首页期刊导航|Robotics & Machine Learning Daily News
期刊信息/Journal information
Robotics & Machine Learning Daily News
NewsRx
Robotics & Machine Learning Daily News

NewsRx

Robotics & Machine Learning Daily News/Journal Robotics & Machine Learning Daily News
正式出版
收录年代

    Northeastern University Reports Findings in Machine Learning (Advances of machine learning-assisted small extracellular vesicles detection strategy)

    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 Shenyang, People’s Republic of China, by NewsRx correspondents, research stated, “Detection of extracellular vesicles (EVs), particularly small EVs (sEVs), is of great significance in exploring their physiological characteristics and clinical applications. The heterogeneity of sEVs plays a crucial role in distinguishing different types of cells and diseases.” Our news editors obtained a quote from the research from Northeastern University, “Machine learning, with its exceptional data processing capabilities, offers a solution to overcome the limitations of conventional detection methods for accurately classifying sEV subtypes and sources. Principal component analysis, linear discriminant analysis, partial least squares discriminant analysis, XGBoost, support vector machine, k-nearest neighbor, and deep learning, along with some combined methods such as principal componentlinear discriminant analysis, have been successfully applied in the detection and identification of sEVs. This review focuses on machine learning-assisted detection strategies for cell identification and disease prediction via sEVs, and summarizes the integration of these strategies with surface-enhanced Raman scattering, electrochemistry, inductively coupled plasma mass spectrometry and fluorescence. The performance of different machine learning-based detection strategies is compared, and the advantages and limitations of various machine learning models are also evaluated.”
      原文链接:
    • NETL
    • NSTL

    McGill University Reports Findings in Endometrial Cancer (Machine learning for prediction of concurrent endometrial carcinoma in patients diagnosed with endometrial intraepithelial neoplasia)

    88-89页
    查看更多>>摘要：New research on Oncology - Endometrial Cancer is the subject of a report. According to news reporting originating from Quebec, Canada, by NewsRx correspondents, research stated, “To identify predictive clinico-pathologic factors for concurrent endometrial carcinoma (EC) among patients with endometrial intraepithelial neoplasia (EIN) using machine learning. a retrospective analysis of 160 patients with a biopsy proven EIN.” Our news editors obtained a quote from the research from McGill University, “We analyzed the performance of multiple machine learning models (n = 48) with different parameters to predict the diagnosis of postoperative EC. The prediction variables included: parity, gestations, sampling method, endometrial thickness, age, body mass index, diabetes, hypertension, serum CA-125, preoperative histology and preoperative hormonal therapy. Python ‘sklearn’ library was used to train and test the models. The model performance was evaluated by sensitivity, specificity, PPV, NPV and AUC. Five iterations of internal crossvalidation were performed, and the mean values were used to compare between the models. Of the 160 women with a preoperative diagnosis of EIN, 37.5% (60) had a post-op diagnosis of EC. In univariable analysis, there were no significant predictors of EIN. For the five best machine learning models, all the models had a high specificity (71%-88%) and a low sensitivity (23%-51%). Logistic regression model had the highest specificity 88%, XG Boost had the highest sensitivity 51%, and the highest positive predictive value 62% and negative predictive value 73%. The highest area under the curve was achieved by the random forest model 0.646. Even using the most elaborate AI algorithms, it is not possible currently to predict concurrent EC in women with a preoperative diagnosis of EIN.”
      原文链接:
    • NETL
    • NSTL

    Findings from Indian Statistical Institute Provides New Data about Machine Learning (From Fuzzy-topsis To Machine Learning: a Holistic Approach To Understanding Groundwater Fluoride Contamination)

    89-90页
    查看更多>>摘要：Current study results on Machine Learning have been published. According to news reporting out of Jharkhand, India, by NewsRx editors, research stated, “Fluoride (F-) contamination of groundwater is a prevalent environmental issue threatening public health worldwide and in India. This study targets an investigation into spatial distribution and contamination sources of fluoride in Dhanbad, India, to help develop tailored mitigation strategies.” Our news journalists obtained a quote from the research from Indian Statistical Institute, “A triad of Multi Criteria Decision Making (MCDM) models (Fuzzy-TOPSIS), machine learning algorithms {logistic regression (LR), classification and regression tree (CART), Random Forest (RF)}, and classical methods has been undertaken here. Groundwater samples (n = 283) were collected for the purpose. Based on permissible limit (1.5 ppm) of fluoride in drinking water as set by the World Health Organization, samples were categorized as Unsafe (n = 67) and Safe (n = 216) groups. Mean fluoride concentration in Safe (0.63 +/- 0.02 ppm) and Unsafe (3.69 +/- 0.3 ppm) groups differed significantly (t-value = -10.04, p＜0.05). Physicochemical parameters (pH, electrical conductivity, total dissolved solids, total hardness, NO3-, HCO3-, SO42-, Cl-, Ca2+, Mg2+, K+, Na+ and F-) were recorded from samples of each group. The samples from ‘Unsafe group’ showed alkaline pH, the abundance of Na+ and HCO3- ions, prolonged rock water interaction in the aquifer, silicate weathering, carbonate dissolution, lack of Ca2+ and calcite precipitation which together facilitated the F- abundance. Aspatial distribution map of F- contamination was created, pinpointing the ‘contaminated pockets.’ Fuzzy- TOPSIS identified that samples from group Safe were closer to the ideal solution. Among these models, the LR proved superior, achieving the highest AUC score of 95.6 % compared to RF (91.3 %) followed by CART (69.4 %). This study successfully identified the primary contributors to F- contamination in groundwater and the developed models can help predicting fluoride contamination in other areas.”
      原文链接:
    • NETL
    • NSTL
    • Elsevier

    China University of Geosciences Reports Findings in Machine Learning (Revealing the drivers and genesis of NO3-N pollution classification in shallow groundwater of the Saying River Basin by explainable machine learning and pathway analysis ...)

    90-91页
    查看更多>>摘要：New research on Machine Learning is the subject of a report. According to news reporting out of Beijing, People’s Republic of China, by NewsRx editors, research stated, “Nitrate (NO-N), as one of the ubiquitous contaminants in groundwater worldwide, has posed a serious threat to public health and the ecological environment. Despite extensive research on its genesis, little is known about the differences in the genesis of NO-N pollution across different concentrations.” Our news journalists obtained a quote from the research from the China University of Geosciences, “Herein, a study of NO-N pollution concentration classification was conducted using the Shaying River Basin as a typical area, followed by examining the genesis differences across different pollution classifications. Results demonstrated that three classifications (0-9.98 mg/L, 10.14-27.44 mg/L, and 28.34-136.30 mg/L) were effectively identified for NO-N pollution using Jenks natural breaks method. Random forest exhibited superior performance in describing NO-N pollution and was thereby affirmed as the optimal explanatory method. With this method coupling SEMs, the genesis of different NO-N pollution classifications was proven to be significantly different. Specifically, strongly reducing conditions represented by Mn, Eh, and NO-N played a dominant role in causing residual NO-N at low levels. Manure and sewage (represented by Cl) leaching into groundwater through precipitation is mainly responsible for NO-N in the 10-30 mg/L classification, with a cumulative contribution rate exceeding 80 %. NO-N concentrations ＞30 mg/L are primarily caused by the anthropogenic loads stemming from manure, sewage, and agricultural fertilization (represented by Cl and K) infiltrating under precipitation in vulnerable hydrogeological conditions. Pathway analysis based on standardized effect and significance further confirmed the rationality and reliability of the above results.”
      原文链接:
    • NETL
    • NSTL

    Capital Medical University Reports Findings in Ear Malformation (Machine learning-based prediction of the outcomes of cochlear implantation in patients with inner ear malformation)

    91-92页
    查看更多>>摘要：New research on Ear Diseases and Conditions - Ear Malformation is the subject of a report. According to news reporting originating from Beijing, People’s Republic of China, by NewsRx correspondents, research stated, “The objectives of this study are twofold: first, to visualize the structure of malformed cochleae through image reconstruction; and second, to develop a predictive model for postoperative outcomes of cochlear implantation (CI) in patients diagnosed with cochlear hypoplasia (CH) and incomplete partition (IP) malformation. The clinical data from patients diagnosed with cochlear hypoplasia (CH) and incomplete partition (IP) malformation who underwent cochlear implantation (CI) at Beijing Tongren Hospital between January 2016 and August 2020 were collected.” Our news editors obtained a quote from the research from Capital Medical University, “Radiological features were analyzed through 3D segmentation of the cochlea. Postoperative auditory speech rehabilitation outcomes were evaluated using the Categories of Auditory Performance (CAP) and the Speech Intelligibility Rating (SIR). This study aimed to investigate the relationship between cochlear parameters and postoperative outcomes. Additionally, a predictive model for postoperative outcomes was developed using the K-nearest neighbors (KNN) algorithm. In our study, we conducted feature selection by using patients’ imaging and audiological attributes. This process involved methods such as the removal of missing values, correlation analysis, and chi-square tests. The findings indicated that two specific features, cochlear volume (Ⅴ) and cochlear canal length (CDL), significantly contributed to predicting the outcomes of hearing and speech rehabilitation for patients with inner ear malformations. In terms of hearing rehabilitation, the KNN classification achieved an accuracy of 93.3%. Likewise, for speech rehabilitation, the KNN classification demonstrated an accuracy of 86.7%. The measurements obtained from the 3D reconstruction model hold significant clinical relevance. Despite the considerable variability in cochlear morphology across individuals, radiological features remain effective in predicting cochlear implantation (CI) prognosis for patients with inner ear malformations. The utilization of 3D segmentation techniques and the developed predictive model can assist surgeons in conducting preoperative cochlear structural measurements for patients with inner ear malformations.”
      原文链接:
    • NETL
    • NSTL

    Studies from Hunan Agriculture University Further Understanding of Agricultural Robots (Intermittent Stop-Move Motion Planning for Dual-Arm Tomato Harvesting Robot in Greenhouse Based on Deep Reinforcement Learning)

    92-93页
    查看更多>>摘要：Investigators publish new report on agricultural robots. According to news originating from Changsha, People’s Republic of China, by NewsRx editors, the research stated, “Intermittent stopmove motion planning is essential for optimizing the efficiency of harvesting robots in greenhouse settings.” Financial supporters for this research include National Major Agricultural Science And Technology Projects; Beijing Nova Program; Baafs Innovation Capacity Building Project. The news editors obtained a quote from the research from Hunan Agriculture University: “Addressing issues like frequent stops, missed targets, and uneven task allocation, this study introduced a novel intermittent motion planning model using deep reinforcement learning for a dual-arm harvesting robot vehicle. Initially, the model gathered real-time coordinate data of target fruits on both sides of the robot, and projected these coordinates onto a two-dimensional map. Subsequently, the DDPG (Deep Deterministic Policy Gradient) algorithm was employed to generate parking node sequences for the robotic vehicle. A dynamic simulation environment, designed to mimic industrial greenhouse conditions, was developed to enhance the DDPG to generalize to real-world scenarios. Simulation results have indicated that the convergence performance of the DDPG model was improved by 19.82% and 33.66% compared to the SAC and TD3 models, respectively. In tomato greenhouse experiments, the model reduced vehicle parking frequency by 46.5% and 36.1% and decreased arm idleness by 42.9% and 33.9%, compared to grid-based and area division algorithms, without missing any targets.” According to the news editors, the research concluded: “The average time required to generate planned paths was 6.9 ms. These findings demonstrate that the parking planning method proposed in this paper can effectively improve the overall harvesting efficiency and allocate tasks for a dual-arm harvesting robot in a more rational manner.”
      原文链接:
    • NETL
    • NSTL
    • Mdpi

    Findings from Czech Technical University Yields New Findings on Robotics (Complex Determination of Automatic Robotic Total Stations’ Measurements’ Accuracy In Underground Spaces and Comparison With Results On the Surface)

    93-94页
    查看更多>>摘要：A new study on Robotics is now available. According to news reporting out of Prague, Czech Republic, by NewsRx editors, research stated, “The total station is the most basic geodetic measuring instrument, locally the most accurate and versatile. Its accuracy is the cornerstone of its use and is defined by the standard deviations of horizontal direction, zenith angle and slope distance measurements.” Financial support for this research came from Grant Agency of CTU in Prague. Our news journalists obtained a quote from the research from Czech Technical University, “These accuracy parameters are given by the manufacturer, but these are only valid under optimum measurement conditions. To ensure the credibility and reliability of the measurements, these values must be periodically ascertained or determined for atypical measurement configurations or measurement conditions. Standardised procedures are used for this purpose, but in our opinion, they do not reflect the full influence of the measuring conditions and other measuring aids. A comprehensive determination of the accuracies (variation components) from the alignment, where all possible influences in a given situation are applied, may be considered the most appropriate for determining the angular accuracy of measurements. Such atypical conditions are certainly represented by geodetic measurements in the confined spaces of an underground mine. Thus, an experimental determination of the accuracy of four different robotic total stations was carried out at the Center of experimental geotechnics in a mine Josef teaching centre (CTU in Prague), and the Forstner method was used to determine the variation components. A network of 6 stations and 8 target points was designed. The grid size was approximately 32x21 m with 4 -31 m lengths. A network with the same configuration was also duplicated at the surface to assess whether the accuracy is different in underground and how the results will correspond to the accuracy claimed by the manufacturers. The result of the testing is that the accuracy claimed by the manufacturers is maintained even under such difficult measurement conditions in narrow corridors and with short sights.”
      原文链接:
    • NETL
    • NSTL
    • Berg Fac Technical Univ Kosice

    Researchers from Chinese Academy of Sciences Report Details of New Studies and Findings in the Area of Machine Learning (Advancing Ocean Subsurface Thermal Structure Estimation In the Pacific Ocean: a Multi-model Ensemble Machine Learning ...)

    94-95页
    查看更多>>摘要：Investigators publish new report on Machine Learning. According to news reporting originating in Qingdao, People’s Republic of China, by NewsRx journalists, research stated, “Estimation of the ocean subsurface thermal structure (OSTS) is important for understanding thermodynamic processes and climate variability. In the present study, a novel multi-model ensemble machine learning (Ensemble- ML) model is developed to retrieve subsurface thermal structure in the Pacific Ocean by integrating sea surface data with Argo observations.” Funders for this research include National Key Research and Development Program of China, National Natural Science Foundation of China (NSFC). The news reporters obtained a quote from the research from the Chinese Academy of Sciences, “The Ensemble-ML model integrates four individual machine learning models to enhance estimation accuracy and reliability. Our results exhibit good agreement between the satellite sea surface temperature (SST) and sea surface salinity (SSS) data and Argo observations, providing validation for the utilization of these datasets in the Ensemble-ML model. The Ensemble-ML model exhibits better performance compared to individual machine learning models, with an average root mean square error (RMSE) of 0.3273 degrees C and an average coefficient of determination (R2) of 0.9905. Notably, incorporating geographical information as input variables enhance model performance, emphasizing the importance of considering spatial context in OSTS estimation. The Ensemble-ML model accurately captures the spatial distribution of OSTS across depths and seasons in the Pacific Ocean, effectively reproducing critical temperature features while maintaining strong agreement with Argo observations. Nevertheless, its performance shows relative weakness within the thermocline layer and the equatorial Pacific region (spanning from 10 degrees S to 10 degrees N latitude), which are characterized by complex circulation systems. Despite these challenges, the Ensemble-ML model effectively reproduces the spatial distribution of OSTS of the Pacific Ocean.”
      原文链接:
    • NETL
    • NSTL
    • Elsevier

    Report Summarizes Robotics Study Findings from Beijing Institute of Technology (An Efficient Two-stage Evolutionary Algorithm for Multi-robot Task Allocation In Nuclear Accident Rescue Scenario)

    95-96页
    查看更多>>摘要：Investigators discuss new findings in Robotics. According to news originating from Beijing, People’s Republic of China, by NewsRx correspondents, research stated, “With the growing maturity of multi -robot system technology, its applications have expanded across various domains. This paper addresses the critical issue of task allocation in nuclear accident rescue scenario, which plays a pivotal role in the success of such operations.” Financial supporters for this research include National Key Research and Development Plan of China, National Natural Science Foundation of China (NSFC). Our news journalists obtained a quote from the research from the Beijing Institute of Technology, “The problem is formulated as a multi -objective optimization problem, taking into account three key indicators: execution time, radiation accumulation, and waiting cost. To effectively tackle this problem, an two -stage evolutionary algorithm is proposed. Firstly, a solution encoding method and a crossover mutation method is devised tailored to the problem’s characteristics. Secondly, a two -stage search strategy is designed. In the first stage, a fixed population size and shift -based density estimation method are used to quickly converge the solution set to the Pareto front. The latter stage uses an infinite size population to find as many Pareto solutions as possible. Finally, a local search strategy is introduced to improve the quality of solution set. In the experimental section, our proposed method is compared with five state-of-the-art algorithms on nine instances of varying scales. Across five evaluation metrics, the proposed algorithm demonstrates competitive performance on all instances.”
      原文链接:
    • NETL
    • NSTL
    • Elsevier

    Shanghai Jiao Tong University Reports Findings in Robotics (Servo torque fault diagnosis implementation for heavy-legged robots using insufficient information)

    96-97页
    查看更多>>摘要：New research on Robotics is the subject of a report. According to news reporting out of Shanghai, People’s Republic of China, by NewsRx editors, research stated, “The reliability of sensors and servos is paramount in diagnosing the Heavy-Legged Robot (HLR). Servo faults stemming from mechanical wear, environmental disturbances, or electrical issues pose significant challenges to traditional diagnostic methods, which rely heavily on delicate sensors.” Our news journalists obtained a quote from the research from Shanghai Jiao Tong University, “This study introduces a framework that solely relies on joint position and permanent magnet synchronous motor (PMSM) information to mitigate dependency on fragile sensors for servo-fault diagnosis. An essential contribution involves refining a model that directly connects PMSM currents to HLR motion. Moreover, to address scenarios where actual servo outputs and HLR cylinder velocities are unavailable, an improved sliding mode observer (ISMO) is proposed. Additionally, a Fourier expansion model characterizes the relationship between operation time and fault-free disturbance in the HLR. Subsequently, the dual-line particle filter (DPF) algorithm is employed to predict fault-free disturbance. The outputs of DPF serve as a feedforward to the ISMO, enabling the real-time servo torque fault diagnosis.”
      原文链接:
    • NETL
    • NSTL