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    Investigators at Queen's University Belfast Discuss Findings in Machine Learning (Machine Learning for Membrane Design In Energy Production, Gas Separation, and Water Treatment: a Review)

    19-20页
    查看更多>>摘要：By a News Reporter-Staff News Editor at Robotics & Machine Learning Daily News Daily News – Data detailed on Machine Learning have been presented. According to news reporting originating from Belfast, United Kingdom, by NewsRx correspondents, research stated, “Membrane filtra- tion is a major process used in the energy, gas separation, and water treatment sectors, yet the efficiency of current membranes is limited. Here, we review the use of machine learning to improve membrane effi- ciency, with emphasis on reverse osmosis, nanofiltration, pervaporation, removal of pollutants, pathogens and nutrients, gas separation of carbon dioxide, oxygen and hydrogen, fuel cells, biodiesel, and biogas purification.” Funders for this research include SEUPB, Bryden Centre project, Interreg Europe, Department for the Economy in Northern Ireland, Department of Business, Enterprise, and Innovation in the Republic of Ireland. Our news editors obtained a quote from the research from Queen’s University Belfast, “We found that the use of machine learning brings substantial improvements in performance and efficiency, leading to specialized membranes with remarkable potential for various applications. This integration offers versatile solutions crucial for addressing global challenges in sustainable development and advancing environmental goals.” According to the news editors, the research concluded: “Membrane gas separation techniques improve carbon capture and purification of industrial gases, aiding in the reduction of carbon dioxide emissions.” This research has been peer-reviewed.
      原文链接:
    • NETL
    • NSTL
    • Springer Nature

    Beijing Technology and Business University Reports Findings in Machine Learning [Predicting the binding configuration and release potential of heavy metals on iron (oxyhydr)oxides: A machine learning study on EXAFS]

    20-20页
    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 the subject of a report. According to news reporting from Beijing, People’s Republic of China, by NewsRx journalists, research stated, “Heavy met- als raise a global concern and can be easily retained by ubiquitous iron (oxyhydr)oxides in natural and engineered systems. The complex interaction between iron (oxyhydr)oxides and heavy metals results in various mineral-metal binding configurations, such as outer-sphere complexes and edge-sharing inner-sphere complexes, which determine the accumulation and release of heavy metals in the environment.” The news correspondents obtained a quote from the research from Beijing Technology and Business Uni- versity, “However, traditional experimental approaches are time-consuming and inadequate to elucidate the complex binding relationships and configurations between iron (oxyhydr)oxides and heavy metals. Herein, a workflow that integrates the binding configuration data of 11 heavy metals on 7 iron (oxyhydr)oxides and then trains machine learning models to predict unknown binding configurations was proposed. The well- trained multi-grained cascade forest models exhibited high accuracy (>90%) and predictive performance (R 0.75). The underlying effects of mineral properties, metal ion species, and environmental conditions on mineral-metal binding configurations were fully interpreted with data mining. Moreover, the metal release rate was further successfully predicted based on mineral-metal binding configurations.” According to the news reporters, the research concluded: “This work provides a method to accurately and quickly predict the binding configuration of heavy metals on iron (oxyhydr)oxides, which would pro- vide guidance for estimating the potential release behavior of heavy metals and remediating heavy metal pollution in natural and engineered environments.” This research has been peer-reviewed.
      原文链接:
    • NETL
    • NSTL

    New Robotics and Mechatronics Study Findings Recently Were Published by a Researcher at Tokyo Institute of Technology (Enhancement of Control Stability Using Double Pulleys for Coupled Tendon-Driven Long-Reach Manipulator 'Super Dragon')

    21-21页
    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 robotics and mechatronics have been published. According to news originating from Tokyo, Japan, by NewsRx correspondents, research stated, “The exploration and inspection of narrow spaces in nuclear facilities require the use of long-reach manipulators.” Funders for this research include Japan Society For The Promotion of Science. The news reporters obtained a quote from the research from Tokyo Institute of Technology: “Coupled tendon-driven manipulators can realize lightweight and slender arms by positioning actuators, such as motors, at the base. Currently, we are developing a coupled tendon-driven super long-reach articulated arm, which we name “Super Dragon.” Super Dragon is a robot arm with a total length of 10 m, a maximum arm diameter of 0.2 m, and 10 joints. In this paper, we focused on the elasticity and elongation of ropes. The joint actuation of Super Dragon utilizes synthetic fiber ropes characterized by their high strength and flexible bending. However, when these ropes are used over long lengths, wire elongation occurs, thus causing the joint angles to exhibit unintended and unstable behavior. Therefore, we focused on a specific posture and employ a double pulley as a mechanical solution to suppress unstable behavior.” According to the news editors, the research concluded: “We introduced stability criteria and performed parametric searches to obtain the appropriate double pulley radii. Subsequently, we identified the position and radius of the introduced double pulley that can be analytically stabilized. By installing a double pulley in the actual machine and conducting experiments, we successfully suppressed the unstable behavior.” For more information on this research see: Enhancement of Control Stability Using Double Pulleys for Coupled Tendon-Driven Long-Reach Manipulator “Super Dragon”. Journal of Robotics and Mechatronics, 2024,36(1). The publisher for Journal of Robotics and Mechatronics is Fuji Technology Press Ltd.
      原文链接:
    • NETL
    • NSTL
    • Fuji Technology Press Ltd

    Reports on Machine Learning from Chinese Academy of Sciences Provide New Insights (Machine Learning Revealing Key Factors Influencing Hono Chemistry In Beijing During Heating and Nonheating Periods)

    23-24页
    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 Machine Learning have been published. According to news originating from Beijing, People’s Republic of China, by NewsRx correspondents, research stated, “Nitrous acid (HONO) is of great interest due to its contribution to hydroxyl (OH) radicals by self-photolysis. Nowadays, machine learning (ML) algorithms are good at capturing complicated non-linear relationships be-tween predictors and dependent variables.” Funders for this research include National Natural Science Foundation of China (NSFC), Beijing Na- tional Laboratory for Molecular Sciences, Youth Innovation Promotion Association of Chinese Academy of Sciences, China Postdoctoral Science Foundation. Our news journalists obtained a quote from the research from the Chinese Academy of Sciences, “Here, using the whole year of 2018 of observed HONO and related pollutant data at an urban site in Beijing, an ML-RF (random forest) model is carried out to predict HONO concentrations and explore the main factors influencing HONO formation mechanisms. ML-RF models show satisfactory performance during the heating, non-heating and whole year periods with R values of 0.95, 0.96 and 0.95, respectively. Primary emissions and diffusion have an obvious influence on ambient HONO during the heating period, while chemical formation processes such as NO2 heterogeneous reaction and photolysis of nitrate are important for HONO during the non-heating period with higher RH and stronger solar intensity. O3 and NH3 are the most important variables for HONO in both periods, indicating the close relationship of HONO with atmospheric oxidation and the important role of NH3 in HONO formation processes. Although there are de-viations due to some variability in HONO formation mechanisms between years, ML-RF models based on 2018 data are able to roughly predict HONO for three periods in 2017 and 2021.”
      原文链接:
    • NETL
    • NSTL
    • Elsevier

    Chongqing University Researcher Illuminates Research in Pattern Recognition and Artificial Intelligence (Saliency and Depth-Aware Full Reference 360-Degree Image Quality Assessment)

    24-25页
    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 pattern recognition and artificial intelligence. According to news originating from Chongqing, People’s Republic of China, by NewsRx correspondents, research stated, “With the widespread adoption of virtual reality and 360-degree video, there is a pressing need for objective metrics to assess quality in this immersive panoramic format reliably.” Financial supporters for this research include Nsfc; Key Projects of Basic Strengthening Plan; Chongqing Talent; Joint Equipment Pre Research And Key Fund Project of The Ministry of Education; Natural Science Foundation of Chongqing, China; Human Resources And Social Security Bureau Project of Chongqing; Guangdong Oppo Mobile Telecommunications Corporation Ltd.. Our news editors obtained a quote from the research from Chongqing University: “However, existing image quality assessment models developed for traditional fixed-viewpoint content do not fully consider the specific perceptual issues involved in 360-degree viewing. This paper proposes a 360-degree image full- reference quality assessment (FR-IQA) methodology based on a multi-channel architecture. The proposed 360-degree FR-IQA method further optimizes and identifies the distorted image quality using two easily obtained useful saliency and depth-aware image features. The convolutional neural network (CNN) is designed for training. Furthermore, the proposed method accounts for predicting user viewing behaviors within 360-degree images, which will further benefit the multi-channel CNN architecture and enable the weighted average pooling of the predicted FR-IQA scores.”
      原文链接:
    • NETL
    • NSTL
    • World Scientific

    State University of New York (SUNY) College of Environmental Science and Forestry Researchers Reveal New Findings on Machine Learning (Mapping Water Clarity in Small Oligotrophic Lakes Using Sentinel-2 Imagery and Machine Learning Methods: A ...)

    25-26页
    查看更多>>摘要：By a News Reporter-Staff News Editor at Robotics & Machine Learning Daily News Daily News – Data detailed on artificial intelligence have been presented. According to news re- porting originating from Syracuse, New York, by NewsRx correspondents, research stated, “Optical remote sensing of water quality poses challenges in small oligotrophic lakes due to the diminishing contribution of constituents to the water-leaving radiance as water clarity increases. For monitoring water clarity over such lakes, this study utilizes machine learning models and data from citizen science to develop effective models for retrieving Secchi disk depth (SDD) in Canandaigua Lake, USA.” Financial supporters for this research include U.S. Department of State And Higher Education Com- mission. Our news correspondents obtained a quote from the research from State University of New York (SUNY) College of Environmental Science and Forestry: “Using Sentinel-2 band ratios as input, we trained random forest (RF), adaptive boosting, extreme gradient boosting, and support vector regression models using spatiotemporally distributed in situ data within 7 days of Senitnel-2 overpass. Each model was optimized using hyperparameter tuning, and cross-validation was used for accuracy assessment to compare the models’ effectiveness in retrieving SDD. The results indicate the superior performance of RF with an R2 of 0.74 and a root mean squared error of 0.72 m. A feature importance analysis for RF indicated the high relevance of the blue and green bands ratio in the estimation of SDD. The RF model was subsequently employed to generate temporal maps for Canandaigua Lake, indicating that water clarity tends to be higher during the early summer months (May and June) but declines during late summer and fall (September and October). This pattern can be closely associated with the increased algal presence in the lake.”
      原文链接:
    • NETL
    • NSTL
    • IEEE

    New Robotics Study Findings Have Been Reported from Chongqing University (Time-jerk Optimal Trajectory Planning for Industrial Robots With Coupled Interpolation Function Selection)

    26-27页
    查看更多>>摘要：By a News Reporter-Staff News Editor at Robotics & Machine Learning Daily News Daily News – Research findings on Robotics are discussed in a new report. According to news reporting out of Chongqing, People’s Republic of China, by NewsRx editors, research stated, “In the con- temporary field of optimal trajectory planning for industrial robots, it is customary to construct trajectories through the manual predefinition of interpolation functions. Unfortunately, this method frequently over- looks the influence of the interpolation function itself on the optimization objectives, resulting in suboptimal outcomes.” Funders for this research include The presented work was supported by the National Key Research and Development Project of China, National Key Research and Development Project of China, National Natural Science Foundation of China (NSFC), Natural Science Foundation of Chongqing, Innovation Group Science Fund of Chongqing Natural Science Foundation, Self-Planned Task of State Key Laboratory of Mechanical Transmission. Our news journalists obtained a quote from the research from Chongqing University, “To remedy this limitation, an optimal trajectory planning method with coupled interpolation function selection is proposed, in which the total task time and the integral squared jerk are defined as optimization objectives. This method minimizes the optimization objectives while also factoring in the optimal interpolation function, and avoiding subjective interference. To address the aforementioned biobjective optimization problem better, an Improved MultiObjective Golden Eagle Optimizer is introduced. Population diversity and the ability to escape local optima are enhanced through the incorporation of Chaotic Mapping, Opposition-Based Learning, Differential Evolution, and adaptive inertia weight strategy into the algorithm. The superiority of the algorithm is validated through a series of simulations on 17 benchmark functions. In the context of the robotic stirring operation within the automated block cast charging process, the proposed method is utilized to derive the time-jerk optimal trajectory.”
      原文链接:
    • NETL
    • NSTL
    • Wiley

    Investigators from Xi'an Jiaotong University Target Artificial Intelligence (Tapping Into the Green Potential: the Power of Artificial Intelligence Adoption In Corporate Green Innovation Drive)

    27-28页
    查看更多>>摘要：By a News Reporter-Staff News Editor at Robotics & Machine Learning Daily News Daily News – Data detailed on Artificial Intelligence have been presented. According to news originating from Xi’an, People’s Republic of China, by NewsRx correspondents, research stated, “In response to growing environmental challenges, there is an urgent need to understand how corporations can leverage new technologies to boost sustainability and eco-innovation. This study addresses this need by investigating Artificial Intelligence adoption (AIA) influence on green innovation (greenovation) performance among Chinese firms as China’s expanding digital economy and severe ecological pressures make it unique study context.” Our news journalists obtained a quote from the research from Xi’an Jiaotong University, “Specifically, panel data on 8722 firm-year observations from Chinese listed firms from 2008 to 2017 is analyzed to test the relationship. The main findings show that higher AIA is associated with increased greenovation, measured through green patents. This positive effect is more pronounced among privately-owned enterprises versus state-owned enterprises. Additionally, financial analysts are found to strengthen the AI-greennovation link through information dissemination and scrutiny. Importantly, the study findings are robust and validated through a battery of tests, including change regression, instrumental variable methods, propensity score match (PSM), and sysGMM. Overall, this study provides novel empirical evidence that AI holds promise as an enabler of corporate eco-innovation.” According to the news editors, the research concluded: “The findings have crucial implications for research and practice regarding leveraging digital technologies for sustainability, especially in emerging economies like China that is undergoing rapid technological change.” This research has been peer-reviewed.
      原文链接:
    • NETL
    • NSTL
    • Wiley

    New Machine Learning Study Findings Recently Were Reported by Researchers at Clemson University (Shifting Roles and Slow Research: Children's Roles In Participatory Co-design of Critical Machine Learning Activities and Technologies)

    28-28页
    查看更多>>摘要：By a News Reporter-Staff News Editor at Robotics & Machine Learning Daily News Daily News – Fresh data on Machine Learning are presented in a new report. According to news reporting originating from Clemson, South Carolina, by NewsRx correspondents, research stated, “Including children’s voices in the design of learning activities and technologies has increasingly become a subject of conversation among researchers and learning designers. Research suggests children have lived experiences that position them as useful contributors in co-designing curricula activities or technologies they will use.” Funders for this research include National Science Foundation10.13039/501100008982, National Sci- ence Foundation (NSF), Office of the Associate Dean for Research in the College of Education at Clemson University. Our news editors obtained a quote from the research from Clemson University, “However, one significant challenge in participatory co-design is engaging children in the co-design of curricula when they have not yet learned the disciplinary content within the curricula. We present our two-year participatory design-based research study in which we co-designed a Critical Machine Learning educational programme with different groups of children at two after-school centres over two consecutive years. In this paper, we characterize the roles children embodied in two cycles of participatory co-design and how the program’s activities impacted these roles. Findings in this study suggest that in two participatory design-based research cycles, children embodied different roles of tester, informant, or designer of both AI learning activities and AI technologies.” According to the news editors, the research concluded: “Based on this design-based research study, we propose that a ‘slow research’ approach that emphasises trust-building and a deep understanding of children’s perspectives can be instrumental in achieving meaningful co-design outcomes.” This research has been peer-reviewed.
      原文链接:
    • NETL
    • NSTL
    • Taylor & Francis

    Liaoning Technical University Researcher Illuminates Research in Machine Learning (Forest Canopy Height Estimation by Integrating Structural Equation Modeling and Multiple Weighted Regression)

    29-30页
    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 artificial intelligence. According to news reporting out of Fuxin, People’s Republic of China, by NewsRx editors, research stated, “As an important component of forest parameters, forest canopy height is of great significance to the study of forest carbon stocks and carbon cycle status. There is an increasing interest in obtaining large-scale forest canopy height quickly and accurately.” Financial supporters for this research include National Natural Science Foundation of China; China Postdoctoral Science Foundation. Our news correspondents obtained a quote from the research from Liaoning Technical University: “Therefore, many studies have aimed to address this issue by proposing machine learning models that accurately invert forest canopy height. However, most of the these approaches feature PolSAR observa- tions from a data-driven viewpoint in the feature selection part of the machine learning model, without taking into account the intrinsic mechanisms of PolSAR polarization observation variables. In this work, we evaluated the correlations between eight polarization observation variables, namely, T11, T22, T33, total backscattered power (SPAN), radar vegetation index (RVI), the surface scattering component (Ps), dihedral angle scattering component (Pd), and body scattering component (Pv) of Freeman-Durden three- component decomposition, and the height of the forest canopy. On this basis, a weighted inversion method for determining forest canopy height under the view of structural equation modeling was proposed. In this study, the direct and indirect contributions of the above eight polarization observation variables to the forest canopy height inversion task were estimated based on structural equation modeling. Among them, the indirect contributions were generated by the interactions between the variables and ultimately had an impact on the forest canopy height inversion. In this study, the covariance matrix between polarization variables and forest canopy height was calculated based on structural equation modeling, the weights of the variables were calculated by combining with the Mahalanobis distance, and the weighted inversion of forest canopy height was carried out using PSO-SVR. In this study, some experiments were carried out using three Gaofen-3 satellite (GF-3) images and ICESat-2 forest canopy height data for some forest areas of Gaofeng Ridge, Baisha Lizu Autonomous County, Hainan Province, China.”
      原文链接:
    • NETL
    • NSTL
    • Mdpi