首页期刊导航|Robotics & Machine Learning Daily News
期刊信息/Journal information
Robotics & Machine Learning Daily News
NewsRx
Robotics & Machine Learning Daily News

NewsRx

Robotics & Machine Learning Daily News/Journal Robotics & Machine Learning Daily News
正式出版
收录年代

    Findings in Artificial Intelligence Reported from Canadian University of Dubai (The Role of Smart Technologies in French Hospitals’ Branding Strategies)

    103-103页
    查看更多>>摘要：Investigators discuss new findings in artificial intelligence. According to news reporting from Dubai, United Arab Emirates, by NewsRx journalists, research stated, “Hospitals resort to different initiatives to build their brands, including media relations, events, and marketing campaigns. However, they face several challenges related to legal frameworks, patients’ new demands, and hospitals’ digital transformation.” Our news editors obtained a quote from the research from Canadian University of Dubai: “This paper analyzes how the best hospitals in France manage smart technologies to enhance their relationships with stakeholders and reinforce their brands. We resorted to the World’s Best Hospitals 2023 to identify the 150 best hospitals in this country. Then, we defined 34 branding indicators to evaluate how each hospital managed smart technologies for branding purposes. We adapted these criteria to different platforms and targets: homepage (patients), online newsroom (media companies), About Us section (suppliers, shareholders, and public authorities), and artificial intelligence department (employees). When analyzing these criteria, we resorted to a binary system and only considered hospitals’ official websites. Our results proved that 98% of hospitals had a website, but not all respected the criteria related to the homepage (4.54 of 11), online newsroom (2.52 of 11), or About Us section (1.56 of 6). The best hospitals in France, according to the number of criteria respected, were Institut Curie-Oncology (20), Institut Gustave Roussy-Oncology (19), and Hopital Paris Saint-Joseph (19).”
      原文链接:
    • NETL
    • NSTL
    • Mdpi

    New Robotics Study Findings Recently Were Reported by Researchers at University of Bordeaux (Multi Actor-critic Ddpg for Robot Action Space Decomposition: a Framework To Control Large 3d Deformation of Soft Linear Objects)

    104-104页
    查看更多>>摘要：Research findings on Robotics are discussed in a new report. According to news reporting from Talence, France, by NewsRx journalists, research stated, “Robotic manipulation of deformable linear objects (DLOs) has great potential for applications in diverse fields such as agriculture or industry. However, a major challenge lies in acquiring accurate deformation models that describe the relationship between robot motion and DLO deformations.” Financial support for this research came from Horizon 2020. The news correspondents obtained a quote from the research from the University of Bordeaux, “Such models are difficult to calculate analytically and vary among DLOs. Consequently, manipulating DLOs poses significant challenges, particularly in achieving large deformations that require highly accurate global models. To address these challenges, this letter presents MultiAC6: a new multi Actor-Critic framework for robot action space decomposition to control large 3D deformations of DLOs. In our approach, two deep reinforcement learning (DRL) agents orient and position a robot gripper to deform a DLO into the desired shape. Unlike previous DRL-based studies, MultiAC6 is able to solve the sim-to-real gap, achieving large 3D deformations up to 40 cm in real-world settings. Experimental results also show that MultiAC6 has a 66% higher success rate than a single-agent approach.”
      原文链接:
    • NETL
    • NSTL
    • IEEE

    Huazhong University of Science and Technology Reports Findings in Rectal Cancer (Radiomics based on T2-weighted and diffusionweighted MR imaging for preoperative prediction of tumor deposits in rectal cancer)

    105-105页
    查看更多>>摘要：New research on Oncology - Rectal Cancer is the subject of a report. According to news reporting out of Wuhan, People’s Republic of China, by NewsRx editors, research stated, “Preoperative diagnosis of tumor deposits (TDs) in patients with rectal cancer remains a challenge. This study aims to develop and validate a radiomics nomogram based on the combination of T2-weighted (T2WI) and diffusion-weighted MR imaging (DWI) for the preoperative identification of TDs in rectal cancer.” Our news journalists obtained a quote from the research from the Huazhong University of Science and Technology, “A total of 199 patients with rectal cancer who underwent T2WI and DWI were retrospectively enrolled and divided into a training set (n = 159) and a validation set (n = 40). The total incidence of TDs was 37.2 % (74/199). Radiomics features were extracted from T2WI and apparent diffusion coefficient (ADC) images. A radiomics nomogram combining Rad-score (T2WI + ADC) and clinical factors was subsequently constructed. The area under the receiver operating characteristic curve (AUC) was then calculated to evaluate the models. The nomogram is also compared to three machine learning model constructed based on no-Rad scores. The Rad-score (T2WI + ADC) achieved an AUC of 0.831 in the training and 0.859 in the validation set. The radiomics nomogram (the combined model), incorporating the Rad-score (T2WI + ADC), MRI-reported lymph node status (mLN-status), and CA19-9, showed good discrimination of TDs with an AUC of 0.854 for the training and 0.923 for the validation set, which was superior to Random Forests, Support Vector Machines, and Deep Learning models. The combined model for predicting TDs outperformed the other three machine learning models showed an accuracy of 82.5 % in the validation set, with sensitivity, specificity, positive predictive value (PPV), and negative predictive value (NPV) of 66.7 %, 92.0 %, 83.3 %, and 82.1 %, respectively.”
      原文链接:
    • NETL
    • NSTL

    Researchers from NITTE (Deemed to be University) Discuss Findings in Machine Translation (Tulu Language Text Recognition and Translation)

    106-106页
    查看更多>>摘要：Fresh data on machine translation are presented in a new report. According to news originating from NITTE (Deemed to be University) by NewsRx editors, the research stated, “Language is a primary means of communication, but it is not the only means; knowing a language does, however, assist speed up the process. Many distinct languages are spoken worldwide, and people use them to communicate.” Financial supporters for this research include Nitte. The news reporters obtained a quote from the research from NITTE (Deemed to be University): “This is only one of the many reasons why language is so crucial. Based on the literature survey, it is evident that there is a lack of available translators for the Tulu language. Despite being prevalent predominantly in Karnataka, the Tulu language has not been as widely spoken as other Indian languages until recently, although it gained enough recognition to become the second language in Karnataka. The purpose of our research work aims at translating the English language into the Tulu language. During the evaluation the system was tested on a dataset consisting of handwritten characters during the evaluation process Convolutional Neural Networks used achieved an accuracy rate of 92%. To translate English to the Tulu language, we employed a parallel sentence dataset for the neural approach and a parallel word dataset for the rule-based approach. The rule-based approach resulted in an 89% accuracy rate for word-based analysis and an 81% accuracy rate for sentence-based analysis for the English-to-Tulu language translation.”
      原文链接:
    • NETL
    • NSTL
    • IEEE

    Peter MacCallum Cancer Centre Reports Findings in Artificial Intelligence [Rapid screening of bacteriostatic and bactericidal antimicrobial agents against Escherichia coli by combining machine learning (artificial intelligence) and UV-VIS ...]

    107-107页
    查看更多>>摘要：New research on Artificial Intelligence is the subject of a report. According to news originating from Melbourne, Australia, by NewsRx correspondents, research stated, “Antibiotics are compounds that have a particular mode of action upon the microorganism they are targeting. However, discovering and developing new antibiotics is a challenging and timely process.” Our news journalists obtained a quote from the research from Peter MacCallum Cancer Centre, “Antibiotic development process can take up to 10-15 years and over $1billion to develop a single new therapeutic product. Rapid screening tools to understand the mode of action of the new antimicrobial agent are considered one of the main bottle necks in the antimicrobial agent development process. Classical approaches require multifarious microbiological methods and they do not capture important biochemical and organism therapeutic-interaction mechanisms. This work aims to provide a rapid antibiotic-antimicrobial biochemical diagnostic tool to reduce the timeframes of therapeutic development, while also generating new biochemical insight into an antimicrobial-therapeutic screening assay in a complex matrix. The work evaluates the effect of antimicrobial action through ‘traditional’ microbiological analysis techniques with a high-throughput rapid analysis method using UV-VIS spectroscopy and chemometrics. Bacteriostatic activity from tetracycline and bactericidal activity from amoxicillin were evaluated on a system using nonresistant O157:H7 by confocal laser scanning microscopy (CLSM), scanning electron microscopy (SEM), and UV-VIS spectroscopy (high-throughput analysis). The data were analysed using principal component analysis (PCA) and support vector machine (SVM) classification.”
      原文链接:
    • NETL
    • NSTL
    • Royal Soc Chemistry

    Department of Radiology Reports Findings in Artificial Intelligence (CT angiography prior to endovascular procedures: can artificial intelligence improve reporting?)

    108-108页
    查看更多>>摘要：New research on Artificial Intelligence is the subject of a report. According to news reporting originating from Mantova, Italy, by NewsRx correspondents, research stated, “CT angiography prior to endovascular aortic surgery is the standard non-invasive imaging method for evaluation of aortic dimensions and access sites. A detailed report is crucial to a proper planning.” Our news editors obtained a quote from the research from the Department of Radiology, “We assessed Artificial Intelligence (AI)-algorithm accuracy to measure vessels diameters at CT prior to transcatheter aortic valve implantation (TAVI). CT scans of 50 patients were included. Two Radiologists with experience in vascular imaging together manually assessed diameters at nine landmark positions according to the American Heart Association guidelines: 450 values were obtained. We implemented TOST (Two One- Sided Test) to determine whether the measurements were equivalent to the values obtained from the AI algorithm. When the equivalence bound was a range of ± 2 mm the test showed equivalence for every point; if the range was equal to ± 1 mm the two measurements were not equivalent in 6 points out of 9 (p-value ＞0.05), close to the aortic valve. The time for automatic evaluation (average 1 min 47 s) was significantly lower compared with manual measurements (5 min 41 s) (p ＜0.01).”
      原文链接:
    • NETL
    • NSTL

    Findings from Johns Hopkins University Provides New Data about Artificial Intelligence [Artificial Intelligence (Ai)-it’s the End of the Tox As We Know It (And I Feel Fine)]

    109-109页
    查看更多>>摘要：Researchers detail new data in Artificial Intelligence. According to news reporting from Baltimore, Maryland, by NewsRx journalists, research stated, “The rapid progress of AI impacts diverse scientific disciplines, including toxicology, and has the potential to transform chemical safety evaluation. Toxicology has evolved from an empirical science focused on observing apical outcomes of chemical exposure, to a data-rich field ripe for AI integration.” Funders for this research include Directorate-General for Research and Innovation, European Union (EU), National Institutes of Health (NIH) - USA. The news correspondents obtained a quote from the research from Johns Hopkins University, “The volume, variety and velocity of toxicological data from legacy studies, literature, high-throughput assays, sensor technologies and omics approaches create opportunities but also complexities that AI can help address. In particular, machine learning is well suited to handle and integrate large, heterogeneous datasets that are both structured and unstructured-a key challenge in modern toxicology. AI methods like deep neural networks, large language models, and natural language processing have successfully predicted toxicity endpoints, analyzed high-throughput data, extracted facts from literature, and generated synthetic data. Beyond automating data capture, analysis, and prediction, AI techniques show promise for accelerating quantitative risk assessment by providing probabilistic outputs to capture uncertainties. AI also enables explanation methods to unravel mechanisms and increase trust in modeled predictions. However, issues like model interpretability, data biases, and transparency currently limit regulatory endorsement of AI. Multidisciplinary collaboration is needed to ensure development of interpretable, robust, and human-centered AI systems. Rather than just automating human tasks at scale, transformative AI can catalyze innovation in how evidence is gathered, data are generated, hypotheses are formed and tested, and tasks are performed to usher new paradigms in chemical safety assessment.”
      原文链接:
    • NETL
    • NSTL
    • Springer Nature

    Researchers at University of Utah Report New Data on Support Vector Machines (A Non-contrast Multi-parametric Mri Biomarker for Assessment of Mr-guided Focused Ultrasound Thermal Therapies)

    110-111页
    查看更多>>摘要：Fresh data on Support Vector Machines are presented in a new report. According to news reporting originating from Salt Lake City, Utah, by NewsRx correspondents, research stated, “<bold>Objective:</bold>We present the development of a non-contrast multi-parametric magnetic resonance (MPMR) imaging biomarker to assess treatment outcomes for magnetic resonance-guided focused ultrasound (MRgFUS) ablations of localized tumors. Images obtained immediately following MRgFUS ablation were inputs for voxel-wise supervised learning classifiers, trained using registered histology as a label for thermal necrosis. <bold >Methods: </bold >VX2 tumors in New Zealand white rabbits quadriceps were thermally ablated using an MRgFUS system under 3 T MRI guidance.” Financial support for this research came from Huntsman Cancer Foundation. Our news editors obtained a quote from the research from the University of Utah, “Animals were reimaged three days post-ablation and euthanized. Histological necrosis labels were created by 3D registration between MR images and digitized H&E segmentations of thermal necrosis to enable voxel-wise classification of necrosis. Supervised MPMR classifier inputs included maximum temperature rise, cumulative thermal dose (CTD), post-FUS differences in T2-weighted images, and apparent diffusion coefficient, or ADC, maps. A logistic regression, support vector machine, and random forest classifier were trained in red a leave-one-out strategy in test data from four subjects. <bold >Results: </bold >In the validation dataset, the MPMR classifiers achieved higher recall and Dice than a clinically adopted 240 cumulative equivalent minutes at 43 degrees C (CEM (43)) threshold (0.43) in all subjects.”
      原文链接:
    • NETL
    • NSTL
    • IEEE

    Studies from National Institute of Technology Rourkela Have Provided New Information about Artificial Intelligence (A Comprehensive Review of Datasets for Detection and Localization of Video Anomalies: a Step Towards Data-centric Artificial ...)

    111-112页
    查看更多>>摘要：Investigators publish new report on Artificial Intelligence. According to news reporting originating from Odisha, India, by NewsRx correspondents, research stated, “Video anomaly detection and localization is one of the key components of the intelligent video surveillance system. Video anomaly detection refers to the process of spatiotemporal localization of the abnormal or anomalous pattern present in the video.” Funders for this research include IMPACTING RESEARCH INNOVATION AND TECHNOLOGY (IMPRINT) INDIA, Ministry of Human Resource Development (MHRD), Government of India, Ministry of Housing and Urban Affairs, Government of India. Our news editors obtained a quote from the research from the National Institute of Technology Rourkela, “The performance of the deep learning-based video anomaly detector depends on the quality and quantity of the video anomaly datasets used for training. However, there is a scarcity of effective video anomaly datasets due to inherent natures such as rareness, context-dependency, and equivocal nature. Further, state-of-theart lacks a review that presents a comprehensive study of video anomaly datasets, including issues associated with the existing datasets, comparative analysis of the available datasets, potential solutions using both model-centric and data-centric approaches. Hence, a comprehensive review of the publicly available video anomaly datasets for video anomaly detection and localization is presented in this article. Further, a comparative study of the existing video anomaly datasets at qualitative and quantitative levels is presented to decide the right strategies for the desired application. Subsequently, model-centric and data-centric approaches required to solve various problems associated with the video anomaly datasets are presented.”
      原文链接:
    • NETL
    • NSTL
    • Springer Nature

    University of Paris Descartes Reports Findings in Prostatectomy (Urinary symptoms change and quality of life after robotic radical prostatectomy: a secondary analysis of a randomized controlled trial)

    112-113页
    查看更多>>摘要：New research on Surgery - Prostatectomy is the subject of a report. According to news reporting originating from Paris, France, by NewsRx correspondents, research stated, “To present the patient-reported QoL outcomes from a prospective, randomized controlled trial comparing the use of pelvic floor muscle training (PFMT) and duloxetine after robot-assisted radical prostatectomy (RARP). We identified 213 men with organ-confined disease having post-RARP urinary incontinence who were randomly assigned to received PFMT, duloxetine, combined PFMT-duloxetine and pelvic floor muscle home exercises.” Our news editors obtained a quote from the research from the University of Paris Descartes, “Urinary symptoms burden was measured by marked clinical important difference improvement (MCID) defined by using the International Prostate Symptom Score(IPSS) difference of -8 points(DIPSS -8). QoL was assessed according to Visual Analog Scale(VAS), King’s Health Questionnaire(KQH), and International Index of Erectile Function(IIEF-5). Multivariable regression analyses aimed to predict MCID, burden of urinary symptoms (IPSS 8), and patients reporting to be satisfied (IPSS QoL 2) or comfortable (VAS 1) post-RARP. Moderate to severe urinary symptoms decreased from 48% pre-operatively to 40%, 34% and 23% at 3mo, 6mo and 12mo post-RARP. After surgery, MCID improvement was observed in 19% of patients, and deterioration in 3.3%. Large prostate was the only factor associated to MCID (OR 1.03 [95%CI 1.01-1.05], p=0.005). At 6mo, patients reached the same degree of preoperative satisfaction. NVB preservation was the only predictor of being comfortable regarding urinary symptoms postoperatively (OR12.8 [CI95% 1.47-111.7],P=0.02 at 3mo) and was also associated to higher median postoperative IIEF-5. Despite UI following RARP, patients with larger prostates experience a reduction of lower urinary tract symptoms (LUTS) within a year, which subsequently elevates QoL.”
      原文链接:
    • NETL
    • NSTL