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    First Affiliated Hospital of Guangzhou Medical University Reports Findings in Ne uromas (Robot-assisted resection of ganglion cell neuroma with a diameter of 78 mm: A case report)

    21-21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 Neuromas is the subjec t of a report. According to news reporting out of Guangzhou, People’s Republic o f China, by NewsRx editors, research stated, “Intrathoracic paragangliomas are t ypically found within the intricate posterior mediastinal region adjacent to the vertebrae, often presenting with substantial volume. Surgical excision of such tumors presents formidable challenges and is conventionally performed via open s urgical procedures.” Our news journalists obtained a quote from the research from the First Affiliate d Hospital of Guangzhou Medical University, “In this report, we present the case of a 53-year-old female patient who presented with the discovery of a left intr athoracic mass during a routine physical examination approximately 1 month prior . She complained of chest tightness and chest pain. She complained of chest tigh tness and chest pain. Magnetic resonance imaging of the chest and brachial plexu s revealed a mass adjacent to the left upper lung hilum, measuring approximately 78 x 63 x 72 mm. The initial suspicion leaned towards a benign lesion. Notably, there was slight compression of the left first thoracic nerve root and mild com pression of the middle and lower trunks of the left brachial plexus. Based on th e morphological features of the tumor and imaging findings, we suspected its ben ign nature. We opted for robot-assisted thoracic surgery to resect the mediastin al tumor. Subsequent postoperative pathology confirmed the diagnosis as a paraga nglioma. The patient did not experience any notable complications post-surgery, and a 6-month follow-up revealed no signs of recurrence.”
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    Graduate University of Advanced Technology Researchers Publish New Studies and F indings in the Area of Machine Learning (Residual energy evaluation in vortex st ructures: On the application of machine learning models)

    22-22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 Kerman, Iran, by NewsRx cor respondents, research stated, “Vortex structures are widely employed for energy dissipation in urban surface water conveyance systems. When transporting wastewa ter through these networks, a substantial amount of water energy is dissipated.” Our news correspondents obtained a quote from the research from Graduate Univers ity of Advanced Technology: “The effectiveness of these structures is usually ev aluated by their efficiency in dissipating energy. Recent literature reviews on vortex structures have emphasized that, despite numerous experimental studies ai med at assessing their hydraulic performance, a reliable mathematical model to p redict the residual energy head ratio remains elusive. In this study, resilient numerical models employing Artificial Intelligence (AI) methodologies (such as n on-parametric regression, decision trees, and ensemble learning) have been struc tured by reliable experimental tests. By analyzing the experiments, three primar y factors, referred to as flow Froude number (Fr), the ratio of sump height (Hs) to shaft diameter (D), and the ratio of drop total height (L) to shaft diameter (D) were determined to estimate the residual energy head ratio. Through experim ental study, the residual energy head ratio is computed as a ratio of downstream flow energy (E2) to upstream flow energy (E1) at vortex structure. During the t raining and testing phases of AI models, the results of statistical tests, servi ng as quantitative evaluations, have shown that ensemble learning models namely Adaptive Boosting (AdaBoost) and Categorical Boosting (CatBoost) models had high er level of efficiency in the E2/E1 predictions and followed by Model Tree (MT), K-Nearest Neighbor (KNN), Support Vector Machine (SVM), Extreme Gradient Boosti ng (XGBoost) and Multivariate Adaptive Regression Spline (MARS). Additionally, t he second-order regression-based equation was obtained from Fully Factorial Meth od (FFM) which had lower level of precision (R = 0.8275, RMSE = 0.1156, and MAE = 0.0846) in the residual energy head ratio predictions when compared with all p redictive AI models.”
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    Study Results from National Taipei University of Technology Provide New Insights into Artificial Intelligence (Explore the driving factors of designers’ AIGC us age behavior based on SOR framework)

    23-23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 reporting out of Taipei, Taiwan, by NewsRx e ditors, research stated, “Despite the widespread recognition of artificial intel ligence’s advantages, it cannot replace human independent thinking and creativit y, especially in fields such as artistic design that require creativity.” The news correspondents obtained a quote from the research from National Taipei University of Technology: “Previous studies often examined its development trend s from the perspective of technical advantages or application processes. This st udy explores the attitudes and acceptance of creative industry practitioners tow ards Artificial Intelligence Generated Content (AIGC) from the perspective of us er behavior modification. Utilizing the Stimulus-Organism-Response Model (SOR) a s the theoretical background, this research integrates the Technology Acceptance Model, Theory of Planned Behavior, and Self-Efficacy to form the research frame work. By employing a mixed-method approach combining quantitative and qualitativ e analyses, data from 226 designers were explored, and structural equation model ing was used to verify the correlations between endogenous factors. The results indicate that users’ facilitating conditions significantly influence self-effica cy, which in turn determines their intention to adopt AIGC. Additionally, semi-s tructured interviews revealed that factors hindering the widespread application of AIGC mainly encompass legal security, ethical risks, and fairness.”
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    University of Cambridge Reports Findings in Knee Osteoarthritis (Predicting rapi d progression in knee osteoarthritis: a novel and interpretable automated machin e learning approach, with specific focus on young patients and early disease)

    24-25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 Musculoskeletal Diseas es and Conditions - Knee Osteoarthritis is the subject of a report. According to news reporting from Cambridge, United Kingdom, by NewsRx journalists, research stated, “To facilitate the stratification of patients with osteoarthritis (OA) f or new treatment development and clinical trial recruitment, we created an autom ated machine learning (autoML) tool predicting the rapid progression of knee OA over a 2-year period. We developed autoML models integrating clinical, biochemic al, X-ray and MRI data.” Financial supporters for this research include Addenbrooke’s Charitable Trust, O RUK/Versus Arthritis, Versus Arthritis, Trinity College Cambridge, NIHR, NIHR Ca mbridge Biomedical Research Centre.
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    Studies from Harbin Engineering University Have Provided New Data on Machine Lea rning (Experimental and Simulation Study on Flow-Induced Vibration of Underwater Vehicle)

    25-25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 reporting originating from Harbin, People’s Republic of China, by NewsRx correspondents, research stated, “At high speeds, flow-induced vibration noise is the main component of underwater vehicl e noise. The turbulent fluctuating pressure is the main excitation source of thi s noise.” Financial supporters for this research include National Natural Science Foundati on of China. Our news correspondents obtained a quote from the research from Harbin Engineeri ng University: “It can cause vibration of the underwater vehicle’s shell and eve ntually radiate noise outward. Therefore, by reducing the turbulent pressure flu ctuation or controlling the vibration of the underwater vehicle’s shell, the rad iation noise of the underwater vehicle can be effectively reduced. This study de signs a conecolumn- sphere composite structure. Firstly, the effect of fluid-str ucture coupling on pulsating pressure is studied. Next, a machine learning metho d is used to predict the turbulent pressure fluctuations and the fluid-induced v ibration response of the structure at different speeds. The results were compare d with experimental and numerical simulation results. The results show that the deformation of the structure will affect the flow field distribution and pulsati ng pressure of the cylindrical section.”
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    Reports on Machine Learning from Mayo Clinic Provide New Insights (Machine Learn ing-based Prediction Models for clostridioides Difficile Infection: a Systematic Review)

    26-26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 out of Rochester, Minnesota, by NewsRx ed itors, research stated, “Despite research efforts, predicting Clostridioides dif ficile incidence and its outcomes remains challenging. The aim of this systemati c review was to evaluate the performance of machine learning (ML) models in pred icting C. difficile infection (CDI) incidence and complications using clinical d ata from electronic health records.” Financial supporters for this research include National Institute of Diabetes an d Digestive and Kidney Diseases, National Center for Advancing Translational Sci ences.
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    West Ukrainian National University Researchers Report Research in Machine Learni ng (OLTW-TEC: online learning with sliding windows for text classifier ensembles )

    27-27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 artificial intell igence are discussed in a new report. According to news originating from Ternopi l, Ukraine, by NewsRx correspondents, research stated, “In the digital age, rapi d dissemination of information has elevated the challenge of distinguishing betw een authentic news and disinformation. This challenge is particularly acute in r egions experiencing geopolitical tensions, where information plays a pivotal rol e in shaping public perception and policy.” Our news correspondents obtained a quote from the research from West Ukrainian N ational University: “The prevalence of disinformation in the Ukrainian-language information space, intensified by the hybrid war with russia, necessitates the d evelopment of sophisticated tools for its detection and mitigation. Our study in troduces the ‘Online Learning with Sliding Windows for Text Classifier Ensembles ’ (OLTWTEC) method, designed to address this urgent need. This research aims to develop and validate an advanced machine learning method capable of dynamically adapting to evolving disinformation tactics. The focus is on creating a highly accurate, flexible, and efficient system for detecting disinformation in Ukraini an-language texts. The OLTW-TEC method leverages an ensemble of classifiers comb ined with a sliding window technique to continuously update the model with the m ost recent data, enhancing its adaptability and accuracy over time. A unique dat aset comprising both authentic and fake news items was used to evaluate the meth od’s performance. Advanced metrics, including precision, recall, and F1- score, f acilitated a comprehensive analysis of its effectiveness. The OLTW-TEC method de monstrated exceptional performance, achieving a classification accuracy of 93% . The integration of the sliding window technique with a classifier ensemble sig nificantly contributed to the system’s ability to accurately identify disinforma tion, making it a robust tool in the ongoing battle against fake news in the Ukr ainian context. The application of the OLTW-TEC method highlights its potential as a versatile and effective solution for disinformation detection. Its adaptabi lity to the specifics of the Ukrainian language and the dynamic nature of inform ation warfare offers valuable insights into the development of similar tools for other languages and regions.”
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    Data on Mental Health Diseases and Conditions Reported by Joao Guerreiro and Col leagues (Transatlantic transferability and replicability of machine-learning alg orithms to predict mental health crises)

    28-28页
    查看更多>>摘要：By a News Reporter-Staff News Editor at Robotics & Machine Learning Daily News – New research on Mental Health Diseases and Condit ions is the subject of a report. According to news reporting from Barcelona, Spa in, by NewsRx journalists, research stated, “Transferring and replicating predic tive algorithms across healthcare systems constitutes a unique yet crucial chall enge that needs to be addressed to enable the widespread adoption of machine lea rning in healthcare. In this study, we explored the impact of important differen ces across healthcare systems and the associated Electronic Health Records (EHRs ) on machine-learning algorithms to predict mental health crises, up to 28 days in advance.” The news correspondents obtained a quote from the research, “We evaluated both t he transferability and replicability of such machine learning models, and for th is purpose, we trained six models using features and methods developed on EHR da ta from the Birmingham and Solihull Mental Health NHS Foundation Trust in the UK . These machine learning models were then used to predict the mental health cris es of 2907 patients seen at the Rush University System for Health in the US betw een 2018 and 2020. The best one was trained on a combination of US-specific stru ctured features and frequency features from anonymized patient notes and achieve d an AUROC of 0.837. A model with comparable performance, originally trained usi ng UK structured data, was transferred and then tuned using US data, achieving a n AUROC of 0.826.”
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    Xi’an Jiaotong University Reports Findings in Robotics (Crossdomain prediction approach of human lower limb voluntary movement intention for exoskeleton robot based on EEG signals)

    29-30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 originating from Shaanxi, People’s Republic of China, by NewsRx correspondents, research stated, “Exoskeleton robot control sho uld ideally be based on human voluntary movement intention. The readiness potent ial (RP) component of the motion-related cortical potential is observed before m ovement in the electroencephalogram and can be used for intention prediction.” Our news journalists obtained a quote from the research from Xi’an Jiaotong Univ ersity, “However, its single-trial features are weak and highly variable, and ex isting methods cannot achieve high crosstemporal and cross-subject accuracies i n practical online applications. Therefore, this work aimed to combine a deep co nvolutional neural network (CNN) framework with a transfer learning (TL) strateg y to predict the lower limb voluntary movement intention, thereby improving the accuracy while enhancing the model generalization capability; this would also pr ovide sufficient processing time for the response of the exoskeleton robotic sys tem and help realize robot control based on the intention of the human body. The signal characteristics of the RP for lower limb movement were analyzed, and a p arameter TL strategy based on CNN was proposed to predict the intention of volun tary lower limb movements. We recruited 10 subjects for offline and online exper iments. Multivariate empirical-mode decomposition was used to remove the artifac ts, and the moment of onset of voluntary movement was labeled using lower limb e lectromyography signals during network training. The RP features can be observed from multiple data overlays before the onset of voluntary lower limb movements, and these features have long latency periods. The offline experimental results showed that the average movement intention prediction accuracy was 95.23 % ? 1.25% for the right leg and 91.21% ? 1.48% for the left leg, which showed good cross-temporal and cross-subject generalizat ion while greatly reducing the training time. Online movement intention predicti on can predict results about 483.9 ? 11.9 ms before movement onset with an avera ge accuracy of 82.75 %.”
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    Shenzhen University Reports Findings in Robotics (A Multifunctional Tactile Sens ory System for Robotic Intelligent Identification and Manipulation Perception)

    30-31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 originating from Shenzhen, People’s Republic of China, by NewsRx correspondents, research stated, “Humans recognize and manipul ate objects relying on the multidimensional force features captured by the tacti le sense of skin during the manipulation. Since the current sensors integrated i n robots cannot support the robots to sense the multiple interaction states betw een manipulator and objects, achieving human-like perception and analytical capa bilities remains a major challenge for service robots.” Financial supporters for this research include National Natural Science Foundati on of China, Natural Science Foundation of Shenzhen Municipality.
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