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    Studies from Huazhong University of Science and Technology Update Current Data on Artificial Intelligence (Application of Artificial Intelligence Technology In the Field of Orthopedics: a Narrative Review)

    85-86页
    查看更多>>摘要：Investigators publish new report on Artificial Intelligence. According to news reporting out of Wuhan, People's Republic of China, by NewsRx editors, research stated, “Artificial intelligence (AI) was a new interdiscipline of computer technology, mathematic, cybernetics and determinism. These years, AI had obtained a significant development by the improvement of core technology Machine Learning and Deep Learning.” Financial support for this research came from National Natural Science Foundation of China (NSFC). Our news journalists obtained a quote from the research from the Huazhong University of Science and Technology, “With the assistance of AI, profound changes had been brought into the traditional orthopedics. In this paper, we narratively reviewed the latest applications of AI in orthopedic diseases, including the severity evaluation, triage, diagnosis, treatment and rehabilitation. The research point, relevant advantages and disadvantages of the orthopedic AI was also discussed combined with our own research experiences.” According to the news editors, the research concluded: “We aimed to summarize the past achievements and appeal for more attentions and effective applications of AI in the field of orthopedics.”
      原文链接:
    • NETL
    • NSTL
    • Springer Nature

    Research from Tongji University Has Provided New Data on Robotics (One-step Solving the Hand-Eye Calibration by Dual Kronecker Product)

    86-87页
    查看更多>>摘要：Researchers detail new data in robotics. According to news reporting originating from Shanghai, People's Republic of China, by NewsRx correspondents, research stated, “Hand-eye calibration is a typical research direction in robotics applications.” Funders for this research include National Natural Science Foundation of China. Our news editors obtained a quote from the research from Tongji University: “The current methods can be divided into two categories according to whether the rotational and translational equations are decoupled for computation: two-step methods and one-step methods. Both one-step and two-step methods generally convert such problems to linear null space computations, which is implemented by the corresponding computational operators. Owing to the booming development of the rotation operators, the two-step methods have been more fully researched. However, due to the limitations of the research on computational operators integrating rotation and translation, the one-step methods still have much scope for research. Dual algebra, as effective mathematical entities for screws and wrenches, provides the theoretical basis for the development of the one-step methods for hand-eye calibration. In this paper, a computational operator for the dual matrices computation was first proposed, i.e., dual Kronecker product.”
      原文链接:
    • NETL
    • NSTL

    Findings from Sri Sivasubramaniya Nadar College of Engineering Broaden Understanding of Nanocomposites (Machine Learning Assisted Metal Oxide-bismuth Oxy Halide Nanocomposite for Electrochemical Sensing of Heavy Metals In Aqueous Media)

    87-88页
    查看更多>>摘要：Investigators publish new report on Nanotechnology - Nanocomposites. According to news reporting from Chennai, India, by NewsRx journalists, research stated, “Heavy metal in excess quantity is one of the major inorganic pollutants in water. It causes several hazards to human life and ecosystem.” Financial supporters for this research include Department of Science & Technology (India), SSN Trust. The news correspondents obtained a quote from the research from the Sri Sivasubramaniya Nadar College of Engineering, “It exists in traces in most of the commonly available drinking water sources from lakes, ponds, wells, etc., However, their presence in treated water is relatively significant. As the treated water is primarily used for agricultural purposes, it is necessary to monitor and measure their concentration. This requires sensing of metals in aqueous medium with good sensitivity and stability. Recently, nanosensors coupled with electrochemical transducer is preferred for analyzing heavy metal in aqueous solutions. In this work, Silver oxide-bismuth oxy bromide coated with nafion is proposed as an electrochemical sensor for detection of heavy metal ions in aqueous solution. Cyclic voltammetry (CV) behavior of the proposed electrode is observed in different electrolytes. Further, Differential Pulse Voltammetry (DPV) study shows that current increases with trace nickel and copper metal ions of different concentration. Further, machine learning (ML) algorithms such as Naive Bayes, ANN, SVM and decision trees are employed for nickel ions to train the cyclic voltammetry data and evaluate its performance. Naive Bayes algorithm provides the best accuracy of 93.2% among all the models. In this article, Silver Oxide Bismuth OxyBromide nanocomposite is identified for the electrochemical detection of Nickel and Copper ions in aqueous solution. The CV and DPV analysis is carried out for with different electrolytes. Linear response is obtained with a correlation coefficient of 98%.”
      原文链接:
    • NETL
    • NSTL
    • Wiley

    Data on Robotics Reported by Researchers at Tongji University (An Accurate Prediction Method of Human Assembly Motion for Human-Robot Collaboration)

    88-89页
    查看更多>>摘要：Investigators publish new report on robotics. According to news reporting from Shanghai, People's Republic of China, by NewsRx journalists, research stated, “In the process of humanrobot collaborative assembly, robots need to recognize and predict human behaviors accurately, and then perform autonomous control and work route planning in real-time.” Funders for this research include Fundamental Research Funds For The Central Universities. Our news correspondents obtained a quote from the research from Tongji University: “To support the judgment of human intervention behaviors and meet the need of real-time human-robot collaboration, the Fast Spatial-Temporal Transformer Network (FST-Trans), an accurate prediction method of human assembly actions, is proposed. We tried to maximize the symmetry between the prediction results and the actual action while meeting the real-time requirement. With concise and efficient structural design, FSTTrans can learn about the spatial-temporal interactions of human joints during assembly in the same latent space and capture more complex motion dynamics. Considering the inconsistent assembly rates of different individuals, the network is forced to learn more motion variations by introducing velocity-acceleration loss, realizing accurate prediction of assembly actions.”
      原文链接:
    • NETL
    • NSTL
    • Mdpi

    Researchers from GITAM University Report Recent Findings in Support Vector Machines (Rg-svm: Recursive Gaussian Support Vector Machine Based Feature Selection Algorithm for Liver Disease Classification)

    89-90页
    查看更多>>摘要：A new study on Support Vector Machines is now available. According to news reporting from Bengaluru, India, by NewsRx journalists, research stated, “Health is an essential concern for everyone, so it is necessary to facilitate medical services that are easily accessible to everyone. The primary goal of this work is to predict liver diseases using a machine-learning strategy that makes use of feature selection and classification techniques.” The news correspondents obtained a quote from the research from GITAM University, “This work proposes the recursive Gaussian support vector machine-based feature selection (RG-SVM) algorithm. It uses the Gaussian kernel of support vector machine and recursive feature selection algorithm for the prediction of liver disease. The proposed RG-SVM algorithm has been evaluated on the Indian liver patient records dataset. Various classification algorithms such as logistic regression, decision tree, k-nearest neighbour, and Naive Bayes are implemented and compared in order to assess the accuracy, confusion matrix and area under curve. The proposed RG-SVM has been compared with other existing algorithms such as logistic regression (LR), decision tree (DT), k-nearest neighbour (KNN), Naive Bayes (NB), and proposed RG-SVM algorithms. The algorithms LR, DT, KNN, NB, and proposed RG-SVM have accuracy values of 73, 80, 81, 54, and 93%, respectively. It clearly shows that the proposed RG-SVM with the support of a recursive feature selection algorithm, outperformed other existing algorithms with an improved accuracy of 14 - 39% 12-20% of reduced MSE error over other compared algorithms. Similarly, the sensitivity and specificity of RG-SVM algorithm produced 5-26% and 34-72% improved results over the existing algorithms.”
      原文链接:
    • NETL
    • NSTL
    • Springer Nature

    University of Minho Reports Findings in Melanoma (Machine Learning-Assisted Optimization of Drug Combinations in Zeolite- Based Delivery Systems for Melanoma Therapy)

    90-91页
    查看更多>>摘要：New research on Oncology - Melanoma is the subject of a report. According to news reporting originating in Braga, Portugal, by NewsRx journalists, research stated, “Two independent artificial neural network (ANN) models were used to determine the optimal drug combination of zeolitebased delivery systems (ZDS) for cancer therapy. The systems were based on the NaY zeolite using silver (Ag) and 5-fluorouracil (5-FU) as antimicrobial and antineoplastic agents.” The news reporters obtained a quote from the research from the University of Minho, “Different ZDS samples were prepared, and their characterization indicates the successful incorporation of both pharmacologically active species without any relevant changes to the zeolite structure. Silver acts as a counterion of the negative framework, and 5-FU retains its molecular integrity. The data from the A375 cell viability assays, involving ZDS samples (solid phase), 5-FU, and Ag aqueous solutions (liquid phase), were used to train two independent machine learning (ML) models. Both models exhibited a high level of accuracy in predicting the experimental cell viability results, allowing the development of a novel protocol for virtual cell viability assays. The findings suggest that the incorporation of both Ag and 5-FU into the zeolite structure significantly potentiates their anticancer activity when compared to that of the liquid phase. Additionally, two optimal AgY/5-FU@Y ratios were proposed to achieve the best cell viability outcomes.”
      原文链接:
    • NETL
    • NSTL

    Report Summarizes Robotics and Automation Study Findings from Technical University Wien (TU Wien) (Highly Maneuverable Humanoid Running Via 3d Slip+foot Dynamics)

    91-91页
    查看更多>>摘要：Fresh data on Robotics - Robotics and Automation are presented in a new report. According to news reporting from Vienna, Austria, by NewsRx journalists, research stated, “Spring loaded inverted pendulum (SLIP) is a template dynamics used to model the steady-state running of humans and animals ranging from cockroaches to horses. This study extends the conventional 3D SLIP model with a foot and an active controller to also model transitioning from stationary to high-speed running and vice versa.” Financial support for this research came from European Research Council (ERC). The news correspondents obtained a quote from the research from Technical University Wien (TU Wien), “It also compares behavioral differences between the conventional deadbeat-controlled 3D SLIP and actively controlled 3D SLIP with a foot, especially during trajectory transitioning. Focusing on humanoid robots, the objective is to enhance the system's trajectory switching and the disturbance rejection performance while keeping the trajectories implementable and forces feasible for the whole body dynamics.” According to the news reporters, the research concluded: “The results are verified on a humanoid robot Kangaroo through simulations in MuJoCo.”
      原文链接:
    • NETL
    • NSTL
    • IEEE

    Yanshan University Reports Findings in Robotics (Nonlinear extended state observer based control for the teleoperation of robotic systems with flexible joints)

    92-92页
    查看更多>>摘要：New research on Robotics is the subject of a report. According to news reporting originating in Qinhuangdao, People's Republic of China, by NewsRx journalists, research stated, “The control of robot manipulator pose is significantly complicated by the uncertainties arising from flexible joints, presenting substantial challenges in incorporating practical operational constraints. These challenges are further exacerbated in teleoperation scenarios, where factors such as synchronization and external disturbances further amplify the difficulties.” The news reporters obtained a quote from the research from Yanshan University, “At the core of this research is the introduction of a pioneering teleoperation controller, ingeniously integrating a nonlinear extended state observer (ESO) with the barrier Lyapunov function (BLF) while effectively accommodating a steady time delay. The controller in our study demonstrates exceptional proficiency in accurately estimating uncertainties arising from both flexible joints and external disturbances using the nonlinear ESO. Refined estimates, in conjunction with operational constraints of the system, are integrated into our BLF-based controller. Consequently, a synchronized control mechanism for teleoperation is achieved, exhibiting promising performance. Importantly, our experimental findings provide substantial evidence that our proposed approach effectively reduces the tracking error of the teleoperation system to within 0.02 rad.”
      原文链接:
    • NETL
    • NSTL
    • Amer Inst Mathematical Sciences-Aims

    Tianjin University of Commerce Researchers Report Recent Findings in Robotics (Energy Consumption Minimization of Quadruped Robot Based on Reinforcement Learning of DDPG Algorithm)

    93-93页
    查看更多>>摘要：Research findings on robotics are discussed in a new report. According to news reporting out of Tianjin, People's Republic of China, by NewsRx editors, research stated, “Energy consumption is one of the most critical factors in determining the kinematic performance of quadruped robots.” Financial supporters for this research include Chunhui Project Foundation of The Education Department of China. The news editors obtained a quote from the research from Tianjin University of Commerce: “However, existing research methods often encounter challenges in quickly and efficiently reducing the energy consumption associated with quadrupedal robotic locomotion. In this paper, the deep deterministic policy gradient (DDPG) algorithm was used to optimize the energy consumption of the Cyber Dog quadruped robot. Firstly, the kinematic and energy consumption models of the robot were established. Secondly, energy consumption was optimized by reinforcement learning using the DDPG algorithm. The optimized plantar trajectory was then compared with two common plantar trajectories in simulation experiments, with the same period and the number of synchronizations but varying velocities. Lastly, real experiments were conducted using a prototype machine to validate the simulation data.”
      原文链接:
    • NETL
    • NSTL
    • Mdpi

    Studies from Indian School of Mines Provide New Data on Machine Learning (Using Rock Physics Analysis Driven Feature Engineering In Ml-based Shear Slowness Prediction Using Logs of Wells From Different Geological Setup)

    93-94页
    查看更多>>摘要：Current study results on Machine Learning have been published. According to news reporting out of Dhanbad, India, by NewsRx editors, research stated, “Shear slowness data are crucial data in rock physics analysis and seismic reservoir characterization. In petrophysical formation evaluation, the use of sonic data is limited, and hence, sonic data, especially shear sonic, are not considered as critical.” Our news journalists obtained a quote from the research from the Indian School of Mines, “In many deep-water wells to save the cost of operations, shear sonic data are not recorded. In these scenarios for rock physics analysis, it becomes necessary to predict shear sonic data from other available datasets. Conventional techniques for shear slowness predictions rely on empirical relations and rock physics modeling. However, these approaches require extensive information as input and additionally carry assumptions and multiple prerequisites. Presently with the advancement of computing power Machine learning (ML) emerges as a robust and optimized technique for predicting precise DTS in quick time and with fewer input datasets. In this study, wells located in the deep-waters of the East Coast of India and penetrated siliciclastic reservoirs of both compacted sand and soft high porosity sands were used as input to train the ML algorithm. Random Forest machine learning algorithm is best used for both classification and regression tasks, and this algorithm is used here for the data prediction. As a comparison, the convolutional LSTM method is also used for data prediction. To comply with the geological variability in the prediction and to enhance the prediction accuracy, rock physics understandings were used as a guide in feature engineering. The RF prediction shows a good match of similar to 93%, and the LSTM model prediction shows similar to 94% correlation at validation well.”
      原文链接:
    • NETL
    • NSTL