查看更多>>摘要：New research on Machine Learning is the subject of a report. According to news reporting originating in Coventry, United Kingdom, by NewsRx journalists, research stated, “The viscoelasticity of cells serves as a biomarker that reveals changes induced by malignant transformation, which aids the cytological examinations. However, differences in the measurement methods and parameters have prevented the consistent and effective characterization of the viscoelastic phenotype of cells.” The news reporters obtained a quote from the research from the University of Warwick, “To address this issue, nanomechanical indentation experiments were conducted using an atomic force microscope (AFM). Multiple indentation methods were applied, and the indentation parameters were gradually varied to measure the viscoelasticity of normal liver cells and cancerous liver cells to create a database. This database was employed to train machine-learning algorithms in order to analyze the differences in the viscoelasticity of different types of cells and as well as to identify the optimal measurement methods and parameters. These findings indicated that the measurement speed significantly influenced viscoelasticity and that the classification difference between the two cell types was most evident at 5 mm/s. In addition, the precision and the area under the receiver operating characteristic curve were comparatively analyzed for various widely employed machine-learning algorithms. Unlike previous studies, this research validated the effectiveness of measurement parameters and methods with the assistance of machine-learning algorithms. Furthermore, the results confirmed that the viscoelasticity obtained from the multiparameter indentation measurement could be effectively used for cell classification. RESEARCH HIGHLIGHTS: This study aimed to analyze the viscoelasticity of liver cancer cells and liver cells. Different nano-indentation methods and parameters were used to measure the viscoelasticity of the two kinds of cells.”
查看更多>>摘要：New research on Robotics - Robotics and Automation is the subject of a report. According to news reporting originating from Munich, Germany, by NewsRx correspondents, research stated, “We propose a fixed-lag smoother-based sensor fusion architecture to leverage the complementary benefits of range-based sensors and visual-inertial odometry (VIO) for localization. We use two fixedlag smoothers (FLS) to decouple accurate state estimation and high-rate pose generation for closed-loop control.” Financial support for this research came from CGIAR. Our news editors obtained a quote from the research from Technical University Munich (TU Munich), “The first FLS combines ultrawideband (UWB)-based range measurements and VIO to estimate the robot trajectory and any systematic biases that affect the range measurements in cluttered environments. The second FLS estimates smooth corrections to VIO to generate pose estimates at a high rate for online control. The proposed method is lightweight and can run on a computationally constrained micro-aerial vehicle (MAV). We validate our approach through closed-loop flight tests involving dynamic trajectories in multiple real-world cluttered indoor environments.”
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, “Robotic autonomous assembly is critical in intelligent manufacturing and has always been a research hotspot. Most previous approaches rely on prior knowledge, such as geometric parameters and pose information of the assembled parts, which are hard to estimate in unstructured environments.” Financial support for this research came from China's National Key Research and Development Program. Our news journalists obtained a quote from the research from Shanghai Jiao Tong University, “This paper proposes a residual reinforcement learning (RL) policy for robotic assembly via combining visual and force information. The residual RL policy, which consists of a visual-based policy and a force-based policy, is trained and tested in an end-to-end manner. In the assembly procedure, the visual-based policy focuses on spatial search, while the force-based policy handles the interactive behaviors. The experimental results reveal the high sample efficiency of our approach, which exhibits the ability to generalize across diverse assembly tasks involving variations in geometries, clearances, and configurations.”
查看更多>>摘要：New research on Nervous System Diseases and Conditions - Carpal Tunnel Syndrome is the subject of a report. According to news reporting originating from Rotterdam, Netherlands, by NewsRx correspondents, research stated, “Surgeons rely on clinical experience when making predictions about treatment effects. Incorporating algorithm-based predictions of symptom improvement after carpal tunnel release (CTR) could support medical decision-making.” Our news editors obtained a quote from the research from Erasmus University Medical Center, “However, these algorithm-based predictions need to outperform predictions made by surgeons to add value. We compared predictions of a validated prediction model for symptom improvement after CTR with predictions made by surgeons. This cohort study included 97 patients scheduled for CTR. Preoperatively, surgeons estimated each patient's probability of improvement 6 months after surgery, defined as reaching the minimally clinically important difference on the Boston Carpal Tunnel Syndrome Symptom Severity Score. We assessed model and surgeon performance using calibration (calibration belts), discrimination (area under the curve [AUC]), sensitivity, and specificity. In addition, we assessed the net benefit of decision-making based on the prediction model's estimates vs the surgeon's judgement. The surgeon predictions had poor calibration and suboptimal discrimination (AUC 0.62, 95%-CI 0.49-0.74), while the prediction model showed good calibration and appropriate discrimination (AUC 0.77, 95%-CI 0.66-0.89, P = .05). The accuracy of surgeon predictions was 0.65 (95%-CI 0.37-0.78) vs 0.78 (95%-CI 0.67-0.89) for the prediction model (P = .03). The sensitivity of surgeon predictions and the prediction model was 0.72 (95%-CI 0.15-0.96) and 0.85 (95%-CI 0.62-0.97), respectively (P = .04). The specificity of the surgeon predictions was similar to the model's specificity (P = .25). The net benefit analysis showed better decision-making based on the prediction model compared with the surgeons' decision-making (ie, more correctly predicted improvements and/or fewer incorrectly predicted improvements). The prediction model outperformed surgeon predictions of improvement after CTR in terms of calibration, accuracy, and sensitivity.”
查看更多>>摘要：A new study on software engineering is now available. According to news originating from ITMO University by NewsRx correspondents, research stated, “This article reviews the integration of machine learning (ML) techniques into Software Engineering (SE) across various phases of the software development life cycle (SDLC).” Our news journalists obtained a quote from the research from ITMO University: “The purpose is to investigate the applications of ML in SE, analyze its methodologies, present findings, and draw conclusions regarding its impact. The study categorized ML applications in SE and assessed the performance of various ML algorithms. Authors identified ML applications in SDLC phases, including requirements analysis, design, implementation, testing, and maintenance. ML algorithms, such as supervised and unsupervised learning, are employed for tasks like software requirement identification, design pattern recognition, code generation, and automated testing. In summary, we find that ML-based techniques are experiencing a substantial surge in adoption within the field of software engineering. Nevertheless, it is evident that substantial endeavors are needed to establish thorough comparisons and synergies among these approaches, perform meaningful evaluations grounded in detailed real-world implementations that are applicable to industrial software development.”
查看更多>>摘要：Researchers detail new data in robotics. According to news reporting originating from Beijing, People's Republic of China, by NewsRx correspondents, research stated, “This study proposes a tightly coupled multi-sensor Simultaneous Localization and Mapping (SLAM) framework that integrates RGB-D and inertial measurements to achieve highly accurate 6 degree of freedom (6DOF) metric localization in a variety of environments.” Funders for this research include National Natural Science Foundation of China; Shenyang Science And Technology Project; Educational Department of Liaoning Provincial Basic Research Project. Our news editors obtained a quote from the research from Beijing Institute of Technology: “Through the consideration of geometric consistency, inertial measurement unit constraints, and visual re-projection errors, we present visual-inertial-depth odometry (called VIDO), an efficient state estimation back-end, to minimise the cascading losses of all factors. Existing visual-inertial odometers rely on visual feature-based constraints to eliminate the translational displacement and angular drift produced by Inertial Measurement Unit (IMU) noise. To mitigate these constraints, we introduce the iterative closest point error of adjacent frames and update the state vectors of observed frames through the minimisation of the estimation errors of all sensors. Moreover, the closed-loop module allows for further optimization of the global attitude map to correct the long-term drift. For experiments, we collect an RGBD-inertial data set for a comprehensive evaluation of VID-SLAM. The data set contains RGB-D image pairs, IMU measurements, and two types of ground truth data.”
查看更多>>摘要：New study results on robotics have been published. According to news originating from the Council of Scientific and Industrial Research (CSIR) by NewsRx correspondents, research stated, “Disturbances experienced by an inspection robot can reduce the quality of its sensor measurements, which can in turn negatively affect the robot's functionality.” Our news reporters obtained a quote from the research from Council of Scientific and Industrial Research (CSIR): “This research aimed to design a Parallel Kinematic Mechanism (PKM) for use as a stabilisation mechanism. The research focused on designing, simulating, building, testing, and analysing the mechanism. The PKM was modelled and simulated using MATLAB®, designed and developed using NX CAD software, and it was tested using a custom-built test rig that could simulate rotational disturbances.” According to the news editors, the research concluded: “Using the metric of absement, the PKM significantly reduced the disturbances, depending on the disturbance induced.”
查看更多>>摘要：New research on Artificial Intelligence is the subject of a report. According to news reporting originating in Istanbul, Turkey, by NewsRx journalists, research stated, “Health is very important for human life. In particular, the health of the brain, which is the executive of the vital resource, is very important.” The news reporters obtained a quote from the research, “Diagnosis for human health is provided by magnetic resonance imaging (MRI) devices, which help health decision makers in critical organs such as brain health. Images from these devices are a source of big data for artificial intelligence. This big data enables high performance in image processing classification problems, which is a subfield of artificial intelligence. In this study, we aim to classify brain tumors such as glioma, meningioma, and pituitary tumor from brain MR images. Convolutional Neural Network (CNN) and CNN-based inception-V3, EfficientNetB4, VGG19, transfer learning methods were used for classification. F-score, recall, imprinting and accuracy were used to evaluate these models. The best accuracy result was obtained with VGG16 with 98%, while the F-score value of the same transfer learning model was 97%, the Area Under the Curve (AUC) value was 99%, the recall value was 98%, and the precision value was 98%.”
查看更多>>摘要：New research on Artificial Intelligence is the subject of a report. According to news originating from Trondheim, Norway, by NewsRx correspondents, research stated, “We have developed a method to automatically assess LV function by measuring mitral annular plane systolic excursion (MAPSE) using artificial intelligence and transesophageal echocardiography (autoMAPSE). Our aim was to evaluate autoMAPSE as an automatic tool for rapid and quantitative assessment of LV function in critical care patients.” Our news journalists obtained a quote from the research from the Norwegian University of Science and Technology (NTNU), “In this retrospective study, we studied 40 critical care patients immediately after cardiac surgery. First, we recorded a set of echocardiographic data, consisting of three consecutive beats of midesophageal two- and four-chamber views. We then altered the patient's hemodynamics by positioning them in anti-Trendelenburg and repeated the recordings. We measured MAPSE manually and used autoMAPSE in all available heartbeats and in four LV walls. To assess the agreement with manual measurements, we used a modified Bland-Altman analysis. To assess the precision of each method, we calculated the least significant change (LSC). Finally, to assess trending ability, we calculated the concordance rates using a four-quadrant plot. We found that autoMAPSE measured MAPSE in almost every set of two- and four-chamber views (feasibility 95%). It took less than a second to measure and average MAPSE over three heartbeats. AutoMAPSE had a low bias (0.4 mm) and acceptable limits of agreement (- 3.7 to 4.5 mm). AutoMAPSE was more precise than manual measurements if it averaged more heartbeats. AutoMAPSE had acceptable trending ability (concordance rate 81%) during hemodynamic alterations.”
查看更多>>摘要：A new study on Machine Learning is now available. According to news reporting from Harbin, People's Republic of China, by NewsRx journalists, research stated, “This paper introduces an intelligent identification method for self-excited aerodynamic equations. The method is based on advanced sparse recognition technology and equipped with a new sampling strategy designed for weak nonlinear dynamic systems with limit cycle characteristics.” Funders for this research include National Natural Science Foundation of China (NSFC), National Key Research and Development Program of China, National Natural Science Foundation of China (NSFC), Natural Science Foundation of Heilongjiang Province, Postdoctoral scientific research development fund of Heilongjiang Province, Heilongjiang Touyan Team and Fundamental Research Funds for the Central Universities. The news correspondents obtained a quote from the research from the Harbin Institute of Technology, “Considering the complexity of the experiment condition and the difficult a priori selection of hyperparameters, a method based on information criteria and ensemble learning is proposed to derive the global optimal aerodynamic self-excited model. The proposed method is first validated by simulated data obtained from some well-known equations and then applied to the identification of flutter aerodynamic equations based on wind tunnel experiments.”
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