查看更多>>摘要：Investigators discuss new findings in Artificial Intelligence. According to news reporting out of Tuscaloosa, Alabama, by NewsRx editors, research stated, "In this study, we examine auditors' relian ce on artificial intelligence (AI) systems that are designed to provide evidence around complex estimates. In an experiment with highly experienced auditors, we find that auditors are more hesitant to rely on evidence from AI-based systems compared to human specialists, consistent with algorithm aversion." Financial support for this research came from Research Council of Norway.
查看更多>>摘要：New research on Robotics is the subjec t of a report. According to news reporting from Beijing, People's Republic of Ch ina, by NewsRx journalists, research stated, "This study compared a multimodal i mage-guided robot and three-dimensionally (3D) printed templates for implanting iodine-125 (I) radioactive seeds in patients with malignant tumours in the skull base and deep facial region. Seventeen patients who underwent I radioactive see d implantation between December 2018 and December 2019 were included." The news correspondents obtained a quote from the research from the Medical Scho ol of Chinese People's Liberation Army (PLA), "The operation time, intraoperativ e blood loss, and accuracy of seed implantation were compared between the multim odal image-guided robot-assisted implantation (experimental) group (n = 7) and 3 D-printed template-assisted implantation (control) group (n = 10). In total, 291 seeds were implanted in the experimental group and 436 in the control group; th e mean error of seed implantation accuracy was 1.95 ± 0.13 mm and 1.90 ± 0.08 mm , respectively (P = 0.309). The preparation time was 26.13 ± 5.28 min in the exp erimental group and 0 min in the control group, while the average operation time was 34.44 ± 6.39 min versus 43.70 ± 6.06 min, respectively. The intraoperative blood loss was 4.96 ± 1.76 ml (experimental) versus 8.97 ± 2.99 ml (control) (P = 0.123)."
查看更多>>摘要：A new study on artificial intelligence is now available. According to news reporting from the Lawrence Berkeley Nation al Laboratory by NewsRx journalists, research stated, "Electron energy loss spec troscopy (EELS) and X-ray absorption spectroscopy (XAS) provide detailed informa tion about bonding, distributions and locations of atoms, and their coordination numbers and oxidation states." Financial supporters for this research include U.S. Department of Energy. The news journalists obtained a quote from the research from Lawrence Berkeley N ational Laboratory: "However, analysis of XAS/EELS data often relies on matching an unknown experimental sample to a series of simulated or experimental standar d samples. This limits analysis throughput and the ability to extract quantitati ve information from a sample. In this work, we have trained a random forest mode l capable of predicting the oxidation state of copper based on its L-edge spectr um. Our model attains an R 2 score of 0.85 and a root mean square error of 0.24 on simulated data. It has also successfully predicted experimental L-edge EELS s pectra taken in this work and XAS spectra extracted from the literature. We furt her demonstrate the utility of this model by predicting simulated and experiment al spectra of mixed valence samples generated by this work."
查看更多>>摘要：New research on artificial intelligenc e is the subject of a new report. According to news reporting from Naples, Italy , by NewsRx journalists, research stated, "Earthquake location and magnitude est imation are critical for seismic monitoring and emergency response. However, acc urately determining the location and the magnitude of off-network earthquakes re mains challenging." Our news correspondents obtained a quote from the research from University of Na ples Federico II: "Seismic stations receive signals from various sources, and it is crucial to quickly discern whether events originated within the area of inte rest. Location determination relies on obtaining ample P-and S-wave readings to ensure accurate and dependable results. Seismic networks vary due to station ch anges or outages, and their variable geometry represents a constraint for tradit ional machine learning models, which rely on fixed data structures. This study p resents a novel approach for real-time classification of local and off-network e arthquakes using the first three associated P picks within an early warning scen ario, and also identifying the event's direction. To handle variable network geo metry, we employ a grid structure over the seismic area. The effectiveness of ou r method was initially validated with data from the Italian National Seismic Net work, selecting Central Italy and Messina Strait subnetworks, and from a subnetw ork of the Southern California Seismic Network; it achieves an inside-outside ac curacy of 95%, 93%, and 96%, and a locati on region accuracy of 93%, 82%, and 97%, respectively. Its robustness was further demonstrated using picks from an earthq uake early warning (EEW) system, the PRobabilistic and Evolutionary early warnin g SysTem (PRESTo) software, to simulate real and noncataloged input data."
查看更多>>摘要：Current study results on Machine Learn ing have been published. According to news originating from Miskolc, Hungary, by NewsRx correspondents, research stated, "Cellular materials are gaining popular ity in today's major sectors. The aim is to develop high-performance materials t o meet customer and application demands." Financial support for this research came from National Research, Development & Innovation Office (NRDIO) -Hungary. Our news journalists obtained a quote from the research from the University of M iskolc, "This study revolves around the beginning of the failure; computed tomog raphy and statistical image analysis assisted with machine learning were employe d to quantitatively characterize the occurring processes within the structure at the beginning of the well-known plateau effect. Simple techniques (e.g., easily obtainable shape parameters, forest decision, 2D image slices) were employed to sort the structural elements into seven classes based on their visual appearanc es."
查看更多>>摘要：Researchers detail new data in Machine Learning. According to news originating from Jaipur, India, by NewsRx correspon dents, research stated, "Identifying disruptive events (riots, protests, natural calamities) from social media is important for maintaining social order and add ressing geopolitical concerns. Existing works on identifying disruptive events u se classical machine learning (ML) models on static datasets." Financial support for this research came from National Supercomputing Mission (I ndia). Our news journalists obtained a quote from the research from the Malaviya Nation al Institute of Technology, "However, social networks are dynamic entities and c annot be practically modeled using static techniques. A viable alternative is th e emerging Continual Machine Learning (CML) approach which applies the knowledge acquired from the past to learn future tasks. However, existing CML techniques are trained and tested on static data and are incapable of handling real-time da ta obtained from dynamic environments. This paper presents a novel DiEvD framewo rk for disruptive event detection using Continual Machine Learning (CML) specifi cally for dynamic data streams. We have used Twitter social media as a case stud y of the real-time and dynamic data provider. To the best of our knowledge, this is the first attempt to use CML for socially disruptive event detection. Compre hensive performance analysis show that our framework effectively identifies disr uptive events with 98% accuracy and can classify them with an aver age incremental accuracy of 76.8%."
查看更多>>摘要：Data detailed on Machine Learning have been presented. According to news originating from Hefei, People's Republic of China, by NewsRx correspondents, research stated, "In the realm of numerical sim ulations concerning vehicle mobility, the establishment of a high-fidelity soil discrete element model often necessitates substantial parameter adjustments to a lign with the mechanical responses of actual soil. In pursuit of a rapid and pre cise calibration of the microparameters of the soil model, this paper describes an approach rooted in machine learning surrogate models." Funders for this research include Youth Innovation Promotion Association of the CAS, Dreams Foundation of Jianghuai Advance Technology Center, Hefei Key Common Technology Research and Development, "Unveiling and Leading" Project.
查看更多>>摘要：New research on Artificial Intelligenc e is the subject of a report. According to news reporting originating from Bari, Italy, by NewsRx correspondents, research stated, "Advances in DNA sequencing r evolutionized plant genomics and significantly contributed to the study of genet ic diversity. However, predicting phenotypes from genomic data remains a challen ge, particularly in the context of plant breeding." Our news editors obtained a quote from the research from the University of Bari ‘Aldo Moro', "Despite significant progress, accurately predicting phenotypes fro m high-dimensional genomic data remains a challenge, particularly in identifying the key genetic factors influencing these predictions. This study aims to bridg e this gap by integrating explainable artificial intelligence (XAI) techniques w ith advanced machine learning models. This approach is intended to enhance both the predictive accuracy and interpretability of genotype-to-phenotype models, th ereby improving their reliability and supporting more informed breeding decision s. This study compares several ML methods for genotype-to-phenotype prediction, using data available from an almond germplasm collection. After preprocessing an d feature selection, regression models are employed to predict almond shelling f raction. Best predictions were obtained by the Random Forest method (correlation = 0.727 ? 0.020, an = 0.511 ? 0.025, and an RMSE = 7.746 ? 0.199). Notably, the application of the SHAP (SHapley Additive exPlanations) values algorithm to exp lain the results highlighted several genomic regions associated with the trait, including one, having the highest feature importance, located in a gene potentia lly involved in seed development. Employing explainable artificial intelligence algorithms enhances model interpretability, identifying genetic polymorphisms as sociated with the shelling percentage."
查看更多>>摘要：Fresh data on Machine Learning are pre sented in a new report. According to news reporting originating from Leiden, Net herlands, by NewsRx correspondents, research stated, "Remote sensing imagery has an ever-increasing impact on important downstream applications, such as vegetat ion monitoring and climate change modelling. Clouds obscuring parts of the image s create a substantial bottleneck in most machine learning tasks that use remote sensing data, and being robust to this issue is an important technical challeng e." Financial supporters for this research include Netherlands Organization for Scie ntific Research (NWO), European Space Agency (ESA) under the Open Space Innovati on Platform (OSIP) research project "Physics-aware Automated Machine Learning (P A-AutoML) for Earth Observations", TAILOR -EU Horizon 2020 research and innovat ion programme.
查看更多>>摘要：Investigators discuss new findings in Robotics. According to news originating from Jiaozuo, People's Republic of China , by NewsRx correspondents, research stated, "Welding is a method of realizing m aterial connections, and the development of modern sensing technology is pushing this traditional process towards automation and intelligence. Among many sensin g methods, visual sensing stands out with its advantages of non -contact, fast r esponse and economic benefits, etc." Our news journalists obtained a quote from the research from HeNan Polytechnic U niversity, "This paper provides a comprehensive review of visualization methods in the context of specific welding processes in the following five aspects. The problem of IWP location is summarized from two directions of active and passive vision. Weld seam identification and tracking methods are discussed in detail ba sed on the morphological characteristics of the weld seam. The feasibility of di fferent weld path planning methods is analyzed based on the point cloud informat ion and the composite vision information. Two types of monitoring means based on infrared sensing and visible light sensing are summarized taking into account t he thermal and morphological characteristics of the weld pool, and welding defec t detection technology is summarized by comparing the intelligent detection algo rithms and the traditional detection algorithms."
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