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 Coventry, United Kingdom, by News Rx correspondents, research stated, “Molecular dynamics (MD) simulations can red uce our need for experimental tests and provide detailed insight into the chemic al reactions and binding kinetics. There are two challenges while dealing with M D simulations: one is the time and length scale limitations, and the latter is e fficiently processing the massive amount of data resulting from the MD simulatio ns and generating the proper reaction rates.” Our news journalists obtained a quote from the research from the University of W arwick, “In this work, we evaluated the use of regression machine learning (ML) methods to solve these two challenges by developing a framework for ethanol adso rption on an Aluminium (Al) slab. This framework comprises three main stages: fi rst, an all-atom molecular dynamics model; second, ML regression models; and thi rd, validation and testing. In stage one, the adsorption of ethanol molecules on the Al surface for various temperatures, velocities and concentrations is simul ated using the large-scale atomic/molecular massively parallel simulator (LAMMPS ) and ReaxFF. The outcome of stage one is utilised for training, testing, and va lidating the predictive models in stages two and three. We developed and evaluat ed 28 different ML models for predicting the number of adsorbed molecules over t ime, including linear regression, support vector machine (SVM), decision trees, ensemble, Gaussian process regression (GPR), neural network (NN) and Bayesian hy per-parameter optimisation models. Based on the results, the Bayesian-based GPR showed the highest accuracy and the lowest training time. The developed model ca n predict the number of adsorbed molecules for new cases within seconds, while M D simulations take a few weeks. This adsorption rate can then be used in macrosc ale simulations to tackle the time and length scale limitations.”
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 ro botics. According to news reporting originating from Guangzhou, People’s Republi c of China, by NewsRx correspondents, research stated, “The speed and accuracy o f navigation road extraction and driving stability affect the inspection accurac y of cage chicken coop inspection robots.” Funders for this research include Guangdong Chaozhou Science And Technology Plan ning Project; State Key Laboratory of Swine And Poultry Breeding Industry (Pi) R esearch Project; Guangdong Province Special Fund For Modern Agricultural Industr y Common Key Technology R&D Innovation Team. Our news reporters obtained a quote from the research from South China Agricultu ral University: “In this paper, a new grayscale factor (4B-3R-2G) was proposed t o achieve fast and accurate road extraction, and a navigation line fitting algor ithm based on the road boundary features was proposed to improve the stability o f the algorithm. The proposed grayscale factor achieved 92.918% se gmentation accuracy, and the speed was six times faster than the deep learning m odel. The experimental results showed that at the speed of 0.348 m/s, the maximu m deviation of the visual navigation was 4 cm, the average deviation was 1.561 c m, the maximum acceleration was 1.122 m/s2, and the average acceleration was 0.2 92 m/s2, with the detection number and accuracy increased by 21.125% and 1.228%, respectively.”
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 reporting from Nanjing, People’s Republic of China, by NewsRx journalists, research stated, “Hydrometeo rological monitoring through satellites in arid and semi-arid regions is constra ined by the coarse spatial resolution of precipitation data, which impedes detai led analyses. The objective of this study is to evaluate various machine learnin g techniques for developing a downscaling framework that generates high spatio-t emporal resolution precipitation products.” Our news reporters obtained a quote from the research from Nanjing Tech Universi ty: “Focusing on the Hai River Basin, we evaluated three machine learning approa ches-Extreme Gradient Boosting (XGBoost), Random Forest (RF), and Back Propagati on (BP) neural networks. These methods integrate environmental variables includi ng land surface temperature (LST), Normalized Difference Vegetation Index (NDVI) , Digital Elevation Model (DEM), Precipitable Water Vapor (PWV), and albedo, to downscale the 0.1° spatial resolution Global Precipitation Measurement (GPM) pro duct to a 1 km resolution. We further refined the results with residual correcti on and calibration using terrestrial rain gauge data. Subsequently, utilizing th e 1 km annual precipitation, we employed the moving average window method to der ive monthly and daily precipitation. The results demonstrated that the XGBoost m ethod, calibrated with Geographical Difference Analysis (GDA) and Kriging spatia l interpolation, proved to be the most accurate, achieving a Mean Absolute Error (MAE) of 58.40 mm for the annual product, representing a 14 % imp rovement over the original data. The monthly and daily products achieved MAE val ues of 11.61 mm and 1.79 mm, respectively, thus enhancing spatial resolution whi le maintaining accuracy comparable to the original product.”
查看更多>>摘要：By a News Reporter-Staff News Editor at Robotics & Machine Learning Daily News Daily News – New research on Autoimmune Diseases an d Conditions - Rheumatoid Arthritis is the subject of a report. According to new s reporting from Puebla, Mexico, by NewsRx journalists, research stated, “This s tudy aimed to investigate the current status and performance of machine learning (ML) approaches in providing reproducible treatment response predictions. This systematic review was conducted in accordance with the PRISMA statement and the CHARMS checklist.” The news correspondents obtained a quote from the research from the National Ins titute of Astrophysics, “We searched PubMed, Cochrane Library, Web of Science, S copus, and EBSCO databases for cohort studies that derived and/or validated ML m odels focused on predicting rheumatoid arthritis (RA) treatment response. We ext racted data and critically appraised studies based on the Transparent Reporting of a Multivariable Prediction Model for Individual Prognosis or Diagnosis (TRIPO D) and Prediction Model Risk of Bias Assessment Tool (PROBAST) guidelines. From 210 unduplicated records identified by the literature search, we retained 29 eli gible studies. Of these studies, 10 developed a predictive model and reported a mean adherence to the TRIPOD guidelines of 45.6 % (95 % CI: 38.3-52.8 %). The remaining 19 studies not only developed a pre dictive model but also validated it externally, with a mean adherence of 42.9 % (95 % CI: 39.1-46.6 %). Most of the articles had an u nclear risk of bias (41.4 %), followed by a high risk of bias, whic h was present in 37.9 %. In recent years, ML methods have been incr easingly used to predict treatment response in RA.”
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 Montreal, Ca nada, by NewsRx correspondents, research stated, “Most solidstate lithium elect rolytes are disordered ionic crystalline materials that possess crystallographic sites that can be vacant or occupied by different ions. The presence of these p artially occupied sites enables lithium diffusion through their lattice and make s such materials promising for developing all-solid batteries.” Financial supporters for this research include Natural Sciences and Engineering Research Council of Canada (NSERC), Triagency Institutional Programs Secretariat through New Frontiers in Research Fund. Our news editors obtained a quote from the research from McGill University, “Hig h-throughput computational screening of such materials must bypass costly DFT sa mpling of disordered configurations and, therefore, commonly relies on the compu tationally efficient Coulomb approximation to find just a few representative low -energy ionic configurations, for which DFT is then used to quickly predict a nu mber of important materials’ properties, such as the electrochemical stability w indow. This work demonstrates, using the Li-La-Ti-O solid electrolyte (LLTO) as an example, that the Coulomb approximation fails to correctly detect the most st able arrangement of Li and La ions in the LLTO, which has a noticeable impact on the accuracy of subsequent computational prediction of the electrochemical stab ility window of the material. The analysis herein shows that the sampling proble m arises from the relatively modest geometry relaxation of the LLTO lattice. A k ernel ridge regression machine learning (ML) method employing the smooth overlap of atomic positions as a structure descriptor (SOAP-KRR) leads to significant i mprovements in detecting the most stable configurations of the LLTO. The univers al ML potential based on the multiple atomic cluster expansion is also found to be reliable but to a lesser extent than SOAP-KRR.”
查看更多>>摘要：By a News Reporter-Staff News Editor at Robotics & Machine Learning Daily News – Data detailed on artificial intelligence have bee n presented. According to news reporting out of Thiagarajar College of Engineeri ng by NewsRx editors, research stated, “Paddy is a major crop in India which is highly affected by the weather variables resulting in drastic reduction of its y ield; adverse all the variables drastically reduce the paddy yield.” Our news correspondents obtained a quote from the research from Thiagarajar Coll ege of Engineering: “In this research, machine learning model was developed for prediction of paddy yield production by linear regression (LR), random forest re gression (RFR), support vector regression (SVR), cat boost regression (CBR), and hybrid machine learning with variance inflation factor (VIF) LR-VIF, RFR-VIF, S VR-VIF, and CBR-VIF techniques. The dataset consists of variables (weather) for more than 15 years collected for the study area which is Madurai district, Tamil Nadu in India. Analysis was carried out by fixing 70% of data cal ibration & remaining 30% for validation in Jupyter n otebook (Python programming). Results showed that CBR-VIF performed having nRMSE value 1.23 to 1.40% for Madurai South, nRMSE value 0.56 to 1.40% for Melur, nRMSE value 1.10 to 1.25% for Usilampatti, and nRMSE va lue 0.75 to 1.10% for Thirumangalam.”
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 artificial intelligence. According to news reporting from Khulna University by N ewsRx journalists, research stated, “Adhesive bonded joints hold significant imp ortance across various industrial sectors in modern engineering, owing to their lightweight nature and myriad advantages.” The news correspondents obtained a quote from the research from Khulna Universit y: “The rising demand for trimaterial joints underscores their utility and versa tility. In these joints, the choice of materials for both adherends greatly infl uences their strength, structural reliability, and overall characteristics. Whil e numerous researches have extensively analyzed stress distributions, their effe cts, and behaviors, many have relied on a one-factor-at-a-time approach, focusin g solely on individual design variables’ effects. However, recognizing the intri cate interplay among various material combinations and their collective impact o n overall performance, this study employs various types of White-box, Black-box, and Grey-box machine learning algorithms to identify an optimized ML model as w ell as predict stress distributions for any random combinations of upper and low er adherend materials. Dataset of total 178 random material combinations were ut ilized for the training phases with 5-fold cross validation and model tuning. Ho wever, the decision tree regressor emerged as the optimized model by comparing t he quantitative metrics of accuracy benchmark as well as the prediction outcomes obtained through all the machine learning models.”
查看更多>>摘要：By a News Reporter-Staff News Editor at Robotics & Machine Learning Daily News Daily News – Fresh data on androids are presented i n a new report. According to news reporting originating from Genoa, Italy, by Ne wsRx correspondents, research stated, “During social interactions, actions can b e performed with different forms as a function of the mood driving them.” Financial supporters for this research include European Research Council. Our news journalists obtained a quote from the research from Italian Institute o f Technology: “These action forms i.e. vitality forms (VFs), have a strong influ ence in human interactions allowing people to immediately understand the attitud e of others. Moreover, it has been demonstrated that the gentle and rude VFs exp ressed by a human agent influence the motor behavior of the receiver. An intrigu ing issue to investigate was to assess whether and how a humanoid agent, able to generate VFs, may induce the same contagion effect on the human partner. To thi s purpose we carried out a kinematic experiment investigating the motor behavior of participants in response to actions (taking request) performed by the iCub r obot with different VFs in video and live interactive contexts. During the exper iment, participants were required to pay attention to the iCub robot request and subsequently to place a ball on a specific target.”
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 Chicago, Illino is, by NewsRx correspondents, research stated, “To discuss the worldwide applica tions and potential impact of artificial intelligence (AI) for the diagnosis, ma nagement and analysis of treatment outcomes of common retinal diseases. We perfo rmed an online literature review, using PubMed Central (PMC), of AI applications to evaluate and manage retinal diseases.” Our news journalists obtained a quote from the research from the University of I llinois Chicago, “Search terms included AI for screening, diagnosis, monitoring, management, and treatment outcomes for age-related macular degeneration (AMD), diabetic retinopathy (DR), retinal surgery, retinal vascular disease, retinopath y of prematurity (ROP) and sickle cell retinopathy (SCR). Additional search term s included AI and color fundus photographs, optical coherence tomography (OCT), and OCT angiography (OCTA). We included original research articles and review ar ticles. Research studies have investigated and shown the utility of AI for scree ning for diseases such as DR, AMD, ROP, and SCR. Research studies using validate d and labeled datasets confirmed AI algorithms could predict disease progression and response to treatment. Studies showed AI facilitated rapid and quantitative interpretation of retinal biomarkers seen on OCT and OCTA imaging. Research art icles suggest AI may be useful for planning and performing robotic surgery. Stud ies suggest AI holds the potential to help lessen the impact of socioeconomic di sparities on the outcomes of retinal diseases. AI applications for retinal disea ses can assist the clinician, not only by disease screening and monitoring for d isease recurrence but also in quantitative analysis of treatment outcomes and pr ediction of treatment response.”
查看更多>>摘要：By a News Reporter-Staff News Editor at Robotics & Machine Learning Daily News Daily News – New research on Phaeochromocytomas is the subject of a report. According to news reporting originating in Olomouc, Cze ch Republic, by NewsRx journalists, research stated, “The topic of the diagnosis of phaeochromocytomas remains highly relevant because of advances in laboratory diagnostics, genetics, and therapeutic options and also the development of imag ing methods. Computed tomography still represents an essential tool in clinical practice, especially in incidentally discovered adrenal masses; it allows morpho logical evaluation, including size, shape, necrosis, and unenhanced attenuation. ” The news reporters obtained a quote from the research from Palacky University, “ More advanced post-processing tools to analyse digital images, such as texture a nalysis and radiomics, are currently being studied. Radiomic features utilise di gital image pixels to calculate parameters and relations undetectable by the hum an eye. On the other hand, the amount of radiomic data requires massive computer capacity. Radiomics, together with machine learning and artificial intelligence in general, has the potential to improve not only the differential diagnosis bu t also the prediction of complications and therapy outcomes of phaeochromocytoma s in the future.”
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