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    Hunan University Reports Findings in Machine Learning (Predicting the Fundraising Performance of Environmental Crowdfunding Projects: an Interpretable Machine Learning Approach)

    76-77页
    查看更多>>摘要：By a News Reporter-Staff News Editor at Robotics & Machine Learning Daily News Daily News – A new study on Machine Learning is now available. According to news report- ing originating from Changsha, People’s Republic of China, by NewsRx correspondents, research stated, “Crowdfunding has become a pivotal fundraising method for environmental organizations. How-ever, the fundraising performance of environmental crowdfunding projects remains subpar, prompting the need for improvements.” Funders for this research include National Natural Science Foundation of China (NSFC), Natural Science Foundation of Hunan Province. Our news editors obtained a quote from the research from Hunan University, “Effectively addressing this challenge entails the precise prediction of each project’s fundraising performance and a comprehensive understanding of the intricate correlations between various features and fundraising success. In response to these imperatives, this study introduces an interpretable framework meticulously designed for pre-dicting the fundraising performance of environmental crowdfunding projects. This comprehen-sive framework integrates ten theoretically significant features to form the predictive model’s feature set. It adopts a diverse array of eight algorithms for training and harnesses SHAP values and ALE plots for insightful post-hoc interpretation, thereby providing valuable insights into the nuanced roles played by these features. Validated on a dataset comprising 3,101 environmental crowdfunding projects from Tencent Charity, the proposed framework outperforms state-of-the -art methods, demonstrating an improvement of 5.9% in predictive performance. Furthermore, the post-hoc interpretation techniques accurately depict the roles of the features.”
      原文链接:
    • NETL
    • NSTL
    • Elsevier

    Reports on Machine Learning Findings from Sichuan University Provide New Insights (Portable Pyrolysis-point Discharge Optical Spectrometer for In Situ Plastic Polymer Identification By Coupling With Machine Learning)

    77-78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 Machine Learning. According to news originating from Chengdu, People’s Republic of China, by NewsRx correspondents, research stated, “Rapid and in situ identification of specific polymers is a challenging and crucial step in plastic recycling. However, conventional techniques continue to exhibit significant limitations in the rapid and field classification of plastic products, especially with the wide range of commercially available color polymers because of their large size, high energy consumption, and slow and complicated analysis procedures.” Funders for this research include National Natural Science Foundation of China (NSFC), National Natural Science Foundation of China (NSFC). Our news journalists obtained a quote from the research from Sichuan University, “In this work, a simple analytical system integrating a miniature and low power consumption (22.3 W) pyrolyzer (Pyr) and a low temperature, atmospheric pressure point discharge optical emission spectrometer (mu PD-OES) was fabricated for rapidly identifying polymer types. Plastic debris is decomposed in the portable pyrolyzer to yield volatile products, which are then swept into the mu PD-OES instrument for monitoring the optical emission patterns of the thermal pyrolysis products. With machine learning, five extensively used raw polymers and their consumer plastics were classified with an accuracy of >= 97.8%. Furthermore, the proposed method was applied to the identification of the aged polymers and plastic samples collected from a garbage recycling station, indicating its great potential for identification of environmentally weathered plastics.”
      原文链接:
    • NETL
    • NSTL
    • Amer Chemical Soc

    Findings from Northwestern Polytechnic University Yields New Findings on Robotics (Cpg-fuzzy Heading Control for a Hexapod Robot With Arc-shaped Blade Legs)

    78-79页
    查看更多>>摘要：By a News Reporter-Staff News Editor at Robotics & Machine Learning Daily News – New research on Robotics is the subject of a report. According to news reporting originating in Xi’an, People’s Republic of China, by NewsRx journalists, research stated, “Based on the central pattern generator (CPG) and fuzzy controller, this paper proposes a heading control method for the directional motion for a new type of blade legged hexapod robot (BLHR). First, the modified Hopf oscillator is used to construct the CPG model of BLHR based on the limit cycle.” Funders for this research include National Natural Science Foundation of China (NSFC), National Natural Science Foundation of China (NSFC), Fundamental Research Funds for the Central Universities. The news reporters obtained a quote from the research from Northwestern Polytechnic University, “Second, the fuzzy controller is applied to adjust the support angles of legs to change the heading of BLHR, thereby correcting the error between the actual and desired heading angle in real-time. Finally, the feasibility and effectiveness of the proposed CPG-Fuzzy control method is verified in Gazebo simulations and real-world experiments. This is the first attempt to combine CPG and fuzzy control in the context of hexapod robot.”According to the news reporters, the research concluded: “In comparison to existing control methods, the proposed CPG-Fuzzy controller can implement heading control of BLHR with better performance and value of further investigation.”
      原文链接:
    • NETL
    • NSTL
    • Springer Nature

    Researchers from Nanjing University of Chinese Medicine Describe Findings in Machine Learning (Using Hs-gc-ms and Flash Gc E-nose In Combination With Chemometric Analysis and Machine Learning Algorithms To Identify the Varieties, Geographical ...)

    79-80页
    查看更多>>摘要：By a News Reporter-Staff News Editor at Robotics & Machine Learning Daily News – Fresh data on Machine Learning are presented in a new report. According to news reporting from Nanjing, People’s Republic of China, by NewsRx journalists, research stated, “Atractylodes lancea (AL) is argued to be the best botanical source of the atractylodes rhizome (AR), which is used within traditional Chinese medicine. However, in recent years there have been a number of issues around the production and use of AR, including authenticity, confusion, and mislabeling between AL and Atractylodes chinensis (AC) isolates, geographical origins, and production modes.” Funders for this research include Key project at central government level: The ability establishment of sustainable use for valuable Chinese medicine resources, Research of assurance-ability improvement of Chinese medicinal resources. The news correspondents obtained a quote from the research from the Nanjing University of Chinese Medicine, “These discrepancies can impact both the quality and commercial value of the crop. In this study, volatile organic compounds from 173 batches of AR isolated from both AL and AC plants were compared using a flash gas chromatography electronic nose (flash GC e-nose) and headspace gas chromatography- mass spectrometry (HS-GC-MS). The flash GC e-nose revealed that the main aromas of AR were spicy, sweety, and fruity, and the flavor differences of Atractylodes lancea from different geographical origins are mainly reflected in sweetness and spicy taste. Furthermore, HS-GC-MS showed that terpenoids are key indicators for determining the quality and further clarifying the origin of AL. Eight terpenoids including 2-pinen-10-ol and beta-elemene were higher in abundance in AL than AC; seven terpenoids including alpha-curcumene and alpha-pinene were higher in abundance in wild AL than cultivated AL; and there were significantly different quantities of ten terpenoids including agarospirol and beta-bisabolene present in samples of AL taken from Jiangsu, Henan and Hubei provinces. Finally, the performance of eight machine- learning algorithms to distinguish between AL and AC, and recognize different regions and production patterns of AL, were compared. Among them, XGBoost had the highest differentiation accuracy of 86.17 +/- 7.48%.”
      原文链接:
    • NETL
    • NSTL
    • Elsevier

    Study Findings from Osaka University Update Knowledge in Androids (Opinion attribution improves motivation to exchange subjective opinions with humanoid robots)

    80-81页
    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 androids. According to news reporting from Osaka, Japan, by NewsRx journalists, research stated, “In recent years, the development of robots that can engage in non-task-oriented dialogue with people, such as chat, has received increasing attention. This study aims to clarify the factors that improve the user’s willingness to talk with robots in non-task oriented dialogues (e.g., chat).” Funders for this research include Japan Science And Technology Agency; Japan Society For The Pro- motion of Science. Our news journalists obtained a quote from the research from Osaka University: “A previous study reported that exchanging subjective opinions makes such dialogue enjoyable and enthusiastic. In some cases, however, the robot’s subjective opinions are not realistic, i.e., the user believes the robot does not have opinions, thus we cannot attribute the opinion to the robot. For example, if a robot says that alcohol tastes good, it may be difficult to imagine the robot having such an opinion. In this case, the user’s motivation to exchange opinions may decrease. In this study, we hypothesize that regardless of the type of robot, opinion attribution affects the user’s motivation to exchange opinions with humanoid robots. We examined the effect by preparing various opinions of two kinds of humanoid robots. The experimental result suggests that not only the users’ interest in the topic but also the attribution of the subjective opinions to them influence their motivation to exchange opinions.”
      原文链接:
    • NETL
    • NSTL
    • Frontiers Media Sa

    Investigators at Harbin Institute of Technology Report Findings in Robotics (Information-theoretic Exploration for Adaptive Robotic Grasping In Clutter Based On Real-time Pixel-level Grasp Detection)

    81-82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 have been published. According to news originating from Harbin, People’s Republic of China, by NewsRx correspondents, research stated, “Robust robotic grasping in clutter still remains a challenging problem despite its great practical value. This article presents an information-theoretic exploration approach that aims to improve the grasp estimation quality in a complex environment.” Financial support for this research came from National Key R&D Program of China. Our news journalists obtained a quote from the research from the Harbin Institute of Technology, “First, a lightweight grasp detector that is composed of inverted residual blocks and a pyramid pooling module is proposed to make more accurate pixelwise predictions in real time, which are then projected to the workspace. To measure the uncertainty of estimations in the workspace, a two-dimensional grasp entropy (2D-GE) is defined, and the Gaussian process is applied to regress the variation of the information gain that is approximated by 2D-GEs. Finally, guided by the information gain, depth cost, and distance cost, our approach is able to actively collect and fuse estimations from multiple informative viewpoints through the exploration that finally converges to a refined best grasp, resulting improved grasping performance with a higher success rate and environmental adaptability in clutter.” According to the news editors, the research concluded: “Simulation and robotic experiment are both performed to demonstrate the effectiveness of our method and compare it to baselines.” This research has been peer-reviewed.
      原文链接:
    • NETL
    • NSTL
    • IEEE

    Findings on Machine Learning Detailed by Investigators at Southwest University of Science and Technology (Mechanical Properties of Mo-re Alloy Based On First-principles and Machine Learning Potential Function)

    82-83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 reporting originating in Mianyang, People’s Republic of China, by NewsRx journalists, research stated, “This study utilizes first principles calculations of density functional theory and Spectral Neighbor Analysis Po-tential (SNAP) machine learning potential to investigate the influence of rhenium concentration and temperature on the fundamental mechanical properties of Molybdenum-Rhenium alloy. The Mo1_ xRex alloys (x = 0.0625-0.5) are constructed using a special quasi-random structure BCC model.” The news reporters obtained a quote from the research from the Southwest University of Science and Technology, “The optimized geometries and lattices are used to calculate elastic constants and derivate mechanical parameters, including Bulk modulus, Young’s modulus, Shear modulus, etc. The results show that with the increase of rhenium content, the me-chanical properties of Mo1_xRex alloy are significantly improved, and higher than pure molybdenum, the best properties are reached at x(Re) = 0.3125. On the other hand, by analyzing the ratio of bulk modulus to shear modulus (B/G) and Poisson’s ratio, the alloying of rhenium can also improve the ductility of molybdenum rhenium alloy. To address the challenge of calculating the high-temperature mechanical properties of Molybdenum-Rhenium alloy, a machine learning potential was developed within a training set called Spectral Neighbor Analysis Potential (SNAP). We accurately predicted the bulk modulus, shear modulus, Young’s modulus, and Poisson’s ratio of Molybdenum-Rhenium alloy over the temperature range of 300-1300 K. Additionally, we provided an accurate description of how temperature affects the mechanical properties and solubility of Molybdenum- Rhenium alloy.”
      原文链接:
    • NETL
    • NSTL
    • Elsevier

    COMSATS University Islamabad Researchers Describe Recent Advances in Machine Learning (Active Machine Learning for Heterogeneity Activity Recognition Through Smartwatch Sensors)

    83-84页
    查看更多>>摘要：By a News Reporter-Staff News Editor at Robotics & Machine Learning Daily News Daily News – New study results on artificial intelligence have been published. According to news reporting originating from Islamabad, Pakistan, by NewsRx correspondents, research stated, “Smartwatches with cutting-edge sensors are becoming commonplace in our daily lives.” Funders for this research include Deanship of Scientific Research At Jouf University; Eu Nextgenera- tioneu Through The Recovery And Resilience Plan For Slovakia. The news journalists obtained a quote from the research from COMSATS University Islamabad: “De- spite their widespread use, it can be challenging to interpret accelerometer and gyroscope data efficiently for Human Activity Recognition (HAR). This study explores active learning integrated with machine learn- ing, intending to maximize the use of smartwatch technology across a range of applications. The previous research on the HAR lacks promising performance, which could make it difficult to make highly accurate recognition. This paper proposes a novel approach to predict human activity from the Heterogeneity Hu- man Activity Recognition (HHAR) dataset that integrates active learning with machine learning models: Random Forest (RF), Extreme Gradient Boosting (XGBoost), K-nearest Neighbors (KNN), Decision Tree (DT), Gradient Boosting (GB) and Light Gradient Boosting Machine (LGBM) classifier to predict hetero- geneous activities accurately. We evaluated our approach to these models on the HHAR dataset that was generated using an accelerometer and gyroscope of smartwatches. The experiments are evaluated on 3 iterations where the results demonstrated that the proposed approach predicts human activities with the highest F1-Score of 99.99%.”
      原文链接:
    • NETL
    • NSTL
    • IEEE

    New Machine Learning Findings from Otto Schott Institute of Materials Research Outlined (Prediction of Phase Composition and Process Resilience In Plasma-assisted Hetero-aggregate Synthesis Using a Machine-learning Model With Multivariate Output)

    84-85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 from Jena, Germany, by NewsRx journalists, research stated, “The synthesis of nanoscale particles and particle aggregates from liquid or gaseous precursors is affected by a variety of trade-off relations, for example, in terms of product composition, yield, or energy efficiency. Machine-supported process evaluation and learning (ML) of these relations enables optimization strategies for advanced material processing.” Funders for this research include German Research Foundation (DFG), German Research Foundation within its priority program. The news correspondents obtained a quote from the research from the Otto Schott Institute of Materials Research, “Such a workflow is demonstrated on the example of plasma-assisted aerosol deposition (PAAD) of alumina powders. Depending on processing conditions, these powders comprise of hetero-aggregate mix- tures of crystalline and amorphous polymorphs. Process optimization toward a specific target composition calls for ML approaches. For this, a sufficiently large and consistent dataset of PAAD input (processing) and output (product) parameters is initially generated by real-world processing, and subsequently extrapo- lated into a cloud of approximate to 106 input-output parameter matrices using Gaussian process regression with multivariate output and input-output feature analysis. It is subsequently demonstrated how not only the phase composition of the obtained alumina powders, but also product resilience to variations in specific processing parameters, or - as a perspective - the energy efficiency of material processing can be predicted.” According to the news reporters, the research concluded: “A machine-learning model with multivariate output is used for predicting process performance and parameter resilience of plasma-assisted aerosol deposition of alumina hetero-aggregates. image.” This research has been peer-reviewed.
      原文链接:
    • NETL
    • NSTL
    • Wiley

    Studies from University of Tennessee in the Area of Machine Learning Described (Quantifying wildfire drivers and predictability in boreal peatlands using a two-step error-correcting machine learning framework in TeFire v1.0)

    85-85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 originat- ing from Knoxville, Tennessee, by NewsRx correspondents, research stated, “Wildfires are becoming an increasing challenge to the sustainability of boreal peatland (BP) ecosystems and can alter the stability of boreal carbon storage.” Funders for this research include U.S. Department of Energy. The news editors obtained a quote from the research from University of Tennessee: “However, pre- dicting the occurrence of rare and extreme BP fires proves to be challenging, and gaining a quantitative understanding of the factors, both natural and anthropogenic, inducing BP fires remains elusive. Here, we quantified the predictability of BP fires and their primary controlling factors from 1997 to 2015 using a two-step correcting machine learning (ML) framework that combines multiple ML classifiers, regression models, and an error-correcting technique. We found that (1) the adopted oversampling algorithm effec- tively addressed the unbalanced data and improved the recall rate by 26.88 %-48.62 % when using multiple datasets, and the error-correcting technique tackled the overestimation of fire sizes during fire seasons; (2) nonparametric models outperformed parametric models in predicting fire occurrences, and the random forest machine learning model performed the best, with the area under the receiver operating characteristic curve ranging from 0.83 to 0.93 across multiple fire datasets; and (3) four sets of factor-control simulations consistently indicated the dominant role of temperature, air dryness, and climate extreme (i.e., frost) for boreal peatland fires, overriding the effects of precipitation, wind speed, and human activities.”
      原文链接:
    • NETL
    • NSTL