查看更多>>摘要：By a News Reporter-Staff News Editor at Robotics & Machine Learning Daily News Daily News – New research on Drugs and Therapies - Central Nervous System Depressants is the subject of a report. According to news reporting out of Rabat, Morocco, by NewsRx editors, research stated, “Recent ad vancements have led to a rise, in the demand for surgical methods with robot-ass isted procedures becoming increasingly popular for addressing the limitations of traditional laparoscopy. However, incorporating surgery involves making changes in the way patients are positioned and logistical planning, which can challenge conventional approaches to providing anesthesia care.” Our news journalists obtained a quote from the research from Mohammed V Universi ty, “Despite these obstacles robotic technology shows potential for bringing abo ut improvements in therapy. Anesthesiologists play a role in ensuring safety and delivering high quality anesthesia care during robotic surgery. Having an under standing of the elements of robotic surgical systems is essential for adjusting anesthesia practices effectively. Keeping up to date with the developments in su rgery is key to achieving optimal outcomes for patients. Effective collaboration between teams and anesthesiologists is essential for managing the complexities of anesthesia during surgery. By promoting communication and cooperation across disciplines healthcare professionals can enhance safety and results. In summary while the introduction of surgery presents challenges in anesthesia care it also offers opportunities for innovation and advancement.”
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 - Carcinomas is the subject of a report. According to news reporting originating from Leuven, Belgium, by NewsRx correspondents, research stated, “An important challenge in the real-world management of patients with advanced clear-cell renal cell carcin oma (aRCC) is determining who might benefit from immune checkpoint blockade (ICB ). Here we performed a comprehensive multiomics mapping of aRCC in the context o f ICB treatment, involving discovery analyses in a real-world data cohort follow ed by validation in independent cohorts.” Our news editors obtained a quote from the research from the University of Leuve n (KU Leuven), “We cross-connected bulk-tumor transcriptomes across > 1,000 patients with validations at single-cell and spatial resolutions, revealin g a patient-specific crosstalk between proinflammatory tumor-associated macropha ges and (pre-)exhausted CD8 T cells that was distinguished by a human leukocyte antigen repertoire with higher preference for tumoral neoantigens. A cross-omics machine learning pipeline helped derive a new tumor transcriptomic footprint of neoantigen-favoring human leukocyte antigen alleles. This machine learning sign ature correlated with positive outcome following ICB treatment in both real-worl d data and independent clinical cohorts. In experiments using the RENCA-tumor mo use model, CD40 agonism combined with PD1 blockade potentiated both proinflammat ory tumor-associated macrophages and CD8 T cells, thereby achieving maximal anti tumor efficacy relative to other tested regimens.”
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 originating from Gliwice, Poland, by NewsRx correspondents, research stated, “Located around the center of multicomponent ph ase space, multi -principal element alloys (MPEAs) are often characterized with a unique blend of contrasting physico-chemical properties, and have a good prosp ective of presenting hardness -ductility synergy. A datasets of MPEAs fabricated by casting, wrought, sintering, annealing procedures, was collected and the mea n values for hardness and elongation was determined as 495.3 HV and 22.16 % respectively.” Financial supporters for this research include National Science Centre, Poland, University Grants Commission, India, European Research Council (ERC).
查看更多>>摘要：By a News Reporter-Staff News Editor at Robotics & Machine Learning Daily News Daily News – A new study on Robotics is now availab le. According to news reporting from Xi’an, People’s Republic of China, by NewsR x journalists, research stated, “To realize robot skill learning in the real wor ld, reinforcement learning algorithms need to be applied in continuous problems with high sample efficiency. Hybrid intelligence is regarded as an available sol ution for this problem, due to the ability to speed up the learning process with human knowledge and experience.” 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 Northwestern Pol ytechnic University, “Therefore, we propose Episode-Fuzzy-COACH (COrrective Advi ce Communicated by Humans), to imitate human fuzzy logic and involve human intel ligence in the learning process. In this framework, human knowledge and experien ce are involved in the learning process, which are provided by human feedback an d fuzzy rules designed by human users. Moreover, it is combined with Path Integr als Policy Improvement ( PI2 ), to realize hybrid intelligence, which is used to realize fast robot skill learning. Throwing Movement Primitives proposed in thi s article is used to represent the policy of ball-throwing skill. According to t he simulation results, the learning efficiency of our method is increased by 72% and 42.86%, respectively, compared with pure PI2 and PI2+ COACH. Ou r method validated in experiments is 46.67% more effective than PI 2+ COACH. The results also show that the performance of our method is not affect ed by users’ knowledge level of the related field.”
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 originating from Harbin, People’s Repub lic of China, by NewsRx correspondents, research stated, “Meal detection is an i mportant technology to ensure success rate of meal -assisting robotics. However, due to the strong interclass similarity and intraclass variability presented by appearance, gesture, and complex traits of meals in different scenarios, it is more challenging to real-time and accurate detect meals.” Funders for this research include National Key R & D Program of Ch ina, Fundamental Research Funds for the Central Universities. Our news journalists obtained a quote from the research from Harbin Engineering University, “To address the above problems, a novel method based on deformable c onvolution and CloFormer (CF) transformer to optimize the YOLOv8s was proposed t o achieve efficient and accurate detection for meal. The YOLOv8s model architect ure was enhanced by introducing deformable convolution to capture finer -grained spatial information, and the CloFormer module was introduced to capture high -f requency local and low -frequency global information through shared weights and context -aware weights, we notated it as DCF-YOLOv8s. The proposed method was ev aluated on meal datasets, which were evaluated separately with baseline model an d several state-of-the-art (SOTA) detection models, and results show that the pr oposed method achieves better performance. Specifically, the proposed method can achieve 88.5% mean average accuracy (mAP) at 43.6 frames per seco nd (FPS), validating its efficiency and accuracy in meal detection for meal -ass isting robotics. The effectiveness of introducing deformable convolution and Clo Former modules was verified by ablation experiments, and validating the importan ce of adopting data augmentation methods.”
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 te lestroke. According to news reporting originating from Besancon, France, by News Rx correspondents, research stated, “Selected patients with large vessel occlusi on (LVO) strokes can benefit from endovascular therapy (EVT). However, the effec tiveness of EVT is largely dependent on how quickly the patient receives treatme nt.” Our news correspondents obtained a quote from the research from University Hospi tal: “Recent technological developments have led to the first neurointerventiona l treatments using robotic assistance, opening up the possibility of performing remote stroke interventions. Existing telestroke networks provide acute stroke c are, including remote administration of intravenous thrombolysis (IVT). Therefor e, the introduction of remote EVT in distant stroke centers requires an adaptati on of the existing telestroke networks. The aim of this work was to propose a fr amework for centers that are potential candidates for telerobotics according to the resources currently available in these centers. In this paper, we highlight the future challenges for including remote robotics in telestroke networks. A li terature review provides potential solutions. Existing telestroke networks need to determine which centers to prioritize for remote robotic technologies based o n objective criteria and cost-effectiveness analysis. Organizational challenges include regional coordination and specific protocols. Technological challenges m ainly concern telecommunication networks.”
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 Shenzhen, People’s Republic of Ch ina, by NewsRx correspondents, research stated, “Despite the clear clinical diag nostic criteria for necrozoospermia in andrology, the fundamental mechanisms und erlying it remain elusive. This study aims to profile the lipid composition in s eminal plasma systematically and to ascertain the potential of lipid biomarkers in the accurate diagnosis of necrozoospermia.” Funders for this research include Shenzhen Science and Technology Innovation Com mittee, Shenzhen Key Medical Discipline Construction Fund, Shenzhen-Hong Kong-Ma cau Science and Technology Program.
查看更多>>摘要：By a News Reporter-Staff News Editor at Robotics & Machine Learning Daily News Daily News – Fresh data on Machine Learning - Suppo rt Vector Machines are presented in a new report. According to news originating from Hyderabad, India, by NewsRx correspondents, research stated, “Thunderstorms are natural disasters that impact people, animals, and the economy. Thunderstor ms’ detrimental repercussions can be avoided by identifying their occurrence in advance.” Our news journalists obtained a quote from the research from the Department of C omputer Sciences and Engineering, “The current work, in this respect, uses soft computing techniques such as K-Nearest Neighbour (KNN), Decision Tree (DT), Logi stic Regression (LR), and Support Vector Machine (SVM) with various kernel funct ions to categorize the occurrence of thunderstorms over Ranchi, India. These tec hniques were trained and tested using two data sets: daily average and hourly me teorological datasets. The primary purpose of this study is to find which datase t-classifier combination is optimal for categorizing thunderstorm occurrence in Ranchi. No classifier was found to adequately classify either the Day Average Da taset or the Modified Day Average Dataset. On the other hand, the Hourly Dataset was found to be more balanced in terms of the number of thunderstorms that occu rred than the Day Average and Modified Average datasets. The F-Score value of th e incidence of thunderstorm incidents after using different classifiers was used to compare the outcomes of these datasets. The results reveal that using SVM wi th radial basis function. The Hourly Dataset is the best for thunderstorm day cl assification. For the overall and only incidence of thunderstorms classes, SVM-R BF gets 0.81 and 0.74 F-Scores, respectively. Other approaches, like grid search and Bagging, have been used to increase SVM-RBF performance.”
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 - Robotics an d Automation is the subject of a report. According to news reporting originating from Alicante, Spain, by NewsRx correspondents, research stated, “Feature-based geo-localization relies on associating features extracted from aerial imagery w ith those detected by the vehicle’s sensors. This requires that the type of land marks must be observable from both sources.” Financial support for this research came from Regional Valencian Community Gover nment. Our news editors obtained a quote from the research from the University of Alica nte, “This lack of variety of feature types generates poor representations that lead to outliers and deviations produced by ambiguities and lack of detections, respectively. To mitigate these drawbacks, in this letter, we present a dynamica lly weighted factor graph model for the vehicle’s trajectory estimation. The wei ght adjustment in this implementation depends on information quantification in t he detections performed using a LiDAR sensor. Also, a prior (GNSS-based) error e stimation is included in the model. Then, when the representation becomes ambigu ous or sparse, the weights are dynamically adjusted to rely on the corrected pri or trajectory, mitigating outliers and deviations in this way. We compare our me thod against state-of-the-art geo-localization ones in a challenging and ambiguo us environment, where we also cause detection losses.” According to the news editors, the research concluded: “We demonstrate mitigatio n of the mentioned drawbacks where the other methods fail.”
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 the University of Novi Sad by NewsRx correspondents, research stated, “Large language models like ChatGPT c an learn in-context (ICL) from examples. Studies showed that, due to ICL, ChatGP T achieves impressive performance in various natural language processing tasks.” Financial supporters for this research include Republic of Serbia Ministry of Ed ucation Science And Technological Development; Science Fund of The Republic of S erbia.
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