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    Findings from Zhejiang Agriculture & Forestry University in the Area of Machine Learning Described (Heterogeneous N-heterocyclic Carbenes Supported Single-atom Catalysts for Nitrogen Fixation: a Combined Density Functional Theory and Machine…)

    84-84页
    查看更多>>摘要：Current study results on Machine Learning have been published. According to news reporting originating in Zhejiang, People's Republic of China, by NewsRx journalists, research stated, “Electrocatalytic nitrogen reduction reaction (NRR) has emerged as a sustainable and eco-friendly alternative for ammonia production at ambient conditions. Exploring highly efficient and selective electrocatalysts for NRR continues to gain significant attention, but remains a challenge.” Funders for this research include National Natural Science Foundation of China (NSFC), Natural Science Foundation of Zhejiang Province. The news reporters obtained a quote from the research from Zhejiang Agriculture & Forestry University, “In this work, we conducted a series of single -atom catalysts (SACs) by embedding 29 kinds of transition metal ™ atoms on the two-dimensional hetero-geneous N-heterocyclic carbene, and systematically investi- gated their catalytic performance for NRR using density functional theory, high-throughput screening, and machine learning. Two promising candidates (TM = Mn and Ta) with high catalytic activity and selectivity were identified, with limiting potentials of -0.51 and -0.53 V, respectively. Moreover, considering solvation effects, the limiting potential for Mn was further reduced to-0.43 V. Machine learning (ML) analysis revealed that the adsorption energy of N2 emerged as an efficient descriptor for NRR activity, and transition metal atomic Mendeleev number (Nm), the molar volume of TM atoms (Vm) and the 1st ionization energy of TM atoms (Im) were intrinsic to the difference in NRR performance of these SACs.”
      原文链接:
    • NETL
    • NSTL
    • Elsevier

    Data from Josip Juraj Strossmayer University Osijek Provide New Insights into Machine Learning (Influence of Thermal Pretreatment on Lignin Destabilization in Harvest Residues: An Ensemble Machine Learning Approach)

    85-85页
    查看更多>>摘要：New research on artificial intelligence is the subject of a new report. According to news originating from Osijek, Croatia, by NewsRx correspondents, research stated, “The research on lignocellulose pretreatments is generally performed through experiments that require substantial resources, are often time-consuming and are not always environmentally friendly.” Our news journalists obtained a quote from the research from Josip Juraj Strossmayer University Osijek: “Therefore, researchers are developing computational methods which can minimize experimental procedures and save money. In this research, three machine learning methods, including Random Forest (RF), Extreme Gradient Boosting (XGB) and Support Vector Machine (SVM), as well as their ensembles were evaluated to predict acid-insoluble detergent lignin (AIDL) content in lignocellulose biomass. Three different types of harvest residue (maize stover, soybean straw and sunflower stalk) were first pretreated in a laboratory oven with hot air under two different temperatures (121 and 175 ℃) at different duration (30 and 90 min) with the aim of disintegration of the lignocellulosic structure, i.e., delignification. Based on the leave-one-out cross-validation, the XGB resulted in the highest accuracy for all individual harvest residues, achieving the coefficient of determination (R2) in the range of 0.756-0.980.”
      原文链接:
    • NETL
    • NSTL
    • Mdpi

    Researchers from Dongguan University of Technology Report Recent Findings in Robotics (Digital Twin-driven 3-d Position Information Mutuality and Positioning Error Compensation for Robotic Arm)

    86-86页
    查看更多>>摘要：Investigators publish new report on Robotics. According to news reporting from Dongguan, People's Republic of China, by NewsRx journalists, research stated, “Robotic arms for industrial applications rely on expensive, complex solutions for high-precision positioning error compensation. Digital twins (DTs) provide virtual representations of physical assets to optimize their engineering performance, which helps address the above issues.” Funders for this research include National Natural Science Foundation of China (NSFC), National Key Research and Development Program of China, Guangdong Province Basic and Applied Basic Research Fund Project, Science and Technology Specialist Project of Dongguan City. The news correspondents obtained a quote from the research from the Dongguan University of Technology, “To address this problem, this article proposes a DT-driven 3-D position information mutuality and positioning error compensation for robotic arm. A DT model is developed and a virtual sensor is modeled geometrically. Information exchange between the physical and virtual sensor enables the comparison of the actual and target arm pose. Through closed-loop alignment of the physical sensor data with the virtual output, the arm joints are dynamically adjusted to reduce positioning errors. Information mutuality significantly reduces the amount of calculation necessary to determine the robotic arm's actual angle of motion.”
      原文链接:
    • NETL
    • NSTL

    New Artificial Intelligence Study Findings Have Been Reported by a Researcher at Technische Hochschule Ingolstadt (Is it worth the hype? Influence of Artificial Intelligence efforts on key financial company metrics)

    87-87页
    查看更多>>摘要：Researchers detail new data in artificial intelligence. According to news originating from the Technische Hochschule Ingolstadt by NewsRx correspondents, research stated, “Artificial Intelligence poses a consortium of multiple digital technologies able to perform tasks which were thought about that they can only be done by humans. To do so, it applies complex learning and decision-making processes based on analysis of structured and unstructured data.” The news editors obtained a quote from the research from Technische Hochschule Ingolstadt: “Currently, AI is assumed to have massive benefits in the areas of efficiency and performance of companies, although the impact on financial key performance indicators (KPI) is still unexplored. The underlying thesis of this research is that the financial impact of AI can already be seen in practice. The research question is whether there is an impact of company-driven AI efforts on financial KPI, like the return on assets (ROA) and the market capitalization. To obtain the intended results, a theoretical and empirical analysis was chosen as particular approach. Firstly, the existing scientific research is examined regarding already measurable financial impacts of digital technologies. In a second step, a regression model for panel data will be applied on a dataset containing financial data of the forty biggest German companies and their respective AI effort per year as a binary variable over a time period of seven years.”
      原文链接:
    • NETL
    • NSTL

    Shanghai Sixth People's Hospital Reports Findings in Machine Learning (Improvements to a GLCM-based machine-learning approach for quantifying posterior capsule opacification)

    87-88页
    查看更多>>摘要：New research on Machine Learning is the subject of a report. According to news reporting originating from Shanghai, People's Republic of China, by NewsRx correspondents, research stated, “Posterior capsular opacification (PCO) is a common complication following cataract surgery that leads to visual disturbances and decreased quality of vision. The aim of our study was to employ a machine- learning methodology to characterize and validate enhancements applied to the grey-level co-occurrence matrix (GLCM) while assessing its validity in comparison to clinical evaluations for evaluating PCO.” Our news editors obtained a quote from the research from Shanghai Sixth People's Hospital, “One hundred patients diagnosed with age-related cataracts who were scheduled for phacoemulsification surgery were included in the study. Following mydriasis, anterior segment photographs were captured using a high- resolution photographic system. The GLCM was utilized as the feature extractor, and a supported vector machine as the regressor. Three variations, namely, GLCM, GLCM+C (+axial information), and GLCM+V (+regional voting), were analyzed. The reference value for regression was determined by averaging clinical scores obtained through subjective analysis. The relationships between the predicted PCO outcome scores and the ground truth were assessed using Pearson correlation analysis and a Bland-Altman plot, while agreement between them was assessed through the Bland-Altman plot. Relative to the ground truth, the GLCM, GLCM+C, and GLCM+V methods exhibited correlation coefficients of 0.706, 0.768, and 0.829, respectively. The relationship between the PCO score predicted by the GLCM+V method and the ground truth was statistically significant (p＜0.001). Furthermore, the GLCM+V method demonstrated competitive performance comparable to that of two experienced clinicians (r = 0.825, 0.843) and superior to that of two junior clinicians (r = 0.786, 0.756). Notably, a high level of agreement was observed between predictions and the ground truth, without significant evidence of proportional bias (p＞0.05). Overall, our findings suggest that a machine-learning approach incorporating the GLCM, specifically the GLCM+V method, holds promise as an objective and reliable tool for assessing PCO progression.”
      原文链接:
    • NETL
    • NSTL
    • Wiley

    Study Findings on Machine Learning Reported by a Researcher at Universidad Senor de Sipan (Machine Learning and Blockchain: A Bibliometric Study on Security and Privacy)

    89-89页
    查看更多>>摘要：Research findings on artificial intelligence are discussed in a new report. According to news reporting originating from the Universidad Senor de Sipan by NewsRx correspondents, research stated, “Machine learning and blockchain technology are fast-developing fields with implications for multiple sectors. Both have attracted a lot of interest and show promise in security, IoT, 5G/6G networks, artificial intelligence, and more.” Financial supporters for this research include Universidad Senor De Sipan-uss. Our news journalists obtained a quote from the research from Universidad Senor de Sipan: “However, challenges remain in the scientific literature, so the aim is to investigate research trends around the use of machine learning in blockchain. A bibliometric analysis is proposed based on the PRISMA-2020 parameters in the Scopus and Web of Science databases. An objective analysis of the most productive and highly cited authors, journals, and countries is conducted. Additionally, a thorough analysis of keyword validity and importance is performed, along with a review of the most significant topics by year of publication. Co-occurrence networks are generated to identify the most crucial research clusters in the field. Finally, a research agenda is proposed to highlight future topics with great potential. This study reveals a growing interest in machine learning and blockchain. Topics are evolving towards IoT and smart contracts.”
      原文链接:
    • NETL
    • NSTL
    • Mdpi

    New Findings from Higher Institute of Engineering Update Understanding of Artificial Intelligence (Artificial Intelligence Based Optimal Coordination of Directional Overcurrent Relay In Distribution Systems Considering Vehicle To Grid Technology)

    90-90页
    查看更多>>摘要：Investigators discuss new findings in Artificial Intelligence. According to news reporting originating in Cairo, Egypt, by NewsRx journalists, research stated, “Optimal coordination of overcurrent relays has become a major challenge in power distribution systems due to the increasing participation of distributed generation (DG) and electric vehicle (EV) charging stations, especially vehicle-to-grid (V2G) technology. This paper proposes a novel approach for optimal coordination of directional overcurrent relays (DOCRs) to achieve the minimum operating time and obtain optimal tuning in terms of time dial setting (TDS) and pick-up current (IP) considering V2G integration.” The news reporters obtained a quote from the research from the Higher Institute of Engineering, “The optimization was conducted using three different Artificial Intelligence (AI) techniques: grey wolf optimization (GWO), cuckoo search (CS), and Hunger Games Search (HGS), and was performed on two systems: IEEE 33-bus system and a local grid located in El-Shorouk City-district#8 (ShC-D8), Cairo, Egypt. The proposed methodology is constructed and validated on ETAP and MATLAB software packages. The simulation results demonstrate that the proposed optimization techniques provide high dynamic performance and accurate coordination of DOCRs. The results show that HGS achieves the minimum operating time of all overcurrent relays as compared with GWO and CS. The effect of V2G integration on the coordination methodology is also considered.”
      原文链接:
    • NETL
    • NSTL
    • Elsevier

    Hubei University of Technology Researcher Adds New Data to Research in Robotics (A Structural Design and Motion Characteristics Analysis of an Inchworm-Inspired Soft Robot Based on Shape Memory Alloy Actuation)

    91-91页
    查看更多>>摘要：New study results on robotics have been published. According to news originating from Wuhan, People's Republic of China, by NewsRx correspondents, research stated, “Inchworms are a widely adopted bio-inspired model for soft crawling robots.” Funders for this research include National Natural Science Foundation of China; Hubei Provincial Key Research And Development Program. Our news editors obtained a quote from the research from Hubei University of Technology: “Taking advantage of the good controllability of Shape Memory Alloy (SMA), this paper designs and manufactures an inchworm-inspired soft robot driven by SMA. Firstly, in the structural design, the paper compares the heat dissipation performance and driving efficiency of SMA actuators with two assembly forms: embedded and external to the silicone body. The external structure assembly design with superior performance is chosen. Secondly, in the analysis of the motion characteristics of the soft robot, a kinematic model is developed. Addressing the issue of inaccurate representation in traditional constitutive models due to difficult-to-measure parameters, such as martensite volume fraction, this paper derives an exclusive new constitutive model starting from traditional models using methods like the Taylor series and thermodynamic laws. The kinematic model is simulated using the Simulink platform to obtain its open-loop step response and sinusoidal signal response.”
      原文链接:
    • NETL
    • NSTL
    • Mdpi

    Sichuan University Reports Findings in Machine Learning (De Novo Atomistic Discovery of Disordered Mechanical Metamaterials by Machine Learning)

    92-93页
    查看更多>>摘要：New research on Machine Learning is the subject of a report. According to news originating from Chengdu, People's Republic of China, by NewsRx correspondents, research stated, “Archi- tected materials design across orders of magnitude length scale intrigues exceptional mechanical responses nonexistent in their natural bulk state. However, the so-termed mechanical metamaterials, when scaling bottom down to the atomistic or microparticle level, remain largely unexplored and conventionally fall out of their coarse-resolution, ordered-pattern design space.” Our news journalists obtained a quote from the research from Sichuan University, “Here, combining high-throughput molecular dynamics (MD) simulations and machine learning (ML) strategies, some in- triguing atomistic families of disordered mechanical metamaterials are discovered, as fabricated by melt quenching and exemplified herein by lightweight-yet-stiff cellular materials featuring a theoretical limit of linear stiffness-density scaling, whose structural disorder-rather than order-is key to reduce the scaling ex- ponent and is simply controlled by the bonding interactions and their directionality that enable flexible tunability experimentally. Importantly, a systematic navigation in the forcefield landscape reveals that, in-between directional and non-directional bonding such as covalent and ionic bonds, modest bond di- rectionality is most likely to promotes disordered packing of polyhedral, stretching-dominated structures responsible for the formation of metamaterials."
      原文链接:
    • NETL
    • NSTL

    Jiaozuo University Researchers Provide Details of New Studies and Findings in the Area of Robotics (MATLAB-based simulation of industrial robots in water environment monitoring)

    92-92页
    查看更多>>摘要：Current study results on robotics have been published. According to news reporting out of Henan, People's Republic of China, by NewsRx editors, research stated, “The use of industrial robots based on MATLAB simulation for water environment monitoring is to monitor the water environment better, improve monitoring efficiency and reduce monitoring costs.” The news correspondents obtained a quote from the research from Jiaozuo University: “The robot can better collect data and can engage in deeper water-specific information. In this paper, based on the discussion of the water environment monitoring robots used in countries around the world for water environment monitoring, we introduce a MATLAB-based simulation of industrial robots in a wide range of water environments to simulate the autonomous data acquisition system. The main advantages are: compared with other robots, it can realize the ‘wide range' of water environment data collection; compared with fixed buoys, it can realize the ‘autonomous' collection of water environment monitoring data and gives the autonomous collection process and hierarchical software progression. The autonomous acquisition process and hierarchical software architecture are presented. The simulation results analysis shows no difference between the simulated data and the predicted data from the historical data using MATLAB- based industrial robots for water environment monitoring."
      原文链接:
    • NETL
    • NSTL
    • Walter De Gruyter