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 Surat T hani, Thailand, by NewsRx editors, the research stated, "Oil palm cultivation th rives as a prominent agricultural endeavor within the southern region of Thailan d, where the country ranks third globally in production, following Malaysia and Indonesia." Financial supporters for this research include National Science, Research And In novation Fund (Nsrf) And Prince of Songkla Universit. Our news correspondents obtained a quote from the research from Prince of Songkl a University: "The assessment of oil palm bunch ripeness serves various purposes , notably in determining purchasing prices, pre-harvest evaluations, and evaluat ing the impacts of disasters or low market prices. Presently, two predominant me thods are employed for this assessment, namely human evaluation, and machine lea rning for ripeness classification. Human assessment, while boasting high accurac y, necessitates the involvement of farmers or experts, resulting in prolonged pr ocessing times, especially when dealing with extensive datasets or dispersed fie lds. Conversely, machine learning, although capable of accurately classifying ha rvested oil palm bunches, faces limitations concerning its inability to process images of oil palm bunches on trees and the absence of a platform for on-tree ri peness classification. Considering these challenges, this study introduces the d evelopment of a classification platform leveraging machine learning (deep learni ng) in conjunction with geospatial analysis and visualization to ascertain the r ipeness of oil palm bunches while they are still on the tree. The research outco mes demonstrate that oil palm bunch ripeness can be accurately and efficiently c lassified using a mobile device, achieving an impressive accuracy rate of 99.89% with a training dataset comprising 8779 images and a validation accuracy of 96.1 2% with 1160 images."
查看更多>>摘要：By a News Reporter-Staff News Editor at Robotics & Machine Learning Daily News-Current study results on Robotics have been publi shed. According to news reporting out of Baltimore, Maryland, by NewsRx editors, research stated, "PurposeEye gaze tracking and pupillometry are evolving areas within the field of tele-robotic surgery, particularly in the context of estimat ing cognitive load (CL). However, this is a recent field, and current solutions for gaze and pupil tracking in robotic surgery require assessment." Financial supporters for this research include Intuitive Surgical, Intuitive Sur gical Technology Advancement Grant. Our news journalists obtained a quote from the research from Johns Hopkins Unive rsity, "Considering the necessity of stable pupillometry signals for reliable co gnitive load estimation, we compare the accuracy of three eye trackers, includin g head and console-mounted designs.MethodsWe conducted a user study with the da Vinci Research Kit (dVRK), to compare the three designs. We collected eye tracki ng and dVRK video data while participants observed nine markers distributed over the dVRK screen. We compute and analyze pupil detection stability and gaze pred iction accuracy for the three designs.ResultsHead-worn devices present better st ability and accuracy of gaze prediction and pupil detection compared to consolemounted systems."
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 originating from Manhattan, Kansas , by NewsRx correspondents, research stated, "Phishing email attacks are among t he most common and most harmful cybersecurity attacks." Funders for this research include Nsf. The news correspondents obtained a quote from the research from Kansas State Uni versity: "With the emergence of generative AI, phishing attacks can be based on emails generated automatically, making it more difficult to detect them. That is , instead of a single email format sent to a large number of recipients, generat ive AI can be used to send each potential victim a different email, making it mo re difficult for cybersecurity systems to identify the scam email before it reac hes the recipient. Here, we describe a corpus of AI-generated phishing emails. W e also use different machine learning tools to test the ability of automatic tex t analysis to identify AI-generated phishing emails. The results are encouraging , and show that machine learning tools can identify an AI-generated phishing ema il with high accuracy compared to regular emails or human-generated scam emails. By applying descriptive analytics, the specific differences between AI-generate d emails and manually crafted scam emails are profiled and show that AI-generate d emails are different in their style from human-generated phishing email scams. "
查看更多>>摘要：By a News Reporter-Staff News Editor at Robotics & Machine Learning Daily News Daily News-New research on Artificial Intelligenc e is the subject of a report. According to news originating from Zhejiang, Peopl e's Republic of China, by NewsRx correspondents, research stated, "The integrati on of artificial intelligence (AI) in ophthalmology has promoted the development of the discipline, offering opportunities for enhancing diagnostic accuracy, pa tient care, and treatment outcomes. This paper aims to provide a foundational un derstanding of AI applications in ophthalmology, with a focus on interpreting st udies related to AI-driven diagnostics." Our news journalists obtained a quote from the research from Zhejiang University , "The core of our discussion is to explore various AI methods, including deep l earning (DL) frameworks for detecting and quantifying ophthalmic features in ima ging data, as well as using transfer learning for effective model training in li mited datasets. The paper highlights the importance of high-quality, diverse dat asets for training AI models and the need for transparent reporting of methodolo gies to ensure reproducibility and reliability in AI studies. Furthermore, we ad dress the clinical implications of AI diagnostics, emphasizing the balance betwe en minimizing false negatives to avoid missed diagnoses and reducing false posit ives to prevent unnecessary interventions. The paper also discusses the ethical considerations and potential biases in AI models, underscoring the importance of continuous monitoring and improvement of AI systems in clinical settings."
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 artificial in telligence have been published. According to news reporting out of Berkeley, Cal ifornia, by NewsRx editors, research stated, "The flexibility of metal-organic f rameworks (MOFs) affects their gas adsorption and diffusion properties." Funders for this research include National Science Foundation; Prytanean Foundat ion; Alfred P. Sloan Foundation. The news reporters obtained a quote from the research from University of Califor nia: "However, reliable force fields for simulating flexible MOFs are lacking. A s a result, most atomistic simulations so far have been carried out assuming rig id MOFs, which inevitably overestimates the gas adsorption energy. Here, we show that this issue can be addressed by applying a machine-learning potential, trai ned on quantum chemistry data, to atomistic simulations."
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 - Prostate Ca ncer is the subject of a report. According to news reporting originating from Fl orence, Italy, by NewsRx correspondents, research stated, "We sought to investig ate whether surgical delay may be associated with pathological upstaging in pati ents treated with robot assisted radical prostatectomy (RARP) for localized and locally advanced prostate cancer (PCa). Consecutive firstly-diagnosed PCa patien ts starting from March 2020 have been enrolled." Our news editors obtained a quote from the research from the University of Flore nce, "All the patients were categorized according to EAU risk categories for PCa risk. Uni- and multivariate analysis were fitted to explore clinical and surgic al predictors of pathological upstaging to locally advanced disease (pT3/pT4 - p N1 disease). Overall 2017 patients entered the study. Median age at surgery was 68 (IQR 63-73) years. Overall low risk, intermediate risk, localized high risk a nd locally advanced disease were recorded in 368 (18.2 %), 1071 (53 .1 %), 388 (19.2 %) and 190 (9.4 %), resp ectively. Median time from to diagnosis to treatment was 51 (IQR 29-70) days. Ti me to surgery was 56 (IQR 32-75), 52 (IQR 30-70), 45 (IQR 24-60) and 41 (IQR 22- 57) days for localized low, intermediate and high risk and locally advanced dise ase, respectively. Considering 1827 patients with localized PCa, at multivariate analysis ISUP grade group 4 on prostate biopsy (HR: 1.30; 95 % CI 1.07-1.86; p = 0.02) and surgical delay only in localized high-risk disease (HR : 1.02; 95 % CI 1.01-1.54; p = 0.02) were confirmed as independent predictors of pathological upstaging to pT3-T4/pN1 disease at final histopathol ogical examination."
查看更多>>摘要：By a News Reporter-Staff News Editor at Robotics & Machine Learning Daily News Daily News-New research on Machine Learning is th e subject of a report. According to news originating from Nancy, France, by News Rx correspondents, research stated, "Magnetically confined fusion plasmas are su bject to various instabilities that cause turbulent transport of particles and h eat across the magnetic field. In the edge plasma region, this transport takes t he form of long filaments stretched along the magnetic field lines." Our news journalists obtained a quote from the research from the University of L orraine, "Understanding the dynamics of these filaments, referred to as blobs, i s crucial for predicting and controlling their impact on reactor performance. To achieve this, highly resolved passive fast camera measurements have been conduc ted on the COMPASS tokamak. These measurements are analyzed using both conventio nal tracking methods and a custom-developed machine-learning approach designed t o characterize more particularly the mutual interactions between filaments. Our findings demonstrate that up to 18% of blobs exhibit mutual intera ctions in the investigated area close to the separatrix, at the border between c onfined and nonconfined plasma. Notably, we present direct observations and radi al dependence of blob coalescence and splitting, rapid reversals in the propagat ion direction of the blob, as well as their dependence on the radial position."
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 are presented i n a new report. According to news reporting originating from Beijing, People's R epublic of China, by NewsRx correspondents, research stated, "Robotic assembly l ines are widely applied in the manufacturing sector to produce a wide range of p roducts because of their efficiency and multifunctionality. The robotic assembly line balancing problem (RALBP) is a combinatorial optimization problem where th e decision variables are task assignment and robot allocation." Financial supporters for this research include National Natural Science Foundati on of China (NSFC), Ministry of Education, China. Our news editors obtained a quote from the research from the Beijing University of Technology, "However, RALBP considering carbon footprint, which is a very sig nificant environmental concern, has scarcely been studied in the literature and a practical ‘cross-station'design is never mathematically formulated. In this pa per, a mixed -integer programming model is proposed to optimize the two objectiv es according to the Pareto principle. A particle swarm algorithm (PSO) with some improvement rules is designed to solve the problem. To examine the efficiency o f the algorithm, computational experiments including five medium-sized and five large -sized datasets are conducted. The results show that the efficiency of PSO is better than that of four other classic algorithms in terms of three evaluati on metrics."
查看更多>>摘要：By a News Reporter-Staff News Editor at Robotics & Machine Learning Daily News Daily News-New research on Machine Learning is th e subject of a report. According to news reporting originating from Giessen, Ger many, by NewsRx correspondents, research stated, "We present a case study on how to improve an existing metal-free catalyst for a particularly difficult reactio n, namely, the Corey-Bakshi-Shibata (CBS) reduction of butanone, which constitut es the classic and prototypical challenge of being able to differentiate a methy l from an ethyl group. As there are no known strategies on how to address this c hallenge, we leveraged the power of machine learning by constructing a realistic (for a typical laboratory) small, albeit high-quality, data set of about 100 re actions (run in triplicate) that we used to train a model in combination with a key-intermediate graph (of substrate and catalyst) to predict the differences in Gibbs activation energies DD of the enantiomeric reaction paths." Our news editors obtained a quote from the research from Justus-Liebig-Universit y, "With the help of this model, we were able to select and subsequently screen a small selection of catalysts and increase the selectivity for the CBS reductio n of butanone to 80% enantiomeric excess (ee), the highest possibl e value achieved to date for this substrate with a metal-free catalyst, thereby also exceeding the best available enzymatic systems (64% ee) and t he selectivity with Corey's original catalyst (60% ee). This trans lates into a>50% improvement in relative D from 0.9 to 1.4 kcal mol. We underscore the transformative potential of machine learning in accelerating catalyst design because we rely on a manageable small data set and a key-intermediate graph representing a combination of catalyst and substrate graphs in lieu of a transition-state model."
查看更多>>摘要：By a News Reporter-Staff News Editor at Robotics & Machine Learning Daily News Daily News-New research on Cancer is the subject of a report. According to news reporting originating from Gdansk, Poland, by New sRx correspondents, research stated, "Circulating tumor cells (CTCs) are tumor c ells that separate from the solid tumor and enter the bloodstream, which can cau se metastasis. Detection and enumeration of CTCs show promising potential as a p redictor for prognosis in cancer patients." Funders for this research include Narodowe Centrum Nauki, Narodowe Centrum Badan i Rozwoju. Our news editors obtained a quote from the research from the Gdansk University o f Technology, "Furthermore, single-cells sequencing is a technique that provides genetic information from individual cells and allows to classify them precisely and reliably. Sequencing data typically comprises thousands of gene expression reads per cell, which artificial intelligence algorithms can accurately analyze. This work presents machine-learning-based classifiers that differentiate CTCs f rom peripheral blood mononuclear cells (PBMCs) based on single cell RNA sequenci ng data. We developed four tree-based models and we trained and tested them on a dataset consisting of Smart-Seq2 sequenced data from primary tumor sections of breast cancer patients and PBMCs and on a public dataset with manually annotated CTC expression profiles from 34 metastatic breast patients, including triple-ne gative breast cancer. Our best models achieved about 95% balanced accuracy on the CTC test set on per cell basis, correctly detecting 133 out of 1 38 CTCs and CTC-PBMC clusters."
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