (77% correct) and p oorest in the neuro-ophthalmology category (58% correct).The chat bot demonstrated intermediate performance on questions from the ocular oncology (72% correct),pediatric ophthalmology (68% correct),uveitis (67% correct),and glaucoma (61% correct) categories.In this study,the recent version of the chatbot accurately responde d to approximately two-thirds of multiple-choice questions pertaining to ophthal mic cases based on imaging interpretation.The multimodal chatbot performed bett er on questions that did not rely on the interpretation of imaging modalities."
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    Reports on Machine Learning Findings from Chengdu University Provide New Insight s (Climate bonds toward achieving net zero emissions and carbon neutrality:Evid ence from machine learning technique)

    50-51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 artific ial intelligence.According to news originating from Sichuan,People's Republic of China,by NewsRx correspondents,research stated,"The Conference of the Part ies (COP26 and 27) placed significant emphasis on climate financing policies wit h the objective of achieving net zero emissions and carbon neutrality.However,studies on the implementation of this policy proposition are limited." Funders for this research include Xiamen University.Our news reporters obtained a quote from the research from Chengdu University:" To address this gap in the literature,this study employs machine learning techn iques,specifically natural language processing (NLP),to examine 77 climate bon d (CB) policies from 32 countries within the context of climate financing.The f indings indicate that ‘sustainability' and ‘carbon emissions control' are the mo st outlined policy objectives in these CB policies.Additionally,the study high lights that most CB funds are invested toward energy projects (i.e.,renewable,clean,and efficient initiatives).However,there has been a notable shift in th e allocation of CB funds from climate-friendly energy projects to the constructi on sector between 2015 and 2019.This shift raises concerns about the potential redirection of funds from climate-focused investments to the real estate industr y,potentially leading to the greenwashing of climate funds.Furthermore,policy sentiment analysis revealed that a minority of policies hold skeptical views on climate change,which may negatively influence climate actions."
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    Technical University Munich (TU Munich) Reports Findings in Artificial Intellige nce (Artificial intelligence support in MR imaging of incidental renal masses:a n early health technology assessment)

    51-52页
    查看更多>>摘要：By a News Reporter-Staff News Editor at Robotics & Machine Learning Daily News-New research on Artificial Intelligence is the su bject of a report.According to news reporting originating in Munich,Germany,b y NewsRx journalists,research stated,"This study analyzes the potential cost-e ffectiveness of integrating an artificial intelligence (AI)-assisted system into the differentiation of incidental renal lesions as benign or malignant on MR im ages during follow-up.For estimation of qualityadjusted life years (QALYs) and lifetime costs,a decision model was created,including the MRI strategy and MR I + AI strategy." The news reporters obtained a quote from the research from Technical University Munich (TU Munich),"Model input parameters were derived from recent literature.Willingness to pay (WTP) was set to $100,000/QALY.Costs of $ 0 for the AI were assumed in the base-case scenario.Model uncertainty and costs of the AI system were assessed using deterministic and probabilistic sensitivit y analysis.Average total costs were at $8054 for the MRI strategy and $7939 for additional use of an AI-based algorithm.The model yi elded a cumulative effectiveness of 8.76 QALYs for the MRI strategy and of 8.77 for the MRI + AI strategy.The economically dominant strategy was MRI + AI.Dete rministic and probabilistic sensitivity analysis showed high robustness of the m odel with the incremental cost-effectiveness ratio (ICER),which represents the incremental cost associated with one additional QALY gained,remaining below the WTP for variation of the input parameters.If increasing costs for the algorith m,the ICER of $0/QALY was exceeded at $115,and the d efined WTP was exceeded at $667 for the use of the AI.This analysi s,rooted in assumptions,suggests that the additional use of an AI-based algori thm may be a potentially costeffective alternative in the differentiation of in cidental renal lesions using MRI and needs to be confirmed in the future.These results hint at AI's the potential impact on diagnosing renal masses."
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    Department of Mechanical Engineering Researchers Publish New Study Findings on Robotics (DDPG-based reinforcement learning for controlling a spatial three-secti on continuum robot)

    52-53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 robotics are presented i n a new report.According to news reporting originating from Constantine,Algeria,by NewsRx correspondents,research stated,"This paper proposes an approach t o controlling a spatial three-section continuum robot using reinforcement learni ng (RL)." Our news editors obtained a quote from the research from Department of Mechanica l Engineering:"Rather than relying on traditional methods that use bending and orientation angles,this study utilizes the curvature of each section to achieve the desired position.The training process involves identifying a 3D workspace for the robot and utilizing a deep neural network to optimize its control polici es.The results demonstrate that the robot's end effector can follow a given tra jectory with a maximum error of 1 [1/mm] b ased on the generated curvature for each section.The Deep Deterministic Policy Gradients (DDPG) algorithm is employed to optimize the robot's control policies.Additionally,the developed DDPG algorithm is compared to the Deep Q-Network (D QN) from a precision standpoint,particularly during the tracking of a circular trajectory."
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    Researchers' from Ontario Tech University (UOIT) Report Details of New Studies a nd Findings in the Area of Robotics (ARSIP:Automated Robotic System for Industr ial Painting)

    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 robotics is the subjec t of a new report.According to news reporting from Oshawa,Canada,by NewsRx jo urnalists,research stated,"This manuscript addresses the critical need for pre cise paint application to ensure product durability and aesthetics.While manual work carries risks,robotic systems promise accuracy,yet programming diverse p roduct trajectories remains a challenge." Financial supporters for this research include Mitacs.The news journalists obtained a quote from the research from Ontario Tech Univer sity (UOIT):"This study aims to develop an autonomous system capable of generat ing paint trajectories based on object geometries for user-defined spraying proc esses.By emphasizing energy efficiency,process time,and coating thickness on complex surfaces,a hybrid optimization technique enhances overall efficiency.E xtensive hardware and software development results in a robust robotic system le veraging the Robot Operating System (ROS).Integrating a low-cost 3D scanner,ca librator,and trajectory optimizer creates an autonomous painting system.Hardwa re components,including sensors,motors,and actuators,are seamlessly integrat ed with a Python and ROS-based software framework,enabling the desired automati on.A web-based GUI,powered by JavaScript,allows user control over two robots,facilitating trajectory dispatch,3D scanning,and optimization.Specific nodes manage calibration,validation,process settings,and real-time video feeds."
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    Scuola Superiore Sant'Anna Reports Findings in Robotics (Design and preliminary validation of a high-fidelity vascular simulator for robot-assisted manipulation )

    54-54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 Robotics is the subjec t of a report.According to news reporting out of Pisa,Italy,by NewsRx editors,research stated,"The number of robot-assisted minimally invasive surgeries is increasing annually,together with the need for dedicated and effective trainin g.Surgeons need to learn how to address the novel control modalities of surgica l instruments and the loss of haptic feedback,which is a common feature of most surgical robots." Financial support for this research came from Intuitive Surgical.Our news journalists obtained a quote from the research from Scuola Superiore Sa nt'Anna,"Highfidelity physical simulation has proved to be a valid training to ol,and it might help in fulfilling these learning needs.In this regard,a high -fidelity sensorized simulator of vascular structures was designed,fabricated a nd preliminarily validated.The main objective of the simulator is to train novi ces in robotic surgery to correctly perform vascular resection procedures withou t applying excessive strain to tissues.The vessel simulator was integrated with soft strain sensors to quantify and objectively assess manipulation skills and to provide real-time feedback to the trainee during a training session.Addition ally,a portable and user-friendly training task board was produced to replicate anatomical constraints.The simulator was characterized in terms of its mechani cal properties,demonstrating its realism with respect to human tissues."
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    University of California Reports Findings in Thyroid Cancer (A scoping review of endoscopic and robotic techniques for lateral neck dissection in thyroid cancer )

    55-55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 Oncology - Thyroid Can cer is the subject of a report.According to news reporting from Sacramento,Cal ifornia,by NewsRx journalists,research stated,"Lateral neck dissection (LND) in thyroid cancer has traditionally been performed by a transcervical technique with a large collar incision.With the rise of endoscopic,video-assisted,and r obotic techniques for thyroidectomy,minimally invasive LND is now being perform ed more frequently,with better cosmetic outcomes." The news correspondents obtained a quote from the research from the University o f California,"The purpose of this paper is to review the different minimally in vasive and remote access techniques for LND in thyroid cancer.A comprehensive l iterature review was performed using PubMed and Google Scholar search terms ‘thy roid cancer' and ‘lateral neck dissection' and ‘endoscopy OR robot OR endoscopic OR video-assisted'.There are multiple surgical options now available within ea ch subset of endoscopic,video-assisted,and robotic LND.The approach dictates the extent of the LND but almost all techniques access levels II-IV,with variab ility on levels I and V.This review provides an overview of the indications,co ntraindications,surgical and oncologic outcomes for each technique.Though data remains limited,endoscopic and robotic techniques for LND are safe,with impro ved cosmetic results and comparable oncologic and surgical outcomes."
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    Studies from Tsinghua University Provide New Data on Machine Learning (Dynamic Traffic Data In Machine-learning Air Quality Mapping Improves Environmental Justi ce Assessment)

    56-56页
    查看更多>>摘要：By a News Reporter-Staff News Editor at Robotics & Machine Learning Daily News Daily News-Current study results on Machine Learn ing have been published.According to news reporting originating in Beijing,Peo ple's Republic of China,by NewsRx journalists,research stated,"Air pollution poses a critical public health threat around many megacities but in an uneven ma nner.Conventional models are limited to depict the highly spatial- and time-var ying patterns of ambient pollutant exposures at the community scale for megaciti es." Funders for this research include National Key Research and Development Program of China,National Key Research and Development Program of China,National Natur al Science Foundation of China (NSFC),China National Postdoctoral Program for I nnovative Talents,Shuimu Tsinghua Scholar Program.The news reporters obtained a quote from the research from Tsinghua University,"Here,we developed a machine-learning approach that leverages the dynamic traff ic profiles to continuously estimate communitylevel year-long air pollutant con centrations in Los Angeles,U.S.We found the introduction of real-world dynamic traffic data significantly improved the spatial fidelity of nitrogen dioxide (N O2),maximum daily 8-h average ozone (MDA8 O-3),and fine particulate matter (PM 2.5) simulations by 47%,4%,and 15%,res pectively.We successfully captured PM2.5 levels exceeding limits due to heavy t raffic activities and providing an ‘out-of-limit map' tool to identify exposure disparities within highly polluted communities.In contrast,the model without r eal-world dynamic traffic data lacks the ability to capture the traffic-induced exposure disparities and significantly underestimate residents' exposure to PM2.5."
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    Bordeaux University Hospital Reports Findings in Kidney Cancer [UroPredict:Machine learning model on real-world data for prediction of kidney c ancer recurrence (UroCCR-120)]

    57-57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 - Kidney Canc er is the subject of a report.According to news reporting originating from Bord eaux,France,by NewsRx correspondents,research stated,"Renal cell carcinoma ( RCC) is most often diagnosed at a localized stage,where surgery is the standard of care.Existing prognostic scores provide moderate predictive performance,le ading to challenges in establishing follow-up recommendations after surgery and in selecting patients who could benefit from adjuvant therapy." Our news editors obtained a quote from the research from Bordeaux University Hos pital,"In this study,we developed a model for individual postoperative disease -free survival (DFS) prediction using machine learning (ML) on real-world prospe ctive data.Using the French kidney cancer research network database,UroCCR,we analyzed a cohort of surgically treated RCC patients.Participating sites were randomly assigned to either the training or testing cohort,and several ML model s were trained on the training dataset.The predictive performance of the best M L model was then evaluated on the test dataset and compared with the usual risk scores.In total,3372 patients were included,with a median follow-up of 30 mon ths.The best results in predicting DFS were achieved using Cox PH models that i ncluded 24 variables,resulting in an iAUC of 0.81 [IC95% 0.77-0.85].The ML model surpassed the predictive performance of the most commonly used risk scores while handling incomplete data in predict ors.Lastly,patients were stratified into four prognostic groups with good disc rimination (iAUC = 0.79 [IC95% 0.74-0.83] )." According to the news editors,the research concluded:"Our study suggests that applying ML to realworld prospective data from patients undergoing surgery for localized or locally advanced RCC can provide accurate individual DFS prediction,outperforming traditional prognostic scores."
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