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    New Findings on Machine Learning from Xi’an Jiaotong University Summarized (Tran s-scale Analysis of 3d Braided Composites With Voids Based On Micro-ct Imaging a nd Unsupervised Machine Learning)

    20-20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 Machine Learning. According to news reporting originating in Xi’an, People’s Rep ublic of China, by NewsRx journalists, research stated, “Voids are unavoidable d uring the manufacturing of 3D braided composites. This study proposes an unsuper vised machine learning method combined with micro-computed tomography (micro-CT) scanning and a progressive damage analysis to analyze defects in these composit es at a trans-scale level.” Financial supporters for this research include National Natural Science Foundati on of China (NSFC), National Key R & D Program of China.
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    Findings from New York University (NYU) Broaden Understanding of Artificial Inte lligence (A Neural Speech Decoding Framework Leveraging Deep Learning and Speech Synthesis)

    21-21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 - Ar tificial Intelligence have been presented. According to news originating from Ne w York City, New York, by NewsRx correspondents, research stated, “Decoding huma n speech from neural signals is essential for brain-computer interface (BCI) tec hnologies that aim to restore speech in populations with neurological deficits. However, it remains a highly challenging task, compounded by the scarce availabi lity of neural signals with corresponding speech, data complexity and high dimen sionality.” Funders for this research include National Science Foundation under Grant No. II S-1912286 National Institute of Health R01NS109367, R01NS115929, R01DC018805 (A. F.), National Science Foundation (NSF), National Institutes of Health (NIH) - US A.
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    Cantonal Hospital Zenica Researchers Provide New Insights into Machine Learning (Sensitivity and specificity of machine learning and deep learning algorithms in the diagnosis of thoracolumbar injuries resulting in vertebral fractures: A ... )

    22-22页
    查看更多>>摘要：By a News Reporter-Staff News Editor at Robotics & Machine Learning Daily News – New study results on artificial intelligence have been published. According to news originating from the Cantonal Hospital Zenica by NewsRx correspondents, research stated, “Clinicians encounter challenges in promptly diagnosing thoracolumbar injuries (TLIs) and fractures (VFs), motivatin g the exploration of Artificial Intelligence (AI) and Machine Learning (ML) and Deep Learning (DL) technologies to enhance diagnostic capabilities. Despite vary ing evidence, the noteworthy transformative potential of AI in healthcare, lever aging insights from daily healthcare data, persists.” The news journalists obtained a quote from the research from Cantonal Hospital Z enica: “Research question: This review investigates the utilization of ML and DL in TLIs causing VFs. Employing Preferred Reporting Items for Systematic Reviews and Meta-Analyzes (PRISMA) methodology, a systematic review was conducted in Pu bMed and Scopus databases, identifying 793 studies. Seventeen were included in t he systematic review, and 11 in the meta-analysis. Variables considered encompas sed publication years, geographical location, study design, total participants ( 14,524), gender distribution, ML or DL methods, specific pathology, diagnostic m odality, test analysis variables, validation details, and key study conclusions. Meta-analysis assessed specificity, sensitivity, and conducted hierarchical sum mary receiver operating characteristic curve (HSROC) analysis. Predominantly con ducted in China (29.41%), the studies involved 14,524 participants. In the analysis, 11.76% (N = 2) focused on ML, while 88.24% (N = 15) were dedicated to deep DL. Meta-analysis revealed a sensitivity of 0.91 (95% CI = 0.86-0.95), consistent specificity of 0.90 (95% CI = 0.86-0.93), with a false positive rate of 0.097 (95% CI = 0.0 68-0.137).”
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    Studies from Wichita State University Update Current Data on Robotics (Soft Robo t Design, Manufacturing, and Operation Challenges: A Review)

    23-23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 robotics have been published. According to news originating from Wichita, Kansas, by NewsRx c orrespondents, research stated, “Advancements in smart manufacturing have embrac ed the adoption of soft robots for improved productivity, flexibility, and autom ation as well as safety in smart factories.” Our news reporters obtained a quote from the research from Wichita State Univers ity: “Hence, soft robotics is seeing a significant surge in popularity by garner ing considerable attention from researchers and practitioners. Bionic soft robot s, which are composed of compliant materials like silicones, offer compelling so lutions to manipulating delicate objects, operating in unstructured environments , and facilitating safe human-robot interactions. However, despite their numerou s advantages, there are some fundamental challenges to overcome, which particula rly concern motion precision and stiffness compliance in performing physical tas ks that involve external forces.”
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    Researcher’s Work from Hefei University of Technology Focuses on Data Intelligen ce (Multi-view Feature Learning for the Over-penalty in Adversarial Domain Adapt ation)

    24-25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 da ta intelligence. According to news reporting from Hefei, People’s Republic of Ch ina, by NewsRx journalists, research stated, “ABSTRACT: Domain adaptation aims t o transfer knowledge from the labeled source domain to an unlabeled target domai n that follows a similar but different distribution.” Our news journalists obtained a quote from the research from Hefei University of Technology: “Recently, adversarial-based methods have achieved remarkable succe ss due to the excellent performance of domaininvariant feature presentation lea rning. However, the adversarial methods learn the transferability at the expense of the discriminability in feature representation, leading to low generalizatio n to the target domain. To this end, we propose a Multi-view Feature Learning me thod for the Over-penalty in Adversarial Domain Adaptation. Specifically, multi- view representation learning is proposed to enrich the discriminative informatio n contained in domain-invariant feature representation, which will counter the o ver-penalty for discriminability in adversarial training.”
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    Tokyo Metropolitan University Researcher Provides Details of New Studies and Fin dings in the Area of Robotics and Mechatronics (Water Droplet Detection System o n Toilet Floor Using Heat Absorption Capacity of Liquid)

    24-24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 robotics and mechatro nics have been presented. According to news originating from Tokyo, Japan, by Ne wsRx correspondents, research stated, “Liquid waste is a type of dirt that is of ten found in toilets.” The news correspondents obtained a quote from the research from Tokyo Metropolit an University: “Detection of liquid waste such as water or urine in the restroom is challenging due to their limited physical appearances, e.g., transparency an d small size. This paper proposes a new method to detect water droplets, includi ng water splashes, on the toilet floor by using the heat absorption capacity of liquid. Water, air, and floor have different heat capacity characteristics. Incr easing temperature difference between water droplets and surroundings is done us ing blowing air on the surface of the detection area. A thermal camera is used t o observe the detection area and an adaptive threshold is implemented to localiz e water droplets.”
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    Jiangxi Cancer Hospital Reports Findings in Liver Cancer (MRIbased clinical-rad iomics nomogram model for predicting microvascular invasion in hepatocellular ca rcinoma)

    25-26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 - Liver Cance r is the subject of a report. According to news reporting originating in Nanchan g, People’s Republic of China, by NewsRx journalists, research stated, “Preopera tive microvascular invasion (MVI) of liver cancer is an effective method to redu ce the recurrence rate of liver cancer. Hepatectomy with extended resection and additional adjuvant or targeted therapy can significantly improve the survival r ate of MVI+ patients by eradicating micrometastasis.” The news reporters obtained a quote from the research from Jiangxi Cancer Hospit al, “Preoperative prediction of MVI status is of great clinical significance for surgical decision-making and the selection of other adjuvant therapy strategies to improve the prognosis of patients. Established a radiomics machine learning model based on multimodal MRI and clinical data, and analyzed the preoperative p rediction value of this model for microvascular invasion (MVI) of hepatocellular carcinoma (HCC). The preoperative liver MRI data and clinical information of 13 0 HCC patients who were pathologically confirmed to be pathologically confirmed were retrospectively studied. These patients were divided into MVI-positive grou p (MVI+) and MVI-negative group (MVI-) based on postoperative pathology. After a series of dimensionality reduction analysis, six radiomic features were finally selected. Then, linear support vector machine (linear SVM), support vector mach ine with rbf kernel function (rbf-SVM), logistic regression (LR), Random forest (RF) and XGBoost (XGB) algorithms were used to establish the MVI prediction mode l for preoperative HCC patients. Then, rbf-SVM with the best predictive performa nce was selected to construct the radiomics score (R-score). Finally, we combine d R-score and clinical-pathology-image independent predictors to establish a com bined nomogram model and corresponding individual models. The predictive perform ance of individual models and combined nomogram was evaluated and compared by re ceiver operating characteristic curve (ROC). Alpha-fetoprotein concentration, pe ritumor enhancement, maximum tumor diameter, smooth tumor margins, tumor growth pattern, presence of intratumor hemorrhage, and RVI were independent predictors of MVI. Compared with individual models, the final combined nomogram model (AUC: 0.968, 95% CI: 0.920-1.000) constructed by radiometry score (R-sc ore) combined with clinicopathological parameters and apparent imaging features showed the optimal predictive performance.”
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    University of Pisa Reports Findings in Robotics (Role of single port robotic sur gery in gynecology)

    26-27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, “Robot-assisted Single-Site Laparoscopy (R-LSS) is a rapidly evolving minimally invasive technique. Although it is a very recent technology, the use of R-LSS have been increasingly report in gynecology, for both benign an d malignant indications.” Our news journalists obtained a quote from the research from the University of P isa, “This review aims to summarize the evolution of this innovative technique a nd to examine its feasibility and safety for gynecological surgical procedures. We evaluated studies dealing about R-LSS in gynecological surgery. We performed a comprehensive literature research on PubMed and the Cochrane Library in Februa ry 2024. Based on the study reviewed, R-LSS seems to be a feasible and effective alternative to other mini-invasive approach in gynecological surgery. R-LSS com bine the advantages of robotics surgery with the aesthetic result of a single in cision. Compare to Single-Site Laparoscopy, it restore triangulation of the inst rument and improve visualization and ergonomic. R-LSS seems to be related to fav ourable intra-e post-operative outcomes.”
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    New Robotics and Automation Findings from University of Maryland Outlined (can a n Embodied Agent Find Your 'cat-shaped Mug'? Llm-based Zero-shot Object Navigati on)

    27-28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 originating from Col lege Park, Maryland, by NewsRx correspondents, research stated, “We present lang uage-guided exploration (LGX), a novel algorithm for Language-Driven Zero-Shot O bject Goal Navigation (L-ZSON), where an embodied agent navigates to an uniquely described target object in a previously unseen environment. Our approach makes use of large language models (LLMs) for this task by leveraging the LLM’s common sense-reasoning capabilities for making sequential navigational decisions.” Financial support for this research came from National Science Foundation (NSF). Our news journalists obtained a quote from the research from the University of M aryland, “Simultaneously, we perform generalized target object detection using a pre-trained Vision-Language grounding model. We achieve state-of-the-art zero-s hot object navigation results on RoboTHOR with a success rate (SR) improvement o f over 27% over the current baseline of the OWL-ViT CLIP on Wheels (OWL CoW). Furthermore, we study the usage of LLMs for robot navigation and pre sent an analysis of various prompting strategies affecting the model output.”
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    Research from Institute of Industrial Has Provided New Study Findings on Robotic s (Smart Perception for Situation Awareness in Robotic Manipulation Tasks)

    28-29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 robotics have been published. According to news originating from the Institute of Industrial by NewsRx correspondents, research stated, “Robotic manipulation in semistructu red environments require perception, planning and execution capabilities to be r obust to deviations and adaptive to changes, and knowledge representation and re asoning may play a role in this direction in order to make robots aware of the s ituations, of the planning domains and of their own execution structures.” Funders for this research include European Commission’s Horizon Europe Framework Program With The Project Intelliman.
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