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    New Findings on Robotics and Automation from Shanghai Jiao Tong University Summa rized (Semi-autonomous Grasping Control of Prosthetic Hand and Wrist Based On Mo tion Prior Field)

    96-97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 - Robotics and Automation are presented in a new report. According to news reporting from Shang hai, People's Republic of China, by NewsRx journalists, research stated, "Graspi ng multiple affordance parts and from arbitrary directions for complex shaped ob jects still remains a challenging problem for prosthetic hand with wrist. We pro pose a semi-autonomous control method that uses only an integrated in-hand camer a to predict the final grasping part on an object as the hand approaches it and obtain the appropriate wrist joint angles and preshape type." Financial support for this research came from National Natural Science Foundatio n of China (NSFC). The news correspondents obtained a quote from the research from Shanghai Jiao To ng University, "We collect approach-grasp motion sequences from human experts to construct a motion prior field (MPF) and derive the prediction model MPFNet by behavior cloning. With noise augmentation and a hybrid regressioncategorization policy training, our prediction model gets less than 2 cm predicting deviation under a small number (15) of demonstrations for each object. We apply our contro l method to a prosthetic hand with a 2 degrees -of-freedom (DoF) wrist, enabling it to grasp multiple parts of complex shaped objects and remain robust under th e position and orientation variation. Compared to state-of-the-art myoelectric c ontrol and semi-autonomous control methods, respectively, our method improves 65 .4%/26.3% in grasp success rate, 40.4%/2 6.3% in control time, and 35.6%/27.8% i n error distance."
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    Research Data from Peking University Update Understanding of Machine Learning (U sing Machine Learning To Construct the Blood-follicle Distribution Models of Var ious Trace Elements and Explore the Transport-related Pathways With Multiomics D ata)

    97-98页
    查看更多>>摘要：By a News Reporter-Staff News Editor at Robotics & Machine Learning Daily News Daily News-Investigators publish new report on Ma chine Learning. According to news reporting from Beijing, People's Republic of C hina, by NewsRx journalists, research stated, "Permeabilities of various trace e lements (TEs) through the blood-follicle barrier (BFB) play an important role in oocyte development. However, it has not been comprehensively described as well as its involved biological pathways." Financial supporters for this research include National Key Research & Development Program of Ministryof Science and Technology of China, Strategy Prio rity Research Program (CategoryB) of Chinese Academy of Sciences, National Natur al Science Foundation of China (NSFC), Yunnan Major Scientific and Technological Projects.
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    New Data from Aerospace Corporation Illuminate Findings in Machine Learning (Mac hine-learning and Physics-based Tool for Anomaly Identification In Propulsion Sy stems)

    98-99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 Machine Learning. According to news reporting from El Segundo, California, by NewsRx jo urnalists, research stated, "Launch anomalies occur frequently during the early phase of a program, with many of the anomalies attributed to propulsion systems. Approaches for identifying and mitigating potential propulsion failures can aid development programs and accelerate the resolution of root cause investigations ." Financial support for this research came from Internal Research and Development. The news correspondents obtained a quote from the research from Aerospace Corpor ation, "In reusable systems, anomaly detection methods can be employed to detect latent system health issues that could become problematic as the system ages. M odern launch support relies on human judgement for redline limit generation and visual family data comparison for many operational aspects, which makes it chall enging to identify failure modes and to diagnose an anomaly. Additionally, famil y data comparison is unavailable for the first few launches of a new vehicle. Au tomated tools to quickly identify system failures of new and reusable systems ca n bridge these gaps. Physics-based modeling and machine learning (PBMML) offers methods that can improve the reliability of new or reusable launch vehicles by i dentifying propulsion anomalies or issues before they jeopardize future space mi ssions. PBMML can then be used to inform corrective actions."
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    Research from College of Art and Design Broadens Understanding of Artificial Int elligence (Integration effect of artificial intelligence and traditional animati on creation technology)

    99-99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 intell igence have been published. According to news originating from Zhanjiang, People 's Republic of China, by NewsRx editors, the research stated, "Despite the advan cements in modern computer hardware and software, the creation of digital animat ion still demands a substantial investment of both manpower and time." Our news editors obtained a quote from the research from College of Art and Desi gn: "This article aimed to explore how artificial intelligence (AI) technology c an be combined with traditional animation creation techniques to achieve better integration effects. By combining intelligent character animation generation wit h hand drawing, a generative adversarial network was used to achieve high-qualit y animation generation. The generator generated realistic animations, and the di scriminator measured the authenticity of the animations by comparing the differe nces between the generator-generated animations and the real animations, which w as used for automated character animation generation. This can greatly reduce th e cost and time of digital animation creation, improve the quality of digital an imation, and provide more innovation for the application of traditional animatio n technology."
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    University of Oklahoma Researcher Broadens Understanding of Machine Learning (Pr edicting Gas Separation Efficiency of a Downhole Separator Using Machine Learnin g)

    100-100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 intelligen ce have been presented. According to news reporting originating from Norman, Okl ahoma, by NewsRx correspondents, research stated, "Artificial lift systems, such as electrical submersible pumps and sucker rod pumps, frequently encounter oper ational challenges due to high gas-oil ratios, leading to premature tool failure and increased downtime. Effective upstream gas separation is critical to mainta in continuous operation." The news reporters obtained a quote from the research from University of Oklahom a: "This study aims to predict the efficiency of downhole gas separator using ma chine learning models trained on data from a centrifugal separator and tested on data from a gravity separator (blind test). A comprehensive experimental setup included a multiphase flow system with horizontal (31 ft. (9.4 m)) and vertical (27 ft. (8.2 m)) sections to facilitate the tests. Seven regression models-multi linear regression, random forest, support vector machine, ridge, lasso, k-neares t neighbor, and XGBoost-were evaluated using performance metrics like RMSE, MAPE , and R-squared. In-depth exploratory data analysis and data preprocessing ident ified inlet liquid and gas volume flows as key predictors for gas volume flow pe r minute at the outlet (GVFO)."
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    Department of Urology Reports Findings in Machine Learning (Applications of mach ine learning in urodynamics: A narrative review)

    101-102页
    查看更多>>摘要：By a News Reporter-Staff News Editor at Robotics & Machine Learning Daily News-New research on Machine Learning is the subject o f a report. According to news originating from Beijing, People's Republic of Chi na, by NewsRx correspondents, research stated, "Machine learning algorithms as a research tool, including traditional machine learning and deep learning, are in creasingly applied to the field of urodynamics. However, no studies have evaluat ed how to select appropriate algorithm models for different urodynamic research tasks."Our news journalists obtained a quote from the research from the Department of U rology, "We undertook a narrative review evaluating how the published literature reports the applications of machine learning in urodynamics. We searched PubMed up to December 2023, limited to the English language. We selected the following search terms: artificial intelligence, machine learning, deep learning, urodyna mics, and lower urinary tract symptoms. We identified three domains for assessme nt in advance of commencing the review. These were the applications of urodynami c studies examination, applications of diagnoses of dysfunction related to urody namics, and applications of prognosis prediction. The machine learning algorithm applied in the field of urodynamics can be mainly divided into three aspects, w hich are urodynamic examination, diagnosis of urinary tract dysfunction and pred iction of the efficacy of various treatment methods. Most of these studies were single-center retrospective studies, lacking external validation, requiring furt her validation of model generalization ability, and insufficient sample size. Th e relevant research in this field is still in the preliminary exploration stage; there are few high-quality multicenter clinical studies, and the performance o f various models still needs to be further optimized, and there is still a dista nce from clinical application. At present, there is no research to summarize and analyze the machine learning algorithms applied in the field of urodynamics."
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    Findings from Swiss Federal Institute of Technology Provides New Data on Artific ial Intelligence (Cheat Sites and Artificial Intelligence Usage In Online Introd uctory Physics Courses: What Is the Extent and What Effect Does It Have On ...)

    101-101页
    查看更多>>摘要：By a News Reporter-Staff News Editor at Robotics & Machine Learning Daily News Daily News-Investigators discuss new findings in Artificial Intelligence. According to news reporting originating in Zurich, Swit zerland, by NewsRx journalists, research stated, "As a result of the pandemic, m any physics courses moved online. Alongside, the popularity of Internet-based pr oblem-solving sites and forums rose." The news reporters obtained a quote from the research from the Swiss Federal Ins titute of Technology, "With the emergence of large language models, another shif t occurred. One year into the public availability of these models, how has onlin e helpseeking behavior among introductory physics students changed, and what is the effect of different patterns of online resource usage? In a mixed-method app roach, we investigate student choices and their impact on assessment components of an online introductory physics course for scientists and engineers. We find t hat students still mostly rely on traditional Internet resources and that their usage strongly influences the outcome of low-stake unsupervised quizzes."
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    Polytechnic University of Madrid Details Findings in Robotics (Vision-based Coll aborative Robots for Exploration In Uneven Terrains)

    102-103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 Robotics are disc ussed in a new report. According to news reporting originating in Madrid, Spain, by NewsRx journalists, research stated, "Exploring tasks in unknown environment s has become a relevant search and rescue robotics approach. Ground robots are a better alternative to rescuers for first exploration." Funders for this research include Programas de Actividades I + D en la Comunidad Madrid, Structural Funds of the EU, Proyectos de I + D + i del Ministerio de Ci encia, Innovacion y Universidades, Proyecto CollaborativE Search And Rescue robo ts (CESAR), ERDF A way of making Europe. The news reporters obtained a quote from the research from the Polytechnic Unive rsity of Madrid, "However, exploration progress is often limited by uneven terra ins that exceed the kinematic capabilities of robots, including those with compl ex locomotion systems. This work proposes an innovative solution based on collab orative behaviours to overcome even terrains. A method employing two collaborati ve robots designed to operate in a marsupial configuration to surmount uneven te rrains has been implemented. These robots, denoted as R1 (enhanced with a mobile ramp) and R2 (serving as an explorer), interact synergistically to expand the e xplored area autonomously. A state machine has been implemented to manage the pr ogression of the mission, based on a perception (RGB-D) system, for both decisio n - making and autonomous execution of the process. In the initial stage, the ter rain and ascent zones to be explored are characterized using point clouds and un supervised learning. Subsequently, the second stage manages the interaction betw een the robots by controlling the R2 ascent through the R1 ramp using artificial vision algorithms and beacons. Outdoor tests have been performed to validate th e method."
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    Research Conducted at University of Southern Florida Has Provided New Informatio n about Artificial Intelligence (Chemistry Students' Artificial Intelligence Lit eracy Through Their Critical Reflections of Chatbot Responses)

    103-104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 originating in Tampa, Florida, by NewsRx journalists, research stated, "Instructors use of Artificial Intelligence (AI) language models (i.e., chatbots) as an educational resource w ill require an understanding of students' AI literacy, namely their ability to c ritically reflect on the relevance, trustworthiness, and quality of these tools in the context of chemistry. This study sought to describe students' AI literacy via open-ended surveys of general chemistry I students and students in an upper -level chemistry elective."The news reporters obtained a quote from the research from the University of Sou thern Florida, "Thematic analysis was used to create a deeper understanding of c hemistry students' AI literacy when considering chatbots. Based on students' res ponses, they were categorized as either with reservations toward chatbots or wit hout reservations toward chatbots. Thematically, students tended to either reaso n with the utility/benefit of the tool or reason with concern toward the accurac y of the tool. Results suggest that students are more of a range between these t wo extremes."
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    Majmaah University Researchers Report Recent Findings in Machine Learning (Tweet Prediction for Social Media using Machine Learning)

    104-105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 new report. According to news originating from Al Majmaah, Saudi Arabia, by NewsRx correspondents, research stated, "Tweet prediction play s a crucial role in sentiment analysis, trend forecasting, and user behavior ana lysis on social media platforms such as X (Twitter)." Our news correspondents obtained a quote from the research from Majmaah Universi ty: "This study delves into optimizing Machine Learning (ML) models for precise tweet prediction by capturing intricate dependencies and contextual nuances with in tweets. Four prominent ML models, i.e. Logistic Regression (LR), XGBoost, Ran dom Forest (RF), and Support Vector Machine (SVM) were utilized for disaster-rel ated tweet prediction. Our models adeptly discern semantic meanings, sentiment, and pertinent context from tweets, ensuring robust predictive outcomes."
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