查看更多>>摘要：New research on Support Vector Machines is the subject of a report. According to news reporting out of Kuala Lumpur, Malaysia, by NewsRx editors, research stated, “Stingless bee honey’s nutritional value is gaining attention, but the impact of harvesting seasons, specifically the rainy (September 2018) and dry (February 2019) seasons in Malaysia on the honey’s physicochemical properties and volatile compounds remains insufficiently explored. This research revealed marginal differences in the physicochemical properties between seasons.” Our news journalists obtained a quote from the research from International Islamic University Malaysia, “However, through individual bee species and cumulative data analysis, honey samples were effectively differentiated based on harvesting seasons. A set of seventeen volatile compounds were identified as potential chemical markers for distinguishing H. bakeri, G. thoracica, and T. binghami honey between rainy and dry seasons. For cumulative data, four significant markers were proposed.”
查看更多>>摘要：Current study results on Support Vector Machines have been published. According to news reporting originating from Jilin, People’s Republic of China, by NewsRx correspondents, research stated, “Tagged neutron detection system combined with support vector machine (SVM) is proposed for the detection of explosives hidden inside walls. The detection system was based on an ING-27 neutron generator as neutron source, two lutetium yttrium silicate (LYSO) detectors as gamma-detectors, and one silicon detector as alpha-detector.” Financial support for this research came from Ministry of Science and Technology of Jilin Province. Our news editors obtained a quote from the research from Northeast Normal University, “The difference in gamma-ray counts within the time window for different samples was combined with the peak area ratios of the elemental peaks in the gamma-energy spectra and used as input vectors for an SVM. A Gaussian kernel function was used as a kernel function and a grid search method as optimization of the penalty factor c and hyperparameter g of the SVM in this experiment. Fivefold cross-validation was used to evaluate the models developed. The correctness of the support vector machine was found to be 100%, 98.3%, and 95% for the target sample detection in that order, and the fivefold cross-validation accuracy was 100%, 97.5%, and 93.3%, respectively.”
查看更多>>摘要：Research findings on Artificial Intelligence are discussed in a new report. According to news reporting out of Amsterdam, Netherlands, by NewsRx editors, research stated, “This editorial introduces and explains the Journal of Management Studies’. We reflect on the use of AI in conducting research and generating journal submissions and what this means for the wider JMS community, including our authors, reviewers, editors, and readers.” Our news journalists obtained a quote from the research from Vrije Universiteit Amsterdam, “Specifically, we consider how AI-generated research and text could both assist and augment the publication process, as well as harm it. Consequentially, our policy acknowledges the need for careful oversight regarding the use of AI to assist in the authoring of texts and in data analyses, while also noting the importance of requiring authors to be transparent about how, when and where they have utilized AI in their submissions or underlying research. Additionally, we examine how and in what ways AI’s use may be antithetical to the spirit of a quality journal like JMS that values both human voice and research transparency. Our editorial explains why we require author teams to oversee all aspects of AI use within their projects, and to take personal responsibility for accuracy in all aspects of their research.”
查看更多>>摘要：New research on Machine Learning is the subject of a report. According to news reporting out of Harbin, People’s Republic of China, by NewsRx editors, research stated, “The maritime sector has diligently endeavored to mitigate fuel consumption to curtail emissions and expendi-tures within the sustainable development framework. To comprehensively analyze ships’ fuel consumption, considering the combined influence of multiple factors, we develop an integrated optimization approach encompassing speed, trim, and speed-trim adjustments under different loading conditions.” Financial supporters for this research include Harbin Engineering University, Ministry of Industry and Information Technology of the People’s Republic of China. Our news journalists obtained a quote from the research from Harbin Engineering University, “Firstly, we employ the fuel consumption prediction models established before, conduct a detailed analysis of the ship’s route in distinct segments, and formulate optimization models by scrutinizing the ship’s actual voyage. Secondly, we conducted single-parameter optimization for speed and trim to achieve the minimum fuel consumption for the entire route. We also implemented a joint optimization approach for simultaneously optimizing speed and trim to enhance fuel efficiency further. Lastly, we applied a smoothing method to the model’s prediction results to solve the step problem; and compared the optimization results before and after smoothing to assess the approach’s effectiveness. The results show that the joint optimization in the ballast condition yielded fuel consumption savings of 12.30% and 11.70% before and after smoothing, respectively; The fuel savings achieved under full load conditions were 10.18% and 9.47%.”
查看更多>>摘要：Data detailed on Machine Learning have been presented. According to news reporting out of Shanghai, People’s Republic of China, by NewsRx editors, research stated, “Arc width is important for understanding the generation mechanism of auroral arcs. However, the continuity or discreteness of the distribution of small and meso-large scale auroral arc widths has not been determined in previous studies.” Financial supporters for this research include National Natural Science Foundation of China (NSFC), Natural Science Basic Research Plan in Shaanxi Province of China, Open Fund of State Key Laboratory of Loess and Quaternary Geology. Our news journalists obtained a quote from the research from Tongji University, “This study employs machine learning techniques to investigate the distribution of arc widths across multiple scales using multifield- of-view (multi-FOV) auroral observations. Based on the 180 degrees, 47 degrees, and 19 degrees auroral observations at the Antarctic Zhongshan Station from February to October 2012, the statistical results demonstrate that the auroral arc width spectrum is continuously distributed across small, meso, and large scales, suggesting that the mechanisms responsible for their generation are capable of producing arcs at all scales. Furthermore, the arc width distribution at each FOV can be well fitted with a log-normal function. We also find that the main widths observed at different FOVs depend on the spatial resolution of the instruments. Our work provides new observational evidence for the generation mechanism of auroral arcs. Auroral arcs are beautiful atmospheric phenomena that occur in the polar regions of the Earth. Understanding their width helps us to understand how they are formed. This study used machine learning techniques to analyze auroral observations and found that the width of auroral arcs is continuous across different scales. We also found that a log-normal function fits the distribution of arc widths well at each scale. In addition, the observed widths at different field-of-views are strongly influenced by the spatial resolution of the instrument used. This research provides new insights into the understanding of auroral arcs.”
查看更多>>摘要：Fresh data on Robotics - Robotics and Automation are presented in a new report. According to news reporting originating from Cambridge, Massachusetts, by NewsRx correspondents, research stated, “We present VERF, a collection of two methods (VERF-PnP and VERF-Light) for providing runtime assurance on the correctness of a camera pose estimate of a monocular camera without relying on direct depth measurements. We leverage the ability of NeRF (Neural Radiance Fields) to render novel RGB perspectives of a scene.” Financial support for this research came from NASA Flight Opportunities. Our news editors obtained a quote from the research from the Massachusetts Institute of Technology, “We only require as input the camera image whose pose is being estimated, an estimate of the camera pose we want to monitor, and a NeRF model containing the scene pictured by the camera. We can then predict if the pose estimate is within a desired distance from the ground truth and justify our prediction with a level of assurance. VERF-Light does this by rendering a viewpoint with NeRF at the estimated pose and estimating its relative offset to the sensor image up to scale. Since scene scale is unknown, the approach renders another auxiliary image and reasons over the consistency of the optical flows across the three images. VERF-PnP takes a different approach by rendering a stereo pair of images with NeRF and utilizing the Perspective-n-Point (PnP) algorithm. We evaluate both methods on the LLFF dataset, on data from a Unitree A1 quadruped robot, and on data collected from Blue Origin’s sub-orbital New Shepard rocket to demonstrate the effectiveness of the proposed pose monitoring method across a range of scene scales.”
查看更多>>摘要：New research on Artificial Intelligence is the subject of a report. According to news reporting from Birmingham, United Kingdom, by NewsRx journalists, research stated, “Peer review is the established method for evaluating the quality and validity of research manuscripts in scholarly publishing. However, scientific peer review faces challenges as the volume of submitted research has steadily increased in recent years.” The news correspondents obtained a quote from the research from Royal Orthopedic Hospital, “Time constraints and peer review quality assurance can place burdens on reviewers, potentially discouraging their participation. Some artificial intelligence (AI) tools might assist in relieving these pressures. This study explores the efficiency and effectiveness of one of the artificial intelligence (AI) chatbots, ChatGPT (Generative Pre-trained Transformer), in the peer review process. Twenty-one peer-reviewed research articles were anonymised to ensure unbiased evaluation. Each article was reviewed by two humans and by versions 3.5 and 4.0 of ChatGPT. The AI was instructed to provide three positive and three negative comments on the articles and recommend whether they should be accepted or rejected. The human and AI results were compared using a 5-point Likert scale to determine the level of agreement. The correlation between ChatGPT responses and the acceptance or rejection of the papers was also examined. Subjective review similarity between human reviewers and ChatGPT showed a mean score of 3.6/5 for ChatGPT 3.5 and 3.76/5 for ChatGPT 4.0. The correlation between human and AI review scores was statistically significant for ChatGPT 3.5, but not for ChatGPT 4.0. ChatGPT can complement human scientific peer review, enhancing efficiency and promptness in the editorial process.”
查看更多>>摘要：Fresh data on Artificial Intelligence are presented in a new report. According to news reporting originating in Denver, Colorado, by NewsRx editors, the research stated, “This study examines the financial opportunities arising from the new Artificial Intelligence (AI) innovation. Firstly, we present the current and projected AI revenue for the upcoming decade.” The news reporters obtained a quote from the research from the University of Colorado Denver, “Secondly, we introduce a valuation model for AI stocks and ETFs, incorporating both AI fundamental and sentiment analyses. We offer two primary models for stock valuation adoption. Our analyses can serve as a benchmark framework for stock valuations and their influence on AI technology.” According to the news reporters, the research concluded: “This study holds particular significance as we witness an enthusiastic embrace of AI technology, potentially signaling the financial market’s entry into an AI bubble.”
查看更多>>摘要：Fresh data on Machine Learning are presented in a new report. According to news reporting from Cambridge, United Kingdom, by NewsRx journalists, research stated, “Previously acquired data could be utilised in predicting glass dissolution kinetics at long times, but the application of machine learning methods needs to be assessed. Here, the dissolution processes of two Li-Na borosilicate ‘base glasses’ at 40 and 90 degrees C were investigated by SEM-EDS, NMR and Raman spectroscopy.” Funders for this research include Engineering & Physical Sciences Research Council (EPSRC), Nuclear Decommissioning Authority (NDA), Nuclear Waste Services (NWS). The news correspondents obtained a quote from the research from the University of Cambridge, “Boron and sodium machine learning predictions were excellent when considering other normalised releases as features. However, extrapolating the training feature space yielded poorer performance and the absence of incorporated waste elements resulted in underestimated predicted long-term lithium and silicon releases. Faster dissolution kinetics were observed for MW than MW-1/2Li but the MW-1/2Li gel layer at 40 degrees C trapped more water. Whilst BO3 rings leached preferentially at 90 degrees C, surface enrichment of BO3 at 40 degrees C suggested [BO4]- transformed prior to dissolution.”
查看更多>>摘要：New research on Neurodegenerative Diseases and Conditions - Alzheimer Disease is the subject of a report. According to news reporting from Stockholm, Sweden, by NewsRx journalists, research stated, “Amyloid and tau aggregates are considered to cause neurodegeneration and consequently cognitive decline in individuals with Alzheimer’s disease (AD). Here, we explore the potential of cerebrospinal fluid (CSF) proteins to reflect AD pathology and cognitive decline, aiming to identify potential biomarkers for monitoring outcomes of disease-modifying therapies targeting these aggregates.” Financial supporters for this research include H2020 Marie Sklodowska-Curie Actions, Royal Institute of Technology. The news correspondents obtained a quote from the research from Karolinska Institute, “We used a multiplex antibody-based suspension bead array to measure the levels of 49 proteins in CSF from the Swedish GEDOC memory clinic cohort at the Karolinska University Hospital. The cohort comprised 148 amyloid- and tau-negative individuals (A-T-) and 65 amyloid- and tau-positive individuals (A+T+). An independent sample set of 26 A-T- and 26 A+T+ individuals from the Amsterdam Dementia Cohort was used for validation. The measured proteins were clustered based on their correlation to CSF amyloid beta peptides, tau and NfL levels. Further, we used support vector machine modelling to identify protein pairs, matched based on their cluster origin, that reflect AD pathology and cognitive decline with improved performance compared to single proteins. The protein-clustering revealed 11 proteins strongly correlated to t-tau and p-tau (tau-associated group), including mainly synaptic proteins previously found elevated in AD such as NRGN, GAP43 and SNCB. Another 16 proteins showed predominant correlation with Ab42 (amyloid-associated group), including PTPRN2, NCAN and CHL1. Support vector machine modelling revealed that proteins from the two groups combined in pairs discriminated A-T- from A+T+ individuals with higher accuracy compared to single proteins, as well as compared to protein pairs composed of proteins originating from the same group. Moreover, combining the proteins from different groups in ratios (tauassociated protein/amyloid-associated protein) significantly increased their correlation to cognitive decline measured with cognitive scores. The results were validated in an independent cohort. Combining brainderived proteins in pairs largely enhanced their capacity to discriminate between AD pathology-affected and unaffected individuals and increased their correlation to cognitive decline, potentially due to adjustment of inter-individual variability.”
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