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    Xi'an Jiaotong University Reports Findings in Thyroid Cancer (Predicting overall survival in anaplastic thyroid cancer using machine learning approaches)

    134-135页
    查看更多>>摘要：New research on Oncology-Thyroid Can cer is the subject of a report. According to news reporting from Xi'an, People's Republic of China, by NewsRx journalists, research stated, "Anaplastic thyroid carcinoma (ATC) is a highly aggressive and lethal thyroid cancer subtype with a poor prognosis. Recent advancements in machine learning (ML) have the potential to improve survival predictions." The news correspondents obtained a quote from the research from Xi'an Jiaotong U niversity, "This study aimed to develop and validate ML models using the SEER da tabase to predict 3-month, 6-month, and 12-month (overall survival) OS in ATC pa tients. Clinical and demographic data for patients with ATC from the SEER databa se (2004-2015) were utilized. Five ML algorithms-AdaBoost, support vector machin es, gradient boosting classifiers, random forests, and naive Bayes-were evaluate d. The data were split into training and testing sets (7:3 ratio), and the model s were tuned using fivefold cross-validation. Model performance was assessed usi ng the concordance index (C-index) and Brier score, with 95% confi dence intervals reported. The gradient boosting model achieved the greatest perf ormance for 3-month survival (C-index: 0.8197, 95% CI 0.7682-0.868 9; Brier score: 0.1802), and the AdaBoost model achieved the greatest performanc e in 6-month survival (C-index: 0.8473, 95% CI 0.7979-0.8933; Brie r score: 0.1775). The SVC model showed superior performance for 12-month surviva l (C-index: 0.8347, 95% CI 0.7866- 0.8816; Brier score: 0.1476). Us ing SHAP with a gradient boosting model, the top five features affecting 6-month OS were identified: surgery, the presence of stage IVC, radiation, chemotherapy , and tumor size. Treatment improved survival, while higher stages reduced survi val, with smaller tumors generally linked to better outcomes. ML algorithms can accurately predict short-term survival in ATC patients."
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    Recent Findings from Shanghai University Provides New Insights into Robotics (So cial Security Fee Reduction, Industrial Robots, and Labor Income Share)

    135-136页
    查看更多>>摘要：Current study results on Robotics have been published. According to news reporting out of Shanghai, People's Republic of China, by NewsRx editors, research stated, "This study explores whether the p olicy of reducing labor payments can have opposite effects to those expected. Sp ecifically, it investigates whether reducing the social insurance contribution r ate can increase labor employment and enhance overall labor income." Funders for this research include National Natural Science Foundation of China ( NSFC), National Office of Philosophy and Social Sciences.
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    Findings from University of Caldas Provide New Insights into Machine Learning (R egulatory-based Classification of Rums: a Chemometric and Machine Learning Analy sis)

    136-137页
    查看更多>>摘要：Investigators discuss new findings in Machine Learning. According to news originating from Manizales, Colombia, by New sRx correspondents, research stated, "The Industria Licorera de Caldas (ILC) sta nds as a major liquor factory in Colombia, specialising in the production of var ious rum types including Tradicional, Juan de la Cruz, Carta de Oro, and Reserva Especial. These rums, as congeneric drinks, are known for their rich content of volatile compounds that define their sensory characteristics." Funders for this research include Industria Licorera de Caldas, Research group G rupo de Investigacion en Cromatografia y Tecnicas Afines (Universidad de Caldas) , Ministry of Science, Technology and Innovation of Colombia, National Council o f Tax Benefits.
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    Studies from Harbin Institute of Technology Describe New Findings in Machine Lea rning (Machine Learning-based Predictors for Maximum Pile Bending Moment of the Soil-pile-superstructure System In Liquefiable Soils)

    137-138页
    查看更多>>摘要：Researchers detail new data in Machine Learning. According to news reporting originating from Heilongjiang, People's R epublic of China, by NewsRx correspondents, research stated, "Accurate and relia ble prediction of the maximum pile bending moment of soil-pile-superstructure sy stem (SPSS) in liquefiable soils is essential for the seismic design. The maximu m pile bending moment can be obtained using experiments that consider only a lim ited number of factors or time-consuming finite element simulations." Financial supporters for this research include National Key R & D Program of China, Research and Development Project of the Ministry of Housing an d Urban-Rural Development, China.
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    Findings in Machine Learning Reported from Gyeongsang National University (A Stu dy on Machine Learning-Based Feature Classification for the Early Diagnosis of B lade Rubbing)

    138-139页
    查看更多>>摘要：A new study on artificial intelligence is now available. According to news reporting originating from Gyeongsang Natio nal University by NewsRx correspondents, research stated, "This research focuses on the development of a machine learning-based approach for the early diagnosis of blade rubbing in rotary machinery." Financial supporters for this research include The Grant Entitled Development of Automatic Predictive Diagnosis Technology; The Gyeongsang National University. The news reporters obtained a quote from the research from Gyeongsang National U niversity: "In this paper, machine learning-based diagnostic methods are used fo r blade rubbing early diagnosis, and the faults are simulated using experimental models. The experimental conditions were simulated as follows: Excessive rotor vibration is generated by an unbalance mass, and blade rubbing occurs through ex cessive rotor vibration. Additionally, the severity of blade rubbing was also si mulated while increasing the unbalance mass. And then, machine learning-based di agnostic methods were applied and the trends according to the severity of blade rubbing were compared. This paper provides a signal processing method through fe ature analysis to diagnose blade rubbing conditions in machine learning."
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    Ruhr-University Bochum Reports Findings in Robotics (Human-Robot Intimacy: Accep tance of Robots as Intimate Companions)

    139-140页
    查看更多>>摘要：New research on Robotics is the subjec t of a report. According to news originating from Bochum, Germany, by NewsRx cor respondents, research stated, "Depictions of robots as romantic partners for hum ans are frequent in popular culture. As robots become part of human society, the y will gradually assume the role of partners for humans whenever necessary, as a ssistants, collaborators, or companions." Financial support for this research came from Bial Foundation. Our news journalists obtained a quote from the research from Ruhr-University Boc hum, "Companion robots are supposed to provide social contact to those who would not have it otherwise. These companion robots are usually not designed to fulfi ll one of the most important human needs: the one for romantic and intimate cont act. Human-robot intimacy remains a vastly unexplored territory. In this article , we review the state-of-the-art research in intimate robotics. We discuss major issues limiting the acceptance of robots as intimate partners, the public perce ption of robots in intimate roles, and the possible influence of crosscultural differences in these domains. We also discuss the possible negative effects huma n-robot intimacy may have on human-human contact. Most importantly, we propose a new term ‘intimate companion robots' to reduce the negative connotations of the other terms that have been used so far and improve the social perception of res earch in this domain."
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    New Findings on Machine Learning from University of Augsburg Summarized (Fast Ap proximation of Fiber Reinforced Injection Molding Processes Using Eikonal Equati ons and Machine Learning)

    140-141页
    查看更多>>摘要：Researchers detail new data in Machine Learning. According to news reporting from Augsburg, Germany, by NewsRx journal ists, research stated, "Injection molding is a popular production process for sh ort fiber reinforced components. The mechanical properties of such components de pend on process-induced fiber orientations which are commonly predicted via nume rical simulations." Funders for this research include Hightech Agenda Bavaria, Bavarian State Govern ment. The news correspondents obtained a quote from the research from the University o f Augsburg, "However, high computational costs prevent process simulations from being used in iterative procedures, such as topology optimization or finding opt imal injection locations. We propose a fast approximation method that extracts n odal features and train a regression model to predict fill states, cooling times , volumetric shrinkage, and fiber orientations. The features are determined by s olving eikonal equations with a fast iterative method and computing spatial mome nts to characterize node-adjacent material distributions. Subsequently, we use t hese features to train feed forward neural networks and gradient boosted regress ion trees with simulation data of a large dataset of geometries. This approach i s significantly faster than conventional methods, providing 20x speed-up for sin gle simulations and more than 200x speed-up in gate location optimization."
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    Research from Jiangsu Second Normal University in the Area of Support Vector Mac hines Published (Support Vector Machine Dynamic Selection of Voting Rule for Coo perative Spectrum Sensing in CUAVNs)

    141-142页
    查看更多>>摘要：Data detailed on have been presented. According to news reporting from Jiangsu, People's Republic of China, by NewsRx journalists, research stated, "Due to the rapid development of unmanned aerial v ehicles (UAVs) communication technology, UAVs are gradually competing with prima ry users (PUs) for spectrum resources. Cognitive radio (CR) technology is a prom ising solution to meet the spectrum requirements of UAVs." Funders for this research include National Natural Science Foundation of China; Open Fund of Key Laboratory of Flight Techniques And Flight Safety, Caac; Projec ted Supported By The Specialized Research Fund For State Key Laboratories.
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    Reports from Aston University Describe Recent Advances in Robotics (An Ontology and Rule-based Method for Human-robot Collaborative Disassembly Planning In Smar t Remanufacturing)

    142-143页
    查看更多>>摘要：Current study results on Robotics have been published. According to news reporting from Birmingham, United Kingdom, by NewsRx journalists, research stated, "Disassembly is a decisive step in the rem anufacturing process of End -of -Life (EoL) products. As an emerging semi -autom atic disassembly paradigm, human-robot collaborative disassembly (HRCD) offers m ultiple disassembly methods to enhance flexibility and efficiency." Funders for this research include RECLAIM project ‘Remanufacturing and Refurbish ment of Large Industrial Equipment', Horizon 2020, National Natural Science Foun dation of China (NSFC). The news correspondents obtained a quote from the research from Aston University , "However, HRCD increases the complexity of planning and determining the optima l disassembly sequence and scheme. Currently, the optimisation process of heuris tic methods is difficult to interpret, and the results cannot be guaranteed as g lobally optimal. Consequently, this paper introduces a general ontology model fo r HRCD, along with a rule -based reasoning method, to automatically generate the optimal disassembly sequence and scheme. Firstly, the HRCD ontology model estab lishes the disassembly -related information for EoL products in a standardised a pproach. Then, customised disassembly -related rules are proposed to regulate th e precedence constraints and optional disassembly methods for each disassembly t ask of EoL products. The optimal disassembly sequence and scheme are automatical ly generated by combining supportive rules with the ontology model. Lastly, the human-robot collaborative disassembly planning of a gearbox is presented as a ca se study to validate the feasibility of the proposed methods. Our method generat es an optimal disassembly scheme compared with other heuristic algorithms, achie ving the shortest process time of 308 units and the fewest number of disassembly direction change of 3 times. Additionally, the reasoning procedure can be easil y tracked and modified."
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    Study Findings on Robotics Reported by a Researcher at Macau University of Scien ce and Technology (Periodic Scheduling Optimization for Dual-Arm Cluster Tools w ith Arm Task and Residency Time Constraints via Petri Net Model)

    143-143页
    查看更多>>摘要：Current study results on robotics have been published. According to news reporting from Macao, People's Republic of Ch ina, by NewsRx journalists, research stated, "In order to improve quality assura nce in wafer manufacturing, there are strict process requirements. Besides the w ell-known residency time constraints (RTCs), a dual-arm cluster tool also requir es each robot arm to execute a specific set of tasks." Financial supporters for this research include Science And Technology Developmen t Fund (Fdct), Macau Sar.
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