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    New Robotics Study Findings Have Been Reported from University of Shanghai for Science and Technology (A Low-Inertia and High-Stiffness Cable-Driven Biped Robot: Design, Modeling, and Control)

    29-30页
    查看更多>>摘要：Investigators discuss new findings in robotics. According to news reporting originating from Shanghai, People’s Republic of China, by NewsRx correspondents, research stated, “In this paper, a biped robot system for dynamic walking is presented.” The news correspondents obtained a quote from the research from University of Shanghai for Science and Technology: “It has two 2-degree-of-freedom (DOF) lightweight legs and a 6-DOF hip. All the joint pulleys of the legs are driven by motors that are placed at the hip using steel cables. Since all the heavy motors are mounted at the hip, the biped robot has remarkably low-mass legs beyond the hip, which guarantees low inertia during walking at high speeds. Utilizing cable-amplification mechanisms, high stiffness and strength are achieved, resulting in better control performance compared to conventional directdriven methods. Techniques are developed to estimate joint-angle errors caused by the elastic deformation of the cables. To achieve smooth control, we introduce the concept of a virtual leg, which is an imaginary leg connecting the hip joint and the ankle joint.”
      原文链接:
    • NETL
    • NSTL

    Manhattan College Reports Findings in Machine Learning (Machine learning descriptors in materials chemistry used in multiple experimentally validated studies: Oliynyk elemental property dataset)

    30-30页
    查看更多>>摘要：New research on Machine Learning is the subject of a report. According to news reporting out of Riverdale, New York, by NewsRx editors, research stated, “Materials informatics employs data-driven approaches for analysis and discovery of materials. Features also referred to as descriptors are essential in generating reliable and accurate machine-learning models.” Our news journalists obtained a quote from the research from Manhattan College, “While general data can be obtained through public and commercial sources, features must be tailored to specific applications. Common featurizers suitable for generic chemical problems may not be effective in features-property mapping in solid-state materials with ML models. Here, we have assembled the Oliynyk property list for compositional feature generation, which performs well on limited datasets (50 to 1000 training data points) in the solid-state materials domain. The dataset contains 98 elemental features for atomic numbers from 1 to 92, including thermodynamic properties, electronic structure data, size, electronegativity, and bulk properties such as melting point, density, and conductivity. The dataset has been utilized peer-reviewed publications in predicting material hardness, classification, discovery of novel Heusler compounds, band gap prediction, and determining the site preference of atoms using machine learning models including support vector machines, random forests for classification, and support vector regression for regression problems.”
      原文链接:
    • NETL
    • NSTL

    Studies from Army Engineering University Reveal New Findings on Robotics (Aoi Minimization Scheme for Short-packet Communications In Energy-constrained Iiot)

    30-31页
    查看更多>>摘要：Investigators discuss new findings in Robotics. According to news reporting out of 30 Nanjing, People’s Republic of China, by NewsRx editors, research stated, “This article is motivated by the requirement of high information freshness in the industrial Internet of Things (IIoT). An industrial robot sends short status packets to a control center (CC), and the timeliness of status updates is measured by the Age of Information (AoI).” Financial support for this research came from National Natural Science Foundation of China (NSFC). Our news journalists obtained a quote from the research from Army Engineering University, “Due to the dynamic change of the wireless channel, the robot needs to send a pilot for channel estimation during each coherence time. Considering the robot is energy-limited, we investigate the average AoI minimization scheme for short-packet communications under the average power consumption constraint. By rationally analyzing state transitions, we first formulate the problem as a constrained Markov decision process and obtain the optimal solution through linear programming (LP). Then, for the problem of high computational complexity caused by too many variables in LP, we propose a heuristic threshold-based status update scheme by exploiting the threshold structure of the optimal solution. Simulation results show that the LP scheme can effectively minimize the average AoI and the threshold-based scheme can achieve near-optimal performance.”
      原文链接:
    • NETL
    • NSTL
    • IEEE

    New Findings from Hongik University in the Area of Robotics Described (Efficient robot tracking system using single-image-based object detection and position estimation)

    31-32页
    查看更多>>摘要：Fresh data on robotics are presented in a new report. According to news reporting from Sejong, South Korea, by NewsRx journalists, research stated, “This study proposes a mother-slave robot tracking system that identifies the target, predicts its location, and tracks it based on a single image.” Funders for this research include Hongik University; National Research Foundation of Korea. Our news reporters obtained a quote from the research from Hongik University: “The proposed system utilizes a Convolutional Neural Network (CNN) for object detection, to identify the target robot. The 31 distance and angle between the robots are then calculated through linear regression analysis, which offers a more efficient and cost-effective solution than traditional methods.” According to the news editors, the research concluded: “The performance of the system was evaluated, resulting in an accuracy of 99.59% for object detection, and an average distance error of 2.04% for the estimated location.”
      原文链接:
    • NETL
    • NSTL
    • Elsevier

    New Findings from Hebei Agricultural University Describe Advances in Machine Learning (A Recognition Method for Aggressive Chicken Behavior Based on Machine Learning)

    32-33页
    查看更多>>摘要：A new study on artificial intelligence is now available. According to news originating from Baoding, People’s Republic of China, by NewsRx correspondents, research stated, “Aggressive behavior is an important indicator of chicken welfare assessment. At present, the aggressive behavior of chickens typically requires human observation for welfare assessment, and the assessment results are influenced by the subjective judgment of humans.” Funders for this research include National Natural Science Foundation of China; Hebei Province Layer/broiler Industry Technology System. Our news correspondents obtained a quote from the research from Hebei Agricultural University: “This paper proposes an aggressive chicken behavior identification method based on a hybrid strategy improved Sparrow Search Algorithm combined with Support Vector Machine (ISSA-SVM). Nine-axis inertial sensors were used to collect the behavioral data of chickens. A total of 231-dimensional feature data in the time and frequency domains of the behavioral data were extracted through a 1 s sliding window. To reduce feature redundancy, the initial population is initialized using circle chaotic mapping instead of random initialization of the original sparrow algorithm to increase the uniformity of the initial population distribution in the feature space; adaptive weights are introduced to increase the search range of the early iteration, and the global optimal solution of the previous generation is introduced to improve the global search capability of the algorithm; the optimal solution is perturbed using the dimension-by-dimension mutation strategy of adaptive t-distribution to increase the diversity of the feature distribution. ISSA-SVM reduced the feature dimensionality from 231 to 17, indicating a reduction of 92.6%. The recognition overall accuracy of ISSA-SVM for aggressive chicken behavior was 94.27%, which improved by 1.33% compared to SVM.”
      原文链接:
    • NETL
    • NSTL
    • IEEE

    Investigators from U.S. Department of Agriculture (USDA) Agricultural Research Service (ARS) Target Machine Learning (Maize Feature Store: a Centralized Resource To Manage and Analyze Curated Maize Multi-omics Features for Machine Learning ...)

    33-34页
    查看更多>>摘要：New research on Machine Learning is the subject of a report. According to news reporting originating from Ames, Iowa, by NewsRx correspondents, research stated, “The big-data analysis of complex data associated with maize genomes accelerates genetic research and improves agronomic traits. As a result, efforts have increased to integrate diverse datasets and extract meaning from these measurements.” Financial supporters for this research include USDA Agricultural Research Service, Iowa State University, USDA-ARS, Corn Insects and Crop Genetics Research Unit. Our news editors obtained a quote from the research from the U.S. Department of Agriculture (USDA) Agricultural Research Service (ARS), “Machine learning models are a powerful tool for gaining knowledge from large and complex datasets. However, these models must be trained on high-quality features to succeed. Currently, there are no solutions to host maize multi-omics datasets with end-to-end solutions for evaluating and linking features to target gene annotations. Our work presents the Maize Feature Store (MFS), a versatile application that combines features built on complex data to facilitate exploration, modeling and analysis. Feature stores allow researchers to rapidly deploy machine learning applications by managing and providing access to frequently used features. We populated the MFS for the maize reference genome with over 14 000 gene-based features based on published genomic, transcriptomic, epigenomic, variomic and proteomics datasets. Using the MFS, we created an accurate pan-genome classification model with an AUC-ROC score of 0.87.”
      原文链接:
    • NETL
    • NSTL
    • Oxford Univ Press

    Findings on Support Vector Machines Reported by Investigators at Shihezi University (An Improved Dcgan Model: Data Augmentation of Hyperspectral Image for Identification Pesticide Residues of Hami Melon)

    34-35页
    查看更多>>摘要：Investigators publish new report on Machine Learning - Support Vector Machines. According to news originating from Shihezi, People’s Republic of China, by NewsRx correspondents, research stated, “The increasing concern over pesticide residues on Hami melon is due to the unregulated use of pesticides, which poses a potential food safety hazard. Thus, it is urgent to propose a method for the rapid and nondestructive detection of pesticide residues on the Hami melon.” Financial support for this research came from National Natural Science Foundation of China (NSFC). Our news journalists obtained a quote from the research from Shihezi University, “This study used shortwave infrared hyperspectral imaging (SWIR-HSI) to identify pesticide residues on the Hami melon. The data augmentation method based on improved deep convolutional generative adversarial networks (DCGAN) was proposed to expand Hami melon’s spectral data with different pesticide residues. To determine the optimal training epoch, the 1-nearest neighbor (1-NN) classifier was used to evaluate the quality of the generated spectra. The effectiveness of the improved DCGAN was verified by three commonly used classifiers, including the decision tree (DT), random forest (RF), and support vector machine (SVM). The results showed that the performance of all three classifiers was improved to varying degrees by the improved DCGAN. The DT, RF, and SVM accuracy was improved by 13.13%, 7.50%, and 11.25%, respectively. Moreover, the SVM model achieved the highest accuracy of 93.13%.”
      原文链接:
    • NETL
    • NSTL
    • Elsevier

    Bambino Gesu Children's Hospital Reports Findings in Machine Learning (Fit of biokinetic data in molecular radiotherapy: a machine learning approach)

    35-35页
    查看更多>>摘要：New research on Machine Learning is the subject of a report. According to news reporting out of Rome, Italy, by NewsRx editors, research stated, “In literature are reported different analytical methods (AM) to choose the proper fit model and to fit data of the time-activity curve (TAC). On the other hand, Machine Learning algorithms (ML) are increasingly used for both classification and regression tasks.” Our news journalists obtained a quote from the research from Bambino Gesu Children’s Hospital, “The aim of this work was to investigate the possibility of employing ML both to classify the most appropriate fit model and to predict the area under the curve (t). Two different ML systems have been developed for classifying the fit model and to predict the biokinetic parameters. The two systems were trained and tested with synthetic TACs simulating a whole-body Fraction Injected Activity for patients affected by metastatic Differentiated Thyroid Carcinoma, administered with [I]I-NaI. Test performances, defined as classification accuracy (CA) and percentage difference between the actual and the estimated area under the curve (Dt), were compared with those obtained using AM varying the number of points (N) of the TACs. A comparison between AM and ML were performed using data of 20 real patients. As N varies, CA remains constant for ML (about 98%), while it improves for F-test (from 62 to 92%) and AICc (from 50 to 92%), as N increases. With AM, [Formula: see text] can reach down to - 67%, while using ML [Formula: see text] ranges within ± 25%. Using real TACs, there is a good agreement between t obtained with ML system and AM.”
      原文链接:
    • NETL
    • NSTL

    Peking University First Hospital Reports Findings in Artificial Intelligence (Establishment and validation of an interactive artificial intelligence platform to predict postoperative ambulatory status for patients with metastatic spinal ...)

    36-37页
    查看更多>>摘要：New research on Artificial Intelligence is the subject of a report. According to news originating from Beijing, People’s Republic of China, by NewsRx correspondents, research stated, “Identification of patients with high risk of experiencing inability to walk after surgery is important for surgeons to make therapeutic strategies for patients with metastatic spinal disease. However, there is a lack of clinical tool to assess postoperative ambulatory status for those patients.” Our news journalists obtained a quote from the research from Peking University First Hospital, “The emergence of artificial intelligence brings a promising opportunity to develop accurate prediction models. This study collected 455 patients with metastatic spinal disease who underwent posterior decompressive surgery at three tertiary medical institutions. Of these, 220 patients were collected from one medical institution to form the model derivation cohort, while 89 and 146 patients were collected from two other medical institutions to form the external validation cohorts 1 and 2, respectively. Patients in the model derivation cohort were used to develop and internally validate models. To establish the interactive AI platform, machine learning techniques were used to develop prediction models, including logistic regression (LR), decision tree (DT), random forest (RF), extreme gradient boosting machine (eXGBM), support vector machine (SVM), and neural network (NN). Furthermore, to enhance the resilience of the study’s model, an ensemble machine learning approach was employed using a soft-voting method by combining the results of the above six algorithms. A scoring system incorporating 10 evaluation metrics was used to comprehensively assess the prediction performance of the developed models. The scoring system had a total score of 0 to 60, with higher scores denoting better prediction performance. An interactive AI platform was further deployed via Streamlit. The prediction performance was compared between medical experts and the AI platform in assessing the risk of experiencing postoperative inability to walk among patients with metastatic spinal disease. Among all developed models, the ensemble model outperformed the six other models with the highest score of 57, followed by the eXGBM model (54), SVM model (50), and NN model (50). The ensemble model had the best performance in accuracy and calibration slope, and the second-best performance in precise, recall, specificity, area under the curve (AUC), Brier score, and log loss. The scores of the LR model, RF model, and DT model were 39, 46, and 26, respectively. External validation demonstrated that the ensemble model had an AUC value of 0.873 (95%CI: 0.809-0.936) in the external validation cohort 1 and 0.924 (95%CI: 0.890-0.959) in the external validation cohort 2. In the new ensemble machine learning model excluding the feature of the number of comorbidities, the AUC value was still as high as 0.916 (95% CI: 0.863-0.969). In addition, the AUC values of the new model were 0.880 (95% CI: 0.819-0.940) in the external validation cohort 1 and 0.922 (95% CI: 0.887-0.958) in the external validation cohort 2, indicating favorable generalization of the model.By using the AI platform, researchers were able to obtain the individual predicted risk of postoperative inability to walk, gain insights into the key factors influencing the outcome, and find the stratified therapeutic recommendations. The AUC value obtained from the AI platform was significantly higher than the average AUC value achieved by the medical experts (P <0.001), denoting that the AI platform obviously outperformed the individual medical experts. The study successfully develops and validates an interactive AI platform for evaluating the risk of postoperative loss of ambulatory ability in patients with metastatic spinal disease.”
      原文链接:
    • NETL
    • NSTL

    National Institute of Nuclear Physics Reports Findings in Robotics (First-in-human validation of a DROP-IN b-probe for robotic radioguided surgery: defining optimal signal-to-background discrimination algorithm)

    37-38页
    查看更多>>摘要：New research on Robotics is the subject of a report. According to news reporting originating from Rome, Italy, by NewsRx correspondents, research stated, “In radioguided surgery (RGS), radiopharmaceuticals are used to generate preoperative roadmaps (e.g., PET/CT) and to facilitate intraoperative tracing of tracer avid lesions. Within RGS, there is a push toward the use of receptor-targeted radiopharmaceuticals, a trend that also has to align with the surgical move toward minimal invasive robotic surgery.” Financial supporters for this research include European Commission, Universita degli Studi di Roma La Sapienza. Our news editors obtained a quote from the research from the National Institute of Nuclear Physics, “Building on our initial ex vivo evaluation, this study investigates the clinical translation of a DROP-IN b probe in robotic PSMA-guided prostate cancer surgery. A clinical-grade DROP-IN b probe was developed to support the detection of PET radioisotopes (e.g., Ga). The prototype was evaluated in 7 primary prostate cancer patients, having at least 1 lymph node metastases visible on PSMA-PET. Patients were scheduled for radical prostatectomy combined with extended pelvic lymph node dissection. At the beginning of surgery, patients were injected with 1.1 MBq/kg of [Ga]Ga-PSMA. The b probe was used to trace PSMAexpressing lymph nodes in vivo. To support intraoperative decision-making, a statistical software algorithm was defined and optimized on this dataset to help the surgeon discriminate between probe signals coming from tumors and healthy tissue. The DROP-IN b probe helped provide the surgeon with autonomous and highly maneuverable tracer detection. A total of 66 samples (i.e., lymph node specimens) were analyzed in vivo, of which 31 (47%) were found to be malignant. After optimization of the signal cutoff algorithm, we found a probe detection rate of 78% of the PSMA-PET-positive samples, a sensitivity of 76%, and a specificity of 93%, as compared to pathologic evaluation. This study shows the first-in-human use of a DROP-IN b probe, supporting the integration of b radio guidance and robotic surgery.”
      原文链接:
    • NETL
    • NSTL
    • Springer Nature